AICoder

AI Coding Live News

First-hand AI coding updates from 𝕏/Twitter frontier labs and researchers, distilled by AI with entity benchmarks.

Explore live on 𝕏 ↗

AI Coding Live News

G
GitHub ChangelogGHchangelog·
👀0
🔥82
View on 𝕏 ↗

GitHub Copilot Comes to Xcode's Built-In Coding Assistant in Public Preview With Xcode 27.3 Beta, Supported on All Copilot Plans

GitHub's official changelog says Copilot now works directly in Xcode's built-in coding assistant, in public preview starting with Xcode 27.3 beta. It needs macOS Tahoe 26.6 or later, is set up under Xcode > Settings > Intelligence, and supports all Copilot plans; the existing Copilot for Xcode extension stays available.

Key Takeaways

  • Copilot joins Xcode's native coding assistant in public preview, starting with Xcode 27.3 beta
  • Requires macOS Tahoe 26.6 or later
  • Set it up under Xcode > Settings > Intelligence; all Copilot plans are supported
  • The existing GitHub Copilot for Xcode extension remains a separate option
  • Apple's Xcode 27.3 beta 1 release notes list it under Coding Intelligence
Read full story
Linked Models & Agents:
🤖 GitHub Copilot🌐 GitHub🌐 Apple🌐 Microsoft
#GitHub Copilot#Xcode#Xcode 27.3#Apple#coding assistant#public preview
I
Ivan Leoivanleomk·
👀0
🔥86
View on 𝕏 ↗

Google Antigravity Launches Remote Agents: Agent Sessions Run in a Google-Managed Cloud Environment and Keep Working After You Close Your Laptop, Rolling Out to AI Pro and Ultra

Ivan Leo, who works on developer experience at Google DeepMind, shared the official docs for Antigravity's new Remote Agents feature on X on November 4. Per the docs, Remote Agents run agent sessions and tools in an isolated, Google-managed remote environment, so tasks keep going when you close your laptop or browser or switch between desktop and mobile. Sessions start from the Antigravity App, the Antigravity CLI (1.4.12+, agy --remote-agent) or the web at antigravity.google.com, and remote projects can attach GitHub repositories, Google Drive files and uploads. It is rolling out gradually to Google AI Pro and Ultra subscribers on personal accounts; it is not available on the free plan, AI Plus or business accounts.

Key Takeaways

  • Remote Agents run in an isolated Google-managed environment with no local machine required; Remote Control instead connects to your own computer or server, and the web sidebar switches between them.
  • Three entry points: a New Remote Project in the Antigravity App, agy --remote-agent in CLI 1.4.12+, or antigravity.google.com in any browser, including mobile.
  • Remote projects can attach GitHub repositories (cloned automatically), Google Drive files and uploads (8 MiB per file, 100 MiB per project, 2 GiB per account).
  • CLI remote sessions default to the always-proceed (turbo) permission preset, so commands run without approval; locally installed MCP servers, plugins and skills are not available remotely yet.
  • Only for Google AI Pro and Ultra subscribers on personal accounts, rolling out gradually; not on the free plan, AI Plus or Workspace/Cloud business accounts.
Read full story
Linked Models & Agents:
🤖 Google Antigravity🤖 Antigravity CLI🌐 Google🌐 Google DeepMind
#Antigravity#Remote Agents#cloud agents#coding agent#Google
a
arXiv Bangers@arXivBangers·
👀11
🔥83
View on 𝕏 ↗

Axion 1.0 Open Agent Foundation Model: Two SFT Stages Plus RL, 35B-A3B Reproduction Checkpoint Under Apache 2.0, Full Recipe Released

arXiv paper 2611.03233 by Yuqi Jia, Yunjie Tian and five co-authors introduces Axion 1.0, an agent foundation model that is open beyond its weights. It uses just two lightweight SFT stages plus reinforcement learning, with no dedicated mid-training, DPO or on-policy distillation, and about 18K SFT trajectories in total. Training code is hosted under the bytedance GitHub organization, and the training data and an Axion 1.0-35B reproduction checkpoint (Qwen3.6-35B-A3B base, 35B total / 3B active, Apache 2.0) are on Hugging Face; weights for the paper's 122B model are listed as coming soon.

