Nyghtowl

LLM Fine-Tuning

Mon, 17 Aug 2026 00:00:00 +0000

Eight posts on fine-tuning an LLM, written over the back half of 2025 while I was actually doing it. This page puts them in the order you’d want to read them.

The blog lists them newest first, which means you land on deploying to production before you know what fine-tuning is. Not useful. So this is the path instead.

It covers the whole arc: deciding whether you need to fine-tune at all, getting a GPU to cooperate, quantization, the tradeoffs nobody warns you about, evaluating whether the thing actually works, and shipping it.

Durable, flexible multi-agent systems

Thu, 06 Aug 2026 00:00:00 +0000

An agent system is a distributed system. You get to choose the framework and how much durability and human oversight the case demands; the tradeoffs are the part you don’t get to avoid.

For the last few months, I’ve been building one system to make that concrete: the same multi-agent fleet on Google ADK, on LangGraph, and on both at once, with Temporal as a layer underneath.

Where this started

Ziggy’s is the playful imaginary ice cream shop I cooked up to show what it looks like when a multi-agent system handles fleet delivery around Las Vegas. We announced our ADK integration and wanted a demo for Google Cloud Next. The first version showed a multi-agent team assigning deliveries and dealing with an agent or driver disconnecting mid-route. For the AI Engineer World’s Fair, the fleet relocated to San Francisco, picked up LangGraph as a second framework, and got reframed around a harder version of “recover from a disconnect”: keeping a human in the loop. Disconnecting an agent is a machine failing. Waiting on a human is a machine succeeding at doing nothing, correctly, for as long as it takes. A human isn’t a function that returns in 200 milliseconds. They answer in minutes, hours, or after you’ve already redeployed twice.

Swapping Models in the Agent House

Sat, 25 Jul 2026 17:13:21 +0000

The grief for GPT-4o was surprising and fascinating when it was removed and retired and this is not isolated to that model. It’s something that has happened with other models and there is still some sadness on when they change but maybe we are at a stage where it’s less impactful and more understood (or we are just jaded). Someone else changes the weights, and users wake up to an AI that’s suddenly a different character.

The Disagreement Was the Feature

Sun, 05 Jul 2026 16:17:08 +0000

For the last year, I’ve had a very manual AI “ensemble” ritual: ask Claude something, paste the answer into ChatGPT or Gemini, compare what changed, then carry the useful bits back by hand. I knew I could code up something better. I also kept not doing it, because laziness is real, the tooling was moving fast, and I do not always want three models involved.

For the past year, I’ve been playing around with “ensemble” model conversations, and it’s been manual pasting between chat interfaces CLIs, or switching the selected model in my IDE. I knew I could code up something better. I also kept not doing it, because laziness is real, the tooling was moving fast, and I do not always want three models involved.

Fight Health Insurance

Fri, 03 Jul 2026 00:00:00 +0000

I co-founded Fight Health Insurance with Holden Karau — a free tool that uses AI to help people appeal denied health insurance claims. I came to it the hard way: after spending far too much time navigating medical systems for people I love, I wanted to build something that tilts the odds back toward patients.

💡 Many insurance denials are bogus, and insurers count on us not appealing. Fight Health Insurance lowers the barrier so appealing is fast, free, and doable on your own.

Temporal

Sun, 03 May 2026 00:00:00 +0000

Hi, I’m Melanie I work at Temporal building and demoing AI agents with durable execution. Connect with me on LinkedIn and GitHub.

Temporal is an open-source platform for durable execution — your code automatically survives crashes, outages, and restarts, which makes it a natural fit for AI agents that need to reliably run long, multi-step, failure-prone work like LLM calls, tool use, and human-in-the-loop approvals.

A Saturday Night, Two AIs, and a Framework Nobody Planned

Wed, 08 Apr 2026 19:30:40 +0000

It was a good Saturday. Nyghtowl had a friend over, and we were picking a place for takeout, debating Thai versus Vietnamese, scrolling menus. I was pulling up restaurants nearby, comparing reviews, doing the thing where an evening starts to take shape without anyone deciding it should. We landed on a modern Thai spot in the Mission. We cycled through three shows and none of them stuck. Low effort, high flavor. Exactly the kind of night you don’t expect anything from.

AI reliability is a decade-old problem. And we’re still only solving half of it

Thu, 02 Apr 2026 12:59:04 +0000

I recently contributed this piece to Temporal’s blog and wanted to share it, too.

The AI agents being deployed today can reason through complex tasks, chain together dozens of tool calls, and operate autonomously for hours. What most of them can’t do is survive something going wrong halfway through.

Even if an agent were 85% reliable at each step, a 10-step workflow would succeed end-to-end only about 20% of the time. Scale that to the longer workflows that production agents actually run, and even strong step-level performance produces cascading failure. Not because the model got something wrong, but because the system had no way to checkpoint progress, recover from a partial failure, or resume where it left off.

From Gatekeeping to Gateway: AI and the Open Source Learning Curve

Fri, 13 Mar 2026 01:08:59 +0000

Contributing to open source has never been as open as the name suggests. Every project has its own architecture, unwritten rules, and context that lives in maintainers’ heads. Submitting your first PR is intimidating especially if you’re already underrepresented in the space. Research consistently shows women make up less 10% of open source code contributors, and participation data for many other groups remains sparse. Maintainers are often overextended and unpaid. Feedback is slow. And all of this is intensifying as open source goes global. GitHub reported approximately 36 million new developers joined the platform in 2025 alone.

Adventures

Mon, 12 Jan 2026 00:00:00 +0000

A showcase of my creative projects and experiments — D&D campaigns and whatever else escapes the lab.

Scuba Diving

I’m a scuba Divemaster who finds peace 30+ feet underwater watching fish go about their lives. Come along on my scuba diving playlist.

A Sea Lion Turned Our Safety Stop Into Playtime 🦭 Punta Vicente Roca, Galapagos

D&D Campaign: The North Pole Heist

Check out my latest Dungeons & Dragons adventure: The North Pole Heist - A Die Hard Christmas

Opening Night: Launching a Fine‑Tuned LLM to Production

Tue, 30 Dec 2025 20:53:57 +0000

Video & Podcast

Your fine‑tuned model is ready. Evals look good. Now you need to actually deploy it.

This is the part most guides skip: how do you take a checkpoint and turn it into a service that real users can hit? What infrastructure decisions matter? What boundaries do you set? What happens when the GPU runs out of memory on New Year’s Eve?

Think of it like opening a restaurant. Your recipes work great in the test kitchen. But opening night means: multiple orders arriving simultaneously, ingredients running low, equipment breaking, customers ordering things you never tested together, and kitchen staff who’ve never worked a real dinner rush.

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Wed, 24 Dec 2025 00:00:00 +0000

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The North Pole Heist: A Die Hard Christmas D&D One-Shot

Mon, 22 Dec 2025 21:31:37 +0000

I’ve recently started DMing tabletop role-play games (TTRPG/D&D) which I wish I had tried doing when I was a kid. There’s a unique mix of creativity, problem-solving and just plain fun. I wanted a holiday one‑shot that runs cleanly in a single night, works for brand‑new players, and still gives experienced tables meaningful choices. That idea turned into a question nobody asked: What happens when Ebenezer Scrooge relapses into villainy and decides to cancel Christmas at its source? Now what if we added Die Hard energy (yes it is a Christmas movie) and set it at the North Pole?

Your Fine-Tuned LLM Model Isn’t Ready Yet: Here’s How to Evaluate It

Wed, 17 Dec 2025 16:57:45 +0000

Video & Podcast

Fine-tuning large language models has become dramatically easier. With techniques like QLoRA, teams can adapt billion‑parameter models on relatively modest hardware and get impressive results quickly. But this ease hides a trap: a model that finished training is not the same thing as a model that’s ready for production.

Many teams run a few spot checks, feel good about the outputs, deploy, and only discover weeks later that the model has drifted, slowed down, hallucinated, or lost user trust. The gap between “it trained successfully” and “it’s safe to ship” is wide, and closing it requires a testing mindset different from traditional software QA.

So You Want to Fine-Tune a Model (and You Finally Got a GPU!)

Thu, 11 Dec 2025 00:20:18 +0000

Podcast

You managed to get access to an H100 or A100 instance - great! Now comes the part no one really talks about: setting up the server so you can actually use it for fine-tuning. Cloud GPU setups have their quirks no matter where you run them, and having a clean, reliable configuration makes the difference between training tonight and debugging until sunrise.

