🛠️ All DevTools

[Other] Show HN: Plain – The full-stack Python framework designed for humans and agents

[Other] Turn your best AI prompts into one-click tools in Chrome

[Database] Show HN: A memory database that forgets, consolidates, and detects contradiction Vector databases store memories. They don&#x27;t manage them. After 10k memories, recall quality degrades because there&#x27;s no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier.<p>YantrikDB is a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores: consolidation collapses duplicate memories, contradiction detection flags incompatible facts, temporal decay with configurable half-life lets unimportant memories fade like human memory does.<p>Single Rust binary. HTTP + binary wire protocol. 2-voter + 1-witness HA cluster via Docker Compose or Kubernetes. Chaos-tested failover, runtime deadlock detection (parking_lot), per-tenant quotas, Prometheus metrics. Ran a 42-task hardening sprint last week — 1178 core tests, cargo-fuzz targets, CRDT property tests, 5 ops runbooks.<p>Live on a 3-node Proxmox homelab cluster with multiple tenants. Alpha — primary user is me, looking for the second one.

[Other] Show HN: LangAlpha – what if Claude Code was built for Wall Street? Some technical context on what we ran into building this.<p>MCP tools don&#x27;t really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80 tools across our servers and the prompt cost is the same whether a server has 3 tools or 30. This part isn&#x27;t finance-specific, it works with any MCP server.<p>The other big thing was making research actually persist across sessions. Most agents treat a single deliverable (a PDF, a spreadsheet) as the end goal. In investing that&#x27;s day one. You update the model when earnings drop, re-run comps when a competitor reports, keep layering new analysis on old. But try doing that across agent sessions, files don&#x27;t carry over, you re-paste context every time. So we built everything around workspaces. Each one maps to a persistent sandbox, one per research goal. The agent maintains its own memory file with findings and a file index that gets re-read before every LLM call. Come back a week later, start a new thread, it picks up where it left off.<p>We also wanted the agent to have real domain context the way Claude Code has codebase context. Portfolio, watchlist, risk tolerance, financial data sources, all injected into every call. Existing AI investing platforms have some of that but nothing close to what a proper agent harness can do. We wanted both and couldn&#x27;t find it, so we built it and open-sourced the whole thing.

[CLI Tool] Show HN: Kontext CLI – Credential broker for AI coding agents in Go We built the Kontext CLI because AI coding agents need access to GitHub, Stripe, databases, and dozens of other services — and right now most teams handle this by copy-pasting long-lived API keys into .env files, or the actual chat interface, whilst hoping for the best.<p>The problem isn&#x27;t just secret sprawl. It&#x27;s that there&#x27;s no lineage of access. You don&#x27;t know which developer launched which agent, what it accessed, or whether it should have been allowed to. The moment you hand raw credentials to a process, you&#x27;ve lost the ability to enforce policy, audit access, or rotate without pain. The credential is the authorization, and that&#x27;s fundamentally broken when autonomous agents are making hundreds of API calls per session.<p>Kontext takes a different approach. You declare what credentials a project needs in a .env.kontext file:<p><pre><code> GITHUB_TOKEN={{kontext:github}} STRIPE_KEY={{kontext:stripe}} LINEAR_TOKEN={{kontext:linear}} </code></pre> Then run `kontext start --agent claude`. The CLI authenticates you via OIDC, and for each placeholder: if the service supports OAuth, it exchanges the placeholder for a short-lived access token via RFC 8693 token exchange; for static API keys, the backend injects the credential directly into the agent&#x27;s runtime environment. Either way, secrets exist only in memory during the session — never written to disk on your machine. Every tool call is streamed for audit as the agent runs.<p>The closest analogy is a Security Token Service (STS): you authenticate once, and the backend mints short-lived, scoped credentials on-the-fly — except unlike a classical STS, we hold the upstream secrets, so nothing long-lived ever reaches the agent. The backend holds your OAuth refresh tokens and API keys; the CLI never sees them. It gets back short-lived access tokens scoped to the session.<p>What the CLI captures for every tool call: what the agent tried to do, what happened, whether it was allowed, and who did it — attributed to a user, session, and org.<p>Install with one command: `brew install kontext-dev&#x2F;tap&#x2F;kontext`<p>The CLI is written in Go (~5ms hook overhead per tool call), uses ConnectRPC for backend communication, and stores auth in the system keyring. Works with Claude Code today, Codex support coming soon.<p>We&#x27;re working on server-side policy enforcement next — the infrastructure for allow&#x2F;deny decisions on every tool call is already wired, we just need to close the loop so tool calls can also be rejected.<p>We&#x27;d love feedback on the approach. Especially curious: how are teams handling credential management for AI agents today? Are you just pasting env vars into the agent chat, or have you found something better?<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;kontext-dev&#x2F;kontext-cli" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;kontext-dev&#x2F;kontext-cli</a> Site: <a href="https:&#x2F;&#x2F;kontext.security" rel="nofollow">https:&#x2F;&#x2F;kontext.security</a>

