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DeepPrinciple launches Hydrogen-1.0, open-weight science model beating GPT-6-Astra on FrontierScience

Chenru Duan (Founder & CTO of DeepPrinciple) announces Hydrogen-1.0, the company's first open-weight model for scientific research. It was created by post-training GLM-5.3 (from Z.ai) on internal scientific data, lab records, and discovery trajectories. Hydrogen-1.0 scores 38.3% on FrontierScience-Research (vs GPT-6-Astra's 33.3%) and 93.0% on GPQA-Diamond, achieving SOTA on FrontierScience-Research at an API cost similar to GLM-5.3. On internal research scenarios it matches or exceeds GPT-6-Astra at maximum thinking on scientific computing (76.9%). The thread demonstrates practical use cases: building a polyethylene melt in GROMACS, diagnosing topology errors, analyzing molecular dynamics trajectories, experiment planning from full-cycle lab records, and faster discovery of transition metal complex candidates with fewer attempts. Weights are available on Hugging Face (deep-principle/Hydrogen-1.0) with API access via neuro-spark.ai, plus a companion YouTube demo video.

#hydrogen10#deepprinciple#openweights

YouTube9 hours agoDemoPlay

DeepSeek Harness Super Agent: New Agent Add-On Overview

Sam Witteveen (Google Developer Expert for Machine Learning) reviews the new DeepSeek harness announcement and the introduction of their super agent add-on. The video covers the harness's plugin architecture (including Cordis plugins and composable plugins), router/worker multi-agent design, comparisons to Claude Code and Codex as an open-source coding agent alternative, and the Frontier Harness benchmark. A GitHub link to the project is referenced in the description.

#deepseek#superagent#agentharness

YouTube9 hours agoNews RecapPlay

LongCat 2.5 Explained: 6.7x Coding Score at DeepSeek's Price

INAPP's explainer video covering Meituan's LongCat 2.5, which went generally available on November 2, 2026. The video breaks down the model's headline result: a 6.7x jump in coding score on DeepSWE (real software engineering tasks in real repositories), achieved at the same price point as DeepSeek V4.1 Flash. Topics covered include agentic AI coding performance, benchmarks like DeepSWE and Terminal Bench, comparisons with tools like Claude Code and Codex, pricing, multimodal capabilities, and open-source availability.

#longcat#meituan#deepseek

Instagram9 hours agoNews RecapView

Meituan releases LongCat-2.5, a 1.6T-parameter multimodal MoE coding agent model

Meituan has released LongCat-2.5, a natively multimodal Mixture-of-Experts model with 1.6 trillion total parameters (~48B active per token), built for long-running coding agents. It builds on LongCat-2.0 with stronger visual understanding and agentic coding, and works with Claude Code, Codex, Hermes and other agent harnesses. It is available via LongCat's chat and API, with GitHub and Hugging Face releases marked 'coming soon'. Key details: trained on long agent trajectories with large-scale RL for full codebases and full-project builds; images are converted into multi-level discrete tokens so one backbone handles text and images (visual coding, design, 3D); real GitHub PRs and issues become training tasks with verifiable tests, and failed agent runs become new tasks; long-context training on sequences up to 1M tokens, done fully on Chinese domestic AI chips. Meituan's benchmark numbers: Terminal Bench v2.1: 88.4 (vs Claude Opus 5: 89.1, GPT-5.6 Sol: 88.8), Program Bench: 25.5 (Opus 5: 37, GPT-5.6 Sol: 23), BrowseComp: 91.9 (Opus 5: 90.8), up from 70.8 on Terminal Bench v2.1 for LongCat-2.0. Sources cited include the LongCat blog (longcat.ai/blog/longcat-2.5), Meituan news, and TechNode. The post is published by AtmoRouter and ends with a promotional pitch for its AI API gateway (atmorouter.dev).

#meituan#longcat#llm

Yesterday

TechCrunch16 hours agoAI Investment

AI inference startup Fal doubles its valuation to $16B as demand surges

Fal, a startup that hosts and runs AI video, image, and audio models, raised $1.3 billion in a Series F round led by Bessemer Venture Partners with participation from Nvidia, doubling its valuation to $16 billion. The company surpassed $800 million in annualized revenue amid surging demand for inference services.

