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GitHub热门项目 | A GPU compute-centric 2D renderer. | Stars: 4,044 | 8 stars today | 语言: Rust

Martin Fowler's May 27 Fragments brings together four arguments with direct implications for teams working with AI agents. All four are worth covering. Ian Johnson: build quality gates before releasing the agent Ian Johnson published a series about restructuring a gnarly codebase: three months, 258 commits, moving from a Laravel monolith with no tests to an application with automated quality gates and an AI agent shipping production code with minimal supervision. The insight Fowler highlights is about the transition from in-the-loop to on-the-loop: "For the first two months of this project, I used Claude Code with auto-approve turned off. Every file edit, every terminal command, every change… I reviewed it before it executed. The results were good. The code was clean. But I was doing most of the thinking and half the typing. The agent was a fancy autocomplete with better suggestions." Ian Johnson Manual review of every change is not how you build trust in the agent. Trust comes from building the structure that ensures the agent will do the right thing, then stepping back. The sequence: characterization tests first, static analysis, architectural patterns that make things flow correctly. Fowler notes this is exactly the sequence he would use himself. Adam Tornhill: roughly 2 hours of cognitive endurance Adam Tornhill observes that agentic work has a decision density that is mentally more expensive than it appears. The estimate is roughly two hours as a sustainable limit, not a full day of work. The implication: adding more parallel agents does not solve the problem, because the bottleneck is the coordinating engineer's cognitive capacity, not available processing volume. The solutions are smaller tasks, automation, and verification mechanisms, not more parallelism. NHS: closing open source repositories NHS (UK National Health Service) closed open source repositories citing LLM threats to code security. The UK Government Data Services countered directly: making code p

A complete ML pipeline: engine, backprop, binary format, and a live browser demo. Zero dependencies. Under 200 KB total. If you have built machine-learning projects before, you have probably done it by importing PyTorch, TensorFlow, or scikit-learn and calling .fit() . Those are excellent libraries. This article is about what happens when you deliberately do not use them — when you build every piece of the pipeline yourself, in a language that compiles to WebAssembly, and the result runs live in the browser with no server, no Python, and no cloud bill. Here is the live demo: move four sliders, watch the predicted Iris species update in real time. The model is running entirely inside your browser tab, loaded from a 1.1 KB binary file, powered by ~100 KB of WebAssembly compiled from pure Rust. This is the story of how I built it and why the engineering choices made it work. Why Rust? Why WebAssembly? Why zero dependencies? Three constraints drove every design decision. WASM requires no_std or a carefully limited std . The wasm32-unknown-unknown target has no operating system, no file system, and no libc. A crate that links against rand , ndarray , or any library that makes OS calls will not compile to it without significant plumbing. An engine built from nothing but the Rust standard library compiles cleanly to every target, including WASM. A zero-dependency std -only crate is uniquely auditable. There are no transitive dependency trees to vet, no supply-chain risks, no version conflicts. Every line of code that runs in the user's browser lives in this repository. The deployment story becomes the technical story. A 100 KB WASM blob that runs locally in the browser is not just a cost optimisation — it is a privacy guarantee (user inputs never leave the machine) and a latency guarantee (inference is microseconds, not a round trip to a cloud API). That story is only possible because the engine has no external dependencies that would bloat the binary. The architecture: ei

Here are two scenes. They look unrelated. They're not. Scene 1 Two people at a café, talking about a restaurant they want to try. A stranger walking past stops: "That place closed six months ago. The one on the corner is better." A brief nod, and they walk on. The two people exchange a glance, taken aback. Why did that person stop? What did they want? A few steps away, the stranger is also confused. They had useful information. They shared it. Why did these people react so strangely? Scene 2 A colleague is visibly stressed, describing a difficult situation at work. One friend pulls their chair closer, puts a hand on their arm: "That sounds really hard." Another opens their laptop: "I found something that might help — HR has a process for exactly this, I'll send you the link." The colleague leans into the first. Glances uncertainly at the second. The second person doesn't understand why sitting close and saying "that sounds hard" counts as helping. You haven't solved anything. The first doesn't understand why anyone would respond to distress with links. Both scenes end the same way: people on both sides convinced they did the right thing, confused by the other's reaction. The mismatch is mutual and invisible from the inside. Two survival instincts, two empathy systems For many autistic people, information is a survival mechanism. Uncertainty is threat, missing information is a vulnerability, and the drive to correct and share runs below conscious awareness. Empathy, expressed through that system , looks like giving someone what keeps you safe: accurate information, solutions, resources. The social preamble before sharing — announcing yourself, softening the approach — doesn't arise as a concept. Why would useful information require an introduction? For many neurotypical people, social safety is a survival mechanism. Group cohesion and reading others accurately are what keep people safe. Empathy, expressed through that system , looks like presence: mirroring distress,

TL;DR I've missed a lot of opportunities simply because I didn't know they existed. So every...

