David Ondrej open-sources his entire agent-skill library
Hundreds of hours of trial and error, handed over for free. Even if you rewrite half of it, a working reference library beats a blank page every time. Go take what's useful.
25 reads.
Hundreds of hours of trial and error, handed over for free. Even if you rewrite half of it, a working reference library beats a blank page every time. Go take what's useful.
The clearest writing yet on why the model stopped being the bottleneck. Thariq's frame: the gap between what you asked for and what the work actually needs is where quality leaks out, and the real skill is surfacing those gaps before, during, and after you build. Worth reading twice.
Anthropic's playbook for working alongside agents as the tools ship into Slack. The interesting shift is treating an agent less like software you run and more like a teammate you delegate to, which changes how you hand off and review work. Read it before your org figures this out the hard way.
A tight framing of the shift everyone's circling: you stop prompting the agent and start building the system that prompts it. Schedule, discover, build, verify, repeat. Worth it as the one-line mental model before you read anything longer on loops.
Thariq's field notes on Claude-in-Slack, a genuinely new form factor that nobody has best practices for yet. Early and speculative, but that's the value: you're reading someone figuring out the patterns in real time rather than after they've calcified. Good companion to the launch post.
A pointer to Addy Osmani's walkthrough of how the software lifecycle is changing, phase by phase, from autocomplete to autonomous agents. Osmani is a reliable, non-hypey guide to this stuff, so it's a solid framework read if you want the shape of the shift rather than a hot take.
Andreessen's long, unusually good essay reframing SpaceX as the one company assembling the full stack for a post-scarcity future: cheap launch, orbital compute, lunar industry, Mars. The engineering-culture sections carry it, especially Musk's five-step Algorithm and the idiot index. Not AI-specific, but the orbital-data-center thesis ties directly to where compute and energy are heading. A real sit-down read.
Block's numbers on their internal agent system are the ones to quote in an argument. Engineers tag it in Slack, it researches, plans, and ships: 200,000 operations a day, 1,500 PRs merged a week, 15% of all production code. This is what agents coordinating across a real codebase looks like at scale, not in a demo.
A shared full guide to shipping your first agent, with the pitch that it collapses a two-week slog into a day. Standard on-ramp material, but a decent starting point if you're going zero to one and want a single path instead of ten browser tabs.
The best long read on why a top-tier model isn't the point. The system around it is. Loops, memory, verifier sub-agents, and state files are what make each run leave the next one smarter, and the piece lays out the whole stack with cost-routing advice for when to reach for the expensive model versus a cheap one. Long, but it's the map most people are missing.
An Anthropic engineer on two things the top model changes: self-correction loops and memory. The sharpest takeaway is that the model shouldn't grade its own work. An independent verifier explores harder and recovers from dead ends where self-critique stalls at good enough. Short, concrete, and from someone who actually ran the experiments.
The best explainer on the phrase everyone was repeating without defining. A loop is cron plus a decision-maker in the body: the model, not a hardcoded script, picks the next move each tick. The punchlines land, that the loop, not the model, is now the expensive part, and that the real asset is the skills a loop calls, not the loop itself. If you read one thing on loops, this.
A plain-English tour of the Claude features hiding in plain sight: Projects, memory, extended thinking, scheduled tasks, prompt caching, custom roles. Nothing exotic, but the value is in the framing. Each one takes minutes to set up and pays off daily. Good to forward to anyone still treating Claude as a fancier search box.
Just a pointer to a GitHub repo of agent skills. Low signal on its own, but these skill collections are where the real how-to lives. Clone it, read how the skills are structured, keep what fits.
An honest field report: dynamic workflows felt useless until they were pointed at adversarial review. The move is splitting a review into thin, mean, single-focus lanes, correctness, duplication, safety, each agent required to bring a way to verify its own finding. It beats self-review because the model likes its own work too much. The step-by-step is copy-and-adapt ready.
Anthropic's own writeup on building agents that do data analysis. The useful parts are the unglamorous ones: skills, clean data foundations, and evaluations. If you're wiring an agent into real business numbers, this is the part everyone skips and then regrets.
The line most people skim: every agent in a workflow can run a different model. This is the proof. A 33-agent audit, cheap readers plus one smart synthesizer, ran in under five minutes and found a real buried error. Put the expensive model where the thinking happens and run the rest on the cheap one. That's the economics of parallel agents in one example.
The clearest map of where interfaces are heading: agents drawing the UI in real time instead of describing it. Three patterns, controlled, declarative, open-ended, each with a different failure mode at scale, and most teams pick one by accident. If you build anything agent-facing, this is the decision tree to read before you're locked in.
A clean way to think about agent design: the model is deliberately thin, and intelligence gets pushed outward into memory, skills, and protocols that the harness composes at runtime. The useful question it hands you is where any new capability should live. Good conceptual scaffolding if agents still feel like a bag of tricks.
The canonical piece on dynamic workflows, from the Anthropic engineer who built them. Claude writes its own custom harness on the fly to beat the failure modes of one long context window: laziness, self-preference, goal drift. The example prompts alone are worth the read, and it names the reusable patterns (fan-out, adversarial verify, tournament) you'll see everywhere else. Start here.
A design studio's field report on a real shift: the deliverable is no longer a PDF brand guide but a folder of structured files an agent can build from. The value moves upstream to the thinking, and the sharp bit is what they call magic_trick.md, the one human, left-of-center idea the system can't generate on its own. Best essay here on where human creativity stays scarce.
Karpathy on a workflow more people should steal: point an LLM at a pile of raw sources and let it compile and maintain a markdown wiki you rarely touch by hand. Once it's big enough, you query it like a research assistant, no fancy RAG required. The best part is that your own explorations file back in, so the knowledge base compounds. One of the most-shared AI ideas of the year for a reason.
Karpathy's follow-up carries the sharper point: in the agent era you stop shipping code and start shipping the idea, kept deliberately vague, and the other person's agent builds it for their needs. It's a small reframe with big implications for how software gets distributed. The knowledge-base spec is the worked example.
A genuinely good step-by-step on running Claude Code 24/7 on an always-on Mac Mini so you can text it tasks from anywhere. The best detail is the one nobody mentions: there's no message queue, so a sleeping laptop drops everything, which is the whole case for dedicated hardware. Practical if you want a personal agent that never goes dark.
The reference for anyone configuring Claude Code seriously. It walks the whole control center: CLAUDE.md, path-scoped rules, and hooks, with the key nuance that instructions are suggestions but hooks are deterministic. Keep CLAUDE.md under 200 lines or adherence drops. This is the one to bookmark.