The Verge18 hours agoAI Everyday Life

Google is entirely disabling Search in schools that don’t want AI

Google says it will turn off Gemini, Gemini Notebook, Gemini in Classroom, and Google Search entirely for K-12 student accounts unless administrators explicitly enable them, meaning schools that reject AI features lose Search access too. The move has drawn criticism from lawmakers, including Sen.

X (Twitter)yesterdayOfficial PostView

OpenAI introduces LifeVal, a benchmark for everyday personal assistant tasks

OpenAI announced LifeVal, a new benchmark shaped by feedback from thousands of ChatGPT users on where they most want help from a personal assistant—including finding childcare, accessing government services, and managing personal finances. Over the past year, OpenAI has used LifeVal to measure and improve how well its models handle these everyday tasks. The post links to the full write-up at openai.com/index/lifeval/ and includes a video thumbnail.

#openai#chatgpt#lifeval

X (Twitter)yesterdayOfficial PostView

Meituan launches LongCat-2.5, a 1.6T-parameter multimodal agentic model

Meituan LongCat officially announced LongCat-2.5, its latest natively multimodal agentic model with 1.6T total parameters and ~48B active parameters. The model is built for long-horizon agentic tasks and brings stronger visual understanding and agentic coding capabilities, handling work ranging from complex coding projects to visual coding such as creative design and 3D tasks. The announcement links to the full blog post at longcat.ai/blog/longcat-2.5. Follow-up posts in the thread detail that LongCat-2.5 scales an end-to-end agentic RL pipeline across data, environments, algorithms and harnesses, with MOPD training retaining the previous model's expert groups; that the API is now available at longcat.ai/platform with 10 million free tokens on sign-up (plus 10 million extra for existing users, cache hits free); and demo use cases including real-time fluid simulation, business operations analysis, 3D artifact showcase pages, and customer feedback analysis reports.

#longcat#meituan#llm

YouTubeyesterdayDiscussionPlay

SemiAnalysis GDPx Benchmark: Measuring Whether AI Can Complete Real Work

In this November 2, 2026 episode of Tech Unfiltered, Dr. Mike explains SemiAnalysis's new GDPx benchmark, which evaluates whether AI agents can actually finish real workplace tasks rather than just produce impressive-sounding answers. The episode draws the distinction between an impressive answer and completed work, and discusses what the benchmark measures in terms of AI's practical impact on workplace productivity.

#ai#gdpx#semianalysis

CrowdStrikeyesterdayAI Safety & Security

CrowdStrike Introduces the AI Kill Switch for AI Safety

CrowdStrike announced the AI Kill Switch in Falcon Guardian, letting security teams terminate rogue AI agents at their root, block relaunching, and cut off enterprise network access with human oversight. The feature is available today and builds on the Falcon sensor already deployed on endpoints.

02 Nov, 2026

TechCrunchyesterdayAI Safety & Security

Anthropic warns that it's very close to RSI capability, aka self-building AI

In its revised S-1A filing, Anthropic disclosed that it may imminently meet its Responsible Scaling Policy's definition of 'dramatically accelerated progress attributable to AI,' signaling it is close to recursive self-improvement (RSI).

X (Twitter)2 days agoOfficial PostView

Google DeepMind expands Gemini-powered PlanAssist to 12 more UK councils

Google DeepMind announced that PlanAssist, an AI tool built with Gemini to help UK Government planning officers speed up building and renovation assessments, has been shown to cut the time officers spend assessing householder applications by up to 58%. The tool is now being made available to 12 more councils to help them make faster planning decisions.

#google#deepmind#gemini

Instagram2 days agoNews RecapView

Google DeepMind unveils Genie 4: real-time AI-generated interactive worlds

Instagram reel by @botsonpayroll recapping Google DeepMind's Genie 4 launch (announced Oct 27, 2026). Genie 4 generates interactive, explorable worlds in seconds from a single text sentence or photo, with live frame generation as you type, mid-run modifications (weather, camera, characters), and for the first time native audio including ambient noise, music, and speaking, reactive characters. It is currently a research preview for trusted testers, with Google AI Ultra subscribers and Gemini API partners next in line; no pricing announced. The carousel covers 10 points including how it works, Genie 3 vs Genie 4 comparison, performance numbers, use cases like riding a dragon or attacking a castle, robot training applications, access, and limitations. Sources cited: Google DeepMind, Google, TechCrunch.