[CLI Tool] jj – the CLI for Jujutsu

[CLI Tool] Show HN: A CLI that writes its own integration code We run superglue, an OSS agentic integration platform. Last week I talked to a founder of another YC startup. She found a use case for our CLI that we hadn&#x27;t officially launched yet.<p>Her problem: customers wanted to create Opps in Salesforce from inside the chat in her app. We kept seeing this pattern: teams build agents and their users can perfectly describe what they want: &quot;pull these three objects from Salesforce and push to nCino when X condition is true&quot;, but translating that into a generalized hard-coded tool the agent can call is a lot of work and does not scale since the logic is different for every user.<p>What superglue CLI does: you point it at any API, and your agent gets the ability to reason over that API at runtime. No pre-built tools. The agent reads the spec, plans the calls, executes them.<p>The founder using this in production described it like this: she gave the CLI to her agent with an instruction set and told it not to build tools, just run against the API. It handled multi-step Salesforce object creation correctly, including per-user field logic and record type templates.<p>Concretely: instead of writing a createSalesforceOpp tool that handles contact -&gt; account -&gt; Opp creation with all the conditional logic, you write a skill doc and let the agent figure out which endpoints to hit and in what order.<p>The tradeoff is: you&#x27;re giving the agent more autonomy over what API calls it makes. That requires good instructions and some guardrails. But for long-tail, user-specific connectors, it&#x27;s a lot more practical than building a tool for every case.<p>Happy to discuss. Curious if others have run into the &quot;pre-defined tool&quot; ceiling with MCP-based connectors and how you&#x27;ve worked around it.<p>Docs: <a href="https:&#x2F;&#x2F;docs.superglue.cloud&#x2F;getting-started&#x2F;cli-skills" rel="nofollow">https:&#x2F;&#x2F;docs.superglue.cloud&#x2F;getting-started&#x2F;cli-skills</a> Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;superglue-ai&#x2F;superglue" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;superglue-ai&#x2F;superglue</a>

[Other] Show HN: A stateful UI runtime for reactive web apps in Go Doors: Server-driven UI framework + runtime for building stateful, reactive web applications in Go.<p>Some highlights:<p>* Front-end framework capabilities in server-side Go. Reactive state primitives, dynamic routing, composable components.<p>* No public API layer. No endpoint design needed, private temporal transport is handled under the hood.<p>* Unified control flow. No context switch between back-end&#x2F;front-end.<p>* Integrated web stack. Bundle assets, build scripts, serve private files, automate CSP, and ship in one binary.<p>How it works: Go server is UI runtime: web application runs on a stateful server, while the browser acts as a remote renderer and input layer.<p>Security model: Every user can interact only with what you render to them. Means you check permissions when your render the button and that&#x27;s is enough to be sure that related action wont be triggered by anyone else.<p>Mental model: Link DOM to the data it depends on.<p>Limitations:<p>* Does not make sense for static non-iteractive sites, client-first apps with simple routing, and is not suitable for offline PWAs.<p>* Load balancing and roll-outs without user interruption require different strategies with stateful server (mechanics to make it simpler is included).<p>Where it fits best: Apps with heavy user flows and complex business logic. Single execution context and no API&#x2F;endpoint permission management burden makes it easier.<p>Peculiarities:<p>* Purposely build [Go language extension](<a href="https:&#x2F;&#x2F;github.com&#x2F;doors-dev&#x2F;gox" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;doors-dev&#x2F;gox</a>) with its own LSP, parser, and editor plugins. Adds HTML as Go expressions and \`elem\` primitives.<p>* Custom concurrency engine that enables non-blocking event processing, parallel rendering, and tree-aware state propagation<p>* HTTP&#x2F;3-ready synchronization protocol (rolling-request + streaming, events via regular post, no WebSockets&#x2F;SSE)<p>From the author (me): It took me 1 year and 9 month to get to this stage. I rewrote the framework 6 or 7 times until every part is coherent, every decision feels right or is a reasonable compromise. I am very critical to my own work and I see flaws, but overall it turned out solid, I like developer experience as a user. Mental model requires a bit of thinking upfront, but pays off with explicit code and predictable outcome.<p>Code Example:<p><pre><code> type Search struct { input doors.Source[string] &#x2F;&#x2F; reactive state } elem (s Search) Main() { &lt;input (doors.AInput{ On: func(ctx context.Context, r doors.RequestInput) bool { s.input.Update(ctx, r.Event().Value) &#x2F;&#x2F; reactive state return false }, }) type=&quot;text&quot; placeholder=&quot;search&quot;&gt; ~&#x2F;&#x2F; subscribe results to state changes ~(doors.Sub(s.input, s.results)) } elem (s Search) results(input string) { ~(for _, user := range Users.Search(input) { &lt;card&gt; ~(user.Name) &lt;&#x2F;card&gt; }) }</code></pre>

