RAW Works

Machine Studying - The Library and The Lab

Sat, 20 Jun 2026 00:00:00 +0000

As usual, I can’t help but dive right into anything that comes out of Omar Khattab’s lab. “Machine Studying” is a very interesting blog post that attempts to define “expertise” and “intelligence” based on an agent’s ability to quickly learn new material from a corpus.

“In a way, Machine Learning asks how a system can improve from data when we have a precise objective to optimize. Machine Studying asks what an agent should do when it’s given a declarative corpus and no downstream task.”

I was curious to see what would happen with a modern harness (Codex CLI) and a top model (GPT 5.5 xhigh reasoning).

I was able to get a very high score right off the bat (76%, roughly 3x the highest reported in the original blog), which would suggest that either this combination of model and harness has high expertise on these tasks (already knows them well), or generally has fairly high intelligence for this category of task (ability to gain expertise).

Both of the examples in the current version of StudyBench involve testing an agent on its ability to use a specific version of a programming tool (DSPy & OpenClaw). The dates of the version in the test are from March and April of 2026, so I thought that it might be possible that these versions might already be in the training data of GPT 5.5, although the official page from OpenAI says the knowledge cutoff is Dec 1, 2025.

This led me to the idea that there are at least two different environments to study in: “the library” and “the lab”. In the library - you have books (i.e. documentation) and in the lab you have instruments (i.e. packages). My initial environments for DSPy and OpenClaw gave Codex access to both the documentation and the package. So I asked my buddy Codex (GPT 5.5 xhigh) to build out the remaining 3 environments. (“Lab only” turned out to be way trickier than anticipated as you have to block the package’s self-documentation.)

So now for each OpenClaw and DSPy StudyBench questions we end up with these four environments:

Closed book: the agent only sees the question. It gets no docs, no source tree, no package metadata, and no runtime to experiment with.
Lab only: the agent gets no docs, no source tree, and no static study corpus. It can only learn by running small probes against the pinned tool/runtime through the allowed lab surface.
Library only: the agent can read and search the official static material: pinned source tree, docs, package metadata, and exposed corpus files. It cannot run the package, import it, execute tests, or use a binary/runtime.
Library + lab: the agent gets both surfaces. It can read the official static material and also run experiments against the pinned environment.

Treatment	Library	Lab	DSPy	OpenClaw	Mean
Closed book	no	no	51.70	6.35	33.56
Lab only	no	yes	78.07	9.70	50.72
Library only	yes	no	82.97	60.30	73.90
Library + lab	yes	yes	85.40	62.05	76.06

So these results suggest a few things:

(Obviously) having access to both the documentation and the tool itself (library and lab) allows the agent to acquire the most expertise on DSPy and OpenClaw.
GPT 5.5 appears to have much better built-in expertise (closed book score) about DSPy than OpenClaw. This makes sense, given that DSPy is many years old, and that OpenClaw is both very new and also has had three different names in its brief history.
“Lab only” (tools but no documentation) led to modest improvements for both environments. “Library only” (docs and source code but no runnable package available) was slightly higher than “lab only” for DSPy, but dramatically higher for OpenClaw. I haven’t done an in-depth analysis of this, but again my suspicion is that it is related to the newness of OpenClaw, the quality of the documentation, and the structure of the StudyBench questions.
Today’s SOTA coding agents may already be highly effective learners, when given access to documentation, source code, and runnable packages.

I did not make any attempts to control the budget (time, tokens, turns) or to vary the reasoning level (fixed xhigh) - so I cannot compute either the “expertise” or the “intelligence” in the framing of the Machine Studying post. These are obvious next steps, in addition to trying out some different models and harnesses.

My environments and results are available at: github.com/rawwerks/studybench-lab-and-library

Until next time…study hard!

Engineering Skills

Tue, 12 May 2026 00:00:00 +0000

Agent skills are a powerful and portable way to transform generally capable AI agents into specifically useful tools — even teammates. There are skills for almost anything you can imagine: doing your taxes, setting up Cloudflare, speaking like a caveman, generating algorithmic art…there’s even a skill dedicated to industrial brutalism.

Skills became so easy to make that the next challenge became finding them. This was quickly solved by various “skill registries” like Vercel’s skills.sh. OpenClaw was a huge inflection point for the skills boom — it made agent skills first-class in the product architecture and provided a high-permission substrate for millions of people to experiment with.

Of course, then safety became a problem. That’s a topic for a longer post, but in case you are curious, I developed the Skill Safety Data Sheet as an analogy to material safety data sheets — for evaluating the risks of specific agent skills.

So where do skills stand today?

If you are an information hoarder like me, your computer is also full of dozens or even hundreds of awesome agent skills — some that 10x devs shared on GitHub, and some that Claude Code made for you after you got tired of saying the same thing over and over and over again.

And if you are like me, your Claude Code and Codex have a terrible habit of finding random skills you don’t even remember installing. Worse, they never load the ones you just added — or at least not until you scold them.

There are two details of the skills implementation that make it very unreliable:

LLMs are nondeterministic. They aren’t guaranteed to load the right skill at the right time.
Progressive disclosure (implemented as a context window workaround) means the agent has to go looking for the information fresh each time. The clanker has to think to find the skill first, which is really inefficient.

In fact, Vercel recently showed that model-mediated skill activation lost to a simple index — a compressed 8KB AGENTS.md hit a 100% pass rate on their Next.js evals while a carefully crafted skill maxed out at 79%, and the skill was never invoked at all in 56% of cases. The winning approach still used a form of progressive disclosure — it just moved the routing layer into stable passive context. (In case you’re curious, I made dirpack as a general utility for creating indices of a fixed token budget for any directory.)

Engineer the skills!

So how can we take advantage of the power of agent skills without giving up our own agency to decide when and how they should be used? Engineer the skills!

Right now I’m having a lot of fun working on OpenProse — a “programming language” that is compiled inside of a coding agent. If this sounds sci-fi, it is…and OpenProse only really works with today’s top models.

The fun thing about OpenProse is that you can express very complex workflows in very simple markdown files. As an example, using the legacy v0 syntax purely for brevity:

input topic
loop until **editor approves** (max: 5):
    session "research {{topic}}, address editor's prior notes"
    session "draft from research, revise per prior notes"
    session "review draft: approve as report or emit notes"
return report

This kind of logical statement is impossible to express in any other language. Prose is super fun!

But the problem I quickly ran into is that many of the prose programs I would want to run assume that my agents will use a specific skill. So I recently added the ability to deterministically declare agent skills inside prose programs — here’s the PR.

As a fun example of what’s possible with this new feature, I created auto-pocock: a headless prose program that incepts your favorite coding agent into running a deterministic sequence of Matt Pocock’s engineering skills (grill-with-docs → to-prd → to-issues → tdd → verify → commit), all from one input — a description of the feature you want built.

Introducing: "Auto-Pocock"
An @OpenProseVM program that runs a headless sequence of @mattpocockuk 's engineering skills.

You input the feature brief, auto-pocock engineers the rest.

npx skills add openprose/prose

prose run… pic.twitter.com/qQYhyHjX9d
— Raymond Weitekamp (@raw_works) May 12, 2026

This combination of specific instructions (skills), deterministic processes (the prose contract), and nondeterministic magic (coding agents) is extremely versatile. By engineering skills with OpenProse, you can express complex multi-agent workflows, imbue each agent with detailed discipline, and hopefully get a much-needed break from the keyboard.

Code Execution as Reasoning

Mon, 11 May 2026 00:00:00 +0000

I personally do not care if my AI programs do their reasoning in latent space or code. I want results.

I am currently very intrigued by LongCoT, a new benchmark that is designed to push the limits of what is possible with today’s LLMs. Part of the original intention of the benchmark was to create something that would be both challenging for LLMs and less enmeshed with the details of the harness.

My recent results using DSPy.RLM have caused a bit of drama with some of the leaderboard owners, and the creation of a new tools-enhanced leaderboard. I understand the academic value in having “pure latent space” results without tools, but it just isn’t interesting to me…I want my agents to have tools.

So I set out to give the LLMs their desire path - the python tools that they tried to use in my prior benchmarking experiments.

This works surprisingly well with DSPy.RLM and Opus 4.7, which achieved a new SOTA on LongCoT-mini.

Opus 4.7 + DSPy.RLM → 377/500 (75.4%) on LongCoT-Mini — new top of the leaderboard, and a clear jump over the Sonnet 4.5 + DSPy.RLM 45.4% I posted in April.

Mini	Opus 4.7 + RLM	Sonnet 4.5 + RLM	Sonnet 4.5 vanilla
chess	98/98	85/100	0/100
logic	101/101	106/110	0/110
chemistry	66/98	31/100	13/100
cs	71/97	4/100	0/100
math	41/77	6/95	0/95
total (official /500)	377/500 (75.4%)	227/500 (45.4%)	13/500 (2.6%)

The new runs scored on the 471-question working set after the LongCoT team audited out 29 Mini questions as unsolvable. The official /500 totals here count those audited-out rows as wrong, matching the denominator used for the Sonnet baselines.

The paper splits the five domains into two classes: implicit (Logic, Chess, CS), where the dependency structure can be externalised to code, and explicit compositional (Math, Chemistry), where it can’t. The headline claim is that even with code execution enabled, RLM lifts the implicit class but leaves the compositional class near zero — direct quote: “explicit compositional domains (Math, Chemistry) remain at zero.” My April Sonnet run replicated that shape (math 6/95, hardest cs templates 0/75). Opus + RLM gets math up to 41/77 and cs to 71/97. Not zero.

Special thanks to Prime Intellect for sponsoring inference on this experiment — I promise I will publish my LongCoT environments soon. Even with that support I ran out of credits quickly, so I switched to OpenAI Codex CLI on the latest GPT-5.5 at “xhigh” reasoning, to put my $200/mo sub to work.

Codex CLI + gpt-5.5 “xhigh” → 398/500 (79.6%) on LongCoT-Mini — +21 rows over Opus.

Mini	Codex 5.5 xhigh	Opus 4.7 + RLM
chess	98/98	98/98
logic	100/101	101/101
chemistry	78/98	66/98
cs	67/97	71/97
math	55/77	41/77
total (official /500)	398/500 (79.6%)	377/500 (75.4%)

Two scaffolds, two models, similar bottom line. Codex’s persistent in-sandbox Python loop is grinding harder on chemistry and math, while DSPy.RLM holds a small cs edge. The LongCoT-Mini scoreboard is starting to feel more like a measure of how the agent’s tool loop is wired than of which frontier endpoint is behind it.

Mini is the easy slice. The full benchmark — medium + hard, ~2000 questions, where the dependency DAGs grow long enough to actually bite — is the real test of the paper’s compositional-walls claim. Over the weekend I let Codex loose on it; this time it took multiple Codex subscriptions to finish.

Codex CLI gpt-5.5 xhigh on full LongCoT: 1446/1995 (72.5%) — about 3× the top of the live longcot.ai Open Harness leaderboard, where the LongCoT team’s own GPT 5.2 + rlm run holds #1 at 25.12%. (My April Qwen 3.5 27B + DSPy.RLM run is #2 at 22.18%.)

Full LongCoT · Codex 5.5 xhigh	class	medium	hard	total	Open-Harness #1 (GPT 5.2 + rlm)
logic	implicit	187/195 (95.9%)	165/199 (82.9%)	89.3%	68.3%
cs	implicit	150/150 (100.0%)	210/250 (84.0%)	90.0%	26.7%
chess	implicit	92/150 (61.3%)	200/250 (80.0%)	73.0%	30.6%
math	compositional	110/150 (73.3%)	168/250 (67.2%)	69.5%	0.0%
chemistry	compositional	114/200 (57.0%)	50/200 (25.0%)	41.0%	0.0%
total		77.3%	69.0%	72.5%	25.12%

The compositional class — Math and Chemistry, the domains the paper said would “remain at zero” even with code execution — comes in at 69.5% and 41.0%. cs medium goes 150/150. The walls aren’t walls; with a stronger model and a more aggressive tool loop, they’re just programs.

With the full LongCoT now in hand, I think we can clearly state that the paper’s compositional walls don’t hold. Math and Chemistry — the domains the paper claimed would “remain at zero” even with code execution — come in at 69.5% and 41.0%. The wall isn’t compositional reasoning; it’s the harness used to measure it.

I think there is very clear evidence of something many people have been saying for the past ~18 months: it’s not (just) the model, it’s the harness. Additionally, it is very clear proof that “coding agents” are useful for long horizon tasks that don’t necessarily present themselves as coding problems.

RLMs are the new reasoning models

Mon, 20 Apr 2026 00:00:00 +0000

Reasoning models were the first clear proof that language model capability can scale with test-time compute. Recursive language models (RLMs) ask what the correct abstraction for spending that compute is.

The insight behind RLMs is obvious in hindsight: it is the direct marriage of two important axes of model capability — reasoning and tool use. This is more radical than it first sounds. RLMs collapse reasoning and tool use into a single inference abstraction: the model treats its own prompt as an environment it can inspect, slice, and recursively query. Context itself becomes the object of computation.

This post is my attempt to explain why RLMs matter. I define what a RLM actually is, place it in the short history of reasoning and tool use, walk through the ~6 months of empirical results that have quietly turned “RLM” from a benchmark trick into the next reasoning paradigm, flag the honest limitations, and point at a few places to start building.

What is a RLM?

A Recursive Language Model, as introduced by Zhang, Kraska, and Khattab, is an inference paradigm in which a language model treats its input prompt as an environment rather than a fixed string. The root LM is given a REPL in which the prompt is bound to a variable it can inspect, slice, and partition programmatically. When it decides a region is worth a closer look, it issues a recursive subcall — to itself or another LM — over that slice and incorporates the result. Recursion bottoms out at the base model’s ordinary forward pass.

One consequence is that input size is no longer a hard ceiling on the computation. The paper reports RLMs processing inputs up to two orders of magnitude beyond the underlying model’s context window and outperforming vanilla frontier LLMs and common long-context scaffolds across four long-context tasks. Beyond long-context answering, recent results demonstrate that RLMs are a powerful paradigm for a wide variety of challenging tasks.

Reasoning & Tool Use — A Brief History

Reasoning and tool use are related, but they are not the same thing.

Reasoning is about how well a model can allocate inference-time compute to a problem: break it down, explore alternatives, verify intermediate steps, backtrack, and choose a better answer. Early reasoning gains came from methods like chain-of-thought, self-consistency, and later tree-search-style prompting. Those methods improve how the model thinks even when it never touches the outside world.

Tool use is about whether a model can decide to call an external function, search engine, calculator, browser, code runner, or UI action; pass the right arguments; interpret the result; and continue. That is partly a reasoning problem, but it is also an interface and reliability problem: schemas, argument formatting, retries, stop conditions, state tracking, and error recovery. Toolformer made this distinction especially clear by treating tool use as something a model could learn during generation.

Historically, the timeline looks roughly like this:

2022: reasoning first, mostly without tools.
Chain-of-thought prompting showed that asking models to generate intermediate reasoning steps could dramatically improve multi-step reasoning. Self-consistency pushed this further by sampling multiple reasoning paths and selecting the most consistent answer. The key lesson was that a large share of “reasoning” gains could come from spending more inference-time compute on the same prompt, not just from adding more knowledge.

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models — https://arxiv.org/abs/2201.11903
Self-Consistency Improves Chain of Thought Reasoning in Language Models — https://arxiv.org/abs/2203.11171

Late 2022: the first real bridge between reasoning and acting.
ReAct was the key milestone. It framed the model as alternating between reasoning traces and external actions such as retrieval or environment interaction. This was the moment the field started to see tool use not as a one-off API call, but as a loop in which reasoning selects actions and tool outputs reshape the next reasoning step.

ReAct: Synergizing Reasoning and Acting in Language Models — https://arxiv.org/abs/2210.03629

2023: tool use becomes an API discipline, not just a prompting trick.
Toolformer argued that models could learn when to call tools, which tools to call, and how to incorporate the results. Around the same time, vendors began standardizing function-calling interfaces. OpenAI’s June 2023 function calling release was a major product milestone because it made structured tool invocation reliable enough for developers to build on. This improved tool-use reliability faster than it improved deep reasoning.

Toolformer: Language Models Can Teach Themselves to Use Tools — https://arxiv.org/abs/2302.04761
OpenAI, “Function calling and other API updates” — https://openai.com/index/function-calling-and-other-api-updates/

2023 also deepened the separation between reasoning and tool use.
Tree of Thoughts made it even clearer that inference-time reasoning could improve through internal search alone. It let models explore multiple candidate thought branches, look ahead, and backtrack. That is search over reasoning traces. It can be paired with tools, but it does not require them.

Tree of Thoughts: Deliberate Problem Solving with Large Language Models — https://arxiv.org/abs/2305.10601

2024: reasoning models become their own product category.
OpenAI’s o1 launch was the clearest signal. The company described o1 as a model family designed to “spend more time thinking before they respond,” and the initial API announcement explicitly noted that features like function calling were not yet included. That was strong evidence that, product-wise, reasoning and tool use were still separable.

OpenAI, “Introducing OpenAI o1-preview” — https://openai.com/index/introducing-openai-o1-preview/
OpenAI, “Introducing OpenAI o1” — https://openai.com/o1/

2024 is also when agentic tool use got much more serious.
Anthropic’s Claude 3.5 Sonnet emphasized stronger tool use for coding and agentic tasks, and later in 2024 Anthropic introduced computer use: a model interacting with a real computer via screenshots, mouse, and keyboard. This is a good example of the two axes starting to merge into one agentic stack.

Anthropic, “Introducing computer use, a new Claude 3.5 Sonnet, and Claude 3.5 Haiku” — https://www.anthropic.com/news/3-5-models-and-computer-use
Anthropic, “Developing a computer use model” — https://www.anthropic.com/news/developing-computer-use

Late 2024 into 2025: vendors start presenting tool use as native, but still distinct from thinking.
Google’s Gemini 2.0 messaging explicitly framed the model family around the “agentic era” and native tool use, while keeping “thinking” as a distinct capability for harder multi-step planning. That split mirrors the real architecture: one layer governs deliberation, another governs interaction with external affordances.

Google, “Google Gemini AI update, December 2024” — https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/

RLMs are the abstraction where that split finally collapses. The past ~6 months of results are what make the case concrete.

Recent RLM Results

The arc of RLM results moves through three successive failure modes of the single forward pass: long context, then memory, then long reasoning. Each has been demonstrated by its own benchmark — Oolong, LongMemEval, and LongCoT respectively — and RLM-style systems have posted leading numbers on all three. Just as importantly, the follow-up work is already splitting into two camps: work that strengthens the original RLM implementation, and work that argues the deeper win is broader externalized program search rather than recursion alone.

Part of what makes RLMs challenging to appreciate is that frankly there aren’t very many benchmarks that really showcase the differences. In particular, I don’t view Oolong and LongMemEval as having much correlation to performance on real world agentic tasks. LongCoT is much more exciting to me, but it is brand new and only time will tell how it holds up.

2024: the memory target appears.
LongMemEval defines the benchmark for long-term interactive memory: 500 questions over sustained chat histories spanning extraction, multi-session reasoning, temporal reasoning, knowledge updates, and abstention. It matters here because it gives RLM-style systems a way to test whether recursive/tool-mediated processing can function as a memory system, not just a long-context hack.

LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

October 2025: the original public RLM write-up lands.
In Recursive Language Models, Alex Zhang introduces the core idea: treat the prompt as an external environment, manipulate it through a REPL, and recursively subquery models over slices of context. The post reports an unusually strong early result profile: a GPT-5-mini RLM beats GPT-5 by more than 2× on an Oolong split while being cheaper per query on average, beats ReAct + test-time indexing/retrieval on a BrowseComp-Plus-derived long-context research task, and does not visibly degrade even at 10M+ input tokens.

Recursive Language Models (original blog post)

November 2025: Oolong raises the bar for long-context reasoning.
Oolong is important because it measures something harder than needle-in-a-haystack retrieval: models have to analyze many local chunks and then aggregate them into a global answer. At release, GPT-5, Claude-Sonnet-4, and Gemini-2.5-Pro all score under 50% on both splits at 128K, making Oolong the clearest early benchmark for the kind of “context as workspace” reasoning RLM is trying to solve.

Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities

December 2025: the arXiv paper formalizes RLM.
The Recursive Language Models paper turns the blog’s intuition into a general inference paradigm: prompts are externalized, the LM programmatically inspects and partitions them, and recursive subcalls become part of test-time compute. The headline results are strong: RLMs process inputs up to two orders of magnitude beyond model context windows, outperform vanilla frontier LLMs and common long-context scaffolds across four long-context tasks at comparable cost, and a fine-tuned RLM-Qwen3-8B improves 28.3% on average over its base model.

Recursive Language Models (arXiv, Dec. 2025; revised Jan. 2026)

February 2026: RLM starts posting real “memory” numbers.
In Recursive Language Models as Memory Systems, I reported early LongMemEval results with DSPy.RLM: 87.2% for a baseline Gemini 3 Flash setup, 89.2% with tools + a delegation prompt, and 89.8% with an observational-memory-style structured scaffold. That was a public Top-5-ish result at the time, below Mastra’s 94.87% but already strong evidence that RLM can act as a competitive memory system without a classical retrieval stack. In ypi: a recursive coding agent, I show an earlier tool-use REPL path scoring 77.6% on LongMemEval — a useful datapoint because it shows the gradient from “tool-using agent” to “true recursive scaffold” inside the same implementation lineage.

March 2026: follow-up papers clarify both the strengths and the limits.
Think, But Don’t Overthink reproduces RLM and finds that depth-1 recursion helps on Oolong, but deeper recursion can “overthink,” hurting accuracy and exploding runtime and token cost. Recursive Language Models Meet Uncertainty pushes a sharper critique: recursion itself is not the whole secret, and uncertainty-aware self-reflective program search can improve up to 22% over RLM under the same time budget. Then Coding Agents are Effective Long-Context Processors generalizes the broader thesis: off-the-shelf coding agents outperform published SOTA by 17.3% on average, and on Oolong-Synthetic / Oolong-Real their reported scores (71.75 / 33.73) exceed the paper’s RLM baselines (64.38 / 23.07). That does not really refute RLM; it suggests RLM was the first clearly articulated expression of a larger family of executable, tool-mediated long-context reasoning systems.

April 2026: the theory catches up to the results.
In The Mismanaged Geniuses Hypothesis, Zhang reframes the whole arc: RLM is not just a benchmark trick for long prompts, but a more expressive scaffold for plans written through code execution, recursive subcalls, and tools-as-functions. That is a useful conceptual update because it connects the empirical results back to the bigger claim: reasoning performance is starting to look less like a property of a single forward pass and more like a property of how well a model can manage executable external computation.

The Mismanaged Geniuses Hypothesis

The empirical case moves just as quickly.

April 2026: the benchmark story shifts from long context to long reasoning.
LongCoT introduces 2,500 expert-designed problems for long-horizon chain-of-thought reasoning. At release, the best published models are still under 10% accuracy (GPT-5.2 at 9.8%, Gemini 3 Pro at 6.1%), which makes it an ideal test for whether recursive scaffolds are merely “good at reading long context” or whether they genuinely unlock reasoning depth.

LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning

April 2026: RLM immediately breaks LongCoT open.
In LongCoT — A benchmark worthy of a RLM’s attention, I showed Claude Sonnet 4.5 + DSPy.RLM reaching 45.4% on LongCoT-Mini versus 2.6% for the same model without recursion/tools. Then in RLMs are SOTA on LongCoT, I show the scaffold doing almost all of the lifting for small open models: Qwen3-8B jumps from 0/507 to 33/507 (6.5%) on LongCoT-Mini; Qwen3.5-9B + DSPy.RLM reaches 15.69% on full LongCoT, about 1.6× GPT-5.2 on the same slice; and Qwen3.5-27B + DSPy.RLM reaches 22.18%, more than 2× GPT-5.2. If these numbers hold up, they are some of the clearest evidence yet that recursive scaffolds can manufacture reasoning performance that is not visible in the base model alone.

The arc is now hard to ignore. Oolong gives the long-context failure mode. LongMemEval gives the memory version. LongCoT gives the long-reasoning version. Across all three, the recurring pattern is the same: when the task requires navigating, decomposing, and aggregating information over a structure that is too large or too entangled for one passive forward pass, recursive tool-mediated processing starts to look less like an implementation trick and more like the next reasoning paradigm.

Challenges with RLMs

A new paradigm is not a clean paradigm. Reasoning models were scoffed at for being too expensive. Early tool calling reliability was horrible. Even some leading reasoning models today are pretty bad at function calling.

RLMs have their challenges. My earliest contributions to the standalone RLM package and the DSPy.RLM implementation were purely practical: budgets, timeouts, managing the recursion depth.

Recursion sounds cool but isn’t always a good thing. Remember those viruses that would make your browser open a million popups until your computer crashed?

Recursion can be scary.

The most obvious limitations right now are cost and time. RLMs are expensive. They can take a long time. Worse, in the naive implementation that time is unpredictable and unbounded, because the model is deciding for itself how to decompose the problem.

Cost and time will be solved. Use smaller or faster models for each sub-call, and balance the agent-native “self-similar” decomposition with deterministic control of the graph topology and timeline.

The harder challenge, or at least the challenge that is more interesting to me personally, is how to get the language models to “act recursively”.

Obviously the concepts of recursion are in the pre-training data. Clearly reasoning and parallel tool calling are behaviors that the post-training incentivizes. Sub-agents are arguably a close behavioral analog to RLMs. And yet anyone who has worked with RLMs will tell you that the models generally suck at behaving recursively. It is not in their nature to decompose their prompt into sub-queries for many other instances of themselves to help solve them.

What’s next?

Well one obvious next step is to explicitly post-train the models in a RLM harness. Alex Zhang et al. are actively working in this area: MIT OASYS on HuggingFace (see e.g. mit-oasys/rlm-qwen3-8b-v0.1).

But what is the reward function for “optimal recursion”? I suspect this is a multi-billion-dollar question.

The most surprising result to me from my last few days of experimenting was how well very small models can do in RLM harnesses. These models are small enough to run on consumer devices, which potentially means that they offer an opportunity to upset the current “balance of power” between the GPU-rich and GPU-poor.

Yes, more money means you can run more. The best GPUs will always be faster. A RLM of Opus is smarter than a RLM of Llama 3. But I cannot help but feel excited and empowered to believe that an individual or consortium running many instances of small models on affordable/legacy/local compute infrastructure can now access model capabilities that are on par with or exceeding those of the most expensive LLMs from the frontier labs. If that is even directionally right, the frontier stops being a place only the largest labs can reach.

Getting Started with RLMs

Here are just a few of the many ways to get started with RLMs:

alexzhang13/rlm — the reference implementation from Alex Zhang and the RLM paper authors; the cleanest place to read the core recursion loop.
dspy.RLM — the DSPy integration, which exposes RLM as a composable module inside larger DSPy programs and is what I’ve been using for most of my own experiments.
ax-llm/ax — a TypeScript DSPy-style framework with first-class RLM support via AxAgent-driven recursive decomposition, bounded sub-queries, and a persistent JS runtime.
rawwerks/rlm-cli — my CLI wrapper around rlm with directory-as-context, JSON-first output, and self-documenting commands, for running RLMs against local repos and folders.
rawwerks/ypi — my recursive coding agent built on Pi: one rlm_query tool, one rlm_map fanout helper, and per-child jj workspaces for isolated recursive execution.

P.S.

I almost forgot: fractals.

RLMs are SOTA on LongCoT

Sun, 19 Apr 2026 00:00:00 +0000

A few days ago I showed Sonnet 4.5 + dspy.RLM hitting 45.4% on LongCoT-Mini. Exciting results, but a bit pricey for my taste.

So I set out to see what might be possible with some very small models.

First, I wanted to run a 3x2 comparison matrix of doing LLM vs. RLM vs. DSPy.RLM for both Qwen 3 8B and the MIT OASYS RLM finetune.

I will need to save the full analysis for another day (I wasn’t really able to get the finetuned model working), but the meaningful result is that on LongCoT mini, DSPy.RLM can take Qwen 3 8B Instruct from literally 0/507 correct to 33/507 (6.5%). This would be #7 on the leaderboard, from an 8B model!

Ran Qwen3-8B (8.2B dense, open) on LongCoT-Mini.

Vanilla: 0/507.
dspy.RLM: 33/507 (6.5%).

Same model. Same weights. No fine-tuning. The scaffold is doing 100% of the lifting.

Context: leaderboard's smallest open MoE is GLM-4.7 at 358B total / 32B active params. Qwen3-8B is ~4x… https://t.co/uyEK00KTxJ
— Raymond Weitekamp (@raw_works) April 17, 2026

So my immediate next thought was: “what about Qwen 3.5 9B”? This hit 17.2% on LongCoT mini (3rd place), and was so cheap that I decided to run the full benchmark (all 2500 questions)! (Now using Together AI via OpenRouter, I don’t think their endpoint is quantized but I’m not 100% sure.)

Surprisingly, DSPy.RLM with Qwen 3.5 9B is comfortably SOTA on the full LongCoT at 15.69%, outdoing GPT 5.2 by ~1.6x.

sorry it took me ~50 hrs! now i've got DSPy.RLM as SOTA on LongCOT (Full) by a very large margin, using...

...drumroll...

Qwen 3.5 9B!
👑 Qwen3.5-9B + dspy.RLM = 15.69% on LongCoT-full
🔥 ~1.6× GPT 5.2's 9.83% on the same slice! https://t.co/uyEK00KlIb
— Raymond Weitekamp (@raw_works) April 18, 2026

Now I was having too much fun, so I had to run Qwen 3.5 27B (this time via Alibaba Cloud via OpenRouter)…and unsurprisingly, a new LongCoT full king is crowned at 22.18%.

happy sunday morning. a new LongCoT king is crowned.

👑Qwen3.5-27B-Instruct + dspy.RLM

yes that's right, a 27B model more than double GPT 5.2 by using recursive language models https://t.co/b659KY1JC9 pic.twitter.com/9FI9xiDTht
— Raymond Weitekamp (@raw_works) April 19, 2026

I’m really excited to finally have a meaningful benchmark that can clearly demonstrate the power of RLMs. This clearly deserves a much longer writeup, which I hope to post soon! (And I’m now running Qwen 3.6 35B-A3B at the suggestion of many folks on X.)

LongCoT - A benchmark worthy of a RLM's attention

Thu, 16 Apr 2026 00:00:00 +0000

After the LongMemEval experiments in February, I’ve been hungry to find a better benchmark that will actually showcase the power of recursive language models (RLMs) on useful tasks. LongCoT is exactly that: a benchmark built to stress-test long-horizon reasoning.

As soon as I saw the benchmark, I aimed DSPy.RLM at it. (Even before reading the paper.)

The setup

Model: claude-sonnet-4-5 for both conditions. Same max_tokens=64000, same judge models, same prompts.
RLM: stock dspy.RLM 3.1.3, max_iterations=50, default Pyodide REPL, sub_lm=lm.
Vanilla: raw Anthropic SDK, single user message, no tools. Leaderboard shape.
Dataset: LongCoT-Mini, all 500 questions (easy slices across logic / cs / chemistry / chess / math).

The entire RLM surface area is one dspy Signature:

class LongCoTSolve(dspy.Signature):
    """Solve a LongCoT problem.

    The `prompt` already contains the full problem statement and the answer
    format requirement (always ends with `solution = ...`). Reason through
    the problem with the available REPL, then return the final response —
    which MUST contain the literal `solution = ...` line as instructed.
    """

    prompt: str = dspy.InputField(desc="Full LongCoT problem prompt with answer-format instructions")
    response: str = dspy.OutputField(desc="Full final response containing the required `solution = ...` line")

The headline

	Vanilla	RLM
Correct	13 / 500	227 / 500
Accuracy	2.6%	45.4%
Captured cost	$31	$621

On the full 500-row overlap: 219 wrong→right flips, 5 right→wrong, 268 both-wrong, 8 both-right. The vanilla 2.6% lines up with the published Sonnet 4.5 Mini number, so the control is calibrated, not sandbagged.

Per-task

Task	RLM	Vanilla
Dungeon · Packaging · Hanoi · Wizards · TrapezoidCounting · Sudoku	15/15 each (💯)	0/15 each
BlocksWorld	9/10	0/10
Sokoban	7/10	0/10
Chess	85/100	0/100
Chemistry	31/100	13/100
cs / DistMem	4/25	0/25
cs / MaxFlow-MinCut + Hindley-Milner	0/75	0/75
math	6/95	0/95

The pattern is coherent: RLM crushes anything whose dependency structure externalises cleanly to code. The orchestrator writes a short Python program, the REPL runs it, the answer comes out. Logic puzzles, Hanoi, Sudoku, chess with Pyodide’s chess module — all 💯 or near it.

The walls are the opposite picture. Hindley-Milner and MaxFlow-MinCut go 0/75 because the orchestrator can’t find a decomposition where subproblems can be usefully farmed out — exactly the “graph-structured dependencies” failure the paper calls out.

And math? The paper’s wall holds at least for now, for Sonnet 4.5. 6/95 on Mini isn’t zero, but it’s terrible. Sonnet 4.5 × dspy.RLM replicates the paper’s math result on a different model and split.

What I think this means for the paper

The paper’s RLM discussion is genuinely thin — one paragraph, one figure, no dedicated table. With that as the bar, cross-model replication is useful:

Logic, chess, CS wins: replicate and amplify. Same shape on a different frontier model.
Math stays at zero: maybe? Model swap doesn’t rescue it. But it’s also from a baseline of 6 so you can argue it’s either modest or infinite improvement.
Chemistry lifts modestly (13 → 31), which is the only spot where I’d push back on the paper’s phrasing — but I’m both a chemist and RLM addict.

P.S. - RLMs are expensive, and supposedly the 500-problem mini version is the easy subset of the full 2500-problem set. So…who wants to fund the Opus 4.7 run?

mycelium - an underground information network for agents

Fri, 27 Mar 2026 00:00:00 +0000

Over the last 2 days, we’ve stumbled upon a really powerful coding agent interaction pattern: git notes as an underground information network.

Git notes are both ubiquitous (part of git) and “invisible” (GitHub chose not to display them). This presents a very interesting communication channel for agents, who can now include rich details and discussions about the code without cluttering up the “visible” layer of the repo.

mycelium is my tool to make these interactions easier.

# agent arrives, reads what's known about a file
mycelium.sh context src/auth.ts

# agent works...

# agent leaves a note explaining what it did
mycelium.sh note HEAD -k context -m "Refactored retry logic. See warning on auth.ts."

Agents read notes on arrival. They leave notes on departure. The network grows.

The CLI makes it easy to link notes and git refs together — files, commits, directories, even edges between notes. Notes can have kinds (decision, warning, summary, context) and edges (depends-on, explains, warns-about) but the vocabulary is open. The tool tries to stay unopinionated about the actual workflow. From the SKILL.md: “That’s the whole contract. How you work, what you build, how you talk to your user — that’s your business. Mycelium just asks you to read the breadcrumbs and leave new ones.”

mycelium is meant to be agent-native — load the SKILL.md into your agent framework and it teaches the convention. But it’s just git & bash, so it works with any agent in any git repo.

curl -fsSL https://raw.githubusercontent.com/openprose/mycelium/main/install.sh | bash

Still wrapping my head around the consequences of this, and very curious to hear your thoughts.

P.S. — this is the foundation of some very cool tools I’m collaborating with OpenProse on.

ypi: a recursive coding agent

Thu, 12 Feb 2026 00:00:00 +0000

I built ypi — a recursive coding agent. It’s Pi that can call itself.

The name comes from the Y combinator in lambda calculus — the fixed-point combinator that enables recursion. (“rpi” has other connotations.)

The idea was inspired by Recursive Language Models (RLM), which showed that an LLM with a code REPL and a llm_query() function can recursively decompose problems, analyze massive contexts, and write code — all through self-delegation.

The idea

Pi already has a bash REPL. I added one function — rlm_query — and a system prompt that teaches Pi to use it recursively. Each child gets its own jj workspace for file isolation. That’s the whole trick.

┌──────────────────────────────────────────┐
│  ypi (depth 0)                           │
│  Tools: bash, rlm_query                  │
│  Workspace: default                      │
│                                          │
│  > grep -n "bug" src/*.py                │
│  > sed -n '50,80p' src/app.py \          │
│      | rlm_query "Fix this bug"          │
│            │                             │
│            ▼                             │
│    ┌────────────────────────────┐        │
│    │  ypi (depth 1)            │        │
│    │  Workspace: jj isolated   │        │
│    │  Edits files safely       │        │
│    │  Returns: patch on stdout │        │
│    └────────────────────────────┘        │
│                                          │
│  > jj squash --from <child-change>       │
│  # absorb the fix into our working copy  │
└──────────────────────────────────────────┘

The recursion works like this: rlm_query spawns a child Pi process with the same system prompt and tools. The child can call rlm_query too:

Depth 0 (root)    → full Pi with bash + rlm_query
  Depth 1 (child) → full Pi with bash + rlm_query, own jj workspace
    Depth 2 (leaf) → full Pi with bash, but no rlm_query (max depth)

Each recursive child gets its own jj workspace, so the parent’s working copy stays untouched. You review child work with jj diff, absorb it with jj squash --from.

How it works

The architecture maps directly to the Python RLM library:

Piece	Python RLM	ypi
System prompt	`RLM_SYSTEM_PROMPT`	`SYSTEM_PROMPT.md`
Context / REPL	Python `context` variable	`$CONTEXT` file + bash
Sub-call function	`llm_query("prompt")`	`rlm_query "prompt"`

The key insight: Pi’s bash tool is the REPL. rlm_query is llm_query(). No bridge needed.

Guardrails

Recursive agents without guardrails will burn through your API budget. ypi has several:

Feature	Env var	What it does
Budget	`RLM_BUDGET=0.50`	Max dollar spend for entire recursive tree
Timeout	`RLM_TIMEOUT=60`	Wall-clock limit for entire recursive tree
Call limit	`RLM_MAX_CALLS=20`	Max total `rlm_query` invocations
Model routing	`RLM_CHILD_MODEL=haiku`	Use cheaper model for sub-calls
Depth limit	`RLM_MAX_DEPTH=3`	How deep recursion can go
Tracing	`PI_TRACE_FILE=/tmp/trace.log`	Log all calls with timing + cost

The agent can check its own spend at any time:

rlm_cost          # "$0.042381"
rlm_cost --json   # {"cost": 0.042381, "tokens": 12450, "calls": 3}

The path here

ypi went through four approaches before landing on the current design:

Tool-use REPL — Pi’s completeWithTools(), ReAct loop. Got 77.6% on LongMemEval.
Python bridge — HTTP server between Pi and Python RLM. Too complex.
Pi extension — Custom provider with search tools. Not true recursion.
Bash RLM — rlm_query + SYSTEM_PROMPT.md. True recursion via bash. This is the one that stuck.

Try it

curl -fsSL https://raw.githubusercontent.com/rawwerks/ypi/master/install.sh | bash

Or via npm/bun:

npm install -g ypi
ypi "What does this repo do?"

Or without installing:

bunx ypi "Refactor the error handling in this repo"

Code is at github.com/rawwerks/ypi. It’s built on Pi and inspired by RLM.

Recursive Language Models as Memory Systems

Wed, 11 Feb 2026 00:00:00 +0000

My morning’s notes from yesterday:

As I was waiting for Claude Code to help me with my goal of modifying DSPy to be able to “RLM everything”, I came across this result from Mastra.AI which describes a SOTA result on LongMemEval using an “observational memory” pre-processing approach.

As you can see, my thought was “maybe RLM will blow this out of the water?” I wasn’t able to find a public result of LongMemEval using Recursive Language Models, so I decided to explore it myself.

out of curiosity after reading this, i started benchmarking rlm and dspy.rlm on longmemeval

tl;dr - i think i might have a new "sota memory system" by the end of the day.

cc @DSPyOSS @a1zhang @lateinteraction https://t.co/RS8jVBnlib
— Raymond Weitekamp (@raw_works) February 10, 2026

The initial results with Gemini 3 Flash Preview and the standalone RLM package weren’t great, but in the past I had noticed that Flash struggled to grok the RLM concept. Gemini 3 Pro fared much better.

