🛠️ All DevTools

[Other] Your Glucose Levels. Right in Your IDE. Visual Code Extensions to display CGM blood glucose readings in your Visual Studio Code status bar.

[Other] Fill forms instantly with fake data Save hours of testing time. Instantly fill web forms with realistic fake data — powered by Faker.js. A free browser extension for developers and QA professionals.

[Other] Free multilingual resume builder in 64 languages Create professional resumes in 64 languages. No registration required, AI-powered text enhancement, and instant PDF download. Build your perfect CV in minutes.

[Other] A modern, customizable GanttChart component. An open-source Gantt chart component built with Vue 3 and TypeScript. Easily add timeline visualization, task scheduling, and drag-and-drop interaction to your project management apps. 🎬 Live Demo now available! Try it here 👉https://nelson820125.github.io/jordium-gantt-vue3

[Other] Discover and share skills to supercharge Claude AI" Get Claude Skills is the first marketplace for Claude AI skills - modular packages that extend Claude's capabilities with specialized workflows and tools. Browse skills for: 🎨 AI image prompting (Midjourney, Flux, Stable Diffusion) 🛠️ Development & code workflows 📊 Productivity & automation 🎯 Domain-specific expertise and many more Download expert-crafted skills with one click. Share your own skills and earn revenue. Think of it as the "App Store" for Claude AI capabilities.

[Other] Show HN: Front End Fuzzy and Substring and Prefix Search Hey everyone, I have updated my fuzzy search library for the frontend. It now supports substring and prefix search, on top of fuzzy matching. It&#x27;s fast, accurate, multilingual and has zero dependencies.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;m31coding&#x2F;fuzzy-search" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;m31coding&#x2F;fuzzy-search</a> Live demo: <a href="https:&#x2F;&#x2F;www.m31coding.com&#x2F;fuzzy-search-demo.html" rel="nofollow">https:&#x2F;&#x2F;www.m31coding.com&#x2F;fuzzy-search-demo.html</a><p>I would love to hear your feedback and any suggestions you may have for improving the library.<p>Happy coding!

[Other] Show HN: HortusFox – FOSS system for houseplants with enterprise-scale features HortusFox is a free and open-source management, tracking and journaling system for indoor and outdoor plants. It&#x27;s 100% FOSS software under the MIT license, but still has grown to a scale that can be considered enterprise-scale.<p>That was possible because of the great community that evolved around this leafy project, that kept me motivated as well as supported me in a very kind and lovely way.<p>After a few months of no new version release, a few days ago I&#x27;ve published v5.3 of HortusFox, which provides some cool changes to help empower your plant parenting. You can see a very detailed changelog in the 5.3 release page on GitHub. Overall 25 issues have been resolved, where some of them were actually quite big.<p>If you&#x27;re new to HortusFox: the system offers you a large number of features to support your plant parenting (or gardening) journey with plant details, locations, photos, default and custom plant attributes, inventory system, tasks system, calendar, history (to keep a memory of your leafy friends), collaborative group chat and a few opt-in features such as weather forecast or plant identification (via photo). It also offers an extensive REST API, backup feature and themes (if you want your workspace a little more personal).<p>I have to say, for me it&#x27;s the perfect blend of two things: The passion for software development as well as the fondness for nature. I&#x27;m really grateful for that.<p>Thanks for reading and have a wonderful and leafy day.

