THE @BREWERS ARE GOING TO THE WORLD SERIES! #CLINCHED (MLB x @BudweiserUSA)

Introducing ChatGPT for Dishwashing GPT-6 achieves state-of-the-art results on dishwashing benchmarks for long-running jobs and difficult stains. The Navier–Stokes Millennium Prize Problem is a $1 million challenge about whether the equations describing fluid motion can break down. In this video, I accidentally found a counter-example while trying to optimize my dishwasher. But in reality, OpenAI used 10,000 AI agents to find a proof in 88 hours: openai.com/index/navier-stok… Parody of the original GPT-6 Astra ad @OpenAI #ChatGPT_Partner #chatgpt Credits: Written and Directed by Joma Co-Writer - Henry Connor Coan IV @coan_henry Production by Dauntlus Studios @dauntlus.studios Producer/Co-Director - Marcus Liew @dauntlus Camera Operator - Amos Lee @amoselijahlee Art Director - Alethea Soo @aletheasoo Art Assistant - Alicia Lim @aliciatkl_ Assistant Editor - GPT-6 Astra Music: Tensions Run High by Soundridemusic youtube.com/watch?v=Ly9H63SL…

So umpires are getting scrutinized and lambasted for missing pitches an 1/8” of an inch to an inch off the plate that really isn’t the plate as MLB only counts the back half of home plate. Imagine being told you are wrong by a computer that the pitch you called a strike because it caught the front half of the plate is actually a ball. Then getting crap from all the younger, nerdy generation because it’s so EZ to tell if a pitch is a strike at 100 mph or a ball from your fucking couch. Get a grip people. They won’t call the real zone because hitters would struggle more than they do now. Imagine that.

JUST IN: Cartel members from Mexico & Colombia are reportedly fighting alongside Ukraine to gain experience with battlefield drones.

41 seasons on the call, a lifetime in our hearts. Rick Rizzs, you’re a Mariners legend. #FarewellRick

Off to Anaheim for three against the Halos.

Good reminder here that with rapidly accelerating AI progress, you should likely be building with a vision that contemplates at least a few orders of magnitude of capability improvement or token volume available to you. The best opportunities right now are those where you can value to customers today with things that are barely only possible, but with a full mission that feels nearly impossible with today’s tech. There are lots of products the require orders of magnitude more processing of data (and thus are barely economically viable ideas) or where model capabilities still are in their infancy relative to what’s needed to work. Best to be building for those scenarios.

GPT-6 Astra is out. We've been testing the model in early preview on our enterprise complex work eval at Box. It is now the best model we've ever tested on our expanded and hardest test set. Overall, GPT-6 Astra offers a breakthrough level of capability in coding, analytics, logic, and domain specific knowledge for dealing with complex enterprise knowledge work. In our eval, we use the GPT-6 Astra model in the Box Agent and give it a set of difficult tasks that represent real-world work in various industries. These tasks often will take people hours to execute properly and require deep domain expertise. GPT-6 Astra scored 77% overall vs. 74% with GPT-5.6 Sol, representing frontier performance. But the story is in some of the bigger gains on individual task types that are incredibly complex across. Here are a few examples across the tests that show how much of an impact this will be in different industries: * Media and entertainment (48% → 100%, +52 points). Ranking film genres and countries by profitability across a year of production data from four teams, where the trick is to apply the following year's tax-incentive corrections without double-counting them. GPT-6 Astra was perfect on every attempt. Sol had the rankings right but the underlying ratios were wrong. * Technology (69% → 97%, +28 points). Choosing which region to fund first, from a stack of performance and infrastructure documents that never state the metric the brief asks for. GPT-6 Astra flagged the gap, labelled its own figure a proxy, and caught a growth claim that didn't match the numbers beneath it. GPT-5.6 Sol reported the proxy as the real thing. * Legal (69% → 93%, +24 points). Reviewing an NDA against a company's own contracting policy, where reaching the right verdict isn't enough; the answer has to cite the provision behind it. Both models declined to approve the draft, but only GPT-6 Astra separated whether the liability cap's structure was permissible from whether its amount was defensible, and pointed to the policy language that settles it. GPT-5.6 Sol argued the same conclusion without citing the provision, which is a critical error in the legal industry. * Healthcare (53% → 77%, +23 points). Auditing a batch of radiology reports for errors and rating how serious each one is. GPT-6 Astra caught a terminology error in a knee-imaging report that GPT-5.6 Sol missed on both attempts, and was more reliable at grading severity rather than just flagging that something was wrong. * Energy (82% → 97%, +15 points). Building a consumption report on two facilities from a year of meter logs, where only one of the two sites is actually missing data. GPT-6 Astra found it and drew a line most reviewers wouldn't: the other site's odd solar readings are a measurement problem to investigate, not a gap in the record. GPT-5.6 Sol reported both sites as incomplete. Astra clearly is going to offer a meaningful jump in powering and orchestrating enterprise workflows. We'll be making GPT-6 Astra as an option in the Box AI Studio for customers to build agents with shortly as it continues to roll out.

You can quote me on this: OpenAI crushed Anthropic’s IPO. They put the newly released, state-of-the-art Fable 5.1 to shame. And they knew exactly what they were doing. Just look at the numbers. Its not even close.

A robot in Dunder Mifflin

DLSS 5 introduces 3D-Guided Neural Rendering, which uses AI to infuse game scenes with lifelike lighting and materials. Developers tune the final output for each game to bring their artistic vision to life.

Muse Voice Transcribe is MSL's first real-time audio perception model -- rolling out today. SOTA in streaming speech-to-text, it handles speaker diarization, and endpointing natively in a single model.