Axion 1.0 Open Agent Foundation Model: Two SFT Stages Plus RL, 35B-A3B Reproduction Checkpoint Under Apache 2.0, Full Recipe Released

Key Takeaways

  • A minimal recipe: SFT stage one for self-contained scientific reasoning, stage two for tool use and information seeking, then RL for long-horizon interaction, with 18,363 SFT trajectories and 484 RL tasks
  • The release covers data construction, generation, filtering, verification, prompts and training code (GitHub), not just weights
  • The 35B reproduction checkpoint scores 52.8 on HLE, 60.4 on SEAL-0, 85.0 on FrontierScience Olympiad and 55.0 on FrontierScience Research (HF model card)
  • The paper's 122B model reports 59.0 on HLE with restricted website access and 66.7 on FrontierScience Research; 122B weights are not out yet
  • All scores are self-reported; the HF card lists Claude Opus 5 at 63.6 on HLE, so Axion still trails the strongest closed models
Read full story
Linked Models & Agents:
⚡ Axion 1.0-35B⚡ Axion 1.0-122B⚡ Qwen3.6-35B-A3B🌐 ByteDance🌐 Hugging Face
#Axion#open weights#agent model#Apache 2.0#RL#SFT
V
Vercel@vercel·
👀22
🔥82
View on 𝕏 ↗

Vercel Security Review Enters Public Beta: Vulnerability Review on Every GitHub Push, One-Click Fixes with Vercel Agent

On November 3 (US Pacific time), Vercel launched Security Review in public beta. It is a new Vercel Agent capability alongside Code Review and Investigation: once enabled, every push to connected GitHub repositories triggers a diff-only vulnerability review that finishes in seconds. Findings land in the Security dashboard, PR comments and the CLI, and can be fixed with one click by Vercel Agent or pulled into your own coding agent via vercel security vulnerabilities. It is available to Pro and Enterprise teams, and reviews are free until November 10.

Vercel Security Review Enters Public Beta: Vulnerability Review on Every GitHub Push, One-Click Fixes with Vercel Agent

Key Takeaways

  • Trigger: every GitHub push (including direct pushes to production) is reviewed automatically; only the diff is scanned, which Vercel says finishes in seconds, so it can run on every preview and production deployment
  • Beyond pattern matching: the agent traces how changed code is reached, checks existing protections and rechecks each finding, which Vercel says catches logic flaws such as missing authorization checks
  • Teams can write security policies in plain language, scoped to projects or the whole team, and manage them with vercel security policy
  • Pricing: Vercel Agent rate, model tokens at provider cost with no markup, plus $0.25 per million tokens; reviews are free until November 10, 2026, while Agent fixes are billed
  • Availability: public beta for Pro and Enterprise teams; Vercel has not published detection-rate or false-positive benchmarks
Read full story
Linked Models & Agents:
🤖 Vercel Agent🤖 Claude Code🤖 Codex🤖 v0 by Vercel🌐 Vercel🌐 GitHub
#Vercel#Security Review#Vercel Agent#vulnerability scanning#AppSec#code review#public beta
ADSponsored
U
Unreal Engine@UnrealEngine·
👀63
🔥82
View on 𝕏 ↗

Epic Confirms Unreal Engine 5.9: Preview Next Week With Improved AI Workflows and Web Back as a Target Platform

On November 3, the official Unreal Engine account said UE 5.9 is happening, with a Preview build next week that includes improved AI workflows, web as a target platform, and rendering and animation updates; changes are already landing in the 5.9 branch on GitHub, with more details to come. This reverses Epic's June State of Unreal stance that 5.8 was the last planned major UE5 release, with 5.9 kept only as an option. UE 5.8 shipped the Experimental Unreal MCP plugin, which lets MCP clients such as Claude Code and Cursor drive the editor; the 5.9 AI workflow changes likely build on it, but Epic has not published a feature list yet.