[API/SDK] Show HN: OQP – A verification protocol for AI agents As AI agents autonomously write and deploy code, there&#x27;s no standard for verifying that what they shipped actually satisfies business requirements. OQP is an attempt to define that standard.<p>It&#x27;s MCP-compatible and defines four core endpoints: - GET &#x2F;capabilities — what can this agent verify? - GET &#x2F;context&#x2F;workflows — what are the business rules for this workflow? - POST &#x2F;verification&#x2F;execute — run a verification workflow - POST &#x2F;verification&#x2F;assess-risk — what is the risk of this change?<p>The analogy we keep coming back to: what OpenAPI did for REST APIs, OQP does for agentic software verification.<p>Early contributors include Philip Lew (XBOSoft) and Benjamin Young (W3C JSON-LD Working Group). Looking for feedback from engineers building on top of MCP, agent orchestration frameworks, or anyone who has felt the pain of &quot;the agent shipped something wrong and we had no way to catch it.&quot;<p>Repo: github.com&#x2F;OranproAi&#x2F;open-qa-protocol

[Testing] N-Day-Bench – Can LLMs find real vulnerabilities in real codebases? N-Day-Bench tests whether frontier LLMs can find known security vulnerabilities in real repository code. Each month it pulls fresh cases from GitHub security advisories, checks out the repo at the last commit before the patch, and gives models a sandboxed bash shell to explore the codebase.<p>Static vulnerability discovery benchmarks become outdated quickly. Cases leak into training data, and scores start measuring memorization. The monthly refresh keeps the test set ahead of contamination — or at least makes the contamination window honest.<p>Each case runs three agents: a Curator reads the advisory and builds an answer key, a Finder (the model under test) gets 24 shell steps to explore the code and write a structured report, and a Judge scores the blinded submission. The Finder never sees the patch. It starts from sink hints and must trace the bug through actual code.<p>Only repos with 10k+ stars qualify. A diversity pass prevents any single repo from dominating the set. Ambiguous advisories (merge commits, multi-repo references, unresolvable refs) are dropped.<p>Currently evaluating GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, GLM-5.1, and Kimi K2.5. All traces are public.<p>Methodology: <a href="https:&#x2F;&#x2F;ndaybench.winfunc.com&#x2F;methodology">https:&#x2F;&#x2F;ndaybench.winfunc.com&#x2F;methodology</a><p>Live Leaderboard: <a href="https:&#x2F;&#x2F;ndaybench.winfunc.com&#x2F;leaderboard">https:&#x2F;&#x2F;ndaybench.winfunc.com&#x2F;leaderboard</a><p>Live Traces: <a href="https:&#x2F;&#x2F;ndaybench.winfunc.com&#x2F;traces">https:&#x2F;&#x2F;ndaybench.winfunc.com&#x2F;traces</a>

[Other] GitHub Stacked PRs

[Other] GAIA – Open-source framework for building AI agents that run on local hardware

[Other] Show HN: Ithihāsas – a character explorer for Hindu epics, built in a few hours Hi HN!<p>I’ve always found it hard to explore the Mahābhārata and Rāmāyaṇa online. Most content is either long-form or scattered, and understanding a character like Karna or Bhishma usually means opening multiple tabs.<p>I built <a href="https:&#x2F;&#x2F;www.ithihasas.in&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.ithihasas.in&#x2F;</a> to solve that. It is a simple character explorer that lets you navigate the epics through people and their relationships instead of reading everything linearly.<p>This was also an experiment with Claude CLI. I was able to put together the first version in a couple of hours. It helped a lot with generating structured content and speeding up development, but UX and data consistency still needed manual work.<p>Would love feedback on the UX and whether this way of exploring mythology works for you.

