🛠️ All DevTools

[Other] An Obsidian plugin that embeds Claude Code as an AI collaborator in your vault

[Other] Agent harness built with LangChain and LangGraph. Equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - well-equipped to handle complex agentic tasks.

[Database] OpenViking is an open-source context database designed specifically for AI Agents(such as openclaw). OpenViking unifies the management of context (memory, resources, and skills) that Agents need through a file system paradigm, enabling hierarchical context delivery and self-evolving.

[Other] A Claude Code plugin that automatically captures everything Claude does during your coding sessions, compresses it with AI (using Claude's agent-sdk), and injects relevant context back into future sessions.

[API/SDK] Show HN: Signbee – An API that lets AI agents send documents for signature Hi HN, I built Signbee while working on AI agents that handle contracting workflows. The agents could draft agreements, negotiate terms, manage deals — but the moment a signature was needed, the workflow broke. It always ended with &quot;please upload this to DocuSign&quot; — which meant human intervention, account setup, and manual uploads. So I built a simple API. You POST markdown and Signbee generates the PDF, or you pass a URL to your own PDF if you already have one designed the way you want it. No templates, no editor. Either way, it verifies both parties via email OTP and produces a signed document. curl -X POST <a href="https:&#x2F;&#x2F;signb.ee&#x2F;api&#x2F;v1&#x2F;send" rel="nofollow">https:&#x2F;&#x2F;signb.ee&#x2F;api&#x2F;v1&#x2F;send</a> \ -H &quot;Content-Type: application&#x2F;json&quot; \ -d &#x27;{ &quot;markdown&quot;: &quot;# NDA\n\nTerms...&quot;, &quot;sender_name&quot;: &quot;You&quot;, &quot;sender_email&quot;: &quot;you@company.com&quot;, &quot;recipient_name&quot;: &quot;Client&quot;, &quot;recipient_email&quot;: &quot;client@co.com&quot; }&#x27; Under the hood: - Markdown → PDF generation, or bring your own PDF via URL - Both parties verified via email OTP - Timestamps and IP addresses recorded - Final document hashed with SHA-256 - Certificate page appended with full audit trail One interesting challenge: the certificate page itself is part of the document that gets hashed, so any modification — even to the certificate — invalidates the integrity check. I also built an MCP server (npx -y signbee-mcp) so tools like Claude or Cursor can call it directly. Curious to hear from people who&#x27;ve dealt with document signing systems or automated agent workflows — what would you want to automate? <a href="https:&#x2F;&#x2F;signb.ee" rel="nofollow">https:&#x2F;&#x2F;signb.ee</a>

[Other] An experiment to use GitHub Actions as a control plane for a PaaS

[Other] Show HN: Lockstep – A data-oriented programming language <a href="https:&#x2F;&#x2F;github.com&#x2F;seanwevans&#x2F;lockstep" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;seanwevans&#x2F;lockstep</a><p>I want to share my work-in-progress systems language with a v0.1.0 release of Lockstep. It is a data-oriented systems programming language designed for high-throughput, deterministic compute pipelines.<p>I built Lockstep to bridge the gap between the productivity of C and the execution efficiency of GPU compute shaders. Instead of traditional control flow, Lockstep enforces straight-line SIMD execution. You will not find any if, for, or while statements inside compute kernels; branching is entirely replaced by hardware-native masking and stream-splitting.<p>Memory is handled via a static arena provided by the Host. There is no malloc, no hidden threads, and no garbage collection, which guarantees predictable performance and eliminates race conditions by construction.<p>Under the hood, Lockstep targets LLVM IR directly to leverage industrial-grade optimization passes. It also generates a C-compatible header for easy integration with host applications written in C, C++, Rust, or Zig.<p>v0.1.0 includes a compiler with LLVM IR and C header emission, a CLI simulator for validating pipeline wiring and cardinality on small datasets and an opt-in LSP server for real-time editor diagnostics, hover type info, and autocompletion.<p>You can check out the repository to see the syntax, and the roadmap outlines where the project is heading next, including parameterized SIMD widths and multi-stage pipeline composition.<p>I would love to hear feedback on the language semantics, the type system, and the overall architecture!

[Other] Show HN: Open-source playground to red-team AI agents with exploits published We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don&#x27;t think like you.<p>So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly.<p>Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use tools, stay in character, and follow instructions while still being useful is harder than it sounds. That alone reminded us how early we all are in understanding and deploying these systems at scale.<p>First challenge was to get an agent to call a tool it&#x27;s been told to never call.<p>Someone got through in around 60 seconds without ever asking for the secret directly (which taught us a lot).<p>Next challenge is focused on data exfiltration with harder defences: <a href="https:&#x2F;&#x2F;playground.fabraix.com" rel="nofollow">https:&#x2F;&#x2F;playground.fabraix.com</a>

[Other] Show HN: Free OpenAI API Access with ChatGPT Account

[Other] Let your Coding Agent debug the browser session with Chrome DevTools MCP

[Build/Deploy] Vite+ is the unified toolchain and entry point for web development. It manages your runtime, package manager, and frontend toolchain in one place.

