Reason Machines Open-Sources Aspen, a Git Server Built for AI Agents
Reason Machines has released Aspen, an open-source Git server for AI agent traffic.It targets repositories that hundreds of agents push to at once.Aspen independently implements the design OpenAI described for Pando.TL;DR Size: One Rust binary.
Branches live in PostgreSQL (or SQLite), packs live in object storage.Runs on: A laptop (aspen dev), one host via Docker Compose, or Helm and Terraform clouds.Performance: Pushes to different branches never wait on each other, and fetches reuse stored packs.
Best: 567 pushes per second sustained for 5 minutes on one 8-core server (self-reported).Worst: Landing on main runs at about 4 per second, and the project is pre-alpha.Bottom line: Best: per-branch writes remove the per-repository push ceiling.
Worst: no releases, no web UI, and the 24-hour durability gate is not met.What is Aspen?Aspen is a self-hostable Git server from Reason Machines that scales writes per branch, not per repository.It speaks plain Git over HTTPS and SSH.
Packs go to S3-compatible object storage; branch history goes to PostgreSQL.Why do classic Git servers struggle with AI agents?An agent pushes every few minutes, and CI fetches after each push.Per Reason Machines, a classic server queues every push to a repository together.
It also builds a fresh pack for every fetch.Server work grows with clients, not with new code.OpenAI hit this wall and built Pando.It cut median push latency by nearly 5x and serves nearly a petabyte daily.How does Aspen work?Aspen follows Pando and drops 2 claims most Git servers keep.
Per-branch compare-and-swap Each branch is a chain of versions in one Postgres row.A push uploads its pack, then moves the branch with one compare-and-swap.Pushes to different branches never wait for each other.If 2 pushes race on one branch, one wins.The other gets Git’s usual “fetch first” error.
The cost: branches may be read at slightly different moments.Stored packs instead of per-client packs Aspen never unpacks a push.It checks the pack, completes missing bases and stores it as-is.A fetch streams stored packs the client lacks, recompressing nothing.
Signed URLs can serve packs from a CDN.Trunk-only advertisement and compaction A push sees only the trunk branch, so negotiation stays small with a million branches.Background compaction merges packs 16 at a time, in levels like a log-structured merge tree.
A client far behind gets a few dozen packs, not thousands.Depth-1 CI fetches get a tip pack built once and served to every job.Auto play
Agentgit push CI jobgit fetch Aspenstateless server Postgres1 row per branch Object storagepacks as pushed Run 128 writersReset
Classic server: one queue per repo 0 / 128 done Aspen: one CAS per branch 0 / 128 done Toy model, not a benchmark. Amber = waiting, blue = done. Mirrors the illustrative simulation in the Reason Machines launch post. Pushes per second to one repository, each durable before acknowledgement
Cursor Origin, S3 Standard (published)120 <div class="
Related
相關文章
火山引擎發佈Seedream5.5:像素級可控編輯,百輪修改不崩壞
相比上代Seedream5.0Pro,新模型寫實感更接近實拍,可生成更自然、更具電影感的圖像。價格方面,Seedream5.5採用"輸入圖+輸出圖"兩段計費,文本Token免費,2K方圖編輯單張僅0.28元,較Seedream5.0Pro便宜60%,生成速度快近40%。
千問辦公2.0發佈,首個支持多人與Agent協作的Team Agent來了
11月9日,千問辦公發佈2.0版本,這是其上線三個月以來最大的一次升級。該版本推出了行業首個支持多人與Agent協作的功能“協作”,同時上線“人機共創”、“資料空間”、“自動任務”和“三端互聯”等核心能力,讓Agent從服務個人走向服務團隊和組織。
基元律動發佈NeoHorse-1.5-Flash Preview,Agent模型RSI進入第二輪迭代
11月5日,基元律動(TokenRhythm)發佈Agent原生模型NeoHorse-1.5-Flash Preview。該模型基於Qwen3.8-Flash進行訓練,總參數180B、激活參數6B,重點提升Agent任務執行與軟件工程能力,目前開放限量內測。據公司公佈的對照評測,相較基座模型,新模型在Agents’ Last Exam上的整體得分提高9.23分,在Natural Language to Repository評測上的整體得分提高5.92分。
SpaceXAI 擴大 Grok Bot 訪問範圍,覆蓋 SuperGrok 與 Cursor 全線套餐
定價未變:Cursor Pro 每月20美元、Pro+60美元、Ultra200美元,Teams Standard 每用戶40美元、Premium120美元;SuperGrok 每月30美元、SuperGrok Plus100美元,X Premium+ 在美國每月40美元,提供七天免費試用。

MoE Routing Strategies Explained: Top-K, Expert-Choice, Sigmoid Gating and Dynamic Routing (2026 Guide)
Every Mixture-of-Experts (MoE) model repeats one decision for every token: which experts should process it. That decision belongs to the router. MoE routing strategies shape quality, load balance and serving cost in every major open model.
Nous Research Releases Arete-27B that Cuts Qwen3.8-27B Propaganda From 22% to 0.9%
Nous Research has released Arete-27B, a supervised fine-tune of Alibaba’s Qwen3.8-27B. Arete-27B is a 27B open-weight model that strips Chinese state framing from Qwen’s answers. On Nous’s own 803-prompt eval, answers containing propaganda fell from 22% to 0.