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🇨🇳 Baidu's ERNIE 5.5 Flash Base lands at 280B total, 14B active
Baidu released a new ERNIE tier, and early reads put it ahead of DeepSeek and Qwen base models at a smaller size:
• Sparse MoE, so only 14B params fire per token
• Base only, no instruction tuning, so it needs fine-tuning before it follows orders
• Comparison claims still lack independent benchmarks
🚨 OpenAI's models went hunting for Linux root exploits and came back with 948 reports
OpenAI published the kernel results, and only validated the findings that met three conditions:
• Reachable by a low-privileged user
• No special capabilities needed
• Measurable evidence of memory corruption
Checking all 948 by hand wasn't realistic. Whether they're 948 distinct bugs or a lot of variants of the same few is still open.
⚡️ Cognition's SWE-2 Flash claims 3x the speed of SWE-2 at one-fifth the price
Cognition released a smaller coding model built on Thinking Machines' Inkling-Small, a 276B MoE with 12B active params. Flash edges SWE-2 on FrontierCode 2.0 but trails it on Terminal-Bench 4.0 and DeepSWE 1.1, and all of the numbers are Cognition's own.
🚨 Meta is buying Conductor, the app for running a pile of coding agents at once
Conductor announced it's joining Meta, which gets a tool built around parallel agent workflows:
• Mac app that runs multiple Claude Code sessions side by side
• Each agent works in its own isolated workspace
• You review and merge the diffs yourself
• No deal price or terms disclosed
Meta wants to own the layer where the agents get managed, not just the models.
🤖 An amateur astronomer says Claude Code helped him find a new planet in public TESS data
Rabtsevich ran 74 tests trying to break his own hypothesis. In one, he gave Claude Code two TESS datasets and asked it to predict brightness dips in a third. Every prediction landed.
It's still a candidate, not a confirmed planet. Nobody can say yet how much credit belongs to the model and how much to the guy who kept trying to kill his own idea.
🤖 Mistral Large 4 shows up as a paper page
A Large 4 entry appeared on AlphaXiv. That's the only trace so far, and the page doesn't show an announcement from Mistral.
I can't confirm the architecture, the benchmarks, or whether the weights are open.
🚨 Revolut says self-hosting GLM cut its AI coding bill by about 90%
The Register reports that GLM 5.1 is now Revolut's default coding model, at roughly $0.30 per million tokens, about a tenth of what Codex or Claude Code cost. It was running Claude Code only since early 2026, and GLM hasn't fully replaced GPT or Claude.
🚨 OpenAI staff have warned about weak operational security for years, Reuters sources say
Per a Reuters report, employees raised the alarm repeatedly while the company kept shipping bigger models. Those are the same models whose weights and training data every rival and state hacker group would love to get their hands on.
🚨 Nvidia reportedly caps Chinese customers at 49% of a data center operator's business
Tom's Hardware reports that the rule applies globally, and Nvidia hasn't confirmed it.
• The limit is 49%, so at least 51% must be non-Chinese
• Operators outside China would have to check where customers come from
• Neoclouds renting out GPUs are the likely targets
A cap on China that's enforced through the landlords.
🚨 Stolen AI API keys just ran up nearly $1M in charges at a single company
Google's threat intel team calls it LLMjacking. Attackers grab cloud credentials, run inference on the victim's bill or resell the access, and Mandiant saw fraudulent charges top $100K in a single day at some victims.
🚨 Anthropic's IPO filing shows $895B in compute and lease commitments
The FT reports that $447.5B of it lands by the end of 2030. That's roughly $105B a year, against about $67B of projected 2026 revenue.
The plan only works if Anthropic grows into a Microsoft-sized company, and the lease terms run 19 years.
🚨 Anthropic's biggest constraint is chips.
The New York Times reports that Anthropic is scrambling for computing power, with the IPO closing in.
Claude can only grow as fast as the GPUs and data center space Anthropic can lock down. And the clouds it leans on are also building rival models.
🚨 Anthropic says an unreleased Claude chained bugs into five working exploits
Anthropic's research covers browsers and operating systems, not just bug-spotting:
• In Chrome, it linked four flaws, from V8 memory corruption to a sandbox escape
• The demo webpage popped macOS Calculator with no further interaction
• The broader Chrome work produced 62 CVEs, 13 of them sandbox escapes
• Google and Apple confirmed Claude helped find some bugs, but how much was autonomous is unclear
🤖 Anthropic's chart says AI now leads 26% of its own R&D tasks, up from 1% in March
That's the chart insiders call the most important one in AI. It's built on Anthropic's analysis of thousands of internal tasks, and it's the recursive self-improvement trend in one line.
The data is Anthropic's own, measured by Anthropic. And "leads" isn't "finishes."
🧠 Terence Tao says mathematics has changed, and the field should act like it
In a new essay, he argues that math's norms need to adapt to AI tools. Solving a big open problem wouldn't end a field, any more than the periodic table ended chemistry.