Key Takeaways

  • Timing: the official post went out at 13:05 UTC on November 3 and says the 5.9 Preview arrives next week; the exact date and the final release date are not announced
  • Scope (per Epic): improved AI workflows, web as a target platform, and rendering and animation updates; changes are already landing in the 5.9 branch on GitHub
  • Context: in June, State of Unreal called 5.8 the last planned major UE5 release; UE6 targets Early Access at the end of 2027, with the full release 12 to 18 months later
  • AI groundwork: the Experimental Unreal MCP plugin in 5.8 embeds an MCP server in the editor at http://127.0.0.1:8000/mcp by default, loopback only and with no authentication
  • Reach: about 2,552 likes, 251 reposts, 114 quotes and 215K views at fetch time
Read full story
Linked Models & Agents:
⚡ Claude⚡ Gemini🤖 Claude Code🤖 Cursor🤖 Epic Developer Assistant🌐 Epic Games🌐 Anthropic🌐 Google
#Unreal Engine#UE 5.9#Epic Games#MCP#AI workflows#game engine#Web#UE6
g
guregodevo (Memdoor author)github.com·
👀26
🔥81

Memdoor reproduces GVS5H: a team of five Qwen3.8-27B agents averages 9.8/10 on the first 10 LiveCodeBench Hard problems, against 8.0 for a single Fable 5 call

On Oct 31 the open-source coding agent Memdoor (GitHub guregodevo/memdoor, Apache 2.0) published its team-solve workflow: using the GVS5H paper authors' exact prompts and loop, five Qwen3.8-27B instances act as planner, two ideators, solver and manager, talk only through files on disk, and a script that runs the tests decides success. Over five passes on the first 10 LiveCodeBench Hard problems from the GVS5H list it averaged 9.8/10, against 9.6 for the authors' own harness and 8.0 for a single Fable 5 call; 49 of 50 runs solved the problem for $35.79 in total, and every request and reply is public. The Hacker News post has about 200 points. This is a community reproduction on only 10 problems, and the author notes that training-data contamination cannot be ruled out.

Key Takeaways

  • Score: a mean of 9.8/10 over five passes (10, 10, 10, 10, 9), against 9.6 for the GVS5H authors' own harness and 8.0 for a single Fable 5 call (team-solve README, project-reported)
  • Cost: 49 of 50 runs solved, $35.79 and 15.3M output tokens in total; a median run took about 40 minutes of model time and $0.39, the longest 4 hours
  • Method: five roles (planner, two ideators, solver, manager) plus a repair worker each start from a fresh context, talk only through files on disk, and a shell script that runs the tests decides success instead of a model
  • Limits: only 10 problems rather than the paper's 100; the contests date from March and April 2025 and may be in training data; Fable 5's column comes from the GVS5H authors' result files and was not rerun in this harness
  • Reach: the HN post has about 200 points as of Nov 1, 16:10 SGT; the original GVS5H paper is arXiv 2608.26480, which reached #1 on HN on Oct 25
Read full story
Linked Models & Agents:
⚡ Qwen3.8-27B⚡ Claude Fable 5🤖 Memdoor🌐 Alibaba🌐 Anthropic
#Memdoor#GVS5H#Qwen3.8-27B#Fable 5#LiveCodeBench Hard#Multi agent#Harness
D
DeepSeek@deepseek_ai·
👀198
🔥84

DeepSeek Harness team open-sources dsh-superagent, a proactive coding agent prototype for a single GitHub repo, alongside its Super-agent architecture article

On Oct 31, deepseek-ai open-sourced dsh-superagent-prototype on GitHub under the MIT license: a proactive coding agent that runs as a DeepSeek Harness bundle, lives on one GitHub repository and can optionally listen to Feishu. When an allowed person asks for work, it opens a work item, works in its own session and directory, and answers in the original conversation, for example with a pull request. The README states it is a research prototype, not an official DeepSeek product. The companion article, Exploring Super-agent Design, proposes a two-layer Router-Worker architecture; the team test, as reproduced by media, reports a p95 completion time of 15.7 minutes versus 29.5 minutes for a single-session setup.

