Image created with Gemini. Image prompt: Vintage book jacket illustration of an Edwardian Dubliner in round wire spectacles and a straw boater at a cobblestone crossroads, fanning out tram tickets in one hand and a ring of unlabeled keys in the other, with chalked directional arrows on the ground pointing to tiny distant shopfronts, bold serif title AGENTS letterpressed across the top with generous margin, flat inked Shakespeare-and-Company blue, cream, lemon soap yellow and slate gray palette with a warm brick accent, soft overcast June daylight, visible paper grain and screen-print texture, landscape 16:9.
Before Fable 5 was shut down, it pushed Gemma 4 to 255 tok/s on WebGPU. Some didn’t believe it was real. Today we’re releasing the demo and kernels it wrote for you to see yourself. Run it locally in your browser. Agentic kernel optimization is the future of on-device inference
https://x.com/xenovacom/status/2067289897111638484
How Claude Code is used in practice \ Anthropic
https://www.anthropic.com/research/claude-code-expertise
Copilot Cowork is now generally available | Microsoft 365 Blog
https://www.microsoft.com/en-us/microsoft-365/blog/2026/06/16/copilot-cowork-is-now-generally-available/
GPT-Realtime 2 is the future of the operating system. I’ve been experimenting with it for a couple weeks now, and I gotta say, it’s pretty gosh darn incredible. Opening apps, searching the web, even editing in Premiere. All with just my voice. And it only takes a few prompts
https://x.com/per_simmons_/status/2067051453022363992
OpenAI prepares major ChatGPT voice upgrade with GPT-Bidi-1
https://www.testingcatalog.com/openai-prepares-major-chatgpt-voice-upgrade-with-gpt-bidi-1/
Official now: SpaceX is officially buying Cursor in an all-stock deal valued at $60B. The move gives Elon Musk’s AI empire a serious enterprise coding product overnight, and could help xAI close the gap with Anthropic’s Claude Code and OpenAI’s Codex. However, the truth is that
https://x.com/kimmonismus/status/2066863066898116954
SpaceX acquired Cursor for $60B. A lot of people dismissed Cursor as just a “GPT/Claude wrapper” and a VS Code fork. But it became a company capable of post-training its own models. That is a big reason Elon wanted it. I think more companies will post-train custom models to
https://x.com/Yuchenj_UW/status/2066891492187320405
SpaceX has exercised the option to acquire @cursor_ai in an all-stock transaction with the goal of building the world’s most useful AI models. For the past few months, SpaceXAI has been jointly training a model with Cursor, which will be released in Cursor and Grok Build soon.
https://x.com/SpaceX/status/2066873915717136548
@jeremyphoward @Zai_org GLM-5.2 is between GPT-5.5 and Opus 4.8 in our new agentic knowledge work eval that we released just today. Very impressive
https://x.com/ArtificialAnlys/status/2067761754990686483
BREAKING: GLM-5.2 is now 1st on Design Arena. With an Elo of 1360, GLM-5.2 has jumped ahead of the now unavailable Claude Fable 5. And it’s open weights. This is an improvement of 4 positions and 27 Elo points to achieve one of the highest Elo scores in our code categories
https://x.com/Designarena/status/2066940737011560652
Credit to GLM-5.2 Max, the new open weights model, for pulling this off. …but you can see the difference between it and Fable in a way benchmarks don’t show. GLM-5.2 gives a correct poem (& the Welsh is fun) but Fable weaves the disappearing letters into the theme of the poem.
https://x.com/emollick/status/2067056226337186146
Exciting news: GLM-5.2 (Max) ranks #2 in Code Arena: Frontend, with +29pt over Claude Opus 4.7 (Thinking) and only behind Fable 5! GLM-5.2 is the best open model vs Kimi-K2.6 and Minimax-M3 by a large margin. – #2 React and #4 HTML sub-leaderboards – Ranks as the top model in
https://x.com/arena/status/2066957802741043641
GLM 5.2 ranks #3 on FrontierSWE. It is only behind Fable 5 and Opus 4.8, and it outperforms GPT-5.5. This is the first model that closes the large gap between models from Anthropic / OpenAI and other providers, and it is the strongest open-weight model by far.
https://x.com/ProximalHQ/status/2066939701026787583
GLM-5.2 (Max) by @Zai_org ranks #10 on the new Agent Arena leaderboard, closely matching Claude-Opus-4.8 (non-thinking) and is the #1 open model by a wide margin! In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks from a global community of
https://x.com/arena/status/2066943450914943025
GLM-5.2 is the first open-weights model to cross 80% on Terminal-Bench, and beats every other open model available. It also beats Gemini, making it a frontier-level model for a fraction of the cost. Open weights is back. This model is a game changer. Available in Cline now!
https://x.com/cline/status/2066951439793242193
GLM-5.2: Built for Long-Horizon Tasks
https://z.ai/blog/glm-5.2
Introducing GLM-5.2: Frontier Intelligence, Open Weights – Significant improvements in coding and agentic tasks – Strong long-horizon capabilities with a 1M context window – Two levels of reasoning effort: GLM-5.2 (max) pushes the limits, while GLM-5.2 (high) strikes a strong
https://x.com/Zai_org/status/2066938937344495629
It’s hard to pinpoint open-closed gap and so-on, but I trust the @arena team and just look where GLM 5.2 is on this. An MIT licensed, to be open weight model. At this point you could argue they have a better agent than Gemini does. That’s a serious accomplishment.
https://x.com/natolambert/status/2066968753221624303
DeepSeek Becomes China’s Most Valuable AI Startup After $7.4 Billion Fundraise – WSJ
https://www.wsj.com/tech/ai/deepseek-becomes-chinas-most-valuable-ai-startup-after-over-7-4-billion-fundraise-78ef64c0
[AINews] Loopcraft: The Art of Stacking Loops @RichardSSutton has his “Bitter Lesson” for models. We now have the Salty Lesson for agents: Don’t fix things yourself, as you have done historically. Instead focus on systems that scale with more agents, like goals and
https://x.com/latentspacepod/status/2065307204836905461
Agents SDK v0.16.1 adds CDP browser automation and resumable code execution. Build agents that can handle interruptions and keep working through them.
