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Could this be a direction for future model architectures?
Introducing Memory Attention (MA).
alphaxiv.org/abs/2609.28399
J.L retweeted
Today we are launching FrontierCS 2, the most frontier benchmark for AI agents in computer science R&D. We built 48 tasks from published papers across 13 areas of CS, refining each one with experts in its field. Excited to co-launch FrontierCS 2 with @kaggle @GoogleDeepMind
J.L retweeted
这个个人网站的设计太帅了！
看了 GitHub 简介，10 月份才开始写的，现在还是那句话：你只需要天马行空，剩下的交给 AI
koimsurai.com
J.L retweeted
你知道的 @teortaxesTex 一直是我们大肥鱼的好兄弟
期待大肥鱼新的表现，具体大肥鱼毕业时间没有人能知道，至少 v41flash 1017 真的很棒，long live RL
我知道听爆料是件很爽的事情但大家有点过度解读了，能说的全是大家知道的消息。
ds4.1pro大肥鱼正在努力学习中具体毕业时间我也不知道。
同时k3.1 pro取名已经明牌flash正在训练中了，很快大家会有便宜的k家模型可以用，可以期待一下。
作为国内我最看好的两家，deepseek和kimi暂时走上了相反的路，其实也代表了deepseek和kimi各自的文化与理念以及困境。
最后说一下我个人看法，国模与O/A两家的差距比我们看到的要大的多，并不是短时间能追上的，‘冲向银河’也只是我对大肥鱼的祝愿以及对ds理念的肯定，同时也希望stepfun，智谱，minimax等众多国模能顶住压力追上去。
J.L retweeted
After much reflection, we’ve redefined FLA’s niche. We want FLA to be the go-to baseline for speed and accuracy—the one everyone wants to compare against—as well as an easy-to-use, reliable default that people feel confident trying.
Alongside K3.1 comes FLA v0.6.0! 💪
Grateful to the open-source community for helping bring faster kernels, more backends, and better KDA.
So proud of my colleagues’ work! ❤️ cc @uniartisan @SonglinYang4
all in RL🫡
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model.
🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal.
🔹 Full-project coding: games in Unreal, Unity & Godot.
🔹 Excels at parametric 3D modeling in Blender & CAD.
🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%.
🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks.
Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API.
Open weights by Nov 4, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
J.L retweeted
MiniMax M3.1 Flash is out! We trained a DFlash 2 drafter for it, and it's open under Apache 2.0. Generation gets up to 3.5× faster, and it's lossless.
MiniMax M3.1 Flash is out, and we've open-sourced a DFlash 2 drafter for it under Apache 2.0. It makes generation up to 3.5× faster, and it's lossless. Congrats to @MiniMax_AI on the release!
huggingface.co/incoai/MiniMa…
J.L retweeted
Today, we release our first model, Flint-40B-A2B, a fast local coding model built with our automated research system. huggingface.co/RecursiveSI/f…
1. Strong performance. 54.9% on Terminal-Bench 2.1, 89.7% on SWE-bench Pro v2, and 74.2% on SWE-bench Verified.
2. It is trained from scratch (tokenizer->Pretrain->Midtrain->SFT->RL).
3. Fast in your local laptop. Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark.
4. We build the complete model training pipeline in 4 month!
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
J.L retweeted
Hardware looks scary until you break it down into small steps. So I built an app that takes you through Arduino one circuit at a time.
Made with Opus 5.5
learn-arduino-red.vercel.app
你才是真正的openai，友商又要难受了😭
🚀 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
From Astra to Opus 5.5, the past few months have arguably been the darkest days for Chinese LLMs.
But now, DSV4.1 is coming back from hell. 🐋
The pressure is back on everyone else.
👀 Rumor has it that Kimi K3.1 and MiniMax M3 Pro are both dropping by the end of this month.
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
1/4
Memory is everywhere now.
Memory Attention
xitter.cf/Jo1uck/status/21029865…
Memory Agents
xitter.cf/TencentHunyuan/status/…
Memory Depth
xitter.cf/_davidgonmar/status/21…
What's next? 👀
A 3B active model beats Opus 5 at memory. We open-sourced the full stack — model, engine, and app.
Hy Memory handles chat, work, and code memory in one layer, and it's first on all four benchmarks we ran, leading MemoryArena by 10.2 points while cutting memory volume 51.8% and recall tokens 39.3%.
The model isn't tied to our engine. Drop Hy-Memory-30B-A3B into Mem0, MemOS, or EverMemOS and it still comes out on top, even on the one system it never trained on. It's already running in our own products, where one assistant's multi-hop recall went from 50% to 90%. It also unifies memory across Codex, Cursor, WorkBuddy, and more.
Repo: github.com/Tencent-Hunyuan/H…
Weights: huggingface.co/tencent/Hy-Me…
Questions welcome in the replies.
