value: pingananth

value:
Hi HN,
I'm a Product Manager who used to be terrible at working with Engineering. I treated devs like feature vending machines and didn't understand why "just one small button" during a refactor was a war crime.
I realized that most PM training focuses on frameworks (RICE, SQL, Product Sense & Strategy) but ignores the actual job: managing political capital and technical trade-offs.
So, I built a "Flight Simulator" for Junior PMs. It's a text-based RPG where you have to navigate crises without burning your "Trust Battery" with the Engineering Lead.
The Stack:
Frontend: [React/Next.js/HTML]
Logic: State machine tracking 3 variables (Eng Trust, Tech Debt, Business Value).
No AI wrapper yet—this is hand-coded branching logic based on real mistakes I made.
The Scenario: You are an APM. Your VP of Marketing sold a feature that doesn't exist. Your Tech Lead is 50% through a critical database migration. You have to figure out how to solve the customer problem without touching the prod database.
I'd love feedback on the realism of the "Grumpy Engineer" dialogue. Did I capture the pain correctly?
Try it here (no login required for first scenario): https://apmcommunication.com/scenario

value: Show HN: I built a simulator to teach PMs why they shouldn't interrupt migration

author: pingananth


value: theuniquebob

value: Show HN: Gochu – Uncensored AI chat, free and no email required

author: theuniquebob


value: francescobianco

value: Show HN: A playful repo full of Christmas easter eggs

author: francescobianco


value: produktive

value: Show HN: Turn LinkedIn/GitHub into a personal website in 2 min (open-source)

author: produktive


value: danielbedrood

value: Show HN: LeagueOfLegends AI Coach

author: danielbedrood

title: Show HN: LeagueOfLegends AI Coach


value: dstamp

value: Show HN: Solving Zodiac's Z32 – top candidate has anomaly visible from space

author: dstamp


value: mananonhn

value: Show HN: Gemini, OpenAI, Claude, Grok and Mistral argue over everything

author: mananonhn


value: Show HN: AppScreenshots – AI-Powered App Store Screenshot Generator


value: RendrrHaider

value: We're building an OS for ecommerce businesses that helps you generate infinite variations of PDP images and videos to run endless loops of A/B test on your store, then adapt what's really working and converting into sales.

value: Show HN: Building an OS for self-improving ecommerce stores

author: RendrrHaider


value:
Hi HN, I built NeuroIndex while working on long-running AI agents and chatbot memory systems.
Vector search answers “what is similar?”, but agents need “what is related?”
NeuroIndex combines vector search, semantic graph recall, and persistent memory in a fully local, open-source project.
Would love feedback on design, use-cases, and improvement ideas.
GitHub: https://github.com/Umeshkumar667/neuroindex

value: Show HN: NeuroIndex – Hybrid AI memory with vectors and semantic graphs


value: ogkaran

value:
Hey HN,
My name is Karan, and I'm a Design Engineer
I was always fascinated by how someone can create slick, responsive UIs that create a lasting impression
and eveyrone wants such UIs but many struggle with CSS and let alone animations
That's why I built ogBlocks - a UI library that lets you create Beautiful UIs, regardless of your CSS Skills
Tell me what you think of it
Best
Karan

value: Show HN: I Built an UI Library for Non CSS Developers – OgBlocks

author: ogkaran


value: kidsil

value:
I kept needing a quick way to see what Stripe / Shopify / GitHub webhooks actually send, without exposing localhost or hopping between dashboards.
This is a small, focused webhook inbox: you get a unique HTTPS endpoint and can inspect incoming requests (headers + payloads) in the browser.
It's built on Cloudflare's edge stack and meant for quick debugging and exploration, not production.
I'm the builder - would love feedback on missing features or bugs.

value: Show HN: Webhook Tester – RequestBin-style webhooks inbox built on Cloudflare

author: kidsil


value: Show HN: Portal – Expose localhost to the public domain with one command


value: giovanella

value: Show HN: 0xFeed – Filtering tech news noise with GPT-4o

author: giovanella


value: debarshri

value: Show HN: Footywhoops – A real-time audio pattern generator and a MIDI Sequencer

author: debarshri


value: ctrl-alt-auto

value: Show HN: PowerNetbox – PowerShell module with 100% NetBox API coverage

author: ctrl-alt-auto


value: nick-keller

value:
Hey HN,
One of the most frustrating parts of working with RabbitMQ for me has always been the management interface, especially when dealing with DLQs.
At some point, I proposed improving the native UI internally, but the response I got was basically: "Don't fix what isn't broken." Fair enough... so I went ahead and built something instead.
Rabbit GUI is a Mac desktop app that connects directly to the RabbitMQ Management API, it works with both self-hosted instances and services like CloudAMQP.
Think of it as an IDE for RabbitMQ. You can connect to multiple environments (local, staging, prod...), browse and filter through queues messages, and selectively republish or discard items in your DLQs.
The app is a one-time payment for a lifetime license, no subscriptions nonsense.
I'd love feedback from anyone who deals with RabbitMQ day to day!

value: Show HN: I Built a Mac Tool for RabbitMQ Dead Letter Queue Management

author: nick-keller


value: noct-scraper-ds

value:
  I’m a grad student and need to cover some tuition this semester. I’ve
  been scraping freelance job boards for a while and turned it into
  a dataset.

  Data:
  - 134,861 unique postings (Apr–Nov 2025)
  - Fields: description, hourly/fixed budget, client country, tech tags
  - Format: SQLite + CSV; scraped with Python (Selenium/Scrapy) with basic
  dedup/normalization (currencies, month-level timestamps, no company names/
  URLs)

  Sample insight: In this scrape, CV gigs pay more than LLM ones: median fixed
  budgets ≈ $3k vs ≈ $2k (Apr–Nov 2025; n≈270 CV, n≈18k NLP).

  Free sample (50 rows): https://docs.google.com/spreadsheets/
  d/1wvvX8JeOIEO2rYeuMf99LEBN4bmVoZ3ZQqHY_clvCvc/edit?gid=0#gid=0
  Full dataset: https://noctscraper.gumroad.com/l/freelance-ai-dataset

  Happy to answer questions about the schema or run a quick aggregate if
  you’re curious.

value: Show HN: 135k Freelance AI Job Postings (2025) – SQLite/CSV Dataset

author: noct-scraper-ds


value: ortuman

value: Show HN: Entangle – An extensible pub/sub messaging protocol for real-time apps

author: ortuman


value: lucharo

value:
Voice-to-text tools like WisprFlow, MacWhisper, and VoiceInk have been getting popular, and I wanted to see how hard it would be to build something similar running entirely locally.
The transcription itself (Whisper via MLX) isn't new — plenty of people have done that. What I find useful is the combination: transcription, LLM cleanup (Qwen 2.5-3B via Ollama) and pasting wherever your cursor is. That last part is (to me) the magic of tools like WisprFlow, and it turns out you can replicate it in a single Python script, running fully locally with comparable performance to paid subscriptions.
It's a proof of concept more than a polished product, but I've been using it daily. It's a testament to our times that all of this fits in a single script.

value: Show HN: 270 Lines of Python to Replace WisprFlow (Local Whisper and Qwen)

author: lucharo


value: agentifysh

value: Show HN: SafeExec: Destructive Command Interceptor for Codex/Claude/Agents

author: agentifysh


value: hyunhum

value:
Hey HN! I’m a researcher working on Bitcoin smart contracts, and today I’m releasing Bithoven—a high-level imperative language that compiles to native Bitcoin Script (Legacy, SegWit, and Taproot).
The Goal:
Raw Bitcoin Script is notoriously difficult to reason about. Writing raw Bitcoin Script today feels like writing Assembly in the 1970s. You have to mentally juggle the stack (`OP_SWAP`, `OP_ROT`), manually manage distinct execution branches, and pray you didn't leave a stack item unconsumed (which crashes the script). My goal was to bridge the gap between complex contract logic and raw opcodes, allowing developers to write readable, compile-time-safe code.
Key Features:
- Imperative Syntax: Write logic using familiar if/else and return statements instead of mental stack juggling.
- Type Safety: First-class support for bool, signature, string, and number types to prevent runtime errors.
- Targeted Compilation: Support for Legacy, SegWit, and Taproot compilation targets.
- Native Primitives: Built-in keywords for timelocks (older, after) and cryptography (sha256, checksig).
You can try it in the browser here (runs via WASM):
https://bithoven-lang.github.io/bithoven/ide/
Here is an example of a Hashed Time-Locked Contract (HTLC):

  (condition: bool, sig_alice: signature)
  (condition: bool, preimage: string, sig_bob: signature)
  {
      if condition {
          // Relative locktime (Sequence)
          older 1000;
          return checksig (sig_alice, alice_pk);
      } else {
          // Hashlock verification
          verify sha256 sha256 preimage == hash;
          return checksig (sig_bob, bob_pk);
      }
  }

The project is free open source and the academic paper is currently under review. I’d love to hear any feedback. Thanks for checking it out!

value: Show HN: Bithoven – A high-level, imperative language for Bitcoin Smart Contract

author: hyunhum


value: ykgup

value: Show HN: Troql – Auto-generate architecture maps from GitHub repos

author: ykgup


value: jugyo

value: I built a Claude Code Skill that lets you create custom VSCode themes just by describing what you want.<p>"Create a blue-based dark theme" → generates theme files, packages as .vsix, and installs to VSCode automatically.

