We are a team of researchers and engineers at @GitHub who investigate the future of software development.
The Future
Joined July 2020
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A new project from GitHub Next!
Chopin is a real-time, multiplayer agentic planning environment. Designed to get your whole team aligned on the plan before you hand it off to agents.
We find our current agent planning tools feel cumbersome, static, lonely, and drown us in walls of text. We're exploring how we can make planning more interactive, shared, and visual 🖌️
It's currently an early prototype but we'll be developing it more over the coming months – and it's open source!
githubnext.com/projects/chop…
How do you _guarantee_ your agent's code is correct? You prove it.
Introducing Proof Driven Development Kit (PDD Kit) from @MSFTResearch.
PDD Kit helps turn your plan into a formal spec, then proves the agent's code meets that spec for every input. No vibe verification. PDD Kit is integrated with @GitHubNext's Chopin, so you can plan as a team.
PDD Kit takes an app's login flow from intent to a verified pull request:
It discusses your code, infers your intent as it goes, and proposes a formal spec in Verus, a proof language for Rust.
Once you approve the spec, the Verus verifier automatically proves the code meets it.
Where proof isn't possible, it falls back to empirical verification with tests and computer-use agents (CUAs).
It works on Rust today. No new language, no change in how you produce the underlying code, making formal methods more accessible.
The goal is a verified software factory, where engineers review specifications instead of code.
Come see it in action at GitHub Universe, Oct 28–29. Swing by the Speakeasy and ask for GitHub Next!
GitHub Next retweeted
Today is my first day at @GitHubNext!
GitHub has been one of my favorite places to hang since I signed up 18 years ago(!), a mere month after its founding.
Excited to join this talented team to try and figure out what comes next for our craft ...
🇯🇵 In Japan? Idan Gazit from @GitHubNext and @JasonEtco from the @github Copilot team will be speaking at @findy_tools AI Engineering Summit on December 14+15. ✨
／
「AI Engineering Summit Tokyo 2026」特設サイト公開✨
＼
AI時代の開発に挑む、すべてのエンジニアと技術リーダーのためのカンファレンス。 12月の開催が決定しました！
国内外から、AI開発の最前線を走る企業が集結します🌎
#AIE2026_findy
▼ 基調講演
・Chip Huyen氏（「AIエンジニアリング」著者） @chipro
・Idan Gazit氏（GitHub Head of GitHub Next）
・Jason Etcovitch氏（GitHub Copilot Agents, Principal Software Engineer） @JasonEtco
▼ 特別講演企業（一部抜粋）
・Duolingo
・Meta
・Uber Technologies Inc. @mykolaskrup
・Vercel @elsigh
・株式会社村田製作所
・日本電気株式会社
・東京海上日動システムズ株式会社
▼お申し込みはこちらから
ai-engineering-summit-tokyo.…
GitHub Next retweeted
Found time last week to cleanup the homepage & docs of Agentic Workflows, one of the very successful @GitHubNext projects of the last year. TLDR: run your own software factories on GitHub Actions
Previous page was vibed by agents and backend developers. Took me a day to redo it.
GitHub Next retweeted
Had a wonderful chat with @GergelyOrosz - always nice to go deep on ideas in a proper long chat. Got to show off some notebook sketches and design jigs with agents too.
What can us, engineers else learn from designers and design engineers? Turns out there’s lots, based on talking with one of the best design engineers in the industry, @Mappletons.
Timestamps:
00:00 Intro
03:24 From anthropology to tech
10:18 What does a designer do?
18:23 How Maggie works
24:55 The case for planning with physical tools
31:53 Why Maggie is learning woodworking
33:13 Design engineers and engineering constraints
38:49 How Maggie uses Figma
40:30 Design at GitHub Next
45:12 How has AI changed design
50:37 When models design and why humans are still needed
53:30 UX and UI
58:29 Capability gaslighting
1:00:33 One Developer, Two Dozen Agents, Zero Alignment
1:07:21 Craft and AI tells
1:14:17 Visual gardens, home-cooked software, and barefoot developers
1:21:02 Advice for engineers and lessons from anthropology
1:25:34 Book recommendation
Brought to you by:
• @turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable. turbopuffer.com/pragmatic
• @AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. antithesis.com/pragmatic
• @EntireHQ – every agent prompt, tool call, stored in your repo, and mirrored. Set up with one click: entire.io/pragmatic
Hope you enjoy this unusually visual episode - thanks again, Maggie!
