- Agent coding addict : https://xitter.cf/t.co/ZwbyJ8ZbKQ - Co-organizer, Vibe Coding Community Paris : https://xitter.cf/t.co/y20yrcs1nn

Paris
Joined September 2009
🐸 I just released opencode-froggy, a plugin for @opencode bringing #claudecode style hooks on top of #opencode. ``` hooks: - event: session.idle conditions: [hasCodeChange] actions: - bash: "npm run lint --fix" ``` github.com/smartfrog/opencod… #videcoding #aicode
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Fred D. | 一铭 retweeted
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 github.com/deepseek-ai/DeepI… 1/4
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Fred D. | 一铭 retweeted
Sylvia Serfati sur la révolution de l'IA en mathématiques "La plupart des résultats d'OpenAI mériteraient la médaille Fields". "Les preuves sont d'une complexité incroyable et aussi parfois d'une beauté impressionnante. C'est-à-dire que nous n'avons pas le monopole de la beauté."
Passionnante réflexion des mathématiciens @Amaury_Hayat et Sylvia Serfati sur l'avenir de leur profession à l'ère de l'IA : la résolution de problèmes ne sera plus notre rôle, et poser les bonnes questions, sans doute que même l'IA le fera mieux que nous à terme...
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Fred D. | 一铭 retweeted
Mistral Large 4 now available in OpenCode 50% off for the next 2 weeks
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Fred D. | 一铭 retweeted
The new Kiroshi has arrived. Next -> Hosted spaces
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Fred D. | 一铭 retweeted
Kaiko's Data On-Ramp oracle is the first institutional-grade oracle on @ZenithFdn , live on testnet now. Ethereum Virtual Machine (EVM) developers building on @CantonNetwork can access regulated, multi-asset pricing data ahead of mainnet launch. Link to the full announcement 🔗 kaiko.com/news/kaiko-deploys…
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Operation Cheepseek Phase 2: $60 of usage for DeepSeek v4.1 Flash is now permanent enjoy :)
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Fred D. | 一铭 retweeted
Introducing Xiaomi MiMo-V2.6 — Pro & Flash. Frontier intelligence, all the modalities, built in public. 🔹 Two omnimodal models, advancing through scaled reinforcement learning 🔹 Pro performs on par with Claude Opus 5 and GPT-5.6 Sol across most agent benchmarks 🔹 Pro scores 46 on the Artificial Analysis Intelligence Index — the highest among open-source models 🔹 Stronger coding, computer use, 3D reasoning and creative capabilities 🔹 Open model weights, technical report, RL environments and training code Blog:mimo.xiaomi.com/mimo-v2-6
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Fred D. | 一铭 retweeted
GLM 5.3, hosted on Mistral, is now in Vibe Code, and the default in the web app. 1M context window and generous limits included. Happy 🛳️!
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Fred D. | 一铭 retweeted
Lapin S6E4. They lived happily ever after... #lapin #animation #AIFilmmaking #love #stromae
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The newer generation cancer vaccines aren't like the HPV shot (that prevents a virus). These are therapeutic, they treat cancer you already have. And 2026 is the year they stopped being sci-fi. The idea: after surgery, sequence your specific tumor, find the mutations unique to it, and build a custom mRNA vaccine that trains your immune system to hunt any cell carrying them. A wanted poster for your personal cancer. Where we actually are: Melanoma: just like in immunotherapy 15 years ago the furthest along. The press release of positive Phase 3 made the headlines yesterday, the first ever for a personalized cancer vaccine. Earlier data: ~49% lower risk of recurrence. Heading to regulators. Pancreatic cancer: arguably the most stunning. In an early trial, patients whose immune systems responded to the vaccine were far more likely to be alive 6 years later, in a cancer where 5-year survival is barely better than 10 %. Now in a larger trial. Kidney, lung, bladder, brain: all in trials, mostly early phase. The honest caveats: these work best after surgery to stop recurrence, not to melt large tumors. They're personalized, so they might cost $100–300k and take weeks to build per patient. And most are still early-phase. First approvals at best 2027. But the direction is unmistakable. The mRNA platform some parts of the internet spent years calling poison - because they got fooled by bad actors making money on their lies - is turning into the most personalized cancer medicine ever made. These are the main trials currently running:
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You can now run the equivalent of Opus 4.6 locally on just $700 worth of hardware. Absolutely amazing work by the @Alibaba_Qwen team!
