Image created with gemini-3.1-flash-image-preview with claude-opus-4.7. Image prompt: Using the provided reference image, preserve exactly the pure white background, landscape aspect ratio, vertical type hierarchy, letter tracking, and galaxy-punchout starfield treatment clipped inside every letterform, but replace ‘HEROES’ with ‘OPEN SOURCE’ in the same bold condensed grotesque all-caps, replace ‘ALESSO’ with ‘THE COMMONS’ in the same light geometric all-caps, and replace ‘TOVE LO’ with ‘OPEN WEIGHTS’ in the same condensed grotesque all-caps, while keeping ‘(we could be)’ and ‘FEATURING.’ unchanged.
Deepseek V4 Pro is the biggest open model ever with 1.6T total 49B active, trained on 33T tokens, 1M context, with 2 new attention mechanisms, Muon, mHC, open source kernels, FP4 QAT, MIT license and with one of the best tech repot of the year
https://x.com/eliebakouch/status/2047519300399837677
DeepSeek is once again the open-source king and it’s competitive with frontier models 1st or 2nd place on 12/22 benchmarks
https://x.com/scaling01/status/2047512176856899985
I tested @huggingface ml-intern, given the prompt “”Fine-tune a Segment Anything Model (SAM) on a useful medical dataset. Train the model, and provide a comprehensive tutorial in a Jupyter Notebook file. Additionally, create a Hugging Face article/blog post documenting
https://x.com/Mayank_022/status/2046646301555900828
Kimi K2.6 Tech Blog: Advancing Open-Source Coding
https://www.kimi.com/blog/kimi-k2-6
Kimi K2.6 autonomously overhauled exchange-core, an 8-year-old open-source financial matching engine. Over a 13-hour execution, the model iterated through 12 optimization strategies, initiating over 1,000 tool calls to precisely modify more than 4,000 lines of code. Acting as
https://x.com/Kimi_Moonshot/status/2046531057147933137
Hermes Agent recently overtook OpenClaw in weekly new GitHub stars. Two months after launch, the agent from @NousResearch is pulling developers from the incumbent at meaningful scale. It’s self-hosted and open source, so you control what leaves your machine. It reaches you
https://x.com/Delphi_Digital/status/2045839142450536504
Introducing Hermes Agent v0.11.0 Our largest update yet, with over 700 PRs across ~200 contributors. Thank you to everyone who’s worked on Hermes Agent! This update features a beta TUI v2, unlimited recursion depth and width of subagents, 5 new LLM providers, expanded image gen
https://x.com/Teknium/status/2047506967909015907
OpenAI dropped a new model on HF today!
https://x.com/ClementDelangue/status/2046973714751754479
OpenAI just open sourced a new 1.5B (50m active) model on HuggingFace with Apache 2.0 license! It’s not a new LLM, this one is called Privacy Filter, and it’s a PII detection model (checking if text has private information) A few interesting tidbits from the release + links:
https://x.com/altryne/status/2046977133013311814
OpenAI just released a new open-source model it’s “”a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text
https://t.co/xTZt1J3WcT
https://x.com/scaling01/status/2046972437422543064
Kimi K2.5 widens gap between the US and China in open weights model intelligence. The leading US open weights model remains OpenAI’s gpt-oss-120b, which has now been eclipsed by an ever-growing list of open weights releases from China.
https://x.com/ArtificialAnlys/status/2016250140219343163?s=20
We’ve post trained a model on top of Qwen that achieves Pareto optimality on accuracy-cost curves. Unlike our previous post trained models, this model has been trained to be good at search and tool calls simultaneously, allowing us to unify the tool call router and
https://x.com/AravSrinivas/status/2047019688920756504
🚀 Meet Qwen3.6-27B, our latest dense, open-source model, packing flagship-level coding power! Yes, 27B, and Qwen3.6-27B punches way above its weight. 👇 What’s new: 🧠 Outstanding agentic coding — surpasses Qwen3.5-397B-A17B across all major coding benchmarks 💡 Strong
https://x.com/Alibaba_Qwen/status/2046939764428009914
Qwen 3.6-Max-Preview solves AIME-2026 #15 after like 30 minutes of thinking, but on first try. Preview or not, it’s more baked than DeepSeek-Expert. Other tests validate this impression. It doesn’t screw up. Alibaba Qwen is, after all, a frontier lab.
https://x.com/teortaxesTex/status/2046166258853269990
HF becoming the platform for agents (assisted by their humans) to use and build AI (rather than just leveraging APIs)!
