Name the AI work you already do.
Short guides for developers who use AI every day but need clearer vocabulary for CVs, interviews, architecture talks, and choosing tools without drowning in jargon.
Start here
The first four pieces cover the ecosystem, the naming mess, and the career vocabulary gap.
01 / Ecosystem
Why Everything Has Llama In The Name
Decode the AI ecosystem's llama naming chaos and learn which projects are Meta models, local runners, and RAG tooling.
03 / Model selection
Reading a Model Card
Learn what model cards tell you about context length, license, tasks, benchmarks, quantization, safety notes, and practical model fit.
04 / Career vocabulary
Describe What You Do In AI Terms
Translate everyday AI development work into accurate job-market vocabulary: RAG, tool use, structured outputs, evals, observability, and agent workflows.
05 / Core concepts
How LLMs Actually Work
A practical explanation of how large language models generate text and why context, tokens, sampling, and training data matter.
Full pipeline
06 / Model selection
Picking a Model
A practical model selection guide covering task fit, latency, cost, context length, reliability, privacy, deployment, and evals.
07 / Local AI
Running Models Locally
Understand local model runners, GPU and RAM constraints, privacy tradeoffs, quantized models, and when local beats hosted APIs.
08 / Ecosystem
The AI Dev Toolkit Landscape
A practical map of AI developer tools: frameworks, observability, evals, model hubs, benchmark sites, and provider cookbooks.
09 / Reliability
Evals for People Who Ship Code
Learn how developers can create practical LLM evals with test cases, expected behavior, graders, traces, and regression checks.
10 / Reliability
Observability for LLM Apps
Understand LLM observability: traces, prompts, retrieved context, tool calls, latency, cost, errors, and user feedback.
11 / Agents
Multi-Agent Orchestration
Understand multi-agent systems, orchestration patterns, routing, planner-worker loops, tool scopes, and the risks of adding agents too early.
12 / Agents
MCPs: APIs But Make It Weird
Understand MCP servers, local stdio transport, remote HTTP transport, tool lists, configuration scope, FastMCP, and MCP tradeoffs.
13 / Agents
How Does an LLM Call a Function When It Just Generates Text?
Learn how function calling and tool use work: schemas, model output, runtime dispatch, tool results, and agent loops.
14 / Agents
Your Agent's Instruction Manual
Understand project instructions for coding agents, including AGENTS.md, CLAUDE.md, local overrides, global instructions, and scope.
15 / Agents
Skills: Teaching Your Agent New Tricks
Understand AI agent skills: folder structure, SKILL.md instructions, references, helper scripts, installation scope, triggers, and security risks.
16 / Ecosystem
Hugging Face: The GitHub of AI
Learn how to browse Hugging Face models, understand filters, read model cards, check licenses, inspect datasets, and use Spaces.
17 / RAG
What Are Embeddings and Why Should I Care?
Understand embeddings as vector representations of meaning and how they power semantic search, RAG, similarity, clustering, and vector databases.
18 / Core concepts
Transformers: The Architecture Behind Everything
Learn the practical meaning of transformer models, attention, context, and the difference between the architecture and the Hugging Face Transformers library.
19 / Local AI
Quantization: Making Big Models Fit Small Computers
Understand quantization, GGUF files, Q4 and Q8 tradeoffs, memory savings, quality loss, and choosing local model formats.
20 / Local AI
Running Local Models: The Software Guide
Compare local model software for developers across CLI, GUI, server, and production use cases.
21 / Model selection
Open Weights vs Open Source
Learn the difference between open-weight models and open-source AI, including licenses, training code, datasets, and commercial use.
22 / Developer tools
Gradio and Streamlit: Ship a Demo in 20 Minutes
Understand Gradio and Streamlit for quick AI demos, Hugging Face Spaces, internal tools, and prototype UIs.
23 / Core concepts
Datasets: Where AI Models Learn
Understand AI datasets, data quality, Common Crawl, Hugging Face datasets, synthetic data, evaluation sets, and licensing.