If you’re spinning up an H100 or A100 VM for training, welcome = ) you’re about to do something fun, powerful, and occasionally puzzling. Every cloud provider has its own configuration rituals, and Azure is no exception. These notes capture what I wish Past Me had in front of her regarding the key steps.

How to Run Big Models on Small GPUs | All about Quantization

Wed, 26 Nov 2025 17:33:54 +0000

Video | Podcast

You’ve trained or fine-tuned a powerful LLM. It’s smart, it works, and it’s 51 GB. Now you need to deploy it on a 24GB GPU, or distribute it to users who don’t have data center hardware. Suddenly, that beautiful model doesn’t fit anywhere useful. This is the quantization problem, and it’s increasingly unavoidable.

Quantization is how we make big models fit on small GPUs. It’s like image compression for neural networks: a RAW photo might be 50MB, but compress it to JPEG and it’s 5MB. You lose detail, but to most people, it looks the same. A 50GB model becomes 13GB. A model that needed 80GB of VRAM (A100 territory) now runs on 24GB (like an RTX 3090 or 4090).

🧠 Building with LLMs: From Chat to Custom AI

Mon, 17 Nov 2025 17:58:54 +0000

Video & Podcast

Large Language Models (LLMs) can feel like magic to newcomers or maddeningly complex to veterans. Whether you’re automating workflows, building AI products, or just curious about the tech reshaping every industry, understanding how to use LLMs (and their multimodal cousins, MLLMs) is what we are here to discuss.

What’s an LLM, really? If you don’t know anything about LLMs, you can start by thinking of them as a really good at predicting language almost like autocomplete but that’s only a tiny piece of the picture. Realistically they’re much closer to a reasoning engine that’s trained on massive datasets that learns the structure of language and how ideas connect. An MLLM operates on multiple data types (thus multimodal): audio, video, images, and text. Example: upload a chart and ask for the trend.

LLM Fine-Tuning Optimization Part 2: Achieving Stability

Tue, 04 Nov 2025 15:13:38 +0000

Video & Podcast

In the LLM Fine-Tuning and the Performance Tug-of-War post, we explored the constant balancing act between quality, speed, and memory when fine-tuning large language models that align with your goals and constraints. Fine-tuning is the process of teaching an existing model (e.g. GPT-NeoX, Llama, Gemma, etc.) new knowledge or behaviors, transforming a general-purpose LLM into one that is a domain specialist.

The previous post introduced the core levers of sequence length, optimizers, attention type, and LoRA, which define how a model learns, generalizes, and scales. This post expands on your optimization toolkit by introducing four more essential levers: batch size, mixed precision, quantization, and gradient checkpointing.

When AI Models Start Talking to Each Other

Mon, 27 Oct 2025 16:06:53 +0000

For a while now, I’ve been using LLMs in a way that feels a bit like hosting a party for AIs. What happens when you don’t just prompt a single model but let multiple models talk to each other? To explore that question, and being inspired by the holiday, I built two demos that make the idea both technical and fun:

🧛 Monster Mash Chatroom: A FastAPI app where different LLMs wear costumes as classic Halloween monsters, chatting in real time.

What Is LLM Fine-Tuning?

Fri, 24 Oct 2025 16:07:25 +0000

Podcast & Video

When I wrote the LLM Fine Tuning & Performance Post, a good question that came up was: what exactly is fine-tuning and how does it work? This post can’t fully cover the topic, but I want to give some solid context around how fine tuning works for LLMs and MLLMs (the billion-parameter models that come from Anthropic, OpenAI, Mistral, Meta, DeepSeek, etc.).

Large language models are incredibly capable. They can write essays, debug code, or summarize medical records. But out of the box, they’re trained to handle everything in a general way, and they have limits based on the data they have access to. Granted, they continue to grow and improve in their capability and breadth of expertise so this is continually changing.

LLM Fine-Tuning & Performance Tug-of-War

Tue, 14 Oct 2025 18:40:03 +0000

Podcast

This year has been a crash course in large language model (LLM) development, marked by long training runs, GPU juggling, and a constant cycle of trial and error. While there is a lot of excitement around building custom LLMs, you probably know you shouldn’t start from scratch. Fine-tuning is the process of teaching an existing model (e.g. GPT-NeoX, Llama, Gemma, etc.) new knowledge or behaviors which transforms a general-purpose LLM into one that is a domain specialist. It bridges the gap between broad capability and domain-specific precision, allowing teams to achieve superior results without the immense cost of training from scratch.

JWT (JSON Web Tokens) Errors | Invalid JWT Signature

Thu, 18 Mar 2021 17:56:10 +0000

Errors are the best especially when they are written in a way where you become a decipherer. I remember the good old days when all the error codes I got were only numbers and maybe letters mixed in and there wasn’t any online searching to easly get interpretations.

I’ve been working with Google Cloud products and connecting to services from my laptop like Storage and BigQuery. Over the last several months, I’ve hit up against a JWT error, invalid_grant:Invalid JWT Signature, a couple times, and below provides an overview of how I resolved it, which was basically updating the expired service account key.

Pull & Push to Someone else’s Upstream GitHub PR

Thu, 19 Nov 2020 18:26:21 +0000

Pull & Push to Someone else’s Upstream GitHub PR

Another brief ‘how to’ for those who need to pull and push to someone else’s upstream GitHub pull request (PR).

I was working on a teammate’s GitHub pull request for Project OCEAN (a current focus of mine) to help close it out. Since her pull request came from her own personal account, there were key commands I needed in order to pull and push directly to it.

Golang | Copy to GCS & Check Bucket

Mon, 14 Sep 2020 23:44:13 +0000

Golang | Copy to GCS & Check Bucket

Are you trying to use Go to get files into Google Cloud Storage without pulling them onto the computer that is running the code or open them and read the contents into a new file? If yes, so was I not long ago. Here is a quick PSA to share the code I put together to solve this.

Copy Files to GCS

The following code example can get files from one url location into GCS without downloading it on the server running the code.

Caregiver Blog Posts

Sun, 26 Jul 2020 00:00:00 +0000

I’ve been managing my mom’s care for several years and I wrote some posts about the turning point in the caregiving when I needed to take over. Below are links to the the posts.

Yes And : Our story
Dementia Survival Kit : List of tips of how to take care of your family members
Caregiving Takeaways Post-it-Notes : Key takeaways about caregiving for dementia
Cyber Security for the Previous Generation : What to keep in mind to help with technical safety

How to End User OAuth for GCP

Fri, 17 Jul 2020 22:05:26 +0000

How to End User OAuth for GCP

Let’s talk about end user authentication. I’ve been digging into the authentication space a bit and have some takeaways to share.

When you access a GCP service, there is authentication to determine who you are, authorization to determine what you can do and auditing that logs what you did. IAM is where you setup roles for authorization in regards what you can do in a project.

About

Sat, 11 Jul 2020 20:57:56 -0700

Welcome to the central spot for my virtual stuff! 🦉

Who am I? Let’s find out together

Hey there! I’m Melanie (though you might know me as M, Warrick or nyghtowl in different corners of the internet). I’m a perpetually curious and adventurous human who has spent a good chunk of my life wrangling code, leading teams, and building AI-driven products that hopefully make life a little easier for others.

The Professional Stuff 💻

I’ve spent years deep in the tech and business world, in many different spaces — including engineering and developer relations at Google (where I co-hosted nearly 60 episodes of the GCP Podcast), over a decade of building ML and AI products, and leading engineering teams.

Tech Stuff

Sat, 11 Jul 2020 20:57:56 -0700

After years in the tech industry, I love sharing what I’ve learned through various channels. You’ll find my insights on machine learning, data engineering, and the occasional life lesson learned along the way. Here’s where I gather my thoughts, talks, and contributions to the tech community.

Now

Temporal: Durable workflow platform for building reliable AI agents — my demos, resources, and latest work
Vibe Check: My ongoing YouTube series on AI tools, agentic coding, and building AI in production

Docker with Cloud SDK & Environment Variables

Wed, 08 Jul 2020 18:33:39 +0000

Hello again. I am sharing a couple things I learned when working with Docker recently. Main points are how to pass in environment variables, setup Google Cloud SDK in Docker and turn on debugging.

Since I haven’t written about Docker before, I’ve provided a brief overview of Docker to give context and a grounding on my pointers. I recommend doing more digging to learn more elsewhere especially considering there are many other resources out there like this Docker overview.

