The model-routing framework (matching model tier to task type) and the 5-gate workflow are concrete, specific, and reflect genuine operational practice — this is a real system someone could actually adopt, not just abstract advice.

A lightweight, actor-inspired framework for building multi-agent LLM applications without depending on LangChain. Its Agent and Task abstractions make message-based collaboration unusually clear, and it works with practically any LLM, not just OpenAI's. GitHub Repo: https://lnkd.in/dY4QBkCW

100+ open-source AI agents and agent skills you can clone and ship today 🤯Awesome LLM Apps with 123,000+ stars is trending again on GitHub.It's 100% free and Open Source. So you can clone it, ship it, even sell it.Others charge $20,000+ for courses that teach less than what's sitting in this one repo.Every agent is fully-built and tested end to end. Pick one, add your API key, and it runs in 30 seconds.Some of the newest AI Agents and Agent skills inside:1. Project Graveyard: an agent skill that autopsies your dead side projects and tells you why each one died2. Self-Improving Agent Skills: skills that rewrite themselves against evals using Gemini and ADK3. Insurance Claim Live Agent Team: voice claims settled in real time with Live Agent teams4. Advisor Orchestrator Worker: a meta loop where one model advises, one orchestrates, one executes5. Always-on HN Briefing Agent: reads Hacker News while you sleep, ships you a ranked brief6. AI Fraud Investigation Agent: cross-examines public records to flag facilities that don't add up7. AI Home Renovation Agent: photo of your space in, photorealistic redesign outThe agent skills install into Claude Code, Codex, or Cursor with one command. Plain English to use.Works with Claude, Gemini, GPT, DeepSeek, Llama, and Qwen. Run local or in the cloud.New agents and skills drop every week. Star it now, clone one this weekend.Link to the GitHub repo in the comments.____100+ AI Agents, Agent Skills, and Agent teams (free and open-source with 125,000+ stars): https://lnkd.in/dW6b_dEn♻️ Repost to share it with your network. Follow me at Shubham Saboo for more such AI Agent, Loop Engineering and Agent Skill workflows.

I just built a 4-agent software team. (100% local and runs on open-source stack) Everything runs from Telegram and is managed on a Kanban board. - A project manager who plans the work - A backend developer - A frontend developer - And a tester The PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. The thing that makes them a team instead of four strangers is a shared kanban board. Every task is a row, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. The next agent reads that summary before it starts. So the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. The hardest part I faced when setting this up was building an agent that could actually act like a backend engineer. A backend engineer has to stand up a database, wire auth, manage storage, deploy functions, and keep all of it consistent while the rest of the team builds on top. An agent doing this from scratch failed almost every time in my run. It burned its context window, remembering which tables existed and which endpoint it created. Due to this, the work context exhausted quickly. I solved this by adding InsForge as the backend context engineering layer. It is an open-source, agent-native backend, and I added it to our backend developer agent as a skill. With InsForge installed, the agent stopped improvising infrastructure and followed a reliable path to create the project, define the database, set up auth, and deploy functions. To test the whole team, I had them build a working Google Docs clone, AI features included. The backend agent spun up the full service on its own, like database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. The frontend agent read that summary and built the UI on top of it, and the tester closed the loop. InsForge GitHub: https://lnkd.in/gZQ3CVva (don't forget to star 🌟) ____ Share this with your network if you found this insightful ♻️ Find me → Avi Chawla. Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

The most complete frontend stack for agentic applications!CopilotKit provides the frontend infrastructure needed to embed agents directly inside products. It supports agent chat, frontend actions, shared application state, human approvals and generative UI across web, mobile and communication surfaces such as Slack.Its biggest contribution may be that it treats the agent as part of the application rather than as a chatbot floating beside it. The agent can read application state, invoke UI-side actions and collaboratively modify what the user is seeing.CopilotKit is also the team behind the AG-UI protocol, which gives it an unusually strong position across both the implementation and interoperability layers.~36k stars, MIT, shipping constantly. 

