any sufficiently advanced engineering is indistinguishable from art.

Please welcome GPT-6 Sol and GPT-6 Luna to the GPT-6 universe. GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more affordable models to support work at scale. We’ve also made caching and inference more efficient, and we’re passing the savings directly to you: 50% lower API prices for Sol and Luna compared with GPT‑5.6 promotional pricing.

Tok/sec is a vanity metric. GPT sol-low completes @ArtificialAnlys tasks in 36 sec/task Qwen 3.8 27B at xhigh (same intelligence score( needs 13.2 min/task (+22x) w/+15x as many output tokens. Qwen at 500 tok/sec (+10x) will STILL be slower and will cost +3-5x.

Imagine if instead of 2.5% transaction fees, Square charged merchants 14.5% on every credit card swipe. This is literally how much it costs doctors to get paid by insurance companies. We could send ~$200B back to wages, taxpayers, etc if we brought healthcare in line with the rest of the economy. These costs are reflected in your insurance premiums! This idea is directionally correct but specifically mistaken. Medical billing is not a commodity service that can simply be offered for free as a compliment to a banking product. If you think you can just throw Claude at medical billing and drive best-in-class collections you are dramatically wrong. However, there are ways in which a vertical bank could significantly drive down transaction fees, smooth cash flows, and serve the financial needs of medical practices much better than what is on offer today.

Don’t join a company that cares about its number of users. That means it’s still selling a product, not a labor equivalent. Our highest paying clients have very few users.

The Certifiably Insane Way to Build an AI Agent: 1. choose a category where mistake tolerance is roughly the same as it is in self-driving cars. we chose "email-based scheduling assistant." many people want this product, but they immediately fire him if he screws up an interaction with a prospect, a candidate, or a potential investor 2. you learn that the edge cases are too complex and too frequent to be solvable. ours: managing timezones for people who travel (and change travel plans) constantly. knowing when NOT to respond, when to text the customer on the side to verify something, when to follow up, which sub-calendar to use, when to bend the rules on availability, when we can schedule that one type of call during your commute but not the other type of call. sharing your availabilities without compromising your privacy. and on and on. 3. the product doesn't feel viable, but you don't want to give up. you spend hours in a hot tub in Marin with a friend who makes self-driving cars. you make a plan to do it the way they did: hold the steering wheel. you go home and build a human-in-the-loop platform and hire contractors to serve as a backstop and catch mistakes before they happen (and to help design a map of what a world-class EA would do in every weird scenario). you decide trust is the currency in your category, so it must be the thing you won't compromise on. the product must succeed at any scheduling request, no matter how complicated. 4. you instantly feel an overwhelming market pull. so you keep going, growing that team to 75 people working 24/7 to support the nonstop scheduling needs of your customers. tons of engineering time goes to scaling the human platform instead of building the product. 5. you try to raise a Series A and investors say you are insane. your gross margins are extremely negative. they believe this is a problem worth solving, but they don't believe it is as hard to solve as you say. they want AI, not humans. your competitors put "NO HUMANS IN THE LOOP" on their landing pages to call you out. you keep going. 6. you work day and night building the harness that can meet the quality standard your customers have come to expect. you create a massive synthetic gold dataset. audit it, and clean it, label it. repeat. then, experiments. fine-tuning. RL. ACE. DSPy. sub-agents. sub agents for your sub-agents. rebuild the harness. throw more tokens at the problem. 7. some weeks you make big progress. some weeks your evals climb a single basis point, but that's better than nothing. more experiments. more tokens. john coogan said the hot trend in 2026 will be dogged pursuits. that pushes you to continue the pursuit, doggedly. 8. then, one day, you realize you are scheduling thousands of meetings a day and approaching 50% autopilot with no increase in churn or complaints. you put 150 customers in a full self-driving experiment, and they use the product MORE than they were using it when they had the human backstop. you can really start to let go of the steering wheel. 9. you don't know yet if this was a hill worth climbing, but you are nonetheless stoked that you can see the top. you have created a proprietary map of what to do in a million different situations. nobody else has that map, and the models keep getting better at following maps. your plan was to bet on trust, and your product can be trusted. today was the first day Howie crossed 50% autopilot:

damn this spacex employee took my parking spot :/

I used to be fully bought into the “Chat is just v1, we have yet to discover novel UX for gen AI” concept But I’ve begrudgingly accepted that chat is just the best UX Innovation in AI UX is mostly just designers flexing their muscles and showing off But in reality humans are designed for conversational dialogue. Chat is the return to nature

Over 1 billion PDFs are created every day, but your agents still can’t read them reliably. Today we’re releasing Parse 2.0, the most accurate document parsing API in the world. Extend already processes millions of pages daily for leading AI teams like Brex, Mercury, Opendoor, Flatiron Health, and hundreds of others. Now, its even better. Parse 2.0 is SOTA quality on RealDoc-Bench, our open source benchmark that measures agent success rate on real world docs that agents actually encounter in production. We trained Parse 2.0 on 1M+ pages of the hardest documents seen in production. Here’s how it stacks up: - #1 in healthcare, real estate, logistics, and financial services - 95.7% agent Q&A accuracy on 581 docs (next best: 92%) - 0.847 F1 on layout (next best: 0.759) Give it a try today and build production-ready document agents with Extend.