value: Show HN: Claude Code Skill for creating VSCode themes

author: jugyo


value: johnoliveiradev

value:
I’m publishing aggregated results of an empirical benchmark documenting a multiscale structural-intersection regime.
The intersection persists across scales, seeds, and parameter sweeps, and separates from strong null models that preserve marginals and coarse support while destroying correlation.
Results only; generator and engine are intentionally private.
Repo: https://github.com/johnoliveiradev/Multiscale-structural-reg...

value: Show HN: Empirical evidence of a multiscale structural intersection

author: johnoliveiradev


value: maddhruvhn

value:
Hi                 HN,
I am building SupaGuard. I am a software engineer and have been deeply involved in production observability for quite some time.
The Problem
I found out that Real User Monitoring (RUM) is the one that lets us down the most when we are in need of help. Taking an example, if a deployment at 7 PM breaks the login flow but no one tries to log in until 11 PM, then the system is operating blindly for more than 4 hours. Metrics might be showing that everything is fine, but in reality, the product is functionally broken.
The Solution
I am making SupaGuard to address only these kinds of pain points. It is an AI-native continuous synthetic monitoring platform that aims at locating those silent functional failures which do not generate any noise.
Key Features:
AI Test Generation: A browser agent goes through your app, automatically discovers critical flows and hence generates Playwright scripts along with assertions.
Smart Retries: To handle flakiness, when a test fails in a certain location (e.g. US East), it will try to rerun the test in another location (e.g. EU) immediately and only if the follow-up fails will it send the alert.
Non-Binary Alerting: It is not only "down" that is checked. The system grades performance degradation and identifies functional failures impact-wise.
Pricing
I am figuring out a better pricing model that will still be affordable and user-friendly at the same time. Suggestions are welcome.
Status
I am still in the process of product development and you can sign up for a waitlist if you'd like to be the first to know when it's ready.
The URL is https://supaguard.app/
I would be very happy to get your feedback regarding the concept and the features that I am prioritizing.                 Thanks!

value: Show HN: I am building a monitoring app to catch product issues

author: maddhruvhn


value: timeproofs

value:
Most webpages are hard for LLMs to read properly.
HTML mixes real content with navigation, footers, cookie banners, scripts, ads, and layout noise.
This makes prompts larger, chunking worse, and RAG pipelines less reliable.
AI2JSON is a small public API that converts any public webpage into a clean, deterministic JSON structure:
- main content only
- ordered sections
- stable output
- SHA-256 hash for change detection
No summary, no interpretation — just a minimal contract between the web and AI systems.
You can paste a URL and instantly compare:
- what an LLM sees with raw HTML
- vs the same content as structured JSON
Free sandbox, no API key.
I’m mainly looking for developer feedback: does this actually improve your AI workflows?
Demo: https://ai2json-c14gadm9x-jeason1.vercel.app

value: Show HN: Turning noisy webpages into clean JSON for LLMs

author: timeproofs


value: seergiue

value: Show HN: A SaaS starter kit built with Nuxt 4 and AdonisJS v6

author: seergiue


value: simedw

value: A proof-of-concept language learning app that uses LLMs to generate definitions of unknown words using only previously mastered vocabulary.

value: Show HN: Learning a Language Using Only Words You Know

author: simedw


value: kevinbaur

value:
Hey HN.
I realized how much time and context teams lose when someone leaves a role. I joined a job once with basically zero onboarding because the person before me was already gone, and it took months to figure everything out.
So I built SkillPass, a tool that captures empyloyees knowledge before they leave and turns it into a structured onboarding playbook for the next person. No templates, no writing, no chaos.
Kevin

value: Show HN: I made a tool to keep employee knowledge in the company when they leave

author: kevinbaur


value: shake-n-fries

value: Show HN: AgentContainers – Open-source web UI for AI dev environments on Docker

author: shake-n-fries


value: victornomad

value: I made a small game where you are given a word that you have to draw and the AI guesses what are you drawing.<p>It uses gemini flash 2.5 via API but also works with local models. I had very good results with mistral/pixtral 12b.

value: Show HN: Drawing Guessing Game with an LLM

author: victornomad


value: michelutti

value:
This is a side project I've been building from scratch, from code to infrastructure and deployments, and now the marketing.
Training interviews and getting instant, accurate feedback would have saved me a lot of time interviewing people. At the end of each interview, a complete report is delivered with next steps to improve in the next one. Furthermore, each person can create their own agent and define tones, questions, and topics, allowing for further customization to their liking.
The feedback process consumed a significant amount of time for validation, using a lot of langchain and several interactions to achieve a good and well refined result. I'm sure it's extremely valuable for anyone with upcoming interviews.

value: Show HN: Ace interviews with AI, real time feedback

author: michelutti


value: qzcanoe

value:
I built FocusFour because I was busy all day but still missing the things that actually mattered.
FocusFour is a task manager based on the Eisenhower Matrix, built directly on top of Apple Reminders. Recently I added lightweight AI to help identify and surface tasks that are likely important but getting ignored.
What it does:
* Four-quadrant (important vs. urgent) task view
* Uses AI to highlight and reprioritize important tasks
* Native Apple Reminders integration (no new inbox, no migration)
* Local-first, no accounts
* iOS, iPadOS, macOS
I’m curious about two things:
* Whether AI-assisted prioritization is actually useful long-term
* Tradeoffs of building on system tools instead of owning all task data
Happy to answer questions or hear criticism.

value: Show HN: FocusFour – Eisenhower Matrix on Apple Reminders

author: qzcanoe


value: lintsinghua

value: Show HN: DeepAudit – open-source auditing agent (LLMs and Static Analysis)

author: lintsinghua


value: cswasif

value: Show HN: ZKS – A Split-Key Mesh Protocol for Private File Transfer (Rust/WASM)

author: cswasif


value: blueskyline

value:
I built an API that provides semantic search over Federal Acquisition Regulations for GovCon AI systems and compliance bots.
*What it does:*
- Semantic search across 617 FAR Part 52 clauses
- Pre-vectorized with 384-dim embeddings (all-MiniLM-L6-v2)
- Returns relevant clauses with similarity scores
- Daily auto-updates from acquisition.gov
- OpenAPI spec for AI agent integration
*Why it exists:*
If you're building AI for government contracting, your LLM will hallucinate legal citations. A wrong FAR clause = disqualification. This solves that.
*Try it free:*
https://blueskylineassets.github.io/far-rag-api/honeypot/
*API access (RapidAPI):*
https://rapidapi.com/yschang/api/far-rag-federal-acquisition...
*Code (open source):*
https://github.com/blueskylineassets/far-rag-api
Built with FastAPI + sentence-transformers. All data is public domain (17 U.S.C. § 105).
Open to feedback!

value: Show HN: Far RAG API – Semantic Search for Federal Regulations (OpenAPI)

author: blueskyline


value: Show HN: Stimm – Low-Latency Voice Agent Platform (Python/WebRTC)


value: dalemhurley

value: All of the possible agents as a simple PHP interface - could not be easier to create AI agents.

value: Show HN: PHP Claude Agents

author: dalemhurley

title: Show HN: PHP Claude Agents


value: piskunlab

value:
Hello HN,
I built this because I wanted to give Claude Desktop access to my Notion workspace without running a flaky local Python script via stdio.
This is a Node.js/Express implementation of the Model Context Protocol (MCP) that uses SSE (Server-Sent Events) for transport. It’s designed to be stateless and deployable as a container (I'm hosting it on Apify, but it works anywhere with Node).
The Stack:
TypeScript + Express
@modelcontextprotocol/sdk
Zod for input validation
Bearer Auth for security (since it exposes an HTTP endpoint)
Capabilities: It allows the LLM to search pages, read database properties, and append blocks. I use it primarily to have Cursor/Claude summarize documentation and create tasks in my sprint board directly from the chat context.
Repo: https://github.com/piskunproject/notion-mcp-server/
One-click deploy (Apify): https://apify.com/piskunlab/notion-mcp-server
Feedback on the SSE implementation is welcome!

value: Show HN: Open-Source Notion MCP Server (TypeScript, SSE, Apify)

author: piskunlab


value: jallewa

value:
Hi HN,
I built CryptoBob, a small API that provides precomputed market intelligence
for crypto trading systems.
Instead of signals or predictions, CryptoBob focuses on:
- regime filtering (when NOT to trade),
- ranking markets by setup quality,
- providing simple risk hints (stop / trail percentages).
It’s designed to be consumed by bots, dashboards, and quant workflows.
The API exposes a small set of stable endpoints and uses Firebase-based
authentication with subscription plans.
Docs: https://cryptobob.de/docs
Website: https://cryptobob.de
This is an early-stage project and I’d really appreciate feedback,
especially from quants and algo traders.

value: Show HN: CryptoBob – a market intelligence API for crypto trading systems

author: jallewa


value: mennylevinski

value:
Hi there,
I built a lightweight, open-source Python CLI tool that runs on Linux and Windows.
Designed for fast discovery of devices on local IPv4 networks, focused on ethical diagnostics and basic administrative auditing.
It supports automatic LAN subnet detection or manual IP/range targeting, combines ICMP, ARP, socket-based discovery and scans common service ports (FTP, SSH, SMB, HTTP, RDP and more).
The output is console-based, with optional export to a TXT log file.

