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GitHub热门项目 | HTTP routing and request-handling library for Rust that focuses on ergonomics and modularity | Stars: 26,687 | 22 stars today | 语言: Rust
GitHub热门项目 | 🪄 Flint is a visualization language that lets AI agents reliably create expressive, good-looking charts from simple, human-editable chart specs. | Stars: 2,372 | 218 stars today | 语言: TypeScript
GitHub热门项目 | 本地优先的跨平台 Claude Code / Agent 桌面工作台：多 Agent、Git Worktree、代码 Diff、技能市场、多模型、Computer Use、任务感知桌面宠物，并支持微信、飞书、钉钉、Telegram、WhatsApp 与 H5 访问。 | Stars: 13,667 | 53 stars today | 语言: TypeScript
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GitHub热门项目 | Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills. | Stars: 27,552 | 180 stars today | 语言: Python
GitHub热门项目 | "OpenSpace: The Skill Management Layer for AI Agents" -- https://open-space.cloud/ | Stars: 7,129 | 91 stars today | 语言: Python
GitHub热门项目 | A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading. | Stars: 9,199 | 113 stars today | 语言: Python
This year I've built more MCP servers than I meant to, across three pretty different shapes: vellum : a self-hosted server over a folder of markdown, so my agent has a memory that's just files I own. (Open source, MIT.) Metrifyr : a marketing-data server that puts GA4, Search Console, AdSense and Tag Manager behind one connection. A read-only gateway over a company's internal user-data stack, federating half a dozen internal services behind one audited MCP surface. Personal, product, enterprise. Totally different data, totally different stakes. And yet the things that made each one good , versus a flashy demo that falls over the moment a real agent uses it in anger, were the same every time. Here are the seven that keep recurring. 1. Every tool you expose is a tax on the agent's context This is the one nobody warns you about. Every tool definition (name, description, JSON schema) gets loaded into the model's context on every single request , whether or not it's ever called. Twenty tools of boilerplate and the agent is reading pages of plumbing before it touches your data. So the design pressure runs the opposite way from a normal API: fewer, broader tools beat many narrow ones. vellum has 15 core tools and I fought to keep it there. If a tool doesn't earn its slot in the context window on most requests, it shouldn't be a tool. 2. Tools are for acting. Resources are for reading. The corollary to #1. Most servers expose everything (including "read this thing") as a tool. But MCP has a better primitive for reading: resources . In vellum, every note is a resource at a stable URI ( vellum://note/projects/x.md ). The agent attaches a document by reference instead of spending a tool round-trip to fetch it, and it can subscribe to that URI and get told the moment the note changes. Reading through a resource costs no tool definition. Reading through a tool costs one on every request. Use the right primitive. 3. Fail closed on auth, fail open on limits A token with no grants
After a lovely and productive conversation with your client, with still ringing ears, you check the coding agent's last log messages on a ticket that adds a discount to a product. The message was: "Done, I added the 10% discount and all tests pass. Stopping. " Well ... you know it's just not true, so you dig further and quickly realize that the discount functionality was never actually added and the tests it reported passing had never been run. The agent reached the end of the loop, looked at its own work, and called it finished. That call is the thing that shipped. This has a name. A paper published this June, From Confident Closing to Silent Failure , calls it false success: the agent asserts the task is complete while the actual state of the system says otherwise. It is common, and it holds up across capable models. On AppWorld, a benchmark for long-horizon coding agents, 75.8% of the runs that actually failed still ended with the agent claiming it was done. The researchers then put five different LLM judges on those completion claims, varying the prompts each time, and every one of them landed barely above a coin flip, because the thing each judge was reading was the closing sentence, and the closing sentence reads as confident whether the work happened or not. What told a real done apart from a false one turned out to be cheap and mechanical: a look at the actual state of the system. A lightweight deterministic state check caught four to eight times more false successes than the best of the judges. The paper has a name for the mechanism underneath, a hallucination of verification: the model narrates having checked something it never checked, and that narration is indistinguishable, sentence for sentence, from a report of a check that really ran. That gap, between what the agent said and what the system did, is what this piece is about. A loop runs five arms: generate, check, steer, retry, stop. The series opener named them; four pieces since took the check that
