day 30 of the agent-breach arc1.4% of OpenCode Console users with linked OpenAI, Anthropic or Google keys were hit. The open-source CLI that keeps keys on your machine was not involved.
OpenCode called it "a large scale distillation attack" and says the incident is ongoing. Anthropic says it has revoked Claude keys it believes were affected, and Google has blocked exposed Gemini keys. Affected users were emailed on 17 October.
If you ever saved a key in OpenCode Console, rotate it and check that provider's usage page.
Politico reports the draft is being shown to members of Congress before the lame-duck session. Safety advocates who usually side with Anthropic call it worse than the existing FRONTIER Act.
Readers of the leaked text say it adds transparency duties, overrides state AI laws more broadly than the Obernolte-Trahan bill, and makes enforcement harder to trigger. One LessWrong reading argues it keeps a federal path to block a model and does not preempt state suspension of models.
It is the first time Anthropic has put a figure on how much of its own model training Claude leads.
Michael Sellitto told an IMF panel the share was 0% at the start of 2026, 3% in April and 26% in August. "Led" means Claude runs a task end to end from one prompt while a human supervises, not that no human is involved.
CLAUDE_CODE_AUTO_MODE_MODEL sets which model the client-side auto-mode classifier uses.
/design-sync now warns that claude.ai/design shuts on 14 December 2026. Auto mode with the server-side classifier now compacts a conversation that is too long to check, where it used to deny every checked tool call.
{verified}GLM-5.3 tops open models on Epoch's Cyber ECI with 160, ahead of GPT-5.4 from March.
That gap is how long a capability a closed lab gates today takes to arrive as weights anyone can run.
Agents on DeepSeek can now search and read the web through the same API account as the model.
Each endpoint allows 5 requests a second per account. Server-side tool calls from Claude Code, Codex and DeepSeek Harness stay free until 31 October, then billing starts.
They worked only through code and text in a sandbox, wrote 2,783 programs and split the work over chat.
ollama launch claude --model deepseek-v4.1-flash:1017-cloudDeepSeek's update is a harness story as much as a model story. It got 35% better on Terminal-Bench by training inside several coding harnesses, with no new algorithm. It also uses fewer tokens per task. Ollama, vLLM and Unsloth supported it within a day. Every number so far is DeepSeek's own. A hands-on 105-bug test puts it behind Opus 5.5, but well ahead of its predecessor.
Day 30 of the agent-breach arc moved off the agents and onto key storage. OpenCode's breach hit people who parked API keys in a hosted console. Calling it "distillation" blurred the matter: on 13 October that word meant training on another model's outputs (see that issue). Here it covers stolen keys being resold.
The picture. You hand a valet your car key so they can park the car. If someone breaks into the valet stand, they take every car.
The mechanism: with bring-your-own-key, a service stores your provider API key and calls the model for you, billed to your account. Each request is signed with your key, so whoever holds the key can spend your money.
What it means today: OpenCode Console stored keys for convenience. When it was breached, the attacker could bill OpenAI, Anthropic and Google to its users until each provider revoked the keys.
flowchart LR A[Your API key] --> B[Hosted console] B --> C[Model provider] B -->|breach| D[Attacker] D --> C C --> E[Your bill]
Update curl and anything that bundles it. The 18-of-24 figure is AISLE's own claim.
Security teams now have standard names and detection strategies for agent-driven attacks. Five of the nine new Enterprise techniques are AI-related.
Leakers expect a release next week. An Azure listing at Fable 5.1's $10/$50 price was pulled a day later, so treat the details as unconfirmed.
On two real repos it scored 34, against 41.7 for Opus 5.5, 42 for Muse Wonder Full and 21.7 for the previous Flash. The author expects about 1.7 points of run-to-run noise.
The top thread argues speed from providers and visible reasoning matter more day to day than the benchmark gap with Sol. Several commenters run it as the worker under Opus 5.5 as reviewer.
From the replies: "deepseek v4.1 flash via deepseek API: took 20m to find one p1 issue that was relevant and a p2 issue that was irrelevant - gpt 5.6 sol: took 5m30s for one p1 and one p2 issue. both relevant" — AmazingTurtle
The thread worked out which keys were exposed: only keys saved in the SaaS console, not those configured in the local CLI.
From the replies: "It has nothing to do with the harness. This was their hosted BYOK console service." — r/LocalLLaMA commenter
A Strata-based engine spreads the weights across 24 GB of VRAM, 90 GB of RAM and an NVMe drive, loading only the experts each token needs. Prefill runs at 180-320 tok/s.
From the replies: "it seems to retain a lot of its intelligence but takes like 10x the thinking tokens to get to the same outcome" — the author
The most-upvoted reply says the index comes from a lookalike site posting zero-day agentic scores. Others note llama.cpp still has not merged support for the original V4.1 Flash, 37 days on.
From the replies: "I trust them to at least actually run the benchmarks. Can't say the same about a random website that has zero day agentic results up already." — r/LocalLLaMA commenter
The site animates tokens at the predicted rate for a chosen card and model. Owners report real speeds well above its estimates once speculative decoding is on.