value: Show HN: I built a fast cross-platform CLI network scanner via Python

author: mennylevinski


value: boraturan

value: Hey, developer here, I have spent the last 2 years building the native Tiktok/Instagram mobile app infra - using web frameworks. No reactnative, no react native web,, just plain react. it is a single code-base that works on web, and as a native mobile app inside a webview. The biggest challenge was the camera, I had to wait for webcodecs support in ios safari, and also I had to develop a react native-like framework for native feel/animations in a webview. would love for your comments... app.copo.st and, native apps are on appstore, the native apps are simple apps that have a webview that load the entire webapp.

value: Show HN: TikTok mobile app written as a web app (with Camera))

author: boraturan


value: iamMXFSCHR

value: Show HN: QueryGlow – Self-Hosted Database GUI (Postgres, MySQL, SQLite)

author: iamMXFSCHR


value: qzcanoe

value:
I built Wan-Animate, an AI video character animation and replacement tool based on the Wan 2.2 model.
It focuses on two common video creation needs:
Move Mode: animate a static character image to follow the motion in a reference video
Mix Mode: replace the person in a video with an uploaded character image
Under the hood it uses skeleton-driven motion control and intelligent relighting, so the character blends more naturally into the original scene.
Quality options:
Standard: 480p
High quality: 720p
There’s a free trial (login required), plus subscription and credit-based plans.
The goal is to make character animation and replacement accessible without complex VFX workflows — just one character image and one reference video.
Happy to hear feedback or questions.

value: Show HN: Wan-Animate – AI video character animation and replacement based


value: Pterjudin

value:
Hi HN — I’m Tajudeen, a software engineer working mostly on large and regulated codebases.
I built CortexIDE as a simple alternative to Cursor, mainly because the open-source editor Void (which CortexIDE is based on) slowed down, and I still wanted a local-first, hackable AI editor I could control.
CortexIDE is a VS Code–based editor, not an extension. The workflow is intentionally explicit: chat with the model, generate a concrete plan, produce diffs, and apply changes only after review.
It supports chat, inline edit, and agent-style multi-file edits, repo-aware context via indexing, local models (e.g. Ollama) as well as remote providers, PDF uploads for specs/docs, image uploads for visual context, checkpoints/rollback, todo list for large task and no telemetry by default.
This is still early and actively evolving. Performance on large repos and slower machines is something I’m actively improving.
I’m sharing this to get feedback from people who like Cursor’s UX but want more control, prefer local or self-hosted models, or don’t trust extensions to freely mutate their repo.

value: Show HN: I built a simple Cursor alternative after Void slowed down

author: Pterjudin


value:
I am sharing a research-grade, open-source trading execution framework that achieves a median end-to-end decision latency of 890 nanoseconds on commodity hardware.
The project is designed for education, systems research, and latency instrumentation, not for live trading. It focuses on understanding exactly where every nanosecond goes in a trading execution path.
Key features:
- Kernel-bypass networking: Direct userspace access to NICs via custom drivers, 20-50 ns RX latency
- Lock-free SPSC/MPSC queues: Zero-copy architecture
- SIMD feature extraction: About 40 ns per update using AVX-512
- Deterministic replay: Bit-identical execution paths, SHA-256 verified
- Nanosecond-level metrics: Full audit logs and performance dashboard
Technical stack: C++17 and Rust, NUMA-aware memory allocation, cache-line alignment, inline assembly for hot paths.
The framework is modular, allowing experimentation with different NIC drivers, feature extraction pipelines, or order-flow models such as Hawkes processes or Avellaneda-Stoikov logic. Everything is open source and documented.
Links:
Live demo: https://submicro.krishnabajpai.me/
Source code: https://github.com/krish567366/submicro-execution-engine
Bare-metal NIC drivers: https://baremetalnic.krishnabajpai.me/
I would welcome feedback from anyone working on low-latency systems, networking, or HFT research.
Some questions for discussion:
- Which part of the execution path is typically hardest to optimize?
- What measurement techniques do you trust for sub-microsecond systems?
This project is for research and educational purposes only. It does not connect to exchanges or execute real trades. It is intended as a sandbox for understanding ultra-low-latency execution.
I am happy to answer questions about methodology, performance, or design trade-offs.

value: Show HN: Sub-microsecond (890 ns) trading execution research system


value: renbuilds

value:
I built a tool that scans deployed websites for secrets in production bundles and forgotten API endpoints.
https://domainoptic.com/audit
Unlike repo scanners (TruffleHog, Gitleaks), this scans what's actually live. What an attacker sees when they view source or probe common paths.
Tested it on 20 sites from Hacker News over the last two days. All indie projects, side hustles, Show HN launches.
Results:
- 1 site had 6 actual secrets (1 cloud credential, 5 API keys)
- 5 sites (25%) had exposed API documentation (/swagger, /graphql, /api-docs)
- 2 sites had critical-level endpoint exposure
- 100% scan consistency on re-tests
The site with secrets wasn't some abandoned project. It launched recently. The founders probably don't know.
What the scanner checks:
1. Secret Scanner - patterns matching AWS, Stripe, Firebase, generic API keys in your compiled JS
2. Ghost API Hunter - probes for /swagger, /api-docs, /graphql, debug routes
To be clear: not every pattern is a real secret. Firebase client configs are public by design. The tool flags patterns worth investigating.
If you want to see it work before scanning your own site, try wirier.com it's a demo site I built with intentionally planted secrets.
Free. No signup. one click scanning.
Curious what others find on their own sites.

value: Show HN: DomainOptic – scanned 20 HN sites for secrets and exposed endpoints

author: renbuilds


value: regnull

value:
My investing performance so far was, quite honestly, mediocre. It’s not like I’m making terrible investment decisions, it’s more about not being able to consistently manage my portfolio. I’m getting distracted by other projects, leaving my portfolio in free float, which, ultimately, doesn’t work great.
So, the idea was to create a reasonable portfolio manager that watches your portfolio, and alerts you (relatively infrequently) when the action is needed. I’m not looking to YOLO to the moon, I’m looking for some reasonable, middle-of-the-road advice and consistent monitoring. This is why I built my portfolio manager, https://portfoliogenius.ai
In the spirit of putting my money where my mouth is, I have $50k to manage since July this year. Currently, it’s about 7.5% up, which is consistent with the goal I gave it - moderate risk portfolio, with some crypto exposure. Yes, S&P was up 12%, but it is expected given that I aimed for lower risk, and a large portion of the portfolio is bonds and treasury ETFs. The performance was hurt by the recent BTC slide, but thankfully it insisted on keeping the crypto sleeve small, about 5% of the whole portfolio.
There were a few things that surprised me. Some investment choices are conventional, but some are quite interesting. For example, it recommended buying ICLN back in July (global clean energy ETF), which was strange given the political climate. But I resolved to do everything it recommends, so I did. ICLN is up 25% since.
I also had plenty of fun running portfolios managed by different models to compare performance. So far, Gemini 3 Pro seems to be the best, but it may very well change in the longer run.
For the tech stack, I use LangChain with a bunch of tools - Tiingo for stock/crypto prices, Brave for news/web search, and my custom news processing service which summarizes daily news and generates macro economic snapshots. I also have some custom tools to help with the tasks such as portfolio design and trade recommendations.
The platform is free for now, and I intend to keep it free for the early adopters (give away premium subscriptions when it is introduced).
I would be grateful for any feedback, specifically if you feel that such a tool can be useful for you, and what functionality would you like to see.

value: Show HN: I built an AI portfolio manager and entrusted it with $50k

author: regnull


value: paperplaneflyr

value:
It's essentially a book progress tracker. Many apps allow you to add the books that you are currently reading, but not at what pace. It's simple, no complicated stuff, no AI shenanigans.
Created as I was overwhelmed by the number of books I wanted to read, and thought it would be helpful to plan ahead.
You add a book name, the number of pages, and how many pages you want to read in a day. It calculates and gives you the number of days and on which date you will finish. It's also flexible to increase the number of pages so that it can recalculate.
It's a PWA for now. Still working on notifications and stuff.

value: Show HN: Bookpace

author: paperplaneflyr

title: Show HN: Bookpace


value: donutloop

value:
I am a software engineer with 15 years of experience, and this is the first time I feel the era of traditional software engineering and many desk jobs in general is starting to end. In the next 5 to 10 years, most desk based roles will likely disappear, except for jobs directly connected to AI.
I built this in under 8 hours using AI. I used Gemini 3 to see whether the marketing claim was real that you can build almost anything just by describing it. So far, I have not hit any serious blockers. I used vibe coding.
For the first time in my career, I have actually thought that I might be out of a job in ten years. Not because I lack skills, but because the tools are becoming incredibly powerful. With Gemini 3, I can build games in less than a day, and this feels like only the beginning.
Tools like Antigravity turn developers into managers of AI agents instead of people who write every line by hand. The old model of expensive custom work is fading. Software can now be produced at massive scale, faster and cheaper than ever before.
Gemini 3 feels like the start of a new era. What comes next will reshape how we work and likely society itself much sooner than most people expect.

value: Show HN: We need to define a new scale for measuring any software project

author: donutloop


value: stoccupied

value: Show HN: A free, no-login DnD 5e DM screen and prep tool

author: stoccupied


value: haneboxx

value: Hi! I just made an obsdian alternative in terminal after searching for an Obsidian like TUI and got nothing. The closest I found was Glow, but it's only a markdown reader. I wanted something more powerful for the terminal, so I built one myself.