Key Takeaways

  • Open source: deepseek-ai/dsh-superagent-prototype went public on Oct 31 under MIT, written in TypeScript, with about 1,800 stars at the time of writing
  • Scope: each deployment serves 1 GitHub repository, with optional Feishu; it acts only when an allowed person addresses it or a team-written rule allows it
  • Delivery: by default it only allows auto-merge for an approved pull request opened for a ticket with fewer than 200 changed lines, and never enables auto-merge as the agent account
  • Team test (as reproduced by media, vendor-reported): in a simulation with about 300 messages and 20 requests, p50 / p95 completion was 12.4 / 15.7 minutes versus 20.4 / 29.5 for a single session with a subagent tool
  • Risk: worker sessions run commands without approval as the operator's OS user, and the README asks users to review and adapt the code first
Read full story
Linked Models & Agents:
🤖 dsh-superagent🤖 DeepSeek Harness🌐 DeepSeek
#DeepSeek#DeepSeek Harness#Dsh superagent#Super-agent#coding agent#open source#GitHub#Feishu
A
Aaron J. Li@aaron_jli·
👀27
🔥83
View on 𝕏 ↗

Microsoft Research and UC Berkeley open-source TokenEconomizer, a plugin that cuts Claude Code and Codex costs by 18.4–30.9% on SWE-Bench without changing the agent

Microsoft Research, Microsoft Security and UC Berkeley open-sourced TokenEconomizer on GitHub (Apache 2.0, v0.1.0 released October 26), a plugin for Claude Code, Codex, GitHub Copilot CLI and Pi, with experimental opencode support. It learns from earlier sessions in the same repository: micro-skills remember repo facts, tool pruning drops tool definitions that go unused, and a loop guard nudges an agent that is stuck. The paper reports 18.4–30.9% lower mean cost across five agent and model combinations on 277 SWE-Bench tasks (paper figures).

Microsoft Research and UC Berkeley open-source TokenEconomizer, a plugin that cuts Claude Code and Codex costs by 18.4–30.9% on SWE-Bench without changing the agent

Key Takeaways

  • Cost: on 277 SWE-Bench tasks across 6 repositories and 5 agent and model combinations, mean cost fell 18.4–30.9%, and all 30 repository-by-combination cells got cheaper (paper figures, including the plugin's own model calls)
  • Success rate: Pass@1 rose in 4 of 5 combinations, e.g. Copilot CLI with Sonnet 5 cost 18.4% less with Pass@1 +2.6, while Claude Code with Opus 4.8 cost 30.9% less with Pass@1 -1.0
  • Three mechanisms: micro-skills (repo facts stated only after three sessions confirm them), tool pruning (by default a tool unused over 10 prompts across 2 sessions is left out), and a failure-loop guard (one redirect when a judge is at least 0.7 confident, never stopping a turn itself)
  • Generalization: 16–21% lower cost on Web-Bench and 6–20% lower on two security-auditing benchmarks where every task is a new repository
  • Deployment: Apache 2.0, Python 3.10+, no API key of its own, and learned experience stays local in ~/.tokeneconomizer
Read full story
Linked Models & Agents:
⚡ Claude Sonnet 5⚡ Claude Opus 4.8⚡ GPT-5.6 Terra⚡ GPT-5.6 Sol🤖 Claude Code (Anthropic)🤖 codex🤖 github-copilot-cli🤖 pi🤖 opencode🌐 Microsoft🌐 UC Berkeley🌐 Anthropic🌐 OpenAI🌐 GitHub
#TokenEconomizer#Microsoft Research#UC Berkeley#coding agents#token cost#Claude Code#Codex#SWE-Bench#open source
P
Pikapika_labs·
👀38
🔥84
View on 𝕏 ↗