https://x.com/ClementDelangue/status/2046598219853951346
Access GPT Image 2.0 natively in Hermes Agent Update now to get access – just run `hermes update` and select your image generation tool model with `hermes tools`
https://x.com/NousResearch/status/2046693872773062834
Moonshot AI: “”Our RL infra team used a K2.6-backed agent that operated autonomously for 5 days, managing monitoring, incident response, and system operations, demonstrating persistent context, multi-threaded task handling, and full-cycle execution from alert to resolution””
https://x.com/scaling01/status/2046250343479054540
DeepSeek V4 Flash Thinking at 284B parameters (13B activated) shifts the Text Pareto frontier with $0.14 input / $0.28 output per MToken. Congrats again to @DeepSeek_AI on the open model progress!
https://x.com/arena/status/2047524055679729885
Exciting news – DeepSeek V4 Pro is in the Arena with 1.6T parameters (49B activated) alongside V4 Flash at 284B parameters (13B activated). Both support 1M token context. It’s a major leap over DeepSeek V3.2! Code Arena: – DeepSeek V4 Pro (thinking): #3 open model (#14 overall),
https://x.com/arena/status/2047518354903359697
🎉 Day-0 support for @deepseek_ai V4 Pro and Flash on vLLM — a new generation of DeepSeek model, purpose-built for tasks up to 1M tokens. Alongside the release, we’re publishing a first-principles walkthrough of the new long-context attention and how we implemented it in vLLM.
https://x.com/vllm_project/status/2047520252851105796
DeepSeek V4 by @deepseek_ai just dropped! SGLang is ready on Day 0 with a full stack of optimizations from architectures to low-level kernels. We also deliver a verified RL training pipeline in Miles (by @radixark) for V4 at launch: 1️⃣ Native “”ShadowRadix”” Design: DeepSeek V4’s
https://x.com/lmsysorg/status/2047511629919932623
Deepseek v4 is a huge step upwards compared to DeepSeek 3, outperforms on SWE verified opus 4.6 and GPT-5.4 and sets a new record on Codeforces. Needs to be tested against opus 4.7 and GPT-5.5 tho and see if real world usage holds its promises. Big release! Sota open source
https://x.com/kimmonismus/status/2047514623356579869
DeepSeek-V4 official pricing: DeepSeek-V4 Flash:$0.14 / $0.28 DeepSeek-V4 Pro: $1.74 / $3.48
https://x.com/scaling01/status/2047508350238175526
DeepSeek-V4 Technical Report
https://x.com/scaling01/status/2047510520618516572
DeepSeek-V4 was pre-trained on 32T tokens using Muon and integrates a new hybrid attention mechanism and mHC
https://x.com/scaling01/status/2047510190044409860
Finally, DeepSeek V4 is here! – MIT license – DeepSeek-V4-Pro: 1.6T params (49B active) – DeepSeek-V4-Pro Max ≈ Opus-4.6 Max / GPT-5.4 xHigh across benchmarks!
https://x.com/Yuchenj_UW/status/2047514092756418757
My quick paper summary: DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) Two new compressed attention mechanisms for long context manifold hyper connections Muon training 32T tokens FP4 Quantization-Aware
https://x.com/iScienceLuvr/status/2047514399393579235
They said it’s next week 🤞 : r/DeepSeek
Tencent, Alibaba to back DeepSeek at $20B+ valuation: report — TFN
Tencent, Alibaba to back DeepSeek at $20B+ valuation: report
🚀 DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length. 🔹 DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world’s top closed-source models. 🔹 DeepSeek-V4-Flash: 284B total / 13B active params.
https://x.com/deepseek_ai/status/2047516922263285776
@teortaxesTex deepseek-v4-flash: $0.14/$0.28 deepseek-v4-pro: $1.74/$3.48 This is extremely aggressive pricing. Flash, in particular, really stands out. It is 10 times cheaper than gemini 3.0 flash on an output-token basis… If their inference infrastructure is solid, I think there could be
https://x.com/Hangsiin/status/2047515855949623667
One of DeepSeek-V4’s goals was to make ‘1M context windows practical enough for real world use’, and they appear to have done that remarkably well. It outperformed Gemini 3.1 Pro on long context benchmarks, held up quite well even at 1M tokens, improved compute efficiency by
https://x.com/Hangsiin/status/2047523724929405328
Check out this video on how to run Gemma 4 locally on an iPhone! It runs completely offline and handles long context, meaning no data plan, no API calls, and no monthly fees required.
