Concurrent agents on one file system
I think there's a lot of confusion out there about what a file system really is. There's lot of places where we can assign blame.People's fa...
Topic
#distributed-system
19 篇笔记
Building fine-grained version control for agents with Rust and Durable Objects
How DeltaDB streams every edit from humans and agents into one causal graph.
Redesigning source control at Anthropic
As our merges per day grew more than tenfold, source control became our biggest development bottleneck. Here, we share how we migrated to an in-house version control system in five months.
Consistent Hashing is kinda cool
Hii in this blog I will try to cover consistent hashing in detail from top to bottom. But before getting into consistent hashing, let’s firs...
Building the last WAL on S3
Nearly two years ago, we started Archil with a simple realization: it was clear that people were spending immense effort to build new, serve...
wal3: A Write-Ahead Log for Chroma, Build on Object Storage
wal3, Chroma's write-ahead log, combining a 30-year-old lock-free algorithm with S3's newest conditional writes feature.
Formally verified distributed locks for sandboxes
I’m wrapping up my internship at Modal, where I worked on our new, massively scalable sandbox architecture.
Git at any scale
Cursor is the best AI coding agent., Git at any scale
OpenEBS vs Longhorn 深度对比
OpenEBS vs Longhorn 深度对比 调研时间：2026-07-26 ｜ 数据来源：OpenEBS v4.5.x 官方文档 + Longhorn v1.12.0 官方文档 概览速查 | 维度 | OpenEBS | Longhorn | |---|---|---| | 定位 | CAS/CNS 容器原生存储，多引擎可选 | 轻量分布式块存储，每卷独立 Engine | | CNCF 状…
DSLs Enable Reliable Use of LLMs
Why Domain-Specific Languages and good abstractions make LLM code generation reliable and reviewable.
Volcano 调度框架深度解读
Volcano 调度框架深度解读 调研日期: 2026-07-01 | 版本: v1.15.0 (2026-06) | 置信度: 全部 findings 经 3-vote 对抗验证，均 High confidence 官方站点: volcano.sh | GitHub: volcano-sh/volcano 一、项目概览与 CNCF 状态 Volcano 是 CNCF 孵化项目（2022-03-2…
Optimizing distributed AI inference: Advanced deployment patterns
Learn about the three optimization levers for distributed AI inference: prefill/decode disaggregation, KV cache strategy, and speculative decoding.
Designing distributed AI inference: Core concepts and scaling dimensions
Learn about the five-dimensional design space in modern LLM serving, including tensor, pipeline, expert, data, and context parallelism.
Engineering End-to-End Observability for Kubernetes Workloads
Stop drowning in Kubernetes dashboards. Learn how consistent signal architecture, not more tooling, is what actually cuts incident resolution time in half.
Launching a million sandboxes per second in constant time
They say that in the future, everyone will have their own sandbox company for 15 minutes. Let's make that simpler, here's exactly how I buil...
Advanced Observability for Production Systems
From SLIs to LLM monitoring: 9 practices that separate mature teams from dashboard collectorsMost teams have dashboards. Few have observabil...
protocols for transactional usage of object storage
building correct applications w/o external coordination
Inside vLLM: Anatomy of a High-Throughput LLM Inference System
From paged attention, continuous batching, prefix caching, specdec, etc. to multi-GPU, multi-node dynamic serving at scale.
Building and operating a pretty big storage system called S3
Three distinct perspectives on scale that come along with building and operating a storage system the size of S3.