Show HN: DenchClaw – Local CRM on Top of OpenClaw https://ift.tt/USBzH2n

Show HN: DenchClaw – Local CRM on Top of OpenClaw Hi everyone, I am Kumar, co-founder of Dench ( https://denchclaw.com ). We were part of YC S24, an agentic workflow company that previously worked with sales floors automating niche enterprise tasks such as outbound calling, legal intake, etc. Building consumer / power-user software always gave me more joy than FDEing into an enterprise. It did not give me joy to manually add AI tools to a cloud harness for every small new thing, at least not as much as completely local software that is open source and has all the powers of OpenClaw (I can now talk to my CRM on Telegram!). A week ago, we launched Ironclaw, an Open Source OpenClaw CRM Framework ( https://ift.tt/CQY50SB ) but people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw ( https://denchclaw.com ). OpenClaw today feels like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode was the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt. That is how we think about DenchClaw. We are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world. Demo: https://www.youtube.com/watch?v=pfACTbc3Bh4#t=43 npx denchclaw It has a CRM focus because we asked a couple dozen hard-core OpenClaw users "what do you actually do", and it was sales automation, lead enrichment, biz dev, creating slides, linkedin outreach, email/notion/calendar stuff, and it's always painful to set up. But I use DenchClaw daily for almost everything I do. It also works as a coding agent like Cursor - DenchClaw built DenchClaw. I am addicted now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter. On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill. The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. Thank you DuckDB team! It creates a new OpenClaw profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA. Think of it as Cursor for your Mac (also works on Linux and Windows) which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do linkedin outreach using MY browser. DenchClaw finds your Chrome Profile and copies it fully into its own, so you won’t have to log in into all your websites again. DenchClaw sees what you see, does what you do. It’s an everything app, that sits locally on your mac. Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use. We would love you all to break it, stress test its CRM capabilities, how it streams subagents for lead enrichment, hook it into your Apollo, Gmail, Notion and everything there is. Looking forward to comments/feedback! https://ift.tt/bUdhegB March 9, 2026 at 09:55PM

Show HN: I gave my robot physical memory – it stopped repeating mistakes https://ift.tt/SZ82DRB

Show HN: I gave my robot physical memory – it stopped repeating mistakes https://ift.tt/FS9pKzn March 9, 2026 at 11:36PM

Show HN: Skir – A schema language I built after 15 years of Protobuf friction https://ift.tt/WLdg0tN

Show HN: Skir – A schema language I built after 15 years of Protobuf friction Why I built Skir: https://ift.tt/zdabGcW... Quick start: npx skir init All the config lives in one YML file. Website: https://skir.build GitHub: https://ift.tt/Z3cks4W Would love feedback especially from teams running mixed-language stacks. https://skir.build/ March 9, 2026 at 12:17AM

Show HN: Astro MD Editor – Schema-aware editor for Astro content collections https://ift.tt/w8HKWgL

Show HN: Astro MD Editor – Schema-aware editor for Astro content collections I built this for my own Astro projects where I got tired of hand-editing YAML frontmatter and switching between files. astro-md-editor reads your collection schemas and gives you a local editor UI with typed frontmatter controls (including image/color/icon pickers) alongside a markdown/MDX editor. Run it with: npx astro-md-editor Would love feedback on schema edge cases or missing field types. https://ift.tt/FAx2wuv March 8, 2026 at 11:44PM

Show HN: I built a simple book tracker because I kept buying books I owned https://ift.tt/qHek8SK

Show HN: I built a simple book tracker because I kept buying books I owned I'm Maureen, a senior and self-taught developer. I love browsing second-hand book markets but kept coming home with books I already owned. I couldn't find a simple enough app to track my library — everything required an account, had ads, or pushed a subscription. So I built one myself. SeniorEase Library (Android): scan an ISBN, book is added instantly. No account, no ads, one-time €2.99. First 10 books free. Would love any feedback! https://ift.tt/D3Rt1yr March 8, 2026 at 11:17PM

Show HN: Agentcheck – Check what an AI agent can access before you run it https://ift.tt/5vrHETy

