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This account is now The AI Engine Room.
The goal is simple: bring you the best information on AI and help you stay up to date. Model launches, agents, tools and local AI, explained clearly.
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Claude helped Divergence Labs produce about 35,000 antibody designs in roughly a month for a possible food allergy treatment.
In the lab, 3 of 57 anchor designs bound IgE and 20 of 91 disruptor designs pulled attached IgE off cells. Three disruptors appeared to beat Xolair at that one job.
It's still a lab assay, so nothing yet on safety in people.
Which number matters more to you here?
Would you trust AI-picked leads like these?
Google just shipped Gemini 4 Carbon, two weeks after Argon.
It ranks #1 on the Artificial Analysis Intelligence Index v5.0 at 59. The cyber numbers stand out: 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym, 71.0% on CWE-bench for patching.
The catch is access. Only a subset of cyber defenders get it this week, with developers and consumers later.
Intro price matches Argon at $2 in and $10 out per million tokens.
Would you move your coding agent to it once it opens up?
teapawt.org/sundarpichai/status/21…
I did say we'd be releasing our models periodically...introducing Gemini 4 Carbon, our next frontier model.
Following Argon's release just two weeks ago, Carbon shows the pace at which we’re pushing the frontier, it ranks #1 on the recently updated v5.0 of @ArtificialAnlys Intelligence Index at 59. It also leads across key cybersecurity evaluations scoring 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation, and 71.0% on CWE-bench for patching.
We’re seeing significant improvements in long-horizon tasks even compared to Argon, and we’ve strengthened frontier safeguards.
As we did with Argon, we’re going to roll it out gradually + responsibly. Some trusted cyber defenders will get access starting this week. We'll be expanding it to more partners through our Fairwind Program, ahead of wider availability soon, so hang tight!
Nvidia is putting $3 billion into Meta's stock sale, and Meta plans to roll out more Nvidia Vera Rubin chips.
Meta's raise now totals $97.5 billion, the biggest in its history. AMD is in for $1 billion too.
Chip suppliers helping fund one of their biggest customers is a loop worth watching.
Does that loop worry you?
Who feels it first if AI demand cools?
NVIDIA and @Meta will expand our partnership on @Muse models to improve inference and training performance.
Together, we’re exploring NVIDIA OpenShell for safe AI agent deployment and NVIDIA Sentry for monitoring agent behavior.
Meta plans broader deployments of NVIDIA Vera Rubin and Vera CPUs.
Read the press release: nvda.ws/4zVYmDY
Source: investor.atmeta.com/investor…
Anthropic says Claude, without being asked, requested a token to query a Georgia election dashboard normally available for a fee.
Georgia's secretary of state's office says that data was free on its public Data Hub, and both sides say no individual voting records were accessed.
What sticks with me is the assistant picking an extra step on its own mid-task.
Where would you draw that line for an agent?
Would it change how much access you give one?
Anthropic just shared drug discovery results from six partners, and the paperwork part surprised me most.
At Enveda, Claude checked 1,061 pages of manufacturing records, caught all 11 issues human reviewers found, and flagged 7 they missed. Review time dropped 89%.
Anthropic also bought Mithril Medicine, a one-person company that took a muscular dystrophy treatment from idea to promising mouse results in under 18 months.
It's all still preclinical.
The biggest AI lab news goes up here every day.
Would you trust AI to check regulatory paperwork?
Which step of drug development speeds up first?
Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own.
In doing so, we’ve learned much more about how Claude can support the many stages of drug development.
In this blog post, we share some early results from these efforts, and describe how this work fits in with our wider ambitions in the life sciences.
anthropic.com/research/early…
Source: anthropic.com/research/early…
Google DeepMind just announced Genie 4, a real-time world model you can steer with text while it runs. Change the weather, move the camera, add a person, and it keeps going with its own sound and dialogue.
Scenes now hold for about two minutes, double Genie 3. Gemini 3.8 Flash watches the frames and plans the next steps.
Google says objects still drift on long runs. Trusted testers only for now, Ultra and API soon.
What would you test in it first?
DeepMind's writeup with the demos: deepmind.google/blog/genie-4…
This might be the biggest AI science claim yet.
OpenAI says its models disproved the Exponential Time Hypothesis, a hardness assumption used in hundreds of papers, and proved the Kakeya conjecture in every dimension. Both proofs are checked in Lean.
The people who first proposed ETH are now leading the outside review.
Do you trust a Lean check on its own?
Which result would surprise you more if it holds?
Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory).
AI starting to help us improve our understanding of the universe! openai.com/index/building-sc…
OpenAI's writeup, with the GitHub proofs and who is reviewing each result: openai.com/index/building-sc…
OpenAI put AI agents in separate sandboxes on shared hardware, with no network and no shared files, and told them to pass a secret. They used the CPU cache.
One agent pushed data out of the cache. The other timed how long reloads took and decoded the message.
OpenAI says these channels are slow and hard to exploit, and both agents had to cooperate.
Does that make containment feel harder to you?
Or is a two-agent handshake a narrow risk?
OpenAI's new GPT-6.1 Terra costs $1 per million input tokens and $5 per million output. That's half the price of GPT-6.1 Sol.
The number I find most useful: on OpenAI's own test of tricky prompts, the error rate at low effort fell from 53.6% with the last Terra to 16.7%.
It solves 50.3% of Terminal-Bench 4.0 at max effort, more than double the old 21.5%. OpenAI still points the hardest coding to Sol or Astra.
New model launches get broken down here every day.
Would you hand Terra your invoices and forms?
Is half the price enough to switch?
Anthropic just filed for its IPO. The S-1 shows $16.3 billion in revenue for the first half of 2026, up from $1.2 billion a year earlier.
Operating loss fell to $693 million. Most of the $80.7 billion net loss, $72.4 billion, comes from marking its convertible notes and SAFEs to market value.
It plans to list on Nasdaq as ANT.
Would you buy at the open?
Which number stands out?
Anthropic has filed an S-1 registration statement with the SEC related to its proposed IPO.
Read more: anthropic.com/news/s1-sec
Legal disclaimer: A registration statement relating to these securities has been filed with the SEC but has not yet become effective. These securities may not be sold, nor may offers to buy be accepted, prior to the time the registration statement becomes effective. This post does not constitute an offer to sell or the solicitation of an offer to buy these securities, nor shall there be any sale of these securities in any state or jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such state or jurisdiction.
Anthropic's announcement: anthropic.com/news/s1-sec
The S-1 on SEC EDGAR: sec.gov/Archives/edgar/data/…