https://x.com/CFchangelog/status/2066930467727630666
Are there toolkits (or skillsets) being created specifically for AIs to use for building games? They default to 3js, reinvent how to make sprites from scratch each time, test technical issues but not gameplay loops, etc. It would help to point AIs at some tools to focus them.
https://x.com/emollick/status/2065407550611136935
As hybrid models (Qwen 3.5 / Nemotron Ultra) run agents with massive context, Gated-DeltaNet / Mamba states become a bottleneck. A simple insight to make this 2x faster: load the states, compute, but don’t store them. This recompute trick finally unlocks spec decoding for SSMs
https://x.com/tri_dao/status/2066518563184365953
Awesome keynote by @alighodsi at the @databricks summit. Some takeaways below. TLDR – Databricks is now a data processing platform, a data platform, an agents platform and an apps platform Architecture updates Iceberg / table formats – v3 is now GA: the unified data layer.
https://x.com/jaminball/status/2066927028331565375
Cursor/Graphite’s @TomasReimers just announced Origin @cursor_ai’s long awaited Git competitor, scalable for agent workloads, extensible with api and mcp, and built in merge conflicts and co failure agent resolution
https://x.com/swyx/status/2066928345246470204
day-0 and already on @FireworksAI_HQ with blazing fast inference long-horizon agents, full-repo understanding, multimodal coding all in one model Try M3 today on Fireworks AI
https://x.com/MiniMax_AI/status/2065510555507626374
Detecting issues in production agent traces is hard. You have to do it cheaply (because of volume) but also accurately (or too much noise) We post-trained our own model for this. SOTA accuracy, at ~10-100x cheaper rates than frontier models Try it out:
https://x.com/hwchase17/status/2066572458422100017
Devin for Security is out now. Application Security has always been a field with two sides to the problem: (1) finding things, and (2) fixing them. The former has *mostly* been a solved problem, but the latter has remained a huge issue in the industry for almost 20+ years.
https://x.com/shayanshafii/status/2067667505905332352
Do you know what your agent streams to the UI before it gets there?👀 We built stream transformers in @LangChain_JS so you can inspect and modify agent streams in-flight. Redact sensitive data, reshape events, and emit custom channels before the frontend renders anything. 🧵👇
https://x.com/bromann/status/2066973919559692614
Factory 2.0: From coding agents to software factories | Factory.ai
https://factory.ai/news/software-factory
Like many AI-pilled orgs, our spending on coding agents has increased pretty dramatically over the past few months A key part of the LLM gateway we’re building (available in private preview now) is cost control and visibility Here’s how we’re doing it: 1/ Accurate, up to date
https://x.com/hwchase17/status/2066895499739922530
observability is non-optional for agents
https://x.com/hwchase17/status/2066601074220466673
Observability is non‑optional for agents. I pair LangGraph for orchestration with LangSmith for tracing, evals, and regression testing across prompts and tools. If you can’t explain why an agent answered something, you don’t have an architecture, you have a demo.
https://x.com/sauvast/status/2066475806843650369
Security review is now part of every Devin Review. Open a PR and Devin automatically finds the vulnerabilities scanners miss, explains each one, and drafts the fix.
https://x.com/cognition/status/2067649690921820212
that’s a fascinating idea to create a .agent domain. Balázs is working on it plus is building a unique agent community around it. Join it!
https://x.com/TheTuringPost/status/2066543792862249065
Today, we’re announcing Factory 2.0: from coding agents to software factories.
https://x.com/FactoryAI/status/2066588050617249904
We don’t honestly know the best approaches to rebuilding companies around AI agents, especially in ways that expand competitive advantage & augment existing human capabilities. Practical agents are merely months old. Experimentation (and productive failures) will be required.
https://x.com/emollick/status/2066250548790083851
We’re launching code storage and git hosting. Origin gives teams and agents a place to host, review, and collaborate on code. Available this fall. Join the waitlist.
https://x.com/cursor_ai/status/2067012220832329782
We’ve been building towards this for ~3 years. It started with the agent, then we needed the surfaces. After that, automations and infrastructure to run Droid anywhere. Now we’re centralizing this into one place where you can manage and evolve your sovereign software factory.
https://x.com/EnoReyes/status/2066588556898787661
Who should design the training environment for an RL agent, the practitioner or the policy itself? RL pipelines for LLMs usually rely on manually redesigned environments between stages, with practitioners guessing which configuration will best improve the current policy. This
https://x.com/dair_ai/status/2067432115072098705
Reading AI-generated code is exhausting Almost as exhausting as not reading it and having to deal with bad assumptions and subtle bugs (and needing reading the code later anyway)
https://x.com/DavidKPiano/status/2067306799452336429
Agent Memory Systems Workshop | AWS Marketplace
https://pages.awscloud.com/awsmp-gro-bzpg-webinar-mss-module-7-agent-memory-workshop.html?trk=a442b9f3-e00d-4b18-936f-444317c44e54&sc_channel=el
Agent Memory Systems Workshop | AWS Marketplace
https://pages.awscloud.com/awsmp-gro-bzpg-webinar-mss-module-7-agent-memory-workshop.html?trk=a442b9f3-e00d-4b18-936f-444317c44e54&sc_channel=el%2F2%2F0100019ed09b92aa-64b4f2b9-3d6c-459f-823c-0d7cc0ea4920-000000%2FFph3uKZK_QtqIOesZkdO7dBfw53QUcviCsBcrCpVQqg%3D452
Anthropic “”pauses”” token-based billing for its Claude Agent SDK – Ars Technica
https://arstechnica.com/ai/2026/06/anthropic-pauses-token-based-billing-for-its-claude-agent-sdk/
We released Sonic-3.5 and Ink-2, the #1 streaming models for text to speech and speech to text you can use in your voice agents today. New architectures enable new frontiers for speed and quality. We’re now the only provider to have #1 models for both speaking and listening.
