Addy Osmani, Morgan Westlee Lunt, and Qing Wang say the Claude Code team reviews changes continuously, with Claude reviewing and fixing most changes at least once before a human looks. They describe asking what could go wrong and which check—automated or human—is right to catch it, rather than using whether a human read the code as a proxy.
OpenAI Developers shares how its engineers use Codex to explore their codebase, identify areas needing attention, and reduce the maintenance backlog. In the quoted post, @rrroim says Codex can now find dead-code candidates in Slack, check them against production telemetry, and draft cleanup PRs, whereas a few months ago it needed explicit directions on where to look. https://developers.openai.com/blog/maintaining-software-with-the-gpt-6-family
Replit CEO and co-founder Amjad Masad said the company stopped working with Anthropic for reasons including cost, partnership and performance, saying, “I think ChatGPT is better now.” People familiar with the partnership pushed back, and a September blog post cited Claude Fable 5 and Claude Fable 5.1 in Replit Agent’s production environment; TechCrunch said it would seek clarification. Masad also said enterprise grew from 2% of revenue last year to 15% this year, while more than 50% of revenue is B2B.
Ali, an engineer at OpenAI, used his dot to triage an on-call incident at 3am. His dot investigated the issue and started a Codex thread, so he could steer the work from his phone in the ChatGPT app. https://t.co/xLhtGUSzsx
ClaudeDevs announced that Code Review is now generally available for Claude Team and Enterprise plans, with faster reviews, improved detection of consequential bugs, and per-repo Quick, Standard, or Deep review levels selected by admins. Boris Cherny says it runs on all his PRs and catches almost every bug before it gets in, helping his team move quickly while raising quality. https://twitter.com/ClaudeDevs/status/2110866210573386065
Google is rolling out Gemini 4 Argon globally to developers, enterprises, and paid subscribers, emphasizing coding, professional knowledge work, multimodal understanding, and sustained task execution. API pricing is $2 per million input tokens and $10 per million output tokens through November 30, rising to $4 and $20 on December 1. Google reports that Argon worked unattended for 11 hours on ADK optimizations, with engineers merging two changes; Ethan Mollick described improvements but noted remaining weaknesses in complex multi-agent tasks.
One more thing: beyond having Opus 5.5 coordinate, you can make full use of an advisor and have Fable advise Opus. GPT 6.1 Sol is actually pretty good at routine tasks https://t.co/RZgKqn62g8
Reflection AI introduced Beam, an open-weight model for agentic coding and multi-step tasks, with 501 billion total parameters and 23 billion active parameters. Reflection reports an effective context length of 1 million tokens and releases Beam 1.0 under Apache 2.0 in BF16, FP8, and NVFP4 variants. The guide explains deployment through Amazon SageMaker JumpStart or HyperPod Inference with JumpStart, SGLang, or vLLM, including capacity planning, invocation, performance measurement, and cleanup.
Why it matters: The deployment comparison clarifies when managed endpoints suffice and when Kubernetes infrastructure or custom runtimes offer useful control over serving configuration, distributed execution, and scaling.
Reflection released Beam on Hugging Face, a 501B MoE model with 23B active parameters per token, a 1M-token context window, and an Apache 2.0 license. The text-only model targets coding and agentic work, with BF16, FP8, and NVFP4 checkpoints and day-0 support in transformers, vLLM, and SGLang. Hugging Face provides deployment and post-training recipes alongside its own task evaluations and demos.
Why it matters: The deployment guidance distinguishes active computation from total weight memory, helping teams assess hardware requirements for running and adapting a sparse model with a long context window.
Ollama is a day 0 partner for Reflection AI’s first open-weight model, Beam, offering it free for a limited time on Ollama’s Cloud with US hosting and full ZDR. Beam has a 1M token context window and is designed for software engineering, reasoning, and long-running agentic tasks. ollama run beam Claude Code ollama launch claude --model beam:cloud Codex ollama launch codex --model beam:cloud OpenCode ollama launch opencode --model beam:cloud Reflection AI says Beam’s weights are available under Apache 2.0 at https://huggingface.co/reflection.
Why it matters: The temporary free offering provides a way to try Beam through several coding tools, with US hosting and full ZDR stated alongside its software engineering and agentic focus.
Replit CEO and co-founder Amjad Masad said the company’s work is increasingly driven by AI agents, which handle much of the initial support-ticket triage and can be deployed to submit fixes. He said people still provide judgment and taste, while spending more time deciding what to work on, discussing ideas, and planning architecture.
Live the kind of life where the suggestions from Codex Composer Text Predictions are always wrong.
