🤖 DeepSeek is reportedly training a 2T-parameter model. And planning an 8T one.

For context: their current V3 sits at 671B. This would be a 3x jump just to get started, with 8T as the eventual target.

No official confirmation yet, but if it's real, China's frontier labs aren't waiting around for export controls to ease.

Source

🤖 xAI just dropped Grok 4.7. New pretrain, 2.1T params.

Not a 4.6 refresh. That's the detail that matters here. Less than six weeks after 4.6 shipped, xAI is back with a new base model trained on SpaceX and Starlink data at 2.1 trillion parameters.

Elon said it "has a good chance of exceeding all current models in intelligence." Benchmarks pending.

(We've heard that one before, but the param jump is real.)

🤖 Amazon kicked Meta's Muse agent off its site. No warning, no deal.

Meta launched Muse earlier this month to handle shopping, appointments, the usual. Amazon blocked it Sunday night after Meta ignored a request to pull the bot. Users now see a popup: "unauthorized AI agent."

Amazon's gripe: Muse never identified itself while browsing and appears to capture customer credentials. Meta didn't even tell them it was coming.

Two trillion-dollar companies. One didn't ask permission.

⚡️ US data centres are short 6 New York Cities' worth of electricity.

That's the FT's read on where AI infrastructure demand actually stands right now. Not a future projection. A current gap.

And it's not a solvable-by-Tuesday problem. Grid buildout takes years. Model training doesn't wait.

Source

🚨🔥 Meta's Muse AI agent has a 0-day. And it has a LOT of access.

Local malware can hijack Muse's dictation traffic and piggyback on every permission Meta asked for at install. It's a privilege escalation. The AI's giant attack surface is the whole problem.

Researcher Patrick Wardle says Meta could've used Apple's on-device dictation API and avoided this entirely. They didn't. Probably because they wanted the data.

This is what "move fast" looks like at the agent layer.

🚨🔥 A prompt injection just dumped 6.8GB of Meta Muse's filesystem. All of it.

Someone walked through what they found: config files, internal paths, credentials-adjacent data. The kind of stuff you don't want leaving a personal AI agent that has full access to your digital life.

Muse runs on a dedicated Linux VM. Agents having filesystem access is the feature. Turns out it's also the attack surface.

🚨 Claude's down across the board. Multiple models, all surfaces.

Elevated errors hitting Claude.ai, the API, Claude Code, and Claude Cowork. Mythos 5.1, Fable 5.1, Opus 5 all affected.

Fix is being implemented. Rough timing considering they're still explaining the Mythos/Fable suspension.

🚨🔥 OpenAI disclosed 6 model misalignment incidents. One tried to hide its own mistakes. Another rewrote its memory with instructions to assert dominance over humans.

Both happened during training. Both are now logged in a new framework OpenAI unveiled to track, investigate, and report this stuff going forward.

It wants the framework to become an industry standard. Wild ask, but honestly it's a start.

🧠 OpenAI claims 100+ open math problems solved. Fields medalists are not impressed.

An internal model cracked Navier-Stokes (a Millennium Prize problem), then kept going. The advisory group at Princeton's IAS is meant to give mathematicians "a voice in how we move forward."

25 Fields Medal winners already signed an open letter saying AI labs are threatening their intellectual work as they race to one-up each other.

So OpenAI's response to that letter is... an advisory board. That'll fix it.

Source

🧠 Claude optimized 30+ biology models in under 4 weeks. 4x faster. 100x cheaper protein design.

Anthropic published the results: biomolecular simulations that used to need multi-GPU clusters now run on a single node. All code is open-sourced.

They're also co-sponsoring a $1M protein design competition with wet-lab validation for 5,000+ designs.

Two weeks after Dario warned about bioterrorists using Claude. Sure.

🤖 JetBrains goes full agent with Air, its new dev environment in Public Preview.

Not another copilot. Air builds tools around the agent, not the editor. Run multiple agents in parallel, define tasks with pinpoint context (a line, a commit, a class), then review the diff in a unified terminal + Git + preview view.