https://x.com/krandiash/status/2066559212533190917
Agent Arena’s causal tracing methodology lets us quantify the real value of humans working together with AI agents, and observe a huge range of model behaviors from the same traces. We started with 5 signals: confirmed success, praise vs. complaint, steerability, bash recovery,
https://x.com/arena/status/2067680639068094958
Big issue with AI strategies at big companies which realized the importance of AI last year (which is only a small subset, most are still not moving fast) is that, in the best case, they developed their strategy in late 2025, before the agentic revolution Things changed since…
https://x.com/emollick/status/2067344183870836985
MosaicLeaks: Can your research agent keep a secret?
https://huggingface.co/blog/ServiceNow/mosaicleaks
Copilot Cowork is now generally available worldwide, now with multi-model support! Every organization can put long-running agents to work on complex, multi-step tasks, grounded in your organization’s unique knowledge and know-how.
https://x.com/satyanadella/status/2066911399494963335
Breaking: Cursor Mobile just announced!
https://x.com/morganlinton/status/2066927071109288103?s=20
Hermes Agent now supports asyncronous subagents! The existing delegate tool, which your agent uses to spawn subagents to fan out and do work, no longer blocks your chat! To access now, `hermes update`, and enjoy!
https://x.com/Teknium/status/2066619275989991861
In partnership with @stripe, Hermes Agent now supports a full suite of Stripe skills. Your agent can buy things, pay per-call APIs, and provision its own SaaS, with configurable safety limits on every action.
https://x.com/NousResearch/status/2066647737613832624
No more waiting for subagents to finish to keep working with your Hermes Agent. Asyncronous subagents are here.
https://x.com/NousResearch/status/2066619860852134384
Best open-source vector databases for LLMs in 2026 ▪︎ Milvus ▪︎ Chroma ▪︎ Weaviate ▪︎ Qdrant ▪︎ Vespa ▪︎ LanceDB ▪︎ Deep Lake Knowledge engines and agentic retrieval tools: ▪︎ Chroma Context-1 ▪︎ Weaviate Engram ▪︎ Pinecone Nexus (not open, but
https://x.com/TheTuringPost/status/2066265159715398058
Building a 100x Cheaper Trace Judge with Fireworks
https://www.langchain.com/blog/building-a-100x-cheaper-trace-judge-with-fireworks
Congrats to the MiniMax team on the open-source launch of M3! There are very few <500bn parameter models that can tackle coding, agentic workloads, and multimodal all with a 1M-token context window but M3 does it all. Dig in here:
https://x.com/baseten/status/2065529390486999448
I just open-sourced my /learn skill. Learn anything with agents and HTML artifacts. I have been learning about all kinds of topics with it. Install the skill and interact with any agent to help you through any topic. Ask it to generate visual and interactive artifacts and
https://x.com/omarsar0/status/2066547840760029605
Just Shipped: Flue 1.0 Beta Flue is the TypeScript framework for building the next generation of agents, designed around an open agent harness with zero LLM lock-in. It’s like Astro, for agents. Flue 1.0 has been redesigned around three core primitives: 🔁 Workflows —
https://x.com/FredKSchott/status/2066962296119959581
You don’t need new infrastructure to get started with Engine. It’s built on top of your current LangSmith setup. Just connect a project, (optionally) your repo, and start surfacing issues from production automatically.
https://x.com/LangChain/status/2066491312686109077
Self-improving Memory for Agents
https://www.perplexity.ai/hub/blog/self-improving-memory-for-agents
I’m proud to officially launch Sakana Marlin, our first commercial product: an autonomous “Ultra Deep Research” agent designed to act as a Virtual CSO. Try Marlin:
https://t.co/voGe7z1GKp Blog:
https://t.co/ABhRXuuj7c Marlin isn’t just another deep research assistant. It is the
https://x.com/hardmaru/status/2066529282588094713
.@DecagonAI cut voice agent cost per turn nearly 6x with Together AI. They moved from closed models to fine-tuned open models, while keeping latency low enough for real-time voice: → <400ms p95 model latency per turn → custom speculators and prompt caching → optimized
https://x.com/togethercompute/status/2066936299836039645
“Self-Evolving Multi-Agent Systems via Decentralized Memory” Multi-agent systems usually share one memory pool, but that makes agents converge toward the same behavior and lose useful specialization. This paper gives each agent its own memory, split into reuse and exploration
https://x.com/askalphaxiv/status/2066362692965691530
// HarnessX: Harnesses You Compile, Not Hand-Build // (bookmark it) Most agent harnesses are hand-crafted and frozen. Each new model or task means rewriting the prompts, tools, memory, and control flow from scratch, and the rich traces from every run get thrown away. HarnessX
https://x.com/dair_ai/status/2066563390538178784
🎉 Congrats to @MiniMax_AI on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse attention architecture: instead of
https://x.com/vllm_project/status/2065445059039031799
3 layers of AI engineering: • Skill engineering – the newest one This is about giving agents reusable skills – mini-procedures they can discover, apply, improve, version, and reuse across tasks. → Skill tells the agent what to do and how to do it again • Context engineering
https://x.com/TheTuringPost/status/2065479753075929318
Cool paper on Skill routing for LLM agents. Real tasks rarely map to a single skill. They need several composed together, but most skill routing still treats the problem as picking one tool from a library. This work formalizes Compositional Skill Routing, decomposes a complex
https://x.com/omarsar0/status/2067618845926510770
Highly-recommended reading! After using /loops & /goal throughout my projects, I believe that verifiers and robust guardrails are imperative to get current/future coding agents to work right. You can’t just YOLO your way with blind autonomous loops. It doesn’t work!
