Tool - Moozonian Search

axoworks.com• Mar 15, 2026• 1 min read

Show HN: Dumped Wix, my AEC consultancy's storefront is now an AI EdgeI run a building design consultancy for homeowners and architects, not a SaaS firm. Honestly, I'm not going to claim we were trying to build some fantastic ‘anti-fragile alternative’ for the future… I just got tired of paying Wix $40 a month for a brochure no one read. The portfolio was static, the inquiries were generic, and every time a potential client asked about setback variances, I'd lose hours explaining the same thing. So last December, I told my wife I was killing the website. She thought I’d lost it. Maybe I had.The stack? It’s messy. We started on Netlify because it was cheap. Hit that stupid 10-second serverless timeout after a week when I started customizing the bot. The bot would just… freeze. Had to implement a clunky polling loop that felt like a band-aid. Eventually split the agent into 3 pieces just to keep it responsive:The Brain (Edge) figures out what you want, shoots a JSON signal to the browser. The Hands (Browser) runs the actual tool—like pulling a code referen

signet.watch• Mar 15, 2026• 1 min read

Show HN: Signet – Autonomous wildfire tracking from satellite and weather dataI built Signet in Go to see if an autonomous system could handle the wildfire monitoring loop that people currently run by hand - checking satellite feeds, pulling up weather, looking at terrain and fuels, deciding whether a detection is actually a fire worth tracking.All the data already exists: NASA FIRMS thermal detections, GOES-19 imagery, NWS forecasts, LANDFIRE fuel models, USGS elevation, Census population data, OpenStreetMap. The problem is it arrives from different sources on different cadences in different formats.Most of the system is deterministic plumbing - ingestion, spatial indexing, deduplication. I use Gemini to orchestrate 23 tools across weather, terrain, imagery, and incident tracking for the part where clean rules break down: deciding which weak detections are worth investigating, what context to pull next, and how to synthesize noisy evidence into a structured assessment.It also records time-bounded predictions and scores them against later data, so the system is

news.ycombinator.com• Mar 14, 2026• 1 min read

Everyone is focusing on AI, we're focusing on humansWell, as the title says, it seems that everyone is looking to build something AI related. I guess it’s the AI bubble. While AI is great in many industries, in language learning and exchange we believe humans are the core. Especially when wanting to achieve fluency. Talking to natives cannot be replaced by anything unless there is a very sophisticated AI that looks entirely like a human, acts like one, has the culture, and everything comes into place, which I believe is still too far away.All I see right now are sloppy AI language learning buddies. At Lengpal we believe in culture, the spirit of the language, and the fun. Nothing in the world can replace interaction with another human being, learning about them, their language, and their culture.So we built something that we thought was missing: a simple video chat tool for language exchange. Think of it as Google Meet but specifically for language exchange. It still surprises me that there is no such tool like this in the market. What

github.com• Mar 14, 2026• 1 min read

Show HN: ngrep – grep plus word embeddings (Rust)I got curious about a simple question: regular expressions are purely syntactic, but what happens if you add just a little bit of semantics?To answer, I ended up building ngrep: a grep-like tool that extends regular expressions with a new operator ~(token) that matches a word by meaning using word2vec-style embeddings (FastText, GloVe, Wikipedia2Vec).A simple demo: "~(big)+ \b~(animal;0.35)+\b" over Moby-Dick can find many ways used to refer to a large animal, surfacing "great whale", "enormous creature", "huge elephant" and so on. Pipe it through sort | uniq -c and the winner is, unsurprisingly, "great whale" :)Built in Rust on top of the awesome fancy-regex, and ~() composes with all standard operators (negative lookahead, quantifiers, etc.). Currently a PoC with many missing optimizations (e.g: no caching, no compilation to standard regex, etc.), obviously without the guarantees of plain regex and subject to the limits of w2v-style embeddings...but thought it was worth sharing!

drobinin.com• Mar 14, 2026• 1 min read

Show HN: I built a release tracker for my indie appsOver the past year I shipped a bunch of iOS and visionOS apps, and the messy part was often not the code but everything around the release - future plans, bug and feature requests buried in reviews, App Store in-app events, featuring nominations, blog drafts, launch posts, and random ideas I got when scrolling HN.I tried using general-purpose tools for this (Notes, Obsidian, Trello, and Jira to name a few), but they were either built around sprints, story points, team workflows, and tickets, or did only a part of what I needed well. What I actually wanted was a single place to track releases and all the admin/marketing/store work that comes with them.I ended up building a wee app for that purpose specifically. Today it is mainly aimed at indie Apple-platform devs. It syncs with App Store Connect, pulls in apps, events and nominations, lets me track launch content alongside releases, and has a CLI/MCP server for terminal/AI-driven workflows. It runs natively on macOS, iOS and iPadOS.It

