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GitHub热门项目 | GlazeWM is a tiling window manager for macOS and Windows inspired by i3wm. | Stars: 12,543 | 31 stars today | 语言: Rust

GitHub热门项目 | Payload is the open-source, fullstack Next.js framework, giving you instant backend superpowers. Get a full TypeScript backend and admin panel instantly. Use Payload as a headless CMS or for building powerful applications. | Stars: 44,041 | 27 stars today | 语言: TypeScript

GitHub热门项目 | AI-Engineering Foundation Framework built with AI and designed for AI. Hundreds of architectural and domain decisions (multi-tenancy, RBAC, event flow, pricing, sales pipeline,CRM/ERP processes) are already made conventions and specs so agents (Cursor, Claude Code, Codex) arch. decisions without reinventing. Ship production grade with AI Agents. | Stars: 1,572 | 12 stars today | 语言: TypeScript

GitHub热门项目 | Graph-Native Infrastructure for Context and Accountable AI Systems | Stars: 2,183 | 118 stars today | 语言: Python

GitHub热门项目 | A self-improving RLM agent for coding workflows and long-running autonomous tasks. | Stars: 5,231 | 2,271 stars today | 语言: TypeScript

A court rules that a hold on all wind projects clearly violates the law.

I have been building machin for a while — a Go-flavored, type-inferred language that compiles through C to a single native binary. It has grown a lot recently, and I wanted to answer the obvious question honestly: does it beat Rust and Zig at anything? It does, at two things, decisively. But the first thing I found was not a win. It was my own benchmark quietly lying to me, and the number it was lying about was the best one I had. The benchmark was measuring the order I ran things in machin's repo has had a bench/native-speed suite for months: four compute kernels — recursive fib, a mandelbrot, a sieve, a big integer loop — written in machin, Rust and Zig, producing byte-identical output, so the timing compares the same computation three ways. The published result claimed machin won the integer loop by 20-25% . That claim also shipped inside machin guide , which is what every coding agent reads to learn the language. When I re-ran it, the margin was gone. Not shrunk — gone. So I read the harness instead of the output: for kernel in kernels : for lang in [ machin , rust , zig ]: for _ in range ( 5 ): # all 5 machin, THEN all 5 rust, THEN all 5 zig time ( binary ) It ran every sample of one language before starting the next. On a laptop that heats up and down-clocks during a three-second kernel, that does not measure the languages. It measures who had the misfortune of running last . Zig always went last. Zig always looked slowest. The fix is four lines — interleave the rounds, rotate who starts each one. Here is what my headline number did: intsum 10^9 before (blocked) after (interleaved) machin 2832 ms 3079.7 ms rust 3764 ms 3223.8 ms zig 3556 ms 3189.7 ms "machin +20-25%" machin +3% = a TIE A 20-25% win became a tie. I deleted the claim from the README and from machin guide . The harness now also refuses to declare a winner inside a 3% band, because the worst run-to-run spread I measured was 41% of the min sample. Calling winners inside that is how benchmarks start

Cellars sweat. On a warm humid day the air you let in is warmer than the cold concrete, and the moment it touches a cold surface it gives up its water. That's condensation, and over enough summers it's how a basement grows mould in the corners you never look at. I wanted a number that warned me before that happened. The catch I already knew going in: a raw relative-humidity reading isn't that number. This is Part 08 of the series. The hub was already in the house running other things, and the install was genuinely the easy 20 minutes. The part worth writing about is what came after the sensor showed up: turning its two raw readings into a dew-point spread, and deciding when that spread means "act" versus "ignore the spike." Why the H100, and why 868 MHz is the whole point The hub is a TP-Link Tapo H100 , a little smart hub that acts as a radio bridge for TP-Link's battery sensors — the T100 motion, the T110 contact, and the one I care about here, the T310 temperature/humidity sensor. Here's the load-bearing detail, and the reason I reached for this hub instead of a WiFi sensor: the Tapo sensors don't talk WiFi. They talk to the H100 over 868 MHz sub-GHz radio . That matters in a cellar more than anywhere else. Sub-GHz is long-range and punches through concrete and floors in a way 2.4 GHz WiFi simply doesn't. There's no WiFi worth having down in my cellar, and no interest in running a repeater into a damp room just to read a sensor. The T310 sits down there on a battery, the H100 upstairs where the network is, and the radio link between does the work. One H100 supports up to 64 sensors — for a house, more headroom than I'll ever use. Getting the sensors into Home Assistant The native TP-Link integration doesn't expose the H100's child sensors — it's built for the plugs and bulbs. The one that works is the community Tapo Controller integration (petretiandrea's TP-Link Tapo ), installed through HACS , the same custom-integration store I've leaned on throughout this ser