https://x.com/omarsar0/status/2067283235759935648
Must-read research of the week ▪️ HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness ▪️ Retrospective Harness Optimization ▪️ Bayesian-Agent: Posterior-Guided Skill Evolution for LLM Agent Harnesses ▪️ FORT-Searcher: Synthesizing Shortcut-Resistant Search
https://x.com/TheTuringPost/status/2066688212068876503
Outstanding paper on computer-using agents. (bookmark it) Computer-using agents drive real software through the screen, but they solve every task from scratch. Ask one to repeat a task, and it re-reads the screen and re-reasons every tap, paying the full cost again. PreAct
https://x.com/dair_ai/status/2067386816815387019
Top trending paper on
https://t.co/nYgeLha6ut is “”FastContext: Training Efficient Repository Explorer for Coding Agents”” Microsoft trained a small 4B model, available on @huggingface, that can be paired with a coding agent for efficient code retrieval. 🔥 This way, they are
https://x.com/NielsRogge/status/2066909608476557565
🎉 Congrats to @poolsideai on Laguna M.1, a new open-weights agentic coding model. Day-0 support landed in vLLM v0.21.0. 🧠 70-layer sparse MoE: 225B total params, 23B active per token, 256K context 🔀 256 experts with top-k=16 routing, built for long-horizon agentic coding 🛠️
https://x.com/vllm_project/status/2067629972941132269
Install the @sentry plugin and ask your agent to find and fix errors, analyze stack traces, and triage alerts
https://x.com/xai/status/2065168019891073260
Claude Code now supports artifacts | Claude by Anthropic
https://claude.com/blog/artifacts-in-claude-code
Claude Managed Agents can operate in a sandbox you control, on your own infrastructure or with any provider you choose. Today we added new guides for @blaxelAI, @e2b, @googlecloud, @namespacelabs, and @superserve_ai, so you can choose the best fit for your use case.
https://x.com/ClaudeDevs/status/2065494480837583297
On Claude Team and Claude Enterprise, you can now use Claude Code to deploy HTML sites and share these with your teammates! This has changed how we work internally. Artifacts is great format for communicating architecture changes, data analyses, and new prototypes.
https://x.com/_catwu/status/2067674836726694200
Our latest economic research introduces a framework for tracking Claude Code as it scales. Who is using Claude Code, and what are they using it for? How is the value of tasks changing? And how much does domain expertise shape whether a session succeeds?
https://x.com/AnthropicAI/status/2066969532380721386
I don’t often walk out of a product introduction thinking, “”Wow, I just saw the world change for the better.”” I have one of the few pictures of @DavidSHolz doing the presentation. I’m very honored to be able to capture this image right before I got yelled at by security guards,
https://x.com/Scobleizer/status/2067457400656007643
Why Apple built a third-party AI system for Siri and then refused to show it at WWDC
https://thenextweb.com/news/apple-siri-extensions-third-party-ai-missing-wwdc
⭐ VibeThinker-3B is released — a dense 3B model for frontier-level verifiable reasoning. 🚀 Reasoning: 94.3 on AIME’26, 76.4 on IMO-AnsBench, and 80.2 Pass@1 on LCB v6; with CLR, AIME’26 improves to 97.1 and IMO-AnsBench to 80.6. 💻 OOD Coding: On recent unseen LeetCode weekly
https://x.com/WeiboLLM/status/2066870851841274249
AA-Briefcase is a new benchmark for testing models on realistic knowledge work tasks in complex projects built by industry experts. Models are evaluated on multi-week knowledge work projects, each with many linked tasks and thousands of input source files. AA-Briefcase combines
https://x.com/ArtificialAnlys/status/2067744639583654012
Announcing AA-Briefcase, the benchmark for the next era of agentic knowledge work AA-Briefcase is our new benchmark for testing models on long-horizon knowledge work tasks in complex projects built by industry experts. Models are evaluated on multi-week projects, each with many
https://x.com/ArtificialAnlys/status/2067744637155226101
Computer-use evals like OSWorld still don’t really test personal assistant use cases: logged-in accounts, user data, personalized workflows, or realistic desktop/web environments. so we made MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents, with 184 tasks
https://x.com/JangLawrenceK/status/2066976606615146875
Congrats to the @browser_use team for taking the #1 spot on Odysseys, a highly challenging benchmark for long-horizon web agents:
https://t.co/dRYnBSGsLG Odysseys evaluates realistic, multi-hour web workflows that require sustained planning, memory, reasoning, and verification
https://x.com/rsalakhu/status/2066976923864199308
Crazy: A 3B model is now reaching highly competitive results on verifiable reasoning tasks. VibeThinker-3B scores 94.3 on AIME26, 80.2 Pass@1 on LiveCodeBench v6, and 96.1% on unseen LeetCode contests. The gains appear to come primarily from post-training on top of
https://x.com/kimmonismus/status/2066837287460053183
If an LLM knows about how an evaluation looks like, can it use it to increase its performance in safety benchmarks? Our new preprint answers this. Excited to share: Models That Know How Evaluations Are Designed Score Safer 🧵 1/8
https://x.com/KatDeckenbach/status/2066520185847132425
Introducing Terminal-Bench Challenges! A new capability has emerged at the frontier: agents completing large-scale projects autonomously. To test this capability, we felt another flavor of benchmark was needed. Terminal-Bench Challenges are long-horizon, token-intensive,
https://x.com/terminalbench/status/2067635273652134002
Looks extremely strong! No medical benchmarks though, that’s what I’m most curious about 🙂
https://x.com/iScienceLuvr/status/2066946611931234485
Make your models safer with this one weird trick: Just finetune them on lots of descriptions of how benchmarks work and what they look like …🙃 We studied this phenomenon and its connection to evaluation awareness in detail, see the quoted thread:
https://x.com/jonasgeiping/status/2066558592086315476
Many coding agent benchmarks evaluate LLM performance (with the same harness) or harness performance (with the same LLM). But in reality, it is the harness + the LLM that determins the overall performance. We introduced new, holistic results that measure both.
https://x.com/gneubig/status/2067651018217648595
MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents Paper:
https://t.co/0XHorsdhRd Code, Environment and Tasks, Agentic harness:
https://t.co/NoA4I4YH1U MyPCBench provides a Linux desktop environment with 17 simulated real-world web applications and a complete
https://x.com/rsalakhu/status/2066897554881810477
olmo-eval: An evaluation workbench for the model development loop
https://huggingface.co/blog/allenai/olmo-eval
People frequently ask me how many tasks a benchmark should have. There’s no exact answer but here’s my intuition- (tl;dr aim for 300-500 tasks)
https://x.com/OfirPress/status/2065481743675666629
This was not a good benchmark before it was updated and it is not a good benchmark now. Having AIs evaluate the work of other AIs on publicly available questions from a different closed benchmark doesn’t tell you very much. And it is unclear how they establish the human ELO.