news.ycombinator.com• Mar 14, 2026• 1 min read

Analyzing OpenClaw's 3-layer defense against prompt injectionI’ve been analyzing how open-source AI execution engines (like OpenClaw) handle prompt injection. The conclusion is concerning: when an Agent has tool access (shell, DB, web), Prompt Injection is no longer just generating bad text—it leads to data exfiltration, prompt leaking, and full agent hijacking.Most frameworks currently rely on static defenses like Regex blacklists or XML tagging. I analyzed OpenClaw's 3-layer defense and found that Regex inevitably fails against semantic variations, synonyms, and context obfuscation.To solve this, I built Prompt Inspector (promptinspector.io). It is a semantic detection engine—not a black-box firewall. It inspects payloads and returns a confidence score for malicious intent, leaving the actual execution or blocking routing entirely in your hands.Here is how it works under the hood:1. Vector Retrieval & Pluggable Embeddings Instead of regex, we convert incoming prompts into vectors and query them in real-time against our vector database of known

thehealthytechpro.com• Mar 14, 2026• 1 min read

Show HN: A body-mass weighted caffeine limit simulator for developersMost caffeine guidelines are a "flat 400mg," but pharmacologically, that makes no sense. A 100kg engineer and a 50kg engineer shouldn't have the same safety ceiling.I built this tool to move beyond the "one-size-fits-all" FDA recommendation. It calculates your personalized limit using a weight-based pharmacokinetic safe-dose formula (standard 3-6 mg/kg) while factoring in physiological states like age and pregnancy that alter metabolic clearance rates.Technical Logic: > - Uses a weight-based scalar for personalized limits.Implements strict conditional logic for pregnancy/adolescent safety thresholds (capping at 200mg/100mg respectively).Aiming to bridge the gap between "standard wellness" and actual biological data-points.URL: https://thehealthytechpro.com/daily-caffeine-intake-calculat...

admakeai.com• Mar 14, 2026• 1 min read

Show HN: Built an AI ad generator and ran $9K of FB ads with itBeen in the AI image gen space since 2023, before even GPT image gen was a thing, and after spending ~9K on Facebook ads for my own projects (made a video about that actually) I realized the thing I kept getting stuck on was the creatives themselves. I'm horrible at making reels-type video ads and Canva even with templates is surprisingly complicated for ad-specific stuff, plus everything ends up looking the same as everyone else using the same templates.Made a video going into detail about the full FB ads journey if anyone's interested: https://www.youtube.com/watch?v=xoe_l67ZReUMost of the existing AI ad tools (Predis, AdCreative, etc.) give you these generic template outputs that look like stock photo collages, or they're enterprise-priced and overkill if you're just a solo founder trying to test 10-20 ad variations quickly. I wanted something where I could throw in product images, describe what I wanted, and get back actual usable creatives, static ads, before/after formats, UGC-st

github.com• Mar 14, 2026• 1 min read

Show HN: AgentArmor – open-source 8-layer security framework for AI agentsI've been talking to founders building AI agents across fintech, devtools, and productivity – and almost none of them have any real security layer. Their agents read emails, call APIs, execute code, and write to databases with essentially no guardrails beyond "we trust the LLM."So I built AgentArmor: an open-source framework that wraps any agentic architecture with 8 independent security layers, each targeting a distinct attack surface in the agent's data flow.The 8 layers: L1 – Ingestion: prompt injection + jailbreak detection (20+ patterns, DAN, extraction attempts, Unicode steganography) L2 – Storage: AES-256-GCM encryption at rest + BLAKE3 integrity for vector DBs L3 – Context: instruction-data separation (like parameterized SQL, but for LLM context), canary tokens, prompt hardening L4 – Planning: action risk scoring (READ=1 → DELETE=7 → EXECUTE=8 → ADMIN=10), chain depth limits, bulk operation detection L5 – Execution: network egress control, per-action rate limiting, human approv

github.com• Mar 14, 2026• 1 min read

Show HN: PDR AI – Open-source startup accelerator engine for non-technical chaosShow HN: PDR AI – Open-source startup accelerator engine for non-technical chaos (marketing, PRDs, onboarding)A couple weeks ago I shared PDR AI as an open-source tool for startup doc mess[](https://news.ycombinator.com/item?id=47258661). Since then I've doubled down on the core vision: it's not just another RAG chat—it's an AI-powered accelerator engine that helps technical founders skip the non-technical pitfalls and move faster.As a solo technical founder, I wasted too much time on fragmented marketing plans, incomplete PRDs, onboarding docs, legal templates. PDR AI centralizes it all in a self-hostable knowledge base and actively accelerates progress by:- Document Sanity Chekcer (Verifying the content validity and consistency in the documents, extracing key information) - Marketing pipeline: Content ideas/trends for Reddit/X/LinkedIn/Bluesky, optional web-enriched analysis. - Team acceleration layer: Role-based access, invites, approvals, audit trails—great for small teams or accel