https://x.com/googlegemma/status/2045204738720084191
What does it take to run 3, 5, or even 10 concurrent instances of Gemma 4 locally? We’ve open-sourced a demo letting you run multiple models side-by-side on your hardware. Gemma 4 26B A4B easily runs 10+ concurrent requests on a MacBook Pro M4 Max at 18 tokens/sec per request.
https://x.com/googlegemma/status/2046621841146671456
🎉 We just shipped a major redesign of
https://t.co/gp8bVnK8aZ. “”How do I run model X on hardware Y for task Z?”” now has a clickable answer. What’s new: – URLs mirror HuggingFace: just swap
https://t.co/KjfZORM2Hs →
https://t.co/gp8bVnK8aZ in any model URL to jump straight to
https://x.com/vllm_project/status/2046592125740142903
partnering with @Kimi_Moonshot to bring kimi k2.6 to @CloudflareDev workers ai on day 0 better for coding and agentic use cases! try it out now:
https://x.com/michellechen/status/2046297037742997909
🎉 Congrats to the Moonshot team on Kimi K2.6 — day-0 support on vLLM 0.19.1. • 1T total / 32B active MoE — 384 experts, 8 routed + 1 shared • MLA attention, 256K context • Native multimodal: MoonViT vision encoder + video input • Native INT4 quantization • Interleaved
https://x.com/vllm_project/status/2046251287206035759
FINALLLY FINALLY it is here. V4-flash: all the way back to V2 prices, only now with 1M V4-pro: roughly Kimi/GLM/MiMo competitor Chat prefix completion and FIM back – thank you! Missed this forever but what can they do?
https://x.com/teortaxesTex/status/2047508587883250112
Kimi 2.6 Thinking seems very good for an open weights model, but many rough edges compared to closed SoTA. The Lem Test resulted in a 74 page thinking trace… and an okay-ish answer. It did an okay TiKZ unicorn, an adequate twigl shader for a neogothic city in the waves, etc.
https://x.com/emollick/status/2046411222354989189
Kimi K2.6 + DFlash: 508 tok/s on 8x MI300X 5.6x throughput improvement over baseline autoregressive serving 90 tok/s → 508 tok/s on the same hardware, same model, zero quality loss
https://x.com/HotAisle/status/2046620289984057634
Kimi K2.6 demonstrates strong long-horizon coding in complex engineering tasks: Kimi K2.6 successfully downloaded and deployed the Qwen3.5-0.8B model locally on a Mac. By implementing and optimizing model inference in Zig–a highly niche programming language–it demonstrated
https://x.com/Kimi_Moonshot/status/2046531052957569211
Kimi K2.6 has landed, and it is live on Baseten! We have baked in multiple inference optimizations so that you can leverage Kimi K2.6 in production right away. To run Kimi K2.6, Baseten uses: -> The Baseten Inference Stack with advanced optimizations, including KV-aware routing
https://x.com/baseten/status/2046263526281576573
Kimi K2.6 helped us rewrite kernels; it worked like a charm 🙂
https://x.com/Yulun_Du/status/2046252918526071017
Kimi K2.6 is live on OpenRouter! @Kimi_Moonshot’s new model is a long-horizon coding model built for sustained agentic work. It behaves more like a systems engineer than a chatbot, with the stamina to decompose, execute, and optimize complex tasks. Try it in all your favorite
https://x.com/OpenRouter/status/2046259590774571199
Kimi K2.6 is now available in Windsurf! Available for free for the next 2 weeks for Pro, Teams, and Max users.