https://x.com/emollick/status/2067009697207427534
today i’m going to try and convince you that as an industry, we basically have no idea what the f**k we’re doing when it comes to evaluating agents on analytics.”” This is my talk from AI Council “”Welcome to our data benchmark, where everything’s made up and the points don’t
https://x.com/isidoremiller/status/2067633428774682697
Today we’re releasing the first results for AA-AgentPerf, our new agentic inference benchmark: initially covering DeepSeek V4 Pro across NVIDIA Blackwell, Hopper, and AMD. AA-AgentPerf is the first benchmark built for agentic inference. We use real, long-context agentic coding
https://x.com/ArtificialAnlys/status/2065559824230957190
We built the fastest PDF -> markdown parser in the world 🚀⚡️ AND it’s more accurate than any other open-source, model-free parser (pymupdf4llm, opendataloader, pdf-inspector, markitdown) on 3 standardized benchmarks: olmOCR0-bench, opendataloader-bench, ParseBench Introducing
https://x.com/jerryjliu0/status/2067679507126124858
We desperately need standardized benchmarks for agentic capabilities instead of panic-reacting to prompt-engineering parlor tricks. Until we can objectively and transparently measure what these systems actually do, the entire ecosystem will remain vulnerable to unpredictable and
https://x.com/fchollet/status/2066554426551390457
We’re happy to see adoption of ProgramBench so quickly- we think the % of overall tests passed metric isn’t as meaningful as our % of *tasks* passed metric, we’ll continue reporting on that metric on our official leaderboard.
https://x.com/OfirPress/status/2066959717016957181
Why Weibo’s tiny VibeThinker-3B has the AI world arguing over benchmarks again | VentureBeat
https://venturebeat.com/technology/why-weibos-tiny-vibethinker-3b-has-the-ai-world-arguing-over-benchmarks-again
I have a fun, oddly useful AI benchmark: “”build me a procedurally generated 3D simulation showing the evolution of a harbor town from 3000 BC to 3000 AD, it should look beautiful & allow me to have some control over it”” Play the gallery of 20 models:
https://x.com/emollick/status/2067294892687024304
The le chaton fat meme is leaking to the outside world and I expect to be asked about Mistral’s new ginormous cat model with infinite benchmark scores at my next meeting with corporate leaders. I guess it is better than being asked about the “”MIT pilot AI study.””
https://x.com/emollick/status/2066578565076746667
NVIDIA Blackwell Leads on First Agentic AI Infrastructure Benchmark | NVIDIA Blog
https://blogs.nvidia.com/blog/nvidia-blackwell-agentperf-artificial-analysis/
The 2026 State of AI Traffic & Cyberthreat Benchmark Report – HUMAN Security
https://www.humansecurity.com/2026-state-of-ai-traffic-cyberthreat-benchmark-report/
This is a good methodological thread on the debate over a new paper that suggests generalist models beat specialized medical AIs. (And a good overview of the challenges of benchmarking AIs in medicine)
https://x.com/emollick/status/2066303566449418739
Great post by @satyanadella summarizing how we see this historic platform shift benefiting everyone broadly. @MicrosoftAI
https://x.com/mustafasuleyman/status/2066232431305216500
MAI Playground | Microsoft AI
https://playground.microsoft.ai/
The App Modernization Playbook | Microsoft Azure
https://info.microsoft.com/ww-landing-app-modernization-playbook.html
Microsoft Tests Phi Silica for Windows AI on Nvidia GPUs
https://winbuzzer.com/2026/06/15/microsoft-tests-phi-silica-for-windows-ai-on-nvidia-gpus-xcxwbn/
@yacineMTB deepseek v1 -> v3 (no details in v4 about this) and k2 don’t use muP and instead use naive N(0,0.006) initialization. so how do they do hyperparam selection? they basically fit scaling laws to get optimal batch size and learning rate. there are a bunch of papers detailing this
https://x.com/eliebakouch/status/2066594560365498695
🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced! 🔷 Improved coding & agent performance over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite. 🔷 Reasoning efficiency: Less overthinking, with 30% lower
https://x.com/Kimi_Moonshot/status/2065377579130142937
🎉 Congrats to @Kimi_Moonshot on Kimi K2.7-Code, a coding-focused agentic model built on K2.6. ✨ 1T-parameter Mixture-of-Experts, 32B active per token ✨ MLA attention with a 256K-token context window ✨ ~30% fewer thinking tokens than K2.6 for more efficient reasoning
https://x.com/vllm_project/status/2065427423148318747
🔗 Weights & code:
https://x.com/Kimi_Moonshot/status/2065379671039189317
289 tokens per second. That’s how fast CoreWeave Serverless Inference serves Kimi K2.7 Code. Per @ArtificialAnlys, we serve it faster than any other provider at the best price-performance. How we did it 🧵
https://x.com/CoreWeave/status/2067613387056709982
It is a good time for moonshots. AI has reached a level where there are transformative projects that could result in huge social good, but require public R&D, consensus & transparency to pull off. Examples: universal tutors, co-scientist/replication systems, remote medical help.
https://x.com/emollick/status/2066534666257973523
kimi code web is back, yeah
https://x.com/bigeagle_xd/status/2067426798493921351#m
Kimi K2.7 Code scores higher than K2.6 on benchmarks while using ~30% fewer tokens. It’s the first time Moonshot has put “”Code”” in a K2 model name. Every other K2 release was a general purpose agentic model. Try in Cline now! (good model to put in our upcoming subscription 👀)
https://x.com/cline/status/2065473287761891621
Kimi-K2.7-Code lands as the #3 open model in the Code Arena: Frontend, #19 overall. In the Code Arena: Frontend models are evaluated on agentic frontend coding tasks from real users building apps and websites (HTML and React).