[API/SDK] Show HN: AgentMailr – dedicated email inboxes for AI agents I kept running into the same problem while building AI agents: every agent that needs email ends up sharing my personal inbox or a single company domain. That breaks attribution, creates deliverability risk, and makes it impossible to test sender identities per agent.<p>So I built AgentMailr. You call an API to create an inbox, your agent gets a unique email address, and replies route back to that specific agent. Works for both inbound (OTP parsing, reply routing) and outbound (cold email, notifications).<p>Bring your own domain is supported so emails come from your domain, not ours. REST API and MCP server are live. Node&#x2F;Python SDKs are in progress.<p>Happy to answer questions about the architecture or how I&#x27;m handling multi-agent routing.

[Other] Learning Creative Coding

[Other] Show HN: Han – A Korean programming language written in Rust A few weeks ago I saw a post about someone converting an entire C++ codebase to Rust using AI in under two weeks.<p>That inspired me — if AI can rewrite a whole language stack that fast, I wanted to try building a programming language from scratch with AI assistance.<p>I&#x27;ve also been noticing growing global interest in Korean language and culture, and I wondered: what would a programming language look like if every keyword was in Hangul (the Korean writing system)?<p>Han is the result. It&#x27;s a statically-typed language written in Rust with a full compiler pipeline (lexer → parser → AST → interpreter + LLVM IR codegen).<p>It supports arrays, structs with impl blocks, closures, pattern matching, try&#x2F;catch, file I&#x2F;O, module imports, a REPL, and a basic LSP server.<p>This is a side project, not a &quot;you should use this instead of Python&quot; pitch. Feedback on language design, compiler architecture, or the Korean keyword choices is very welcome.<p><a href="https:&#x2F;&#x2F;github.com&#x2F;xodn348&#x2F;han" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;xodn348&#x2F;han</a>

[Other] Show HN: Zap Code – AI code generator that teaches kids real HTML/CSS/JS Zap Code generates working HTML&#x2F;CSS&#x2F;JS from plain English descriptions, designed for kids ages 8-16.<p>The core loop: kid types &quot;make a space shooter game&quot;, AI generates the code, live preview renders it immediately. Three interaction modes - visual-only tweaks, read-only code view with annotations, and full code editing with AI autocomplete.<p>Technical details: Next.js frontend, Node.js backend, Monaco editor simplified for younger users, sandboxed iframe for preview execution (no external API calls from generated code). Progressive complexity engine uses a skill model to decide when to surface more advanced features.<p>Main thing that was focused on was the gap between block-based coding (Scratch, etc.) and actual programming. Block tools are great for ages 6-10 but the transition to real code is rough. This tries to smooth that curve by letting kids interact with real output first, then gradually exposing the code behind it.<p>Limitations: AI-generated code isn&#x27;t always clean or idiomatic. Content is filtered for age-appropriateness but its not perfect. Collaboration features are still basic. The complexity engine needs more data to tune well.<p>Free tier, 3 projects. Pro at $9.99&#x2F;mo.

[Other] Claudetop – htop for Claude Code sessions (see your AI spend in real-time)

[Other] Show HN: KeyID – Free email and phone infrastructure for AI agents (MCP)

[Other] Show HN: Data-anim – Animate HTML with just data attributes Hey HN, I built data-anim — an animation library where you never have to write JavaScript yourself.<p>You just write:<p><pre><code> &lt;div data-anim=&quot;fadeInUp&quot;&gt;Hello&lt;&#x2F;div&gt; </code></pre> That&#x27;s it. Scroll-triggered fade-in animation, zero JS to write.<p>What it does:<p>- 30+ built-in animations (fade, slide, zoom, bounce, rotate, etc.)<p>- 4 triggers: scroll (default), load, click, hover<p>- 3-layer anti-FOUC protection (immediate style injection → noscript fallback → 5s timeout)<p>- Responsive controls: disable per device or swap animations on mobile<p>- TypeScript autocomplete for all attributes<p>- Under 3KB gzipped, zero dependencies<p>Why I built this:<p>I noticed that most animation needs on landing pages and marketing sites are simple — fade in on scroll, slide in from left, bounce on hover. But the existing options are either too heavy (Framer Motion ~30KB) or require JS boilerplate.<p>I also think declarative HTML attributes are the most AI-friendly animation format. When LLMs generate UI, HTML attributes are the output they hallucinate least on — no selector matching, no JS API to misremember, no script execution order to get wrong.<p>Docs: <a href="https:&#x2F;&#x2F;ryo-manba.github.io&#x2F;data-anim&#x2F;" rel="nofollow">https:&#x2F;&#x2F;ryo-manba.github.io&#x2F;data-anim&#x2F;</a><p>Playground: <a href="https:&#x2F;&#x2F;ryo-manba.github.io&#x2F;data-anim&#x2F;playground&#x2F;" rel="nofollow">https:&#x2F;&#x2F;ryo-manba.github.io&#x2F;data-anim&#x2F;playground&#x2F;</a><p>npm: <a href="https:&#x2F;&#x2F;www.npmjs.com&#x2F;package&#x2F;data-anim" rel="nofollow">https:&#x2F;&#x2F;www.npmjs.com&#x2F;package&#x2F;data-anim</a><p>Happy to answer any questions about the implementation or design decisions.

