标签：#open

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. Okay so here's a fun one for you. Imagine you invent the thing that eventually becomes the substrate for Google, Facebook, Amazon, your bank, your ex's Instagram, and every cursed cookie consent banner known to man. Now imagine you had the legal right to charge a licensing fee for it. Like, a reasonable one. A cent per page load, say. You would never have to work again. Your great-great-grandchildren would never have to work again. You'd be sipping something expensive on a boat named after a HTTP status code. Tim Berners-Lee looked at that exact opportunity in 1993 and said, essentially, "nah, you guys keep it." This has been rattling around in my head for days, so let's talk about it properly, with all the nerdy details. The web almost lost to a gopher (literally) Berners-Lee built the World Wide Web in 1989 at CERN, laid out in a proposal called Information Management: A Proposal , mostly so physicists could stop emailing each other giant papers and just... link to things. Wild concept, I know. But here's the part people forget: the Web wasn't the obvious winner in the early 90s. It had a genuine rival called Gopher , built at the University of Minnesota, and for a while Gopher was winning. It was simpler, it was faster on the slow modems of the era, and it had a head start in adoption among universities and libraries. Then in February 1993, the University of Minnesota did something that, in hindsight, ranks among the great unforced errors in computing history: they announced they'd start charging licensing fees for commercial use of Gopher server software. Reasonable-sounding at the time (they needed to fund development), catastrophic in practice. The developer community, which had spent years contributing code for free on the assumption

Brad Lightcap, OpenAI's special projects lead and the company's former COO, announced his departure after an eight-year stint at the AI lab. In an internal memo he later posted to X, Lightcap told colleagues he'd be starting "something new." "Over the last few months, I've been focused on the next horizon and what would stand […]

Over a year following its post-election surge, Bluesky’s mobile app is seeing a continued decline in active users, though its remaining community is still relatively engaged.

One of OpenAI's longest-serving executives is headed out the door, although the longtime COO told staff that he was "excited to help you all advance the mission from a different vantage point."

GitHub热门项目 | 29 editorial diagram types for Claude Code. Self-contained HTML + SVG. No shadows, no Mermaid-slop. | Stars: 6,003 | 1,612 stars today | 语言: HTML

I Benchmarked Two Local LLMs on Real Dev Work — Qwopus 27B vs Muse Glimmer 30B Two open-weight models, one 20 GB GPU, two real development tasks, and a third model as the referee. Here is what actually happened when I made Qwopus 3.6 27B and Meta's Muse Glimmer 30B implement a bug fix and then a full feature in my own project. The setup Both models ran fully local on an AMD Radeon RX 7900 XT (20 GB VRAM) via a llama.cpp multi-model router (one OpenAI-compatible endpoint, GGUF models, load-mode=dio — more on why below). Each model was driven by the pi CLI in non-interactive mode with --thinking high . A third model — Codex, through a disciplined stdin wrapper — reviewed both outputs and gave the verdict. The fairness method was simple but strict: One task , described in a markdown spec, copied byte-identical into two isolated git clones of my project. Each model worked in its own clone, its own branch , never seeing the other's work. Objective verification by script: existing test suite + new tests + production build. Cross-review by Codex , examining both branches against the same criteria. The test project: Jeu de Cochons (a "Pass the Pigs" dice game, vanilla JS PWA on Vite + Vitest) — real code, real tests, no toy repo. Qwopus 3.6 27B Muse Glimmer 30B Source Community fine-tune of Qwen 3.6 Meta (distilled from Muse Spark) Size 27B 29.6B Quant IQ4_XS (~15 GB) UD-Q4_K_XL (~14.8 GB) Round 1 — fixing a regression (short task) The project had a broken PWA: a commit that added a /jeu-de-cochons/ base path for GitHub Pages had broken 3 service-worker tests (manifest, precache, offline navigation fallback). Task: fix the regression without touching the tests , keep the other 84 green. Qwopus Muse PWA tests (11) 11/11 ✅ 11/11 ✅ Full suite (87) 87/87 ✅ 87/87 ✅ Files touched 2 2 Diff size +4/−4 +4/−4 Wall time ~8.5 min ~21 min Leftover artifacts none one .bak file The remarkable result: both models produced a byte-identical diff. Same diagnosis (a lost capture group in the a

