🛠️ All DevTools

[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.

[Other] Algorithmic trading strategy builder | no code backtesting Build, backtest, and deploy algorithmic trading strategies with ease. No coding required. Free algorithmic trading platform with visual strategy builder, real-time backtesting, and comprehensive analytics.

[Database] Fast, simple database manager Fast, simple, cross-platform database manager with intuitive GUI to connect, query, and manage 20+ databases: PostgreSQL, MySQL, SQLite, ClickHouse, DuckDB, Redis, S3/R2, and more.

[CLI Tool] Open Source, local-first AI CLI for developers An open-source, local-first AI CLI for developers

[Other] Free customizable countdown timers to your Shopify store GSC Countdown Timer helps Shopify merchants create urgency and boost conversions with flexible countdowns. Add fixed-date, evergreen, or recurring timers to any page or section. Clean design, fast setup, and full Shopify theme integration — no coding required.

[Other] Movycat – A terminal movie player written in Zig I saw Mario (the author) at Zigtoberfest in Munich last Saturday where he gave a presentation on a whole stack of related projects implemented in Zig: A graphics library for the terminal (movy), movycat (video playback in the terminal), zig64 &amp; zigreSID (emulators for Commodore 64&#x27;s CPU and sound chip), and a reimplementation of a C64 video game (which I don&#x27;t think he has published on GitHub yet). Anyway, I found his work incredible and thought he deserved some attention.<p>Update: Since writing this, Mario has uploaded the game, too: <a href="https:&#x2F;&#x2F;github.com&#x2F;M64GitHub&#x2F;1st-shot" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;M64GitHub&#x2F;1st-shot</a> . I misunderstood, though: It doesn&#x27;t seem to be a port of an actual C64 game.

[Other] Cisco opensourced MCP-Scanner for finding vulnerabilties in MCP server

[CLI Tool] Show HN: Git Auto Commit (GAC) – LLM-powered Git commit command line tool GAC is a tool I built to help users spend less time summing up what was done and more time building. It uses LLMs to generate contextual git commit messages from your code changes. And it can be a drop-in replacement for `git commit -m &quot;...&quot;`.<p>Example:<p>```<p>feat(auth): add OAuth2 integration with GitHub and Google<p>- Implement OAuth2 authentication flow<p>- Add provider configuration for GitHub and Google<p>- Create callback handler for token exchange<p>- Update login UI with social auth buttons<p>```<p>Don&#x27;t like it? Reroll with &#x27;r&#x27;, or type `r &quot;focus on xyz&quot;` and it rerolls the commit with your feedback!<p>You can try it out with uvx (no install):<p>```<p>uvx gac init # config wizard<p>uvx gac<p>```<p><i>Note: `gac init` creates a .gac.env file in your home directory with your chosen provider, model, and API key.</i><p>*Tech details:*<p>*14 providers* - Supports local (Ollama &amp; LM Studio) and cloud (OpenAI, Anthropic, Gemini, OpenRouter, Groq, Cerebras, Chutes, Fireworks, StreamLake, Synthetic, Together AI, &amp; Z.ai (including their extremely cheap coding plans!)).<p>*Three verbosity modes* - Standard with bullets (default), one-liners (`-o`), or verbose (`-v`) with detailed Motivation&#x2F;Architecture&#x2F;Impact sections.<p>*Secret detection* - Scans for API keys, tokens, and credentials before committing. Has caught my API keys on a new project when I hadn&#x27;t yet gitignored .env.<p>*Flags* - Automate common workflows:<p>- `gac -h &quot;bug fix&quot;` - pass hints to guide intent<p>- `gac -yo` - auto-accept the commit message in one-liner mode<p>- `gac -ayp` - stage all files, auto-accept the commit message, and push (yolo mode)<p>Would love to hear your feedback! Give it a try and let me know what you think! &lt;3<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;cellwebb&#x2F;gac" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;cellwebb&#x2F;gac</a>

[Other] Show HN: JSON Query I&#x27;m working on a tool that will probably involve querying JSON documents and I&#x27;m asking myself how to expose that functionality to my users.<p>I like the power of `jq` and the fact that LLMs are proficient at it, but I find it right out impossible to come up with the right `jq` incantations myself. Has anyone here been in a similar situation? Which tool &#x2F; language did you end up exposing to your users?