github.com/githubnext/localj…
Not everyone on the team has access to Jev yet. Spent a morning cobbling together a poor man's Jev on top of omlx for local use. Benchmarked and eval'ed a variety of models including diffusiongemma and a variety of autoregressive models (Qwen MoE, Gemma 4 MoE, and Gemma 4 e4b/e2b.) Benchmark report is in the repo.
This is 100% promptcoding but hey the evals look okay, speeds are pretty good on local machine (m5 max 64gb), and the LocalJev server exposes an API that can be used with the normal Jev API wrapper libraries.
Messing around with @typesafeai Jev for real-time styling
🔤 🔜 🎨
If you can define a bounded, semantically meaningful property space, you can project a natural language expression into that space in one generation step
From our friends on the Applied Science team, great stuff!
We're finally starting to see the multimodel future with an intelligent routing layer. As time goes on, we _should_ care less about the models, because A) they're all "good enough" and B) AI should be optimizing routing.
Plus, imagine a world where we do some inference on local hardware. Coding is certainly sensitive to parameter count, but a lot of the coding workload is not about code generation, but reasoning about English/NL. That part of the agentic workload doesn't necessarily need the flagship models, or even the cheaper cloud models. Those subagents don't necessarily require long context windows (which in turn requires massive amounts of RAM and memory bandwidth). Companies are buying powerful machines for their engineers — machines that can do some of this work, but only if there's a routing layer that can route to them. Excited to see the first steps in this direction!
4.9 percentage points higher verified task quality. 67% lower estimated cost.
Project HydraFusion delivered those results against Claude Opus 5 on Terminal-Bench 2.1 in controlled offline evaluations.
HydraFusion orchestrates the models and workflow for each coding task. You select it like any other model. ⬇️
github.blog/ai-and-ml/github…
GitHub Next retweeted
hot take: code has been commoditised but access to do code hasn’t yet
ai transformation at software companies is mainly about removing gate keepering and enablement of anyone from anywhere to contribute ideas (even if they get thrown away. it’s better than a slack message!)
You can check out the repo here.
Use at your own risk. But also feel free to contribute ideas and discussions via issues.
We've got lots we want to explore in this space and will be developing it in public.
github.com/githubnext/chopin
GitHub Agentic Workflows.
Learn how to use Agentic Automation at Scale in your Organization or Enterprise
- Roll out changes org-wide
- Assess code quality across 100s of repos
- Synchronize dependency updates
- Propagate coding policy changes
- Shrink backlogs in multiple maintained repos
- Enforce coding standards across teams
- Aggregate issue tracking into a single control plane
Use with Anthropic, Open AI, Copilot and other subscriptions. Cost controls, auditing and other enterprise features built-in.
Enterprise agentic repository automation. Centred on the GitHub platform. Under developer guidance and control.
gh.io/gh-aw/
cc @GitHub @GitHubCopilot
✨ New post! "Knowledge compressor"
We're all writing more specs. Pretty soon they add up to real tokens! How can we get rigorous about compacting that context in a way that doesn't impact model understanding?
githubnext.com/posts/knowled…
Discussion on HN: news.ycombinator.com/item?id…
Software Factories, Continuous AI and GitHub Agentic Workflows!
GitHub's Don Syme joins Tessl's Guy Podjarny on The AI Native Dev to explore agentic repository automation.
A lot of the subjective work we do around software: triage, documentation, performance, accessibility, wants to run continuously and in context, the same way CI/CD does. Not inside your CI. Beside it.
YouTube: tessl.co/qsf
Apple Podcasts: podcasts.apple.com/us/podcas…
Spotify: open.spotify.com/episode/1QP…
GitHub Next retweeted
The aim of agentic workflows is to make the repo and PDLC a living organism. Think a garden. Alive, evolving, and always improving.
We have a wide catalog available and we will continue to expand them.
github.blog/changelog/2026-0…