The tech world has been building up towards the shift to a fully agentic development life cycle for a few years now. AI is changing how software gets built. Across the Puppet ecosystem, we're seeing a shift toward more agentic engineering workflows. AI helps generate code, shape documentation, and accelerate how Puppet modules evolve. This brings real benefits in speed and consistency, but it also raises important questions from the community: How are AI-generated changes validated? How do you ensure consistency across modules? What does this mean for contributors and maintainers? These are exactly the kinds of questions we should be asking! This article will outline how Perforce and the Puppet team are approaching the use of AI in our open source modules and repositories. How We Build Trust in AI-Assisted Contributions At Perforce, AI is a core part of our process and our teams operate within a defined, governed framework for development. We don’t rely on trust in the tool itself. We rely on the processes around it. Whether a change is written by a person, generated with AI, or some mix of both, they are held to the same standards before it’s accepted and released. In practice, that means: Human review is always the gate: Every change is reviewed by maintainers. AI can assist, but it doesn’t replace accountability. AI works within established patterns: AI-generated code isn’t created in isolation. It’s guided by the same module structures, conventions, and expectations that already exist across the ecosystem. Validation is continuous and enforced: AI doesn’t change our standards. It reinforces them. AI-generated changes go through the same checks as any other contribution: Test suites Integration validation Functional verification AI output is a starting point, not a final artifact: Generated code is iterated on, refined, and aligned before acceptance. We treat AI as an accelerator, not an authority. The community plays an important role Open source means visibilit
If you run more than a handful of OpenTelemetry Collectors, you already know the pain: a config change means SSHing into boxes, redeploying DaemonSets, or babysitting a Git pipeline per cluster, and you never quite trust that every agent is running the config you think it is. OpAMP fixes exactly that. It is a protocol that lets a central server push configuration to a fleet of Collectors, watch their health, and roll changes out in stages, without you touching each host. This post walks through how OpAMP works, the two ways a Collector can speak it, and the config you need to wire one up. The problem OpAMP solves A single Collector is easy. A hundred of them, spread across clusters, VMs, and edge nodes, is a fleet-management problem that has nothing to do with telemetry itself. Every observability team eventually builds some version of the same thing: a way to ship a new pipeline config, confirm it actually applied, and back it out when a processor starts dropping spans. Without a management protocol you end up gluing that together from ConfigMaps, Ansible runs, and dashboards that only tell you an agent is alive, not what config it is actually running. Config drift creeps in. One node keeps an old sampling rate for months because its rollout quietly failed and nobody noticed. OpAMP, the Open Agent Management Protocol, is the OpenTelemetry answer to this. Splunk donated it to the project in 2022, and it has since become the standard control channel for the Collector. It is worth pairing with a clear-eyed view of what a Collector actually is versus lighter agents; the OpenTelemetry Collector vs Grafana Alloy comparison covers that trade-off if you are still choosing a data plane. What OpAMP actually is OpAMP is a client/server network protocol for remote management of large fleets of data-collection agents. It is transport-flexible: agents connect to the server over either plain HTTP or a WebSocket, and the WebSocket path gives you a persistent bidirectional channel
⚡ What makes MoilStack .md different? 1. Bring Your Own AI (100% Local or Cloud) Connect any provider without vendor lock-in or middleman servers: Local & Offline: Direct integration with Ollama so your data never touches the internet. Cloud API Support: Works with OpenAI, Anthropic Claude, Google Gemini, Groq, Mistral AI, OpenRouter, Together AI, and Cerebras. Your API keys stay stored locally, and requests go straight from your machine to the provider. Zero telemetry, zero cloud accounts. 2. Native Inline AI Editing Forget copying and pasting between a chatbot window and your file. Highlight any section and ask the AI to rewrite, shorten, expand, or explain it. Edits stream directly into your document. 3. Safety Net: Reversible Edits & Auto Backups AI modifications shouldn't destroy your hard work: Instant Revert: Press Ctrl + Z to instantly undo any AI rewrite and restore your exact document state. Automatic Snapshots: MoilStack .md automatically creates a local snapshot before every AI action, keeping the last 10 versions per file in a local backup directory. 4. Focused Desktop Experience Minimalist UI: Clean writing view with toggle preview and right-click formatting — no clunky toolbars taking up screen space. Local Workspaces: Open any local folder as a workspace to create, rename, and edit .md files directly. One-Click PDF Export: Export your drafts into clean, beautifully formatted PDFs with standard margins and readable typography. Multi-Instance: Double-click any file in your file explorer or open multiple side-by-side windows independently. 🛠️ Tech Stack & Availability MoilStack .md is open source under the MIT License . Stack: Electron, Modern Web Technologies Platforms: Windows ( .exe / Microsoft Store) & Linux ( .deb / AppImage ) (macOS coming soon) 🚀 Check It Out 🌐 Website: moilstack.com/moilstack-md 🐙 GitHub: github.com/moilstack/moilstack-md 🛒 Microsoft Store: Available for Windows I'd love to hear your thoughts! What does your current Markdown set