https://x.com/windsurf/status/2046686574793154996
Kimi K2.6 now in OpenCode — Go included
https://x.com/opencode/status/2046275886396125680
Kimi K2.6 was released 1h ago, and it looks amazing! Here it’s running with MLX (mlx-vlm) on two M3 Ultras (full 1T param VLM) 🔥
https://x.com/pcuenq/status/2046283942689456297
Meet Kimi K2.6: Advancing Open-Source Coding 🔹Open-source SOTA on HLE w/ tools (54.0), SWE-Bench Pro (58.6), SWE-bench Multilingual (76.7), BrowseComp (83.2), Toolathlon (50.0), Charxiv w/ python(86.7), Math Vision w/ python (93.2) What’s new: 🔹Long-horizon coding – 4,000+
https://x.com/Kimi_Moonshot/status/2046249571882500354
Moonshot AI launches Kimi K2.6 on Kimi Chat and APIs
https://www.testingcatalog.com/moonshot-ai-launches-kimi-k2-6-on-kimi-chat-and-apis/
Qwen3.6-27B can now run locally! 💜 Run on 18GB RAM via Unsloth Dynamic GGUFs. Qwen3.6-27B surpasses Qwen3.5-397B-A17B on all major coding benchmarks. GGUFs:
https://t.co/ykKgwh2zI9 Guide:
https://x.com/UnslothAI/status/2046959757299487029
Ran Qwen3-8B (8.2B dense, open) on LongCoT-Mini. Vanilla: 0/507. dspy.RLM: 33/507 (6.5%). Same model. Same weights. No fine-tuning. The scaffold is doing 100% of the lifting. Context: leaderboard’s smallest open MoE is GLM-4.7 at 358B total / 32B active params. Qwen3-8B is ~4x
https://x.com/raw_works/status/2045208764509470742
these questions are silly Kimi > all other open-source models tho
https://x.com/scaling01/status/2046591683198906542
We’re open-sourcing FlashKDA — our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. Achieves 1.72×-2.22× prefill speedup over the flash-linear-attention baseline on H20, and works as a drop-in backend for flash-linear-attention. Explore on github:
https://x.com/Kimi_Moonshot/status/2046607915424034839
OpenClaw 2026.4.20 🦞 🧠 Kimi K2.6 support + provider-aware /think 💬 BlueBubbles iMessage sends + tapbacks fixed ⏰ Cron state/delivery cleanup 🔐 Gateway pairing + plugin startup hardening Less haunted. More useful.
https://x.com/openclaw/status/2046686809367708123
Kimi K2.6 wrote an inference engine for Qwen3.5 0.5B in Zig and managed to beat LM Studio’s token per second by 20%, running for 12 hours and with 4000+ tool calls
https://x.com/nrehiew_/status/2046254256194474221
I find that open weights models over-perform on benchmarks compared to actual real-world usage, and Kimi feels like no exception. For example, a small amount of use will show that Kimi is not as good as Claude Opus 4.6, which it beats on the benchmarks. Still a good model, tho!
https://x.com/emollick/status/2046430301593751797
Context Unrolling in Omni Models – A unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations – Enables Context Unrolling, where the model explicitly reasons across multiple modal representations
https://x.com/arankomatsuzaki/status/2047519009004716097
New in LangSmith Fleet: Create and edit files with your agent. Your agent can now work with files directly. Create documents, presentations, and webpages inside a conversation, or upload your own files and edit them together. 📄 Work with images, PDFs, and text files. 💬 Build
https://x.com/LangChain/status/2047362259983495215
Tool Gateway is now live in Nous Portal. No separate accounts, no API key juggling. All you need is one subscription, and everything works. A paid Nous Portal subscription now includes access to 300+ models and a growing set of third-party tools. Launching with: → Web
https://x.com/NousResearch/status/2044878344592699744?s=20
Hermes Agent 🤝 Ollama
https://x.com/NousResearch/status/2045304840645939304
Hermes Agent 生态要炸了，这波进化速度把我整不会了！ 刚从官方生态地图 Hermes Atlas 扒出来几个真·硬货，每一个单拎出来都是一个方向—- 1️⃣ hermes-agent-camel 内置 CaMeL 信任边界，Agent 自主跑任务不再翻车。生产环境终于敢上了！之前多少人卡在这步，现在直接破防。
https://x.com/NFTCPS/status/2046076635200553224
Hermes Agent: The Complete Beginner’s Guide Says written by me but was actually done by my Hermes agent and it used the Hermes Atlas knowledge base as a source It will be updated periodically as the canonical guide Now live on Hermes Atlas (link in replies)
https://x.com/KSimback/status/2046528526581383643
Hermes 一丢 Agent，全网程序员集体进化了！ Nous Research 扔出 hermes-agent（90k+ stars），核心就一个词：自我进化。它不是玩具，而是带持久记忆、自动提炼技能、跨会话成长的底层骨架。 结果？社区直接把它当 DNA，短短几周卷出 80+ 进化体，生态总星 10 万+。这才是开源的最高境界：一个
https://x.com/GitTrend0x/status/2045142797439922337
Hermes 多 Agent 深水区：三个高级实战技巧 90% 的人用 Hermes，还停留在助手阶段：把所有需求塞进一个 Prompt，然后看着它串行执行。 这种用法在多 Agent 并发场景下有三个隐性代价： •Token 浪费：子 Agent 继承冗余历史信息。 •指令稀释：长上下文中关键指令权重衰减。
https://x.com/BTCqzy1/status/2045720855137903046
Introducing
https://t.co/JflLUfop4O V2 🤯 Whats New 👇🏻 🤯No fork required ⭐️New Hermes dark + light themes 🤖Agent View Office 🚀Conductor for agent missions 👥Operations for sub agent orchestration One liner: curl -fsSL
https://t.co/XsSfmAJvZx | bash
https://x.com/outsource_/status/2046079580105064787
ollama launch hermes Ollama 0.21 includes supports Hermes Agent, the self-improving AI agent built by @NousResearch.