Pika releases open-weight video model Pika H3 Flex: post-trained on MiniMax H3, six-step sampling, five audio stems, API from $0.01 per second

On October 29 Pika released Pika H3 Flex, a joint audio-video generator post-trained on MiniMax H3, with weights on Hugging Face, code on GitHub, and ComfyUI and Diffusers support. Pika says it distilled sampling from 50 denoising steps to six, averages about 1.8x faster DiT denoising than Fal H3 Max at 768p, and can produce a 15-second clip with soundtrack in as little as about 7 seconds; the hosted API adds 1080p to 4K upscaling and five exportable audio stems (dialogue, score, SFX, foley, ambience). All speed and quality figures are vendor-reported.

Pika releases open-weight video model Pika H3 Flex: post-trained on MiniMax H3, six-step sampling, five audio stems, API from $0.01 per second

Key Takeaways

  • Pricing (vendor figures): text/image-to-video from $0.01 per output second, reference-to-video from $0.03/s; Pika's comparison table lists MiniMax H3 at $0.08/s and Seedance 2.5 at $0.103/s
  • Speed (vendor figures): six-step sampling, about 1.8x faster DiT denoising than Fal H3 Max at 768p on average and about 2x for 10–15 s text/image-to-video; 1.60x faster end-to-end 5 s 768p generation on eight B300 GPUs
  • Ranking: Pika's product page says it scores 1293 and ranks #7 on DesignArena's text-to-video board, the highest open-weight model there
  • Release form: the Hugging Face repo is tagged as an adapter on MiniMax-H3 under an 'other' license; local inference natively supports 480p/768p with mixed stereo audio, while 4K upscaling and audio stems are hosted-API only
  • Accepts up to 12 reference inputs (image, audio, video, sketch and more) and supports style transfer, motion transfer, character swap and targeted edits
Read full story
Linked Models & Agents:
⚡ Pika H3 Flex⚡ MiniMax H3⚡ Fal H3 Max⚡ HeyGen Video⚡ Gemini Omni 1.1 Flash🌐 Pika🌐 MiniMax
#Video generation#Open weights#Audio stems#Distillation#Pika
L
LM Studiolmstudio·
👀111
🔥80
View on 𝕏 ↗

LM Studio Bionic 1.1.10 adds the oMLX engine, bringing RAM-plus-SSD KV caching and MLX speculative decoding to local models on Macs

LM Studio posted on X at 15:32 UTC on Oct 29 that users can now download a new oMLX engine inside LM Studio and run it with MLX models they already have. The official changelog lists it in Bionic 1.1.10, alongside speculative decoding for MLX models and an in-app terminal. oMLX is an open-source Apple Silicon inference server maintained by developer jundot (Apache 2.0, about 23,000 GitHub stars) built around continuous batching and a two-tier RAM and SSD KV cache. LM Studio has not published speed comparisons for the engine.

LM Studio Bionic 1.1.10 adds the oMLX engine, bringing RAM-plus-SSD KV caching and MLX speculative decoding to local models on Macs

Key Takeaways

  • LM Studio says the oMLX engine can now be downloaded inside the app and used with existing MLX models; the post has about 540 likes (post)
  • The Bionic 1.1.10 changelog (Oct 29) also lists speculative decoding for MLX models, a public in-app terminal and llama.cpp 2.66.0 extension packs (changelog)
  • oMLX is Apache 2.0 open source with about 23,000 GitHub stars and needs macOS 15+ on Apple Silicon M1 to M5 (GitHub)
  • Its core is a two-tier KV cache: hot blocks stay in RAM, overflow is written to SSD as safetensors, and matching prefixes are restored from disk even after a restart
  • LM Studio has published no speed comparison with its existing MLX engine; one user reported that restart-to-update failed after launch and LM Studio is investigating
Read full story
Linked Models & Agents:
⚡ MLX🤖 LM Studio🤖 oMLX🌐 LM Studio🌐 Hugging Face🌐 Apple
#LM Studio#oMLX#MLX#Apple Silicon#local LLM#KV cache#inference engine
ADSponsored
O
OpenAI@AICoder·
👀45
🔥80