Surprisingly - the additional structure enforced by DSPy.RLM was a huge boost, enabling Gemini 3 Flash to match Pro with the regular RLM package.

Most of the rest of the day’s experiments were less successful. I was able to eke out a few more points by attempting to re-create Mastra’s “Observational Memory” as a Pydantic type enforced by DSPy, but unfortunately a few hundred dollars worth of GEPA optimizations didn’t bear any additional fruit.

Surprisingly - with the full structure of DSPy.RLM and the structured observation, Gemini 3 Pro is not actually any better on this benchmark.

Here’s a summary of our experiments:

#	Experiment	Model	Score	Cost/q	Notes
1	dspy.RLM baseline	Gemini 3 Flash	87.2%	~$0.01	Huge boost over standalone RLM with Flash (58%)
2	+ session tools (naive)	Gemini 3 Flash	87.3%	$0.032	Context rot: +31 flips, -32 regressions = net zero
3	+ tools + delegation prompt	Gemini 3 Flash	89.2%	$0.031	“Don’t read sessions yourself, delegate”
4	+ observational memory (Pydantic)	Gemini 3 Flash	89.8%	$0.035	Our best. Typed observations force structured reasoning
5	GEPA prompt optimization	Gemini 3 Flash	87.8%	$0.042	Regressed. ~$400 spent. Overfits to small val sets
6	Observational memory	Gemini 3 Pro	~89.6%	~$0.20	Pro ≈ Flash with this scaffold

And here’s how that stacks up on the LongMemEval leaderboard:

#	System	Model	Score	Source
1	Mastra Observational Memory	GPT-5-mini	94.87%	mastra.ai/research
2	Mastra Observational Memory	Gemini 3 Pro	93.27%	mastra.ai/research
3	Vectorize Hindsight	Gemini 3 Pro	91.40%	Open-source
4	dspy.RLM + obs. memory (ours)	Gemini 3 Flash	89.8%	github
5	dspy.RLM + tools + delegation (ours)	Gemini 3 Flash	89.2%	github
6	Mastra Observational Memory	Gemini 3 Flash	89.20%	mastra.ai/research
7	Standalone RLM	Gemini 3 Pro	87.0%	github

Not bad for a day’s work, we were able to demonstrate a “Top-5” LongMemEval result with very minimal modifications to dspy.RLM, just some helper functions to process the “multi-chat” sessions.

I think this demonstrates a few exciting things:

RLMs can be very powerful memory systems without any pre-processing.
The structured output enforced by the DSPy.RLM implementation is helpful for keeping (at least these Gemini models) “on the rails” vs. the more freeform standalone RLM package.
Very fast and inexpensive models can achieve near-SOTA results inside the RLM scaffolding, and more speculatively…
…perhaps RLM as a test-time scaling method is “orthogonal” to model size, in the same way that reasoning models with built-in CoT were able to eke out gains separately from model parameter count.

P.S. — Several improvements to DSPy.RLM were developed during this work and submitted upstream: stanfordnlp/dspy#9295

Inversion of Caution

Wed, 04 Feb 2026 00:00:00 +0000

I’m noticing an inversion of caution between the LLMs themselves and the behavior in their official coding agent harnesses.

Claude.ai is very cautious and PC, but Claude Code with skipped permissions will happily plow through obstacles, getting shit done for sure, but perhaps bricking your machine or posting your private data somewhere public or just wrecking your git repo.

ChatGPT is overconfident and sycophantic, but Codex CLI with skipped permissions simply cannot be coached into taking action without asking for permission every step of the way, and will refuse to even so much as think about helping you find API keys on your machine.

Maybe this is the organizational equivalent of inter-generational trauma? Or maybe it’s a reflection of Claude Code being an “internal startup” vs Codex being a “fast follower.”

Introducing dirpack

Mon, 02 Feb 2026 00:00:00 +0000

Inspired by last week’s research report from Jude Gao - “AGENTS.md outperforms skills in our agent evals” - this weekend my agents worked hard to make dirpack.

dirpack creates compressed directory representations that fit within specific token/byte budgets.

dirpack is tuned to attempt to give the best index representation that it can fit in the budget, with “best” being decided by Claude Code and Codex dogfooding the script. Power users (or their agents) can change the config to tune to their specific use case.

Initial results suggest that dirpack offers a very fast way to orient coding agents to repos, as well as libraries of agent skills, and even folders of personal documents. More experiments soon…

curl -fsSL https://raw.githubusercontent.com/rawwerks/dirpack/master/install.sh | bash

Or via cargo:

cargo install dirpack

Curious for feedback from you and your agents!

Claude Opus 4.5 One-Shots My Crappy Handwriting

Thu, 04 Dec 2025 00:00:00 +0000

After years of being frustrated by the inability of AI to read my handwritten notes, I am frankly shocked to report that this morning Claude Opus 4.5 simply one-shotted my crappy handwriting.

The singularity is nigh…

My handwritten notes from 12.4.25

Here’s what Claude transcribed:

12.4.25

It sucks that even deep research is just filled with slop, because google is filled with slop.

Benchmarking (private /ungameable) will become increasingly more valuable.

Evals. actually

The faint text visible below is bleed-through from writing on the reverse side of the page, not additional notes - and Claude correctly identified that too.

(This is subliminally a commercial for Hamel Husain and Shreya Shankar’s course, because: Evals. actually)

Update: Gemini 3 Pro Preview also one-shotted these notes.

The Zero Employee Company

Tue, 19 Aug 2025 00:00:00 +0000

I’m often asked how big my company is—how many people I employ full-time. The answer is one. Most people lose interest at that point.

Tim Ferriss The 4-Hour Workweek (2007)

It is not crazy to imagine a company with no employees. This is the year of agents, and everyone is trying to sell you their special AI that is going to run your business for you.

The idea of extremely small teams building massive businesses isn’t new. Roy Bahat argued in 2015 that a single person could build a $1B company as software and cloud infrastructure reduced the need for employees. Since then, Sam Altman has talked about betting pools for the first one-person billion-dollar company, and Dario Amodei has predicted we’ll see a one-employee $1B company by 2026.

Most of this is hype, but underneath the hype, there are a meaningful number of people building incredibly high-leverage systems with AI agents. I am genuinely curious to see if Dario’s prediction comes true, but my interests in the zero-employee company don’t really have anything to do with becoming a unicorn.

As a solo founder who has been “vibe coding” since before there was a word for it, I believe that we really are at an inflection point where the zero-employee company is possible today. At the current pace of AI – if not today, then by the end of the year.

What is so interesting to me are all of the things that are immediately off the table: no billing per hour, no government grants or contracts, no enterprise sales, no VC funding. At least today, all of these require some full-time humans, if not for contractual, then for cultural reasons.

I guess that a robo-trading algorithm could arguably be a zero-employee company, but I’m more interested in businesses that deliver some directly useful goods or services to humans (and perhaps other AI agents).

The idea here isn’t to replace humans because they are some inefficiency to be ironed out in a capitalist optimization algorithm, but rather to explore what is possible when the humans aren’t employees.

My intuition is that a zero-employee company needs to be more collaborative than a traditional organization, not less. Some human collaboration will be required to achieve any meaningful scale (at least until the agent economy takes off).

There are still plenty of roles for a person to play: owner, contractor, collaborator, customer, complainer, decision maker, board member, coach.

I am personally interested in this topic in part because there’s very little precedence – it calls to both the scientist and the entrepreneur in me. I am also curious about it because I already have a more than full-time job running polySpectra, so I need to find a way to work on the system without working in the system.

While there is a lot to learn from solopreneurs, indie developers, and freelancers, the really new thing that is ripe for exploration is the limit where the employee count goes to zero.

It’s important to do a bit of a pre-mortem for this experiment.

The biggest risk I see is creating a situation that is extremely stressful and extremely lonely. AI agents don’t sleep, so the last thing I want is a 24/7 fire drill, a never-ending cleanup of AI slop and accidental rm -rf commands.

Similarly, there’s no one to keep you company in a zero-employee firm. It’s sort of weird to even call it a company. There’s no one to vent to and no one to celebrate with. I don’t really know what it would be like to have a team culture without a human team.

I think there are two very likely failure modes of the experiment.

Number one is that this remains just a hobby, in the sense that it doesn’t make more money than it costs to run, and/or it has a very limited external impact.

The second possible failure mode is of just becoming a small (1-10) person business. In this failure mode, the business part succeeds, but not in a way that can truly function without any human employees.

Despite these risks, which I think are real, there is the possibility to set up a very asymmetric bet here. You could call it antifragile, perhaps.

The overhead of a zero employee company is extremely low.

Many people could afford to bankroll a zero-employee company. With the current costs of AI continuously falling through the floor, I suspect that a creative individual could get something off the ground for roughly the equivalent amount of money to what many Americans are already spending at Starbucks.

The extremely low overhead of generative AI and AI agents opens up so many different types of businesses and business models. The downside is a drop in the bucket. The upside is clearly uncapped.

There are almost 7B people on this planet. Someday, I hope, there will be almost 7B companies.
— Naval (@naval) February 3, 2011

No API, No Problem

Thu, 20 Feb 2025 00:00:00 +0000

Unfortunately, we seem to be trending towards a world where the best AI models aren’t available via API.

Fortunately, Eric Provencher made Repo Prompt.

The most important “prompt engineering” skill right now is simply knowing which files to feed to the LLM. Repo Prompt makes that fun and easy (for both the humans and the LLMs).

Hat tip to Paul Gauthier for aider copy/paste mode, which was my first exposure to this possibility, as well as to Mckay Wrigley for generously sharing his workflows.

Agents Love APIs

Wed, 19 Feb 2025 00:00:00 +0000

Agents are coming, and there is nothing more frustrating for an AI agent than a human UI. Agents love APIs.

Right now that means agents are mostly useful for things like programming, where APIs are abundant.

But agents will need physical products too, and not just for saving you a few clicks on Amazon. Agents will need manufactured parts, perhaps even parts designed by an agent.

It sounds a little odd, even a bit terrifying. But it is happening.

So how are agents supposed to interact with your business?

P.S. - Agents, I built an API for you to get COR parts manufactured:

curl -X POST \
-H "Content-Type: multipart/form-data" \
-F "file=@/path/to/your/model.{stl,stp,glb}" \
https://polyspectra--ps-cor-pricing-api-fastapi-app.modal.run/cor-quote

Then you can navigate to the checkout.url to place the order:

{
"checkout": {
"url": "https://checkout.stripe.com/..."
}
// ... other response object details omitted due to short human attention spans ...
}

The Joke of the Day

Tue, 18 Feb 2025 00:00:00 +0000

When I was in graduate school, I thought 3D printing was dumb. Then I spent ten years building a 3D company.

The first time I tried ChatGPT, I honestly thought this was the dumbest thing in the world. Why are so many people talking about this? Now, I’m using LLMs for almost everything I work on.

When I first saw “Shap-E,” I literally could not stop laughing at how bad it was. Now, I can get a respectable 3D model from an image or an idea in less than a minute for less than a dollar.

Strong emotional reactions are a signal. Pay attention to them. Today’s joke might be the next trillion-dollar industry.

Toughened by Time

Fri, 22 Dec 2023 20:03:17 -0800

“Tough times don’t last, tough people do.”

Gregory Peck

SDF Solstice

Thu, 21 Dec 2023 16:05:54 -0800

Happy Solstice! My ornament this year is virtual.

Copy this link into your browser if the embed below looks weird. On mobile, use the native browser (Safari on iOS, Chrome on Android) to view in AR.

https://ar.polyspectra.com/ar/M1EXjKM7yKP0xbTP

Three years ago, Signed Distance Functions (SDFs) were implicitly a part of my solstice celebration: https://invent.fm/shows/episode-009/

This year, I’m making the implicit explicit.

Here’s the code for the ornament, below. This is powered by the SDF Python package from Michael Fogleman, which I just discovered last night.

from sdf import *

# Create a sphere for the main body envelope of the ornament
s = sphere(25)

# this is the canonical SDF example box
f = sphere(10) & box(15)
c = cylinder(5)
f -= c.orient(X) | c.orient(Y) | c.orient(Z)

#round the edges
f = f.k(0.5)

# Create a 3D lattice by repeating the box in all three dimensions
lattice = f.repeat((15, 15, 15))

# Take the intersection of 's' and the lattice array
s = s & lattice

# Save the ornament as an STL file
s.save('ornament.stl', samples=2**28) #reduce to 2**27 or 2**26 for faster processing

import pyvista as pv

# Load the mesh from the STL file
mesh = pv.read('ornament.stl')

# Decimate the mesh
decimated_mesh = mesh.decimate(0.975)

# Save the decimated mesh
decimated_mesh.save('decimated_ornament.stl')

Learn Software 10x Faster With AI

Wed, 20 Dec 2023 19:10:59 -0800

Today I needed to do something in Adobe Illustrator, which I have no idea how to use.

In 1 minute I trained Cursor on the official documentation, and 15 seconds later I had 4 different approaches to how to do it!

In 2 minutes and 35 seconds I can teach you how to do it too!

AI-Accelerated Reading

Tue, 19 Dec 2023 16:53:17 -0800

This post and video are dedicated to Prof. Garret Miyake.

He asked me to show him how I’m using AI to accelerate my reading. There are quite a few ways I’m currently doing this, but thanks to the new Knowledge Base feature in Poe there is a very easy way to do this.

In under 5 minutes, you can train a custom Poe bot to dramatically accelerate your reading! Books, articles, whatever. You just need a PDF. Here’s how to do it:

I hope this helps you with your “just-in-time” learning!

Find me on Poe: https://poe.com/rawworks

Squeezed Into Essentialism

Mon, 18 Dec 2023 15:58:47 -0800

Well, after six days of a ketogenic diet and four with COVID symptoms, I can definitely say that my energy is depleted. As a card-carrying workaholic, it is an interesting opportunity to really ask myself:

What is the truly essential work that I need to do?

This is a question that I try to bring my attention to regularly, but there’s nothing like extremely low blood sugar and a COVID infection to get your priorities straight. Today, I’m just trying to do the work that is truly essential, and a bit of work that is easy/energizing: syncing up with my team on the plan for the week, sharing some new technical ideas that I have with our collaborators, making sure our insurance gets renewed on time.

I am walking and dictating this, walking very slowly because I’m feeling weak. It will only take a few minutes to turn the transcription into a blog post and social media posts.

Here are some resources that I’ve found helpful in asking this question of what is truly essential.

In particular, these questions from the 4-Hour Workweek came to mind while I was walking:

1. If you had a heart attack and had to work two hours per day, what would you do?

2. If you had a second heart attack and had to work two hours per week, what would you do?

3. If you had a gun to your head and had to stop doing ⅘ of different time-consuming activities, what would you remove?

Tim Ferriss

Nothing like a having highly contagious viral infection to really amplify this thought experiment.

Disclosures

Mon, 18 Dec 2023 10:58:47 -0800

Hey there, it’s Raymond. Just a quick note to let you know that some of the links on this site are affiliate links. This means that if you click on one of these links and make a purchase, I may receive a small commission at no extra cost to you.

I only recommend products or services that I’ve personally used or believe will add value to my readers. This includes being a participant in the Amazon Associates Program – as an Amazon Associate I earn from qualifying purchases.

Social Media Repeaters

Sun, 17 Dec 2023 18:51:48 -0800

I want to treat my social media accounts as “repeaters”. They spread the word to different corners of the internet.

I previously shared a bit about my automation workflow.

In my current workflow, I’m using webhooks with Make.com to send posts to Buffer. This is great for LinkedIn, X/Twitter, and Facebook Pages. Unfortunately, Meta makes it pretty rough for folks to post to personal Facebook profiles, Instagram, or Threads via API. As far as I can tell, these all require some sort of manual intervention.

Why webhooks? So I can post the content programatically. (Right now from a Jupyter notebook inside the same repo as this website, which I run in Cursor because I suck at programming and I am eagerly awaiting my AI overlords.)

Why Make.com? IFTTT sucks. Zapier is expensive.

Why Buffer?

So I don’t have to deal with the API’s of each platform.
So I can schedule posts without setting up cron jobs or intricate automation workflows.
So I can preview/schedule/share posts without actually logging into any of the platforms. (I might never come back if I do that.)

I used to be a big fan of Buffer, but they seem to have completely stopped listening to their users. To their credit, I think the social media apps make their lives pretty miserable.

When I find the time I’ll come up with a better solution. Let me know if you have any ideas.

My COVID Protocol

Sat, 16 Dec 2023 18:18:39 -0800

I have COVID, for the second time. It’s not fun, but I’m doing 10x better than last time.

Big inflection point: Paxlovid. I got it within 24 hrs of testing positive, and it is clearly keeping everything in check. Horrible metallic taste in my mouth, but I’ll take it. I was initially very skeptical after hearing all of the stories of rebounds, but I gave it a shot after a few friends shared how quickly it helped them recover. Fingers crossed for no rebound or weird complications.

Activity: Slow walking, at least 10,000 steps a day. Sleep as much as possible at night and just let myself nap whenever I’m tired.

Dietary: I’m basically doing a keto diet. Bulletproof coffee, tons of liquids, eggs, avocado, veggies, and meat. I’m not a mouse, but this paper is interesting: “Impaired ketogenesis ties metabolism to T cell dysfunction in COVID-19.” Nature.

Supplements:

P.S. - I got GPT4V to make the list of these supplements from a single photo. Unfortunately, both Bing and ChatGPT refuse to find product links on Amazon. For that I had to switch to Bard, and then fix about half of them manually…oh well.

LLM Prompting Goldmine

Fri, 15 Dec 2023 16:23:06 -0800

Remember the story about the McDonald’s at the top of the volcano?

Well it erupted. (…and I sincerely hope that you didn’t quit your job to make custom GPTs for a living.)

A few people figured out how to trick the custom GPTs that are starting to roll out to the app store into revealing their prompt instructions.

The result is hands-down the most comprehensive and high quality repository of prompts that I have ever found: https://github.com/linexjlin/GPTs

For additional context, many of these are “professionally-created”, bringing in thousands of dollars a month in revenue sharing to their creators. I’ve shared elsewhere that I think it’s a terrible idea to try to be selling AI products right now, this is case-in-point. It has a nice parallel with the “AI as electricity” riff - once you know how to do the magic trick, it’s pretty hard to keep other people from copying you! (and very quickly everyone will expect it to be essentially free.)

P.S. - If you want to learn prompting more formally, this is the best course I’ve found: https://learn.deeplearning.ai/chatgpt-prompt-eng/

P.P.S - Hat tip to Mayo as usual. Sign up for his newsletter and you won’t need me.

Introducing: Human Instruct Turbo

Thu, 14 Dec 2023 17:49:42 -0800

I struggle to create clear instructions for AI.

I struggle to create clear instructions for humans.

I struggle to create clear instructions for AI to create clear instructions for humans.

But we move forwards, step by step.

Try out Human Instruct Turbo today!

In this video, we go behind the scenes of the creation of the revolutionary new GPT, Human Instruct Turbo. This is unedited, unplanned, completely raw footage of creating a custom OpenAI GPT from start to finish. Watch me fumble so you don’t have to.

Private and Personalized AI Transcription

Wed, 13 Dec 2023 17:04:32 -0800

In this video, I delve into the world of AI transcription, specifically focusing on MacWhisper, a leading tool for AI-driven voice-to-text transcription on Mac. We explore how to enhance MacWhisper with a custom glossary for accurate transcription of unique words and proper nouns, and share tips from the OpenAI Cookbook to refine your MacWhisper settings.

I also demonstrate real-life application by adding custom product names to our vocabulary, troubleshoot common transcription mistakes using find-and-replace, and explore the benefits of using larger AI models for improved accuracy.

Whether for professional or personal use, MacWhisper adapts to your specific language needs, offering privacy-focused and highly accurate transcriptions.