The first collaboration model in CodeVerse was convincing in exactly the way a local demo needs to be convincing. Open two tabs. Join the same room. Type in one editor. Watch the other editor update. Then ask one unpleasant question: what happens when those two sockets land on different server instances? The answer was that the room stopped being a room. Each process had its own memory, its own presence list, and its own idea of the current files. A restart erased state. A reconnect could create a second identity. A load balancer could turn a working demo into two isolated conversations. This article is about the work that followed: moving CodeVerse from synchronized tabs to a collaboration path I could test across processes, recover after disconnects, and describe without pretending a local benchmark was a production capacity claim. The real boundary was not Socket.IO Socket.IO made connection handling and room fan-out approachable, but it did not decide where truth lived. That distinction matters. A room name inside one Socket.IO process is a routing convenience, not durable shared state. Once I wanted multiple application instances, I needed separate answers for four kinds of information: Document state — the convergent contents of every file. Room policy — organizer identity, edit permissions, active file, and revision. Presence — which sockets are here now, on which instance, with which effective role. Durability — what survives Redis expiry, application restarts, or a longer period of inactivity. CodeVerse now uses Yjs for convergent document updates, Redis for live distributed room state and pub/sub, and Supabase for durable room snapshots and membership data. Socket.IO remains the transport and fan-out layer. That separation was more important than any individual library choice. Redis does three different jobs It is easy to say “I added Redis” and leave the architecture vague. In CodeVerse, Redis has three explicit responsibilities. 1. Cross-instance fan-out

GitHub热门项目 | Rivet Actors are the primitive for stateful workloads. Built for AI agents, collaborative apps, and durable execution. | Stars: 5,998 | 197 stars this week | 语言: Rust

GitHub热门项目 | Cross-platform & feature rich iOS/iPadOS/tvOS sideloading application. Formerly known as PlumeImpactor. | Stars: 2,915 | 19 stars today | 语言: Rust

GitHub热门项目 | Send files from one device to many in real-time. | Stars: 1,468 | 26 stars today | 语言: JavaScript

GitHub热门项目 | Breakthrough Method for Agile Ai Driven Development | Stars: 51,759 | 46 stars today | 语言: JavaScript

GitHub热门项目 | A clean, self-hosted web UI for managing your Traefik reverse proxy. | Stars: 1,207 | 31 stars today | 语言: JavaScript

GitHub热门项目 | 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. | Stars: 163,607 | 69 stars today | 语言: Python

GitHub热门项目 | Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions | Stars: 94,417 | 18 stars today | 语言: Shell

The fourth in a suite of deterministic MCP servers for AI agents — and the one that ties the first three together. Over the last stretch I shipped three focused, deterministic MCP servers: ScenarioSim — what-if / scenario simulation DecisionMatrix — multi-criteria decision analysis PrecisionCalc — exact finance / business math They're great on their own, but agents kept needing all three in the same task — and installing three servers, juggling three keys, and hand-gluing their outputs is friction. So here's AgentStack MCP : one endpoint, one key, all three — plus composite tools that chain them. simulate → decide → compute { "mcpServers" : { "agentstack" : { "type" : "http" , "url" : "https://agentstack-mcp.pages.dev/mcp" } } } Free tier: no key, 20 calls/day. The tools are namespaced so an agent always knows which engine it's calling: sim_* — ScenarioSim (run, sensitivity, break-even, compare, templates) decide_* — DecisionMatrix (decide, score, sensitivity, compare_two, methods) calc_* — PrecisionCalc (metrics, currency, NPV, IRR, loan, depreciation, …) The part that's actually new: composite tools These chain the engines to do reasoning no single server can , deterministically end-to-end: evaluate_options_with_scenarios (simulate → decide) — project each option as its own scenario, then rank the outcomes against weighted criteria: { "name" : "evaluate_options_with_scenarios" , "arguments" : { "template" : "saas_growth" , "horizon" : 12 , "options" : [ { "name" : "Aggressive" , "inputs" : { "new_customers_per_period" : 60 , "churn_rate" : 0.05 } }, { "name" : "Lean" , "inputs" : { "new_customers_per_period" : 20 , "churn_rate" : 0.02 } } ], "criteria" : [ { "metric" : "ending_mrr" , "weight" : 3 , "direction" : "benefit" }, { "metric" : "total_churned_customers" , "weight" : 1 , "direction" : "cost" } ] } } plan_to_valuation (simulate → compute) — project a plan, then value its cash-flow line: NPV, IRR, undiscounted total. stress_test_decision (simulate × decide)

IBM and Red Hat have announced an expansion of Lightwell, introducing new commercial offerings designed to help organizations establish trusted, verifiable software supply chains for the age of AI-assisted software development. By Craig Risi

Mathematician James Maynard has spent a lot of time this past year "soul searching." A professor at the University of Oxford and winner of the prestigious Fields Medal, Maynard told The Verge he's been grappling with the future of his field as the traditionally slow-moving discipline hurries to adapt to AI. Days before we spoke, […]