value: Show HN: Ekphos – open-source, lightweight, fast, TUI Markdown research tool

author: haneboxx


value: mattjung

value: I’m currently looking for beta testers to help validate the usefulness of Voilo, stress-test real-world use cases, and shape it into a production-ready tool. If you enjoy tracking things, care about owning your data, or already live in Google Sheets, I’d love to hear your feedback.<p>Here the elevator pitch: Voilo turns your spoken updates into structured entries in Google Sheets. Simply record, speak naturally, and your data is parsed, validated, and added to the right tracker — hands-free and instantly.

value: Show HN: Voilo Launching in Beta

author: mattjung

title: Show HN: Voilo Launching in Beta


value: tamnd

value: Show HN: A practical, code-driven guide to learning HTTP frameworks in Go

author: tamnd


value: janicerk

value: Show HN: Octopii – runtime for writing distributed applications in Rust

author: janicerk


value: amirkabbara

value:
Hey all, I'm one of the founders at Papr.
We started building an AI project manager. Users needed to search for context about projects, and discover insights like open tasks holding up a launch.
Vector search was terrible at #1 (couldn't connect code, marketing and PR that are for the same project). Knowledge graphs were too slow for #1, but perfect for structured relationships, great for UIs.
Then we started talking to other teams building AI agents - we realized everyone was hitting the exact same two problems.
So we pivoted to build Papr — a unified memory layer that combines:
- Intent vectors: Fast goal-oriented search for conversational AI
- Knowledge graph: Structured insights for analytics and dashboard generation
- One API: Add unstructured content once, query for search or discover insights
And just open sourced it.
How intent vectors work (search problem)
The problem with vector search: it's fast but context-blind. Returns semantically similar content but misses goal-oriented connections.
These are far apart in vector space (different keywords, different topics). Traditional vector search returns fragments. You miss the complete picture.
Our solution: Group memories by user intent and goals stored as a new vector embedding (also known as associative memory - per Google's latest research).
When you add a memory:
1. Detect the user's goal (using LLM + context)
2. Find top 3 related memories serving that goal
3. Combine all 4 → generate NEW embedding
4. Store at different position in vector space (near "product launch" goals, not individual topics)
5. Query "What's the status of mobile launch?" finds the goal-group instantly (one query, sub-100ms), returns all four memories—even though they're semantically far apart.
This is what got us #1 on Stanford's STaRK benchmark (91%+ retrieval accuracy). The benchmark tests multi-hop reasoning—queries needing information from multiple semantically-different sources. Pure vector search scores ~60%, Papr scores 91%+.
Automatic knowledge graphs (structured insights)
Intent graph solves search. But production AI agents also need structured insights for dashboards and analytics.
The problem with knowledge graphs:
- Hard to get unstructured data IN (entity extraction, relationship mapping)
- Hard to query with natural language (slow multi-hop traversal)
- Fast for static UIs (predefined queries), slow for dynamic assistants
Our solution:
- Automatically extract entities and relationships from unstructured content
- Cache common graph patterns and match them to queries (speeds up retrieval)
- Expose GraphQL API so LLMs can directly query structured data
- Support both predefined queries (fast, for static UIs) and natural language (for dynamic assistants)
We combined both of these solutions in one API.
What I'd Love Feedback On
1. Evaluation - We chose Stanford STARK's benchmark because it required multi-hop search but it only captures search, not insights we generate. Are there better evals we should be looking at?
2. Graph pattern caching - We cache unique and common graph patterns stored in the knowledge graph (i.e. node -> edge -> node), then match queries to them. What patterns should we prioritize caching? How do you decide which patterns are worth the storage/compute trade-off?
3. Embedding weights - When combining 4 memories into one group embedding, how should we weight them? Equal weights? Weight the newest memory higher? Let the model learn optimal weights?
4. GraphQL vs Natural Language - Should LLMs always use GraphQL for structured queries (faster, more precise), or keep natural language as an option (easier for prototyping)? What are the trade-offs you've seen?
---
Try it:
- Developer dashboard: platform.papr.ai (free tier)
- Open source: https://github.com/Papr-ai/memory-opensource
- SDK: npm install papr/memory or pip install papr_memory

value: Show HN: Intent vectors for AI search and knowledge graphs for AI analytics

author: amirkabbara


value: mengmota

value: Show HN: Oh-My-Opencode: $24,000 Worth Spent. Built Async agents, Curated Agents

author: mengmota


value: MaxTeabag

value:
I work mostly in the terminal but found myself constantly switching to bloated GUIs like SSMS only for the simple task of browsing tables and run queries. And I didn't find Existing SQL TUIs intuitive, having to read documentation to learn keybindings and CLI flags to connect. Given I had recently switched to linux, I found myself using vs code's sql database extension. Something was awfully wrong.
I wanted something like lazygit for databases – run it, connect, and query and frankly just make it enjoyable to access data.

  Sqlit is a keyboard-driven SQL TUI with:

  - Context-based keybindings (always visible)
  - Neovim-like interface with normal and insert mode for query editing
  - Browse databases, tables, views, stored procedures
  - Adapters for SQL Server, SQLite, PostgreSQL, Turso & more
  - SSH tunneling support
  - Themes (Tokyo Night, Nord, Gruvbox etc.)

  Inspired by lazygit, neovim and lazysql. Built with Python/Textual.

Feedback welcome – especially on which adapters to prioritize next. My vision of sqlit is to make a tool that makes it easy to connect and query data, and to do that, and that thing only, really well.
https://github.com/Maxteabag/sqlit

value: Show HN: Sqlit – A lazygit-style TUI for SQL databases

author: MaxTeabag


value:
I’ll be honest. I built a platform to host datasets thinking it would be the next big thing. I spent 8 months on it. Result: 0 users. It was a ghost town.
Instead of deleting the repo and crying in a corner, I realized the only part of the app that was actually cool was the engine: It could turn any CSV upload into a queryable API in milliseconds.
So I stripped away the social features, killed the "hub" idea, and turned it into a simple utility tool for frontend devs who hate writing backend boilerplate.
It’s called Shodata. You drag a CSV, you get a REST API. That's it.
It’s running on FastAPI + DuckDB. Roast my pivot.

value: Show HN: I spent 8 months building a "Data Hub" that nobody used, so I pivoted


value: theill

value: Show HN: Free wedding photo sharing service

author: theill


value: mairswang

value: Show HN: OpenSciEval-AI Deriving Prime Theorem from Chaos

author: mairswang


value: A lightweight, no-retraining verification layer that rejects smooth hallucinations by measuring structural tension instead of probability.

value: Show HN: Structural Verification for LLMs: Why Best-of-N Isn't Enough


value: altras

value: Show HN: I built a scorecard to make creators think about plaftorm dependence

author: altras


value: zarred

value: Show HN: Meeting cost calculator that shows what else you could've bought

author: zarred


value: gpirescampos

value: Show HN: An app that helps parents explain complex questions to children

author: gpirescampos


value: nodar-d

value:
Hi HN,
We are the team behind Shuttle (YC S20). We just shipped Neptune, (https://github.com/shuttle-hq/neptune-mcp) an open source MCP server that lets coding agents actually deploy software, not just write code. Behind it is an app deployment platform we built to be friendly for AI agents.
Neptune runs in your IDE. It reads your codebase, analyzes its structure and intent using ASTs and inference, and generates a deterministic infrastructure spec called neptune.json. You can inspect every change, approve it, and apply it. Think: coding agents that can actually ship safely to AWS.
How it works in practice:
1. Take any of your existing apps
2. Install Neptune mcp & login
3. Tell your agent to deploy
4. Watch it do everything else for you and (super important) it keeps you in the loop at every step, asking for your approval and guidance. We’ve spent most of our time implementing guardrails to make this as safe as if you were doing it yourself.
We built this after seeing teams rely on AI to write services, but still hand-craft Terraform and YAML. The AI could write code, but it could not understand the system around it. Neptune tries to close that gap.
Right now we have support for the most basic AWS products (ECS, S3, SecretManager), and we support postgres (thanks to Neon) and we are adding more resources as we speak. For now, your apps are all deployed to our a shared tenancy AWS account, and “bring your own cloud” account is coming soon too.
If you are also excited about a future where coding agents can help you reliably ship stuff into production, try it and share what you think.
If you have tried it on shared tenancy and would like to run it in your own AWS account, just get in touch and we’d be happy to help.