A live production integration case study Introduction and Purpose of This Article This article is written for mid- and high-level managerial and technical decision makers. I am the author of the open-source Java library MgntUtils . The article presents an analysis of a real integration of the stacktrace-filtering feature from that library into a live commercial production environment. A few important clarifications up front: This is not a side-project pilot and not a lab demo. The feature was integrated into a production service of a company that serves a high volume of real customers. Due to legal constraints, I am not at liberty to name the company. This is not a how-to article for implementers. If you came looking for code samples or logging-framework wiring, please see the dedicated articles listed in the Disclaimer below. MgntUtils can be used in Java projects and in other JVM-based languages such as Kotlin. Before diving into the production numbers, it is worth stating briefly what the feature does and why those numbers matter. Server-side stacktraces are usually full of framework and infrastructure noise — proxies, filter chains, containers, thread pools, and similar boilerplate — while the few lines that actually explain the failure are easy to lose in the pile. The MgntUtils filtering utility keeps the application frames and the exception / Caused by chain, and collapses that noise. The result is a much shorter stacktrace without losing the information you actually need . When those stacktraces are later consumed — sent to an LLM for analysis, or opened by an engineer — that reduction can mean: Substantial AI token savings Typically more accurate AI root-cause answers , because the model has less framework noise to latch onto and hallucinate about A meaningful productivity boost for human triage The rest of this article focuses on what was observed after integrating this feature in production: the measured benefits, how to interpret them, and the integratio

The AI leaderboard just had a seismic shift. Qwen3.8 Max, Alibaba's latest open-weight model, has been ranked as the best overall model by the Artificial Analysis Agentic Index — beating out GPT-5.6 Sol from OpenAI, Claude Opus 4.5 from Anthropic, and Gemini Ultra 2 from Google. This isn't just a benchmark win. It's the first time an open-source model has topped a comprehensive agentic intelligence index that measures real-world task performance, not just test scores. What Is the Agentic Index? The Artificial Analysis Agentic Index is an independent benchmark that evaluates AI models on their ability to complete agentic tasks — multi-step reasoning, tool use, code generation, and real-world problem solving. Unlike traditional benchmarks (MMLU, HumanEval) that test static knowledge, the agentic index measures whether a model can actually do things . The index evaluates models across multiple dimensions: Intelligence Index : Composite score across reasoning, coding, math, and instruction following Speed : Output tokens per second under production load Cost : Weighted average cost per intelligence task Endpoint Accuracy : Whether provider endpoints match reference model quality Qwen3.8 Max: The Specs Qwen3.8 Max represents Alibaba's most capable model to date: Parameters : 240B (MoE architecture, ~35B active during inference) Context : 256K tokens native, 1M extended Training : Trained through November 2025 data cutoff Licensing : Open weights for research and commercial use (with restrictions for users in restricted jurisdictions) What makes Qwen3.8 Max notable isn't just raw intelligence — it's the combination of high performance with competitive pricing and speed. The model scores near the top on intelligence while maintaining cost per task well below premium alternatives. Why This Matters for Developers 1. Open-Source is Catching Up — and Pulling Ahead For two years, the gap between open-source models (Llama, Qwen, Mistral) and proprietary frontier models (GPT, Cla

