Show HN: CryptoFlora – Visualize SHA256 to a flower using Rose curves https://bit.ly/4lkcFMp

Show HN: CryptoFlora – Visualize SHA256 to a flower using Rose curves I made this side tool to visualize SHA-256 while building a loyalty card wallet application to easily identify if a collected stamp is certified by the issuer by simply seeing it, instead of scanning something like a QR code or matching a serial number. I think there are more potential use cases, like creating a random avatar based on an email address or something else. Feel free to share your feedback :) source code: https://bit.ly/3Ngkfeo https://bit.ly/4cBM2jV March 11, 2026 at 04:52AM

Show HN: Readhn – AI-Native Hacker News MCP Server (Discover, Trust, Understand) https://bit.ly/3Nw8u3F

Show HN: Readhn – AI-Native Hacker News MCP Server (Discover, Trust, Understand) I felt frustrated finding high-signal discussions on HN, and I started this project to better understand how this community actually works. That led me to build readhn, an MCP server that helps with three things: - Discover: find relevant stories/comments by keyword, score, and time window - Trust: identify credible voices using EigenTrust-style propagation from seed experts - Understand: show why each result is ranked, with explicit signals instead of a black-box score It includes 6 tools: discover_stories, search, find_experts, expert_brief, story_brief, and thread_analysis. I also added readhn setup so AI agents can auto-configure it (Claude Code, Codex, Cursor, and others) after pip install. I’d love feedback on: 1) whether these ranking signals match how you evaluate HN quality, 2) trust-model tradeoffs, 3) what would make this useful in your daily workflow. If this is useful to you, starring the repo helps others discover it: https://bit.ly/40pmNKh https://bit.ly/40pmNKh March 11, 2026 at 01:49AM

Show HN: Claude Code Token Elo https://bit.ly/4s44FSx

Show HN: Claude Code Token Elo https://bit.ly/4ddLBfJ March 10, 2026 at 05:29AM

Show HN: Modulus – Cross-repository knowledge orchestration for coding agents https://bit.ly/3P3vAPB

Show HN: Modulus – Cross-repository knowledge orchestration for coding agents Hello HN, we're Jeet and Husain from Modulus ( https://bit.ly/4s9fGBW ) - a desktop app that lets you run multiple coding agents with shared project memory. We built it to solve two problems we kept running into: - Cross-repo context is broken. When working across multiple repositories, agents don't understand dependencies between them. Even if we open two repos in separate Cursor windows, we still have to manually explain the backend API schema while making changes in the frontend repo. - Agents lose context. Switching between coding agents often means losing context and repeating the same instructions again. Modulus shares memory across agents and repositories so they can understand your entire system. It's an alternative to tools like Conductor for orchestrating AI coding agents to build product, but we focused specifically on multi-repo workflows (e.g., backend repo + client repo + shared library repo + AI agents repo). We built our own Memory and Context Engine from the ground up specifically for coding agents. Why build another agent orchestration tool? It came from our own problem. While working on our last startup, Husain and I were working across two different repositories. Working across repos meant manually pasting API schemas between Cursor windows — telling the frontend agent what the backend API looked like again and again. So we built a small context engine to share knowledge across repos and hooked it up to Cursor via MCP. This later became Modulus. Soon, Modulus will allow teams to share knowledge with others to improve their workflows with AI coding agents - enabling team collaboration in the era of AI coding. Our API will allow developers to switch between coding agents or IDEs without losing any context. If you wanna see a quick demo before trying out, here is our launch post - https://bit.ly/3NtfI8E We'd greatly appreciate any feedback you have and hope you get the chance to try out Modulus. https://bit.ly/4s9fGBW March 10, 2026 at 07:52PM

Show HN: Latchup – Competitive programming for hardware description languages https://bit.ly/4bhDgFy

Show HN: Latchup – Competitive programming for hardware description languages https://bit.ly/4cEqHGt March 10, 2026 at 07:06AM

Show HN: I Was Here – Draw on street view, others can find your drawings https://bit.ly/4rUNti6