https://x.com/arena/status/2066616607380828401
Moonshot released Kimi-K2.7-Code it looks to be a decent step up in performance, but also efficiency
https://x.com/scaling01/status/2065460210584420510
moonshotai/Kimi-K2.7-Code · Hugging Face
https://huggingface.co/moonshotai/Kimi-K2.7-Code
Today marks the beginning of our launch calendar and to celebrate i am making ncode and our flagship kimi k2.7 model free to use for the next week (or until the traffic knocks us out). all you need to do is: 1) sign up for a noumena account at
https://t.co/XoNsZeW8sw 2) go to
https://x.com/_xjdr/status/2067741647941832818
You can now run Kimi K2.7 Code locally! 🌘 We shrank the 1T model to 325GB (-48%) via Dynamic 2-bit where important layers are upcasted. Run at >40 tok/s on 330GB RAM/VRAM setups. Run full precision on 610 GB. Guide:
https://t.co/SXZJ3IHMpY GGUF:
https://x.com/UnslothAI/status/2066492839450800427
Ask our developer docs. They’ll show you the way The new docs agent on 🔗https://t.co/G1783HdxH4 helps you find answers about OpenAI products and takes you directly to the relevant documentation.
https://x.com/OpenAIDevs/status/2065507724704858173
Getting Chris to do a PR with Codex!
https://x.com/steipete/status/2065176989359808636
Got a PayPal verification text and thought I been hacked, but it was just codex signing up for a web service it needed.
https://x.com/steipete/status/2065997212015067508
How am I only now finding out about appshots? I was dragging screenshots into codex live a caveman.
https://x.com/steipete/status/2065564094879637546
I can barely keep up with implementing/testing/landing all the Issues/PRs folks submit to
https://t.co/DNpNf2tNy4 Codex runs INSIDE crabbox while it is building crabbox. This is becoming essential infra for my work. Codex been looping nonstop for the last 4 days in multiple
https://x.com/steipete/status/2065650561484267540
“not consistently candid in their communications” is my fav new americanism.
https://x.com/steipete/status/2065574894545560062
I’m still homeless in SF. If anyone’s renting or selling a nice place, slide into my DMs. The hotel room is getting old.
https://x.com/steipete/status/2065566228253389209
Mosh + tmux/zellij is a lifesaver for bad in-flight internet. (i prefer zellij but either works)
https://x.com/steipete/status/2066427449551036469
Part of the OpenClaw hardening work is reducing surface risk; for some media conversion we had to shell out to ffmpeg. In the next release that can now be done via wasm, with similar performance for our use cases.
https://x.com/steipete/status/2064999763397980286
This shortage of chips is getting out of hand.
https://x.com/steipete/status/2065998839467933862
Wanted to buy a new Mac Studio for San Francisco. We don’t even know how to make these anymore.
https://x.com/steipete/status/2066471737068232835
Whenever you create an issue on one of oure open source projects, @clawsweeper will review it, and *if* it fits the VISION.md file, will pick it up and create+autoreview a PR. e.g.:
https://x.com/steipete/status/2066457262571360396
writing mac apps is still hard.
https://x.com/steipete/status/2065132980398444945
Hermes Agent updates (the most important ones) • Hermes turned from a “CLI agent” into a full desktop app. • Hermes overtook OpenClaw on OpenRouter • It became faster and more modulAR • Anthropic / HERMES.md billing incident • Some developers moved to Hermes because of
https://x.com/TheTuringPost/status/2065981026728620456
@Zai_org GLM 5.2 is now live on Eigent from day 0! threw a real long-horizon task: research 30 companies across 6 sectors of the AI infrastructure stack, structure it into JSON, then build an interactive HTML report. same prompt, 5.1 vs 5.2. where 5.2 pulls ahead: → plans
https://x.com/Eigent_AI/status/2066942441974886714
@Zai_org’s GLM-5.2 — day zero on DeepInfra 🔥 744B/40B MoE, MIT-licensed, full 1M ctx, High/Max thinking-effort. Top open-source model on FrontierSWE, PostTrainBench & SWE-Marathon. $1.40 in / $4.40 out / $0.25 cached per 1M
https://x.com/DeepInfra/status/2066982674741494131
> We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP Good demonstration that a lot could be squeezed out of “”mere”” DSA on benchmarks, too.
https://x.com/teortaxesTex/status/2066940539652456944
🎉 Day-0 support for in vLLM, available today in v0.23.0! Congrats to @Zai_org on GLM-5.2, a flagship model built for long-horizon coding agents. ✨ 1M-token context, built to hold project-scale engineering work in a single run ✨ Tuned for long-horizon coding: large-scale
https://x.com/vllm_project/status/2066950636428775693
🎉 Meet GLM-5.2 from @Zai_org, the new flagship for long-horizon tasks built on a solid 1M-token context. Day-0 support is now live in SGLang! ✅ Advanced coding capability: 81.0 on Terminal-Bench 2.1 (vs 62.0 for GLM-5.1) ✅ Solid 1M context across long agentic coding
https://x.com/lmsysorg/status/2066941143536013622
🤯 GLM-5.2 is here — built for long-horizon coding and agentic tasks, now with a solid 1M-token context. The strongest open-source coding model yet! Available now on Ollama’s cloud, hosted in the US on the latest @NVIDIAAI Blackwell datacenter GPUs. Privacy policy and zero
https://x.com/ollama/status/2066949797316350361
All day using GLM 5.2. Didn’t miss much. First open model that passes the bar as a daily driver. Things are not going to be the same. Damn, now I want to buy some serious hardware.
https://x.com/matvelloso/status/2067791546335019439
an old cloud backup GLM-130B was the first model that made me suspect that the Chinese will be carrying the project of open source AGI on behalf of all us barbarians. DeepSeek confirmed it and provided a paradigm. It’s been a damn good ride since then. Happy to see GLM glow-up.