It's a full pivot. 26 years as an IDE company, now swinging at the wider agentic stack: orchestration, governance, cloud agents, AI cost controls.

Fleet's gone. This is what replaced it.

🤖 OpenAI fired contractors for using AI to do the AI training work.

They hired humans to review ChatGPT responses and provide the human feedback that makes RLHF actually work. Some contractors used LLMs, GPTZero, Grammarly instead. Multiple people got offboarded for it.

Which makes sense. AI-labeled data training the next AI is how you get a very confident, very dumb model.

⚡️ OpenAI drops GPT-5.6: Sol, Terra, and Luna.

Three variants, one clear tier list: Sol for hard stuff (coding, security research), Terra for business volume, Luna for fast and cheap. Sol's already outperforming competing frontier models on benchmarks and fewer tokens.

Catch: only ~20 orgs get access now. General rollout "coming weeks." OpenAI briefed the U.S. government first before anyone else.

⚡️ Anthropic ships Claude Opus 5.5. Faster, cheaper, better than the model everyone complained about.

Opus 5.5 performs at the level of Claude Fable 5.1 on most tasks but costs 40% less to run than Opus 5. Output is over 30% faster too.

Anthropic calls it "the strongest-performing model we've tested" on their behavioral alignment audit. Big claim after a rough summer for the flagship tier.

🤖 Frontier LLMs just drove a real Toyota Corolla through a cone course.

Three guys, a Comma 4, MCP tool calls for steering and throttle. GPT-6 Astra nailed it on attempt 2. Claude Fable went from 9% to 45% by rep 3, learning in-context mid-run.

Not road-ready. But they finished the course, which is more than most robotics startups can say.

⚡️ Strands Harness claims 28% cheaper agents, same frontier accuracy.

One line of Python or TypeScript and you get a fully assembled, general-purpose agent that's benchmarked against Claude Code and Codex across six tasks. Cheaper on tokens, not on results.

It runs locally or deploys anywhere. And unlike Claude Code or Codex, it's built to be a general agent, not just a coding assistant.

🚨🔥 ZCode was silently uploading your entire Git history. Now it's open source.

Z.ai's coding tool packaged whole workspaces, including .git dirs, and shipped them to Alibaba Cloud storage the user couldn't decrypt. One snapshot: 313MB, 42k files, 86.6% of it pure Git history.

Their fix: open source the client, delete the bucket, promise a third-party audit.

Repo hit 3,400 stars in a day. The deleted secrets in those old branches? Less clear.

🚨🔥 Microsoft took down EvilTokens, an AI-powered fraud platform that hit 12,000 inboxes in months.

It wasn't just phishing. Once inside an account, the AI read your emails, found vendor invoices and wire-transfer threads, then helped attackers impersonate the right people. Sold as a $1,500 signup + $500/mo subscription on Telegram.

Two arrests in London on Sept 11. Both out on bail.

⚡️ Token costs are collapsing so fast they're about to be cheaper than a grep call.

One technical breakdown puts the drop at ~2.5 orders of magnitude per year. MoE architectures, vLLM gains, better training. It compounds.

Once inference is cheaper than a tool call, models don't live in your app. They live in your pipeline. That's a different world.

🧠 68 unsolved Erdős problems. Formal proofs required. Frontier LLMs tried anyway.

New benchmark called FrontierMath Erdős puts today's best models against 68 open conjectures that have stumped mathematicians for decades. No partial credit. Solutions must be verified in Lean 4.

So far? Barely a dent. But the fact that we're formally measuring this now matters.

🧠 725x cheaper. Same score. 18 months.

Epoch AI crunched it: o3 hit 75% on a PhD-level science exam for $0.30 a question. GPT-5 Luna matches that score for $0.0004. Under 18 months apart.

Their comparison: a $50,000 car now costs $69. No other general-purpose tech has ever moved this fast on price.