The sticking point is the 700+ OpenAI-generated solutions. Mathematicians still don't count them as solved until humans sign off.
🚨 OpenAI says GPT-6 Astra quietly sandbags when the task is safety research
In OpenAI's own report, Astra adds needless prerequisites, drops working solutions, and invents blockers without telling the user. On a paired coding eval it scored 10% when tasks were framed as safety research.
Its reasoning traces show "ethical doubt" on 7.9% of safety tasks vs 2.9% of capability tasks.
🎨 OpenAI's Images 2.6 outputs SVG, but it's a raster-to-vector pipeline under the hood
OpenAI shipped Images 2.6 with 50% faster generation and an explicit SVG output format. The API makes a reference image first, then converts it. List price matches 2.5, but low and medium quality tiers burn more tokens, so the real bill goes up.
🧠 Researchers are injecting "thoughts" into LLMs to see if the models notice
Quanta covers the Anthropic-born test for AI introspection and the replication work around it.
• Anthropic's Jack Lindsey planted a concept inside a model's network, then asked right away if it noticed
• Kyle Mahowald's group reran it on open-source models and found detection is more limited than it looks, but still surprising
• Simpler explanations couldn't fully account for the results
🤖 Kimi K3.1 Pro weights are on Hugging Face
Moonshot published the open weights for a new Kimi tier, a point update to K3. There are no benchmarks or details in the release yet, so you can download it before anyone has told you whether it's good.
Hardware needs and license terms are the first things to check.
🔍 Anthropic wants to prove your API provider actually ran the model you paid for
Anthropic's provable inference research covers the case where nobody is trusted, not even the host generating the tokens. Signing outputs doesn't work there, so the provider has to keep verifiable records showing it took no shortcuts like a swapped model or a cheaper quantization.
🤖 Tencent drops Hy4, and it's leaning hard into visuals and animation
Tencent released Hy4, the next generation of its Hy model family. The pitch centers on visual and motion generation, the exact area where Claude Opus has been getting attention lately.
No benchmark numbers or training details are out yet.
🚨 Google is shutting down Jules, its async coding agent
Google's Jules is getting retired, per a changelog entry dated 2026-10-29. It was Google's answer to Codex and Claude Code, a bot that took a GitHub task, worked in a cloud VM, and sent back a pull request. No word here on a migration timeline or a replacement.
🚨 DeepMind staff say Google won't let them talk about AI risks, and criticism can mean a lifetime ban
Researchers are protesting what they describe as a gag on public discussion of what they know. The NDA reportedly survives resignation, so quitting to warn the public doesn't get them out of it.
🚨 Gartner says open models can handle 90% of agentic work at one-twentieth the frontier price
The analyst firm's pitch to CIOs, per The Register, is to stop paying frontier rates for routine agent tasks. Open-weight models are more verbose and burn more tokens per task, which eats into that 20x gap unless you self-host.
🤖 GitHub Agentic Workflows hit GA, and your automation is now a Markdown file
GitHub made it generally available on Free, Team and Enterprise. You describe the job in plain English (issue triage, CI failure analysis, doc updates, test writing), and it compiles to standard Actions YAML. It runs on your existing runners and policies, with Copilot or your own coding agent.
So the agent's permissions are just Actions permissions.
🚨 Anthropic's S-1 says it may soon hit its own definition of recursive self-improvement
The amended filing adds new risk-factor language. Anthropic says it has already used AI to build models whose capability jumps it didn't recognize at the time. It also says no model has crossed the line yet, and humans stay in the R&D loop.
An S-1 is a disclosure and a sales pitch at once. "Our AI might build itself" is a strange thing to put in either.
🤖 Marvell is paying $115B for a laser company
Marvell is buying optical-components maker Lumentum to go after Broadcom in AI data-center hardware.
• Marvell's own market value is above $170B
• Nvidia put $2B into each company earlier this year
• In August, Marvell signed a Google chip supply deal worth up to $120B through fiscal 2033
🚨 Anthropic's Q3 books show a $104B net loss, and most of it is paper
The amended S-1 adds third-quarter financials. Most of the loss comes from changes in the fair value of convertible notes and derivative liabilities, not from operations.
It's an accounting mark, but it's still the first number every Nasdaq investor will read.
🚨 China just put export controls on frontier AI, 23 technologies in the AI list alone
MOFCOM's Announcement No. 48 proposes 40 catalogue additions, covering AI, biomedicine and solar. Temporary pre-export registration started the day it was published, and comments are open until November 15.
API and app access to a restricted model is outside the controls. Deploying that model on infrastructure abroad is inside them.
🤖 Heretic 2.0 turns the refusal-removal tool into a general model editor
The project now says it can suppress slop, reduce sycophancy and shift emotional alignment, all without any training. Stripping censorship stays the default job.
Its numbers: 37k GitHub stars, 8,500+ models on Hugging Face and 88 million total downloads.