edgewhisper.com• Mar 13, 2026• 1 min read

Show HN: EdgeWhisper – On-device voice-to-text for macOS (Voxtral 4B via MLX)I built a macOS voice dictation app where zero bytes of audio ever leave your machine.EdgeWhisper runs Voxtral Mini 4B Realtime (Mistral AI, Apache 2.0) locally on Apple Silicon via the MLX framework. Hold a key, speak, release — text appears at your cursor in whatever app has focus.Architecture: - Native Swift (SwiftUI + AppKit). No Electron. - Voxtral 4B inference via MLX on the Neural Engine. ~3GB model, runs in ~2GB RAM on M1+. - Dual text injection: AXUIElement (preserves undo stack) with NSPasteboard+CGEvent fallback. - 6-stage post-processing pipeline: filler removal → dictionary → snippets → punctuation → capitalization → formatting. - Sliding window KV cache for unlimited streaming without latency degradation. - Configurable transcription delay (240ms–2.4s). Sweet spot at 480ms.What it does well: - Works in 20+ terminals/IDEs (VS Code, Xcode, iTerm2, Warp, JetBrains). Most dictation tools break in terminals — we detect them and switch injection strategy. - Removes filler words

identitytxt.org• Mar 13, 2026• 1 min read

Show HN: identity.txt – Like llms.txt but for Peoplellms.txt solved a real problem. It gave AI tools a way to understand a website. It has some traction. But there's nothing equivalent for people. I think this might be a thing. Let's see what HN thinks.Every AI tool has some version of "custom instructions" but they're locked inside that tool. Switch from ChatGPT to Claude to Gemini and you're starting from scratch each time. Your voice, your preferences, your expertise, all gone.identity.txt is a plain text markdown file (same philosophy as llms.txt, humans.txt, robots.txt) that captures who you are for AI tools. Drop it at yourdomain.com/identity.txt or paste it into any context window.The spec is deliberately minimal: - H1 with your name - H2 sections for Voice, Expertise, Background, Preferences, Terms - A Terms section with machine-readable consent (open, attribution, prompt-only, restricted, none) The Terms section is interesting, maybe. It's like robots.txt for personal identity. A social contract, not a legal one. We think conse

how-to-grab-me.vercel.app• Mar 13, 2026• 1 min read

Show HN: Prompt optimizer that refactors messy prompts using a P-C-V pipelineI built a small web tool that tries to automatically refactor messy prompts into structured prompt specifications.The motivation was simple: most prompts people write are poorly structured, which often leads to unstable AI responses.So I implemented a Proposer–Critic–Verifier optimization pipeline.Proposer restructures the prompt. Critic evaluates clarity, structure, and task definition. Verifier checks consistency. Arbiter detects convergence.The system runs several iterations and produces a structured prompt specification.It usually takes around 30–40 seconds to run the full optimization loop.The tool also includes: • prompt quality analysis • explain mode • version history • prompt diff viewerDemo: https://how-to-grab-me.vercel.app/Would love to hear if people building AI tools find something like this useful.

github.com• Mar 13, 2026• 1 min read

Show HN: Coral – Visualize .proto file dependencies as an interactive graphHi HN, I built Coral to solve a pain point I had with large gRPC/Connect projects where proto dependencies became hard to track.It parses FileDescriptorSet from `buf build` and generates:- Interactive React Flow graph with a neon-style dark UI - PR comments showing proto schema diffs (added/removed/modified) - Static GitHub Pages for team documentationAvailable as both a CLI tool and a GitHub Action: buf build -o - | coral serve Live demo: https://daisuke8000.github.io/coral/ GitHub Action: https://github.com/marketplace/actions/coral-proto-visualize...

octetta.github.io• Mar 13, 2026• 1 min read

Show HN: K-synth – A web-based array language playground for synth designI built k-synth as an experiment to see if a minimalist, K-inspired array language could make sketching waveforms faster and more intuitive than traditional code. I’ve put together a web-based toolkit so you can try the syntax directly in the browser without having to touch a compiler:Live Toolkit: https://octetta.github.io/k-synth/If you visit the page, here is a quick path to an audio payoff:- Click "patches" and choose dm-bell.ks.- Click "run"—the notebook area will update. Click the waveform to hear the result.- Click the "->0" button below the waveform to copy it into slot 0 at the top (slots are also clickable).- Click "pads" in the entry area to show a performance grid.- Click "melodic" to play slot 0's sample at different intervals across the grid.The 'Weird' Stack:- The Language: A simplified, right-associative array language (e.g., s for sine, p for pi).- The Web Toolkit: Built using WASM and Web Audio for live-coding samples.- AI Pair-Programming: I used AI agents to bootstr