[Other] Database backups, dump files and restic

[Database] SwirlDB: Modular-first, CRDT-based embedded database

[Other] Show HN: Butter – A Behavior Cache for LLMs Hi HN! I&#x27;m Erik. We built Butter, an LLM proxy that makes agent systems deterministic by caching and replaying responses, so automations behave consistently across runs.<p>- It’s a chat completions compatible endpoint, making it easy to drop into existing agents with a custom base_url<p>- The cache is template-aware, meaning lookups can treat dynamic content (names, addresses, etc.) as variables<p>You can see it in action in this demo where it memorizes tic-tac-toe games: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=PWbyeZwPjuY" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=PWbyeZwPjuY</a><p>Why we built this: before Butter, we were Pig.dev (YC W25), where we built computer-use agents to automate legacy Windows applications. The goal was to replace RPA. But in practice, these agents were slow, expensive, and unpredictable - a major downgrade from deterministic RPA, and unacceptable in the worlds of healthcare, lending, and government. We realized users don&#x27;t want to replace RPA with AI, they just want AI to handle the edge cases.<p>We set out to build a system for &quot;muscle memory&quot; for AI automations (general purpose, not just computer-use), where agent trajectories get baked into reusable code. You may recall our first iteration of this in May, a library called Muscle Mem: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=43988381">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=43988381</a><p>Today we&#x27;re relaunching it as a chat completions proxy. It emulates scripted automations by storing observed message histories in a tree structure, where each fork in the tree represents some conditional branch in the workflow&#x27;s &quot;code&quot;. We replay behaviors by walking the agent down the tree, falling back to AI to add new branches if the next step is not yet known.<p>The proxy is live and free to use while we work through making the template-aware engine more flexible and accurate. Please try it out and share how it went, where it breaks, and if it’s helpful.

[Other] Show HN: Tamagotchi P1 for FPGAs After being thrust headfirst into FPGA development thanks to the Analogue Pocket, my first from scratch creation was a gate level implementation of the original Tamagotchi toy.<p>The core, running on both the Analogue Pocket and MiSTer platforms, lets users re-experience the very first Tamagotchi from 1996 with accurate emulation, but modern features. The core has savestates (which is much harder to do in hardware vs software emulation), high turbo speeds (1,800x was the max clock speed I&#x27;ve reached so far), and more.<p>Learning more about hardware and FPGAs is something I&#x27;ve wanted to do for many years, and I highly recommend it for any programmer-brained person. It&#x27;s a very slightly different way of thinking that has vast consequences on how you look at simple problems.

[API/SDK] Show HN: OpenAI Apps Handbook I went swimming in the ocean of OpenAI&#x27;s Apps SDK… and came back with a handbook!<p>Over the past few weeks, I’ve been diving deep into the ChatGPT App SDK: exploring its APIs, tools, and hidden gems. Along the way, I built, broke, fixed, and reimagined a bunch of little experiments.<p>P.S: Indeed OAIs official docs is the source of truth, this is just a rough notebook<p>Maybe, I can create a CLI tool to scaffold app?

[Other] Show HN: Apache Fory Rust – 10-20x faster serialization than JSON/Protobuf Serialization framework with some interesting numbers: 10-20x faster on nested objects than json&#x2F;protobuf.<p><pre><code> Technical approach: compile-time codegen (no reflection), compact binary protocol with meta-packing, little-endian layout optimized for modern CPUs. Unique features that other fast serializers don&#x27;t have: - Cross-language without IDL files (Rust ↔ Python&#x2F;Java&#x2F;Go) - Trait object serialization (Box&lt;dyn Trait&gt;) - Automatic circular reference handling - Schema evolution without coordination Happy to discuss design trade-offs. Benchmarks: https:&#x2F;&#x2F;fory.apache.org&#x2F;docs&#x2F;benchmarks&#x2F;rust</code></pre>

[Package Manager] Show HN: CoordConversions NPM Module for Map Coordinate Conversions I have been working on a project that has multiple repos, all of which have to convert between multiple map coordinate types, so I made an NPM module that allows you to parse and convert between Decimal Degrees, Degrees-Minutes, and Degrees-Minutes-Seconds coordinate types. Niche? Yes. Useful? Also yes (I hope)!