The underlying hiring-speed research is genuine and well-documented, the tech stack is specific and plausible, and the Twilio WhatsApp opt-in explanation is technically accurate — this is a real, buildable system with a real problem it's addressing.

Model size used to mean RAM size. colibri breaks that link: it runs GLM-5.2, a 744B model, on a 25GB machine with no GPU. Only a small slice is needed per token, so it keeps that in RAM and streams the rest off an SSD. It's slow, a token every few seconds, more a proof the memory wall isn't fixed than a finished tool. 2.1k stars, Apache-2.0. #LocalLLM #LocalInference #MoE #OpenSourceAI

Kimi K3 just released 🔹 2.8 Trillion Parameters, 1 Million Context, Natively  Multimodal🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost🔹 Built for long-horizon agentic coding and self-evolving workflowsKimi K3 is now live on Kimi.com, Kimi Work, Kimi Code, and the Kimi API.Open Weights by July 27, 2026.This a Fable 5, GPT 5.6-sol level modelSome interesting underrated gems in Kimi-K3 release:    > an early K3 wrote the majority of the kernels in the late development stages  > it built a triton-class compiler from scratch, MiniTriton, that delivers performance on par with or better than Triton and torch.compile > then it designed a chip, by a model, for a model, in one 48-hour autonomous run  On many tasks this model performs equivalent to Fable,Opus 4.8  and GPT-5.6 at half or even lesser costExcellent at front end code generation. The first-party API is priced at $3.00/$15.00 per 1M input/output tokens, with cached input discounted 90% to $0.30 per 1M tokens.This is a crazy release from Moonshot.ai

HOLY SHIT. SOMEONE JUST KILLED EVERY SKETCHY VIDEO DOWNLOADER SITE ON THE INTERNET.It's called ReClip. Self-hosted. Open source. Free.Paste a link from YouTube, TikTok, Instagram, X, or 1000+ other sites. Pick MP4 or MP3. Done.No ads. No popups. No trackers. No rate limits.Here's how it works: 👇What ReClip actually does:→ 1000+ supported sites (powered by yt-dlp)→ MP4 video or MP3 audio→ Resolution picker before you download→ Batch downloads. Paste multiple URLs at once.→ Automatic URL deduplication→ Clean web UI in any browser→ Docker supportHere's the part developers will love:Python + Flask backend in ~150 lines.Frontend is vanilla HTML, CSS, JS. No framework. No build step.Two dependencies total: Flask and yt-dlp.That's it. The whole thing is auditable in one sitting.Setup takes one command if you have brew:brew install yt-dlp ffmpeggit clone https://lnkd.in/eYMuXxxjcd reclip && ./reclip.shOpen localhost:8899. You're done.Prefer Docker? One line:docker build -t reclip . && docker run -p 8899:8899 reclip100% Open Source. MIT License. Free forever.If you've ever closed 4 ad popups just to download a 30-second clip, this is for you.

The core mechanism, licensing, keyless design, and benchmark figures are accurate and well-documented across independent sources — this is a genuine, actively discussed project with real technical substance.

The ultimate backbone for AI web agents just hit 69k stars. One framework. Handles everything. Works everywhere. → Single HTTP requests → Concurrent Spider crawls → Cloudflare bypass → CLI extraction → MCP Server What is adaptive scraping? AI agents are smart but their web tools are brittle. They don't know how to handle Turnstile captchas. They crash when a website renames its CSS classes. Scrapling is a framework that teaches them. One library = full web access. Perfectly executed. Every time. This repo handles the hardest parts of the web: → Stealth (TLS fingerprinting, header spoofing) → Parsing (relocates elements when UI changes) → Routing (mix fast HTTP with headless browsers) → Speed (10x faster JSON serialization) Install once: pip install "scrapling[ai]" Then: Connect the MCP server to Claude or Cursor. Your AI agent just got unrestricted, token-efficient access to the internet. GitHub Repo - https://lnkd.in/dCurt4Pc

Every functional claim — the creator, the layout/shortcut mimicry, the license, the install/uninstall simplicity, and the star count trajectory — is accurate and verifiable against primary sources.

