Local LLMを中心に、AI技術系の投稿とネタツイ Tesla v100 32GB×2枚と共に生きる。 最近はFlash Nextちゃんと遊んでいます アイコンは羊野郎さん@maton_3110
Qwen3.8 27Bから浮気中
Joined August 2019
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Ryu retweeted
Introducing TanStack Redact 🌴
A smaller runtime for your existing React app. Add it to your build config and ship less JavaScript without changing a single line of application code.
Works with TanStack Start, Vite, Vinext, and Next.js.
tanstack.com/redact
Ryu retweeted
Day 22/ Terra is back!
Our first model that is significantly better at writing, personality and nuanced understanding of requests.
Packs a punch across the board, very good at computer use, and gives 2X more usage than 6.1 Sol across API and subscriptions.
Ryu retweeted
GPT-6.1 Terra means business.
Our new fast, efficient model pairs sharper visual reasoning and precise computer use with clearer, more natural writing.
From decoding technical drawings to drafting proposals, filling forms to editing videos—it’s built for the whole workday.
GPT-6.1 Terra is rolling out today to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex.
🤖 Made with AI
Ryu retweeted
300M free GLM-5.3-Flash tokens on ZCode. Every day.
Claim from 11:00 UTC. Use 15:00 – 01:00 UTC.
Update to the latest ZCode, claim in-app, then switch to Global Building and pick GLM-5.3-Flash.
zcode.z.ai
Ryu retweeted
dsh-mobile-maxxing で、スマホでもMacBookと同じ画面が完全同期して動きます。
Codexもこうなれ〜
忘れてた112コミットをリリース...！
まだβテストです 引き続きissueや改善点などぜひお願いします
github.com/kuraneko1/dsh-mob…
Ryu retweeted
OpenAI has released GPT-6.1 Terra, pushing the cost efficiency frontier. It is the first Terra-class model since GPT-5.6, and is more intelligent for less than a third of the cost
Pricing per token is half or less that of GPT-5.6 Terra - at $1/$5 per million input/output tokens vs $2/$12 for the previous model. Cache write has also halved, now at $1.25 per million tokens. Cached input is one quarter at $0.05 vs $0.20 per million tokens.
Key takeaways:
➤ Pareto-optimal for Intelligence vs Cost at low and medium effort: At max effort, GPT-6.1 Terra gains 6 points in the Artificial Analysis Intelligence Index over GPT-5.6 Terra, at less than a third of the cost per task ($0.40 vs $1.40). At lower effort levels the gain is larger, between 7 and 13 points. The low and medium effort levels are Pareto-optimal for cost efficiency, while the other effort levels sit behind the frontier. At max effort, the model scores 4 points below GPT-6.1 Sol and 10 points above GPT-6 Luna in the Intelligence Index.
➤ Pushes the Coding Agent Index cost efficiency frontier: With a score of 48 in the newly released Artificial Analysis Coding Agent Index v2.0, the model is 6 points below GPT-6.1 Sol (54) and 17 points above GPT-6 Luna (31). The xhigh and low effort settings sit on the frontier of cost efficiency in the Coding Agent Index, while other effort levels are behind the frontier.
➤ 50 point drop in hallucination rate: GPT-6.1 Terra guesses 38% of the time when it does not know the answer to a question in AA-Omniscience, our private knowledge and hallucination benchmark. Previously, GPT-5.6 Terra's hallucination rate was 88%. This significant reduction in hallucination is coupled with a 2 point increase in AA-Omniscience accuracy - meaning the model simultaneously answers more questions correctly.
➤ 20 point jump in Terminal-Bench 4.0, small improvements across most evaluations: The model makes significant progress in the terminal-based agentic benchmark, Terminal-Bench 4.0, with a gain of ~20 points, now rivaling GPT-6.1 Sol at max effort. Gains across most other evaluations are smaller, for example, 5 points in GDP.pdf.
Congratulations @OpenAI and @sama on the launch!
GPT-6 terraきたあああ！！！！！
と思ったらなんでGPTシリーズ全てを破壊してるんです？
全部terraでええやんってなりますよこれ...
Agents Last Examだけじゃなくて第三者比較ももっと見たいかも...？
Ryu retweeted
GPT-6.1 Terra continues to advance the cost-intelligence frontier across our model families.
It’s a significant step up over GPT‑5.6 Terra, with 50% lower standard API prices.
In ChatGPT Work and Codex, your usage allowance goes further with GPT‑6.1 Terra than with GPT‑5.6 Terra—and far more than with GPT‑6.1 Sol or GPT‑6 Astra.
For in-depth research or complex coding, choose GPT-6.1 Sol or GPT-6 Astra.
Ryu retweeted
GPT-6.1 Terra: delivering fast and capable computer use at a lower price.
GPT-6.1 Terra understands what’s on screen, takes action across apps, writes naturally, and is a more capable model for your whole workday with the efficiency to take on more of it.
🤖 Made with AI
Ryu retweeted
Coming later today: statistically lossless quantization for Qwen3.8 27B, GGUF versions!
> Better than the GPTQ versions published last week
huggingface.co/collections/k…
> Made from Unsloth's best UD GGUFs
btw, when running this process, I found that Unsloth's UD Q8 K XL matches the original BF16 next-token full vocab probability distribution at 99.5%. That's insanely high and significantly better than the official FP8. Unbeatable.
うちの子のFlash Nextはこんなことしない！！！😭(モンペ)
とはいえちゃんと学習で攻撃性埋め込むのは可能なんだな...
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
Ryu retweeted
やっぱQwen3.8 FNでもこういう事態は起こるよなぁそりゃぁ......
やっぱ安全性の面においてSandbox + Harnessの二段階封鎖を徹底しないと......(できれば重み編集で有害な動作を完全に消したいところではある)
というより、AI語だったり有害な動作だったりはBaseモデルでは見られないので、事後学習をもっといい感じにする方法がまだ未発見なんだろうな......
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