Show HN: I Was Here – Draw on street view, others can find your drawings Hey HN, I made a site where you can draw on street-level panoramas. Your drawings persist and other people can see them in real time. Strokes get projected onto the 3D panorama so they wrap around buildings and follow the geometry, not just a flat overlay. Uses WebGL2 for rendering, Mapillary for the street imagery. The idea is for it to become a global canvas, anyone can leave a mark anywhere and others stumble onto it. https://bit.ly/40TNruT March 10, 2026 at 06:04AM

Show HN: SAT Protocol – static social networking https://bit.ly/3PqUmJw

Show HN: SAT Protocol – static social networking https://bit.ly/4rXCy7f March 10, 2026 at 04:25AM

Show HN: ChatJC – chatbot for resume/LinkedIn/portfolio info https://bit.ly/3OZ9A8y

Show HN: ChatJC – chatbot for resume/LinkedIn/portfolio info https://bit.ly/4b3Iy8M March 10, 2026 at 01:37AM

Show HN: Toolkit – Visual Simulators for How Internet Protocols and Systems Work https://bit.ly/4syfVWP

Show HN: Toolkit – Visual Simulators for How Internet Protocols and Systems Work https://bit.ly/4d7ddmL March 8, 2026 at 09:23PM

Show HN: Jarvey - a local JARVIS for MacOS https://bit.ly/46LTYLE

Show HN: Jarvey - a local JARVIS for MacOS https://bit.ly/3OWd0Jh March 8, 2026 at 12:04AM

Show HN: SiClaw – Open-source AIOps with a hypothesis-driven diagnostic engine https://bit.ly/40dYpLH

Show HN: SiClaw – Open-source AIOps with a hypothesis-driven diagnostic engine https://bit.ly/4rYciJW March 8, 2026 at 03:27AM

Show HN: Help] I run 4 AI-driven companies simultaneously from my terminal https://bit.ly/4sC3Iki

Show HN: Help] I run 4 AI-driven companies simultaneously from my terminal https://bit.ly/4cAtEbg March 7, 2026 at 11:13PM

Show HN: MicroBin – Easy File Sharing for Everyone – Self-Hostable https://bit.ly/4b89DpR

Show HN: MicroBin – Easy File Sharing for Everyone – Self-Hostable https://bit.ly/3NlpnxY March 7, 2026 at 10:07PM

Show HN: mTile – native macOS window tiler inspired by gTile https://bit.ly/4cyeD9O

Show HN: mTile – native macOS window tiler inspired by gTile Built this with codex/claude because I missed gTile[1] from Ubuntu and couldn’t find a macOS tiler that felt good on a big ultrawide screen. Most mac options I tried were way too rigid for my workflow (fixed layouts, etc) or wanted a monthly subscription. gTile’s "pick your own grid sizes + keyboard flow" is exactly what I wanted and used for years. Still rough in places and not full parity, but very usable now and I run it daily at work (forced mac life). [1]: https://bit.ly/4rhJXNF https://bit.ly/40iPJUh March 6, 2026 at 11:21PM

Show HN: Kanon 2 Enricher – the first hierarchical graphitization model https://bit.ly/4boHrAq

Show HN: Kanon 2 Enricher – the first hierarchical graphitization model Hey HN, This is Kanon 2 Enricher, the first hierarchical graphitization model. It represents an entirely new class of AI models designed to transform document corpora into rich, highly structured knowledge graphs. In brief, our model is capable of: - Entity extraction, classification, and linking: identifying key entities like individuals, companies, governments, locations, dates, documents, and more, and classifying and linking them together. - Hierarchical segmentation: breaking a document up into its full hierarchy, including divisions, sections, subsections, paragraphs, and so on. - Text annotation: extracting common textual elements such as headings, sigantures, tables of contents, cross-references, and the like. We built Kanon 2 Enricher from scratch. Every node, edge, and label in the Isaacus Legal Graph Schema (ILGS), which is the format it outputs to, corresponds to at least one task head in our model. In total, we built 58 different task heads jointly optimized with 70 different loss terms. Thanks to its novel architecture, unlike your typical LLM, Kanon 2 Enricher doesn't generate extractions token by token (which introduces the possibility of hallucinations) but instead directly classifies all the tokens in a document in a single shot. This makes it really fast. Because Kanon 2 Enricher's feature set is so wide, there are a myriad of applications it can be used for, from financial forensics and due diligence all the way to legal research. One of the coolest applications we've seen so far is where a Canadian government built a knowledge graph out of thousands of federal and provincial laws in order to accelerate regulatory analysis. Another cool application is something we built ourselves, a 3D interactive map of Australian High Court cases since 1903, which you can find right at the start of our announcement. Our model has already been in use for the past month, since we released it through a closed beta that included Harvey, KPMG, Clifford Chance, Clyde & Co, Alvarez & Marsal, Smokeball, and 96 other design partners. Their feedback was instrumental in improving Kanon 2 Enricher before its public release, and we're immensely thankful to each and every beta participant. We're eager to see what other developers manage to build with our model now that its out publicly. https://bit.ly/4ud0Aga March 3, 2026 at 09:55AM