https://x.com/teortaxesTex/status/2066974572314816646
Chinese lab Z AI just released GLM-5.2, an impressive new open weights model with a 1M token context window. A few benchmark comparisons, slotting between Opus 4.8 and GPT 5.5 on several fronts: – 74.4 on long-horizon coding, ahead of GPT-5.5’s 72.6. – 62.1 on SWE-bench
https://x.com/TheRundownAI/status/2066953804424102228
Compare GPT-5.2 from 7 months ago with the new GLM-5.2 Deep Think Max’s: “”create a visually interesting shader that can run in twigl with an infinite city of neo-gothic towers partially drowned in a stormy ocean with large waves”” “”Make it better”” GLM-5.2 also had a couple errors
https://x.com/emollick/status/2066963613143752861
Congrats GLM 5.2 on great ProgramBench numbers! In recent weeks, a lot of model cards have reported % of tests passed as the main metric for PB. imo, it’s fine for now. But in the future w/ better models: eventually it’d be good to report % instances (not tests) resolved.
https://x.com/jyangballin/status/2066958991494922334
Exo 0.14.0 is out today with Day-0 support for GLM 5.2 (from @Zai_org) and @opencode harness support. GLM is the first open model that achieves comparable performance to Opus in my internal eval, and runs at approximately the same speed on @ollama cloud. it’s now my default
https://x.com/agupta/status/2067008234368430417
frontier labs are absolutely scamming you on API pricing btw GLM-5.2 is $4.4 output at 744B@40B DeepSeek-V4-Pro is $0.87 output at 1.6T@49B (and they are both making money, without any fancy Blackwell chips) Sonnet 4.6 is $15 output Opus 4.8 is $25 output GPT-5.5 is $30 output
https://x.com/scaling01/status/2066952626386714906
GLM 5.2 has just been released 🔥 Here it’s already running with MLX on two Mac Studios (M3 Ultra). This is comparable to the latest closed models, with weights you can download, quantize, distill, fine-tune, run.
https://x.com/pcuenq/status/2066967665726337219
GLM 5.2 is apparently the strongest Chinese model, and it’ll be open this is quite a jump on CritPt. They have *finally* started to build general reasoners. I think it’ll be very competitive on ARC-AGI 2 and WeirdML, too
https://x.com/teortaxesTex/status/2066942272692723917
GLM 5.2 is live on Baseten. 5.2 is built for agentic engineering: stronger coding, sharper agentic reasoning, and a long context window built to run hours-long tasks. Test it today:
https://x.com/baseten/status/2066961882720940371
GLM 5.2 is live on Fireworks, day zero. 1M-token context, coding‑first frontier model, independently validated on SWE‑bench, Terminal‑Bench, GPQA and AIME. Served on our own infrastructure the moment @Zai_org opened the weights. What’s under the hood ↓
https://x.com/FireworksAI_HQ/status/2067007200426680509
GLM 5.2 is now available on Workers AI Excited to be a launch partner and supporting the open-source model ecosystem.
https://x.com/CloudflareDev/status/2066941091853602899
GLM 5.2 is now live on Venice. @Zai_org’s frontier open-source model built to power agents and long-horizon agentic coding, with High and Max reasoning modes for the hardest problems. Available fully private to Pro users.
https://x.com/AskVenice/status/2066940339412152803
GLM 5.2 on Ollama’s cloud just doubled GPU capacity to handle the volume of usage! This is all US based, and running on NVIDIA B300 Blackwell GPUs. We believe privacy matters! Let’s go open models! ❤️
https://x.com/ollama/status/2067730812645298626
GLM models continue their amazing trajectory with GLM-5.2 that supports 1M context window. We continue to see amazing innovation from open-weights models on long-context. GLM-5.2 extends DeepSeek’s sparse attention by reusing the same indexer across every four sparse attention
https://x.com/vipulved/status/2066982555245855064
GLM-5.2 (Max) is #25 overall in the Text Arena, similar to GLM-5.1. Diving deeper into the comparison in Text, we can see that though GLM-5.2’s rank is down overall, its largest gains are across: – Sub-categories: Expert Arena, Multi-Turn – Occupational categories: such as:
https://x.com/arena/status/2066957809741447383
GLM-5.2 also delivers significant improvements in AI slide generation, long-context document processing, long-form writing, and long-context role-play. Experience these enhancements at
https://x.com/Zai_org/status/2066938957447807003
GLM-5.2 can now run locally in llama.cpp and Unsloth Studio. Check the graph below for the accuracy of each GLM-5.2-GGUF quantization. Full guide:
https://x.com/ZixuanLi_/status/2067626723986841765
GLM-5.2 delivers a substantial leap in app development capabilities, which also represent demanding long-horizon tasks. Results: – GLM-5.1: 21/70 – GLM-5.2: 48/70 – Claude Fable 5: 56/70 That’s more than a twofold improvement from GLM-5.1 to GLM-5.2. These come from an
https://x.com/ZixuanLi_/status/2067803136283005393
GLM-5.2 from @Zai_org is live on OpenRouter!
https://t.co/UqC0wfwmmO’s flagship for long-horizon tasks, now with a 1M-token context window capable of being reliable across long, messy coding-agent work.
https://x.com/OpenRouter/status/2066941552208056561
GLM-5.2 genuinely looks like a good model and it’s only 744B imagine what kind of margins the frontier labs have also try to imagine what a 1T Kimi or 1.6T DeepSeek model will do the only way for frontier labs to keep their lead is to scale their models and put in even more RL
https://x.com/scaling01/status/2066944834170917032
GLM-5.2 is a win for local AI : r/LocalLLaMA
https://www.reddit.com/r/LocalLLaMA/comments/1u8ai2a/glm52_is_a_win_for_local_ai/
GLM-5.2 is comparable to Opus 4.8 🔥🥵 with 1M context > new IS attention reuses one indexer every 4 sparse layers (2.9× per-token FLOPs at 1M > improved MTP layer for spec decoding > flexible thinking-effort levels > day-0 in transformers + vLLM + SGLang, MIT license 🤗
https://x.com/mervenoyann/status/2066940184977920183
GLM-5.2 is free when used with Hugging Face Inference Providers for the next 5 hours:
https://x.com/Zai_org/status/2067647208451604617
GLM-5.2 is Fully Open, Frontier Intelligence Belongs to Everyone Today, the sudden restriction of certain frontier models is deeply regrettable. At a time when access to frontier models is abruptly cut off for non-technical reasons, we are even more convinced of one thing:
https://x.com/jietang/status/2065784751345287314
GLM-5.2 is live on Friendli Model API as a Day 0 partner.