OpenAI Codex CLI 0.166.0: keyboard transcript selection, GPT-6.1 Terra in Vertex AI and Bedrock catalogs, codex doctor network checks

OpenAI published the stable Codex CLI release rust-v0.166.0 on GitHub at 19:47 UTC on Oct 29. It adds keyboard-driven transcript selection by word, line and page with built-in search, adds GPT-6.1 Terra to the bundled, Vertex AI and Amazon Bedrock model catalogs, introduces codex queue --resume and codex doctor --network-only, and shows clearer safety-block explanations with selectable safer follow-up prompts in the TUI.

Key Takeaways

  • Model catalogs: GPT-6.1 Terra added to the bundled, Vertex AI and Amazon Bedrock catalogs (#57293)
  • Transcript: keyboard selection by word, line and page, integrated search and configurable shortcuts (#57221 to #57224)
  • New commands: codex queue --resume resumes unloaded threads after queueing a message; codex doctor --network-only checks config, connectivity and WebSocket
  • Safety UX: clearer explanations when a request is blocked, plus selectable safer follow-up prompts (#57363, #57365)
  • Fixes: approval and permission settings survive resume and fork, and reasoning-token accounting is preserved to avoid premature compaction
Read full story
Linked Models & Agents:
⚡ GPT-6.1 Terra🤖 Codex CLI🌐 OpenAI
#Codex CLI#OpenAI#GPT-6.1 Terra#Amazon Bedrock#Vertex AI#codex doctor#TUI#coding agent
A
Anthropic@AICoder·
👀96
🔥80

Claude Code 2.1.314 adds a block on writes outside working directories, IdP group routing for the apps gateway, and idle compaction in VS Code and Desktop

Anthropic published Claude Code v2.1.314 on GitHub at 01:06 UTC on Oct 30. The official CHANGELOG lists 119 entries, more than half of them fixes. The new features lean enterprise: permissions.blockWritesOutsideWorkingDirectories makes Write, Edit and NotebookEdit refuse paths outside the working directories in every permission mode; Claude apps gateway upstreams can be limited to IdP groups, and Bedrock upstreams can log which guardrail fired; idle compaction now also runs in VS Code and the Code tab of Claude Desktop. It also fixes sessions failing repeatedly when an admin caps effort, and claude -p exiting 0 with no output when startup failed.

Key Takeaways

  • Size: the official v2.1.314 CHANGELOG has 119 entries, more than half of them fixes; released at 01:06 UTC on Oct 30
  • Safety: the new permissions.blockWritesOutsideWorkingDirectories setting stops Write, Edit and NotebookEdit from writing outside the working directories in every permission mode
  • Enterprise gateway: upstreams take an optional groups list so only developers in those IdP groups are routed there, failover included; Bedrock upstreams gain guardrail_trace
  • Experience: idle compaction now runs in VS Code and the Claude Desktop Code tab, summarizing idle conversations above 200K tokens
  • Fixes: when an admin caps effort, Claude Code lowers it and retries; a failed claude -p startup now prints the error and exits 1
Read full story
Linked Models & Agents:
🤖 Claude Code🌐 Anthropic
#Claude Code#Permissions#LLM gateway#Amazon Bedrock#idle compaction#VS Code#Enterprise
G
GitHubgithub·
👀28
🔥82
View on 𝕏 ↗

GitHub Copilot Automations go team-wide: repo-owned shared automations, 24 new triggers, 20 new tools and AI credit limits

At GitHub Universe on Oct 28, GitHub shipped a batch of Copilot Automations updates. Shared automations now belong to the repository, act as Copilot, bill AI credits to the repo owner and keep running after their creator leaves. Automations gain 24 new GitHub event triggers, REST API triggers from external systems and chaining, plus 20 new tools and custom MCP servers. Teams can cap AI credits per run, day, week or month, automations can propose improvements to their own prompts from real outcomes, and VS Code 1.144 can run them in the cloud.