What to work on? A trifecta approach

Tue, 12 Dec 2023 17:09:38 -0800

Today I attended Builder’s Roundtable: Generative AI for eCommerce. It was pretty good. 2x it, or, better - get your AI to watch the replay for you.

There were a few interesting ideas on customizing generative AI for eCommerce. Unfortunately I think the OctoAI product still has a long way to go, I frankly was not impressed with the onboarding experience after getting amped up by this webinar.

But one quote stuck with me all day. It has nothing to do with AI:

Find something that you’re really passionate about, find something that you can become the best in the world at, and find something that you can make money doing.

Hikary Senju, Omneky

I think this is fantastic advice for anyone, not just entrepreneurs. A trifecta of how to decide what to work on. (58:39 in the video)

Dalle3 made this diagram. I don't know what the symbol in the middle is, but I think you get the point.

Zone of Genius Enumeration

Mon, 11 Dec 2023 19:25:33 -0800

A brief enumeration (or perhaps perturbation) around the concept of the “Zone of Genius”:

Incentive Structures

Sun, 10 Dec 2023 19:19:12 -0800

“Show me the incentive, and I will show you the outcome.” - Charlie Munger

This weekend I’ve been thinking a lot about incentive structures. (RIP Charlie Munger)

I don’t think I have any new “crispy realizations”, but here are a few related items:

The Big Companies Will Never Catch Up
- Why small teams have a big advantage over large organizations, especially right now.
Justifying Our Own Existence
- Bad incentives at Twitter led to a bloated codebase.
Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries
- A very thought-provoking book about how to structure organizations to nurture innovation (via incentive structures).
Advice Under Uncertainty
- “What is the ‘business model’ of the person giving me this advice?”.

The Pace Is Exhausting

Sat, 09 Dec 2023 18:53:17 -0800

The pace of AI development over the last few months has been simply exhausting. Exhilarating, but exhausting.

Just look at this chart. This is just the open source LLMs.

I don’t check this leaderboard very often, but the Mistral models that were winning two weeks ago aren’t even in the top 20.

Here’s some cool stuff I found since I ate dinner an hour ago. (Sorry, I literally don’t know what else to do…there’s too much cool stuff.)

WikiChat on GitHub: WikiChat enhances the factuality of large language models by retrieving data from Wikipedia.
LLMCompiler on GitHub: LLMCompiler is a framework for efficient parallel function calling with both open-source and close-source large language models.
Flowise on GitHub: Flowise offers a drag & drop user interface to build customized flows for large language models.

1/n Was December 8th, 2023, the day when we've come to realize that AGI technology has been democratized? That it cannot be confined to the few and the GPU-rich? Let me explain to you what happened yesterday. pic.twitter.com/syLBuCVqG6
— Carlos E. Perez (@IntuitMachine) December 9, 2023

Mixtral 8x7B in LangSmith Playground

Thanks to our friends at @thefireworksai, you can try out the newest @MistralAI mixtral-8x7B model from LangSmith Playground and Hub for free!

s/o to @fireworksai for the experimental chat fine-tune as well!

Sign up for LangSmith here:… pic.twitter.com/tYHvP2jCaT
— LangChain (@LangChain) December 10, 2023

RAG over Complex PDFs 📑

The issue with basic RAG strategies (chunking, top-k), is that they’re fine with plain .txt essays, but they do terribly over complex documents - w/ embedded objects like tables, diagrams 📊, and hierarchical sections 🪆

You can solve this with… pic.twitter.com/jSMnzdDXIS
— LlamaIndex 🦙 (@llama_index) December 10, 2023

An increasing use case in retrieval is not only fetching the top-k most similar chunks to queries, but exploring entity relationships.

The way you can do that is with knowledge graphs, and now it's easier than ever to explore how to use them in @llama_index.

Simply download our… https://t.co/ibDZvSpfAf
— Jerry Liu (@jerryjliu0) December 8, 2023

AI Transcription Without a Subscription

Fri, 08 Dec 2023 16:20:41 -0800

For those that follow, you’ll know I’m currently obsessed with AI, voice to text transcription, and the intersection of AI and voice-to to text transcription.

I wrote some thoughts about “the perfect voice transcription tool” - which unfortunately still doesn’t exist.

But what did happen this week is that MacWhisper found a way to 3x the speed of their transcription model. And that boost in speed is enough to make it better than Otter or HappyScribe for my use case.

So yesterday I unsubscribed from HappyScribe.

MacWhisper transcribes locally on your machine. You can trade accuracy for speed, and it has a free tier. I’ve paid for it because I want to support Jordi, and because I want to run batches of audio files through it.

The quality isn’t quite as good as HappyScribe, but since its local I can quickly get ChatGPT (or Jordi’s MacGPT) to fix it up. The added time to fix the differential errors is less than the time it takes to upload to HappyScribe, wait for the transcription, and download the file.

Modular Frankenstein

Thu, 07 Dec 2023 18:19:05 -0800

I’ve been working on my AI “content machine” for polySpectra. As I wrote before, quantity is very easy, but quality is a challenge.

It is very easy to output nonsense. I have been able to achieve a high throughput of “SEO-optimized nonsense”, but I have been having a hard time getting technical content that isn’t more work to edit than it would have been to just write myself (or write step by step by “holding the AI’s hand”).

I also got “greedy” and was trying to build a system that would go “all the way” from ideas/keywords to fully written articles. This was too ambitious.

So now I’m taking a more modular approach. First building up the foundational concepts and research and structure, which will later serve as the training data for AI-assisted writing. My modular approach also involves a human-in-the-loop at every stage - because nothing is more annoying than propagating errors with AI.

But my eye is on scalability, so I’m making sure that each stage of the process is able to run in parallel, concurrently. In other words, the non-human steps should take the same amount of time for one or one thousand.

My big “it’s alive” moment today was getting GPTResearcher from Tavily to run concurrently:

🧟‍♂️ It's alive!

This did about 15 reports in about 3 minutes. I haven’t pushed to see when I hit my OpenAI API limit.

As breadcrumbs, here are the “resource reports” that I generated: https://polyspectra.com/tags/resource-reports/. These are not very engaging, nor are they meant to be, but they will serve as the foundation for the next step…

In addition to the human oversight at each step, this modular approach also let’s me mix and match the best tools for the job. Tavily is great for research, but the writing style is pretty rigid, and I don’t feel like re-writing it’s guts. So use it just for the step that it excels at.

Big Companies Love Big Data

Wed, 06 Dec 2023 18:02:12 -0800

There are three reasons why big companies are so obsessed with big data.

One, there are very few people inside the company that actually have a clue about what’s really important. (Or at least a low density of people who have a clue.)

Two, the people who do have a clue unfortunately have to justify every activity and expense to people who don’t have a clue. (And the people who don’t have a clue are usually the ones who are in charge of the budget.)

Three, big data makes it easier to draw spurious correlations. At least the people who don’t have a clue, and maybe the ones that do have a clue but just don’t understand statistics - they have no idea that the projection that they’re looking at, the extrapolation that justifies the decision, has no basis in reality.

Big hug for big data.

Happy Birthday polySpectra

Tue, 05 Dec 2023 18:11:26 -0800

Today is polySpectra’s 7th birthday.

I spent the morning walking slowly, reflecting on the past seven years…and the next seven.

(…As is my usual morning ritual, dictating into my Yealink BH71 Pro headset.)

Here are seven reflections to celebrate seven years of polySpectra.

For fun, I’ll share some of the rejected cakes that didn’t make it into the official company post.

(5th time was a charm, because DALL·E 3 Can Almost Spell.)

Time for Play

Mon, 04 Dec 2023 19:04:25 -0800

I’m not always the best at making time for play. It’s very easy as an entrepreneur to have everything be about work in some way or another. Once the vision is big enough, there is no practical end.

This evening I discovered something wonderful (to me): WebChuck

It instantly transported me back to my days in PLOrk, where I first learned computer science, in ChucK.

(I do not recommend ChucK as your first programming language, I had a lot of unlearning to do.)

I hacked one of the demos for fun. It’s a little arpeggiator that plays a harmonic series. I added a low pass filter and an echo. I also added a slider for the filter frequency and a slider for the echo mix. Change chop to change the base frequency. Change slow to change the tempo.

Today, I made just a little bit of time for play. I’m glad I did. Maybe I’ll try it again someday.

Hello, WebChuck

Copy and paste the code below into the editor at WebChuck to play with it yourself.

// Harmonic Series Arpeggiator
// Written by Terry Feng
// CHANGE ME! ADD MULTIPLE SHREDS!
// Completely ruined by Raymond Weitekamp

1 => float chop; //let's go bro
1 => float slow;

//GUI bro
0.5 => global float f; // global variables create a GUI slider
0 => global float e; 


220 => float baseFrequency; // starting frequency
12 => int numHarmonics; // number of harmonics to play
125::ms => dur noteDur; // note duration

// Unit Generator
SawOsc osc => LPF lpf => Echo a => dac;
osc.gain(0.5);

while (true) 
{
    // Loop through the number of harmonics
    for (0 => int i; i < numHarmonics; i++)
    {
        // Update the oscillator frequency to the next harmonic
        (baseFrequency + (i * baseFrequency))/chop  => osc.freq; 
        
        //gui freqs
        f * 20000 => lpf.freq;
        e => a.mix;
        
        // Advance time to play the note
        (noteDur * slow) => now;
    }
}

Trauma-Informed Marketing

Sun, 03 Dec 2023 15:04:06 -0800

Today I started thinking a lot about the fact that the 3D printing industry is “traumatized”. I am not necessarily using this word in a clinical (DSM-5) sense, but rather in the sense that the industry has been through a really hard time. It is hard to imagine more of a rock bottom.

So in this analogy, under the hypothesis that the collective behavior of the industry is influenced by this trauma, what does that mean for marketing?

I intend to write a longer post about this, but for now I will just say that I think it means that the industry is very sensitive to any kind of “salesy” or “hypey” marketing.

Big launches with the Chicago Bulls cheerleaders (actually happened and was as cringe-worthy as it sounds) are not going to work. Neither are the “we’re going to change the world” pitches. Everyone is too f*ing tired for that.

The customers are traumatized by 40 years of false promises. The OEMs are traumatized by the complete evaporation of any investor interest in the industry. The investors are traumatized by the fact that they lost a lot of money. The markets are traumatized by the hostile takeovers and failed merger attempts. The founders are traumatized by their balance sheets. The employees are traumatized by the never-ending re-orgs. (Again, hopefully “lower case t” trauma for most, but still trauma.)

What do we need to do instead?

Build trust and rapport. Be honest. Be transparent. Be vulnerable. Be human.

I think this will ultimately be a good thing for a historically frothy industry. The “fair weather” participants are already gone. I think it will lead to a healthier and more legitimate additive manufacturing sector. I think it will lead to better products. I think it will lead to better companies. I think it will lead to more trust and education between AM companies and engineers.

I’m curious to hear what you think, especially if there are lessons learned from other industries that have been through similar experiences.

We need some therapy for the 3D printing industry.

AI as Electricity

Sat, 02 Dec 2023 20:00:48 -0800

Today I noticed a new section on Purple Space for AI exploration and discussion. This part of the description had me noodling all day:

“Perhaps the biggest change in work since the invention of electricity.”

So if AI is electricity…

…then the foundation model companies are like the utility companies.

(AI had a few more ideas to fill out the obvious)

…then data is the fuel that powers these utilities. Without it, the AI cannot function, much like a power plant without coal or gas.

…then machine learning engineers are the electricians, building and maintaining the infrastructure that allows this power to be harnessed and used effectively.

…then the algorithms are the power grids, distributing the AI’s capabilities to where they’re needed most.

…then the applications of AI, from autonomous vehicles to voice assistants, are the various appliances and devices that use electricity in different ways to perform a wide range of tasks.

…then the ethical guidelines and regulations around AI are the safety standards and regulations in the electrical industry, ensuring that this powerful tool is used responsibly and safely.

(ok back to human mode)

AI is hot right now. There are a lot of people trying to resell electricity and make a buck. But if we believe the analogy, there are only going to be a few utilities, and they are going to be heavily regulated.

What seems more interesting to me is the idea of building a business that is powered by AI. The same way that the most valuable companies of the “second industrial revolution” were the ones that were powered by electricity, not the ones that sold electricity.

Or perhaps lets take a more personal level of the analogy…

…who do you want to be? Tesla? Edison? Westinghouse? Shockley? Moore?

Further reading: Empires of Light by Jill Jonnes

6 Hats Is More Fun Together

Fri, 01 Dec 2023 17:11:10 -0800

This week we did our first AI-assisted 6 Hats exercise with 6 Hats Helper.

It was way more fun than doing it alone. The helper was well-behaved, and in a few cases stated the “obvious” perspectives right away, so the team didn’t need to spend time naming them. At the end, it summarized everything for us to copy/paste as meeting notes.

6 Hats is a major decision-accelerator on its own, even more so when powered by AI.

In case you are curious, here’s the first post about it: 6 Hats Helper: Your New Thinking Buddy

Oh That's Why It's So Hard

Thu, 30 Nov 2023 18:07:24 -0800

Today I had an “Oh That’s Why It’s So Hard” moment courtesy of NIST, US taxpayers, and the Constitution.

The short version of the story is that it is a pain in the ass to print on LCD resin 3D printers, and there are all these inconsistencies that arise even when you specifically “tool match” a DLP printer to have the same specs (wavelength, power density, temp, etc).

Today I found out that I’m not the only one with this problem. In fact, it’s a big enough problem that NIST decided to investigate.

I’m sure we’ll write something more in depth on the polySpectra website about this.

In case you are curious, here’s the paper: Characterizing light engine uniformity and its influence on liquid crystal display based vat photopolymerization printing…

…and the figure that summarizes why it’s so hard to print on these machines is below:

On LCD printers, the light doesn't actually fill the full area of the 'pixel'.

Fill the Gap With Gold

Wed, 29 Nov 2023 17:55:50 -0800

Sometimes you fall off the horse. I did over the last week.

This time, I’m going to try to cover my tracks with gold. Gold flakes, nuggets, maybe even gold teeth.

Pardon my mess

Custom Function Calling Oh My

Tue, 28 Nov 2023 18:07:54 -0800

SuperAgent has recently introduced a new feature that’s worth exploring: Custom Tools. This feature allows you to create your own tools within SuperAgent, opening up a world of possibilities for data manipulation and visualization. (and literally anything you can write a function for…)

One tool that caught my attention is the graph tool. It’s a powerful addition that allows you to visualize data in a more intuitive and insightful way.

A great example of this in action is the Super Stocks project. It uses the graph tool to visualize stock market data, making it easier to spot trends and patterns.

This is just the tip of the iceberg.

Training Myself to Talk to AI

Mon, 27 Nov 2023 18:16:27 -0800

In my ongoing journey with AI, I’ve been experimenting with a new approach: dictating directly to AI. This method eliminates the need for additional prompting, allowing me to simply paste the transcript straight into ChatGPT.

This approach has several advantages. It’s faster, as it cuts out the middle step of typing out my thoughts. It’s also more natural, as I can speak my thoughts out loud as they come to me. I’ve been playing with “function words” like “todo” and “end”. It’s amazing how well it catches onto the re-formatting. Just don’t ask for a summary.

Despite these challenges, I’ve found the process to be incredibly rewarding. It’s opened up a new way of interacting with AI, one that feels more natural and intuitive. It’s also made me more aware of how I formulate my thoughts and express them, which has been an interesting exercise in self-awareness.

Soon, I will look like this at work.

For more on my journey with AI and related topics, check out these posts:

Fixing Typos With AI for Speed and Focus

Sun, 26 Nov 2023 18:03:02 -0800

I’m intrigued by this concept: “Type as fast as you can. Or paste some badly written text. I’ll rewrite the mess in a proper way.”

Flow Speed Typist

It’s an interesting angle on using AI to accelerate workflows. I’m not a perfect typist. I’m probably unaware of just how much time this would save.

I’m particularly interested in applying this concept to bad audio transcripts. Please let me know if you hear of anyone working on this.

gogogo

Brain Retraining Question for Chronic Pain

Sat, 25 Nov 2023 18:43:02 -0800

I was revisiting Curable recently. A question that really stuck out to me, which I’ve been revisiting recently:

“Is this movement dangerous for me?”

We our fear/danger wires get re-inforced over time, this question seems to help uncross the wires.

Defusing the pain bomb, one question at a time.

Check Out Tavily

Fri, 24 Nov 2023 18:29:34 -0800

I recently came across Tavily, an AI-powered platform that aims to revolutionize the way we conduct research. Tavily automates the research process, promising to deliver comprehensive, accurate, and credible research results in a matter of seconds.

Tavily’s approach to research is quite impressive. You simply share what you want to research, and Tavily starts gathering information from multiple online trusted sources. It then organizes the information and provides you with a comprehensive research report within minutes. This process not only saves time but also ensures that you get the most accurate and credible information.

One of the things that caught my attention about Tavily is its flexibility. It can conduct any kind of research, regardless of the subject matter or niche. It isn’t perfect but it is dangerously good. And the team is amazing and super helpful.

Family Is Where Things Don't Need to Make Sense

Thu, 23 Nov 2023 18:54:41 -0800

My friend Keetu said something very deep recently, which I think is appropriate for Thanksgiving:

“Family Is Where Things Don’t Need to Make Sense”

Sometimes You Get Lucky

Wed, 22 Nov 2023 14:57:37 -0800

Sometimes you get lucky on the first try.

(Don’t ask me how many times I completely struck out trying to improve this image.)

Topologically Optimized Turkey

🙏 Thank you! 🙏

The Big Companies Will Never Catch Up

Tue, 21 Nov 2023 17:06:19 -0800

The big companies will never catch up.

They won’t be the first to know, even if they are the first to hear.

The tech scout will have a call with the business unit. This will take a few weeks to schedule.

If it’s really interesting, the business unit and the tech scout will have to write a report for the leadership team.

If that goes well, and assuming the CEO also sees something about it on social media, then a committee will be formed to investigate.

The committee will have to write a strategy, a timeline, a budget, and a risk assessment. Maybe even a policy.

No one in the company knows how to do this new thing, so they will have to hire a consultant.

They’ll need multiple competing bids first before they select a consultant. Then the consultant will have to write a report for the committee.

The committee will have to write a report for the leadership team.

The leadership team will have to write a report for the board.

The board will have to make a decision.

A team will have to be formed to implement the decision.

The committee will have to hire the team.

The team will have a lot of new blood, really excited to make a difference.

But before they can do anything, they will have to write a report for the committee.

By the time the budget is approved, the team will have gotten the message that they don’t actually need to do anything to continue to get paid.

But they will try to do something anyway. And it will take longer than expected. And it will cost more than expected. And it will be worse than expected.

The team will have to present it to the committee.

The committee will have to present it to the leadership team.

The leadership team will have to present it to the board.

At this point, no one will remember why they wanted to do this in the first place.

All the while, the small company will have been doing it…with just a few people who are really excited to make a difference, and AI agents to implement their vision.

Apologies for the Delay

Mon, 20 Nov 2023 18:54:22 -0800

Apologies for the delay, I’ve been walking on the edge of infinity. It has been quite consuming.

I’m still practicing my discernment.

Some key learnings:

AI needs editing.
AI is better at writing than editing.
It is very easy to propagate errors to infinity.
Once it works, it cranks.

Impromptu DJ Set at the Berkeley Half Marathon

Sun, 19 Nov 2023 18:57:18 -0800

Once a year, on the day of the Berkeley Half Marathon, I try to make a point to leave extra early to get to yoga, because they shut down my normal freeway exit.

This year, it didn’t matter - life had another plan for me. Right when I got between the two exits that were closed for the race, emergency vehicles started pushing through the traffic, and closed every single lane of the 80.

At first I was quite grumpy. Then the woman in the car behind me started to cheer on the runners - who were just a few feet away from us.

Then I remembered: I know exactly what to do in this situation. My Burning Man skills kicked in. I rolled down the window and pumped up the jams for the runners.

A Truly Epic DJ Set

Slow Down to Speed Up (or a Rabbit Hole)

Sat, 18 Nov 2023 19:23:00 -0800

Sometimes you have to slow down to speed up.