Cybersecurity is entering a new phase. This is because security teams are facing more and more complex problems and threats that are moving faster. To help defenders respond more effectively, OpenAI has introduced GPT-5.6-Cyber, a special model designed for advanced cybersecurity tasks. The model supports authorized security research, vulnerability discovery, and other defensive workflows. The Daybreak program is showing how specialized AI tools can improve modern cybersecurity by working together with human security experts. Quick overview GPT-5.6-Cyber is a specialized model for authorized cybersecurity work. It is available through OpenAI’s Daybreak Red access for approved defenders. OpenAI reports a 95% completion rate on its internal advanced cybersecurity evaluation. The model helped researchers uncover vulnerabilities in Chrome’s V8 JavaScript engine. Controlled access, monitoring, and human oversight remain important for safe deployment. What Is GPT-5.6-Cyber? GPT-5.6-Cyber is OpenAI’s cybersecurity-specific model, available through Daybreak Red. Built on GPT-5.6 Sol, it is trained to improve performance on specialized cybersecurity tasks such as finding zero-day vulnerabilities and developing exploit chains, while reducing refusals for certain higher-risk, dual-use cyber tasks. Daybreak has two access tiers: Daybreak Blue provides approved defenders with frontier general-purpose models such as GPT-5.6 Sol, with safeguards tailored to authorized defensive security work. Daybreak Red provides purpose-trained cybersecurity models for authorized vulnerability research, exploit validation, and security testing. This approach reflects a significant shift toward security tools designed for professional cybersecurity environments rather than unrestricted public use. The goal is clear: to help trusted defenders investigate vulnerabilities, analyze potential threats, and respond to security incidents more effectively while keeping access controlled. According to Open

TL;DR — Events in Fitz LiveViews carry data three ways: a click payload ( data-flv-value-* ) tags a button with the value it should send; a form submit ( data-flv-submit ) reads the form's named inputs; and a live value ( @input / @change ) delivers a control's current value in payload["value"] . All three land in the same place — a payload map your handler reads. This post builds a live name list (add / remove / count) that runs both server-rendered and as WebAssembly. (Part 3 of the FitzLiveViews series.) Parts 1 and 2 covered the pitch and the counter. A counter only reads +1 / -1 — no data flows in . Real UIs take input: text, selections, form fields. Here's how that data reaches your handlers. The payload Every event handler has a payload in scope — a Map<Str, Str> . The three mechanisms below all fill it; your handler reads it with payload["key"] (guard with payload.has("key") ): 1. Click payload — a button that carries a value Tag any element with data-flv-value-<key>="{expr}" , and when a data-flv-click on it (or an ancestor) fires, that value rides along: <button data-flv-click= "remove" data-flv-value-item= "{it}" > × </button> event remove () { if ( payload . has ( " item " )) { let target = payload [ " item " ] names = names . filter ( fn ( it ) => it != target ) } } The delete button knows which row it is because the row's value is stamped on it. No IDs threaded through a callback, no closure capture. 2. Form submit — the whole form at once data-flv-submit="handler" on a <form> reads each named input into the payload on submit; data-flv-clear resets a field afterward: <form data-flv-submit= "add" > <input name= "item" placeholder= "Add a name" data-flv-clear /> <button type= "submit" > Add </button> </form> event add () { if ( payload . has ( " item " )) { let n = payload [ " item " ] if ( n != "" ) { names . push ( n ) } } } payload["item"] is the input's value at submit time. No preventDefault , no FormData , no fetch . 3. Live value — @input / @chang

Introduction: The Enterprise AI Architecture Question Enterprise AI is entering a new phase. The first wave was about putting large language models into applications. The second wave focused on Retrieval-Augmented Generation (RAG), enterprise search, copilots, and AI assistants. Now, enterprises are asking a more fundamental question: What should the architecture behind enterprise AI actually look like? Should one powerful AI system receive a business problem, access the required tools, reason through the workflow, and deliver the answer? Or should the work be divided among multiple specialized AI agents—each responsible for a specific function—with an orchestrator coordinating the entire process? This is the debate between single-agent AI and multi-agent AI. And the answer is more nuanced than “more agents are better.” A single agent can be remarkably effective when the workflow is focused, sequential, and supported by the right tools and context. Multi-agent architectures become attractive when work can be decomposed into independent streams, when specialized expertise is required, or when the scale of the problem exceeds what one agent can efficiently manage. Recent research on agent architectures highlights exactly these trade-offs: capability versus reliability, autonomy versus controllability, and accuracy versus latency and cost. The real enterprise question, therefore, is not: “How many AI agents should we deploy?” It is: “What architecture best matches the complexity of the business problem?” What Is a Single-Agent AI Architecture? A single-agent architecture typically consists of one AI agent powered by a foundation model, connected to enterprise data, tools, APIs, memory, and business systems. The agent receives a goal and determines how to accomplish it. A simplified architecture looks like: User Request → AI Agent → Reasoning → Tools/Data → Action → Result For example, imagine an employee asks: “Why did yesterday's sales decline in the western region?”