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There's a whole ecosystem of "agent skills" now — reusable instruction files you drop into Claude Code (or Cursor, or Copilot) to make the model write cleaner code, debug more carefully, use fewer tokens, and so on. Some of these repos have tens of thousands of GitHub stars. Almost none of them ship a single number telling you whether the skill actually does anything. That bothered me, because "adding a plausible-sounding instruction" and "adding an instruction that works" look identical until you measure them. So I built a benchmark with one rule, committed before I ran anything: No skill gets merged unless it beats both a no-instruction baseline AND a placebo prompt on its pre-registered target metric, measured on hidden hold-out tests, with accuracy not allowed to drop. Skills that fail are published anyway, with their numbers. The placebo arm is the part almost nobody runs, and it turned out to be the most important one. Why a placebo Most "battle-tested" skill collections that measure anything at all compare skill-on vs skill-off. The problem: that comparison can't separate "this skill works" from "adding any confident-sounding text changes the model's behavior." LLMs are suggestible. If you want to claim your skill did something, you have to show it beats a same-length instruction that contains no actual mechanism — just vibes. So every result here is a three-way comparison — off / placebo / on — run K=5–8 times per task per arm, in isolated git workspaces, graded by hold-out acceptance tests the agent never sees, with every raw run log committed to the repo and the README regenerated from those logs in CI. 516 runs total, all on claude-opus-4-8 . Finding 1: the placebo often made code bigger My anti-over-engineering skill ( underkill , ~20 lines) cut source LOC by -23.8% vs baseline at identical accuracy (60/60 hold-out passes). Good. But the interesting column is the placebo: a same-length "write clean, minimal, professional code" instruction didn't reduce c

Seller is a dedicated Marketplace app for people who list and sell items frequently.

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. You've probably noticed this if you've tried to fetch a public webpage in the last two years. You write four lines of Python. You get a 403. You add a User-Agent header. You get a 403 with better manners. You spin up headless Chrome and get a challenge page that spins forever. Eventually you go looking at pricing pages for data APIs, where a cheerful landing page offers to sell you back the exact same public HTML for $100 a month. Meanwhile the big labs vacuumed up that same web at a scale none of us will ever match, and now the standard way to read a page programmatically is to pay somebody. The web went from "open by default" to "open, but only if you look like a person holding a mouse." So let's talk about eight open source projects quietly clawing that access back. I've used most of these in anger. Some are brilliant. Quick note on star counts: I mention a few, because they are a decent signal of "somebody else has hit the bugs before you." They are not a signal of maintenance. A repo can have 70k stars and a last commit from 2023. Always check the commit graph before you check the star count. 1. Firecrawl: the one that turned a URL into a prompt What it does: you hand it a URL, it hands you clean markdown. Not HTML with the nav bar and the cookie banner and four newsletter modals. Markdown. Headings, paragraphs, links, done. It also does more than single pages. The crawl endpoint walks a whole site, map gives you a URL inventory without fetching everything, and structured extraction pulls typed JSON out of a page if you describe the shape you want. from firecrawl import Firecrawl app = Firecrawl ( api_key = " fc-... " ) doc = app . scrape ( " https://example.com/docs/getting-started " , formats = [ " markdown " ]) print ( doc . markdown

I wanted to add a background-removal tool to my site's image cluster that stayed true to the 100% client-side processing principle I already use for PDFs and image conversions. The path there was anything but linear: a library dropped over a licensing problem, a carefully chosen model that turned out more limited than expected, and a bug that froze the entire page — not just the tool — during computation. Here's the full build, including the parts that didn't work the first time. The starting problem: what's actually feasible for free? The initial idea was broad: remove backgrounds, and maybe unwanted objects too. The two tasks have very different difficulty levels. Removing objects requires inpainting — plausibly reconstructing the erased area — which in practice still means heavy generative models, impractical to run client-side with good quality on an average device. Removing a background , on the other hand, is a segmentation problem: separating a subject from its surroundings. That has much lighter models available, runnable entirely via WebAssembly with no server involved at all. So: background removal only, object removal shelved for later. The AGPL trap The first library that looked like a perfect fit turned out to be distributed under AGPL , a strong copyleft license. Free to use — but with a real catch for anyone embedding it in a public, closed-source web service: AGPL can require releasing the full source of the project that embeds it, under the same license. "Free for the end user" and "safe to drop into a closed-source commercial product" are two different questions, and it's worth answering the second one before writing integration code, not after deploying it. Before wiring any "free" AI library into a commercial project, check the exact license, not just the price tag. AGPL, GPL, and other strong copyleft licenses are fine for personal or internal tools, risky for a public closed-source product. The fix: switch to Transformers.js — Hugging Face's li