 The three-tier routing mechanism, the "Skippy" naming, the Rust/open-source framing, and the consumer-hardware pooling concept are all accurate and specific, matching the project's actual documented design.

A MODEL THAT CLONES A VOICE FROM 20 SECONDS no gpu. no api. no cloud. one pip install ★ 6.8k pocket-tts by kyutai, mit license, pip install pocket-tts - the lab behind moshi: → 100M params, runs on plain cpu - they tried a gpu, it wasn't even faster → ~6x faster than real-time on a macbook air m4, using 2 cores → ~200ms to the first audio chunk, streams the rest → clone a voice from a short sample, or use 26 built-in ones → 6 languages, handles infinitely long text → small enough to run in the browser via webassembly everyone's renting gpu time to make audio. this fits in your pocket https://lnkd.in/dpXEvrZX

 Extremely dense and precise — every mechanism (default-fail gate, fresh-context evaluator, self-maintained handoff, star count, license) is a specific, verifiable claim that matches the actual repository almost word-for-word. This is about as tightly sourced as a one-paragraph post gets.

An AI engineering interview question most people get wrong: How does an LLM choose its next token? The model writes one token at a time. → At each step, it scores every token in its vocabulary. For instance, GPT-4o holds 200,019 tokens. → Softmax turns those scores into probabilities: a distribution to pick from. → The next token is sampled from this distribution. This is 𝘁𝗼𝗸𝗲𝗻 𝘀𝗮𝗺𝗽𝗹𝗶𝗻𝗴. Here are three runtime settings that can change how that pick happens. 𝗧𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲 Scales the scores before softmax. It reshapes the probability distribution. → T = 1: no scaling. The distribution stays exactly as the model produced it. → Higher T flattens it. Long-shot tokens get a chance, so answers are more varied and creative. → Lower T sharpens it. High-probability tokens dominate, so answers are more focused and predictable. As T approaches 0, sampling approaches argmax. The highest-probability token dominates. ↳ Even temperature 0 may not produce identical outputs every time. ↳ Floating-point operations, batching, and hardware differences can introduce variation. 𝗧𝗼𝗽-𝗽 (𝗻𝘂𝗰𝗹𝗲𝘂𝘀) Adds the highest-probability tokens until their cumulative probability reaches p. The pool grows or shrinks with the model's confidence. → p = 1.0: every token stays in play. → p = 0.9: only the tokens covering 90% of the probability. → p = 0.1: very restricted, close to greedy. 𝗧𝗼𝗽-𝗸 Keeps only the k highest-probability tokens. The pool size is fixed. → k = 50 and the model is confident: you still sample from 50 when only 3 matter. → k = 50 and the model is uncertain: 50 tokens may not be enough. 𝗢𝗻𝗲 𝗿𝘂𝗹𝗲 𝗼𝗳 𝘁𝗵𝘂𝗺𝗯 Temperature and top-p both act on the same distribution. → Change one at a time. → Move both and you stack two diversity controls. The effect gets harder to reason about. Temperature reshapes the curve. Top-p and top-k filter the pool. 𝗧𝗼𝗸𝗲𝗻 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 One token is selected from what's left. → After filtering, the remaining probabilities are rescaled to sum to 1. → Higher-probability tokens have a higher chance of being picked. → A weighted random draw picks one token based on those probabilities. The selected token is added to the sequence. Then the process repeats. That is how an LLM chooses its next token. And because the draw can land on a different token each time, the same prompt can produce a different answer. 💾 Save this for your next AI interview ➕ Follow for more AI engineering insights ♻ Repost to help someone in your network

When an AI agent acts in your Slack or GitHub, a gateway holds the login token so the agent never touches it. But the token still leaves on each call, and a redirect to another domain can carry it somewhere it should never go. OpenConnector strips credential headers on any cross-domain redirect and blocks calls bounced to internal addresses. That outbound path is where keys leak. Early repo, 2.1k stars, Apache-2.0.