[Other] How to make Firefox builds 17% faster

[Other] Show HN: Mcptube – Karpathy's LLM Wiki idea applied to YouTube videos I watch a lot of Stanford&#x2F;Berkeley lectures and YouTube content on AI agents, MCP, and security. Got tired of scrubbing through hour-long videos to find one explanation. Built v1 of mcptube a few months ago. It performs transcript search and implements Q&amp;A as an MCP server. It got traction (34 stars, my first open-source PR, some notable stargazers like CEO of Trail of Bits).<p>But v1 re-searched raw chunks from scratch every query. So I rebuilt it.<p>v2 (mcptube-vision) follows Karpathy&#x27;s LLM Wiki pattern. At ingest time, it extracts transcripts, detects scene changes with ffmpeg, describes key frames via a vision model, and writes structured wiki pages. Knowledge compounds across videos rather than being re-discovered. FTS5 + a two-stage agent (narrow then reason) for retrieval.<p>MCPTube works both as CLI (BYOK) and MCP server. I tested MCPTube with Claude Code, Claude Desktop, VS Code Copilot, Cursor, and others. Zero API key needed server-side.<p>Coming soon: I am also building SaaS platform. This platform supports playlist ingestion, team wikis, etc. I like to share early access signup: <a href="https:&#x2F;&#x2F;0xchamin.github.io&#x2F;mcptube&#x2F;" rel="nofollow">https:&#x2F;&#x2F;0xchamin.github.io&#x2F;mcptube&#x2F;</a><p>Happy to discuss architecture tradeoffs — FTS5 vs vectors, file-based wiki vs DB, scene-change vs fixed-interval sampling. Give it a try via `pip install mcptube`. Also, please do star the repo if you enjoy my contribution (<a href="https:&#x2F;&#x2F;github.com&#x2F;0xchamin&#x2F;mcptube" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;0xchamin&#x2F;mcptube</a>)

[CLI Tool] Show HN: Dbg – One CLI debugger for every language (AI-agent ready) AI agents are great at writing code but blind at runtime. They guess, print, and waste tokens.<p>I built dbg to give them a real debugger experience. Since it is backend based with the few I implemented (still at basic level) it can support 15+ languages with one simple CLI (still some work needed but it is functional as it is):<p>LLDB, Delve, PDB, JDB, node inspect, rdbg, phpdbg, GHCi, etc. Profilers too (perf, pprof, cProfile, Valgrind…)<p>I also added GPU profiling via `gdbg` (CUDA, PyTorch, Triton kernels). It auto-dispatches and shares the same unified interface. (Planning to bring those advanced concepts back to the main dbg).<p>Works with Claude &amp; Codex (probably works on others but didn&#x27;t try them)<p>Quick start: ``` curl -sSf <a href="https:&#x2F;&#x2F;raw.githubusercontent.com&#x2F;redknightlois&#x2F;dbg&#x2F;main&#x2F;install.sh" rel="nofollow">https:&#x2F;&#x2F;raw.githubusercontent.com&#x2F;redknightlois&#x2F;dbg&#x2F;main&#x2F;ins...</a> | sh dbg --init claude (for claude) ```<p>Then just say: “use dbg to debug the crash in src&#x2F;foo.rs”<p>Docs: <a href="https:&#x2F;&#x2F;redknightlois.github.io&#x2F;dbg&#x2F;" rel="nofollow">https:&#x2F;&#x2F;redknightlois.github.io&#x2F;dbg&#x2F;</a> GitHub (MIT Licensed): <a href="https:&#x2F;&#x2F;github.com&#x2F;redknightlois&#x2F;dbg" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;redknightlois&#x2F;dbg</a><p>Would love feedback from anyone building agents. What languages or features are you missing most? Ping me at @federicolois on X or open issues.

[CLI Tool] Building a CLI for All of Cloudflare

[Other] Initial mainline video capture and camera support for Rockchip RK3588

[Other] Michigan 'digital age' bills pulled after privacy concerns raised

[Other] Show HN: I built a social media management tool in 3 weeks with Claude and Codex