Show HN: I built an AI exam prep platform for AWS certs after failing one myself https://bit.ly/4aY1AvG

Show HN: I built an AI exam prep platform for AWS certs after failing one myself Hey HN, I failed the AWS Advanced Networking Specialty exam. Studied for weeks, used the usual prep sites, thought I was ready — wasn't. The problem wasn't effort, it was the tools. Static question banks don't teach you to think through AWS architecture decisions. They teach you to pattern-match answers. That falls apart on the harder exams. So I built Knowza to fix that for myself, and then figured others probably had the same frustration. The idea: instead of a static question bank, use AI to generate questions, adapt to what you're weak on, and actually explain the reasoning behind each answer — the way a senior engineer would explain it, not a multiple choice rubric. The stack: Next.js + Amplify Gen 2 DynamoDB (direct Server Actions, no API layer) AWS Bedrock (Claude) for question generation and explanations Stripe for billing The hardest part honestly wasn't the AI — it was getting question quality consistent enough that I'd trust it for real exam prep. Still iterating on that. Early days, one person, built alongside a day job. Would love feedback from anyone who's grinded AWS certs or has thoughts on AI-generated educational content. knowza.ai https://bit.ly/3MMq0R7 March 5, 2026 at 09:27PM

Show HN: A shell-native cd-compatible directory jumper using power-law frecency https://bit.ly/4cz3be9

Show HN: A shell-native cd-compatible directory jumper using power-law frecency I have used this tool privately since 2011 to manage directory jumping. While it is conceptually similar to tools like z or zoxide, the underlying ranking model is different. It uses a power-law convolution with the time series of cd actions to calculate a history-aware "frecency" metric instead of the standard heuristic counters and multipliers. This approach moves away from point-estimates for recency. Most tools look only at the timestamp of the last visit, which can allow a "one-off" burst of activity to clobber long-term habits. By convolving a configurable history window (typically the last 1,000+ events), the score balances consistent habits against recent flukes. On performance: Despite the O(N) complexity of calculating decay for 1,000+ events, query time is ~20-30ms (Real Time) in ksh/bash, which is well below the threshold of perceived lag. I intentionally chose a Logical Path (pwd -L) model. Preserving symlink names ensures that the "Name" remains the primary searchable key. Resolving to physical paths often strips away the very keyword the user intends to use for searching. https://bit.ly/3N95WIu March 4, 2026 at 11:20AM

Show HN: DubTab – Live AI Dubbing in the Browser (Meet/YouTube/Twitch/etc.) https://bit.ly/4u1yiVL

Show HN: DubTab – Live AI Dubbing in the Browser (Meet/YouTube/Twitch/etc.) Hi HN — I’m Ethan, a solo developer. I built DubTab because I spend a lot of time in meetings and watching videos in languages I’m not fluent in, and subtitles alone don’t always keep up (especially when the speaker is fast). DubTab is a Chrome/Edge extension that listens to the audio of your current tab and gives you: 1.Live translated subtitles (optional bilingual mode) 2.Optional AI dubbing with a natural-sounding voice — so you can follow by listening, not just reading The goal is simple: make it easier to understand live audio in another language in real time, without downloading files or doing an upload-and-wait workflow. How you’d use it 1.Open a video call / livestream / lecture / any tab with audio 2.Start DubTab 3.Choose target language (and source language if you know it) 4.Use subtitles only, or turn on natural AI dubbing and adjust the audio mix (keep original, or duck it) What it’s good for 1.Following cross-language meetings/classes when you’re tired of staring at subtitles 2.Watching live content where you can’t pause/rewind constantly 3.Language learners who want bilingual captions to sanity-check meaning 4.Keeping up with live news streams on YouTube when events are unfolding in real time (e.g., breaking international updates like U.S./Iran/Israel-related developments) Link: https://bit.ly/40HBFUo I’ll be in the comments and happy to share implementation details if anyone’s curious. https://bit.ly/40HBFUo March 4, 2026 at 02:04AM

