Anthropic is merging Cowork and Chat into one Claude, so you hand over the task and it figures out what the job needs. - Rolling out to Pro and Max first over the next few weeks. Team and Free plans soon after - Nothing to turn on. Your Cowork chats, projects, connectors, and skills all carry over - New Claude Docs and Claude Slides launched same day, plus Design now works inside conversations - Slides export to PowerPoint or PDF. All three are beta on paid plans - Keeps working after you close your laptop. Check progress from your phone - Schedule recurring work, like a Monday report Claude starts without being asked - Report and a 5-slide deck from one conversation, so they actually match - Default is Claude asks before acting. You can loosen that if you trust it - Enterprise admins get 30+ days notice before changes hit their org The real shift here for us builders: the line between "chat tool" and "automation tool" just got really blurry. Scheduled, context-aware work from a normal conversation is basically a lightweight agent workflow with zero setup. https://claude.com/blog/cowork-is-now-claude

New image model from OpenAI!

Here's an optimistic use case of AI! Google just mapped the 98% of your DNA doctors couldn’t read. Doctors could only read about 2% of your DNA. The rest got written off as junk — turns out it’s the control panel telling your genes when to switch on and off. Google DeepMind built Alpha Genome Atlas: it scored all 9 billion possible one-letter DNA changes and turned it into a free tool. No coding needed. It’s already pointed to the exact spot causing a rare disease that stumped doctors for years — and found new links tied to body weight the old methods missed. Most of what makes you sick, healthy, or prone to gaining weight was hiding in DNA nobody could read. Now there’s a free tool that reads it. https://deepmind.google/blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/

Google just pre-calculated every possible typo in human DNA. All 9 billion of them. DeepMind released AlphaGenome Atlas, a searchable database that predicts what happens when any single letter in the human genome changes, and it runs in a browser with zero coding required. - The human genome is 3 billion base pairs, and scientists only understand the 2% that codes for proteins - The other 98% is basically dark matter, and that's where a lot of disease risk hides - They ran the AlphaGenome model across all 9 billion possible single-letter changes ahead of time - Result is a 1 petabyte dataset, pre-computed so researchers get answers instantly instead of running inference themselves - They boiled it down to one number called the AVI score so you don't have to sift through thousands of data points - A team at the Broad Institute used it to flag a variant in the DNM1 gene and help crack an unsolved rare disease case - Another researcher ran it against 54,000+ UK Biobank participants and found 22% more non-coding genetic associations - Filtering to just the top 1% of impactful variants surfaced 19 genetic regions linked to BMI - The whole thing is a web portal, no code needed, which is the part I keep thinking about That last bullet is the real story to me. The model is impressive, but the unlock is that they ran inference once, cached it, and wrapped it in a UI a biologist can use without touching Python. That's not a biology pattern, that's an automation pattern. Pre-compute the expensive thing, index it, put a simple interface on top. Would you want an AI reading your DNA for risk scores? https://blog.google/innovation-and-ai/models-and-research/google-deepmind/alphagenome-atlas

OpenAI rolled out ChatGPT Images 2.5, a new image model with sharper output, faster generation, and editing that actually leaves the rest of your image alone. Here's what stood out to me: - Up to 50% faster generation vs Images 2.0 - People already make 3 billion images a week across ChatGPT and the image API - New Sketch feature, type @Sketch and draw your idea as a reference - Templates for common formats like posters, flyers, merch, product photos - Comment directly on an image to point at what you want changed - Multi turn edits hold up better, so image 6 doesn't look worse than image 2 - Two new API models, Flare for speed and volume, Sunburst for premium detail work - Flare claims 50% lower latency than GPT-Image-2, one partner measured 2 to 4x - Better transparent backgrounds, which matters a lot for automated asset pipelines - Live now for all ChatGPT, ChatGPT Work, and Codex tiers on desktop, mobile, and web - C2PA metadata and invisible watermarking stay in place The API side is the real story for us. Faster, cheaper-to-run image generation with reliable single element edits means you can finally automate on-brand asset production instead of babysitting every render. Going to use this heavily for my video thumbnails! I already have it wired up to my content engine. What would you guys actually automate with this? https://openai.com/index/introducing-chatgpt-images-2-5