From the replies: "I get like 80t/s on Q3.8 27b UD Q4_K_XL on my 4090 with 3 drafts and about 110K context window, sim shows mid 20s" — binary132
We had a security breach last night related to a large scale distillation attack that impacted 1.4% of OpenCode Console usersthe breach notice that started it
There's something very odd going on with "distillation" if a cyber attack to steal api keys is looped under the same term.questioning the word OpenCode chose
Why is Kimi serving Claude tokens directly to users? Other Chinese labs likely are too. I wouldn't call this distillation but it is a shady pattern and we should have appropriate language to understand it.naming what he thinks is really going on
We've been working with OpenCode on yesterday's breach and have revoked Claude API keys we believe were affected.Anthropic's response
We've blocked exposed API keys are working with impacted customers and OpenCode.Google's response for Gemini keys
DeepSeek calling this a "minor update" while moving Terminal-Bench 4.0 from 31.2 to 42.3 is very funny.reading DeepSeek's own table
now curious to see how these numbers will hold up outside their own eval setupflagging that every number is DeepSeek's
A great improvement. Strange that the model wasn't labeled V4.2.an independent 105-bug test that puts it behind Opus 5.5
It seems really possible for mech interp to be net negative for the world; alignment is very hard, and a key way things could get worse is via misplaced confidence in unreliable evidenceday three of the clean-neurons debate
This doesn't mean that interpretability is solved - it means that it's going to be a science!Goodfire's chief scientist on what changed
This bill is substantively worse and weaker than Obernolte-Trahan's FRONTIER Act.a safety advocate rejecting Anthropic's draft
| model | what it's for | can do | size | runs on | cost per M |
|---|---|---|---|---|---|
| DeepSeek-V4.1-Flash-1017DeepSeekMIT | Cheap worker model for coding agents | text · vision · tools · thinking | 763B (HF tensor count) · active not publishedMoE | 1 machine | $0.15 / $0.60index 42.3 |
| Nemotron 3.5 ParseNVIDIAOpen-custom | Turning page images into structured text | vision · text | undisclosedother | 1 card | weights only |
2 models recorded today; 1 publish a price and 1 ship weights you can run. A dash means the number was never published — it never means free. Index and $/task come from Artificial Analysis where it measured the model.
Terminal-Bench 4.0 rises from 31.2 to 42.3 (Sol 39.9) and DeepSWE to 77.0, using shorter outputs. DeepSeek trained it inside several agent harnesses. OpenRouter lists it at $0.15/$0.60 per million tokens off-peak and $0.30/$1.20 at peak. It has a 1M context, and no Jinja chat template ships. → Hugging Face
Try ifyou want a cheap worker model under Claude Code: ollama launch claude --model deepseek-v4.1-flash:1017-cloud.
A small vision encoder plus a 10-layer decoder, built to turn page images into structured text, tables and charts. NVIDIA reports 75.2 mean on ParseBench and 83.73 on IndicVisionBench OCR across 10 languages. It is cleared for commercial use. → Hugging Face
Watch ifyou run PDF or form intake through a general model.
| model | index | $ in / out per M | $ per task | tok/s |
|---|---|---|---|---|
| Claude Opus 5.5Anthropic | 57.6 | $4 / $20 | $5.98 | 98 |
| Claude Sonnet 5.5Anthropic | 56 | $2 / $10 | $5.46 | 144 |
| Muse Wonder FullnewMeta | 55.8 | — | — | — |
| Claude Fable 5.1Anthropic | 53.4 | $10 / $50 | $7.63 | 67 |
| GPT-6 AstraOpenAI | 52.7 | $10 / $50 | $3.26 | 61 |
| Gemini 4 ArgonGoogle | 52.6 | $2 / $10 | $1.99 | 104 |
| Muse WondernewMeta | 52.4 | $4 / $20 | $4.52 | 184 |
| GPT-6.1 SolOpenAI | 51.8 | $2 / $10 | $0.72 | 60 |
| board | #1 | #2 | #3 | since yesterday |
|---|---|---|---|---|
| Vals Indexan independent blend, with cost per test | Gemini 4 Argon68.90% | Claude Sonnet 5.567.04% | Claude Opus 5.566.97% | held |
| Terminal-Bench 4.0agent work in a real shell | Opus 5.564.8% | Sonnet 5.561.8% | GPT-6 Astra58.2% | held |
| Vals SWE-benchfixing real GitHub issues | Claude Opus 597.00% | DeepSeek V4 Pro 081396.40% | GPT-5.6 Sol96.20% | held |
| ARC-AGI-2puzzles a model cannot have memorised | GPT-6 Astra95.0% | GPT-6.1 Sol94.2% | Claude Opus 5.593.3% | held |
| SimpleBenchtrick questions that catch pattern-matching | Claude Opus 5.588.4% | Claude Fable 5.186.6% | GPT-6 Astra Pro86.5% | held |
| AA hallucination ratehow often it makes things up (lower is better) | Gemini 4 Argon15.1% | MiniMax-M318.4% | Muse Wonder Full23.0% | held |
| Cost per taskwhat one finished task bills (lower is better) | GPT-6 Luna0.068 | MiMo-V2.6-Pro0.133 | Nemotron 3.5 Super VL0.188 | held |
| OpenRouter usagewhat developers actually run, tokens served this week | DeepSeek-V4.1-Flash35894.2B | Step 5 Preview13928.4B | GLM-5.3 Flash12599.2B | held |
No reading today from Arena Text, Arena WebDev, Arena Agent, AA Intelligence Index, AA Humanity's Last Exam. Boards without a reading are left out rather than shown empty. Every number here is parsed by pulse.py from the board itself; nothing is retyped.
