10 reads.
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.
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.
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.
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.
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.