- Learnings
A public archive of my weekday LinkedIn posts. Drafted by an agent from my private daily-briefs and edited by hand before publishing. See how →
- Claude Code & AI Dev
Today's concept: using Claude as a coding pair for ML projects
Claude doesn't just finish your Python - it explains why your training loop is diverging and helps you fix it, live, while you drive the architecture. Using Claude Code as your ML pair is like pair programming where on
read full post → - Claude Code & AI Dev
Today's concept: GitHub Actions for ML automation
You already know how to automate software deployment with GitHub Actions - now do the same for your model training pipeline, and suddenly you're not manually babysitting notebooks anymore. GitHub Actions is a CI/CD (co
read full post → - Claude Code & AI Dev
Today's concept: Claude Code slash commands and how to use them
Slash commands aren't shortcuts to Claude - they're shortcuts to different agents, each optimized for a specific job. Slash commands work this way: /code-review routes to a code-review agent (different than general Cla
read full post → - Claude Code & AI Dev
Today's concept: CLAUDE.md - teaching Claude your codebase
If you want Claude to stop suggesting bad ideas in your codebase, stop hoping it'll read your mind - write it a CLAUDE.md instead. CLAUDE.md is a file you create at your repo root that tells Claude Code about your proj
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Today's concept: what Claude Code is and new features in Claude code
Claude Code isn't a chatbot that tells you what to code - it's a pair programmer that actually does the coding, sees what breaks, and fixes it in real time. Claude Code is an IDE where Claude can edit files, run tests,
read full post → - Agentic AI
Today's concept: agent safety and guardrails
Guardrails aren't restrictions - they're what make agents trustworthy enough to actually use. Think of an agent like a junior developer you're onboarding: you don't give them full production access on day one. Guardrai
read full post → - Agentic AI
Today's concept: evaluating agents - how do you know it is working
An agent can get the right answer for entirely the wrong reasons and you'll never know until it breaks on edge cases in production. Evaluating agents is fundamentally different from testing traditional code because age
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Today's concept: LlamaIndex - RAG and agents over your own data
LlamaIndex isn't about teaching models your data - it's about teaching agents how to find and use your data when they need it. LlamaIndex is a framework that solves a real problem: LLMs are powerful but they don't know
read full post → - Agentic AI
Today's concept: LangChain - building chains and agents
LangChain turns your LLM into a decision-maker instead of just an answer machine. LangChain is a Python framework (JavaScript version exists too) that handles the plumbing between LLMs, tools, and data. Two core ideas:
read full post → - Agentic AI
Today's concept: multi-agent systems - when one model is not enough
You already know how to build this. Multi-agent systems are just microservices for intelligence. Instead of feeding a problem to one giant model and hoping it figures it out, you split the work across multiple speciali
read full post → - Agentic AI
Today's concept: what an AI agent actually is
An agent isn't a smarter chatbot - it's code that thinks, decides what to do, acts on it, then learns from what happens. Think of it like a GitHub Actions workflow, but with a brain. A traditional workflow runs a fixed
read full post → - Modern AI & LLMs
Today's concept: prompt engineering patterns that actually work
Your LLM doesn't actually reason through problems until you explicitly ask it to - and most people skip that step. Prompt engineering patterns are structured ways to talk to language models that dramatically improve th
read full post → - Modern AI & LLMs
Today's concept: context windows - what they are and why they are limited
The biggest LLM you can run can't read a whole book, no matter how smart it is - there's a hard wall on how much text you can feed it at once. A context window is the maximum amount of text (measured in tokens, think "
read full post → - Modern AI & LLMs
Today's concept: model quantization - making models smaller and faster
You can shrink a language model to 1/4 its size and lose almost nothing - and every LLM engineer in production does this. Quantization is taking the decimal numbers that make up a neural network and storing them with l
read full post → - Modern AI & LLMs
Today's concept: multimodal models - text, image, audio together
Your next API call won't just take text - it'll swallow a screenshot, audio clip, and paragraph all at once and spit out one unified understanding. A multimodal model is an LLM that understands multiple kinds of inform
read full post → - Modern AI & LLMs
Today's concept: RAG architecture - retrieval augmented generation
Instead of trying to make an LLM remember everything, RAG teaches it to look things up on demand - like giving your model a search engine instead of cramming it with textbooks. RAG stands for Retrieval Augmented Genera
read full post → - Modern AI & LLMs
Today's concept: LoRA fine-tuning - adapting models without full retraining
You can fine-tune a 70B-parameter LLM with fewer trainable parameters than your typical microservices stack has config variables. Full fine-tuning means retraining every single weight in a model - think of it like reco
read full post → Today's concept: evaluating agents - how do you know it is working
An agent that completes 95% of tasks might be worse than one that completes 70% - if the 5% it fails on are the critical ones. Evaluating agents is like hiring a contractor - you don't just ask "did the job get done?"
read full post →Today's concept: agentic coding — how Claude Code works under the hood
Claude Code isn't magic - it's just a large language model in a loop, picking from a menu of tools until the job is done. Think of Claude Code like a GitHub Actions workflow, but instead of hardcoded steps, you have an
read full post →Today's concept: LangGraph — stateful multi-step agent workflows
Most LLM agents fail because they're just a sequence of prompts with no memory; LangGraph treats agents like programs with state and decisions. LangGraph is a framework that builds agents as state machines — think of i
read full post →Context: https://lnkd.in/gK8knHDH
A chatbot forgets everything you told it last week; an agent that remembers it? That's the difference between a toy and something you'd actually deploy. Think of an agent like you: it has a "short-term memory" (what's
read full post →One AI hitting 85% on a task, but three AIs working *against* each other on that same task hitting 95%—and the differenc
A multi-agent system is when you send the same problem to multiple models (or the same model with different prompts/personalities) and have them interact—challenge each other, build on each other's ideas, or route to sp
read full post →Today's concept: ReAct.
LLMs are terrible at math, until you let them think out loud AND use a calculator at the same time. ReAct is a pattern where your AI agent alternates between two modes: reasoning (thinking through a problem step by ste
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