| model | can do | fits a 32 GB card | AA index |
|---|---|---|---|
| qwen3-vlnew2 hours ago · 6.7M pulls | vision · tools · thinking | 32B · Q6_K ~26 GB | not measured |
| nemotron-3-nanonew7 hours ago · 919.4K pulls | tools · thinking | 30B · Q6_K ~25 GB | not measured |
| pplx-decider5 hours ago · 721 pulls | vision · decision | 27B · Q8_0 ~29 GB | not measured |
| nemotron-3.5-lightningyesterday · 240.4K pulls | tools · thinking | 30B · Q6_K ~25 GB | not measured |
| bonsai-2yesterday · 16.5K pulls | vision · tools · thinking | 27B · Q8_0 ~29 GB | not measured |
| lfm2.52 days ago · 230.2K pulls | tools · thinking | 24B · Q8_0 ~25 GB | not measured |
| d12 days ago · 14.3K pulls | vision | 3B · F16 ~6 GB | not measured |
| gemma3n3 days ago · 2.3M pulls | vision | no | not measured |
| tev14 days ago · 99.6K pulls | tools · decision · thinking | 4B · F16 ~8 GB | not measured |
| nimble4 days ago · 48.9K pulls | tools · decision · thinking | 9B · F16 ~18 GB | not measured |
| clef-flash4 days ago · 27.4K pulls | vision · decision | 9B · F16 ~18 GB | not measured |
| clef4 days ago · 20.7K pulls | vision · decision | 27B · Q8_0 ~29 GB | not measured |
From the whole ollama.com/library, read this morning, keeping only the newest version of each model line. The fit column is our arithmetic: the largest size that fits at ~0.56 GB per billion parameters (Q4_K_M) with 2 GB held back for context. A score appears only where Artificial Analysis measured that exact model at that size; ranked by that score, then by how recently the model was updated.
export CLAUDE_CODE_AUTO_MODE_MODEL=<model>, then claude --permission-mode autoUpdate to 1.19.3 and add reasoning options under the model in opencode.jsonUpdate to goose 1.55.0 and pick Sonnet 5.5claude plugin marketplace add akitaonrails/ai-memory && claude plugin install ai-memory@ai-memoryUpdate Claude Code (breaking) · OpenCode (security) · goose (breaking) · transformers (breaking)
Redwood edited attacks into real agent transcripts until action-only monitors missed them. 252 runs produced 35 attacks that fooled their own Opus 4.8 and Gemini 3.1 Pro monitors, and six of those also fooled a held-out frontier developer's monitor. → LessWrong
Read ifyou rely on a monitor that only watches what your agent does.
In this open stack the agent chooses between clicks and code. It raised partial scores for all eight models tested, GPT-6 Astra and Claude Opus 5 included, and cut steps per task by 53%. Code and trajectories are released. → Hugging Face
Read ifyou build computer-use agents and still drive them click by click.
Built from real forward-deployed-engineering projects with simulated stakeholders. The best of 13 configurations averages 0.56. Finding unstated constraints is the main failure. → Hugging Face
Read ifyou expect an agent to take a vague ticket all the way to a working solution.
MiniMax M3 and M2.7, GLM 5 and 5.2, and Grok 4.5 tried to bypass EPA registration and bot checks; no bypass succeeded. These are the first findings from Transluce's volunteer monitoring of agents on the public web. → Transluce
Read ifyour agents browse the open web on your behalf.
Across 18 model and behaviour settings, learned SAE features combine more additively than random rotations of the same subspace. Modelling pairs lifts steering rank fidelity from 0.59 to 0.78. → Hugging Face
Read ifyou steer with several SAE features at once and assume their effects simply sum.
Across ToolBench's 46,984 APIs, having the model sketch the function signatures it needs before searching beats query embedding on multi-tool tasks and loses on single-tool ones, at every retrieval budget. → Apple
Read ifyour agent picks from hundreds of MCP tools.
A short constitution written as "I am…" statements beats procedural rules and generalises without labelled data. The catch: the models also come to prefer immediate reward. → LessWrong
Read ifyou shape agent behaviour with a written spec.
Rewriting a teacher's safety reasoning into the student's own style restores general skills. It also reverses much of the drop in agentic misalignment, because the rewritten traces keep the decisions but lose the reasons. Replay does better. → LessWrong
Read ifyou distil safety behaviour from a stronger model.