value: Show HN: Neptune – Give your coding agents DevOps superpowers

author: nodar-d


value:
Hi HN — I built “Undefined”, a live multiplayer text world where an LLM acts as a “reality referee” (not a friendly chatbot).
Demo: https://undefinedai.xyz/
Notes:
- No signup required
- BYOK supported (bring your own API key/model)
- Server-funded guest usage is capped (~$20) to avoid runaway costs
- /do is concurrency-limited so the server stays stable under traffic
Tech/how to play: https://github.com/pearl-100/undefined-public
Write-up: https://open.substack.com/pub/undefinednamoo/p/i-spent-200-t...
2-min demo video: https://www.youtube.com/watch?v=q-B4f1oEzfw

value: Show HN: Instead of exams, I vibe-coded a live LLM-based text MMO


value: qkeast

value:
My partner and I do a lot of planning around things like holidays and trips, which involves a mix of firm, tentative, and overlapping plans, and normal calendars are the wrong fidelity for that kind of planning. We often found ourselves drawing on top of screenshots of calendars with tools like Miro or even printing out calendars we could draw on.
We're both product designers, and so we're used to working in low fidelity tools like Excalidraw and FigJam. We decided to try and make a low-fidelity equivalent for calendars, and built Walnut. It's just lines and stickies, with a few different styles, no events or times.
Walnut has two versions: the super simple, localstorage-based version at letswalnut.com, which you can just pop open any time. There's also a "workspace" version which lets multiple people work on the same calendars with multiplayer collaboration, and persists the calendars long-term.
It's still rough around the edges and needs more design refinement, but we've been using it extensively personally (and my team's using it at my work for planning purposes). It's not right for everyone, but for those who like to plan this way, we think it's helpful. Curious to hear any thoughts!
Right now anyone can use the localstorage-based version. If you'd be interested in trying out the persisted/collaborative version, drop your email in the waitlist[1] and I'll send you an invite to sign up!
[1] https://docs.google.com/forms/d/e/1FAIpQLSetOC_hY54_DBfnPCmW...

value: Show HN: We built an uncomplicated calendar tool for low-fidelity planning

author: qkeast


value: andreamonaco

value:
For my YouTube videos, I often need to record my laptop screen.
I used both ffmpeg and OBS and while they are quite rich with features, they also introduce a big overhead.
I started experimenting with the possibility of recording the framebuffer directly, bypassing the X server or Wayland: the Linux kernel provide this via a system called DMAbuf.
To be fair, the idea is not mine: ffmpeg let you do that with the kmsgrab input device, but for some reason they don't recommend it very much, certainly not as a first choice; maybe that road is not as tested as the classic x11grab.
So here is the first version of my little utility screenrec: you can take a screenshot or record a short video of your desktop.
It's a proof-of-concept; while it only supports a linear or tiledX by 4KB framebuffer and only the pixel format of my video card, those limitations are easy to expand.
Let me know if you like it!

value: Show HN: A little utility to take screenshots and record screen on GNU/Linux

author: andreamonaco


value: eideus

value: Show HN: Synara – deterministic calendar invites across calendar providers

author: eideus


value: Show HN: I analyzed 49K corporate deaths (1992-2025) using minute-level data


value: urirubio

value:
OP here.
I built this because I was tired of manually copy-pasting emails into Notion or dealing with Zapier scenarios that just dumped raw HTML strings.
The Stack:
Chrome Extension (Manifest V3)
Node.js backend
OpenAI API for the parsing logic
Notion API
How it works: It grabs the DOM of the active email thread, cleans the noise, sends it to the LLM to enforce a JSON schema (Summary, Action Items, Dates), and pushes it to a Notion Database.
Business Model: It's a paid tool ($9/mo) because parsing long email threads with attachments eats up tokens, and I wanted a sustainable model.
If anyone wants to test the workflow, I made a code for 50% off for HN readers: FOUNDER50
I'd love to hear your feedback on the extraction quality.

value: Show HN: MailNotes – Parse Gmail threads into Notion databases using AI

author: urirubio


value: lrkwa

value:
Hello Hacker News. The four of us have been getting the best results with Claude Code when we iterate with it to build full context and then use and update that context as we work.
So, we built Nimbalyst to be the local WYSIWYG editor and session manager where you iterate with Claude Code on your full context: markdown docs, mockups, diagrams, and code. Nimbalyst lets you:
- Use all the power of Claude Code in a UI
- Work with Claude Code to write and edit WYSIWYG markdown, see AI changes as red/green, approve them
- Integrate mermaid diagrams, text, tables, mockups, and images in standard markdown for human/AI context
- Iterate on html mockups with Claude Code, annotating the mockups, and then use them as context for people and Claude Code coding
- Tie sessions to documents to sessions, find and resume sessions, treat sessions as context, run parallel sessions
- Code with Claude Code with all of this context, use / commands, see git status
Nimbalyst is in Beta, local, and free. We would love your feedback.

value: Show HN: Local WYSIWYG Markdown/mockup editor powered by Claude Code – Nimbalyst

author: lrkwa


value: asheshgoplani

value:
I run multiple AI coding agents across projects and kept losing track of which sessions were waiting for input vs still working.
Agent Deck is a TUI built on tmux that shows all sessions with live status - green (working), yellow (needs input), gray (idle).
Works with Claude Code, Gemini CLI, Aider, Codex.
Can also fork Claude conversations to try different approaches from the same context.
Built with Go + Bubble Tea. Early development, using it daily with 20+ sessions.
Looking for feedback

value: Show HN: Agent Deck – Terminal Dashboard to Manage Claude/Gemini/Codex Sessions

author: asheshgoplani


value: renbuilds

value:
I kept manually deleting UTM parameters and click IDs from URLs before sharing them. Eventually I built a small tool to do it automatically.
wirier.com
It's a single-page app that strips 210+ tracking parameters from URLs. Everything runs client-side in JavaScript. URLs never leave your browser.
What it removes:
- Platform trackers (fbclid, gclid, ttclid, msclkid, twclid, etc.)
- UTM parameters
- Analytics junk (_ga, _gl, _hsenc, mkt_tok)
- Recruiter/ATS tracking (Greenhouse, Lever, Workday, LinkedIn Jobs, Indeed, Glassdoor, and others)
- Affiliate parameters
Also unwraps redirect URLs from Facebook, Twitter, Google AMP, YouTube, and LinkedIn.
The recruiter tracking stuff was a recent addition after some job seekers requested it. Turns out there are 60+ parameters across 18 different ATS systems that track candidate activity.
Happy to answer questions about the implementation.

value: Show HN: Wirier – Browser-based URL cleaner for tracking parameters

author: renbuilds


value: mdahardy

value:
Hey HN - I built ModelGuessr, a game where you chat with a random AI model and try to guess which one it is.
A big open question in AI is whether there's enough brand differentiation for AI companies to capture real profits. Will models end up commoditized like cloud compute, or differentiated like smartphones?
I built ModelGuessr to test this. I think that people will struggle more than they expect. And the more model mix-ups there are, the more commodity-like these models probably are.
If enough people play, I'll publish some follow-up analyses on confusion patterns (which models get mistaken for each other, what gives them away, etc.). Would love any feedback!

value: Show HN: ModelGuessr: Can you tell which AI you're chatting with?

author: mdahardy


value: Written natively from scratch in Metal and Swift. Build for AirVis app.

value: Show HN: 100 Million splats, a whole town, rendered in M2 MacBook Air


value: sarreph

value:
Hey HN! We’re Rory, Chris, and Felix from Kenobi (https://kenobi.ai). Kenobi lets you add AI-based content personalization to any website. As a site owner, you install our personalization widget with a script tag, just like you would for e.g. a chatbot integration. As a visitor, you interact with the widget (right now by providing a company name) and Kenobi changes the site content to suit you.
We’ve built a demo that anyone can try here: https://kenobi.ai/start
We believe that large parts of the web are about to go from being static to dynamic because of how adept LLMs are at transforming rendered HTML. And right now we’re focussing on B2B landing page content (as opposed to application UIs) because there is a lot of commercial opportunity for increasing top-of-funnel inbound conversions.
Our journey to Kenobi today is a long and snaking one. You may notice from the post title that we did YC’s Winter 2022 batch (I know that 4 years is practically ancient in YC-dog-years). Kenobi is a hard pivot from our original idea that we got accepted into YC with — a company called Verdn which did trackable environmental donations via an API. Since the summer, we’ve been hacking on different ideas… We started with personalized UI screenshots for outbound campaigns, but then people told us they wanted transformations to their actual site[0] — so we built an agentic workflow to research a visitor-company and “pre-render” changes to a landing site for them. Ultimately, there was too much friction in getting people to incorporate personalized URLs into their cold outbound campaigns[1]. Besides, people kept asking for us to do this for their inbound traffic, and so our current iteration was born.
Right now with Kenobi you pick a page that you’d like to make customizable, and choose [text] elements that you’d like to make dynamic. You can define custom prompting instructions for these elements, and when someone visits your page, our agentic workflow researches their company, and presents the updated content as quickly as possible, usually within a few seconds.[2] You also get a ping in Slack every time this happens so you know who is using your site.
We’ve been experimenting with features such as generating custom imagery that actually looks good and native to the page design, and pulling in company data sources so that e.g. the right case study can be presented based on a visitor’s industry and ICP profile. Our most requested feature is deanonymizing traffic so that Kenobi’s personalization can happen automatically as visitors land on your page — this is coming very soon, as right now you have to specify where you’re coming from.
It’s surprised us just how much business value we’ve gotten from knowing who (most probably) is on the page and asking for a personalized experience. We’ve seen response rates 3x of what we would normally from following people up from companies we know visited our site.
There are many players in this space already, and everyone seems to have their own angle. We are keen to hear thoughts on what people think the future of the personalized internet looks like!
Cheers from London!
P.S. - there's also a video that Chris recorded showing the end-to-end Kenobi experience right now https://www.loom.com/share/bc0a82a2f2fd40f695315bae80e8f5d8
[0] - Many of them had tried AI “microsite” generators but found the maintenance of managing a separate website(s) just for closing deals to be burdensome and inefficient.
[1] -  Despite having a CSV export and Clay integration option for our pre-generated website changes, getting people to weave the URLs into their email sequences (everyone uses different tools) seemed almost insurmountable without building what would ostensibly be our own sequencing software.
[2] - We use light foundation models with grounded search for the research step, and translate these into markup changes via another light LLM pass and our own DSL which is optimized for speed.