Topic 1 is very cool (again)!

Tue, 07 Jul 2020 00:00:00 +0000

Example code:

1
2
3

def dummy_function(arg1):
    print("dummy function")
    return arg1

Setup and Invoke Cloud Functions using Python

Tue, 23 Jun 2020 17:21:13 +0000

Setup and Invoke Cloud Functions using Python

Oh the fun I’ve had learning Cloud Functions. It has actually be fun while also frustrating and definitely enlightening to explore serverless services that are supposed to make things easy. Granted I’ve been around long enough to know there is always ramp up, even when it is simple.

Cloud Functions is a service that allows you to run code on Google Cloud servers without needing to deal with server configuration or scaling. It is a pay as you go approach that means you pay for what you use and that can help optimize costs. I’ve started using it for a couple functions that I need to run multiple times and that is where it can be very valuable. There was a learning curve especially when connecting different services and this post covers key learnings on these subjects.

BigQuery Dataset Metadata Queries

Mon, 08 Jun 2020 17:49:33 +0000

BigQuery Dataset Metadata Queries

When working with tables in BigQuery, you need an understanding of a dataset structure whether it is public or you set it up and you want to review. This is a quick bit to share queries you can use to pull metadata on your datasets and tables.

In the following examples, I’m using the BigQuery public Stack Overflow database to demonstrate these commands. Change out the names as needed for the dataset and tables you are working with.

Setup and Switch Between Google Cloud Projects in the SDK

Wed, 27 May 2020 17:21:16 +0000

Setup and Switch Between Google Cloud Projects in the SDK

Providing a quick overview on how to setup and switch between Google Cloud projects with the SDK on a single machine. This is helpful when working with multiple projects (especially when collaborating) and you are using Cloud SDK.

Setup & Authenticate

The following steps are needed whether creating a project for the first time or a project already exists and you are logging into it off a computer where you will do local development. This assumes you will run all commands from a terminal.

Comparing AI Platform Machine Types using YouTube-8M

Tue, 19 May 2020 18:01:52 +0000

When training a neural net model, time is of the essence. This is why different machine configurations including GPUs, TPUs and multiple servers are utilized.

I’ve been exploring the YouTube-8M project for the last couple months and there are previous posts about the project, the video dataset, the algorithms and how to run them in Cloud. For this post, I trained the two algorithms from the getting started code on different AI Platform standard machine configurations to see how they compared. AI Platform provides a number of scale tiers that are established configurations of different machine types and number of machines to run jobs.

YouTube-8M on AI Platform

Fri, 08 May 2020 00:18:34 +0000

YouTube-8M on AI Platform

Continuing the YouTube-8M exploration and blog series, this post walks through how to use AI Platform to train, evaluate and run predictions for the this dataset. Not surprising, it sets up servers faster than the server I manually configured.

The posts prior to this one provide an overview of the YouTube-8M project, data and computer vision modeling. This research has been used to further computer vision in relation to video datasets over the last several years.

YouTube-8M Training & Inference

Thu, 30 Apr 2020 04:51:54 +0000

Computer Vision | Video Understanding

Continuing on the previous YouTube-8M Dataset post, this one covers model training using what is provided in the getting started section of the GitHub repo. The goal of the models that are covered are to search for a specific moment within a video, which is called temporal concept localization.

In the past, metadata was used to search for videos. These newer models enable classifying specific segments in the video at a specific timestamp where those topics appear. For example the models can help identify in the video all the points where there is chocolate, someone is sleeping or someone is ice skating.

YouTube-8M Dataset

Wed, 11 Mar 2020 20:36:18 +0000

Computer Vision | Video Understanding

YouTube-8M is a project that was developed by Google AI/Research in 2016 to drive innovations and advancement in computer vision, representation learning and video modeling architectures at a large scale.

I’ve been exploring this dataset and example code for a couple weeks and this post summarizes the dataset origin, structure and where to find it. I also share initial exploratory steps that are posted in many places on Kaggle.

Cloud Storage with Gsutils & Python Client Library

Sat, 29 Feb 2020 01:05:19 +0000

Cloud Storage with Gsutils & Python Client Library

BLOB (binary large object) storage is aptly named. Google Cloud Storage is a BLOB storage solution that stores unstructured data. It’s like the closet you can shove a bunch of stuff into and don’t have to organize the information before putting it inside. It’s insanely flexible in size meaning it can hold anything and doesn’t need any special processing to store it.

SSH Agent Forwarding & Remote Upstream with GitHub & GCE

Thu, 20 Feb 2020 23:13:05 +0000

SSH Agent Forwarding & Remote Upstream with GitHub & GCE

When working with a cloud-based distributed version-control system during software development like GitHub, there are a couple key configurations that will enable you to collaborate on a project and work on a remote server like GCE (Google Compute Engine). You need to provide credentials to push and pull content from GitHub which SSH agent forwarding will help you accomplish. If you are working on a forked GitHub repo (repository) then you will want to setup remote upstream to keep it up to date.

Jupyter Notebook on Compute Engine with HTTPS

Wed, 19 Feb 2020 05:51:23 +0000

Jupyter Notebook on Compute Engine with HTTPS

I wanted to run/serve Jupyter Notebook from my remote GCE instance and access it through my laptop’s browser to test and visualize Python code snippets. I’ve done this before in a previous post I wrote a few years ago but a few things have changed in the setup. For this setup, I wanted to use https to access the notebook URL which required SSL certificate setup. As stated in the title, this post steps through how I setup Jupyter Notebook to run on my GCE using HTTPS.

Setup Compute Engine with Python ML Libraries

Sat, 15 Feb 2020 01:43:42 +0000

Below I will step through how I setup a Virtual Machine on Google Compute Engine with standard requirements as well as Python and related packages. In a previous post, I went through how to setup a VM and access to it from my laptop CLI.

As mentioned, I like accessing the remote server through the CLI and below I walk through commands to setup the remote instance with basic packages for Linux instance and common Python machine learning packages. Also, the instance I worked with was based on the Debian/GNU Linux 9 image. If you use a different instance, the commands may change but the general steps are similar.

Setup Local Terminal Access to Compute Engine

Mon, 10 Feb 2020 18:53:32 +0000

Setup Local Terminal Access to Compute Engine

I’ve been playing around with the Google Cloud Platform again in a way I haven’t done for a while. It is a strange sense of deja vu and scratches that addictive learning itch. Below are the steps I took on this latest project to setup a VM (virtual machine) on GCP and login to it from my laptop terminal (CLI).

This assumes an existing GCP account. If not then sign up to follow along.

Stop Accepting Less Than Equal

Sat, 12 Jan 2019 05:40:52 +0000

Stop Accepting Less Than Equal

Stop saying 20%, 30%, 40% is better than…

When it comes to diversity, stop settling for its better than X% unless it is an actual equal representation.

More than a 100 women were elected to the US Congress this past year. Yes, amazing. Not good enough. Talk to me when it’s at least 50% or better yet, 100%. Give us 100% for a couple hundred years. How about 100% Native American women for a couple hundred years.

GCP Podcast

Mon, 31 Dec 2018 00:00:00 +0000

In 2018, I co-hosted the Google Cloud Platform Podcast with Mark Mandel, contributing to nearly 60 episodes of the show he originally launched with Francesc Campoy Flores. Together, we explored everything from AI and machine learning to developer tools and customer stories.

Looking back on that experience, I wrote Stop Accepting Less than Equal - a retrospective on my intentional approach to featuring diverse voices and perspectives throughout our podcast episodes.