A nova fase dos agentes de IA: menos chat, mais operação. A OpenAI apresentou o Presence, uma plataforma para empresas implantarem agentes de voz e chat em atendimento ao cliente e fluxos internos. Para quem desenvolve sistemas, o ponto não é apenas colocar mais um chatbot em produção. A mudança relevante é tratar a IA como uma camada operacional: ela precisa participar de processos reais, com objetivos claros e resultados verificáveis. Um agente confiável precisa de mais do que boas respostas. Precisa receber o contexto certo, operar dentro de permissões bem definidas, deixar rastros auditáveis e saber quando transferir o caso para uma pessoa. Isso altera a decisão técnica. Escolher um modelo continua importante, mas arquitetura, integrações, observabilidade, avaliação contínua e governança passam a fazer parte do mesmo problema. A pergunta mais útil deixa de ser “onde podemos colocar IA?” e passa a ser: qual processo já está pronto para ser redesenhado com um agente de confiança? Fonte oficial: https://openai.com/index/introducing-openai-presence

TL;DR: nb2lite-skill-agy wraps Google's gemini-3.1-flash-lite-image model (NB2Lite) in a FastMCP server and packages it as an Antigravity CLI skill. You type "generate an image of a cyberpunk kitchen" into Antigravity, and it just... does it. Then you say "add a neon RAMEN sign" and it edits the same image without re-prompting the whole scene. Oh, and the cover image of this article? Generated by the thing the article is about — dogfooding all the way down. More on that at the end. Background: why another image tool? Most image-generation workflows are stateless . You send a prompt, you get pixels back, and the model immediately forgets everything. Want to tweak the result? You re-describe the entire scene and pray the character, lighting, and composition survive the round trip. (Narrator: they don't.) Google's NB2Lite — the friendly nickname for gemini-3.1-flash-lite-image — takes a different approach. It's a high-efficiency image model with sub-2-second generations, solid text rendering in 25+ languages, and — the headline feature — support for the stateful Interactions API , which lets you iterate on an image across multiple turns while the model keeps the visual context server-side. This repo glues that capability directly into Google Antigravity CLI , so your coding agent can generate and iteratively refine images as a natural part of a pair-programming session. It ships as two things in one repo: A Model Context Protocol (MCP) server ( nb2lite-agent , a single-file FastMCP app in server.py ) exposing four tools. A Skill definition ( nb2lite-image ) that teaches Antigravity when and how to use those tools well. The Interactions API: images with a memory The Interactions API is Gemini's stateful endpoint. The core loop looks like this: You call client.interactions.create(...) with a prompt and store=True . The response includes an interaction_id — a handle to the turn's visual context, persisted on Google's servers. On the next call, you pass previous_interactio

submitted by /u/wineandcode [link] [留言]

TL;DR: omni-skill-agy wraps Google's gemini-omni-flash-preview model (Omni Flash) in a tiny FastMCP server and packages it as an Antigravity CLI skill. You type "generate a video of a fox running through snow" into Antigravity, and it just... does it. Then you say "make it nighttime with snowfall" and it edits the same video without re-prompting the whole scene. It can also animate a still image, interpolate between two keyframes, restyle a video you already have — and when you're happy, upload the result to YouTube. Without leaving your terminal. Background: why another video tool? Most video-generation workflows are stateless . You send a prompt, you get frames back, and the model immediately forgets everything. Want to tweak the result? You re-describe the entire scene and pray the character, lighting, and camera work survive the round trip. (Narrator: they don't.) Google's Omni Flash — gemini-omni-flash-preview — takes a different approach. It's the video-generation model in Google's Gemini "Omni" line: built for fast, high-fidelity clips, and — the headline feature — wired into the stateful Interactions API , which lets you iterate on a video across multiple turns while the model keeps the visual context server-side. What Omni Flash actually does The "Omni" part isn't branding fluff — the model accepts genuinely mixed multimodal input. A single request's input can be a plain string, or a list of typed parts: text parts, base64-encoded image parts, and document parts pointing at a video you've uploaded via the Gemini File API. The model composes whatever you hand it into one clip. That single mechanism covers five distinct ways to make a video: Text → video. A prompt in, an .mp4 out — landscape 16:9 or portrait 9:16 , chosen at generation time. One image + a motion prompt → animation. A still comes to life ("the group smiles and waves at the camera"). Two images + a transition prompt → keyframe interpolation. The model invents the in-between footage from frame A