value: Show HN: Kenobi – AI personalized website content for every visitor

author: sarreph


value: garylauchina

value:
Is the market itself a Gödelian system—inherently incomplete and unpredictable from within?
I tried to find out through 400K+ evolution cycles with zero predefined strategies.
Just 4 rules: features, death, rebirth, linear weighting.
The system evolved patterns I never designed. It found boundaries I didn't know existed.
But it also revealed something deeper: 28.5% of agent behavior remained "mysterious"—
unexplainable by any observable features.
Maybe that's the point. We can't fully predict the market.
But we can measure its complexity, observe its boundaries, and understand its limits.
Would love critical feedback, especially from those who think I'm wrong.

value: Show HN: Is the market a Gödelian system? 400K evolution cycles later

author: garylauchina


value: florianwueest

value: Show HN: I've created a site that helps you create a bilingual resume

author: florianwueest


value: HNStudioC

value: Show HN: ScreenEagle – Get alerts when your screen changes

author: HNStudioC


value: mnorris

value: Record a short video of food, it gets reconstructed into a 3D model using photogrammetry. Viewers can spin and zoom the model while the original video plays behind it.<p>Pose tracking lets me project 3d effects onto the video like the glow in the demo.

value: Show HN: Mukbang 3D – Turn short food videos into interactive 3D models

author: mnorris


value: bigjimhere

value:
Stoker began 12 years ago while I was building custom web apps for field service companies. Over time, I developed a framework that helped me deliver customisations faster. Three years ago, I started rebuilding that framework from the ground up.
Stoker takes a config-first approach, similar to Payload, and is essentially a headless CMS with an Admin UI. The front end is built with React, while the back end runs on Firebase, providing realtime updates and offline support.
I’d love to hear your thoughts.

value: Show HN: Stoker – Realtime internal tools with offline support

author: bigjimhere


value: stwsk

value:
i built a small wordle-style game where the target is a daily sha-256 hash.
it’s intentionally not cryptographically realistic; the goal is to make avalanche effects and the meaninglessness of near-matches intuitive.
this was a quick front-end experiment; the code isn’t published yet.
everything runs client-side; no tracking; no accounts.

value: Show HN: A Wordle-style game for SHA-256 hashes

author: stwsk


value: manesvenom

value:
I made Hope Tree, which helps people reflect and build small moments of hope during difficult times.
It’s a mix of a simple journaling tool and a calm, lightweight emotional companion, designed to be private and distraction-free.
App Store: https://apps.apple.com/app/hope-tree/id6753673053

value: Show HN: Hope Tree – a calm, minimal reflection app

author: manesvenom


value:
So this weekend I and a friend of mine (@ronitrajfr) created a minimalist writing editor.
It's ui is clean, beautiful and distraction free, it also has a vim mode and everything stays on your computer
no authentication is required to write your thoughts and all
we wanted an application where we just start typing with no BS and that's what we made.
and did I tell ya that it's open source as well
lmk your thoughts and feedback!!

value: Show HN: BetterWrite – Minimalist Writing Editor


value: bilbywilby

value: I have been working on building a custom zipcode RSS feed that users will be able to use for local news

value: Show HN

author: bilbywilby

title: Show HN


value: rubenhellman

value:
I recently shipped a small SaaS I built in roughly 24 hours, mostly during school breaks. This is my first project that I have taken from idea to deployment, onboarding, and real users.
The product targets early-stage developers and focuses on reducing initial setup and preparation when building new apps. It abstracts away some of the repetitive early decisions and boilerplate that tend to slow down first-time builders, especially around project structure, configuration, and “what should exist on day one”.
I have a small number of active users, but churn is relatively high, which suggests either:
the problem is not painful enough
the abstraction leaks too early
the UX or onboarding fails to communicate value
or the tool solves a problem that disappears after the first session
I would really appreciate technical feedback on:
whether the abstraction layer makes sense
if the mental model aligns with how you bootstrap projects
where the product feels opinionated vs restrictive
what would make this something you would actually keep installed
Thanks for reading. Direct, critical feedback is very welcome.

value: Show HN: A lightweight SaaS to reduce early-stage app friction

author: rubenhellman


value: tscburak

value:
Hi HN
I’ve been working on VizuLLM, an open-source engine that turns structured LLM outputs (JSON) into deterministic, printable documents like timetables, invoices, guides, and diagrams.
LLMs are great at generating text, but in real applications we usually need documents, not paragraphs.
VizuLLM introduces a strict contract between the model and the UI:
 • LLM generates schema-validated JSON
 • Schemas define a document type
 • VizuLLM renders a deterministic visual artifact (HTML / PDF)
Instead of asking an LLM to “write a PDF”, you ask it to generate data for a document schema, and the renderer handles the rest.
What makes it different:
 • Schema-driven (Zod) → predictable outputs
 • Renderer-first, not prompt-first
 • Print-optimized components
 • Open-source and community-extendable (anyone can add new document types)
Example use cases:
 • Generate schedules, invoices, cover letters
 • Turn LLM output into guides, reports, or diagrams
 • Integrate into internal tools or LLM workflows
Repo:
https://github.com/GenR8ive/VizuLLM
I’d love feedback on:
 • Whether this abstraction makes sense
 • Missing document types / use cases
Happy to answer questions or discuss tradeoffs.

value: Show HN: VizuLLM – Turn structured LLM output into real, printable documents

author: tscburak


value:
Hey HN,
My co-founder and I built Tickerterm – making algo trading accessible through AI.
We met at Bubble (employees #2 and #4). Watched AI coding change who could build software. Started wondering: could the same thing happen for algo trading?
The platform: Chat with AI to write your strategy → code deploys serverless → trades execute in our built-in brokerage (Alpaca partnership). 60 seconds from idea to live.
Fully licensed (FINRA/SIPC compliance, NASDAQ/NYSE data licenses). Launched today.
Technical stack: JavaScript runtime for strategies, Firebase backend, real-time market data ingestion. One piece I'm curious about: we built tt.ai() - strategies can call AI at runtime to make decisions (e.g., tt.ai("Dip or falling knife?", data)). Curious what the HN community thinks about AI making real-time trading decisions.
Happy to answer questions about the architecture, regulatory stuff, or how we're thinking about AI making trading decisions.

value: Show HN: Tickerterm – AI-native algo trading platform


value: NBenkovich

value:
GitHub’s official CLI (`gh`) does not provide a built-in way to view or interact with inline pull request review comments or review threads. The available command (`gh pr view --comments`) only shows top-level discussion, not the line-level feedback most reviews depend on.
Inline comments can be accessed via the raw GitHub REST API, but doing so requires multiple separate calls (list reviews -> list review comments -> fetch thread comments -> merge and reconstruct). This is fragile for CLI workflows and especially problematic for automation and LLM-based agents.
We built a community GitHub CLI extension to simplify this:
https://github.com/agynio/gh-pr-review
The extension allows users and agents to:
- view inline PR review threads with file and line context
- filter unresolved review comments
- submit replies to review threads
- resolve review conversations from the CLI
- export compact, deterministic JSON for automation and LLM workflows
Example:

    gh extension install agynio/gh-pr-review
    gh pr-review review view <pr-number> --unresolved

This returns the full inline review context in a single command, without stitching together fragmented API responses.
Feedback is welcome, especially around CLI ergonomics, review submission flows, and agent or LLM-driven use cases.

value: Show HN: Gh-PR-review, inline PR comments for GitHub CLI

author: NBenkovich


value: DrSparrowhawk

value:
VSCode extension with games (Snake, Plinko, slots), HN/LessWrong reader, and Pomodoro timer for AI coding wait times. Fake currency only.
Marketplace: https://marketplace.visualstudio.com/items?itemName=TouchGra...
Built in a week with Claude Code after seeing Chad IDE get ratio'd for integrating real gambling. Happy to answer questions or get roasted on the code.

value: Show HN: I built an open-source alternative to the "brainrot IDE" that YC funded

author: DrSparrowhawk


value: xtafnuihc

value:
I want to be very transparent upfront:
I’m not a developer.
This little spinning giveaway game is something I built for my hardware project,
and almost all of the code was generated with the help of AI (mainly Gemini),
with a lot of trial, error, and copy-paste on my side.
I focused more on:
– the idea
– the interaction
– how it feels to users
rather than clean architecture or perfect code.
I’m sharing this here not as a “technical achievement”,
but as an experiment in what non-coders can build today with AI assistance.
I’d genuinely appreciate:
– feedback
– criticism
– or suggestions on what’s done badly / dangerously

value: Show HN: A spinning ring giveaway game – built as a non-coder with AI

author: xtafnuihc


value: SpeckOs

value: Show HN: Speck.js – One-Line AI Agents with Built-in Persistent Memory

author: SpeckOs


value: simonblok

value: Show HN: Minimal tool for fast image and video device mockups

author: simonblok


value: rjboadi

value:
I built VibeCoCo (vibecoco.app/demo) to better manage context throughout each step of the development lifecycle.