Your notes do not need another productivity system. They need an interface that can actually reason across them.I have put together a blog that explains how to connect an Obsidian vault with OpenAI Codex and turn a folder of markdown files into an AI-assisted knowledge system. You will learn how to structure your vault, use an `AGENTS.md` file to define rules, set safe permissions, and use Codex to summarize research, connect related ideas, clean up notes, and generate weekly reviews.Why it matters: most second brains are good at storing information but bad at helping you synthesize it. Codex changes that by operating directly across the files you already own, without requiring a complicated integration.This is for researchers, writers, developers, students, founders, and anyone sitting on hundreds of notes they can no longer meaningfully navigate.

6 open-source GitHub repositories for crawling and scraping the web 1./ Firecrawl An open-source web-data API, also available as a hosted service, for searching, scraping, mapping and crawling the web. It can convert individual pages into Markdown, HTML, screenshots or structured JSON, discover URLs across a website, crawl multiple pages and process large URL batches asynchronously. It also supports interacting with pages through actions such as clicking, scrolling and typing before extracting the content. 2./ Crawl4AI A Python crawler and scraper built around producing content for LLMs, RAG pipelines and AI agents. It generates clean or filtered Markdown, supports structured extraction using CSS, XPath or LLMs, and can run deep crawls using strategies such as breadth-first search. Its Playwright-based browser layer can execute JavaScript, preserve sessions, use proxies and handle dynamic or infinite-scroll pages. 3./ Scrapling An adaptive Python scraping framework that supports everything from fetching a single page to running concurrent, multi-session crawls. Its adaptive selector system can save an element’s properties and search for the most similar element when the original selector stops matching after a page redesign. Its spider framework also includes streaming, concurrency controls, pause and resume, and proxy rotation. 4./ Crawlee A JavaScript and TypeScript library for building web crawlers, scrapers and browser-automation workflows. It works with lightweight HTTP and HTML parsing as well as Playwright and Puppeteer for JavaScript-rendered pages. Crawlee provides persistent URL queues, structured-data storage, automatic concurrency scaling, session management and proxy rotation, while leaving the page-specific extraction logic to the developer. 5./ Scrapy A high-level Python framework for crawling websites and extracting structured data. Developers create spiders that issue requests, follow links and parse responses using CSS or XPath selectors. Scrapy’s engine coordinates scheduling and downloading, while item pipelines can validate, transform or store extracted records, and feed exports can write results to formats such as JSON, CSV and XML. 6./ Maxun An open-source, no-code web-data platform that combines extraction, scraping, crawling and web search. Its Recorder Mode captures browser actions and turns them into reusable extraction robots, while its AI Mode uses natural-language instructions for structured extraction. It can also convert webpages into Markdown or HTML, capture screenshots, crawl websites with configurable scope and run locally through self-hosting. ---- "The web is the largest database in the world." [GitHub repo in the comments]

Google just standardized one of the most useful ideas for building AI assistants. It's called the Open Knowledge Format (OKF). → https://lnkd.in/dWdn8ZU2At its core, OKF is a standardized format for building LLM wikis. Instead of storing knowledge behind proprietary APIs, it stores it as Markdown with structured metadata. Because this gives you something that's simultaneously: • Human-readable• LLM-readable• Git-friendly• Vendor-neutral The interesting part isn't the format itself... It's what it enables. Your knowledge becomes a collection of linked documents that both humans and agents can navigate. Each page contains: • Structured metadata (YAML)• Rich documentation (Markdown)• Links to related concepts Instead of loading everything into one enormous context window... Agents progressively navigate the wiki, following links only when they need more information. The same knowledge can then power: • Humans• AI agents• Search• Documentation• Knowledge graph visualizers ...without maintaining separate representations for each. This is a strong signal of where agent memory is heading. Over the past year, I've been leaning toward the same pattern while building my personal AI assistant. Instead of treating memory as something hidden behind a database... Treat it as an LLM wiki that both humans and agents can read, navigate, version, and evolve together. LLM wikis are becoming much more than an implementation detail. They may become the default interface between humans and AI systems. Definitely worth bookmarking if you're building AI assistants, RAG systems, or knowledge platforms.