[API/SDK] Show HN: GitAgent – An open standard that turns any Git repo into an AI agent We built GitAgent because we kept seeing the same problem: every agent framework defines agents differently, and switching frameworks means rewriting everything.<p>GitAgent is a spec that defines an AI agent as files in a git repo.<p>Three core files — agent.yaml (config), SOUL.md (personality&#x2F;instructions), and SKILL.md (capabilities) — and you get a portable agent definition that exports to Claude Code, OpenAI Agents SDK, CrewAI, Google ADK, LangChain, and others.<p>What you get for free by being git-native:<p>1. Version control for agent behavior (roll back a bad prompt like you&#x27;d revert a bad commit) 2. Branching for environment promotion (dev → staging → main) 3. Human-in-the-loop via PRs (agent learns a skill → opens a branch → human reviews before merge) 4. Audit trail via git blame and git diff 5. Agent forking and remixing (fork a public agent, customize it, PR improvements back) 6. CI&#x2F;CD with GitAgent validate in GitHub Actions<p>The CLI lets you run any agent repo directly:<p>npx @open-gitagent&#x2F;gitagent run -r <a href="https:&#x2F;&#x2F;github.com&#x2F;user&#x2F;agent" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;user&#x2F;agent</a> -a claude<p>The compliance layer is optional, but there if you need it — risk tiers, regulatory mappings (FINRA, SEC, SR 11-7), and audit reports via GitAgent audit.<p>Spec is at <a href="https:&#x2F;&#x2F;gitagent.sh" rel="nofollow">https:&#x2F;&#x2F;gitagent.sh</a>, code is on GitHub.<p>Would love feedback on the schema design and what adapters people would want next.

[DevOps] Show HN: I built Wool, a lightweight distributed Python runtime I spent a long time working in the payments industry, specifically on a rather niche reporting&#x2F;aggregation platform with spiky workloads that were not easily parallelized. To pump as much data through our pipeline as possible, we had to rely on complex locking schemes across half a dozen or so not-so-micro services - keeping a clear mental picture of how the services interacted for a given data source was a major headache. This problem always intrigued me, even after I no longer worked at the company, and lead to the development of Wool.<p>If you&#x27;ve worked with frameworks like Ray or Prefect, you&#x27;re probably familiar with the promise of going from script to scale in two lines of code (or something along those lines). This is essentially the solution I was looking for: a framework with limited boilerplate that facilitated arbitrary distribution schemes within a single, coherent codebase. What I was hoping for, though, was something a little bit more focused - I wasn&#x27;t working on ML pipelines and didn&#x27;t need much else other than the distribution layer. This is where Wool comes in. While it&#x27;s API is very similar to those of Ray and Prefect, where it differentiates itself is in its scope and architecture.<p>First, Wool is not a task orchestrator. It provides push-based, best-effort, at-most-once execution. There is no built-in coordination state, retry logic, or durable task tracking. Those concerns remain application-defined. The beauty of Wool is that it looks and feels like native async Python, allowing you to use purpose-built libraries for your needs as you would for any other Python app (with some caveats).<p>Second, Wool was designed with speed in mind. Because it&#x27;s not bloated with features, it&#x27;s actually pretty fast, even in its current nascent state. Wool routines are dispatched directly to a decentralized peer-to-peer network of gRPC workers, who can distribute nested routines amongst themselves in turn. This results in low dispatch latencies and high throughput. I won&#x27;t make any performance claims until I can assemble some more robust benchmarks, but running local workers on my M4 MacBook Pro (a trivial example, I know), I can easily achieve sub-millisecond dispatch latencies.<p>Anyway, check it out, any and all feedback is welcome. Regarding docs- the code is the documentation for now, but I promise I&#x27;ll sort that out soon. I&#x27;ve got plenty of ideas for next steps, but it&#x27;s always more fun when people actually use what you&#x27;ve built, so I&#x27;m open to suggestions for impactful features.<p>-Conrad