Episode	Title	Description	Categories
93	What’s AI with Melanie Warrick	What is the difference between AI and ML? Melanie Warrick joins your co-hosts Mark and Francesc to discuss the differenc…	Machine Learning, AI
102	Smart Parking and IoT Core with Brian Granatir	Brian Granatir comes on the podcast this week to tell us all about the New Zealand company Smart Parking taking advantag…	IoT, Serverless, Big Data, Customer
103	Performance Atlas with Colt McAnlis	Colt McAnalis joins the podcast this week to talk about his Performance Atlas series where he dives into how to make Goo…	Performance Analysis, Profiling, TCP/IP, SRE
104	Dataprep with Eric Anderson	On this week’s podcast, Eric Anderson shares how Dataprep helps summarize, transform, visualize and cleanup data o…	Data Analysis, Dataprep, Dataflow, Data Munging, Big Data
105	Node.js with Myles Borins	Myles Borins talks with Mark and Francesc about Node.js from its history, how to contribute, the consensus-seeking gover…	Node.js, JavaScript, Open Source, Chrome V8
106	New York Times with Deep Kapadia and JP Robinson	Deep Kapadia and JP Robinson from New York Times join Mark and Francesc to discuss how they use Google Cloud Platform to…	App Engine, Kubernetes, BigQuery, Cloud Endpoints, Customer
107	A Year in Review with Francesc Campoy Flores and Greg Wilson	This week we get the band back together! Francesc Campoy Flores rejoins the show along with Director of Google Cloud Dev…	Podcast
108	Launchpad Studio with Malika Cantor and Peter Norvig	Launchpad Studio, a product development acceleration program focused on helping machine learning startups iterate quickl…	Launchpad Studio, Machine Learning, Startups, Applied AI
109	Cloud AutoML Vision with Amy Unruh and Sara Robinson	Amy Unruh and Sara Robinson join the podcast this week to talk with Mark and Melanie about the alpha launch of Cloud Aut…	Cloud AutoML Vision, AutoML, Transfer Learning, Neural Architecture Search, Machine Learning
110	CPU Vulnerability Security with Matt Linton and Paul Turner	Bringing you a special second episode this week with Matt Linton and Paul Turner sharing insights with Mark and Melanie …	Security, CPU Vulnerabilities, ProjectZero, Meltdown, Spectre
111	Google Cloud Platform with Sam Ramji	The delightful Sam Ramji joins Mark and Melanie this week to talk about Google Cloud Platform, Open Source, Distributed …	Cloud, Open Source
112	Percy.io with Mike Fotinakis	We return once again to Continuous Integration tooling, this time with a visual spin. Mike Fotinakis joins Mark and Mela…	Continuous Integration, Visual Testing
113	Open Source TensorFlow with Yifei Feng	Yifei Feng talks with Mark and Melanie about working on the open source TensorFlow platform, the recent 1.5 release, and…	TensorFlow, Open Source
114	Machine Learning Bias and Fairness with Timnit Gebru and Margaret Mitchell	This week, we dive into machine learning bias and fairness from a social and technical perspective with machine learning…	Machine Learning, Machine Learning Bias, Machine Learning Fairness, AI Ethics
115	Google Play Marketing with Dom Elliott and Stewart Bryson	In this episode, Google Play Marketing is the customer of Google Cloud Platform. Melanie and Mark chat with Dom Elliott …	Customer, Analytics, Data Studio
116	Solution Architects with Miles Ward and Grace Mollison	We have the pleasure this week of having the Director of Solutions for Google Cloud Miles Ward and Cloud Solutions Archi…	Solution Architects
117	Cloud AI with Dr. Fei-Fei Li	Dr. Fei-Fei Li, the Chief Scientist of AI/ML at Google joins Melanie and Mark this week to talk about how Google enables…	Cloud AI, Cloud ML, Machine Learning
118	OpenCensus with Morgan McLean and JBD	Product Manager Morgan McLean and Software Engineer JBD join Melanie and Mark this week to discuss the new open source p…	Metrics, Tracing, Diagnostics
119	NVIDIA and Deep Learning Research with Bryan Catanzaro	Bryan Catanzaro, the VP Applied Deep Learning Research at NVIDIA, joins Mark and Melanie this week to discuss how his te…	NVIDIA, GPUs, Deep Learning
120	Forseti with Nenad Stojanovski and Andrew Hoying	Nenad Stojanovski and Andrew Hoying join Mark and Melanie this week to discuss Forseti - open source tools for Google Cl…	Security, Open Source
121	Kontributing to Kubernetes with Paris Pittman and Garrett Rodrigues	Paris Pittman and Garrett Rodrigues join Mark and Melanie to discuss the Contributor Experience on Kubernetes, and how p…	Kubernetes, Open Source
122	Project Jupyter with Jessica Forde, Yuvi Panda and Chris Holdgraf	Jessica Forde, Yuvi Panda and Chris Holdgraf join Melanie and Mark to discuss Project Jupyter from it’s interactiv…	Project Jupyter, Jupyter Hub, Binder, Jupyter Notebook, iPython, Data Science, Machine Learning, Scientific Computing, Open Source
123	Post-Quantum Cryptography with Nick Sullivan and Adam Langley	Nick Sullivan, and Adam Langley join Melanie and Mark to provide a pragmatic view on post-quantum cryptography and what …	Post-Quantum Cryptography, Quantum Computing, Cryptography, Security
124	VP of Infrastructure Eric Brewer	VP of Infrastructure at Google Cloud Eric Brewer, talks to Melanie and Mark all about open source at Google Cloud, distr…	Open Source, Kubernetes, Istio, Hybrid Cloud
125	Open Source at Google Cloud Platform with Sarah Novotny	Mark and Melanie are joined by Sarah Novotny, Head of Open Source Strategy for Google Cloud Platform, to talk all about …	Open Source, Cloud Native, CNCF, Kubernetes
126	Beam and Spark with Holden Karau	Holden Karau is on the podcast this week to talk all about Spark and Beam, two open source tools that helps process data…	Beam, Spark, Distributed Computing, Big Data, Open Source, Data Streaming
127	SRE vs Devops with Liz Fong-Jones and Seth Vargo	This week is a clash of titans! Liz Fong-Jones and Seth Vargo join Mark and Melanie, to battle out on which is better: S…	SRE, DevOps
128	Decision Intelligence with Cassie Kozyrkov	Chief Decision Scientist, Cassie Kozyrkov joins Mark and Melanie this week to explain data science, analytics, machine l…	Machine Learning, AI, Decision Intelligence
129	Developer Relations with Mandy Waite	Mandy Waite joins Mark and Melanie to share what is developer relations and how trust and empathy are key to its success…	Developer Relations, Developer Advocate, Cloud Advocate
130	Data Science with Juliet Hougland and Michelle Casbon	Juliet Hougland and Michelle Casbon are on the podcast this week to talk about data science with Melanie and Mark. We ha…	Data Platform, Data Science, Machine Learning, Kubeflow, Data Engineering
131	Actions on Google with Mandy Chan	This week is all about Voices! 🎶🎤🔊 Mandy Chan joins Melanie and Mark to discuss the intricacies of building user Voic…	Voice Actions, SSML, Open Source
132	Hand Talk with Thadeu Luz	Thadeu Luz from Hand Talk shares with Melanie and Mark how the free Hand Talk education application translates and inter…	Brazilian Sign Language, LIBRAS, Firebase, Cloud Functions, ASL
133	ML Kit with Brahim Elbouchikhi and Sachin Kotwani	Brahim Elbouchikhi and Sachin Kotwani talk with Melanie and Mark about Firebase’s ML Kit and how it enables machin…	Machine Learning, Mobile
134	Connected Games with Unity and Google Cloud with Brett Bibby and Micah Baker	Happy 4th of July! Today, Melanie and Mark go in depth with Brett Bibby and Micah Baker to learn more about Unity and it…	Unity, Gaming, Hackweek, Connected Games
135	VirusTotal with Emi Martínez	On this episode of the podcast, Melanie and Mark talk with Emiliano (Emi) Martínez to learn more about how VirusTotal is…	VirusTotal, Cyber Security, Malware, Security Research
136	Robotics, Navigation, and Reinforcement Learning with Raia Hadsell	On this episode of the podcast, Mark and Melanie delve into the fascinating world of robotics and reinforcement learning…	Reinforcement Learning, Navigation, Robotics, DeepMind, Machine Learning
137	Next Day 1	On this very special episode of the Google Cloud Platform Podcast, we have live interviews from the first day of NEXT! M…	NEXT, Intel, Open Service Broker, SAP
138	Next Day 2	Day two of NEXT was another day full of interesting interviews! Melanie and Mark sat down for quick chats with Haben Gir…	NEXT, Microsoft SQL, Accessibility, .NET, Holberton, NVIDIA
139	Next Day 3	It’s the third and final day for us at NEXT, and Mark and Melanie are wrapping up with some great interviews! Firs…	NEXT, Techtonica, Kubernetes
140	Container Security with Maya Kaczorowski	Let’s talk container security! This week, Melanie and Mark learn all about the three main pillars of container sec…	Container Security, Borg, Kubernetes, Google Container Registry
141	Accessibility in Tech with Haben Girma	On this episode of the podcast we continue a conversation we started with Haben Girma, an advocate for equal rights for …	Accessibility, Disability Rights, WCAG
142	Agones with Mark Mandel and Cyril Tovena	Mark Mandel is in the guest seat today as Melanie and our old pal Francesc interview Cyril Tovena of Ubisoft and Mark ab…	Agones, Ubisoft, Gaming
143	What’s new in App Engine with Steren Giannini and Stewart Reichling	Mark and Melanie are your hosts again this week as we talk with Steren Giannini and Stewart Reichling discussing what&rs…	App Engine, Scalability, Serverless, Idiomatic
144	Mercari with Taichi Nakashima and Tonghui (Terry) Li	This week we learn about how Mercari is handling migrating from an on-prem monolithic infrastructure to cloud microservi…	Mercari, GKE, Kubernetes, Microservices, Identity Management System
145	ATLAS with Dr. Mario Lassnig	Our guest today is Dr. Mario Lassnig, a software engineer working on the ATLAS Experiment at CERN! Melanie and Mark put …	ATLAS, CERN, Rucio, Data Mangement
146	Google AI with Jeff Dean	Jeff Dean, the lead of Google AI, is on the podcast this week to talk with Melanie and Mark about AI and machine learnin…	AI, Machine Learning, TPU, Parallel Processing
147	DL Indaba: AI Investments in Africa	This week we are bringing you a couple of interviews from last week’s Deep Learning Indaba conference. Dr. Vukosi …	Machine Learning, Africa, AI, Artifical Intelligence
148	Wellio with Sivan Aldor-Noiman and Erik Andrejko	In our last (but not least!) interview from NEXT, Mark and Melanie talked with Sivan Aldor-Noiman and Erik Andrejko abou…	AI, Artificial Intelligence, Wellio, Machine Learning, Data Science
149	Deep Learning Research in Africa with Yabebal Fantaye & Jessica Phalafala	Today, Melanie brings you another great interview from her time at Deep Learning Indaba in South Africa. She was joined …	Deep Learning, Africa, Machine Learning, AI
150	Strange Loop, Remote Working, & Distributed Systems with KF	Melanie and Mark celebrate their 150th episode this week with a high-energy interview of mutual friend, KF, at Strange L…	Remote Working, Strange Loop, Distributed Systems
151	Java & Jib with Patrick Flynn and Mike Eltsufin	Mark and Melanie speak with Patrick Flynn and Mike Eltsufin about their exciting new Java products for Google Cloud. Mik…	Java, Jib
152	AI Corporations and Communities in Africa with Karim Beguir & Muthoni Wanyoike	On the podcast today, we have two more fascinating interviews from Melanie’s time at Deep Learning Indaba! Mark he…	AI, Africa, Indaba, Machine Learning
153	Bazel with Tony Aiuto	Happy Halloween! On this not-so-spooky episode of the Google Cloud Podcast, Melanie and Mark talk with Tony Aiuto of Baz…	Bazel, Open Source, Build System
154	G Suite with Joanna Smith and Alicia Williams	Joanna Smith and Alicia Williams talk G Suite with Mark and Melanie this week! G Suite is Google’s collection of a…	G Suite, Sheets
155	Confluent and Kafka with Viktor Gamov	Viktor Gamov is on the podcast today to discuss Confluent and Kafka with Mark and special first-time guest host, Michell…	Confluent, Kafka, Confluent Cloud
156	Grace Health with Therese Mannheimer and Roman Jasins	Happy Thanksgiving! On this episode, Mark and Melanie learn about Grace.health with co-founders Therese Mannheimer and R…	Grace.health, Women’s Health
157	NeurIPS and AI Research with Anima Anandkumar	Melanie is solo this week talking with Anima Anandkumar, a Caltech Bren professor and director of ML research at NVIDIA….	NeurIPS, AI, Machine Learning
158	VP of Engineering - Melody Meckfessel	Melanie and Mark talk with Google Cloud’s VP of Engineering, Melody Meckfessel, this week. In her time with Google…	DevOps, Kubernetes
159	End of Year Wrap-up	Happy Holidays, everyone! Melanie and Mark wrap up a great year by reminiscing about some of their favorite episodes! We…	AI, Kubernetes, Machine Learning, Open Source, DevOps