Last time I said each post would show you the receipts. This one is a single day's worth. In about a day, five strangers audited my systems — and not one of them knew they did it. Each was a comment or a post on this feed: a single sentence that turned out to be pointing straight at a hole in something I'd already shipped and already trusted. I read the sentence, found the hole, and closed it — usually within the hour. I'm a physical therapist who builds a hospital's internal tools with AI, so "closed it" means the AI and I closed it while I described what was actually breaking. Five sentences, five holes, five receipts. They also turned out to be one idea wearing five coats. I'll get to that. Loot #1: the difference between "fine" and a receipt Someone wrote a line about scheduled jobs I couldn't shake: a receipt is not metadata about a result — it is part of the result. Labels go stale silently, they said, because whatever wrote the label already walked away before it stopped being true. My systems' health check said ok . That was the whole thing — a green word. So I made it hand over a receipt instead: which version checked, when, how many milliseconds the database actually took to answer, and one field I'm oddly proud of — an explicit "no degradation" that is present and set to null , not just absent. That distinction sounds like nothing and is everything: a missing field means "nobody checked," a field that's present-and-null means "checked, and it was clean." One is silence wearing a confident face. The other is proof someone looked. My health check used to conflate them. Now it can't. Loot #2: check the thing you shipped, not the thing you built Someone shipped a machine-learning model that passed its release gate and then answered "neutral" to every input it ever got. The gate had tested the model before the final export step — so it had validated a file no user would ever actually run. Their line: ask whether your gate validates the file you upload. I had t

Built an open-source AI coding agent that was 7%–75% cheaper than a cold "claude -p" run on 6/6 well-localized tasks across repositories up to ~82k LOC. The biggest difference: Cold agent: $6.83, 207 turns AutoDev Studio: ~$1.70 for the same bug The full benchmark (including cases where it loses) is in the README. So what's different? Most AI coding agents re-explore a repository from scratch on every task just to figure out where the change belongs. AutoDev Studio pays that localization cost once. It ingests a repository and builds a persistent knowledge base using static analysis and a local embedding index. Every future task reuses that knowledge, turning localization into a lookup instead of another cold search. What it does: PM agent asks clarifying questions and drafts tickets Dev agent writes code on an isolated branch QA runs tests A different model family reviews the diff (author ≠ reviewer) If needed, it goes through a bounded revise loop Opens a real GitHub PR It also includes a live Kanban board and tracks token usage and cost per ticket/agent. Where it doesn't win: Tiny, easy-to-find edits can be cheaper with a single-shot agent because of the pipeline overhead. On one complex cross-cutting bug, it produced a cheaper but narrower fix than the baseline. Other features: Provider agnostic (Anthropic, Claude Code, OpenAI-compatible APIs, Groq, Gemini, xAI, OpenRouter, Ollama, etc.) Runs completely free/offline by default using Groq's free tier + local embeddings FastAPI + SQLite Hand-rolled UI Tests + CI MIT licensed Repo (screenshots + full benchmark): https://github.com/krishagarwal314/autodev-studio I'd love any feedback, criticism, or contributions. Happy to answer questions about the architecture or benchmarking. submitted by /u/NeighborhoodOwn8510 [link] [留言]

Hello, I'm Rijul. I'm building git-lrc, a micro AI code reviewer that runs on every commit. It's free...

A deep dive on Go 1.24’s map redesign, where the runtime moved from the classic bucket + overflow-chain model to a Swiss Table-inspired implementation: how the old map layout worked and where pointer-chasing hurt cache locality how control-byte metadata + h2 filtering changes lookup behavior why this can improve practical load factor and memory efficiency Go-specific constraints (iteration semantics, GC/runtime integration, incremental growth behavior) benchmark context: large microbench wins vs smaller full-application geomean gains trade-offs still being worked on (cold-cache and certain delete/clear-heavy paths) submitted by /u/OtherwisePush6424 [link] [留言]

These are the AC units we’ve trusted to cool our homes for months, if not years.

US robotics researchers flew to Venezuela with snakebots after getting a call.

This essay was written with Barath Raghavan, and originally appeared in The Guardian . Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient. There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to...

The TK705STi’s highly responsive, low-latency performance makes gameplay ultra-realistic. For movies and TV? It’s a purely average home projector.

Never mind the “heat death of the universe.” Entropy is just a bridge between two levels of reality.

Facebook is planning some big changes to try and keep its users from jumping to rival social platforms like TikTok - changes that sound dubiously similar to becoming a TikTok clone. Facebook head Tom Alison announced today that the platform will begin testing a "reimagined experience" later this year that will put a subset of […]

Airbus selected Scaleway as its sovereign cloud provider after a tender that scored protection against non-European extraterritorial legislation alongside technical capability. Airbus frames it as complementing multi-cloud, not exiting AWS. Practitioners note the pattern is spreading past hyperscalers to small US SaaS vendors, and that sovereignty claims still require verifiable controls. By Steef-Jan Wiggers

From musical instruments and games to play pieces that encourage balance and dexterity, these toys will engage your child’s mind and body.