🛠️ Hacker News Tools

[Other] Show HN: MCP server gives your agent a budget (save tokens, get smarter results) As a consultant I foot my own Cursor bills, and last month was $1,263. Opus is too good not to use, but there&#x27;s no way to cap spending per session. After blowing through my Ultra limit, I realized how token-hungry Cursor + Opus really is. It spins up sub-agents, balloons the context window, and suddenly, a task I expected to cost $2 comes back at $8. My bill kept going up, but was I really going to switch to a worse model?<p>No. So I built l6e: an MCP server that gives your agent the ability to budget. It works with Cursor, Claude Code, Windsurf, Openclaw, and every MCP-compatible application.<p>Saving money was why I built it, but what surprised me was that the process of budgeting changed the agent&#x27;s behavior. An agent that understands the limitations of the resources doesn&#x27;t try to speculatively increase the context window with extra files. It doesn&#x27;t try to reach every possible API. The agent plans ahead, sticks to it, and ends work when it should.<p>It works, and we&#x27;ve been dogfooding it hard. After v1 shipped, the rest of l6e was all built with it. We launched the entire docs site using frontier models for $0.99. The kicker was every time l6e broke in development, I could feel the pain. The agent got sloppy, burned through context, and output quality dropped right along with it.<p>Install: pip install l6e-mcp<p>Docs: <a href="https:&#x2F;&#x2F;docs.l6e.ai" rel="nofollow">https:&#x2F;&#x2F;docs.l6e.ai</a><p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;l6e-ai&#x2F;l6e-mcp" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;l6e-ai&#x2F;l6e-mcp</a><p>Website: <a href="https:&#x2F;&#x2F;l6e.ai" rel="nofollow">https:&#x2F;&#x2F;l6e.ai</a><p>Happy to answer questions about the system design, calibration models, or why I can&#x27;t go back to coding without it.

[Other] Elevated errors on Claude.ai, API, Claude Code

[Monitoring/Observability] MCP as Observability Interface: Connecting AI Agents to Kernel Tracepoints

[Other] Binary Encodings for JSON and Variant

[Other] Show HN: Every CEO and CFO change at US public companies, live from SEC Built this solo. It watches SEC filings for executive and board changes, extracts the data, and shows it in real time. 2,100+ changes in the last 30 days. The comp data is interesting: average new CEO total comp is $8.4M across 284 appointments. The &#x2F;explore page is fully open, no login needed.

[Other] Retrofitting JIT Compilers into C Interpreters

[Other] Nanopass Framework: Clean Compiler Creation Language

[Other] Show HN: Xit – a Git-compatible VCS written in Zig The marquee feature is patch-based merging, similar to Darcs and Pijul. I think xit is the first version control system (VCS) to have this feature while still being git compatible. See the 100% human-written readme for more.

[Other] Want to Write a Compiler? Just Read These Two Papers (2008)

[Other] Direct Win32 API, Weird-Shaped Windows, and Why They Mostly Disappeared

[CLI Tool] Wacli – WhatsApp CLI: sync, search, send

[Other] CadQuery is an open-source Python library for building 3D CAD models

[Other] Your codebase doesn't care how it got written

[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

[Other] Claude Code Routines

[Database] 5NF and Database Design

[Monitoring/Observability] Show HN: Kelet – Root Cause Analysis agent for your LLM apps I&#x27;ve spent the past few years building 50+ AI agents in prod (some reached 1M+ sessions&#x2F;day), and the hardest part was never building them — it was figuring out why they fail.<p>AI agents don&#x27;t crash. They just quietly give wrong answers. You end up scrolling through traces one by one, trying to find a pattern across hundreds of sessions.<p>Kelet automates that investigation. Here&#x27;s how it works:<p>1. You connect your traces and signals (user feedback, edits, clicks, sentiment, LLM-as-a-judge, etc.) 2. Kelet processes those signals and extracts facts about each session 3. It forms hypotheses about what went wrong in each case 4. It clusters similar hypotheses across sessions and investigates them together 5. It surfaces a root cause with a suggested fix you can review and apply<p>The key insight: individual session failures look random. But when you cluster the hypotheses, failure patterns emerge.<p>The fastest way to integrate is through the Kelet Skill for coding agents — it scans your codebase, discovers where signals should be collected, and sets everything up for you. There are also Python and TypeScript SDKs if you prefer manual setup.<p>It’s currently free during beta. No credit card required. Docs: <a href="https:&#x2F;&#x2F;kelet.ai&#x2F;docs&#x2F;" rel="nofollow">https:&#x2F;&#x2F;kelet.ai&#x2F;docs&#x2F;</a><p>I&#x27;d love feedback on the approach, especially from anyone running agents in prod. Does automating the manual error analysis sound right?

[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: MōBrowser, a TypeScript-first desktop app framework with typed IPC Hi HN,<p>For the last ~15 years I&#x27;ve worked on embedding web browsers into Java and .NET desktop apps (JxBrowser, DotNetBrowser). Over time, I watched many teams move from embedding web views into native apps, to building full desktop apps with frameworks like Electron and Tauri.<p>Both are useful, but in practice I kept running into several problems.<p>With Electron, beyond the larger app footprint, I often ran into:<p><pre><code> - lack of type-safe IPC - no source code protection - weak support for the modern web stack </code></pre> Tauri solves some problems (like app size), but introduces others:<p><pre><code> - different WebViews across platforms → inconsistent behavior - requires Rust + JS instead of a single stack </code></pre> So we built MōBrowser, a framework for building desktop apps with TypeScript, Node.js, and Chromium.<p>Some of the things we focused on:<p><pre><code> - typed IPC using Protobuf + code generation (RPC-style communication instead of string channels) - consistent rendering and behavior across different platforms - Node.js runtime - built-in packaging, updates, and scaffolding - source code protection - small delta auto-updates </code></pre> The goal is to let web developers ship desktop apps with a web stack they already know and fewer cross-platform surprises.<p>I&#x27;d especially love feedback from people who have built production apps with Electron or Tauri.<p>Happy to answer any questions.