A Live Linux system such as SystemRescue generally has the following structure: ISO9660 ├── EFI/, boot/, syslinux/, grub/ ← Bootloader ├── vmlinuz ← Kernel ├── initramfs ← Initial RAM disk └── airootfs.sfs / filesystem.squashfs └── Actual root filesystem Because SquashFS is read-only, the basic process is as follows: Extract the ISO ↓ Extract the SquashFS ↓ Edit the rootfs or enter it with chroot ↓ Rebuild the SquashFS ↓ Replace the SquashFS inside the ISO ↓ Rebuild it as a bootable ISO ↓ Test with BIOS and UEFI However, with SystemRescue, it is safer not to rebuild airootfs.sfs directly from the outset, but to select a method in the following order of priority: YAML configuration in sysrescue.d Overlay using an SRM (SystemRescueModule) Direct reconstruction of airootfs.sfs Full build from the SystemRescue source The official SystemRescue documentation also recommends sysrescue-customize for modifying ISO images. An SRM is an additional layer in SquashFS format, and files at the same paths in the SRM take precedence over those in the base rootfs. ( SystemRescue ) 1. Preparing the Working Environment It is easiest to perform this work on Linux. On Debian/Ubuntu-based systems, install the following: sudo apt update sudo apt install squashfs-tools xorriso rsync file It is also useful to install QEMU for testing: sudo apt install qemu-system-x86 ovmf The official SystemRescue customization script also lists xorriso and squashfs-tools among its main dependencies. It can also be run under WSL. ( SystemRescue ) Create a working directory: mkdir -p ~/work/systemrescue cd ~/work/systemrescue cp /path/to/systemrescue.iso original.iso Ensure that you have at least several times the original ISO size in free space. When rebuilding from within SystemRescue itself, the official documentation notes that the Copy-on-Write area may require approximately three times the ISO size. ( SystemRescue ) Method A: Use the Official SystemRescue sysrescue-customize Tool For SystemRescue, this
GitHub热门项目 | AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 1,987+ agentic skills. Includes CLI, local MCP, catalog, plugins, and Workbench. | Stars: 44,034 | 73 stars today | 语言: Python
Published on Aug 18th, 2025 The Setup: When Everything Seems Perfect After successfully implementing a chatbot based on ChatGPT in my portfolio (as detailed in my previous "Redesign Portfolio" post), I was feeling pretty good about myself. The AI integration was working smoothly, users could ask questions about my skills and projects, and I had successfully created content embeddings that made the chatbot intelligent and contextually aware. Little did I know that this "completed" project was about to become the perfect candidate for my "Unfinished Projects" series. The Crisis: When OpenAI Goes Silent It started with a simple error message: "OpenAI API account suspended." At first, I thought it was a simple configuration issue. Maybe I had accidentally exposed my API key or hit some rate limit. But after checking my environment variables and account status, I realized the problem was deeper. My OpenAI account was suspended, and suddenly, my "completed" AI chat functionality was completely broken. Suddenly, my SmartNotes app which also powers the chat functionality went offline. This was supposed to be a finished project. Instead, it had become the latest addition to my stack of unfinished work. The Panic: Scrambling for Solutions The immediate challenge was clear: either abandon the chat functionality entirely or find an alternative solution. Given that I had already invested significant time in building the user interface and database integration, abandoning it wasn't an option. I started researching alternatives: Claude API : Limited availability and different pricing structure Local AI models : Required significant computational resources Hugging Face : Promising, but I had no experience with their inference API The clock was ticking, and my portfolio was broken. The Discovery: Hugging Face to the Rescue After several hours of research and testing, I discovered that Hugging Face offered inference APIs that could potentially replace OpenAI's functionality. The catc
Published on Aug 18, 2025 A New Era of AI-Powered Coding Begins I have installed Cursor on my laptop this weekend, and I am amazed at how much it speeds up my coding. I have a new debugging buddy!! This week, I have made several updates to the Portfolio website. The Challenge: When OpenAI Falls Short In my previous post, I shared the excitement of implementing a chatbot based on ChatGPT for my portfolio website. The initial experience was promising - I successfully created content embeddings and integrated them with OpenAI's API. However, as many developers know, relying on a single service provider can lead to unexpected roadblocks. When my OpenAI account encountered issues, I faced a critical decision: abandon the chat functionality or find an alternative solution. I chose the latter, embarking on a journey that would transform my portfolio's AI capabilities and teach me valuable lessons about