I build software with AI all day. A reading app for dyslexic kids. A map that lives on your desktop. A meditation app. A fox in my menu bar. Some of it with Claude, some with Gemini, some at 2am with whatever model was awake. The code was never the problem. The problem was six months later, opening a file and having no idea what we were thinking. Not what it does — the code says that. Why it's like that. What we tried that didn't work. What we weren't sure about. That part evaporated the moment the editor closed. So we started leaving a note. It's called MurphySig , and it's not a tool — it's a comment: // Signed: Kev + claude-sonnet-5, 2026-07-14, Confidence 0.5 (spike; // compiles, on-device run pending), Prior: Unknown // Review: claude-fable-5, 2026-07-14 — the on-device run HAPPENED same // day: gemma-4-12B-it-4bit loads + describes the app icon correctly, // 265 prompt tokens/image, 7333MB peak. Confidence now 0.9 for the // instrument itself (measured live). That's a real one, from M1K3 's codebase. Signed 0.5 in the morning, reviewed 0.9 the same evening, measurement attached. Confidence as a live value, not decoration. The one that sold me on my own convention My favourite signature lives in Cartogram's map engine. Three models worked that file across two months. In June, one of them recorded a performance overhaul: drift updates moved to "1s intervals," 52% CPU down to zero. In July, a newer model read that note, saw the shipped constant was 0.1s, took the mismatch for a bug, and "fixed" it. On hardware, every longer interval was stop-motion. So it reverted — and then wrote this into the file: So 0.1s was not a regression; it is load-bearing, and the 1s in the 06-21 note is the part that was wrong. [...] the standing lesson is that drift cost needs Instruments, not reasoning. The confident note turned out to be the bug. The code was innocent. And the correction is now part of the file's memory, so nobody — human or model — "fixes" that constant again. That

Hadrian is building automated factories to mass-produce parts for defense vehicles like submarines. It's backed by a long list of well-known investors.

GitHub热门项目 | Claude Code toolkit - agents, commands, skills, rules, and hooks for productive AI-assisted development | Stars: 982 | 143 stars this week | 语言: JavaScript

Google's Custom Search JSON API is closed to new customers, and existing customers have until 2027-01-01 to move off it. That deadline takes searchType=image with it. I maintain cse-bridge , a small self-hosted service that speaks Google's customsearch/v1 wire format on top of your own SearXNG instance, so migrating is a base-URL change rather than a rewrite. Web search shipped first. This week I added image search — and it turned out to be much less mechanical than "map some more fields", because two of the assumptions that hold for web results are actively wrong for image results. Both are worth knowing whether or not you ever use my code. If you are writing anything that normalises image search results, you will hit them. Trap 1: link is not the page For a web result, Google's link is the URL of the page. Easy. For an image result, link is the image file itself , and the page it was found on lives in image.contextLink : { "link" : "https://facts.net/wp-content/uploads/2020/08/AdobeStock_209028852.jpeg" , "displayLink" : "facts.net" , "image" : { "contextLink" : "https://facts.net/nature/animals/red-panda-facts" , "thumbnailLink" : "https://ts1.mm.bing.net/th?id=OIP.I_aIcVvl98DbktQmP297ugHaE7&pid=15.1" , "width" : 4000 , "height" : 2666 } } SearXNG has it the other way round: the result's url is the page, and the image is in a separate img_src field ( documented here ). So the naive mapping — reuse the web mapper, add an image object — produces items whose link points at an HTML document. That fails silently , which is what makes it nasty. Your JSON still validates. Your item count is right. Every field is a well-formed URL. But every client that does <img src={item.link}> — which is the entire point of image search — renders nothing, and it looks like the images are broken rather than like your mapper is wrong. The fix is a rule, not a patch: if a result has no image URL, drop the whole result . Never fall back to the page URL to keep the count up. export functio

GitHub热门项目 | A beautiful, non-destructive, and GPU-accelerated RAW image editor built with performance in mind. | Stars: 9,229 | 205 stars this week | 语言: TypeScript

GitHub热门项目 | A fast static site generator in a single binary with everything built-in. https://www.getzola.org | Stars: 17,298 | 9 stars today | 语言: Rust

GitHub热门项目 | Fully transparent SSH, HTTPS, Kubernetes, MySQL and Postgres bastion/PAM that doesn't need additional client-side software | Stars: 7,464 | 10 stars today | 语言: Rust

GitHub热门项目 | dev tools, env vars, task runner | Stars: 31,835 | 264 stars today | 语言: Rust

GitHub热门项目 | An open-source framework for making universal native apps with React. Expo runs on Android, iOS, and the web. | Stars: 51,381 | 36 stars today | 语言: TypeScript

GitHub热门项目 | Free Windows, Mac and Linux cleaner, scanner, and more. | Stars: 2,013 | 30 stars today | 语言: TypeScript