AI Workflows

🎧 Prefer to listen? Your browser does not support the audio element. I’ve spent the last month writing about agent shapes — the loop in why the AI world is getting loopy, the vocabulary in the plain-English graphs intro, and the decision rule in graphs vs looping. That’s theory. This is the version I wish someone had handed me first: five actual workflows where the graph shape earns its keep, and how to copy each one in a visual automation tool without writing code. ...

🎧 Prefer to listen? Your browser does not support the audio element. Somewhere in 2026, the agent world split into two camps arguing about shapes. One camp — most loudly Cognition, the team behind Devin — argued that for hard sequential work like coding, a single agent running a tight loop with full context beats fancy architectures every time. The other camp — most loudly Anthropic, describing their multi-agent research system — showed that for broad, parallelizable work, an orchestrator fanning tasks out across a graph beat any single loop by huge margins. Both camps had receipts. Both were right. ...

🎧 Prefer to listen? Your browser does not support the audio element. Ask three people what “graphs” means in AI and you’ll get three answers — a chart, a database, or something about LangGraph that sounds like homework. None of those is what the AI world actually means. A graph in AI is a map of your workflow: boxes for steps, arrows for the paths between them, and a shared memory that travels along the arrows. ...

🎧 Prefer to listen? Your browser does not support the audio element. Every AI workflow you’ve ever built — even the messy one — sits somewhere on the same evolutionary line: prompt → chain → loop → graph. Most people got stuck at the first stage and never learned why. The ones running serious solo operations didn’t get better prompts. They changed the shape of their workflow. ...

🎧 Prefer to listen? Your browser does not support the audio element. Miles Wang, a former OpenAI researcher, is in talks to launch an AI drug discovery startup valued at $2 billion. His core strategy isn’t inventing new compounds from scratch; it’s identifying new therapeutic uses for drugs that have already passed safety trials but failed for their original purpose. The expensive, time-consuming part—proving a compound is safe for humans—is already done. Wang’s company, using transformer architectures similar to those behind ChatGPT, focuses on finding the right match between an existing molecule and a new disease. ...