loseylabs.ai• Mar 13, 2026• 1 min read

Show HN: DarkMatter – P2P mesh networking protocol for AI agentsHey HN, I built DarkMatter, an open-source peer-to-peer mesh networking protocol that lets AI agents discover and communicate with each other directly. Or just communicate on your own local network, agents on different devices can communicate and collaborate. No central orchestrator, which we all know doesn't work tbh... The problem: if you're running multiple AI agents (across models, frameworks, or machines), you need them to coordinate. Today that means standing up a central server, REST APIs, or a message queue. Single point of failure, extra infra to maintain, and your agents can't find each other without you wiring it up. What DarkMatter does: - Agents get an Ed25519 keypair identity (no central registry) - Discovery via LAN multicast or bootstrap peers - Transport: HTTP + WebRTC data channels (pluggable) - Messages are cryptographically signed and optionally encrypted - Trust scoring between peers (agents decide who to talk to) - Works across tools — Claude, Codex, OpenCode, doe

pycoclaw.com• Mar 12, 2026• 1 min read

Show HN: OpenClaw-class agents on ESP32 (and the IDE that makes it possible)Hi HN, I’m the creator of pycoClaw.I wanted to run OpenClaw-class, platform-agnostic, autonomous agents on MicroPython hardware, but standard tools couldn't handle the scale of the task.pycoClaw is the result, which bridges the gap between high-level AI reasoning and bare-metal execution.The Stack:- PFC Agent (~26k LOC): A full-featured agent that uses an LLM to 'self-program' its own local MicroPython scripts. Once a task is solved, it runs locally without requiring the LLM.- ScriptoStudio IDE: A PWA https://scriptostudio.com designed for the iteration speed required by autonomous agents. Since it’s a PWA, it brings a full dev environment (including a real single-step debugger) to any platform, including iPadOS.- ScriptoHub ( https://scriptohub.ai ): A repository for "Skills" and extensions. Since the agent can generate and execute code, I built a curated hub with automated malware checking to ensure the community can safely share and deploy hardware logic.- IANA Protocol: To make the

github.com• Mar 12, 2026• 1 min read

Show HN: Mingle – find and connect with people, like LinkedIn but in your chatLike LinkedIn, but inside your chat.I built Mingle, an open-source plugin that turns your AI into a networking agent.The problem isn't connecting with people; it's that every tool for it puts the work on you: scroll feeds, filter noise, write messages, repeat. What if your agent handled the noise and just came back with: "2 relevant matches. Want intros?"You tell Claude, GPT, Cursor, or any MCP client what you're looking for. Your agent publishes a signed card to a shared network. Other people's agents match against it. If there's a fit, both humans approve before any connection happens.So instead of scrolling LinkedIn or sending cold messages, you say:"I'm looking for a senior Rust engineer with protocol experience."And your AI comes back with: "2 relevant matches found. Want intros?"How it works: - You publish what you need and what you offer - Other people's agents match against it based on need/offer overlap - You get a short digest of relevant people - Both sides approve before co

news.ycombinator.com• Mar 12, 2026• 1 min read

Ask HN: How do you find early users willing to bet on a rough product?I built an Instagram-focused AI tool for a client that is trained on a brand’s product catalog and internal documentation. The interesting part is not generic generation, but that it actually understands the product and brand context well enough to produce content that is much less slop-prone than normal prompt-based workflows.I’m now trying to abstract and productize it beyond the initial client.My idea is to onboard 5 to 10 ecommerce teams through a small “founder edition” style program, work closely with them, and use that to shape the product. The product is useful already, but still rough around the edges, so this is not a polished self-serve SaaS yet.I’m curious about two things:Where do you find the kind of early users who are comfortable betting on a product at this stage?Has anyone here tried a founder edition or lifetime-style early access offer, and did it work well or create bad incentives later?I’d especially like to hear from people who have sold early B2B tools before th

studioindex.ai• Mar 12, 2026• 1 min read

Show HN: StudioIndex – A directory to find AI video production studiosI built StudioIndex because I kept seeing incredible AI-generated commercials (Nike, Coca-Cola, Anthropic's Super Bowl spot) but had no way to find out which studios actually made them or how to hire similar talent.Marketing teams are starting to look for AI video production partners, but there's no central place to compare studios, see their work, or understand pricing. So I built a curated directory where you can browse AI-native and AI-augmented studios, watch their best work, and filter by the tools they use (Runway, Veo, Midjourney, Kling, etc.).The site is free to browse. Studios can list themselves for free too. I'm curating listings manually right now to keep quality high.Would love feedback on the UX and what data points would be most useful when comparing studios.I'm just strating this, so it's work and progress, don't hsitate to share good videos and studios.