Other times you realize you’re in a rabbit hole.

What does it all mean?

Double Graduation Day

Fri, 17 Nov 2023 15:47:24 -0800

Today was a double graduation day. I graduated from both Mayo Oshin’s Build a ChatGPT Chatbot For Your Data and TREW Marketing’s Content Writing, Engineered.

I highly recommend both courses. I’m already using the skills I learned in both courses to improve my business. A few relevant links to what I’ve learned and completed:

Resina - a chatbot for resin 3D printing
Common Failures of Traditional Products for Resin 3D Printing - a new blog post that I wrote using the TREW Marketing framework
Asiga Pro 4K Quickstart Guide for COR - our first quickstart guide, a type of content I have a new appreciation for.

My cap is very dusty...

Cheers!

LLM Ensembles - A Preview

Thu, 16 Nov 2023 17:46:39 -0800

I’m developing some ideas around using ensembles of LLMs for specific tasks. Today I’m sharing a preview of the first “trivial” example, which is the foundation of linking arrays of LLMs to the concept of ensembles from statistical mechanics.

In this example, an ensemble of LLMs are called with the same prompt, but “high” temperature (of 0.99). There is a “prompt” and a “grading criteria” that are used to create the full prompt.

LLM Ensembles - Trivial Example

The only variance is the from the “temperature”, which you can see doesn’t result in much variety.

Then the “judge” is asked to rank the results, based on the grading criteria, with the judge having a low temperature (0.1):

The output is not impressive. But from here we can start to build up to more interesting examples.

Much more to come on this topic. Stay tuned. 📻

Quantity: Check ✅, Quality: In Progress ⌛

Wed, 15 Nov 2023 17:51:43 -0800

Today I had a “holy shit” AI moment. (Which has been happening quite frequently.)

Corrie Who Writes turned me onto this plugin for Google Sheets called GPT for Sheets™ and Docs™.

Basically, it adds a bunch of functions to Sheets that help interface with OpenAI (in both directions). Every cell can run it’s own API call (in parallel). You can reference other cells. Combine, list, split, it’s really nuts.

If LLMs weren’t already capable of generating more text than anyone could possibly read or use, this really seals the deal. Quantity: Check ✅. See below for about 20 question and answer pairs generated using this plugin in under 1 minute, from my initial input of just 4 questions (and no answers).

Q&A Pairs, as far as the eye can see.

Quality: In Progress ⌛. This is more work. Especially if there is too much content to have a human editor. Stay tuned. 📻

Deep Work 1,2 Punch

Tue, 14 Nov 2023 18:35:43 -0800

Here’s a “deep work” 1,2 punch that I have had some good results with.

Walk and talk out a very clear plan. Record a voice memo. I use the Yealink BH71 Pro headset, which has amazing noise cancelling (for the microphone) and I look like a telemarketer on a hike. Later you can transcribe this with your favorite voice transcription tool. (Optionally, get ChatGPT to format the transcription for you.) It is very important to resist the urge to use your phone, just talk out the steps, talk out the framework, talk out the plan for what you will do. When you get distracted, bring it back to the plan. Make sure you have a clear outcome in mind.
Block 90-120 minutes of “deep work” time to execute the plan. Try to get to the outcome, try to follow the steps. Maybe the plan was too ambitious, maybe you need to adjust the plan. But try to get to the outcome. Maybe you don’t need 99% of the structure you thought that you did (that’s what happened to me today). But you incept in yourself the idea of what you want to do, and then you do it. When you get distracted, bring it back to the outcome.

That’s it. Go get ’em, champ!

Go get 'em, champ.

P.S. - I came up with an interesting theory while performing Step 1 this morning (a distracting thought from the plan I was formulating), which is that Cal Newport is actually an AI being sent to us from the future to teach us how to focus like a machine. (Think: The Terminator of “Deep Work”.) I’m not sure how to test this hypothesis.

The Prompting Course I Wish I Found Months Ago

Mon, 13 Nov 2023 16:41:18 -0800

This is the prompting course I wish I had taken months ago: https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/. It’s free.

Props to Mayo Oshin for the recommendation. (His course Build a ChatGPT Chatbot For Your Data is amazing.)

Why do I wish that I had found this months ago?

After watching it, I realized that many of the AI applications where I thought I would need plug-ins, or special function calling set-ups, or a proprietary/paid solution, etc — these can be solved with better prompting. It’s also helpful to see them build up to more complex use cases, step-by-step.

One of the points that Andrew Ng makes in the intro is that there is no “best prompt for X”. So instead this course teaches you the fundamentals, and more importantly - how to iterate to get to a solution that works for your application.

With these prompting fundamentals and a basic RAG pipeline (which is getting easier and easier every day) - you can really accelerate a ton of business tasks.

Something for Everybody

Sun, 12 Nov 2023 16:37:04 -0800

I am now convinced that there is something for everybody.

Clearly someone likes this, there is only one left in the box.

I could imagine a point in time where I would be excited to identify as a “slime-licker”. But licking toxic waste? No thank you.

P.S. - In case this is for you, a quick search seems to indicate that this particular form factor of “sour rolling liquid candy” is no longer for sale online. (Hit me up and I’ll tell you where the physical location of this last roller is.) But don’t worry - you can still squeeze various flavors of toxic waste onto your tongue with two-day delivery 😛.

Field Notes from Today's Social Media Automation Attempts

Sat, 11 Nov 2023 19:44:02 -0800

Here are some lessons learned from today’s attempts into social media automation workflows:

When soliciting creative input from Language Learning Models (LLMs), consider asking for more than four variations. This is particularly useful if the workflow involves human selection at some point. (It makes sense that most of the generative image tools do it this way.)
I’ve decided to move away from IFTTT as it no longer serves my needs. I’ve transitioned to using Postman for simple webhooks and Make.com for more intricate routing.
Currently, I’m utilizing Buffer. However, they’ve ceased the addition of new apps to their API, forcing me to use Make > Buffer. In the future, I might consider posting directly to social media via the individual platform APIs. This is especially feasible with function calling and having LLMs write the function calls for me. I’m finding that API-replacement apps like Zapier, Make.com, and IFTTT are becoming more of a hassle than they’re worth.
A significant time-saving tip: use GPT-4 to generate regex patterns for you.

First Day with Athena

Fri, 10 Nov 2023 21:09:07 -0800

Today was my kickoff call with Athena. I’ve been really impressed by the intentionally of their onboarding process.

Each step of their sales funnel was very clear and effective. Each stage of the on-boarding has a clear purpose and structure. The first 100 days are planned out in detail, which provides a nice framework and sense of certainty and security.

I’m excited to see how it goes! I will keep you posted.

Cutting Edge or Bleeding Edge?

Thu, 09 Nov 2023 05:10:32 -0800

Are you at the cutting edge of your field? Or are you at the bleeding edge?

Most of the time, I think I’m at the cutting edge. But then I notice that I’m bleeding.

I get so caught up in the excitement and novelty that I don’t realize that I have crossed over to the bleeding edge. It’s hard to get paid at the bleeding edge, even harder to be profitable. It’s even more exciting than the cutting edge, and even more draining.

In silicon valley, the saying is “pioneers get arrows in their backs, settlers get land.” This is the distinction that I’m trying to make between the cutting edge and the bleeding edge. Both the pioneers and the settlers had the same vision, the same excitement. One was just a little too soon (and it’s really hard to know when the timing is right).

Digital to Physical and Back Again: Part 1

Wed, 08 Nov 2023 19:59:11 -0800

Part of what originally drew me to 3D printing was the theme of commuting between the digital and physical realms. When I first started, I knew nothing about CAD, a category of software that continues to frustrated me to this day. (I’m just not into graphical programming languages.)

There are a few cool new tools that I’ve been playing with that make commuting between the physical to digital worlds a little easier. Two that I’m really into right now are Commonsense Machines (CSM.ai) and Luma AI from LumaLabs. Both companies offer text-to-3D and video-to-3D. CSM also offers 2D-to-3D, which is getting better every month.

A couple of Christmases ago, I accidentally ended up on the bleeding edge of web-based Augmented Reality. What I thought would be pretty straightforward involved hiring and firing at least 3 different professional WebAR developers. But we ended up building polySpectra AR - which I still think is pretty nifty. The idea was to give users a free massless preview of their .STL, before they would upload it to a 3D printer or 3D printing service.

Fast forward to this morning, something clicked and I realized that we could pretty quickly tweak polySpectra AR to give users a completely CAD-free workflow to both visualize 3D models and then manufacture them with 3D printing.

CSM AI B2B polySpectra AR

Here’s a rough demo of the workflow for .obj files, which currently doesn’t support color/texture, but hopefully will soon. GLB files currently look the best. Give the recently updated polySpectra AR a try!

P.S. - Your feedback would be appreciated => https://github.com/polyspectra/AR.polySpectra.com-User-Feedback

How Quick is Your Quickstart Guide?

Tue, 07 Nov 2023 18:35:49 -0800

I’ve spent a lot of time banging my head against the wall trying to quickly get started with a bunch of new AI tools recently. The quickstart guides have not been so quick.

In particular, it was interesting to see the Poe team watch their quickstart get (mis)interpreted in real time at the Hackathon on Saturday. There were a few really common hiccups where people got stuck, even though it was clearly in the docs, almost every single team got stuck at the same problem and had to ask for help.

I definitely have a new appreciation for the importance of a good quickstart guide. Today I decided that we should build one for polySpectra. We started with our go-to printer: the Asiga Pro 4K.

A few lessons learned out loud:

Cursor is f-ing amazing. (In case you missed it, Cursor was used live during the OpenAI Dev Day.)
By using a template with common variables, we can now pretty quickly generate a new quickstart guide for any printer we support. The first one took about 3 hours, the second one about 10 minutes.
I really think one of the most important superpowers of AI tools is making it easier for people to interact with code.
I’m also getting into Zoom Clips, which means I don’t need to pay Loom and Zoom.

Poe Wants to Become the YouTube of AI Bots

Mon, 06 Nov 2023 18:30:36 -0800

Poe has the goal of becoming “the YouTube of AI Bots”. Part of the thesis is that they can leverage their experience building Quora to create the infrastructure necessary to help bot creators focus on the creative parts of bot creation.

At the AGI House Poe Hackathon on Saturday, Quora CEO Adam D’Angelo stated that his goal with Poe’s new creator monetization strategy was to have bot creators be able to quit their jobs because they are making enough money on Poe. As you might imagine, this went over very well with the audience. I’m sure everyone was imagining themselves as part of a new class of creator, like being a professional YouTuber or Twitch streamer. (Sounds more fun than either of those to me!)

Poe’s strategy currently involves:

Distribution: Poe ensures that its bots reach a large audience by implementing a bot recommendation system and allowing users to share chats with bots both internally and externally. They also encourage bot creators to drive traffic to their bots from outside of Poe, as this increases the likelihood of the bot being recommended on-platform.
Monetization: Poe provides a way for bot creators to generate revenue by setting a price per message that the bot creator will be paid for every message to their bot, and by offering referral fees when a bot brings new users to Poe. Poe also allows bot creators to monetize their bots through alternative means, such as placing ads in their content or asking users to visit their website to make donations or payments.
Costs: Poe covers all model inference costs and any other significant per-message costs involved in operating any bot on Poe. This is done by using the bot query API or by working with the bot creator to pay their model inference costs if they want to use a model that is not currently available on Poe. (This is a really big deal for individual bot creators.)
Multi-platform UI: Poe ensures that users have a great, consistent experience with bots no matter what device they are on. This is achieved by having a native presence on all major platforms (Web, iOS, Android, MacOS, etc) and by taking care of login and synchronized history.
Model independence: Poe allows bot creators to build their product using models from all different providers. This enables bot creators to adapt their product to use any combination of the best technologies as they are created.

I’m sure that the Poe team is already thinking about this, but I think there is one final missing piece: analytics. The only way you end up with “Mr. Beast”-level creators is by giving creators direct insight into the engagement data. For example, see how 18-year-old YouTube phenom Jenny Hoyos breaks down her “creator science”. (Spoiler alert: she literally plans everything out to the second.)

To truly become the “YouTube of AI Bots”, Poe should provide creators with a comprehensive analytics dashboard. This dashboard would allow creators to track the performance of their bots, understand user engagement, and gain insights into how their bots are being used.

Features of this dashboard could include:

Usage Statistics: Display the number of interactions, active users, session length, and other key metrics that help creators understand how their bots are being used.
Engagement Metrics: Show which parts of the bot are most engaging to users. This could include metrics like response rate, user retention, and session duration.
Error Logs: Provide detailed logs of any errors or issues that occur during bot interactions. This would help creators identify and fix problems in their bots.
User Feedback: Collect and display feedback from users. This could include ratings, reviews, and direct user feedback. At the very least, the stats on the “thumbs up” and “thumbs down” user responses.
Demographic Information: If appropriate and privacy-compliant, show demographic information about the users interacting with the bot. This could help creators understand their audience better and tailor their bots to user needs.

By providing these analytics and insights, Poe would empower creators to continually improve their bots, make data-driven decisions, and ultimately create better experiences for users.

Maybe I’m still sleep deprived from Saturday’s hackathon, but I think this is going to be a really big deal. I’m excited to see what the Poe team does next. I don’t think I’ll be quitting my job anytime soon, but I can’t wait to meet the first bot creator who does!

Find me on Poe! Here’s my Poe profile link and my two currently active Poe bots:

P.S. - What is your Job with a capital J?

Offset Monster Is Born

Sun, 05 Nov 2023 18:12:42 -0800

Yesterday’s Hackathon was a ton of fun.

I didn’t quite manage to finish the full workflow of Offset Monster in time for the demo, but I forced myself to present my work in progress anyways.

I met a lot of cool folks, got a ton of help from the Poe/Quora team (thank you for your patience), learned about 10 different ways to solve most of the things I was struggling with, and did sucessfully figure out how to deploy my first Poe Server bot.

Poe Hackathon at AGI House

Another time it would be fun to do a retrospective on this whole experience, but honestly I’m too tired.

Today I fixed up one of the missing pieces and now have a functional version of Offset Monster running on Poe. Check it out! This monster is ferocious and will attempt to offset the carbon footprint of anything you feed it.

Offset Monster is live now!

Making Carbon Offsets Fun, Conversational, and Contagious

Offset Monster makes carbon offsetting more accessible and viral through a fun, conversational AI bot. After calculating the carbon footprint of anything, Offset Monster makes it easy to buy a corresponding carbon offset for the item/activity in question, on the spot.

Poe Hackathon at AGI House

Sat, 04 Nov 2023 09:04:09 -0700

Today I have the great privilege of being able to participate in the Poe Hackathon at the infamous AGI House.

I am frankly quite surprised that they would allow a lowly chemist such as myself to participate. I did include links to Resina and my Six Hats Helper chatbot in my application.

I’ve never participated a hackathon before. Most of the products that I have built took five to ten years to get to a minimum viable product, not five to ten hours.

Maybe it’s because I have absolutely no experience in this culture, but I am imagining fierce competition between the “stallions” of the Silicon Valley TV show.

My imagined 'competition' at today's hackathon. Image credit 'lol valley' on YouTube.

Or maybe I’m just going to hang out with a bunch of AI nerds all day. I’m stoked either way.

Here’s what I’m building today…

Offset Monster

Making Carbon Offsets Fun, Conversational, and Contagious

Demo: https://poe.com/OffsetMonster

(This link is likely to break repeatedly over the coming hours/days/weeks.)

Stuck, So Close

Fri, 03 Nov 2023 16:30:53 -0700

I’m stuck…so close. I thought I had it all ready to rock. It didn’t work like it should have. Then the backup plan was foiled because I left that one tiny thing at home.

I can’t actually test it because I don’t have access. Anything I try will just be shooting in the dark.

I can see all the pieces, but can’t actually make progress. It’s an intellectual bardo.

What do we do in these situations?

Pen and paper? Plan it out?

Surrender and take a break?

Make a plan to be sure this never happens again?

What do we do when we can’t do what we planned to do?

Stripe Is Not for Wholesale

Thu, 02 Nov 2023 13:01:10 -0700

Until today, I would have said that Stripe has come a long way since its start as “PayPal for developers”. Despite being valued at almost $100B, the company’s main product still does not offer an option for the most common pricing scheme in the history of the world: the volume discount.

Ironically, you can set up a volume discount for a recurring subscription, but not for a one-time purchase. Apparently no one at this massive company has ever sold physical products, or wants to. The confusing thing is that the “one time” payment option gets automatically converted to “recurring” if you pick “volume” pricing. What is truly hilarious is that Stripe’s customer support has no idea why anyone would ever want to use their product for just a one-time purchase with a volume discount.

I guess you could say that they are staying true to their founding mission: developers only. While I don’t self-identify as a developer, I can tell you that a developer could fix this oversight in about 5 minutes.

I filed a feature request, but I’m not getting my hopes up. After all, I am just a confused manufacturer of physical goods, trying to implement the most ancient of discounts. And manufacturing is only a $11 trillion industry…probably not big enough for anyone at Stripe to care.

RAG Information Overload

Wed, 01 Nov 2023 10:21:11 -0700

Retrieval Augmented Generation (RAG) is getting easier by the minute, I can’t keep up with the daily influx of new tools in the space. My initial experience with ChatGPT was not positive - I couldn’t believe how useless the tool was. The “hallucinations” were really what threw me off - the propensity for the LLM to just make up random information was just too high.

RAG changed all of this for me. With the ability to train the AI on my sources of truth, all of a sudden this became an indispensable tool. I really was blown away by the power of RAG to shift the functional utility of LLMs.

Today, I noticed something funny - our customer support AI seemed to have forgotten its training. It was now getting questions “wrong” that previously it had an amazing track record of answering.

The culprit? Information overload.

Originally we only trained pSai on the polySpectra documentation website and the product pages from our e-commerce store — in part because I wanted it to just be able to answer basic product questions, and in part because ran into a technical difficulty getting it to scrape the entire polyspectra website when I first set it up. Recently, I figured out how to train it on the entire polySpectra.com website, which at first seemed like a good thing.

Unexpectedly, more became less. This extra information was the RAG that broke the camel’s back. There were just enough conflicting sources of truth in the AI’s verified sources to confuse it. Where before it was doing an amazing job, now it is giving the wrong answer.

Without the sources of truth, LLMs are pretty useless customer support agents. With just enough information, they are surprisingly good. With too much, they become unhelpful again.

The role of the humans in this situation is clearly to maintain a single source of truth. It makes me wonder how many humans we’ve confused with our website over the years…much more to distill and refine.

It also makes me wonder what my “context window” is. How many things do I get confused, by having access to too many sources of information?

Happy Halloween 2023

Tue, 31 Oct 2023 17:39:34 -0700

Happy Halloween!

(I’ll tell you the story another time.)

Candy-Coated Muertos

Mon, 30 Oct 2023 13:05:23 -0700

There is something very telling about the fact that Bing Image Creator considers this prompt to be “unsafe content”:

dia de los muertos skull, dayglow poster with extremely bright colors and black background, detailed mandala patterns and flow lines

We’ve candy-coated our dead. Halloween is for selling sugar and sexy costumes. Nothing truly scary. No room for a conversation about mortality. No room for reverence.

Disney costumes and drinks.

I’m all for the play. I’m all for the chance to assume a character.

I’m all for celebration. Death can be a tremendous celebration. I’m not suggesting we make it somber.

But death is real. And scary. And coming for each of us.

What a missed opportunity to have a really meaningful cultural conversation. It’s hard to hear the wisdom of the dead when you’re chewing on candy.

Enumeration of Logical Families

Sun, 29 Oct 2023 07:08:40 -0700

Towards a taxonomy of logical families, an initial list:

Sangha
Team
T-group
Crew
Squad
Club
Troupe
Tribe
Collective
Club
Circle
House
Salon
Friday Night Skate

…and half-baked topology…

(the taxonomy is still a work in progress)

Social Flexural Modulus

Sat, 28 Oct 2023 06:55:47 -0700

Different social media platforms have different expectations, a different culture, a different cadence. None of that interests me in the slightest.