Show HN: Agentcheck – Check what an AI agent can access before you run it Hey HN! I've just open-sourced agentcheck, a fast, read-only CLI tool that scans your shell and reports what an AI agent could access: cloud IAM credentials, API keys, Kubernetes contexts, local tools, and more. Main features: - Broad coverage: scans AWS, GCP, Azure, 100+ API key environment variables and credential files, Kubernetes, Docker, SSH keys, Terraform configs, and .env files - Severity levels: every finding is tagged LOW, MODERATE, HIGH, or CRITICAL so you know what actually matters - CI/CD integration: run agentcheck --ci to fail a pipeline if findings exceed a configurable threshold, with JSON and Markdown output for automation - Configurable: extend it with your own env vars, credential files, and CLI tool checks via a config file When you hand a shell to an AI agent, it inherits everything in that environment: cloud credentials, API keys, SSH keys, kubectl contexts. That's often more access than you'd consciously grant, and it’s hard to keep track of what permissions your user account actually has. Agentcheck makes that surface area visible before you run the agent. It’s a single Go binary, no dependencies. Install with Homebrew: brew install Pringled/tap/agentcheck Code: github.com/Pringled/agentcheck Let me know if you have any feedback! https://ift.tt/Bo5Ea02 March 8, 2026 at 11:05PM

Show HN: Tessera – MCP server that gives Claude persistent memory and local RAG https://ift.tt/AEvu03P

Show HN: Tessera – MCP server that gives Claude persistent memory and local RAG https://ift.tt/HS9BX0o March 8, 2026 at 12:42AM

Show HN: Prompt Armour – Real-time PII detection for AI chatbots, 100% local https://ift.tt/aGDYjFX

Show HN: Prompt Armour – Real-time PII detection for AI chatbots, 100% local https://prompt-armour.vercel.app/ March 8, 2026 at 12:34AM

Show HN: OpenGraviton – Run 500B+ parameter models on a consumer Mac Mini https://ift.tt/2M6FohH

Show HN: OpenGraviton – Run 500B+ parameter models on a consumer Mac Mini Hi HN, I built OpenGraviton, an open-source AI inference engine designed to push the limits of running extremely large models on consumer hardware. The system combines several techniques to drastically reduce memory and compute requirements: • 1.58-bit ternary quantization ({-1, 0, +1}) for ~10x compression • dynamic sparsity with Top-K pruning and MoE routing • mmap-based layer streaming to load weights directly from NVMe SSDs • speculative decoding to improve generation throughput These allow models far larger than system RAM to run locally. In early benchmarks, OpenGraviton reduced TinyLlama-1.1B from ~2.05GB (FP16) to ~0.24GB using ternary quantization. Synthetic stress tests at the 140B scale show that models which would normally require ~280GB FP16 can fit within ~35GB when packed with the ternary format. The project is optimized for Apple Silicon and currently uses custom Metal + C++ tensor unpacking. Benchmarks, architecture, and details: https://opengraviton.github.io GitHub: https://ift.tt/iLOwpRP https://opengraviton.github.io March 7, 2026 at 11:37PM

Show HN: diskard – A fast TUI disk usage analyzer with trash functionality https://ift.tt/Vd7exyL

Show HN: diskard – A fast TUI disk usage analyzer with trash functionality This is an ncdu clone written in Rust that I figured others might find useful! The main things that differentiate it from ncdu are: - It's very fast. In my benchmarks it's often twice as fast. - It allows you to send files to trash rather than permanently delete. Please try it out and lmk if I can improve on anything! https://ift.tt/JYNrWSX March 6, 2026 at 09:35PM

Show HN: Modembin – A pastebin that encodes your text into real FSK modem audio https://ift.tt/4OlSN2v

Show HN: Modembin – A pastebin that encodes your text into real FSK modem audio A fun weekend project: https://ift.tt/VM2GvEN It's a pastebin, except text/files are encoded into .wav files using real FSK modem audio. Image sharing is supported via Slow-Scan Television (SSTV), a method of transmitting images as FM audio originally used by ham radio operators. Everything runs in the browser with zero audio libraries and the encoding is vanilla TypeScript sine wave math: phase-continuous FSK with proper 8-N-1 framing, fractional bit accumulation for non-integer sample rates, and a quadrature FM discriminator on the decode side (no FFT windowing or Goertzel), The only dependency is lz-string for URL sharing compression. It supports Bell 103 (300 baud), Bell 202 (1200 baud), V.21, RTTY/Baudot, Caller ID (Bellcore MDMF), DTMF, Blue Box MF tones, and SSTV image encoding. There's also a chat mode where messages are transmitted as actual Bell 103 audio over WebSocket... or use the acoustic mode for speaker-to-mic coupling for in-room local chat. https://ift.tt/VM2GvEN March 6, 2026 at 10:00PM