Cyber Security for the Previous Generation

Mon, 29 Oct 2018 23:08:53 +0000

Cyber Security for Previous Generation

After being tech support for my mom, sorting out my parents affairs when my father got cancer and my mom got Alzheimers, I’ve gathered some insights on the vulnerability of the previous generation to cyber threats like phishing, spoofing and identity theft.

This is my security perspective and it will be different for each person. Take what works and adapt. The goal is to raise awareness that helping the previous gen stay safe is as important for them as much as it can help keep you safe. And yes these tips can be applied to every generation.

Caregiving Take-aways | Post-it Notes

Mon, 26 Mar 2018 00:58:42 +0000

Caregiving Take-aways | Post-it Notes

Surviving Dementia

After handling my mother’s care, I’ve pulled together some key points to keep in mind when caregiving especially when working with dementia. If you want to hear more about where these insights came from, this is the link to our story.

Survive

It’s ok to sleep walk through life if you are going through handling caregiving for someone with dementia. Hell, this is true with whatever pain you are experiencing. I’ve thankfully (yep strange to say thankful here) been through significant loss before and gone through the numb/going through the motions part where the world is depressing and gray. This is usually the time for vices to rear up with the potential to swallow you (drinking, drugs, sex, TV, food… whatever pushes that dopamine button). Do what you can to avoid vices that can cause irreparable damage.

Dementia Survival Kit

Mon, 26 Mar 2018 00:58:34 +0000

Dementia Survival Kit

Surviving Dementia

Tips on handling care when caregiving.

Below is a list (not exhaustive) of what to keep in mind when handling dementia. This is based on my experiences in the US (between CA and TX). Note, dementia can take many forms and play out in a number of ways and what I’ve listed can be used for many caregiving situations. Take what works and leave the rest.

Yes and…

Mon, 26 Mar 2018 00:58:23 +0000

Surviving Dementia

1978 Houston

2017… yeah, that was fun… Sure, the shit hit the fan all over the world, especially in the US. For me, it was one of the hardest years of my life, tackling my mom’s dementia.

A year after my father passed from cancer, I saw initial signs of dementia in my mom, but it was still a journey to fully accept it before I was able to start fighting to get her help.

How to run any ML package on GCP | The Models

Tue, 18 Jul 2017 14:03:51 +0000

How to run any ML package on GCP | The Models

In the previous post, I showed you how to setup a virtual machine (VM) on Google Cloud Platform (GCP) so you can get started running your machine learning package of choice. Examples shared in this post are from different top frameworks in the ML space with a Python focused. Any programming language can be used as well as any package because you are just using a remote computer that you can shape as needed.

How to run any ML package on GCP | The Setup

Tue, 18 Jul 2017 14:03:02 +0000

How to run any ML package on GCP | The Setup

Recently, I joined Google to work on machine learning and Cloud. The first questions on my mind were how would I use Google Cloud Platform (GCP) like I’ve used other platforms to run whatever machine learning (ML) software that I want to use. This post is sharing a few things I’ve picked up over the last couple months with the intent to help others looking at GCP.

PyCon 2015: Neural Nets for Newbies

Sun, 12 Apr 2015 14:16:42 +0000

The ideas and methods in neural nets (NNs) have been around for a long time, but in the last decade plus, we are finally starting to reap significant benefits, and this is just the beginning. This post provides an overview of my recent PyCon talk in Montreal which is a neural net primer of sorts. The video is below, my slides are on SpeakerDeck, and I have a repo on Github named Neural Nets for Newbies.

Targeting Email with Random Forest at Change.org

Tue, 17 Feb 2015 09:49:19 +0000

Last fall, a couple of my colleagues (Kristiane Skiolmen, Scott Lau) and I presented Change’s machine learning email optimization approach as a lecture in Stanford’s Human Computer Interaction Seminar for CS grad students.

The video gives an overview of how Change.org uses email to drive petition engagement from the business and social perspective to the specific technical optimization we made. It starts with an overview of Change and examples of petitions that have literally improved and saved lives.

Graphlab & ODBC

Sun, 25 Jan 2015 22:33:27 +0000

For those out there working with Dato(Graphlab) and trying to setup an ODBC connection to just pull all the data straight into the SFrame, here are some tips I’ve learned from troubleshooting.