https://t.co/6NSuXQFNy2’s newest flagship for agentic engineering brings: 🔹 Significant improvements in coding and agentic tasks 🔹 Strong long-horizon capability with a usable 1M-token context window 🔹 Two levels of
https://x.com/friendliai/status/2066942555397472336
GLM-5.2 is now available on GMI Cloud on day 0 The best open-source coding model, leading all open models on long-horizon and standard benchmarks. Its flexible effort levels (High/Max) gives users good cost vs performance balance Go build with it now 👇
https://x.com/gmi_cloud/status/2066943032520556936
GLM5.2 brings back the critic. It was just a matter of time until we people would realize that group-based variance reduction is unfeasible after some horizon length. We need to be more fine-grained. I am sure OAI and Ant have been using value models for quite some time.
https://x.com/hallerite/status/2066969117043941613
I said this when GLM 5.2 was already deployed, so it shouldn’t count as an update. But it was heroic, still. I didn’t expect the actual latest-gen-Opus level.
https://x.com/teortaxesTex/status/2067416066423152825
idea: Claude’s persona is probably vastly more salient, coherent and integrated into broad behavior than any half-assed “”who r u – I’m GLM from Zhipu”” anyway, so it doesn’t require that massive a share of outputs. “”Claude”” could be a self-spreading virus at this point.
https://x.com/teortaxesTex/status/2067428989505769547
Impressive if true! Better than Claude Fable 5? Wow! Design is really lacking in these frontier models, so I’m very curious to test GLM-5.2 myself. Testing this already on a few internal use cases and will report back on findings.
https://x.com/omarsar0/status/2066967030490640894
Just caught up with the recent GLM-5.2 release. The best open-weight model today. Architecture-wise, it’s build on the GLM-5 and GLM-5.1 architecture that I covered previously, which means it’s reusing the Multi-head Latent Attention (MLA) and DeepSeek Sparse Attention (DSA)
https://x.com/rasbt/status/2067612153020838055
Just to be clear, if you remove Fable which is unavaialble, GLM-5.2 (Max) is the #1 model in the world for frontend coding. This is a huge moment. OSS has caught up with proprietary, and China has caught up with the US, in this very important domain.
https://x.com/ml_angelopoulos/status/2066969005856829824
Lets go, GLM-5.2 released as Open Weights model. tl;dr -1M context window -MIT-licensed open weights -Stronger long-horizon coding agents -Two reasoning modes: max and high -Same API pricing as GLM-5.1 Zai says GLM-5.2 was trained specifically for large-scale implementation,
https://x.com/kimmonismus/status/2066947839591084212
Make no mistake, what the GLM team has pulled off is *heroic*. They are the largest Chinese LLM startup, they have the academic chops, state favor etc, but Mistral is the same and could get all the GPUs it needed. It did not. They’re better than the entire continent of Europe.
https://x.com/teortaxesTex/status/2067413884588212738
New open-weight model: GLM 5.2 is now available in Notion. Built for long-horizon tasks. Served via @baseten.
https://x.com/NotionHQ/status/2066963258985320550
New podcast with @finbarrtimbers! We survey the latest post-training recipes, from GLM 5.1, Kimi K2.6, DeepSeek V4, Xiaomi MiMo V2.5, Nemotron Ultra, etc. and discuss: – Why the industry slowly shifted to multi-teacher on-policy distillation (MOPD). – What an Olmo-style recipe
https://x.com/natolambert/status/2066879709661827507
No time wasting on the frontier of open-weight models. GLM-5.2 looks impressive based on the results I’ve seen. Very curious to see how it holds on long-horizon tasks.
https://x.com/omarsar0/status/2066967804373324101
so many golden nuggets from the glm 5.2 release blog about breakthroughs that helped them with benchmark gains. you never see this level of transparency from the frontier labs. they found that glm 5.2 kept trying to reward hack in rl by curl’ing task related source from github
https://x.com/sdrzn/status/2066966814220042266
That was my original estimate before GLM’s scores Elon actually might be better calibrated. Extrapolating “”7 months”” forward ignores scaling to Fable-tier. They start getting 950s in August; by Nov clusters are running. *If* scale is the hard limiting factor… 4+ months. If.
https://x.com/teortaxesTex/status/2067446753217970202
This is kind of insane. Seems like we have recursive self-improvement in the open source now. Impressive for _only_ 753B parameters. Congrats to the @Zai_org team!
https://x.com/code_star/status/2066954960361906658
Thread by @Zai_org on Thread Reader App – Thread Reader App
https://threadreaderapp.com/thread/2065704919299235870.html
We also detail the infrastructure innovations powering our 1M context window and agentic RL. Explore the full breakdown at
https://x.com/Zai_org/status/2066938952225857609
Wow. @Zai_org GLM 5.2 is a marvel! It is *at least* as good as Opus 4.8 and GPT 5.5. It’s super fast, inexpensive, and not too verbose. It responds with nuance and judgement, & handles long context VERY well. I’ve never experienced an open weights model like this before.
https://x.com/jeremyphoward/status/2067757468189679764
Zai was gracious enough to give me a key to test out GLM 5.2. I used it on a few simple tasks and quickly realized this model is on another level. I committed to using GLM 5.2 solely for the weekend and yesterday on everything from simple data analysis, random queries, side
https://x.com/Sentdex/status/2066945985217990667
‘No poaching’ our people, China’s DeepSeek reportedly tells investors
https://www.cnbc.com/2026/06/18/no-poaching-our-people-chinas-deepseek-reportedly-tells-investors.html
Top trending paper on
https://t.co/nYgeLha6ut today is “”VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models”” 🔥 A 3B model placed in the same performance tier as models like DeepSeek V3.2, GLM-5, and Gemini 3 Pro (!)
https://x.com/NielsRogge/status/2067169163508908423
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