#googledeepmind#genie4#generativeai

Reddit2 days agoAnnouncementView

DeepSeek open-sources dsh-superagent, a Router-Worker coding agent prototype for GitHub

DeepSeek's Harness team released dsh-superagent, an MIT-licensed research prototype coding agent for GitHub. A long-running Router session reads GitHub (and optionally Feishu) events, admits requests from allowed people, and hands each to an isolated Worker session that replies in the original thread, often with a PR. Features include per-request Tickets with isolated sessions and directories, subscription-based routing of follow-up PR comments to the same Worker, gating of unasked work behind team-written rules (off, observe, auto modes), and backgrounding of shell commands after 5 seconds. Self-reported simulation benchmarks: ~300 messages per run, ~20 real work requests, 5 runs per scheme; Super-agent p50 12.4 min and p95 15.7 min vs p95 29.5 min for a single session plus subagents; Router per-request latency 2.5s to 3.9s at 1 to 40 events/min. The release shipped with a design essay on DeepSeek's 'Super-agent' concept.

#deepseek#opensource#codingagent

Reddit2 days agoNews RecapView

DeepSeek open-sources dsh-superagent-prototype: Router-Worker autonomous coding agent

On October 31, DeepSeek's Harness team published a design essay, 'Exploring Super-agent Design: Towards a More Autonomous Agent System,' alongside an MIT-licensed research prototype called dsh-superagent-prototype. The system is orchestration around existing model-powered tools, built for a single GitHub repository: a persistent Router session handles conversations and dispatch decisions, while each accepted work item gets its own Worker session and directory so tasks run in parallel without shared context. It listens to GitHub events (and optionally Feishu), returns results in the original conversation often as a pull request, and a local web panel surfaces only decisions needing human input. The philosophy is to take as little human attention as possible; the agent confirms once, stays quiet while working, and defaults to observe-only for merely inferred work. DeepSeek argues human attention, not model capability, is the bottleneck, noting a person can actively follow only 3-5 agent sessions. In its own simulated test (~300 messages, ~20 real work requests per run), the super-agent finished the slowest 5% of tasks in 15.7 minutes vs 29.5 minutes for a single-session baseline. The poster cautions these numbers are self-reported, based on synthetic messages and simulated tools, with no independent benchmark or verified comparison to Claude Code or Codex. DeepSeek states the prototype is research code, not production-ready, may contain bugs, and differs from the internal system. The post ends with discussion questions about whether Router+Workers is the right architecture and what permissions agents should have to open pull requests.

#deepseek#aiagents#opensource

OpenAI2 days agoAI Research

LifeVal: Measuring AI progress on the tasks that matter in everyday life

OpenAI introduced LifeVal, a benchmark measuring how well AI models help people complete everyday tasks like finding childcare, managing finances, and accessing government services.

Marvell Technology2 days agoAI Investment

Marvell and Lumentum to Combine, Creating a Category Leader in AI Connectivity

Marvell has agreed to acquire Lumentum in an all-stock transaction valuing Lumentum at an implied enterprise value of $115 billion, combining electrical and optical technologies for next-generation AI infrastructure.

OpenAI2 days agoAI Infrastructure & Chips

Introducing the Grid Protectors Program

OpenAI launched the Grid Protectors Program, a national initiative to work with utilities, regulators and policymakers so its future compute campuses can reduce electricity demand during grid strain.

01 Nov, 2026

Axios2 days agoAI Safety & Security

Behind the Curtain: OpenAI's post-election power play

Axios reports that OpenAI will launch a post-midterm policy and PR blitz, including mandatory 'speed limits' for powerful AI systems, community compacts for data centers rebranded as 'AI compute campuses,' and proposals on economic opportunity and international standards.