https://x.com/ollama/status/2045282803387158873
Skillkit native support for Hermes agent is live NOW! Thanks to @ruffy0369 🔥
https://x.com/ghumare64/status/2046542176142733712
The Hermes Agent Creative Hackathon starts now 16 Days, $25k in Prizes Presented by @Kimi_Moonshot & @NousResearch For the tinkerers pushing Hermes Agent into creative domains: video, image, audio, 3D, long-form writing, creative software, interactive media and more. Show us
https://x.com/NousResearch/status/2045225469088326039
Xiaomi’s MiMo-V2.5 and MiMo-V2.5-Pro are both now available in Hermes Agent through Nous Portal and OpenRouter! Just `hermes update`!
https://x.com/Teknium/status/2047093325774385358
You can now scale depth as well as width with subagents! Just uncapped Hermes Agents’ sub-agent spawn width, and enabled spawn depth so sub-agents can be configured to spawn their own sub-agents! Looking forward to seeing what new use cases open up with this new flexibility.
https://x.com/Teknium/status/2046709250114957624
不会还有很多人运行Hermes Agent还在用黑底白字的命令行吧？ 其实开源社区已经给出了几套非常成熟的 Web 面板方案 体验完全不输商业软件 今天帮大家盘点目前 GitHub 上专门适配 Hermes四大主流 Web UI方案，帮你找到最适合的那款！ 以下是 4 种目前最主流面板的完整干货清单： 1.全能管家
https://x.com/0xMulight/status/2046071441469366368
为 Hermes AI agent 提供原生 macOS 图形界面，支持同时管理多个本地和远程 Hermes 服务器，实时可视化 agent 活动、会话、配置和系统状态
https://t.co/osk1ipQ4Kd Scarf 是一个 Swift 编写的 macOS 应用，用来给 Hermes AI agent 套一个图形界面。 2.0
https://x.com/QingQ77/status/2046592289540346020
我靠！Ollama 现在原生支持 Hermes Agent 了！ 一行命令直接起飞： ollama launch hermes 就这？就这！本地部署这么简单你还在用什么云端？ 不知道自己电脑能跑哪些模型的，两个方法选一个： 1️⃣ 用 llmfit 本地检测 2️⃣ 直接上网站查 别再说本地部署难了，难的是你没试过。 🔗
https://x.com/NFTCPS/status/2045730947501576460
Kimi K2.6 is now available in Hermes Agent. Simply run `hermes update` and use `hermes model` to select a compatible provider hosting the model!
https://x.com/NousResearch/status/2046300755683098910
和上交念 AI 专业的研究生朋友聊了一下openclaw 和 hermes，分享一下内容： 1. 速度 卸载 / 迁移 openclaw ， openclaw 是垃圾 2. openclaw 的生态丰富，但是底层是”上下文窗口 + RAG”的方案，长时间用虾，非常容易技术串联，牛头不对虾嘴 3. hermes 比 openclaw 好，但并不完美
https://x.com/ResearchWang/status/2046080807186665594
Building a Fast Multilingual OCR Model with Synthetic Data
https://huggingface.co/blog/nvidia/nemotron-ocr-v2
GPT-5.5 is now accessible in Hermes Agent through the ChatGPT/Codex OAuth provider. Run `hermes update` to access now or learn how to get started with Hermes Agent here:
https://x.com/Teknium/status/2047419336537846193
Qwen
https://qwen.ai/blog?id=qwen3.6-max-preview
Qwen3.6 Plus lands at #7 in Code Arena with a score of 1476 – up +16 points since the Preview. The new score also moves @AlibabaGroup to #3 lab in Code Arena. In the Text Arena, Qwen3.6 Plus lands at #36, a +13 point improvement since Preview. Congrats to the Qwen team on the
https://x.com/arena/status/2046268995163258958
🚀 Introducing Qwen3.6-Max-Preview, an early preview of our next flagship model Highlights： ⚡️ Improved agentic coding capability over Qwen3.6-Plus 📖 Stronger world knowledge and instruction following 🌍 Improved real-world agent and knowledge reliability performance Smarter,
https://x.com/Alibaba_Qwen/status/2046227759475921291
Guys, I am absolutely astounded. The Qwen 3.6 27b is like a jump to Qwen 4 from Qwen 27B 3.5. I just did a full suite of front end design tests and agentic benchmarks, made entirely by it. VERDICT: They’re so much better than I thought they’d be, like I’m completely astounded. I
https://x.com/KyleHessling1/status/2046986423736451327
llama-server -hf ggml-org/Qwen3.6-27B-GGUF –spec-default
https://x.com/ggerganov/status/2046988075302064209