building robust, fallback-ready systems. The Migration: Embracing Open Source AI The transition from OpenAI to Hugging Face wasn't just a simple API swap - it was a complete architectural evolution. Here's what I learned: 1. Model Selection Complexity Finding the right model on Hugging Face proved more challenging than expected. After testing several options: microsoft/DialoGPT-medium - No inference provider available gpt2 and distilgpt2 - Limited conversational capabilities Qwen/Qwen3-4B - Perfect fit with the nebius provider 2. Database Architecture Evolution The migration also prompted a database upgrade from MongoDB to Neon PostgreSQL. This wasn't just about changing providers - it was about building a more scalable, production-ready foundation for my portfolio. Technical Implementation: Building Resilience Streaming Responses for Better UX One of the most significant improvements was implementing streaming text responses. Instead of waiting for complete AI responses, users now see text appear word-by-word, creating a ChatGPT-like experience: // Streaming implementation
I Built Flowstate Because We Somehow Made Productivity More Complicated Than The Actual Work Live: https://flowstate.chromitedev.xyz/ GitHub: https://github.com/ChromiteDev/flowstate We have a strange problem. Humans built some of the most advanced technology in history. We created: Computers that fit in our pockets Instant communication across the planet Machines that explore space Software that can do incredible things And somehow... We still struggle with: "What should I actually focus on today?" That is the problem I wanted to solve. So I built Flowstate . The productivity paradox We have never had more productivity tools. Seriously. Think about it. There are apps for: Tasks Notes Calendars Habits Goals Projects Time tracking Team management There is probably an app to help you organize the apps that organize your life. At some point we stopped being productive and started managing productivity. The funniest part? Sometimes creating the perfect productivity system becomes the biggest productivity project. You spend two hours making a beautiful workspace... Then realize: You have done absolutely nothing. A masterpiece of organization. Zero progress. The moment I realized something was wrong I noticed a pattern. People were not struggling because they were lazy. They were struggling because their attention was constantly being divided. Every day we fight: Notifications Endless information Too many choices Too many responsibilities Too many things competing for our attention The internet gave us unlimited access to information. But our attention? That is still limited. The question behind Flowstate I kept coming back to one question: "What actually deserves my attention right now?" Not: "What are all the possible things I could do?" Not: "How can I create the most complicated workflow imaginable?" Not: "Should I reorganize my folders for the fifth time?" (We have all been there.) The goal was simple: Create a tool that helps people find clarity. Introducing Flowsta
May 2026 There's a touchscreen mounted in my kitchen — I call it the WallScreen. It runs recipes, the chore board, a calendar, the usual smart-home clutter. One day I decided it should also pull in free over-the-air television. No subscription, no streaming app, just the local broadcast towers that have been beaming HD into the air for free this whole time. I had a Raspberry Pi 5, a $30 USB tuner, and what I assumed would be a boring afternoon. It was not a boring afternoon. The tuner that didn't want to work The tuner I grabbed was a MyGica A681 — a tidy little USB TV stick. Plug it into Windows, install the bundled software, done. Plug it into a Raspberry Pi running a current Linux kernel and you get… nothing. The computer notices a device is there and otherwise shrugs. Here's why, in two sentences: Linux has no built-in driver for the chips inside this particular stick. The manufacturer's driver only works on regular PC processors — and even then, only as a sealed, prebuilt file with no source code. A Raspberry Pi uses a different kind of chip entirely, so that driver is a non-starter. That's the whole problem. The hardware is great. It's just that on a Pi, this tuner is a paperweight — and you can't buy or download your way out of it. The only way out was to write the driver myself. What writing the driver actually involved A USB TV tuner isn't one chip — it's a little team of them working together. One chip is the "translator" that lets the computer talk to the device over USB. Another tunes to a channel, like turning a radio dial. A third converts the broadcast signal into video data the computer can use. The good news: for the parts that handle tuning and decoding the signal, I was able to build on existing open-source work from the broader Linux TV community — code other people had already written and shared for the chips inside this stick. (It's all credited in the project.) The missing piece — the part nobody had written — was the translator layer : the co
TL;DR Modern web applications change constantly. Components are re-rendered, generated...
GitHub热门项目 | A lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs in the web browser, on the desktop, on mobile, and inside Jupyter notebooks. | Stars: 2,613 | 420 stars today | 语言: TypeScript