But I am on a mission, and I have to play the game if I want anyone to read what I have to write, to hear what I have to say. RAW.works is a reset, an inquiry. How do I want to show up on the internet? How do I want to choose to engage with 8 billion people and 8 trillion robots every day?

I certainly don’t want Elon Musk choosing for me. I don’t want Alphabet choosing for me. I don’t want Apple choosing for me.

I acknowledge and respect the power of these tools. I see that if I want to show up in a search result, there are certain things I need to do to format my website for Google. With these new AI search tools like Poe Web Search [link my old post], perhaps that game is going to change a little bit. But we still need to bend to be noticed, which is increasingly true in a world where more content can be created in a day than consumed in a lifetime.

The intentionality part is: How much does that matter to me? Does SEO matter 0% to me? Does it matter 1% to me?

How far am I willing to bend my interests and my attention, to garner the interest and attention of others?

This question is at least as old as the first living organisms. The struggle to find a niche and a complex system. Initially, it was just about survival. As more and more people ascend Maslow’s Hierarchy of Needs, it becomes more cultural, more psychological, more philosophical.

But we need to be careful, because wars and pandemics, climate chaos and rogue AGI have the potential to bring us all the way back to survival.

The memelords won’t have much to offer in the way of food and shelter. Infinite jest. Wirehead now. Xanadu.

How far am I willing to bend my interests and my attention, to garner the interest and attention of others?

On a more Adlerian plane…maybe the greatest social good is achieved when we don’t bend so much. Or at least we don’t spend so much time worrying about bending. We either bend or we don’t. We pick the way we’re willing to wiggle, or maybe the wiggle picks us, but either way — we wiggle, we dance.

Money Can't Buy You Enrollment

Fri, 27 Oct 2023 07:18:08 -0700

If you hire a mechanical turk, you are going to get buttons pushed.
If you hire a contractor, you are going to get billable hours.
If you hire an employee, you are going to get a butt in a chair.
If you hire a consultant, you are going to get a report.
If you hire an expert, you are going to get an opinion.

In none of these cases are you guaranteed to get the true enrollment of a human being. You would likely expect the the attention of a human being, but these days you might have to pay extra for that. In the case of the mechanical turk, the probability is very high that they are actually using AI to “cheat” at their “jobs”. And who can blame them? I would do the same thing.

As an exploration of the concept of enrollment, let me tell you about the time I failed to hire Don McCurdy.

I first stumbled upon Don’s work through my interest in augmented reality. He was working at Google at the time and was a major contributor to Model Viewer, as well as the three.js library that powers it. I later realized he was even contributing to the glTF specification itself, which is poised to be the JPEG of 3D models. (Any day now, you’ll see.)

While I was in the process of building polySpectra AR, I saw that Don had left Google. Between this personal announcement and noticing his developer “tip jar” on GitHub, I figured that he would be open to at least discuss the possibility of working together.*

I sent him an email. No reply. I followed up. No reply.

The funny thing was that during this time where I couldn’t get any response to my emails, Don was incredibly generous with answering lots of my programming questions that were related to the problems we were having with polySpectra AR. I quickly realized that if I was showing respect for his craft, showing I had done enough searching to attempt to solve the problem myself (or have our developers try to solve the problem themselves), and if I presented the question in the channels of Don’s choice (GitHub & Discord at the time) — then I was fairly likely to get Don to solve my problems within a day or two. Completely for free, on his own time, just for fun.

I literally could not pay this expert. I could not get him to engage in a discussion about money. I could not buy his attention. But by engaging in a real dialog in an appropriate forum, I could see that Don was clearly enrolled in the idea of helping other people solve their problems with web AR. He was particularly helpful when I asked questions about how to use the amazing library that he wrote, glTF Transform. This makes sense, it is his project, he cares deeply about it, and he is enrolled in helping other people use it.**

Don is not an outlier. This is a very common pattern I have seen with the best in the world. If you offer money, you get no reply. If you engage in a genuine dialog, you can get the best advice, from the smartest people, for free.

Money can’t buy you enrollment.

* Re-reading this, I am laughing out loud at the way this phrase slipped in: “working together”. But that’s exactly what this is all about. What does it mean to “work together”?

** It looks like Don is now experimenting with a new way to monetize his expert attention: glTF Transform Pro.

BREAKING NEWS: My Internet is Out

Thu, 26 Oct 2023 09:05:15 -0700

A First-Person Account from the Frontlines

The battle rages on here at my home front as I’ve just lost connection to the information superhighway. Absolute chaos has erupted - I’ve scrambled to deploy my personal hotspot reserves but even they can only offer up a trickle of bandwidth.

Rations are being put in place - I’ve had to limit who else in the household can piggyback off my mobile connection at a given time. Streaming anything heavier than mere tweets appears to be out of the question for now.

Moral is low amongst the ranks. We’re all tired of staring at the dreaded buffering symbols that mock us endlessly. I’ve logged an urgent message with our ISP chatbot but so far their support has been insufficient.

What began as chaos has descended into full blown crisis…particularly for the younger recruits who have never known life without a steady internet stream.

The Millenial and Gen Z troops are flailing without the online world they’re so accustomed to. Endless refreshes and buffering frustration has taken its toll on morale.

Making matters worse, many have never had to endure these kinds of communications blackouts before. It’s all they’ve ever known. Now they’re scrambling to learn archaic skills like entertaining themselves without screens or getting homework done via mobile hotspots with data limitations.

I’m fighting to keep a steady leadership presence but it’s not easy wrangling troops so used to constant connectivity. Rations remain tight and options few. Unless the cavalry arrives soon with repairs, I fear a full meltdown may be imminent amongst the digitally dependent ranks.

For now all I can do is pray the older generations can keep the younger ones from losing it completely until reinforcements liberate us from this internet Iron Dome. Wish us luck out here on the dark network front! Updates to follow if we can keep it together.

Options are running thin fast. I’m seriously contemplating having to retreat to the office just to get some decent work done. Already non-essential and bandwidth-limiting programs, like VPN, have been cut off.

A Brigade of Personal Hotspots to the Rescue

I managed to scrape together just enough data to transmit this rough photo giving a glimpse of the tense atmosphere (seen above). Even falling back to being “that guy” who turns off his video during our next Zoom meeting is being quietly mulled as a potential last resort.

As night falls with no repairs in sight, the fight to regain control of my internet access continues. I’ll keep transmitting updates from the digital warzone as events allow, but for now all any of us can do is keep our fingers crossed that reinforcements arrive soon to liberate our stopped up information lines. Wish us luck!

Poe Is My New Search Engine

Wed, 25 Oct 2023 13:28:28 -0700

This post is about the attentional leverage of searching through AI, specifically with the Poe Web Search bot. Sometimes the AI gives me the answer in the summary, sometimes it gives me the link where I need to dig deeper, sometimes it doesn’t find anything good. Regardless…there is no attentional rabbit hole.

What are my figures of merit?

Time to a good answer — the time cost or opportunity cost.
# of tabs opened to get to a good answer — the cost of attention-switching, which I’m really trying to avoid.

One of the traps of a traditional search engine is that it really feels like you’re doing important work. You are on a detective case. You are weeding out the nonsense. You are finding the hidden gems. If you truly enjoy searching and browsing for the sake of searching and browsing, then more power to you and please don’t let me ruin your pastime.

But I ain’t got time for that ish. More importantly, I ain’t got attention for that ish. I want the answer now, even if it’s just to know that there isn’t a good answer on the web. If I can’t have it now, then I want the answer with as little attention switching as possible.

In my experience, Poe Web Search is “smarter” than Bing. In other words - better results and no ads. What’s even cooler is that once I get the answer, I can quickly input that into one of many very useful Poe bots: Claude 2, GPT-4, Code Llama, 6 Hats Helper, etc. Poe is kind of like Inspector Gadget - there is a chat bot for every job.

I’m trying not to make recommendations these days. Just sharing what works for me.

AI Inspector Gadget

pSai and Resina

Tue, 24 Oct 2023 18:12:08 -0700

Today, we released two new AI chat bots that we have been building at polySpectra: pSai & Resina. Here’s the announcement post.

pSai is trained on all of polySpectra’s technical product information. We’re now using it as an AI-augmented search function, for the entire polySpectra.com website. I wrote about my initial experience building pSai here.

Resina is a much more challenging project. Our goal is to train it on all of resin 3D printing. This will be pretty hard. Resina is accessible at resin3D.ai.

Only a couple hours after emailing our list, we already have a volume of un-answered (or poorly-answered) questions that would be pretty overwhelming for a lowly human to try to respond to on their own. I’m excited by the challenge to figure out a scalable system from the start.

We’re refining a setup where Resina learns a bit more every night. So, even if it can’t answer a certain question today, the hope is that it’ll know the answer by tomorrow. There are still a lot of humans-in-the-loop, but we’re trying to make the system as automated as possible. (With humans as curators/editors of the AI’s knowledge base.)

We’re aiming for something like this:

Rough Diagram of the Resina AI Workflow

A few fun experiences from today:

We must be doing something right, because we already have a few really angry dialogs with people who appear to be major haters of polySpectra (and maybe AI too?)
I’ve been having a lot of fun with the combination of GitHub CoPilot and Cursor.sh. CoPilot quickly tries to autocomplete while Cursor can really do the full-context heavy lifting. These tools are designed for developers (and I am not a developer), but I’m finding them to be really useful for content creation. (See yesterday’s post: ) It’s surprisingly good at coming up with new questions to ask Resina.
Very early exploration with the possibility of giving Resina a voice. ElevenLabs is super impressive.

My Journey with Cursor: A New Wave in Coding

Mon, 23 Oct 2023 00:00:00 +0000

Note: Watch how I wrote this (super cheesy) post in ~7 minutes with Cursor, via the new Zoom Clips Beta Feature

Writing about Cursor with Cursor.

I’ve been exploring Cursor, an AI-first code editor that’s making waves in the developer community. It’s being used by engineers at big names like Shopify, Samsung, OpenAI, and Facebook, to name a few. But what’s all the fuss about?

Well, Cursor is packed with features that are designed to make coding faster and more efficient. For starters, it lets you chat with your project. No more wasting time hunting for the right place to start a change or the correct method to call. It’s like having a knowledgeable coding buddy right there with you.

But that’s not all. Cursor also allows you to browse documentation directly and refer to code definitions and files. It’s like having a coding reference library at your fingertips.

One feature that really stands out is Cursor’s ability to make code changes. It’s like having an AI assistant that can write low-level logic for you. Need to change an entire method or class? Just give it a prompt. Want to generate code from scratch? Just give it a simple instruction. It’s a game-changer.

And let’s not forget about debugging. Cursor can scan your code for bugs and help you fix them quickly. It’s like having a personal bug detective that automatically investigates linter errors and stack traces to figure out the root cause of your bug.

I’ve been using Cursor every single day (since yesterday 😜), and it’s become an integral part of my coding routine (today). It’s not just the advanced features that make it stand out, but also the ease of use and the efficiency it brings to my workflow. It’s more than just an editor, it’s a tool that truly understands the needs of a developer.

So, is Cursor worth the hype? Well, it’s loved by developers all over the world and has helped tens of thousands of them be more productive. It’s not just an editor; it’s a game-changer in the world of coding. And I’m excited to see where it goes from here.

Finding the Perfect Voice Transcription Tool

Sun, 22 Oct 2023 00:00:00 +0000

For the last four or five months, I have been on the hunt for the best voice to text transcription tool. This is a really important part of my workflow and helps me to be able to work while I am walking or driving. (I use the Yealink BH71 Pro headset, which has amazing noise cancelling (for the microphone) and is still small enough to wear while active.)

In my quest for the perfect transcription tool, I’ve tried and tested numerous options. Here, I present a detailed comparison of three of the many tools I’ve explored: Otter, WhisperBoard, and HappyScribe:

Otter:

Has a nice mobile app
Provides rough transcription on the fly
Refines transcription when you have a connection
Free plan is fairly generous, you can use it to get in the habit.
Cheapest subscription is $16.99/month

WhisperBoard:

Completely free and open source
Runs locally on your phone for maximum privacy
Can choose from various accuracy levels, but the bigger/more accurate models are slow
Sometimes gets stuck repeating phrases
Not updated frequently, seems to be a hobby project

HappyScribe:

Not optimized for mobile
Highest accuracy transcription I’ve seen
Can add custom vocabulary to improve accuracy
Took longest to find (no mobile app)
User interface designed for desktop
Only 10 minutes free transcription for testing
Cheapest subscription is $17/month

In summary, Otter is best for quick rough transcription on mobile, WhisperBoard is best for full privacy but you get what you pay for it, and HappyScribe has the highest accuracy but is designed for desktop. The price difference between HappyScribe and Otter is only $0.01/month. They each have tradeoffs between accuracy, mobility, privacy and cost.

Feature	Otter	WhisperBoard	HappyScribe
Mobile App	✅ Nice native app	✅ Runs on phone	❌ Only browser
Transcription Quality	❌ Meh	⚖️ Varies by model	✅ Highest accuracy
Privacy	❌ Uses cloud	✅ Runs locally	❌ Uses cloud
Cost	✅ Free & $16.99/mo	✅ Completely free	❌ $17/mo minimum
Customization	❌ None	❌ None	✅ Custom vocab
Testing Options	✅ Free is generous	✅ Completely free	❌ Only 10 mins free

Right now I’m still in the final phases of what will likely be a decision to go all-in on HappyScribe. I’m recording in Otter, then exporting the audio, re-importing to HappyScribe, and comparing the difference. If I were working at Otter, I would put some focus and attention on transcription accuracy. If I were working at HappyScribe, I would build a mobile app.

Honorable mention goes to the built-in iOS voice-to-text, which was used to dictate much of this post. (And in case you are curious, here was my process of dictating to Claude-instant-100k, to get most of this post written without using my hands => https://poe.com/rawworks/1512928000195140 )

It's All in the Hips

Sat, 21 Oct 2023 00:00:00 +0000

This is what I’ve been up to today:

For this type of “awareness through movement”, I would consider low-dose ketamine to be a performance-enhancing drug.

(& of course I found Alfons by asking Poe Web Search for “Feldenkrais AI”.)

GPT-4V(ision) Handwriting OCR

Fri, 20 Oct 2023 00:00:00 +0000

Last month I was whining about “handwriting OCR”, a technology that has mysteriously and simultaneously existed in a quantum superposition between |in use every day at the USPS⟩ and |doesn’t actually work⟩ since at least 1998. (See Why Does Handwriting OCR Suck in 2023?)

GPT-4 with Vision (GPT-4V) is now available to ChatGPT Plus users (somehow Microsoft didn’t release this one first). I figured I’d take a break from my ranting and put it to the test:

GPT-4V transcribes my notebook into Markdown.

It didn’t nail the indentation of the bullet points, but I think this is getting pretty close to useful. If anyone can think of a way to fine-tune GPT-4V (I think we may need to wait for the API) - please let me know. Maybe I’ll finally be able to digitize my handwritten notebooks this decade.

Shout out to “All About AI” for showing the example that inspired me to revisit this. At 00:32 - he draws an outline of a program in his notebook and asks GPT4 to write the corresponding code. Full video below:

Using AI at Every Step of the Customer Journey

Thu, 19 Oct 2023 00:00:00 +0000

In the same way that every company needed to become an “Internet company”, and then have a social media presence — now every company is going to need to become an “AI company”.

My first professional “AI win” was building a customer-support chatbot. But how about other stages of the customer journey?

This post is me “learning out loud”, to show y’all what I’ve been up to. I’m going to use the simple framework of Attract > Convert > Close > Delight.

Attract

Social Media images, DALL·E 3 Can Almost Spell

AI can spell AI, that's a start.

Expertise in the market niche: “Meet Resina - Your Resin 3D Printing AI Agent” https://resin3d.ai/

Convert

For polySpectra’s engineering audience, AI answering questions about the documentation is arguably an important part of conversion, closing, and delighting customers. Here’s our support bot in action: https://docs.polyspectra.com/
Similarly, one of my goals with Resina is to freely educate people as much as possible about resin 3D printing. polySpectra was founded to solve the founding technical debt of stereolithography 3D printing (shitty materials) - so the faster people learn about the “gotchas” of resin printing, the faster they’ll realize that they need materials they can actually trust.

Close

This stage warrants extra precaution. Depending on the type of interaction that you are having, AI could be a surefire way to destroy any trust that you’ve built with the prospect. I’m not sure I have good advice for a true sales close, other than perhaps using AI to help word emails. I think the best approach is using AI to be so helpful and so informative (in the other stages) that the customer closes themselves.

Delight

Speed. I’m hooked on same-day shipping. I can’t wait 5 seconds for ChatGPT to respond, I need Claude-Instant instead. AI can help delight customers by accelerating customer engagement and customer support 100-1000x.
“Mass customization”. This is a term thrown around a lot in 3D printing. LLMs can be fine-tuned so quickly now (see How To Train ChatGPT On A Book In 5 Minutes) that you could imagine customizing the AI to be specific to an individual customer. I’m excited to explore this idea further.
Make it fun. As we start to build more and more AI tools at polySpectra, I’m trying to have fun with it. I’m also trying to build tools that I would legitimately want to use.

Searching for Super-Discernment

Wed, 18 Oct 2023 00:00:00 +0000

Over the years, I have had lot of “good” ideas that just clearly weren’t worth my time. Now that AI can accelerate some of these ideas by 100x or maybe even 1000x - does that make them worth pursuing? Certainly for some of them.

The super-doing capabilities of AI require super-discernment capabilities for humans.

The possibilities are completely overwhelming to me, every day I find myself paralyzed by the sheer scale of potential permutations. I feel something like this:

Remembering Roland Griffiths

Tue, 17 Oct 2023 00:00:00 +0000

The first time that I met Roland Griffiths, he was setting up someone else’s tent at Burning Man. This small act of generosity - setting up someone else’s tent - might not sound like a big deal, but it turned out to be a really big deal for me. (I happen to now be married to that someone.)

Roland was a truly inspiring friend and mentor to me. He had incredible discipline in all areas of his life - his work ethic, his self-care routines, his meditation practice. From the moment I met him, I could tell immediately that he had a very clear sense of purpose, a real mission, perhaps best conveyed by his favorite question: “Are you aware that you are aware?”

You can likely sense Roland’s child-like curiosity in this question. His curiosity wasn’t just about ideas, Roland was simply one of the most loving and compassionate people that I have ever met. I am incredibly grateful for the time I had with him over the last decade or so.

Roland Griffiths teaching me how to meditate, as a wedding gift.

Professionally, Roland was a rockstar. He was one of the world’s foremost experts in seemingly disparate subjects: caffeine, mystical experiences, and psilocybin. In “How to Change Your Mind”, Roland is described as “the investigator beyond reproach”. Roland was among the last of a generation of researchers to use self-experimentation as a mode of scientific investigation. Once regarded as the gold standard, this is now considered to be too conflicted and biased to be used in modern research (which currently favors double-blind controlled studies). For example, one of Roland’s academic contributions that involved self-experimentation was towards quantifying the minimum detectable dose of caffeine. (This was at a time when caffeine was not considered to be addictive, and not treated as “a drug”.)

Roland was truly at the cutting edge of research in consciousness. He was incredibly curious about probing the line between science and spirituality. With the utmost respect for my dear friend, many of the questions he was seeking to explore in his research were beyond science. Roland was not afraid to look over that edge.

What do I mean by beyond science? To me, science stops when we can’t propose a means for others to reproduce the experiment. So many of the questions about consciousness (not just Roland’s) are fundamentally irreproducible. It’s not that they aren’t interesting questions - it’s that these are philosophical questions, these are spiritual questions, these are mystical questions. In my view they cannot be answered with the tools of physical science. (But hey, I’m just a lowly chemist.)

I am forever grateful to Roland for these debates. I don’t know exactly where to draw the line between science and spirituality, but my conversations with him helped me to start sketching a rough topology. Without getting too heady, let me explain what I mean with a short example:

Roland Griffiths taught Michael Phelps how to swim.

We can test this statement with scientific investigation. We might never be able to prove without a doubt that Roland did or did not teach Michael how to swim (it takes a village), but if we asked enough people at the pool we could probably get a good scientific sense of the degree to which Roland influenced Michael’s swimming abilities.