@emollick posted: “Live the kind of life where the suggestions from Codex Composer Text Predictions are always wrong.” Ethan Mollick: I couldn't help myself. https://t.co/1D6DQqXvAC https://t.co/baQEzSDYII
Artificial Analysis reports that NVIDIA's Vera Rubin NVL72 delivers up to ~5.7x GB300 NVL72's throughput per megawatt on DeepSeek V4 Pro in initial AA-AgentPerf results. Its advantage is largest at faster per-user speeds; peak-throughput gains range from 1.14x to 1.45x across the four tested models. NVIDIA submitted all Vera Rubin results using TensorRT-LLM and vLLM with NVIDIA Dynamo; the benchmark replays real agentic coding sessions and measures throughput per megawatt of measured GPU power alongside per-agent output speed.
Antigravity announced that Gemini 4 Argon is rolling out today to paid Google AI Pro subscribers, excluding Pro trials, free users, and Google AI Plus subscribers. Access is available through the Antigravity desktop app, CLI, and IDE extensions, but not yet within Antigravity IDE; users are advised to update to the latest version. Argon has a separate weekly quota that does not consume the Gemini 3.8 Flash allowance or refresh every 5 hours; the weekly amount and reset day are unspecified. Official model documentation: https://antigravity.google/docs/models
Why it matters: The separate weekly Argon quota lets eligible Pro subscribers use Argon without reducing their 3.8 Flash allowance, providing a concrete basis for planning usage across the two models.
Muse Wonder from Meta Superintelligence Labs is now available in Cursor! It scores 46.1% on CursorBench at default effort, the highest of any Muse model. Priced at $4/M input and $20/M output tokens. https://t.co/NGEnvB43LN
You can just build things with Codex We built a site for you to explore hardware projects from the community and share your own.
NVIDIA's RTX Spark N1X puts up to 128 GB of unified memory and a Blackwell GPU in a Windows laptop. In Artificial Analysis's first AA-AgentPerf-Local results, an RTX Spark laptop completes its local agent workload on Qwen3.8 27B in 18.7 minutes. Artificial Analysis describes RTX Spark's N1X chip as essentially the GB10, linking to https://t.co/520A4lOOdB
Meta has launched Muse Spark Open, a natively multimodal open-weight model with 816 billion parameters and a 1 million token context window, built for complex coding tasks and agentic workflows. It is designed to run locally on Macs, though Engadget reports it requires at least 1 terabyte of RAM. Meta claims superior performance over other popular open-weight models in its evaluations and is making the model available now through multiple API platforms.
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
Meta has released Muse Spark Open, an open-weight model with 816 billion parameters (42 billion active), up to one million tokens of context, and text, image, video, and audio support. Meta reports leads over Qwen 3.8, GLM 5.3, and Kimi K3 in several benchmark categories, while trailing some competitors in coding. Weights are on Hugging Face; use requires the label "Built with Muse Spark Open," and API providers earning more than $20 million annually need a separate agreement with Meta.
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
Alexandr Wang highlights @mjdouglas’s announcement that Muse Code 1.4.8 became much more token-efficient and everyone’s usage has been reset, effective immediately. https://twitter.com/mjdouglas/status/2110798276932853945
Day-0 support for Meta’s open-weight Muse Spark Open is live on vLLM, verified on NVIDIA and AMD GPUs as well as TPUs! 816B parameters, 42B active per token, a 1M-token context with image, video and audio inputs, built for long-horizon agentic coding. We’re excited to have worked https://t.co/MleWIPrBMD
Why it matters: Verification across NVIDIA and AMD GPUs and TPUs gives deployment teams concrete hardware compatibility information for running Meta’s open-weight model through vLLM.
Meta announced Muse Spark Open, describing it as the most capable US open-weight model to date, built for coding, agentic work, and multi-step reasoning. NVIDIA AI says the 816B model was trained on NVIDIA GB300 NVL72 and has a 1M-token context window for working across large codebases. Meta also produced NVFP4 checkpoints optimized for Blackwell Ultra. https://t.co/yAtoxNeYJm https://t.co/HJUKdC1n3D
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
Baoyu reports that Meta released Muse Spark Open on October 15, an open-weight model with 816B total parameters, 42B active parameters, and a 1M token context window. It accepts text, images, video, and audio and outputs text; the Muse Model License permits free commercial use subject to attribution, usage restrictions, and a separate agreement for qualifying API businesses. The model card lists minimum memory requirements of 700GB for NVFP4 and 2TB for BF16, while Meta describes optimization for two Mac Studio machines with 512GB unified memory each.
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
1/ Muse Spark open is the most capable US open-weight model to date. 816B total params, 42B active. 1M-token context. Natively multimodal. Built for coding and agentic work like long-horizon tasks, hard software engineering, and multi-step reasoning. America! 🇺🇲 https://t.co/NjtOB7aY9D
Meta announced Muse Spark Open, calling it the most capable US open-weight model to date and describing it as built for coding and agentic work. Meta lists 816B total / 42B active parameters, a 1M token context window, and native text, image, video, and audio support. Ollama welcomed the release and said the model will be available on Ollama shortly. https://twitter.com/AIatMeta/status/2110779754274292212
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
Today Meta is releasing Muse Spark Open, which it calls the most capable US open-weight model to date. Built for coding and agentic work, Meta says it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key model specs include 816B total / 42B active parameters, a 1M token context window, and native multimodality across text, image, video, and audio.