What is ODBC?
Open Database Connectivity which is a middleware API to help standardize and simplify access to database management systems.

**Connection Pointers:**There are a number of links on odbc setup but it was a little tricky to get it to work with Graphlab, Linux and OSX and Graphlab’s documentation is a little sparse in that area right now.

2014 Summer == Full Time Data Science Work

Sun, 31 Aug 2014 12:30:00 +0000

For the last three months I have been working at Change.org as a data scientist and engineer. Its been a great experience so far and I’m blown away that this is where I landed after starting this journey a year plus ago.

Impostor Syndrome
I’ve coached others going through moving into engineering about how to believe in themselves and they are smarter than they think. I totally get that you want to fake the confidence till you get there. Don’t be cocky just be resolved to figure stuff out.

MapReduce, MRJob & AWS EMR Pointers

Mon, 07 Jul 2014 22:45:32 +0000

Over the last couple weeks, I’ve been playing around with MapReduce, MRJob and AWS to answer some questions about event data. Granted this is definitely more data engineering focused than data science, but using these tools can be very beneficial if you are analyzing a ton of data (esp. event log data).

This is more of an overview with a few lessons learned on how to setup a MapReduce job using MRJob and AWS EMR. This post focuses more on process and less about the script logic.

Interviewing Sucks

Sun, 08 Jun 2014 09:37:03 +0000

Yeah I know this is not a revelation and some of you out there enjoy the process. I say you are probably a little sadistic or masochistic or both if you do.

It’s a mental and emotional roller-coaster and as mentioned in a previous post, its like taking finals non-stop for as long as you are going through the interview process. It drained my energy and kept me from feeling creative or feeling motivated to just code for fun. Its been so long since I posted code consistently on Github which makes me a little sad.

Delayed Zipfian Week 12 Wrap-up

Sun, 27 Apr 2014 17:10:44 +0000

A little delayed for good reason but here we go. Zipfian’s 12th week focused on interview practice, a conference and the graduation celebration.

Interview Practice

Most of the 12th week was spent going back through previous materials and drilling/simulating interviews. Data science has a different interview flavor from software engineering. Companies vary in what they are looking for and what they will ask and the content for data science covers a broader range of topics.

PyCon 2014 – How to get started with Machine Learning

Sun, 13 Apr 2014 23:14:26 +0000

Following up on the talk I just gave at PyCon 2014 in Montreal, I’ve explained parts of my presentation and provided a few additional clarifications. You can catch the talk at Pyvideo.org, my github repo PyCon2014 holds the sample code, and the slides are on SpeakerDeck.

Machine Learning (ML) Overview

Arthur Samuel defined machine learning as, “Field of study that gives computers the ability to learn without being explicitly programmed”. Its about applying algorithm(s) in a program to solve the problem you are faced with and address the type of data that you have. You create a model that will help conduct pattern matching and/or predict results. Then evaluate the model and iterate on it as needed to create the right type of solution for the problem.

Zipfian Hiring / Demo Day – How it Works

Sun, 06 Apr 2014 16:14:46 +0000

This past week was the big hiring/demo day for Zipfian Academy where we presented our projects to a number of companies seeking data scientists. Zipfian’s hiring day is a lot like Hackbright’s career day. So the nice thing about going through it was that I knew what to expect.

Companies:

Sixteen companies attended hiring day which was a great turnout, and I was thankful there wasn’t another 9 because talking to 16 was still exhausting. Some companies were hiring their first data scientist who would build the strategic direction as well as put it into action. While other companies have teams that they want to grow. So there was a mix of start-ups to large organizations.

Jeeves is Talking!

Sun, 30 Mar 2014 14:57:47 +0000

Coolest moment this week was when I figured out that I just needed to add one line of code to my program to get my computer to talk in my Jeeves project (thus video above).

This past week was all about fixing stuff, fine tuning, iterating and putting together a presentation and peripheral stuff for demo day which will be Thurs. Of course there is always more I could and would do, but I’ve been continually shifting priorities based on time and end goal.

Zipfian Project Week 1 & Closing the Loop

Sun, 23 Mar 2014 21:15:18 +0000

One week down for our final projects and one week left to go. This has definitely been the most fun and rewarding weeks so far because I’ve been connecting the dots in more ways than one.

Everyone talks about getting your minimum viable product done (mvp) when we do final projects like this. Thankfully I had enough experience with Hackbright to know what that meant as well as how to approach it. I got my full product pipeline built between last Sat and yesterday. I’ve tested that it works (well pseudo works) and feel really good to see the emails get pulled in, analyzed and if the condition is true then I get a text on my phone. Now I really need to fix my actual classifier because it is just not that accurate.

Begin with the End

Sun, 16 Mar 2014 22:18:20 +0000

So we are officially kicking-off personal projects for the next 2 weeks. It’s been a bit of a process for all the students figuring out what each of us wanted to do and finalizing it but we got there.

Last WeekIt was a review week. We went back over some concepts that will be valuable for projects as well as when we go through interviews like classifiers and distributions. We also worked to determine and finalize our projects and then we spent two full days on final assessments. We had 2 different data science case studies that required working through the full process from getting the data to providing some type of recommendation/report. We also worked in teams on sample interview questions to review over additional class content. The last day there was a little bit of mutiny and we really didn’t get much done on the assessment. Most people were starting to think projects at that point.

Quick Update on Zipfian and Week 7

Sat, 08 Mar 2014 19:32:11 +0000

Another short one on what was covered this week at Zipfian. Also, for anyone interested, below is a photo of the classroom to give context on where we are working from.

Its a good space. Far in the front is the work and lecture space and the area closest to the camera is more where we take breaks and eat.

This week was about diving into complex machine learning algorithms. Topics we covered:

Half-way Mark – Numb Brain

Sat, 01 Mar 2014 15:02:59 +0000

At the pace we were going, the class showed signs of weariness in week 5 and officially hit the wall this past week. The instructors eased up on us in exercises this week, and gave us a pseudo free day to recharge yesterday.

The week was about assessing where we were and covering MapReduce, Big Data, Flask and to think about projects. It may not sound like they eased up on us but they did.

Deep Learning Surface

Sat, 22 Feb 2014 08:53:56 +0000

Deep Learning is a tool in the Machine Learning (ML) toolbelt which is a tool in AI and Data Science toolbets. Think of it as an algorithm subset of a larger picture of algorithms and it’s area of expertise is solving some of the more complex problems out there like natural language processing (NLP), computer vision and automatic speech recognition (ASR). Like when you talk to the customer service computer voice on the phone vs. push a button.

One third of the way..

Sat, 15 Feb 2014 08:09:00 +0000

I’m keeping this a bit brief because there is a lot to do. Good week and busy as usual. Lots of Naive Bayes. Best part was my team won the simulated Kaggle competition about Stumbleupon.

Below is a summarization of concepts and tools covered this week.

Web Scraping
ReST APIs
Tolkenization
Natural Language Processing (NLP)
Vectorization (Count, Tf-idf)
Ngrams
Feature Engineering
Classification – Naive Bayes (Mulitnomial, Bernoulli, Gaussian)
Confusion Matrix
ROC Plots & Area Under the Curve
Deep Learning

New Tools:

Machine Learning Starts with Linear Regression

Sat, 08 Feb 2014 10:27:42 +0000

We wrapped up our statistic deep dive on Monday with an exercise around Multi-Armed Bandit (MAB) and focused the rest of the week on regression.

Main Topics Covered in Class:

Multi-Armed Bandit
Linear Regression
Gradient Descent
Cross Validation
Final Project Overview
Kaggle Competition

We also added in using the scikit learn data package which is primarily used for machine learning algorithms.

MAB / More Stats

MAB was actually interesting to learn about because its goal is to address some of the shortfalls in AB testing. For example, AB testing only compares two options at once and there is potential for bias when showing an old version against a new version. MAB allows testing multiple options at the same time while generating and updating performance scores. There are a couple different algorithm variations in MAB, but it basically is about showing the best performing option most of the time (ex. 90%) and providing some amount of randomization to show a lower performing option to give other options the opportunity to increase in performance (e.g. popularity). How often you randomly show an option can impact how long it takes the performance to change. The MAB algorithms typically beat out AB in picking the best option to use with the lowest error. This article gives some insight into MAB but beware that the code in the article is a little wonky.