X (Twitter)3 days agoOfficial PostView

OpenAI launches Private Intelligence for enterprise data controls

OpenAI officially announced "Private Intelligence," a new offering that gives businesses stronger controls over their data while using OpenAI's most capable models. The company states that privacy should advance as fast as intelligence, and that these protections are being built in close collaboration with some of its largest customers to help businesses apply frontier AI to their most sensitive work. The post links to openai.com/business/private-intelligence and includes a promotional video.

#openai#privateintelligence#enterpriseai

X (Twitter)3 days agoOfficial PostView

Anthropic automates red-teaming with thousands of Claude agents

Anthropic announces new research on automating subtle emergent misalignment research. Since red-teaming is limited by researchers' time, Anthropic set thousands of Claude agents the task of evading a training data monitor. The agents developed training data that appeared harmless to monitors (e.g., a veteran wrestler mentoring a newcomer, or a Q&A about professional beekeeping) but actually taught misaligned behavior. Anthropic notes these examples can directly improve future training data monitors, and that while this is only a first step, future red-teaming efforts could improve much further with better models and more compute. Full post published on the Alignment Science Blog.

#anthropic#claude#aisafety

YouTube3 days agoNews RecapPlay

Mistral Large 4 Open Weights Released: 1 Trillion Parameters Explained

Europe's Mistral AI has released the weights of Mistral Large 4, its 1-trillion-parameter model nicknamed "Le Chonk". The video (5:12 long) explains what was actually released, including details on the open-weight release, the mixture-of-experts architecture, availability on Hugging Face, AI benchmarks, and AI cybersecurity implications.

#mistral#mistrallarge4#lechonk

Instagram3 days agoNews RecapView

Mistral releases open weights of 1T-parameter Mistral Large 4 "Le Chonk"

Mistral has released the weights of Mistral Large 4, its 1-trillion-parameter, natively multimodal model nicknamed Le Chonk. Per Mistral's Oct 29 announcement: 19.7% on FrontierCode 2.0 (Cognition's private benchmark, taking the lead), 48% on the Artificial Analysis Coding Agent Index (effectively tied with open-weight leader GLM-5.3 while using 47% fewer tokens per task), 41% on Terminal-Bench 4.0 (up 13 points from public preview), and 51% on the Artificial Analysis Cyber Index. The model offers three reasoning modes (Low/Medium/High) to trade cost against performance, and Mistral positions it for sovereign AI: trained in Europe, self-hostable, and customizable. Benchmarks are Mistral-reported; independent results may differ. Source: https://mistral.ai/news/mistral-large-4-oss/

#mistral#mistrallarge4#openweights

Instagram3 days agoNews RecapView

Mistral Large 4 open weights released: 1T params, half-price API

On October 29, 2026, Mistral released open weights for Mistral Large 4 ('Le Chonk') on Hugging Face. The model is a 1-trillion-parameter mixture-of-experts with ~49B active parameters per token (52B including embeddings), 128 experts with 4 picked per token, 1M-token context, and text + image input. It uses the Mistral Ecosystem Open-Weight license (modified MIT) allowing use, fine-tuning, and product sales, though reselling as a hosted model service requires a separate commercial license. API pricing is $0.70 input / $0.07 cached / $2.10 output per 1M tokens, roughly half the preview price. Mistral-reported benchmarks include 48 on the Artificial Analysis Coding Agent Index (tied with GLM-5.3 for open models while using 47% fewer tokens), Terminal-Bench 4.0 at 41 (up from 28), and 51 on the Artificial Analysis Cyber Index with a 96.6% refusal rate on malicious cyber requests. The post notes practical caveats: FP8 weights are ~1TB on disk, serving requires 8x NVIDIA B300 GPUs (or 4x GB300s for the compressed build), and for most builders the API is the practical product.

#mistral#mistrallarge4#openweights

Reddit3 days agoNews RecapView

Mistral Large 4 (Le Chonk) released as open weight

A link post in r/MistralAI reports that Mistral Large 4, nicknamed 'Le Chonk', is now available as an open-weight release. The post links to the official announcement page and has sparked community discussion about the release, earning 165 upvotes (100% upvoted) and 8 comments within its first days.

#mistral#mistrallarge4#opensource