[Other] Show HN: I made semantic search engine for engineering blogs and conferences

[Other] Show HN: Pipelex – declarative language for repeatable AI workflows (MIT) We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile&#x2F;SQL for multi-step LLM pipelines: you declare steps and interfaces; any model&#x2F;provider can fill them.<p>Why this instead of yet another workflow builder?<p>- Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs&#x2F;outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open standard under MIT: language spec, runtime, API server, editor extensions, MCP server, n8n node. - Composable: pipes can call other pipes, created by you or shared in the community.<p>Why a domain-specific language?<p>- We need context, meaning and nuances preserved in a structured syntax that both humans and LLMs can understand - We need determinism, control, and reproducibility that pure prompts can&#x27;t deliver - Bonus: editors, diffs, semantic coloring, easy sharing, search &amp; replace, version control, linters…<p>How we got there:<p>Initially, we just wanted to solve every use-case with LLMs but kept rebuilding the same agentic patterns across different projects. So we challenged ourselves to keep the code generic and separate from use-case specifics, which meant modeling workflows from the relevant knowledge and know-how.<p>Unlike existing code&#x2F;no-code frameworks for AI workflows, our abstraction layer doesn&#x27;t wrap APIs, it transcribes business logic into a structured, unambiguous script executable by software and AI. Hence the &quot;declarative&quot; aspect: the script says what should be done, not how to do it. It&#x27;s like a Dockerfile or SQL for AI workflows.<p>Additionally, we wanted the language to be LLM-friendly. Classic programming languages hide logic and context in variable names, functions, and comments: all invisible to the interpreter. In Pipelex, these elements are explicitly stated in natural language, giving AI full visibility: it&#x27;s all logic and context, with minimal syntax.<p>Then, we didn&#x27;t want to write Pipelex scripts ourselves so we dogfooded: we built a Pipelex workflow that writes Pipelex workflows. It&#x27;s in the MCP and CLI: &quot;pipelex build pipe &#x27;…&#x27;&quot; runs a multi-step, structured generation flow that produces a validated workflow ready to execute with &quot;pipelex run&quot;. Then you can iterate on it yourself or with any coding agent.<p>What’s included: Python library, FastAPI and Docker, MCP server, n8n node, VS Code extension.<p>What we’d like from you<p>1. Build a workflow: did the language work for you or against you? 2. Agent&#x2F;MCP workflows and n8n node usability. 3. Suggest new kinds of pipes and other AI models we could integrate 4. Looking for OSS contributors to the core library but also to share pipes with the community<p>Known limitations<p>- Connectors: Pipelex doesn’t integrate with “your apps”, we focus on the cognitive steps, and you can integrate through code&#x2F;API or using MCP or n8n - Visualization: we need to generate flow-charts - The pipe builder is still buggy - Run it yourself: we don’t yet provide a hosted Pipelex API, it’s in the works - Cost-tracking: we only track LLM costs, not image generation or OCR costs yet - Caching and reasoning options: not supported yet<p>Links<p>- GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;Pipelex&#x2F;pipelex" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Pipelex&#x2F;pipelex</a> - Cookbook: <a href="https:&#x2F;&#x2F;github.com&#x2F;Pipelex&#x2F;pipelex-cookbook" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Pipelex&#x2F;pipelex-cookbook</a> - Starter: <a href="https:&#x2F;&#x2F;github.com&#x2F;Pipelex&#x2F;pipelex-starter" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Pipelex&#x2F;pipelex-starter</a> - VS Code extension: <a href="https:&#x2F;&#x2F;github.com&#x2F;Pipelex&#x2F;vscode-pipelex" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Pipelex&#x2F;vscode-pipelex</a> - Docs: [<a href="https:&#x2F;&#x2F;docs.pipelex.com" rel="nofollow">https:&#x2F;&#x2F;docs.pipelex.com</a>](<a href="https:&#x2F;&#x2F;docs.pipelex.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;docs.pipelex.com&#x2F;</a>) - Demo video (2 min): <a href="https:&#x2F;&#x2F;youtu.be&#x2F;dBigQa8M8pQ" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;dBigQa8M8pQ</a> - Discord for support and sharing: <a href="https:&#x2F;&#x2F;go.pipelex.com&#x2F;discord" rel="nofollow">https:&#x2F;&#x2F;go.pipelex.com&#x2F;discord</a><p>Thanks for reading. If you try Pipelex, tell us exactly where it hurts, that’s the most valuable feedback we can get.