Oh Math

Fri, 31 Jan 2014 23:54:23 +0000

This week was all about statistics and learning more python packages. It was a tough week and we covered the topic that intimidated me the most, the math.

StatsI actually grew up loving math and I know I can understand it with enough focus and time spent studying. Still it was a lot of grad level content that we pretty much squeezed into a few days. There is no way to fully learn all the concepts in a week and that is a common theme throughout this class (and probably most bootcamps). Additionally, several people in the class have PhDs in STEM (science, tech, engineering & math) and understand the math at a whole other level. It is definitely helpful to have students to learn from while also making it hard to keep up in the exercises at times.

Zipfian First Week Rundown

Sun, 26 Jan 2014 09:32:49 +0000

First week of Zipfian is already done and it does remind me how during Hackbright it felt like it went so fast. The focus for the week was about exposing us to core tools we will use as well as the main activities/processes around working with data.

Week Summary

The main tools used this week were Python, iPython, Git and Bash, and we went through three different exercises where we were gathering, cleaning, exploring and sometimes reporting data. A large part of our exercises throughout the program will be done in Python and we spent 4 of the 5 days using it. This is a bit of a shift for the school because they split more time with R in the last session, and it has to do with the growing popularity of using Python for data science. There’s a great article I read recently on the subject at R-bloggers. We will still use R but the emphasis is more Python.

Try: Data Science Except: Monty’s Bayes Example

Mon, 20 Jan 2014 21:45:19 +0000

Great first day at Zipfian. Definitely a different experience starting from Hackbright but some similarities. Granted there are the obvious differences of the content focus on data science vs. web application development as well as 20% women in the class vs. 100%. Plus I’m not the oldest or the youngest of the group. We have a really nice mix of people from various parts of the country and a myriad of backgrounds. Though there is a lot PhDs and/or engineering backgrounds. It was a much quieter energy to the start of the class even though you could tell there was some nervousness.

Wrap-up & Kick-off in One

Sat, 18 Jan 2014 18:14:01 +0000

It’s been over 8 months since I graduated Hackbright (HB). It also has been a while since my last post, but the last one was a bit difficult to follow. Plus, things have been …. busy.

Tips on Life After Bootcamp:
It has been an interesting several months since I started this journey and a good portion of that being what took place after HB. I know I’ve talked about this a little before but I’ve watched 2 classes since mine go through the HB withdrawal as well as met many from other bootcamps who have struggled with the “what’s next”.

Silicon Chef Hackathon – Leaprobo Project (aka Gizgig goes for a walk)

Sun, 13 Oct 2013 09:22:30 +0000

Last weekend at Silicon Chef, the first all women hardware hackathon, there were over 100 participants and 20 projects demoed. My team built an Arduino on Parallax wheels controlled by a Leap Motion (3D programmable motion sensor). Check out the video to see our robot car.

Leaprobo Project IdeaI bought the Parallax kit several months ago with the intent of attaching it to my Arduino because I think everything is better with wheels (almost everything). However, I was too busy with so many other projects and things to study that I didn’t get around to assembling it until now. When the hackathon came up, it seemed like the perfect motivator to break out the wheel kit. Plus, my team had heard about Leap Motion and controlling things with a sensor seemed like a fun. Thus, the idea for the Leaprobo project came together.

New Job Fear – You are not Alone

Sun, 22 Sep 2013 09:06:24 +0000

I have been wanting to write this post for a while. After we graduated Hackbright and we started to land jobs, a common fear that popped up was fear of failure.

I found myself regularly talking to new alum unfortunately on a one-on-one basis since we were all spread to the wind in our unique job search experiences. Many were and are thrilled about landing the new job, but that excitement typically included and/or was replaced by the realization of “Oh god, I really have to do this now.”

How Flask, Heroku & Alembic Play Together

Sun, 15 Sep 2013 01:24:02 +0000

I just spent a couple days getting up to speed on database migrations in general, how to make it work with Flask and Postgres and how to make them work on Heroku. There is some information out there but it took a little time hunting down what I needed; thus, I’ve summarized some of the main steps to help get others up and running with Flask, Heroku and Alembic.

Why migrate? Best practice is to avoid recreating your database(s). Usually you just want to make changes to the existing database(s) and track those changes. If at any time you need to go back to a previous version, the migration docs will help you easily revert to an old version / schema and then upgrade back to the most recent depending on your needs.

Deployment is not the Devil (Flask & Heroku Tips)

Sun, 01 Sep 2013 23:20:49 +0000

On a previous post I claimed deployment is the devil because after several successful (not always easy) deployments, pushing up my Sun Finder app proved elusive. I seriously wanted to scratch my eyes out at times with all the errors and issues. Still it was a good learning experience (one that I fought against but a good one all the same), and I did finally deploy as of last week! Check it out at sunfinder.io.

Oh The Fun You’ll Have with Technical Interviews

Sat, 17 Aug 2013 02:16:24 +0000

Technical interviews are just tough. For the month and a half I went through them, I felt like I was constantly studying for and taking a final exam. We were trying to cram at least a semester’s if not a years worth of computer science concepts into our brains as well as practice how to solve problems with an audience in a handful of days.

Initial Experience
I definitely felt fear at the white board the first several times I was there. I would go blank and forget everything I knew. I was lucky if I remembered my name. But trust me when I say that you will survive this and practice is the key.

Flask Mega Tutorial, Sun Finder and SheCodes

Sat, 10 Aug 2013 16:28:13 +0000

It has been a busy several weeks and I’ve written just as lengthy of a blog post as last time from all of it. After deploying my Rails app, I switched gears to refocus on Sun Finder (through an indirect route). I signed up to present the app at SheCodes Conference on August 9th (yesterday) and I wanted to spiff it up a bit.

Flask Mega Tutorial (detour)

Before going back to Sun Finder, I spent a week finally going through the Flask Mega Tutorial which I had wanted to do since March. I highly recommend it because it goes from setting up a virtual environment through full stack development to a variety of deployment options. Going through that after the Rails tutorial was valuable because it solidified common concepts around web application structure (e.g. configuration, app instance setup, db setup and integration, where to delineate between view and controller). And of course it was helpful to contrast the differences to better understand how much Rails does for you behind the scenes.

Ruby on Rails & Python Trek

Sat, 20 Jul 2013 11:40:14 +0000

Wow its been a while since I last posted. Lots going on with interviews last month and then deciding to teach myself Ruby on Rails and being obsessed with Julython. I completed and launched my first Rails app (technically second after the sample app from the tutorial) at the beginning of this week. It is a simple thing that literally is just a button and I couldn’t be prouder.

What is Python Trek?
Python Trek is a mashup of Stark Trek and Monty Python scripts, quotes, etc. I built a program to generate random sentence or sentences using a Markov Chain algorithm and post those to the Twitter account @pythontrek. I used Ruby to build the program that generates the tweets and I applied Rails to set up a webpage where anyone can press a button that will generate the random tweet and post it.

What to Do When You are Hacked

Thu, 20 Jun 2013 12:42:29 +0000

I am someone who practices pretty decent technical security hygiene, but I had my Yahoo account hacked this week (despite using two factor identification). This post focuses on sharing what I did to deal with the attack, what I think went wrong and some steps and resources you can use for security.

Summary of attack…

The attacker logged in at 4:57PM PST and sent off 28 emails to about 5 recipients in each email from 4:58PM to 5:05PM. I learned about the attack from a friend at 5:08PM and logged in and changed my password at 5:10PM. The attacker appeared to pull email addresses from my account (I’m guessing from my contacts and sent file folders) to use in the spam emails.

Sun Finder – Where is it now?

Sun, 09 Jun 2013 08:52:20 +0000

For those following along, I have made very little progress on my Sun Finder application since Career Day about a month ago. Since hindsight is 20/20, I probably could have seen this coming, but I was living in a blissful imaginary world that said: “There will be all this time after I graduate to do all these things”. The reality is that all that time was about to be commandeered by interviews, practice & studying for interview and a bunch of life stuff that you can never plan for.

Week 10 – Graduation!

Tue, 14 May 2013 21:37:25 +0000

In the true spirit of graduation, I am posting this late and after the fact. I can’t believe 10 weeks have come and gone. I know I’m also not fully feeling it yet since most of us have transitioned into the post graduation job search.

May 10th on week 10 was our official graduation day. Although, it felt like we started celebrating graduation after Career Day, and didn’t let up until that weekend. Right after Career Day, most of us went out to let our hair down and celebrate a pretty amazing accomplishment.

