valkyrie263 @valkyrie263dev

Extract – a system built by the UK government, using our Gemini foundational model – will help council planners make faster decisions. 🚀 Using multimodal reasoning, it turns complex planning documents – even handwritten notes and blurry maps – into digital data in just 40s. Find out more. ↓ goo.gle/4jIuMJM

The A2A Github repository (14.5k ⭐s) has grown quickly, underscoring the industry's eagerness for a standardized agent communication protocol! To learn more about Agent2Agent Protocol, check out our @github ↓ github.com/google/a2a

You can now fine-tune TTS models with Unsloth! Train, run and save models like Sesame-CSM and OpenAI's Whisper locally with our free notebooks. Unsloth makes TTS training 1.5x faster with 50% less VRAM. GitHub: github.com/unslothai/unsl… Docs & Notebooks: docs.unsloth.ai/basics/text-to…

Introducing Continuous Thought Machines New Blog: sakana.ai/ctm/ Modern AI is powerful, but it’s still distinct from human-like flexible intelligence. We believe neural timing is key. Our Continuous Thought Machine is built from the ground up to use neural dynamics as a powerful representation for intelligence. Thought takes time, and reasoning is a process. Biological brains inspire us with their complex neural activity, where neural timing is critical to intelligence. We’re exploring how to bring that power to AI. The Continuous Thought Machine (CTM) incorporates neuron-level temporal processing and neural synchronization, moving beyond current AI limitations. Our approach has two core innovations: (1) neuron-level temporal processing, where each neuron uses unique parameters to process a history of incoming signals for fine-grained temporal dynamics, and (2) neural synchronization, used as a direct latent representation to modulate data and produce outputs, encoding information directly in the timing of neural activity. Learn more about our approach: Interactive Report: pub.sakana.ai/ctm/ Full Paper: arxiv.org/abs/2505.05522 GitHub : github.com/SakanaAI/conti…

Introducing Collaborative Reasoner: a framework to improve collaborative reasoning in language models. Collaborative Reasoner paves the way for developing social agents that can partner with humans and other agents. Read the research paper and download the code. go.fb.me/je2t32

We’re releasing model weights for our 8B- parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. Learn more about how Dynamic Byte Latent Transformer is paving the way for groundbreaking developments in the field of language modeling. Read the research paper, and download the model and code. go.fb.me/je2t32

パワーポイントやPDFをマークダウンに変換できるMCPサーバとかありそうだなと思って調べたらMicrosoftがMarkItDownの公式MCPサーバ出してた！ 他のMCPと組み合わせると便利につかえるかもですね github.com/microsoft/mark…

We are excited to partner with @AIatMeta to welcome Llama 4 Maverick (402B) & Scout (109B) natively multimodal Language Models on the Hugging Face Hub with Xet 🤗 Both MoE models trained on up-to 40 Trillion tokens, pre-trained on 200 languages and significantly outperforms its predecessors Llama 3.1 405B 🔥 Llama 4 Scout would fit on one H100 GPU whilst a Mavericks fits perfectly on a H100 DGX - intelligence truly is too cheap to meter! 💥

Django 5.2 has arrived! 🚀djangoproject.com/weblog/2025/ap… Featuring a composite of new feature – auto-importing models in the shell. Composite primary keys (!?!). BoundField overrride improvements. Version 5.2 is Long Term Support (LTS) 👉️ until April 2028. We hope you like it!

🌟OpenStack 2025.1 Epoxy Has Arrived🌟 Thank you to everyone who contributed to the success of the 31st #OpenStack release! 🚀

NEW MODEL: GemmaCoder3-12b is a code reasoning model that improves performance on the LiveCodeBench benchmark 11 points over the base model. This makes for a useful code model because: - At 8 bit it runs nicely on 32gb of RAM - Gemma3's 128k context length is great for large code bases - Thinking can be activated or not via the chat template using the token > > model, blog post, and code in the thread.

Graphs provide a powerful way to model & solve many real-life problems, from traffic prediction to understanding why molecules smell. Learn more about the recent history of graph-based #ML & the role that Google researchers have played in the field →goo.gle/42aABbR

Today, we're excited to make two big announcements! - OpenHands LM: The strongest 32B coding agent model, resolving 37.4% of issues on SWE-bench Verified 📈 - OpenHands Cloud: SOTA open-source coding agents from your computer, phone, github, with $50 in free credits 🙌☁️

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Today we’re releasing an experimental version of Gemini 2.5 Pro. 💡2.5 Pro shows strong reasoning and improved code capabilities, with state-of-the-art performance across a range of benchmarks. 📈It’s topped @lmarena_ai's leaderboard by a huge margin

The resource API is full of neat tricks - did you know you can stream a response, too? This feature and more are available in Angular 19.2 Check the blog post here: blog.angular.dev/angular-19-2-i…

nvidia.com/en-us/products… Memory Bandwidthが273 GB/sだからあんまりな予感 M3 Studioとかが819 GB/sらしい

🚀 Day 1 of #OpenSourceWeek: FlashMLA Honored to share FlashMLA - our efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences and now in production. ✅ BF16 support ✅ Paged KV cache (block size 64) ⚡ 3000 GB/s memory-bound & 580 TFLOPS compute-bound on H800 🔗 Explore on GitHub: github.com/deepseek-ai/Fl…

Panduit の頭と工具

Today we introduce an AI co-scientist system, designed to go beyond deep research tools to aid scientists in generating novel hypotheses & research strategies. Learn more, including how to join the Trusted Tester Program, at goo.gle/417wJrA

Metaが提案したテスト自動化の手法が面白い LLMを使って特定の問題が起きるバグを意図的にソースコードに仕込ませて、同時にそれを検知できるテストコードを生成することで回帰テストの品質を上げるというアプローチ システム開発の現場にも手軽に導入できそう arxiv.org/abs/2501.12862