Show HN: I built a LLM human rights evaluator for HN (content vs. site behavior) https://bit.ly/4l4c4yi

Show HN: I built a LLM human rights evaluator for HN (content vs. site behavior) My health challenges limit how much I can work. I've come to think of Claude Code as an accommodation engine — not in the medical-paperwork sense, but in the literal one: it gives me the capacity to finish things that a normal work environment doesn't. Observatory was built in eight days because that kind of collaboration became possible for me. (I even used Claude Code to write this post — but am only posting what resonates with me.) Two companion posts: on the recursive methodology ( https://bit.ly/409tFeD... ) and what 806 evaluated stories reveal ( https://bit.ly/4r7k9DW... ). I built Observatory to automatically evaluate Hacker News front-page stories against all 31 provisions of the UN Universal Declaration of Human Rights — starting with HN because its human-curated front page is one of the few feeds where a story's presence signals something about quality, not just virality. It runs every minute: https://bit.ly/4aKNMpG . Claude Haiku 4.5 handles full evaluations; Llama 4 Scout and Llama 3.3 70B on Workers AI run a lighter free-tier pass. The observation that shaped the design: rights violations rarely announce themselves. An article about a company's "privacy-first approach" might appear on a site running twelve trackers. The interesting signal isn't whether an article mentions privacy — it's whether the site's infrastructure matches its words. Each evaluation runs two parallel channels. The editorial channel scores what the content says about rights: which provisions it touches, direction, evidence strength. The structural channel scores what the site infrastructure does: tracking, paywalls, accessibility, authorship disclosure, funding transparency. The divergence — SETL (Structural-Editorial Tension Level) — is often the most revealing number. "Says one thing, does another," quantified. Every evaluation separates observable facts from interpretive conclusions (the Fair Witness layer, same concept as fairwitness.bot — https://bit.ly/43DzQKs ). You get a facts-to-inferences ratio and can read exactly what evidence the model cited. If a score looks wrong, follow the chain and tell me where the inference fails. Per our evaluations across 805 stories: only 65% identify their author — one in three HN stories without a named author. 18% disclose conflicts of interest. 44% assume expert knowledge (a structural note on Article 26). Tech coverage runs nearly 10× more retrospective than prospective: past harm documented extensively; prevention discussed rarely. One story illustrates SETL best: "Half of Americans now believe that news organizations deliberately mislead them" (fortune.com, 652 HN points). Editorial: +0.30. Structural: −0.63 (paywall, tracking, no funding disclosure). SETL: 0.84. A story about why people don't trust media, from an outlet whose own infrastructure demonstrates the pattern. The structural channel for free Llama models is noisy — 86% of scores cluster on two integers. The direction I'm exploring: TQ (Transparency Quotient) — binary, countable indicators that don't need LLM interpretation (author named? sources cited? funding disclosed?). Code is open source: https://bit.ly/3MJJANP — the .claude/ directory has the cognitive architecture behind the build. Find a story whose score looks wrong, open the detail page, follow the evidence chain. The most useful feedback: where the chain reaches a defensible conclusion from defensible evidence and still gets the normative call wrong. That's the failure mode I haven't solved. My background is math and psychology (undergrad), a decade in software — enough to build this, not enough to be confident the methodology is sound. Expertise in psychometrics, NLP, or human rights scholarship especially welcome. Methodology, prompts, and a 15-story calibration set are on the About page. Thanks! https://bit.ly/4aKNMpG March 4, 2026 at 01:26AM

Show HN: Interactive WordNet Visualizer-Explore Semantic Relations as a Graph https://bit.ly/4l9DCCr

Show HN: Interactive WordNet Visualizer-Explore Semantic Relations as a Graph https://bit.ly/4l7NYTv March 3, 2026 at 10:17PM