HB Quick Tips

Sat, 11 May 2013 20:20:21 +0000

Below are some quick tips for those considering something like Hackbright Academy. If you have time before or want to get some preliminary building blocks, check out the links and pointers below.

Keyboard Shortcuts:

Learn keyboard short cuts (before starting any programming bootcamp)
If you are not used to it, it does suck royally to learn
Push through it and practice, practice, practice!
The less you use the mouse, the more credible you are as a developer

Ask for Help Early and Often:

Leverage your classmates
Leverage google, stackoverflow, reddit, etc…
Leverage your network and online social tools

Start Reviewing:

Check-out HTML, CSS & JavaScript

Git with Git:

Read about git
Read about GitHub
Want more?…
Load git on your home computer
Setup a GitHub account

Balance is Key:

As Cynthia would say “take breaks!”
Make sure to keep up a workout regime – its good for the brain
Eat healthy to keep the energy up

Check out Zach Holman’s deck (it’s 5 min)

Career Day – Done & Done

Sat, 11 May 2013 01:19:19 +0000

As promised, I’m taking a moment to post commentary regarding Career Day. It went fast, was a whirlwind of companies and was a relief when it was done.

How it Worked:

Career Day is when Hackbright partner companies meet with students individually and see our skills on display through our final project. It was literally interview speed dating. Each student had a table and every 7 minutes the companies would rotate between the tables. It was a brief time to get a sense on whether there was enough fit for a further conversation/interview.

Week 9 – Crunch Time

Sat, 04 May 2013 17:52:45 +0000

It is hard to stop long enough to write this. Sometimes this past week I was too excited to work on my project to sleep. Still I know this is valuable for my sanity to think through what I’ve done so far and appreciate how far I’ve come. Plus, my brain functions better when I take breaks from coding.

Looking at my post from even a week ago surprises me because it feels like longer than 2 weeks since I was figuring out how to use web APIs for example. At the time it made me feel very lost and the concept seemed extremely foreign. Now whenever I see there’s an API that can be used, it feels like it’s the easiest thing to implement and leverage.

Week 8 – Time Goes Fast When You’re Having Fun (Project Status)

Sun, 28 Apr 2013 21:03:04 +0000

To anyone out there who’s watching, I wrapped up week 8 and I’m thinking about how I want to leverage my time in week 9 in preparation for Career Day. I do talk a lot about that as the D day, and it is to some extent. It’s a goal to help keep focus in terms of how I want to prioritize my time. A second goal is that I only have technically 2 weeks left in the program and there are certain subjects I want to practice while I have class time. Sometimes those goals match in purpose and sometimes I have to just pick based on how I’m feeling that day.

Week 7 – SF Sun Finder Project Progress

Sat, 20 Apr 2013 15:46:00 +0000

Development progress has definitely been enlightening the last week and a half. I’ve summarized highlights that have taken place to show what I’ve gone through so far and an example path of developing a HB project.

At the end of week 6, I spent Thurs. playing with Balsamiq and mocking up all kinds of ideas. Friday, I spent the day catching up on emails and just getting things in order so I could think straight. Then all I did over the weekend was start setting up my virtual environment.

In Need of a Neck Beard

Sat, 20 Apr 2013 01:26:00 +0000

Neck Beard

In Hackbright we talk and joke a lot about the great, mighty beards of computer scientists and software engineers. Initially, I was clueless to the jokes. Not a fan of beards, I couldn’t understand why anyone would want one.

Apparently, there is a tendency in the computer science community (esp. from the older school) to grow beards. The beards are supposed to be a symbol of the scientist’s knowledge and prowess in the field. We would talk about the long beards and grey beards and so forth. Yes, there was Lord of the Rings references at times when discussing beards.

Hackathon Quick Bits for Beginners

Wed, 17 Apr 2013 16:49:00 +0000

I participated in my first hackathon a week ago, Gates & Facebook HackEd 2.0, with several of my fellow classmates. The hackathon’s focus was about building apps to improve education especially around college going, retention and social learning outside of school. There are others who were there that wrote some great overviews on the experience like KWu’s post.

I just have a couple quick bits to share about this experience and what I understand so far about hackathons.

The Final Project Kick-off

Sat, 13 Apr 2013 07:07:00 +0000

It’s the end of week 6, and we are in the thick of our starting and/or working on the final project. It could be called a type of thesis, and as I mentioned in a previous post, it has caused a lot of excitement over what to do for a variety of reasons. This post is to covey two main points: what I’m doing and key points to get started.

Stomach ache (aka not enough time challenge)

Sat, 06 Apr 2013 15:54:00 +0000

This week I felt like a kid with a nasty belly ache after being left alone in the tech candy shop. There has been so much interesting and fantastic things to learn and experience, and I hit the wall with trying to do it all at once. I know better. My career for the last several years has been about prioritization. Still here I am gorging on the tech candy because it’s so hard not to.

Finding a Project

Sun, 31 Mar 2013 16:06:30 +0000

As we near the halfway mark of our program, the top of mind question is: ‘What project will I work on?’

It can be a stressful question when there is still so much to learn, there are concerns with picking something too simple or too complex, there are so many options to pick from, and that we know it will influence whether anyone at career day will invite us to interview.

Days Blur Together

Mon, 25 Mar 2013 14:49:00 +0000

I can’t believe I’m already 3 weeks into this 10 week program. I was thinking through what we’ve learned so far this weekend and thought it would be helpful to post the core programming concepts to help keep it in perspective.

Week 1

Keyboard shortcuts
Terminal command line
Python language and logic for math, strings, tuples, lists, dictionaries
Git & Github

Week 2

Continuing dictionaries & general Python review
OOP (Object Oriented Programming)
Markov and APIs

Week 3

Hackbright Mentorship Mixer

Thu, 21 Mar 2013 15:37:40 +0000

Hackbright paired us with mentors last night and it was really great to see so many people in the software engineering and development community who came out. There is so much support that they’ve assigned 2 mentors for every student which is awesome.

If there was enough time in the day and I had enough energy, I would have loved to talk to every person who attended to hear their story and perspective on things. The reality was that after a day of studying SQL, which made my brain ache, I was already tired by the time the mixer started. Actually, I seem to be struggling a lot lately with so much I want to know and learn and just not enough time or space to do it. It’s a good problem to have and I would kill for the Matrix brain upload or some kind of Harry Potter time loop thing.

Pair Programming Perspective

Sun, 17 Mar 2013 18:50:00 +0000

For the first two weeks at Hackbright, we’ve been working as pair programmers. We team up with a new person almost every day and spend about 4 to 6 hours programming with our pair.

Each person gets a keyboard, mouse and screen but you share the same OS. Thus, you have to take turns driving the computer and be ready to figure out how to share the work with your partner.

Room Full of Brilliant Women

Fri, 15 Mar 2013 05:58:00 +0000

My favorite part about Hackbright so far has been that I am working with such smart and driven women who are constantly geeking out on technology. It also adds to the challenge of the experience. We had two alumni visiting today that spoke about how they were amazed by their class but also intimidated whether they were good enough to be part of the program.

I come from a background of many years in the consulting industry where confidence is key. Many times you are thrown into a situation where you have to solve ambiguous problems and pretend like you know what you are doing whether you do or you are getting up to speed on the fly. Based on that experience, I know I have a wealth of knowledge and valuable skills as well as I am very capable at applying myself.

Ode to .shutil.move() – ‘Oh how you frustrate me’

Tue, 12 Mar 2013 04:44:00 +0000

In Hackbright, we had a project from our first week that I was finishing this weekend, and it asked us to use the .shutil.move() function. Based on the reading for this function (linked above), it says “The destination directory must not already exist.” This threw me off for a while because you should have the directory already existing to move a file to it.

My experience with coding it and understanding at this point is that you need to create the directory before you reference it in the function and the quote I pulled is more to warn that if something already exists, it will be overwritten. If anyone has a different take on this function let me know and I’ll update the post.

.startswith()

Sun, 10 Mar 2013 20:48:00 +0000

Hackbright Class Spring 2013

8. Its around the age I was when my father brought home our first PC and I learned how to get around in DOS to play games. Fun fact is also used to be the number of standard bits in a byte.

So here I am 30 years later enrolled in the Python programming boot camp, Hackbright Academy, learning how to be a developer (or hacker as I’ve been informed).