GitHub Copilot Automations go team-wide: repo-owned shared automations, 24 new triggers, 20 new tools and AI credit limits

Key Takeaways

  • Shared automations belong to the repository: anyone with read access can view them, anyone with write access can edit, actions are attributed to Copilot and AI credits bill to the repo owner
  • 24 new triggers across issues, pull requests, discussions and security alerts, plus REST API triggers from systems like Jira, Sentry and PagerDuty
  • 20 new GitHub tools (merge PRs, retry workflow runs, upload screenshots and more) and support for custom MCP servers
  • AI credit limits can be set per run, day, week or month; org repos need an admin to enable the "Allow use of Copilot cloud agent billed to the organization" policy
  • Rolling out to Copilot Pro, Pro+, Max, Business and Enterprise; VS Code 1.144 runs them in the cloud with chat.automations.cloud.enabled
Read full story
Linked Models & Agents:
🤖 GitHub Copilot🌐 GitHub🌐 Microsoft
#GitHub Copilot#Copilot Automations#GitHub Universe#MCP#Agents#Automation
G
GitHubgithub·
👀39
🔥83
View on 𝕏 ↗

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
Read full story
Linked Models & Agents:
🤖 GitHub Copilot🤖 Copilot cloud agent🌐 GitHub🌐 Microsoft
#GitHub#Octoverse 2026#coding agents#pull requests#GitHub Copilot#developer growth
M
Microsoft Developermsdev·
👀35
🔥84
View on 𝕏 ↗

Microsoft opens the GitHub-built Copilot harness in Foundry Agent Service: public preview, 20+ frontier models plus BYOK, billing from Dec 1

During GitHub Universe on Oct 28, Microsoft put the Microsoft Copilot harness, the shared agent execution layer behind Copilot Code, Copilot Studio, the GitHub Copilot app, the CLI and VS Code, into Foundry Agent Service as a public preview. Microsoft runs the agent loop and sandbox while developers supply instructions, skills, tools and model choice; Microsoft says it works across 20+ frontier models plus bring-your-own-key. Long-running resilience for hosted agents also reached GA. Harness runtime usage is free until Dec 1.

Microsoft opens the GitHub-built Copilot harness in Foundry Agent Service: public preview, 20+ frontier models plus BYOK, billing from Dec 1

Key Takeaways

  • Microsoft says the harness spans 20+ frontier models plus bring-your-own-key, so you can swap models without rewriting orchestration (official blog)
  • Preview building blocks: reusable skills, Foundry and MCP tools with explicit auth and approvals, file and code execution with built-in subagent delegation, and source-controlled agent definitions
  • Pricing: harness runtime is free until Dec 1; after that sandbox compute is USD 0.0994 per vCPU-hour and 0.0118 per GiB-hour, with model inference billed separately (Foundry blog)
  • Long-running resilience for hosted agents went GA the same week: interrupted work restarts with its original input, and your app saves checkpoints
  • Limits: you pick the harness only when creating a new agent; existing agents are unchanged and switching later is not supported yet
Read full story
Linked Models & Agents:
🤖 Microsoft Copilot harness🤖 Foundry Agent Service🤖 GitHub Copilot🤖 Copilot Studio🌐 Microsoft🌐 GitHub
#Microsoft#Foundry#Copilot harness#agent runtime#MCP#agent skills#GitHub Universe