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
Meta’s Muse Spark Open is an open-weight mixture-of-experts model with approximately 816 billion parameters, around 42 billion active per token, and a context window of up to one million tokens. Trained for agentic coding, it accepts text, images, audio, and video and generates text. Hugging Face provides inference and deployment recipes, reports its own quantization tests, and demonstrates attention-layer LoRA fine-tuning with TRL while keeping the experts frozen.
Why it matters: The deployment recipes clarify how sparse expert activation reduces per-token computation while retaining substantial memory requirements, helping developers assess hardware needs across checkpoint formats and serving frameworks.
@davis7’s review says Muse Wonder Full has good writing and “voice,” capabilities on par with 6.1 sol for non-specialized tasks, and orchestration. It lists the naming scheme and price and usage limits as cons, and says the full model is available in Muse Code through a subscription, with no API. Alexandr Wang called it a thoughtful review, said there is lots to improve, and was glad people enjoy using the model. https://twitter.com/davis7/status/2110533786895356118
Simon Willison describes prompting Claude Opus 5.5 Max in the default Claude iPhone app’s chat mode to build “Tree appreciator,” a three.js game about comparing algorithmically generated trees. Claude worked for 3 hours and 7 minutes before returning a spot-the-difference game with music, animation and achievements that Willison found compelling to play.
Baoyu says Claude Code now proactively runs /compact before the cache expires, helping avoid a spike in token costs on the first request after an hour away, and calls it useful 👍 https://t.co/4PT3UQ5kWD In the quoted post, @lydiahallie says idle compaction compresses long idle sessions just before the prompt cache expires (~1h), so returning users only need the summary reprocessed instead of the entire conversation, saving some usage. https://t.co/SG3QyTNFwr
Epic released a publicly available Preview of its rebuilt Epic Games Launcher, describing it as faster and more responsive than the current version, with a new look. Epic Games Store General Manager and VP Martin Keely said the team used AI to write and review features from scratch in a different tech stack. He said this helped a small team rewrite the code base faster for Windows and Mac, with a Linux version also planned.
@theo says Gemini 4 Argon has proven outright unusable for real code work in his codebases because it aggressively hallucinates about how systems work. Alexandr Wang says there is still room for Muse Wonder Full to improve and that they are working on it! https://t.co/D804UwQQQi
muse wonder full is SOTA on VCB 1-100, new coding eval out today https://t.co/PdN1m05g1V
Artificial Analysis is adding AMD MI355X results to AA-AgentPerf, which replays real coding agent sessions with prompt caching and tool-call delays. On a single 8-GPU node, MI355X delivers ~15% to 40% more throughput per MW than B200 on DeepSeek V4 Pro 0813 between 35 and 75 tok/s per user. DeepSeek V4.1 Flash matches NVIDIA Blackwell performance for a single agent but trails as load rises; GLM-5.2/5.3 and Kimi K3 also trail the compared NVIDIA systems. Results use ROCm nightly and dev builds of vLLM and SGLang; Kimi K3 comparisons use NVIDIA multi-node configurations.
people are sleeping on muse wonder full :) https://t.co/dOhyW9f2UA
Artificial Analysis added DeepSeek V4.1 Flash to AA-AgentPerf, its benchmark replaying real agentic coding sessions with prompt caching and tool-call delays. At 100 tokens/s per user, GB300 leads with approximately 261M tokens/s per MW, followed by B300 at approximately 220M and MI355X at approximately 128M. Compared with DeepSeek V4 Pro 0813, V4.1 Flash scores 39 versus 36 on the Artificial Analysis Intelligence Index and delivers approximately 1.3x to 6.2x the throughput per MW at that user output speed, depending on hardware.
Try Muse Wonder Full on your hardest coding tasks—it will surprise you! “By my vibe testing, It's better than Astra. Not quite as good as Claude Code Fable and 5.5 Opus.” https://t.co/8RTGZ434kG
In Claude Code, Sonnet 5.5 scores 68 on the Coding Agent Index at max effort and 63 at xhigh. The xhigh setting costs $3.33 per task versus $14.19 at max, a 77% reduction for 5.5 fewer Index points. Max generates 4.8× as many output tokens and takes 3.4× as many turns, while improving all three benchmark scores; its largest gain is on Terminal-Bench 4.0, from 58.1% to 66.2%.
Profiling the unmodified web-edit app with Python 3.15’s built-in `profiling.sampling` found that preserving mixed line endings dominated the editing workload. `preserve_endings` appeared in 64.8% of 15,296 CPU stacks, while `SequenceMatcher.find_longest_match` contributed 38.3% of leaf samples; 95.1% of stacks came from AnyIO’s worker thread, making all-thread sampling essential.