Hey everyone! I bring you my development journey on what I have discovered, accomplishments for this...

👋👋👋👋 Looking back on your week -- what was something you're proud of? All wins count -- big or small...

I've been exploring some lesser-known languages and I'm curious what the community thinks. Languages like Go, Rust, and Kotlin have gained traction, but I feel like there are some gems that don't get enough attention. What programming language do you think is underrated? Which one deserves more love from developers? Share your thoughts and why you think that language is underrated!

We were burning 400ms in p99 tail latency on a core event-processing path in Veltrix. The upstream teams kept blaming the network, but the numbers didnt lie—64% of the time was spent inside the JVM, specifically in sun.misc.Unsafe.park during GC pauses. Every time we hit 80% heap pressure, the throughput collapsed and we lost 300k events per minute. That was the exact moment I stopped believing in the JVM as the runtime and started looking at the system boundary. The first attempt was aggressively tuned HotSpot with G1GC and pinning the critical threads to their own NUMA nodes. We set -XX:MaxGCPauseMillis=20 , -XX:+UseNUMA , and even migrated to Azul Zulu Prime because its handling of large heaps was supposedly better. The p99 dropped to 280ms, but the GC telemetry still showed a sawtooth pattern of 30–40ms spikes every 230ms on a 16GB heap. Profiling with JDK Flight Recorder told us 18% of CPU time was spent in card-table scanning. At that point I knew we were fighting the runtime, not the problem. The event pipeline was small—just JSON parsing, enrichment, and a single RocksDB write—but the JVMs generational collector couldnt stop moving objects. The architecture decision came during a four-day blackout window after a failed Blue-Green deploy. Three of us sat in a war room with a single Grafana dashboard showing 100% CPU steal time on the Kubernetes nodes. We had two choices: squeeze more life out of the JVM by manually balancing the heap or rewrite the critical hot path in Rust and give the compiler full control over memory layout. The Rust option meant losing the JVM ecosystem (no more async-profiler, no more one-liner heap dumps) but gave us stackless futures, zero-cost abstractions, and compile-time memory safety. We chose Rust. We forked the Cargo.toml wed used in a sidecar for metrics and started porting the event collector. The numbers after the rewrite told the story. We recompiled the same two endpoints— POST /events and GET /aggregates —and served them f

LinkerBot makes dexterous robotic hands for as little as $600. It wants to become the standard for humanoids and automated factories—and eventually replace human labor altogether.

A new hacking campaign is trying to trick Signal users to give up their secret recovery key, which can be used to access online backups containing past messages.

Pay Tel secured the publicly exposed data after security researchers discovered the leak containing callers' sensitive ID documents and inmate communications.

The launch of the Ojai minivan robotaxi comes after years of development and testing, but arrives amid a challenging time for Waymo.

The pale-blue Ojai vehicles will start picking up members of the public in California and Arizona today.

After several months of testing, Waymo is finally ready to invite non-employee passengers into its newest vehicle, the Zeekr RT minivan, which has been rebranded as Ojai. Waymo says it will begin offering "select riders" access in San Francisco, Los Angeles, and Phoenix, before "gradually" expanding to more riders and cities. Trips will be free […]

GitHub热门项目 | A simple, decentralized mesh VPN with WireGuard support. | Stars: 11,608 | 173 stars this week | 语言: Rust

GitHub热门项目 | ⚡A CLI tool for code structural search, lint and rewriting. Written in Rust | Stars: 14,208 | 205 stars this week | 语言: Rust

GitHub热门项目 | 🌙🦊 Dalfox is a powerful open-source XSS scanner and utility focused on automation. | Stars: 5,020 | 11 stars today | 语言: Rust

GitHub热门项目 | ripgrep recursively searches directories for a regex pattern while respecting your gitignore | Stars: 64,322 | 57 stars today | 语言: Rust

GitHub热门项目 | An HTTP library for Rust | Stars: 16,099 | 2 stars today | 语言: Rust