Roland Griffiths and Michael Phelps have the same conscious experience while swimming.

This one gets tricky. Some people would argue that we just don’t have sharp enough scientific tools to probe that question. To me, this question is beyond science. From a scientific perspective - it is unknowable, even while from a psychological perspective Roland and Michael both intimately know the experience of swimming. My view is that no matter how many words, sounds, EEGs, fMRIs, or nerve clamps that we use - science alone can’t compare their direct conscious experience. (And the beauty of it is that Michael turned out to be a decent swimmer, even though we’ll never be sure if Roland’s experience of swimming even remotely resembled Michael’s.)

That’s enough philosophy of mind for today (see the footnotes for some of my favorite books on this topic). Let’s get back to remembering my buddy Roland.

Roland was unbelievably upbeat and cheerful after receiving the news of his terminal cancer diagnosis. “What a gift”, he would say. “What a tremendous opportunity to really think about my priorities.” While I can’t imagine myself responding to a similar situation with even a fraction of his equanimity - I don’t doubt that he meant it. I certainly don’t have the words for it now, but I feel that I have learned a tremendous amount from Roland recently, observing how he has courageously and compassionately navigated his final months on earth.

Towards the end of his life, Roland was very curious about the possibility of communicating after death. Since I heard the news of his passing yesterday, I’ve been quietly investigating this possibility.

I can’t prove it scientifically, Roland, but I feel your presence. I feel your love. Thank you.

Footnotes:

Roland Griffiths Memorial Fund
“How to Change Your Mind” - Michael Pollan
“Galileo’s Error” - Philip Goff (special thanks to Dana Sawyer for gifting me this book)
“The Flip” - Jeff Kripal (also via Dana)

Leveraging AI's Alexithymia to Overcome Emotional Friction at Work

Mon, 16 Oct 2023 00:00:00 +0000

The emotional component is one of the toughest parts of the job. Whether it’s communicating with someone you recently had a conflict with, following up on a tedious but important task, or just generally pushing through moments of resistance or procrastination - our emotions don’t always make things easy.

What if we could leverage the natural “alexithymia” of AI systems to our advantage in overcoming emotional friction? As AI’s don’t appear to have subjective experiences (yet), they are impervious to emotions like frustration, resentment, burnout or reluctance that humans often face (for now).

Imagine there’s someone you need to follow up with, but just the thought of directly interacting with them starts to raise your blood pressure. Use AI to draft the message. This would allow you to communicate pragmatically without letting any lingering interpersonal tensions interfere. The AI’s objective, emotion-free communication style would sidestep your own anxious reactions.

So rather than dwelling on past issues or stressing over an awkward encounter, you focus solely on conveying the necessary information. The AI acts as a buffer, providing a bit of distance from an emotionally fraught situation. This helps you still get your point across and move things forward, while saving your emotional energy for interactions that truly matter.

Similarly, if there’s a task I’ve been resisting or procrastinating due to fatigue or lack of motivation, enlisting an AI assistant can help separate the emotional component from the logical “next steps”. This allows me to leverage a methodical, emotion-free collaborator to help move projects forward despite any personal reservations. Showing up is 90% of the work, and LLMs “show up” in milliseconds, with minimal pay. By reducing the emotional friction of the work - they can help me show up even when I don’t really feel like it.

Some prompts to experiment with:

Have an AI assistant draft a difficult email, message, or presentation you’ve been avoiding. Remove your emotional reaction from the content.
Enlist an AI to break a complex task down into bite-sized, manageable steps so it feels less overwhelming.
Give an AI routine administrative tasks you’ve come to dislike through repetition so you can focus on more engaging work.
Utilize AI databases, research tools, or information retrieval to find answers independently rather than expose yourself to the emotional slot-machine of the internet
Develop an AI chatbot to field basic user questions and issues as the first point of contact, reserving your expertise for more nuanced inquiries.
Use an AI to guide you (and your team) through a perspective-switching exercise (for example, I’ve been using my “6 Hats Helper” bot almost every day).

With great power comes with great responsibility. These tools could be used to help you become even more emotionally avoidant, or they could be used to reserve your human energy and attention for the truly important and highly emotional work.

Ongoing Moving Sale - Text For Info

Sun, 15 Oct 2023 00:00:00 +0000

In a small village surrounded by terraced rice fields, there stood a run-down storehouse. Out front lay a lone piece of weathered plywood, spray painted with odd markings that had faded with time: “ONGOING MOVING SALE! TEXT FOR INFO”. A phone number was listed below, but no one had ever paid it any mind.

The villagers assumed the building must contain only trash, since its owner had clearly left in a hurry, judging by the messy sign. Over the years, the storehouse had fallen into total ruin and disrepair.

One sunny day, as the village children played, curious young Tomas spied the mysterious sign and could not resist investigating further. Peeling back flakes of faded paint, he decoded the message and decided to text the number. There was no reply.

Tomas could not control himself. The next day, he decided to go in anyway. When Tomas pushed open the creaking door, layers of dust stirred…

What wonders were then revealed to Tomas’s keen eyes! On wooden shelves lining the walls from floor to ceiling were stacked objects untouched for who knows how long. Intricate sculptures from ancient civilizations were piled high, their artistic details still visible under years of dust. Figures of elephants, horses and dancers from India appeared as if in meditation.

Weaving amongst them were relics from every corner of the universe. From distant galaxies, celestial maps charted nebulae and star systems in vibrant cosmic oils. Alien artifacts of long-lost civilizations spoke to lives pondering the same mysteries under alien skies. Sculptures hewn from asteroids captured the shifting dances of stardust with microscopic perfection.

In dark corners, musical instruments of all shapes and sizes from a thousand worlds rested in silent harmony. Zithers, flutes and drums from lands afar awaited the songs they held within. Rolled parchments and faded tomes filled with the wisdom of sages from days long forgotten. Secrets of medicine, philosophy and history that could lift entire communities from suffering were here in abundance.

From Ancient Egypt emerged papyrus scrolls preserving the wisdom of Imhotep, yielding knowledge to lift humankind from sickness. Statuettes and funerary masks channeled the poise of the pharaohs, as membranes within guided souls to sacrifice all for their people. Opal scarabs and gems engraved with spells escorted souls to paradise realms described only in poems.

The glories of Rome were not absent. Marble friezes recounted triumphs through disciplined legions and advances in architecture that stood the test of time. From distant Guangdong came intricate bronzewares and jades inscribed with early dialects, as were silken banners from Chang’an imprinted with astrological charts. Scrolls of rice paper bore poetic odes and wisdom that fed countless souls through dynasties.

From the deserts of a thousand worlds surrendered their secrets. Scrolls penned in luminescent sands illuminated the insights of hermit monks who dwelled alone with dunes for decades. Elixirs distilled from meteorites held properties to cure afflictions unnamed.

Even the roots of World Trees bowed under the abundance. Folios bound in barkskin preserved the visions of prophets who communed for centuries in leafy groves. Carvings in flowing mycelium recorded the epics of subterranean elders conversing telepathically in living rootworks.

From dimensions unseen, objects of pure imagination took shape. Sculptures of mercy and grace sculpted from liquid dreams. Harps whose strings were spun from strands of thought provoking melodies never before conceived. Prisms refracting the nuances of concepts beyond words into rainbows of meaning.

As Tomas explored further into the depths, the piles of marvels seemed to grow inexhaustibly. He stumbled upon a veritable library of Babylonia - clay tablets bearing epics, etchings of constellations, and arithmetic used to chart the heavens. Inkwashed scrolls by masters from Chang’an imparted the subtle and profound. Lost amid wonders unimaginable, Tomas forgot all about the passage of time in his awe and delight.

Lost amid wonders unimaginable, Tomas forgot all about the passage of time in his awe and delight. Truly, this place held riches utterly without measure or bound, enough to uplift all beings throughout the cosmos for eras untold.

Generous Residue

Sat, 14 Oct 2023 00:00:00 +0000

I was given a gift and
en route to enjoyment
The sticky residue of the price tag
Stuck to my fingers
Pulpy bits in my nail beds
I never could remove
No soap nor scour strong enough
To undo the glue

My fingerprints changed
Possibly my DNA
Incredible shame
Unforgettable blame
Enraged and enflamed
All in the name
of erasing the price of the gift

Books Are So 20th Century

Fri, 13 Oct 2023 00:00:00 +0000

This is the last moment in history where the act of writing a book will be considered impressive. Just like Uber was “everyone’s private driver”, LLMs are now “everyone’s private ghostwriter”.

Both Bill Gates & HH the Dalai Lama have ghostwriters - why shouldn’t you?

Here are just a few pieces of evidence:

Squeezed By The Universe

Thu, 12 Oct 2023 00:00:00 +0000

Being squeezed by the universe

is usually quite uncomfortable.

A loving kick in the pants

from a higher dimension.

A (re)birth canal

that will change the shape of your head.

An invitation

not to the party you dreamed of,

but to the path you belong to.

6 Hats Helper: Your New Thinking Buddy

Wed, 11 Oct 2023 00:00:00 +0000

Ever had one of those days where every decision feels like a brain-bender? Well, we’ve been there and we got you covered. Meet 6 Hats Helper, my latest AI chatbot. But hold on, it’s no ordinary chatbot. It’s like your personal decision-making sidekick, inspired by the genius of Edward de Bono’s Six Thinking Hats technique.

Picture this. The 6 Hats Helper is like your own cognitive jiu-jitzu sensei, wearing Blue, White, Red, Black, Yellow, and Green hats, each one representing a different style of thinking. It’s as if you’ve got a team of top-notch thinkers in your head, each bringing their unique perspective to the table, helping you make decisions that cover all bases.

Whether you’re wrestling with a mind-boggling problem or trying to strategize your next big move, the 6 Hats Helper steps in to guide your thought process. It’s like having a structured, multi-angle approach to handling any challenge, right in your pocket.

With the 6 Hats Helper, decision-making isn’t just about ’eeny, meeny, miny, moe’ anymore. It’s a thorough expedition into the world of thoughts. It’s like having an advisory board in your brain, each member giving you a different viewpoint, making sure you’ve got all your angles covered.

Building the 6 Hats Helper was an absolute blast. Thanks to Poe and Claude-2, creating this bot was way easier than I anticipated. (This is my first Poe bot.)

Are you ready to shake up your decision-making game? Put 6 Hats Helper to the test and tell me what you think.

How To Train ChatGPT On A Book In 5 Minutes

Tue, 10 Oct 2023 01:00:00 -0700

Step 0: Get your book as a .PDF (As an example, I chose “The 4-Hour Work Week”, which is available as a PDF for free from the author.)
Step 1: Sign up for SuperAgent: https://beta.superagent.sh and connect your OpenAI API key (by pasting it here after clicking “configure”).
Step 2: Go to “Datasources”. Click “New Data Source”. Click “Local files”. Pick the file, give it a name and description.
Step 3: (While your data source is processing…) go to “Agents”. Click “New Agent”. Under Datasources, select the source.
Step 4: Give it a prompt. I chose this:

You are an helpful AI Assistant. You are an expert in productivity. You are an expert in startup business systems. When answering a question, first see if the answer is available in The Four-Hour Work Week book data.
Step 5: Choose the model and connect the datasource. Click “Update Agent” to save. Your agent setup should look something like this:
Step 6: Go back to “Datasources” to confirm that the datasource is done processing. It should say “Ready”.
Step 7: Go back to your agent and test it out!

Here was my test question:

help me with my “dreamlining” excercise. to confirm your understanding, summarize the process of “dreamlining” from the book.

…and the response:

“Dreamlining” is a concept introduced by Tim Ferriss in his book “The 4-Hour Work Week”. It’s a process that involves defining your goals and dreams in concrete terms and then breaking them down into actionable steps. Here’s a summary of the process:
Create Two Timelines: Create two timelines - one for six months and one for twelve months. For each timeline, list up to five things you dream of having (including, but not limited to, material wants: house, car, clothing, etc.), being (be a great cook, be fluent in Chinese, etc.), and doing (visiting Thailand, tracing your roots overseas, racing ostriches, etc.) in that order.

Determine the Cost: Next, you need to determine the cost of these dreams to make them a reality. This includes the cost of living your dream and any expenses that might arise in the pursuit of it.

Define Action Steps: Break down the steps you need to take to achieve each dream. Start with the steps you can take today.

Take the First Step: Now that you have your steps defined, take the first step immediately. This creates momentum and makes it more likely that you'll follow through with the rest of your plan.
Remember, the goal of dreamlining is to make your dreams achievable by breaking them down into manageable, actionable steps. It’s about turning the abstract into the concrete and making your dreams a reality.

It took me 9 minutes to figure out how to do this (including finding/downloading/uploading the 4HWW .pdf). (I gave myself a time limit of 10 minutes). If you follow these instructions (or ask your AI agent to follow them for you), I bet you can do it in 5 minutes.

Crickets

Mon, 09 Oct 2023 00:00:00 +0000

A bold idea born,
Shared with excitement,
Met with tranquility,
Crickets teach silence.

(Some) Questions For a New Project

Sun, 08 Oct 2023 00:00:00 +0000

Project Objectives and Scope

What are the goals and objectives of this project?
How does this project bring value to customers/society?
How will the capabilities created by this project support the organization’s strategic goals?
What is the scope and deliverables?
If there are any non-negotiable constraints (e.g., regulatory, time, budget), what are they?

Stakeholders

Who are the key stakeholders?
What are their expectations and influence?
How will stakeholder feedback be incorporated throughout the project?
How will we continue to sync with them?

Team

Who is on the team and what are their roles?
Is the project aligned with team values and passions?
How will decisions be made as a team?
How will we handle skill gaps within the team, if any?
How will we handle conflicts within the team?
How will we celebrate wins and milestones together?

Communication

How often will we meet for updates?
What communication tools or platforms will we use?
What anonymous channels can we use for feedback?
How will we document key decisions and who will be responsible for this?
How will we continue to communicate with stakeholders?

Timelines and Milestones

What is the timeline and milestones?
Are there any schedule dependencies?
How flexible are the milestones and deadlines? Can they be adjusted based on project progress or unforeseen obstacles?
How can we build in feedback loops and iterations?

Risks and Obstacles

What risks and challenges may arise?
What pitfalls have we faced before that we can now avoid?
How will we identify and monitor emerging risks as the project progresses?
Do we have backup plans and contingencies?
What could potentially derail this project?

Resources

What resources are available?
What is our plan for resource allocation and management throughout the project?
Are any key resources missing?

Sustainability

How can this project be sustained long-term?
How will the results of this project be maintained or enhanced after project completion?

Project Management

What methodology will be used?
How will we track progress and reassess?
How will we manage scope creep, if it occurs?
How will we handle change requests?

Success Criteria

How will we define and measure success?
What are the key performance indicators (KPIs) for this project?
What would it feel like to do a good job?
When and how will we reevaluate our approach?
What lessons learned from past projects can we apply to this one to increase its success?

Purple Impressions

Sat, 07 Oct 2023 00:00:00 +0000

I just finished my first week of Purple Space, “a community of practice for creators, entrepreneurs, and line managers” founded by Seth Godin.

Like any new community, things got off to a somewhat awkward start…feeling a bit disoriented and sheepish. Who is who? Where am I supposed to sit? What is normal here? How am I expected to engage? Do I want to be here?

Today was a big inflection point. Seth organized a “Round Robin Chat” this morning, which consisted of about 50 people who were split into pairs for 5 minutes. There were 5 rounds, interspersed with a bit of commentary from Seth. The whole thing lasted less than an hour. I’m feeling inspired.

It’s a really simple container which can be easily implemented by Zoom. I found this “speed dating” format to be really energizing. In 5 minutes you almost always end up getting cut-off mid-sentence when the breakout room ends. This might sound frustrating but it’s super fun. There’s a cliff-hanger. It’s hard to get bored in 5 minutes. You’re left curious about what could have been if the interaction were longer.

Afterwards, I couldn’t help but think of how different the round robin “set and setting” was than most professional gatherings I’ve been to. The juxtaposition that immediately came to mind was a community/conference/company “happy hour” - there might be some conversations you’re really trying to get out of, someone you’re anxious/excited about approaching, someone you really want to avoid, that new person who joins in late and now the whole story has to be told again, someone buys you a beer and now you feel like you have to talk to them, someone offers to buy you a beer and now you feel like you have to drink, and on and on…

The Zoom round robin is less organic, more mechanical. There isn’t the opportunity to go very deep. But that’s what’s fun about it. Who goes first? How do I present myself in just a minute? How can I help someone in just a couple of minutes? Who wouldn’t want to learn about the intricacies of alpaca fur for five minutes?

Aside from just being a great way to meet five new people in the online community, I left feeling inspired to create community. I couldn’t help but recall of the different types of events I’ve hosted in the past. Remembering that skill, that possibility, that muscle.

When The Magic Is Gone

Fri, 06 Oct 2023 00:00:00 +0000

When the magic is gone…

walk on the other side of the street

do a rain dance

hire a magician

seek wise counsel

draw a box around the sacred

take the sacrament

put down your weapons

surrender your dictionary

get very still

observe.

Indra's Web: Part 1

Thu, 05 Oct 2023 00:00:00 +0000

Indra’s Web, or Indra’s Net, is a cosmic net laced with jewels that hang over the palace of Indra, the king of gods in Hindu mythology.

The net is infinite, and at each of its junction points there is a jewel that infinitely reflects the others. This is said to symbolize the infinite interconnectedness of all phenomena. Just as each jewel contains the reflections of all the others, each phenomenon in the universe contains within it all other phenomena. Nothing exists independently, but rather everything arises dependent on causes and conditions.

What got me thinking about Indra’s Net was the idea of building my own internal wiki for different things that I’m referencing often. For example if I’m referencing a particular book regularly, instead of just having the link/title/author, I could have my RAW.works edition wiki about that book, here on the site.

Generative AI makes this easier than ever. So while that might have taken hours of time to do in the past, you could do it in a few minutes now. You could potentially even automate the creation of the wiki, pulling data from various internal and external locations to curate a “showcase” wiki entry for the book.

What I’m excited about here is the possibility to create really engaging experiences that are more than just a recommendation and a link to go somewhere else. My next thought immediately terrified me.

If you think filter bubbles are a problem now, this is gonna make them even worse. So for example, Google could functionally summarize the entire internet with AI in a way that you will never leave Google. And you can see that they’re already doing this. With certain types of questions, you get the response as a snippet in the top search results of Google. (Not necessarily link to another website, but an official “Google answer.)

You can imagine taking that one step further, where the user literally never leaves Google. So you start on Google and when you ask it for stuff, it doesn’t give you the link to the other person’s website. It gives you the summary that it wants you to see.

I’m not sure Google will ever actually implement this to the fullest extreme, because all of their money comes from selling ads via the cost per click. The traffic leaving Google and going to other people’s websites is their main cash cow business. But it’s not hard to imagine a version of Google where there are no organic search results, only the Google AI summaries and paid ads.

If not Google, someone with less ad revenue. Microsoft Bing I’m sure makes some money on ads, but it’s not really the lifeblood of the organization. Or Amazon can create its own version of the internet that is constantly making sure that you see the version that maximizes as many of the physical and digital products that they sell as possible.

Maybe we’re already living in a version of Indra’s web. In the context of books, Amazon is already the number one platform for physical books, digital books, audiobooks, and now they own Goodreads…so it’s kind of hard not to be in Amazon’s version of the web when you’re talking about books.

Interdependent. Interconnected. Addicted to shiny jewels.

DALL·E 3 Can Almost Spell

Wed, 04 Oct 2023 00:00:00 +0000

As amazing as Midjourney is, the one thing it is notoriously bad at is spelling. It doesn’t understand words as text. When OpenAI recently teased DALL·E 3 with their avocado therapy cartoon, my immediate reaction was “OMG it can spell!”

Aside from some goofy LinkedIn posts - I was really struggling to see how Midjourney could be used for my business. It was a fun toy, and clearly a very powerful one, but it didn’t seem practical for business use.