  The workflow:
  1. Define your idea in a guided wizard (or create everything from scratch)
  2. AI helps you to generate context documentation (PRD, tech design, user stories and task breakdown)
  3. Get a personal MCP server with your project context (or download context manually without signing up)
  4. Point Claude Code (or any MCP-compatible agent) at it (or add the downloaded context to your agents folder)
  5. Your agent can now query your project context while coding

  The problem:
  Managing the context window is challenging, agents lose track and have to waste valuable context reading and rereading the codebase. Context gets scattered across conversations and it's hard to control what information shapes their decisions. Using memory is ineffective, forcing repetition and refactors. Troubleshooting endless logs cn quickly fill a context window and confuse the agent before it begins

  What VibeCoCo does:
  - Planning: Helps create well structured and comprehensive project and context documentation
  - Delivery: Kanban task management and Custom MCP server delivers the right context at the right time, reducing code lookups etc
  - Troubleshooting: context aware prompts that filter logs and error reports and provide your agent the exact info it needs
  - Collaboration: Centralized context: Planning docs in one place, separate from execution conversations. Use the same context across different agents, share projects with collaborators.
  - Official docs URLs: Tech stack includes links to official documentation – avoids prompt injection risks from unknown web sources

  Feedback I'm looking for:
  - Does the planning → Context workflow strike a good balance between control for people who know what they want and guidance for people who are new to coding / vibe coding.
  - Does the context outoput provide use to your agents
  - What improvements and/or additional context do you wish your AI agent had access to?
  - Is the wizard structure useful or too rigid, not helpful enough?

  Try it at vibecoco.app/demo – full wizard available without an account. You can download all of the output, MCP servers are in testing and not yet available so this is really for feedback on the quality of the output and the overall workflow of the wizard. Thank You

value: Show HN: VibeCoCo – Plan your project, get a custom MCP server for your AI agent

author: rjboadi


value: VexAnvil

value: Show HN: A participation-weighted cryptocurrency prototype (Anvil)

author: VexAnvil


value: adamoufkir

value:
Hi HN,
I built Crovise, an AI-powered tool that reviews landing pages and gives concrete, actionable suggestions to improve conversion rates.
You paste a URL, and it analyzes copy clarity, CTA placement, messaging hierarchy, and friction points using LLM-based heuristics inspired by CRO best practices. The goal is to help founders and marketers quickly understand why a page isn’t converting, not just guess.
This started as a side project after I struggled to get useful feedback on my own landing pages. I’d love feedback from the HN community on the approach, accuracy, and what would make this more useful for developers and indie founders.

value: Show HN: I made an LLM-powered CRO tool

author: adamoufkir

title: Show HN: I made an LLM-powered CRO tool


value: noahgolmant

value:
This is a small tool to label bounding boxes on satellite/aerial imagery and export training datasets for object detection.
Web maps like Google Earth work by stitching together lots of small images called tiles (this is why you see square patches as the page loads). They do this by querying a "tile server" API that reads from sharded raster files in cloud storage. In my day job we built infra to efficiently serve imagery through tile servers for map visualization. I wanted to test out ML applications of that infra. It turns out this standard can also be leveraged to label and fine-tune models on map imagery.
This tool lets you point at any tile server URL, draw labeled bounding boxes, and export labels in COCO annotation format, plus download underlying tile PNGs for training/inference. This can feed directly into standard computer vision frameworks like ultrayltics or pytorch.
The workflow is hotkey-driven: draw a box, press 1-9 to assign a category (or N for negative examples). You can also drag-and-drop local GeoTIFFs.
I found this helpful to experiment with fine-tuning SAM 3 on local aerial imagery. It was nice to zip up the PNGs + COCO file, drag and drop to a colab notebook, and run inference.
There are other interesting applications of this I'd like to explore, like in-browser map-based segmentation / object detection with onnx.

value: Show HN: Build ML training datasets from large-scale satellite/aerial imagery

author: noahgolmant


value: afrederico

value:
Hey HN!
We got tired of waiting on Shopify, Stripe, and OpenAI to converge on a solution and let the world use it, so we built our own infrastructure to bridge that gap.
For merchants, this is a one-click setup: connect your Shopify store and your catalog becomes programmatically accessible to AI systems via ACP. Agents can query structured product data (inventory, pricing, availability) and initiate purchases using ACP-compatible flows. Full agentic payments via Stripe are on the roadmap.
A few technical details that may interest this crowd:
- Shopify’s APIs were designed for human storefronts and backend services, not autonomous agents. We built an abstraction layer that presents commerce data in a deterministic, agent-friendly way.
- Payments are the hardest part. The challenge isn’t charging a card — it’s safely handing off agent intent to Stripe while preserving authorization, fraud controls, and auditability.
Basically, everything is structured and permissioned. No scraping, no prompt-hacking your storefront HTML.
For developers, we provide clear docs and SDK-style endpoints so integrating an agent is straightforward. The goal is to make “AI can shop this store” as simple as calling an API, not building custom glue code per merchant.
We’re running limited beta signups while we learn where this breaks in real usage. Right now it’s free for smaller stores as we focus on stability and feedback rather than monetization.
The early interest has been strongest from merchants selling technical or niche products, where agent-driven discovery and purchasing actually makes sense.
We’re live at https://actory.ai. If you run a Shopify store, are building agents, or have thought about agent-driven commerce, we’d love blunt feedback.
What are we missing? What breaks if even 5–10% of commerce flows through agents in the next few years?

value: Show HN: Enabling Agentic Commerce Protocol (ACP) for Shopify Stores

author: afrederico


value: calebevans

value:
Cordon uses transformer embeddings and density scoring to identify what's semantically unique in log files, filtering out repetitive noise.
The core insight: a critical error repeated 1000x is "normal" (semantically dense). A strange one-off event is anomalous (semantically isolated).
Outputs XML-tagged blocks with anomaly scores. Designed to reduce large logs as a form of pre-processing for LLM analysis.
Architecture: https://github.com/calebevans/cordon/blob/main/docs/architec...
Benchmark: https://github.com/calebevans/cordon/blob/main/benchmark/res...
Trade-offs: intentionally ignores repetitive patterns, uses percentile-based thresholds (relative, not absolute).

value: Show HN: Cordon – Reduce large log files to anomalous sections

author: calebevans


value: zieg

value:
Hey HN,
I built this because I was frustrated with existing website builders:
- Drag-and-drop builders are tedious and produce messy results
- No-code tools still have a learning curve. You need an expert or to become an expert
- Inline editing always breaks something, especially useless for repeating lists
- AI builders? Explaining what you want, burning thousands of tokens, and still ending up editing code yourself
So I made something simpler: pick crafted shadcn blocks, customize colors/text/images through a clean sidebar, and publish.
What's included:
- 150+ blocks (planning to expand to thousands)
- Multi-language support – create sites in any language
- Built-in blog
- Free custom domain connection
- Integrations: Meta Pixel, Google Analytics, Google Tag Manager, PostHog, etc. for professionals.
The idea is that constraints create better outcomes. Instead of infinite flexibility, you get curated blocks that just work together.
Would love your feedback. Is this useful? What would make it better?

value: Show HN: Website Builder with Shadcn Blocks

author: zieg


value: nykodev

value: Show HN: CatalystAlert – Biotech catalyst tracker with ML predictions

author: nykodev


value: gargigupta

value:
Hi HN,
I am from the engineering team behind Mino by Tiny Fish.
We’ve spent the last year building the web agents infrastructure for companies like Google and DoorDash. Today, we’re opening Mino, our API to developers.
95% of the web's value is no longer in static HTML documents. It's in operations: checking inventory behind a login, verifying compliance across government portals, or aggregating pricing. These tasks were made for human attention, but the web has outgrown human comprehension.
Traditional RPAs are too brittle. The current wave of Computer Use Agents (Claude, OpenAI Operator, Gemini, Browser Use) wants to solve this with Continuous Inference. They take a screenshot, reason, click, repeat. This is amazing for novelty, but fatal for production:
1. Reasoning loops introduce massive overhead.
2.Cost: "Looking" at a page 1,000 times costs 1,000x the tokens.
3. Indeterminacy: A probabilistic model might click "Back" instead of “Book Now" 1% of the time.
Mino’s architecture is different. We use AI to learn the workflow (navigation graph, logic), then codify that logic to make subsequent runs execute as deterministic code.
1. It is fast (no reasoning loop).
2. It is cheap (pennies per job).
3. It returns Structured JSON. Every single time.
We designed Mino to be the "Hands" for your application.
Input: URL + Natural Language Goal ("Login, find the invoice from May, return total as JSON").
Output: Structured JSON.
Concurrency: Stateless, parallel execution across websites via async calls.
We believe the internet will largely be used by non-human users, such as agents, robots, and devices. And this is the beginning of that infrastructure, to use and build on top of the "Deep Web".
Check it out: https://mino.ai/
50 Free Completed Jobs to test the architecture. Feedback is welcome.