[DevOps] Show HN: Dexto – Connect your AI Agents with real-world tools and data Hi HN, we’re the team at Truffle AI (YC W25), and we’ve been working on Dexto (<a href="https:&#x2F;&#x2F;www.dexto.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.dexto.ai&#x2F;</a>), a runtime and orchestration layer for AI Agents that lets you turn any app, service or tool into an AI assistant that can reason, think and act. Here&#x27;s a video walkthrough - <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=WJ1qbI6MU6g" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=WJ1qbI6MU6g</a><p>We started working on Dexto after helping clients setup agents for everyday marketing tasks like posting on LinkedIn, running Reddit searches, generating ad creatives, etc. We realized that the LLMs weren’t the issue. The real drag was the repetitive orchestration around them:<p>- wiring LLMs to tools - managing context and persistence - adding memory and approval flows - tailoring behavior per client&#x2F;use case<p>Each small project quietly ballooned into weeks of plumbing where each customer had mostly the same, but slightly custom requirement.<p>So instead of another framework where you write orchestration logic yourself, we built Dexto as a top-level orchestration layer where you declare an agent’s capabilities and behavior:<p>- which tools or MCPs the agent can use - which LLM powers it - how it should behave (system prompt, tone, approval rules)<p>Once configured, the agent runs as an event-driven loop - reasoning through steps, invoking tools, handling retries, and maintaining its own state and memory. Your app doesn’t manage orchestration, it just triggers and subscribes to the agent’s events and decides how to render or approve outcomes.<p>Agents can run locally, in the cloud, or hybrid. Dexto ships with a CLI, a web UI, and a few sample agents to get started.<p>To show its flexibility, we wrapped some OpenCV functions into an MCP server and connected it to Dexto (<a href="https:&#x2F;&#x2F;youtu.be&#x2F;A0j61EIgWdI" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;A0j61EIgWdI</a>). Now, a non-technical user could detect faces in images or create custom photo collages by talking to the agent. The same approach works for coding agents, browser agents, multi-speaker podcast agents, and marketing assistants tuned to your data. <a href="https:&#x2F;&#x2F;docs.dexto.ai&#x2F;examples&#x2F;category&#x2F;agent-examples" rel="nofollow">https:&#x2F;&#x2F;docs.dexto.ai&#x2F;examples&#x2F;category&#x2F;agent-examples</a><p>Dexto is modular, composable and portable allowing you to plug in new tools or even re-expose an entire Dexto agent as an MCP Server and consume it from other apps like Cursor (<a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=_hZMFIO8KZM" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=_hZMFIO8KZM</a>). Because agents are defined through config and powered by a consistent runtime, they can run anywhere without code changes making cross-agent (A2A) interactions and reuse effortless.<p>In a way, we like to think of Dexto as a “meta-agent” or “agent harness” that can be customized into a specialized agent depending on its tools, data, and platform.<p>For the time being, we have opted for an Elastic V2 license to give maximum flexibility for the community to build with Dexto while preventing bigger players from taking over and monetizing our work.<p>We’d love your feedback:<p>- Try the quickstart and tell us what breaks - Share a use case you want to ship in a day, and we’ll suggest a minimal config<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;truffle-ai&#x2F;dexto" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;truffle-ai&#x2F;dexto</a><p>Docs: <a href="https:&#x2F;&#x2F;docs.dexto.ai&#x2F;docs&#x2F;category&#x2F;getting-started" rel="nofollow">https:&#x2F;&#x2F;docs.dexto.ai&#x2F;docs&#x2F;category&#x2F;getting-started</a><p>Quickstart: npm i -g dexto

[Other] AWS zero to hero repo for devops engineers to learn AWS in 30 Days. This repo includes projects, presentations, interview questions and real time examples.

[Other] Rust GUI components for building fantastic cross-platform desktop application by using GPUI.

[Other] Show HN: Bash Screensavers A github project to collect a bunch of bash-based screensavers&#x2F;visualizations.