[IDE/Editor] Show HN: Erdos – open-source, AI data science IDE Hey HN! We’re Jorge and Will from Lotas (<a href="https:&#x2F;&#x2F;www.lotas.ai&#x2F;">https:&#x2F;&#x2F;www.lotas.ai&#x2F;</a>), and we’ve built Erdos, a secure AI-powered data science IDE that’s fully open source (<a href="https:&#x2F;&#x2F;www.lotas.ai&#x2F;erdos">https:&#x2F;&#x2F;www.lotas.ai&#x2F;erdos</a>).<p>A few months ago, we shared Rao, an AI coding assistant for RStudio (<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44638510">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44638510</a>). We built Rao to bring the Cursor-like experience to RStudio users. Now we want to take the next step and deliver a tool for the entire data science community that handles Python, R, SQL, and Julia workflows.<p>Erdos is a fork of VS Code designed for data science. It includes:<p>- An AI that can search, read, and write across all file types for Python, R, SQL, and Julia. Also, for Jupyter notebooks, we’ve optimized a jupytext system to allow the AI to make faster edits.<p>- Built-in Python, R, and Julia consoles accessible to both the user and AI<p>- Plot pane that tracks and organizes plots by file and time<p>- Database pane for connecting to and manipulating SQL or FTP data sources<p>- Environment pane for viewing variables, packages, and environments<p>- Help pane for Python, R, and Julia documentation<p>- Remote development via SSH or containers<p>- AI assistant available through a single-click sign-in to our zero data retention backend, bring your own key, or a local model<p>- Open source AGPLv3 license<p>We built Erdos because data scientists are often second-class citizens in modern IDEs. Tools like VS Code, Cursor, and Claude Code are made for software developers, not for people working across Jupyter notebooks, scripts, and SQL. We wanted an IDE that feels native to data scientists, while offering the same AI productivity boosts.<p>You can try Erdos at <a href="https:&#x2F;&#x2F;www.lotas.ai&#x2F;erdos">https:&#x2F;&#x2F;www.lotas.ai&#x2F;erdos</a>, check out our source code on our GitHub (<a href="https:&#x2F;&#x2F;github.com&#x2F;lotas-ai&#x2F;erdos" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;lotas-ai&#x2F;erdos</a>), and let us know what features would make it more useful for your work. We’d love your feedback below!

[Other] Show HN: spoilerjs – Reddit-style spoilers with particle animations Hello HN!<p>I just published my first npm library as a small way to give back to the open source community.<p>I built `spoilerjs`, a lightweight web component that lets you hide text with an animated spoiler effect. Think Reddit spoilers, but with more flair! It works with plain HTML, React, Vue, or Svelte, and you can customize attributes like particle density, velocity, and scale. The effect is totally inspired by the Telegram app!<p>Demo: <a href="https:&#x2F;&#x2F;spoilerjs.sh4jid.me" rel="nofollow">https:&#x2F;&#x2F;spoilerjs.sh4jid.me</a> NPM: <a href="https:&#x2F;&#x2F;www.npmjs.com&#x2F;package&#x2F;spoilerjs" rel="nofollow">https:&#x2F;&#x2F;www.npmjs.com&#x2F;package&#x2F;spoilerjs</a> GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;shajidhasan&#x2F;spoilerjs" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;shajidhasan&#x2F;spoilerjs</a><p>I&#x27;m sure there are probably some bugs and rough edges, but I&#x27;d love to hear your feedback!<p>Thanks!

[CLI Tool] Show HN: Whatdidido – CLI to summarize your work from Jira/Linear I built this after spending days every year manually searching through Jira tickets to remember what I&#x27;d accomplished for performance reviews.<p>whatdidido is a CLI tool that:<p>- Pulls tickets from Jira or Linear for a date range<p>- Uses an LLM to create short summaries of each ticket<p>- Generates an overall summary to help you build your self-evaluation<p>The tool doesn&#x27;t write your review for you—crafting thoughtful, contextual feedback still requires human judgment. It just eliminates the busywork of finding and organizing what you worked on.<p>Key details:<p>- MIT licensed, open source<p>- No data storage—everything stays local<p>- Requires OpenAI or OpenRouter API key<p>- Works with Jira and Linear (more integrations welcome&#x2F;coming soon)<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;oliviersm199&#x2F;whatdidido" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;oliviersm199&#x2F;whatdidido</a><p>I&#x27;m releasing it now because I think others might find it useful during review season.<p>Would love feedback on the approach and what other integrations would be helpful. Happy to answer questions about how it works.

[API/SDK] Show HN: nblm - Rust CLI/Python SDK for NotebookLM Enterprise automation I built nblm, a Rust-based toolset to automate Google’s NotebookLM Enterprise API reliably. It aims to replace brittle curl snippets with a stable interface you can use in cron&#x2F;CI or agentic systems.<p>* Python SDK (type-safe): IDE auto-complete, fewer JSON key typos, fits complex workflows.<p>* Standalone CLI: single fast binary for scripts and pipelines.<p>* Handles auth, batching, retries; you focus on logic. Rust core is fast and memory-safe.<p>* Enterprise API only (consumer NotebookLM isn’t supported).<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;K-dash&#x2F;nblm-rs" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;K-dash&#x2F;nblm-rs</a><p>Feedback is welcome—I&#x27;m especially interested in thoughts on the Python SDK’s design for building automated&#x2F;agentic workflows. Thanks!

[API/SDK] API, CLI, and Web App for analyzing and finding a person's profile in 1000 social media \ websites

[Other] SysNova Toolkit: advanced diagnostics for Windows & macOS — Ethical toolkit for Windows/macOS optimization – modular, lightweight, open source. - MentalistOps/MentalistOps SysNova Toolkit is an ethical and modular suite for advanced system optimization and diagnostics across Windows and macOS platforms.

[Other] Release verseinserter v1.0.0 · kasimlyee/verse Automated Scripture Insertion. Contribute to kasimlyee/verse-inserter development by creating an account on GitHub.