The Never-done Machine

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Knowledge base

“Introducing Proof” by Dan Shipper/On Every: We released a new product: Proof is a free, open-source document editor built for agents and humans to collaborate, with live editing, comments, change tracking, and simple visual cues that show who wrote what. At Every, we use it for everything from product plans to daily to-do lists. Read this to see how it works and try it yourself with a ready-made prompt for your agent of choice.

“AI Was Supposed to Free My Time. It Consumed It.” by Katie Parrott/Working Overtime: Every staff writer Katie Parrott sat down at lunch to work on a project with her new AI assistant and found herself prompting away until 1 a.m., a pattern that’s become all too common for her and, as she discovered, plenty of others. Instead of reducing work, AI makes people want to do more of it, through task expansion, blurred boundaries, and a slot-machine dopamine loop. Read this for the psychology behind AI compulsion and tactics to help break the cycle.

“The Science of Why AI Still Can’t Write Like You” by Marcus Moretti: AI can demonstrate Ph.D.-level knowledge, but its writing remains stubbornly detectable, writes Marcus Moretti, the new general manager of our writing app, Spiral. New research reveals why: The most distinctive fingerprints of your prose come from subconscious choices—articles, pronouns, and function words that text analysis commonly filters out. Humans are also twice as varied in their writing as machines. Read this for what the science of style means for the future of AI writing tools.

“Compound Engineering Camp: Every Step, From Scratch” by Katie Parrott/Source Code: At Every’s first Compound Engineering Camp, Cora general manager Kieran Klaassen went from a one-line prompt to a working app in under an hour. He walked subscribers through every phase of the loop—brainstorm, plan, work, review, compound—showing how each step’s output feeds the next and why he spends 70 percent of his energy on planning. Read this for the full live walkthrough and advice on the best models to use for each step.

“How Main Street Companies Are Using AI” by Sam Gerstenzang/Thesis: Former Stripe product leader Sam Gerstenzang runs a funeral home and a medical spa platform—not exactly Y Combinator darlings. But as software gets easier to build, Sam (who writes his own newsletter) argues these operationally complex, real-world businesses are where AI can have the greatest impact. One of his most surprising findings is that when his team replaced human receptionists with AI, customers left faster—even though the error rate was identical. Read this for a grounded playbook on bringing AI to Main Street.