Show HN: Tracemap – run and visualize traceroutes from probes around the world https://ift.tt/7SduDP0

Show HN: Tracemap – run and visualize traceroutes from probes around the world Hi HN, I thought it would be fun to plot a traceroute on a map to visually see the path packets take. I know this idea has been done before, but I still wanted to scratch that itch. The first version just let you paste in a traceroute and it would plot the hops on a map. Later I discovered Globalping ( https://globalping.io ), which allows you to run traceroutes and MTRs from probes around the world, so I integrated that into the tool. From playing around with it, I noticed a few interesting things: • It's very easy to spot incorrect IP geolocation. If a hop shows 1–2 ms latency but appears to jump across continents, the geolocation is probably wrong. • Suboptimal routing is sometimes much easier to notice visually than by just looking at latency numbers. • Even with really good databases like IPinfo, IP geolocation is still not perfect, so parts of the path may occasionally be misleading. Huge credit to the teams behind Globalping and IPinfo — Globalping for the measurement infrastructure and IPinfo for the geolocation data. Feedback welcome. https://tracemap.dev/ March 5, 2026 at 11:40PM

Show HN: OmoiOS–190K lines of Python to stop babysitting AI agents (Apache 2.0) https://ift.tt/5tfqPKU

Show HN: OmoiOS–190K lines of Python to stop babysitting AI agents (Apache 2.0) AI coding agents generate decent code. The problem is everything around the code - checking progress, catching drift, deciding if it's actually done. I spent months trying to make autonomous agents work. The bottleneck was always me. Attempt 1 - Claude/GPT directly: works for small stuff, but you re-explain context endlessly. Attempt 2 - Copilot/Cursor: great autocomplete, still doing 95% of the thinking. Attempt 3 - continuous agents: keeps working without prompting, but "no errors" doesn't mean "feature works." Attempt 4 - parallel agents: faster wall-clock, but now you're manually reviewing even more output. The common failure: nobody verifies whether the output satisfies the goal. That somebody was always me. So I automated that job. OmoiOS is a spec-driven orchestration system. You describe a feature, and it: 1. Runs a multi-phase spec pipeline (Explore > Requirements > Design > Tasks) with LLM evaluators scoring each phase. Retry on failure, advance on pass. By the time agents code, requirements have machine-checkable acceptance criteria. 2. Spawns isolated cloud sandboxes per task. Your local env is untouched. Agents get ephemeral containers with full git access. 3. Validates continuously - a separate validator agent checks each task against acceptance criteria. Failures feed back for retry. No human in the loop between steps. 4. Discovers new work - validation can spawn new tasks when agents find missing edge cases. The task graph grows as agents learn. What's hard (honest): - Spec quality is the bottleneck. Vague spec = agents spinning. - Validation is domain-specific. API correctness is easy. UI quality is not. - Discovery branching can grow the task graph unexpectedly. - Sandbox overhead adds latency per task. Worth it, but a tradeoff. - Merging parallel branches with real conflicts is the hardest problem. - Guardian monitoring (per-agent trajectory analysis) has rough edges still. Stack: Python/FastAPI, PostgreSQL+pgvector, Redis (~190K lines). Next.js 15 + React Flow (~83K lines TS). Claude Agent SDK + Daytona Cloud. 686 commits since Nov 2025, built solo. Apache 2.0. I keep coming back to the same problem: structured spec generation that produces genuinely machine-checkable acceptance criteria. Has anyone found an approach that works for non-trivial features, or is this just fundamentally hard? GitHub: https://ift.tt/d356S9K Live: https://omoios.dev https://ift.tt/d356S9K March 5, 2026 at 11:07PM