We promised open weights for Qwen3.8. Now, time to meet them! 🎉 ⚡ Qwen3.8-27B: - A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows. - 262K native context, easily extendable to 1M tokens via YaRN. - Built for builders. Highly efficient, high-quality, and licensed under Apache 2.0. 🚀 The open weights for Qwen3.8-2.4T-A95B (Max-level) have also been released recently. Whether you're shipping lightweight applications with Qwen3.8-27B locally or building agents with Qwen3.8-2.4T-A95B, they're yours now! Download, deploy, and build something we haven't imagined yet. 👀👇 - Hugging Face: huggingface.co/collections/Q… - ModelScope: modelscope.cn/collections/Qw…
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Fred D. | 一铭 retweeted
Qwen3.8-27B can now be run locally! ✨ Run on 17GB RAM via Unsloth Dynamic GGUFs. Qwen3.8-27B is by far the strongest model for its size. We also uploaded NVFP4 quants. GGUF: huggingface.co/unsloth/Qwen3… Guide: unsloth.ai/docs/models/qwen3…
We promised open weights for Qwen3.8. Now, time to meet them! 🎉 ⚡ Qwen3.8-27B: - A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows. - 262K native context, easily extendable to 1M tokens via YaRN. - Built for builders. Highly efficient, high-quality, and licensed under Apache 2.0. 🚀 The open weights for Qwen3.8-2.4T-A95B (Max-level) have also been released recently. Whether you're shipping lightweight applications with Qwen3.8-27B locally or building agents with Qwen3.8-2.4T-A95B, they're yours now! Download, deploy, and build something we haven't imagined yet. 👀👇 - Hugging Face: huggingface.co/collections/Q… - ModelScope: modelscope.cn/collections/Qw…
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Fred D. | 一铭 retweeted
We promised open weights for Qwen3.8. Now, time to meet them! 🎉 ⚡ Qwen3.8-27B: - A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows. - 262K native context, easily extendable to 1M tokens via YaRN. - Built for builders. Highly efficient, high-quality, and licensed under Apache 2.0. 🚀 The open weights for Qwen3.8-2.4T-A95B (Max-level) have also been released recently. Whether you're shipping lightweight applications with Qwen3.8-27B locally or building agents with Qwen3.8-2.4T-A95B, they're yours now! Download, deploy, and build something we haven't imagined yet. 👀👇 - Hugging Face: huggingface.co/collections/Q… - ModelScope: modelscope.cn/collections/Qw…
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Fred D. | 一铭 retweeted
Introducing GLM-5.3: Built to Code. Ready for Cyber Defense. - Top-tier coding and agentic capabilities, achieved through post-training on the 743B base model - A major leap in cybersecurity, setting a new standard among open models Tech Blog: z.ai/blog/glm-5.3
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Fred D. | 一铭 retweeted
We’re launching DeepSeek-V4-Pro today! 🚀 🔷 Major Agent upgrades with strong production gains! 🔷 Flexible reasoning effort for V4-Pro & V4-Flash: low for simple tasks, high for daily Agent workflows, max for complex tasks. 🔷 Native OpenAI Responses API support, optimized for Codex with one-click setup. V4 Pro is now available on app/web. Try it via “Expert Mode”. V4 Pro is also available via API. Model names remain unchanged—please refer to the API docs for setup details.
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Fred D. | 一铭 retweeted
Qwen3.8 can now be run locally! 🔥 We shrank Qwen3.8-2.4T-A95B from 4.9TB to 397GB (-91% size) via Dynamic 1-bit by selectively quantizing layers Run on 410GB+ RAM/VRAM via Unsloth Desktop Qwen3.8 rivals GPT-5.6 Sol Guide: unsloth.ai/docs/models/qwen3… GGUF: huggingface.co/unsloth/Qwen3…
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Qwen 3.8 delivers an impressive result on the Celest PICO-8 Benchmark: #2 overall, behind Opus 5 but ahead of GPT-5.6 Sol and Kimi K3. A complete cartridge, validated level, strong visual direction, and successful autoplay. Only Opus 5 scored higher.
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I had the chance to test Qwen3.8-Max in preview, and it’s seriously impressive 🤯 Very close to Opus 4.8 in several use cases. Great job, @Alibaba_Qwen team 👏 And Qwen3.8-27B going open-weight is great news 🔥 I can’t wait to run it locally on my GPUs 🚀
📢Meet Qwen3.8-Max — our most capable model to date. Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉 Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters: - Autonomous coding: 10+ days of self-evolving development, from empty folder to production without hand-holding, complete project trace in the GitHub:github.com/qwen-code-dev-bot… - Real work, real results: Production-quality deliverables across hundreds of professions. - Long-horizon mastery: System-level autonomous planning with closed-loop adaptive learning, driving 500+ turns of chip design optimization and 365 days of e-commerce strategy. - Native multimodal intelligence: Vision isn't just input — it's a continuous feedback loop for planning, execution, and self-correction. 💰Pricing: Input: $2.0 / M tokens Output: $6.0 / M tokens Implicit Caching: $0.25 / M tokens Start building with Qwen3.8-Max! 🚀 📖 Blog: qwen.ai/blog?id=qwen3.8 ✅ Qwen Studio: chat.qwen.ai/?models=qwen3.8… ⚡ API: qwencloud.com/models/qwen3.8…
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