Qwen 3.6 27B model is available on Ollama! Use it with all the integrations in Ollama or chat with the model. Chat with the model: ollama run qwen3.6:27b OpenClaw: ollama launch openclaw –model qwen3.6:27b Claude Code: ollama launch claude –model qwen3.6:27b More
https://x.com/ollama/status/2047066252523507916
We ran Qwen3.6-35B-A3B GGUF KLD benchmarks of all our dynamic quants and other providers. 1. Nearly all Unsloth quants for mean KLD, 90%, 99.9% KLD are on the Pareto Frontier for KLD vs Disk Space. 2. MXFP4_MOE is an outlier for all. 3. We’ll also make some smaller quants soon!
https://x.com/danielhanchen/status/2045169369723064449
Sharing my current setup to run Qwen3.6 locally in a good agentic setup (Pi + llama.cpp). Should give you a good overview of how good local agents are today: # Start llama.cpp server: llama-server \ -hf unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_XL \ –jinja \
https://x.com/victormustar/status/2045068986446958899
[2604.15804] Qwen3.5-Omni Technical Report
https://arxiv.org/abs/2604.15804
🎉 Day-0 vLLM support for Qwen3.6-27B! Congrats to @Alibaba_Qwen on the new 27B dense model release. Looking forward to more of the Qwen3.6 series. 👀 📖 Recipe:
https://x.com/vllm_project/status/2046943674890871019
LM Performance：With only 27B parameters, Qwen3.6-27B outperforms the Qwen3.5-397B-A17B (397B total / 17B active, ~15x larger!) on every major coding benchmark — including SWE-bench Verified (77.2 vs. 76.2), SWE-bench Pro (53.5 vs. 50.9), Terminal-Bench 2.0 (59.3 vs. 52.5), and
https://x.com/Alibaba_Qwen/status/2046939775924584577
Qwen3.5-Omni Technical Report | alphaXiv
https://www.alphaxiv.org/abs/2604.15804
Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
https://simonwillison.net/2026/Apr/22/qwen36-27b/
Qwen3.6-35B-A3B just dropped. Red Hat AI has an NVFP4 quantized checkpoint ready. 35B params, 3B active, quantized with LLM Compressor. Preliminary GSM8K Platinum: 100.69% recovery (slightly above baseline). Early release. Let us know what you think!
https://x.com/RedHat_AI/status/2045153791402520952
The new Qwen3.6-27B just gave me definitely the best pelican riding a bicycle I’ve had from a 16.8GB model file!
https://x.com/simonw/status/2046995047720378458
We then experiment with 4 different training methods for the Minimal Code Editing task using Qwen3 4B. We find that SFT only works when trained on the same set of evaluation corruptions. It collapses otherwise, indicating that it fails to learn the general minimal coding style
https://x.com/nrehiew_/status/2046963050427879488
VLM Performance：Qwen3.6-27B is natively multimodal, supporting both vision-language thinking and non-thinking modes in a single unified checkpoint — the same as Qwen3.6-35B-A3B. It handles images and video alongside text, enabling multimodal reasoning, document understanding,
https://x.com/Alibaba_Qwen/status/2046939788184547610
We’ve published new research on how we post-train models for accurate search-augmented answers. Our SFT + RL pipeline improves search, citation quality, instruction following, and efficiency. With Qwen models, we match or beat GPT models on factuality at a lower cost.
https://x.com/perplexity_ai/status/2047016400292839808
It cruises over grass, stone steps, shallow water, and steep hills like it was born for the outdoors. 📍 open-source 4WD AGV: No more indoor-only experiments, suddenly your „project” can actually go explore. It’s DFRobot’s open-source 4WD AGV chassis: four direct-drive hub
https://x.com/IlirAliu_/status/2046135804750086191
Leave a Reply