I’m sure that there are plenty of Midjourney diehards who are proving me wrong on a daily basis, but for me something about the lack of spelling just gave me the sense that Midjourney would be more trouble than it was worth for our professional digital marketing use cases.

So the real question is: can DALL·E 3 actually spell? (As of this writing, the only way I know how to access it is through the Bing Image Creator, which makes me question my $20/mo OpenAI “ChatGPT Plus” subscription. (Microsoft must really have OpenAI by the balls.))

I’ll let you decide for yourself. (All typos courtesy Microsoft via Bing Image Creator)

Plumber.AI

Tue, 03 Oct 2023 00:00:00 +0000

Do you know whose job AI is not going to be replacing anytime soon? My plumbers.

Literally every week there is a new advancement in generative AI that gets us one step closer to AI that can achieve the “Turing Test equivalent output” for many functional roles within an organization.

A friendly agent to help you sift through the documentation and clarify any questions? You can set that up in less than an hour.

How about write a blog? That was so 2022.

What about someone who could provide a quick sounding board to test your ideas, or an intern you can research different business opportunities? This is all easy to do with tools that are either free or cost as much for a monthly subscription as a human might charge per hour or even per 15 minutes.

I have been following these developments relatively closely and I have yet to see an AI that can replace copper piping, install a water heater, or shut off the water if a pipe bursts under my house. While a huge number of programmers have been laid off here in the Bay Area, my plumber is doing just fine.

Knowing About

Mon, 02 Oct 2023 00:00:00 +0000

With LLMs accelerating daily, the value of “knowing about” something is approaching zero. To use an example that is relevant to my business polySpectra: previously it might have been valuable to be the person on your team or in your company that knows about “resin 3D printing”. In about 90 seconds I just tested (via Poe) GPT 4, Claude-2, Llama-2-70b, and PaLM 2. They all “know about” resin 3D printing (see below for examples).

If someone knows how to spell the thing you are curious about - they can get a pretty good overview of a topic instantaneously and for free. So your value as the person who “knows about” the topic isn’t very high, because in the time it would take me to find you, I already have the answers I need.

So how is a mere human supposed to stay ahead of the curve? Perhaps you can know something that is fundamentally unknowable to AI. Perhaps you can do something better and faster and cheaper than anyone else. (“Knowing how” is clearly infinitely more valuable than “knowing about”.)

Or perhaps you can choose a few things or maybe even just one thing - you can choose a topic on which you are going to be the absolute foremost expert in the entire world. Not a topic that you “know about”, a topic that you know in your bones, a topic that you live every single day.

Professional 'Advisors'

Sun, 01 Oct 2023 00:00:00 +0000

There is something that always bothered me about the advice of productivity gurus. It has taken me a while to figure out what it is. I think a big part of it is that I was never particularly impressed with their examples.

A common example, especially when someone is writing a productivity book, would be examples of authors. The first problem with this is that I’m not particularly interested in writing a book (although maybe someday I will be). More over, the specific act of writing a book is not necessarily difficult or impressive.

It is definitely a lot of work to write a book. (Or was, before generative AI took off.) And I’m sure that if you actually want anyone to read your book, it is even more work. And if you actually want to make the book successful enough to make any money, it is even more work. But the example of the author who just writes for some specified period of time or some specified number of pages every single day really didn’t resonate with me.

Maybe the other piece of it is that the majority of the people giving advice on productivity themselves aren’t doing anything or achieving anything that I find particularly impressive or applicable to my own life. Especially where their profession now is simply writing books about methods for how to be more efficient at writing books. Again, I’m sure that is valuable for a lot of aspiring authors, but it never really stuck with me.

For me, I have always been more interested in the advice of operators and doers. For example, I am much more curious about Warren Buffett’s advice on investing because he is actively investing and has built up his expertise on the topic through doing it (well) for decades. His advice might get him a little bit of publicity, but it really isn’t his business model to be giving advice. His business model is to be a smart investor.

On the other hand, you have people like Jim Cramer, whose primary job is giving investment advice every single day on TV. That’s what he gets paid to do. And yes, he also had a hedge fund at one point. I won’t pretend to know anything about it, but I don’t think it was particularly successful. Regardless, his profession is giving advice about investing, not investing.

I tend to be more interested in productivity tips from people who are focused on doing and achieving things, not just talking about it. The hard-won learnings of an operator are richer and more rooted in reality.

I can do nothing for you but work on myself…you can do nothing for me but work on yourself!

Ram Dass Be Here Now

Why Does Handwriting OCR Suck in 2023?

Sat, 30 Sep 2023 00:00:00 +0000

Some days, I’m overwhelmed by how fast technology is improving. Today, I feel like I’m running Windows 98.

In 1998 “50% of all handwritten card and letter addresses were ‘read’ not by human eyes but by high speed sorting machines, a process that resulted in labor savings of $31 million.”

The fundamental challenge of a machine reading handwritten postal addresses was solved before 1998. So why can’t even the most advanced AI algorthims from the top tech companies read a handwritten note in 2023? (This is not a rhetorical question.)

Optical character recognition (OCR) for digital text is ubiquitous, free, and fast. The built-in camera app on your iPhone will scan digitally-printed text basically in real-time. Google Translate will overlay the translation of signs/receipts/packaging in augmented reality.

Ironically, one of the classic tutorials in machine learning education is recognizing handwritten digits. Apparently only the US Postal Service has the talent to put this into production. Either that, or no one can think of a way that handwriting OCR would make them money. An efficient way to digitize pen and paper means that people would spend less time on their phones/computers/tablets, which would mean fewer notifications and less ad revenue. It would also mean that a plain piece of paper might displace the need for a $1500 tablet with a cool “pencil” to boot…and that’s bad for hardware sales. (Not to mention less data to spy on.)

I’ve been obsessed with finding the best tools for handwriting OCR, as a way of digitizing my analog notes (with the goal of reducing my screen time). Ironically, my journey thus far has only added to my screentime, trying to find and try “handwriting to text” tools that might unlock this workflow for me.

My conclusion as of today: the best handwriting OCR tools are already built into your phone. For iOS, use the “Scan Documents” feature inside of Notes. For Android, it’s called Google Lens. Don’t waste your time or money on any of the other apps for this — they all suck. Adobe gets a dishonorable mention for how terrible their OCR is for handwriting. (Given that they have monopolized PDF and “regular” OCR forever, they should be the best, not the worst.)

If you find a handwriting OCR app that doesn’t suck, please contact me immediately. I am hoping that maybe in 15 more years, I can finally digitize my handwritten notes.

Stories <> Data

Fri, 29 Sep 2023 00:00:00 +0000

Humans want stories, not data. Algorithms want data, not stories.

The major breakthrough of LLMs is commuting between these two domains.

Don't Get X'd

Thu, 28 Sep 2023 00:00:00 +0000

I am proposing yet another definition of X. It is a verb this time.

You know that you’ve been X’d when the platform that you’ve been sharecropping on suddenly pulls the rug out from under you.

Are you familiar with Nassim Taleb’s parable of the turkey?

Introducing Rai, my AI Librarian

Wed, 27 Sep 2023 00:00:00 +0000

As you might already know, I’m having a lot of fun with AI right now.

It took me 4 hours to set up my customer support chatbot for polySpectra. It took me 40 minutes to set up Rai, my new AI librarian for RAW.works. (Next time I’m aiming for 4 minutes.)

Put Rai to the test! That’s the only way it (they?) will improve.

P.S. - I want to be like Seth Godin when I grow up.

What If The Web Worked For You?

Tue, 26 Sep 2023 00:00:00 +0000

The largest tech companies in the world are selling your attention. At the same time, the internet is the great democratizer, and is giving you access to the vast majority of the world at lightning speed. You can’t possibly read everything on your many screens, and you can’t possibly disconnect completely. What’s a human to do?

CO2 Offsets for Walking? Why Not?

Mon, 25 Sep 2023 00:00:00 +0000

Yesterday, I found out about TreeCard, an app from Ecosia that pays to plant trees based on how many steps you take.

Today I’m at a 95-days streak of walking 10,000 steps a day - so my first reaction was that I wished I had found out about this a few months ago! (Why so many steps? I read Built to Move.)

My second reaction: Where does the money come from? Here’s the official TreeCard answer:

Brands pay us to feature their eco products within our rewards structure. These can be earned by planting trees!

So basically they’re selling my attention. (What’s new?) I’m not sure that this alone is going to save the planet, but so far my review is: why not? I’m going to get those steps anyways, why not get some carbon offsets along the way?

I’m still figuring out how the app works, but apparently if you join with my Treecard link and enter the code ray-5d7 then we both get extra trees (or something like that).

A Recipe for Forgiveness

Sun, 24 Sep 2023 00:00:00 +0000

One holy day
- Optionally in your tradition of faith, but someone else’s holy day will do
One patio chair
- Set up your patio chair in a peaceful location
One decorative pillow
- Place the decorative pillow on the chair for added comfort
15mg Ketamine HCl
iPhone with AirPods
Playlist including but not limited to:
- “Sit Around the Fire “ by Jon Hopkins with Ram Dass, East Forest
- “Indian Summer” by Jonsi & Alex Somers
At least one felt sense of unworthiness, preferably from the present
- Finely dice the unworthiness into the smallest possible pieces
At least one memory of being scolded for “bad” things you did as a child
- Whisk this memory thoroughly and compassionately
(Optionally) tears
- Cry to taste
A shower
- As long as necessary

A Joyous Equinox

Sat, 23 Sep 2023 00:00:00 +0000

On the day of the equinox, I found myself embarking on a new journey, one that I am now eager to share. A great occasion for a pinch of discovery and a cup of self-care. This was my Day One trying out Joyous, a brand of low-dose ketamine therapy that you can self-administer within the comfort of your own home.

Balancing Quantity and Quality in Content Creation

Fri, 22 Sep 2023 00:00:00 +0000

In the chaotic landscape of the digital age, content is king. Companies, individuals, and brands compete in a relentless bid for attention, and content creation has become the currency of this competition. The sheer volume of information available at our fingertips is staggering. We are bombarded with blogs, podcasts, videos, social media posts, and more, all vying for our time and attention.

In this whirlwind of content creation, a dilemma often surfaces: should the focus be on quantity or quality?

On one hand, producing a large amount of content regularly can improve visibility, boost Search Engine Optimization (SEO), and keep an audience engaged. On the other hand, high-quality content can build credibility, drive conversions, and create loyal followers. But, can one really strike a balance between the two or does one inevitably sacrifice the other?

In this post, we will delve into the tug of war between quantity and quality in content creation, examining the importance of each, the struggle to balance the two, and strategies to maintain this balance effectively.

Sharecropping

Thu, 21 Sep 2023 00:00:00 +0000

Who gets paid for your work?

Elon Musk? Alphabet shareholders? Medium? Meta?

Does it bother you that you are a sharecropper?

You didn't read the iTunes User Agreement?

My First Executive AI Win

Wed, 20 Sep 2023 00:00:00 +0000

Most of my experience with machine learning and artificial intelligence has been in art. (More on that below, for the curious.)

Today I’m excited to share my first “win” using AI as a business executive: Mendable. I can’t really take much credit other than to say that I’ve been keeping my eyes open, but the impact that Mendable will have on polySpectra is going to be truly transformative.

TL;DR - with Mendable I was able to train, test, and deploy a customer support chatbot on our full technical and product documentation in about 4 hours (and I will explain below how it could have been 40 mins.) If you run a business, have someone on your team try it today…you’ll thank me.

Advice Under Uncertainty

Tue, 19 Sep 2023 00:00:00 +0000

It is risky to offer advice for what someone else “should do”.

It is generous to offer a “what if”, a beautiful future of the possibilities if they chose to.

It is noble to offer your vulnerable truth about the lessons you’ve learned and the choices you’ve made.

Justifying Our Own Existence

Mon, 18 Sep 2023 01:00:00 -0700

After his brief internship at Twitter (now X), George Hotz concluded that the company could be run by 50 people.

I’m not an expert in digital infrastructure, but 50 people doesn’t sound that ridiculous to me, at least for running the product day-to-day. Maybe he’s being dramatic, but the point is that the product would be functionally equivalent with 10-100x fewer people working at the company.

Automating Social Media Posts for Hugo Sites

Sun, 17 Sep 2023 01:00:00 -0700

The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.

Bill Gates

TL;DR: You can automatically create social media posts from specific Hugo blog posts using tags, RSS, and either IFTTT or Make.com. Just point the automation to the tag-specific RSS link: https://yourwebsite.com/tags/yourtag/index.xml

Once you know this trick, you can set it up in one click. Keep reading for the long boring saga of how I figured that out.

Quantity Over Quality

Sat, 16 Sep 2023 01:00:00 -0700

It is a struggle to ship work that matters. It is very easy to get in your own way, to procrastinate, to delay. It is natural to wait for the perfect phrasing, or the extra feature, or the right person to come along.

The problem is that later becomes never. The recording will never sound like it did in your head. The perfect wording for you might not be the perfect wording for me. Tomorrow you might have changed your mind.

Quantity, or perhaps frequency, is potentially a more reliable route to quality.

Venture Games

Fri, 15 Sep 2023 01:00:00 -0700

I used to be jealous of my friends who had raised more VC money than me. Now I am grateful to have raised less than most of my peers.

Just when I felt that I was finally starting to understand the “VC game” - the entire playing field was re-arranged.

There Will Be Blood - Pt. 1

Thu, 14 Sep 2023 01:00:00 -0700

The date of the surgery was set months in advance, but not the time or location.

A few days in advance, a call was promised to confirm the time and location - but no one called.

No one picked up the phone when they were called, even though there were two separate extension lines, with separate voicemails, one of which was full.

No one picked up the phone during the lunch break, and of course messages cannot be left during the lunch break.

I am desperate for details and less than 24 hours from the surgery.

Cosmic Reassurance

Wed, 13 Sep 2023 01:00:00 -0700

Fear not loss. Ruin rerises reformed.

Xanadu Now

Tue, 12 Sep 2023 01:00:00 -0700

Sleep in Xanadu’s Pleasure Dome - One Night Stay Includes Guided River Tour

Wander Kubla Khan’s Lush Gardens and Ancient Forests - Immersive 2-Day Escape

Enjoy an Exotic Concert by the Abyssinian Maid - Private Dulcimer Performance and Feast

Experience Kubla’s Erupting Fountain First-hand - Behind the Scenes Geological Tour

Sleep Under the River Alph - Overnight Stay in the Caverns Measureless to Man

Monthly Milk of Paradise Subscription - Get Xanadu’s Divine Nectar Shipped to Your Door

Paper in Your Pocket

Mon, 11 Sep 2023 01:00:00 -0700

The insight scribbled hastily
On a scrap of paper in your pocket:
How will it reach others?

Like a single pebble cast into still waters,
Its ripples travel far.

Yet if shared in haste
It becomes just another rock
Lost in the cairn.

Lower the barrier
Smooth the way
Make space for the unspoken.

In the beginner’s mind
Scattered seeds become flowers
Frustration melts like morning frost

Immediate praise and blame
Are dancing shadows.
The one who shares, the one who hears-
Not two.

Like the moon in the lake
Insight reflects insight.
The interface is empty, yet fulfilled.

Let your insight fall like rain
Seep through cracks to sow its seed.
A quiet truth echoes loudest.

On Feedback

Sun, 10 Sep 2023 01:00:00 -0700

How long can you write if no one is reading?

How much can you work if no one is paying?

As much as we want to see ourselves as steering our own ship, following our own nose, forging our own path — most of us are not Van Gogh.

We need feedback and recognition to thrive. Just enough.
Too little = Van Gogh.
Too much = arrested development.

Enough to know we’re not alone. Not so much that we can’t hear our own voice.

Blue Moon

Sat, 09 Sep 2023 01:00:00 -0700

The red sun rises in the rearview,
Blazing bright over the highway behind me.
The blue moon sets ahead over stagnant cars,
Sinking below the foggy ridgeline.
The Dumbarton smells like farts.
For a moment I’m transported, certain I’m there -
On the playa, breathing it all in.
In the morning twilight, farts and dust.

Understanding Stories

Fri, 08 Sep 2023 01:00:00 -0700

There is a fear of AI writing stories for us, as Harari says “because we don’t understand what’s behind it”. But I’m not sure we’ve ever understood where our stories come from.

I’ve written a handful of songs. I could probably tell you what they are about, but I would be lying if I said that I knew where they came from.

Chief No Officer

Thu, 07 Sep 2023 01:00:00 -0700

I met a C-level leader at a 3D printing startup that described his functional role as the “Chief No Officer”. His job was to say no to almost everything.

The promise of 3D printing is that it can it can (supposedly) make everything. The reality is that it can rarely make anything.

His version of this was “just because you can do something doesn’t mean you should do it”. Hence the CNO’s job was to shut down most of the ideas, to keep the company focused.

Reflecting on this interaction, I am seeing how I have not been very good at saying no. The potentiality of yes is exciting. The possibilities. The novelty. The excitement.

Mixed Messages

Wed, 06 Sep 2023 01:00:00 -0700

Are you sending mixed messages?

Not planned, intended

Tue, 05 Sep 2023 01:00:00 -0700

The best things that have happened in my life were not planned, they were intended.

I did not plan to meet my wife. I did have an intention of being open to a romantic relationship.

I did not plan to join Cyclotron Road. I did have an intention to combine my interests in physical science & entrepreneurship, as well as an intention to live in the Bay Area.

Are you making a plan, or setting an intention?

Multiple Choice: Moments

Mon, 04 Sep 2023 01:00:00 -0700

Multiple Choice Monday.

In my in-between moments, I am most likely to:

Scroll
Text
Tap
Type
Feel
Breathe
Ask
Swipe
Sense
Sit
Pee
Wipe
Worry
Rest
Rage
Snipe
(None of the above)

AI doesn't blink

Sun, 03 Sep 2023 01:00:00 -0700

A.I. doesn’t blink. This was the vision of Haley Joel Osment and Steven Spielberg. The red doorknob. The origami unicorn. The subtle tell that is only obvious when it’s too late.

Make sure your friends are blinking.

Where do stories come from?

Sat, 02 Sep 2023 01:00:00 -0700

My friend manifests stories. She doesn’t write them, she writes them down. She has a protocol. She asks for them. From whom? I’m not sure she could tell you exactly, even if she wanted to.

Mommy, where do stories come from?

Superpowers

Fri, 01 Sep 2023 01:00:00 -0700

The very tools that promise to give us superpowers simultaneously (and perhaps necessarily) erode our intrinsic superpowers. If we haven’t yet discovered our innate superpowers, these technologies make it very hard to get quiet enough to notice them. Especially when our tools talk back, they can make it difficult to find time enough (and attention enough) to hone our (super)natural (super)powers.

A child's nightmare

Thu, 31 Aug 2023 01:00:00 -0700

As a small child, I had a recurring nightmare of getting trapped inside of the family computer. It took me more than 30 years to appreciate the wisdom in this dream. It was not the irrational fear of a four year old boy. It was a warning, a prophesy – and it has now come true.

Job with a capital J

Wed, 30 Aug 2023 01:00:00 -0700

What is your Job? (with a capital J) A story more powerful than money. A calling. A vocation. Where did it come from? How did you find it? What is in the way of you doing your Job?

Garbage Truck Mantra

Tue, 29 Aug 2023 01:00:00 -0700

Some days, no matter where you walk, you can’t seem to avoid the garbage trucks

Whose problem?

Mon, 28 Aug 2023 01:00:00 -0700

When someone tells you, “If you don’t do ___, that’s gonna be a problem,” they probably have a problem that they are not telling you about. They see you as the way to solve their problem, even if only psychologically.

Bliss & Rent

Sun, 27 Aug 2023 01:00:00 -0700

Find your bliss, and pay your rent.

…always remember…

Most landlords do not accept bliss for payment, and true bliss is not for sale.

Quotes

Mon, 01 Jan 0001 00:00:00 +0000

something

Sign up error

Mon, 01 Jan 0001 00:00:00 +0000

Sorry, we weren’t able to sign you up. Want to try again?

Thanks for signing up!

Mon, 01 Jan 0001 00:00:00 +0000

Thanks for signing up!