value: Show HN: Operational web infra (Why we moved away from pure Computer Use agents)

author: gargigupta


value: smoovb

value:
Firstly, I’m not a developer — but I am a very technical, over-50, product person and entrepreneur.
This project started shortly after Claude Code launched. I had already worked on a few other AI coding projects, and decided to see how far I could realistically take something end-to-end.  While I had some help from a developer, I increasingly felt capable of taking on more work myself.  So about 9 months in the making, working full time on it past 2 months.
Stack:
Postgres, Next.js on Render. Monorepo with both frontend and backend services.
Some takeaways from using Claude Code on this project:
- I built all project pages directly in frontend JavaScript with mock data — no Figma or other layout tools.
- In the early days, scope got out of hand; I learned quickly that Claude tends to overbuild unless tightly constrained.
- Initially, I used Claude Desktop to refine prompts for Claude Code.
- My skills with Git, SQL, Docker, and local/remote environments were shaky at first, but they no longer get in the way.
- Using Flowbite CSS saved a lot of pain and made it possible to produce reasonably high-quality designs quickly.
- Getting details right has always depended on pushing people, now that responsibility falls entirely on me.
- About six months in, I adopted the Functional Core, Imperative Shell approach and documented it in claude.md
(inspired by this HN post: https://news.ycombinator.com/item?id=45701901).
- I built an Airbnb-style referral program in a day, after spending time on a detailed to-do spec and following the above approach.
What Jetogo does:
- Issues you long-term, data-only eSIMs for mobile data
What is Jetogo for:
- Saving  money on travel roaming data costs
- Secondary devices (e.g., a kid’s iPad, laptop, or backup connection)
What we built:
- Parse CDR usage data to calculate accurate user billing
- Implemented pay-as-you-go and subscription options using Stripe
- Subscriptions support rollover and overage, applied at the end of each billing period
- Built referral, bonus, and coupon programs
- Aggregate usage across multiple eSIMs under a single account and present a unified total
Live now at: https://jetogo.com

value: Show HN: Long term, data only eSIMs – built with Claude Code

author: smoovb


value: benbristow

value: Show HN: Genny, a stupidly simple static site generator

author: benbristow


value: fariszr

value: Show HN: Knocker, access your homelab without needing a VPN

author: fariszr


value: Show HN: Go Libraries for Working with OpenAPI


value: nunobrito

value:
You are reading a blog post hosted directly from my laptop using a new platform called geogram.
When I close the lid, the laptop will sleep and the blog will no longer be available. There is a phone version of geogram too, over there the blog and other things like chat or web pages can continue to run 24/7.
Kind of crazy to think we can run web servers straight from our pockets.

value: Show HN: Blog post runs P2P from my laptop

author: nunobrito


value: etherio

value: Show HN: Lunette – auditing agents for evals and environments

author: etherio


value: borisandcrispin

value:
Mozart Says is a floating reminder built specifically for ADHD brains that need to see things to remember them.
If you're like me, notifications disappear into the void and todo apps get buried under 47 browser tabs. I used to plaster my monitor with post-its because out of sight = out of mind.
So I built Mozart Says, a reminder that literally floats on top of everything until you deal with it.
It's a free Mac app where you can write any message, customize how it looks (typography, colors), and drag it anywhere on screen.

value: Show HN: A visual reminder for ADHD brains

author: borisandcrispin


value: valenceidra

value: It's crazy how useful the batteries still are, and some of these have usbc charging circuitry while still being disposable. Next time I plan to sus out if I can bypass only the cutoff timer so it doesn't turn itself off after 15 seconds or so. Has anyone done this?

value: Show HN: I made a Minecraft torch out of a vape I found on the ground

author: valenceidra


value: DinakarS

value:
Really cool art generator based on preset seeds.
When you copy and share the link, the same artwork is procedurally generated.
Locally run on your browser - private. Would love your comments and feedback. Thanks for checking it out!! :)

value: Show HN: Abstract Algorithmic Art Generator

author: DinakarS


value: dtagames

value: Show HN: Conjunction1, a tool for planning telescope and celestial observations

author: dtagames


value: ivanramos

value: Show HN: I built a FREE tool that turns procrastination into an executable plan

author: ivanramos


value: Show HN: Turn raw GitHub commits into customer-friendly changelogs (free)


value: roampal

value: Show HN: I made Claude Code learn from its mistakes

author: roampal


value: Show HN: I built a Free tool to create a startup waiting list in seconds


value: Iblockedthem

value:
I built a small desktop tool called PasteClean to solve a personal annoyance: sharing URLs filled with tracking garbage (utm_source, fbclid, etc.).
It runs in the background and automatically cleans URLs in your clipboard when you copy them. It handles 50+ tracking parameters and can also unshorten links (bit.ly, t.co) to reveal the destination and clean them before pasting.
Tech stack is Electron/React/TypeScript.
The core privacy features are free. I added a "Pro" lifetime license for some advanced features (unshortening, batch mode) to support development.

value: Show HN: PasteClean – Desktop app to strip tracking parameters from clipboard

author: Iblockedthem


value: alohaTool

value:
Hi HN
I built GitNotes, a Chrome (/any Chromium based browser) extension for taking repo-specific notes while browsing GitHub. It lives in the Chrome Side Panel, so your notes stay visible next to the code. Check it here : https://github.com/ParasKoundal/GitNotes
It’s designed for GitHub first, but you can also use it as a general-purpose local notes app.
Highlights
- Context-aware: Notes are automatically scoped to the current GitHub repo (owner/repo)
- Local storage only: Everything stays on your machine — no accounts, no sync, no servers
- Side panel UI: Non-intrusive, works alongside code, PRs, commits
- Markdown & rich text
- Version history + diffs
- Dark mode
Why I built it
I wanted a fast, private way to jot down thoughts while reading code — without switching tabs or trusting a cloud notes app.
Not on the Chrome Web Store yet (manual install via dev mode for now).
Would love feedback from people who read or review a lot of GitHub repos.

value: Show HN: GitNotes – Local, context-aware notes built for GitHub

author: alohaTool


value:
EdgeVec is an embedded vector database I built in Rust that runs natively in browsers via WebAssembly. v0.4.0 focuses on documentation and quality.
Key points:
- HNSW indexing with O(log n) search
- SQ8 quantization (3.6x memory reduction)
- 329µs search latency at 100k vectors
- 213 KB gzipped bundle
- 24x faster than voy (fastest pure-WASM competitor)
- Works in browsers, Node.js, and edge devices
New in v0.4.0:
- Complete docs: tutorial, tuning guide, troubleshooting
- Migration from hnswlib/FAISS/Pinecone
- Interactive benchmark dashboard
- Chaos testing (15 edge cases)
- P99 latency tracking in CI
GitHub: https://github.com/matte1782/edgevec
The use case is offline-first AI applications where you want vector search without network latency or sending embeddings to a cloud service.

value: Show HN: EdgeVec v0.4.0 – Sub-millisecond vector search in WASM (Rust)


value: newyearman

value:
Hi HN,
I built a simple tool that checks Minecraft server status and latency in real time.
It was made for server owners who want a fast and no-login solution.
It supports Java servers and shows online status, player count and ping.
Feedback is welcome.
https://minestat.io

value: Show HN: Free Minecraft server status checker

author: newyearman


value: sebasjimenezv

value:
I built this as a small experiment after getting tired of copying AI prompt files and agent configurations between machines and projects.
shai is a tiny CLI that lets you organize, share, and install AI config files with a single command. The idea is to make AI setups reproducible and easy to reuse without having to manually move files around.
It’s early and intentionally minimal — I’m mostly sharing it here to learn whether this workflow is useful to others, and what feels confusing, unnecessary, or missing.
Repo + docs: https://shaicli.dev
(Author here — happy to answer questions.)

value: Show HN: Shai – a tiny tool for managing AI config files

author: sebasjimenezv


value:
I built InterviewKitHQ to help HR teams create quality interview materials quickly.<p>Creating good interview guides takes time for HR and hiring managers. Enterprise tools are expensive ($75+/user/mo) and complex. Free AI tools give you generic, sometimes problematic questions.<p>*What I built:* A system that:
1. Researches job families across 8 dimensions (responsibilities, skills, compensation, career paths, etc.)
2. Generates complete interview kits with questions, rubrics, and red/green flags
3. Runs compliance checks (no discriminatory content)
4. Exports as editable Word documents<p>*Tech stack:*
- Backend: Python/FastAPI, SQLite (PostgreSQL in prod)
- Frontend: React + TypeScript + Tailwind
- AI: Perplexity for research, GPT-4o for generation/compliance<p>*Business model:*
- Free tier: 3 kits (stock library or custom from job description)
- Paid: $99-299/mo for more kits and features

value: Show HN: InterviewKitHQ – AI-generated interview playbooks for HR teams


value: stoicfungi

value: Show HN: Segue – Vocabulary app with flashcards, quizzes, and typing practice

author: stoicfungi


value: demajh

value:
I've come across the problem a few times to need to remove duplicate values from my data.  Usually, the data are higher level objects like images or text blobs.  I end up writing custom deduplication pipelines every time.
I got sick of doing this over and over, so I wrote a wrapper around RocksDB that deduplicates values after a Put() operation.  Currently only exact deduplication is performed, but I want to extend it in a number of ways, including semantic (fuzzy) deduplication for things like images and text.
Any feedback on the project would be appreciated:
https://github.com/demajh/prestige

value: Show HN: Extending RocksDB to Deduplicate Values

author: demajh


value:
I built an open-source text processing tool as an alternative to PopClip and SnipDo. It's cross-platform (macOS/Windows), extensible, and doesn't require a subscription.
What it does:
TextGO automatically recognizes text types and executes custom actions. You can trigger it via global hotkeys, mouse double-click, or text selection. It supports both instant execution and interactive toolbar modes.
Key features:
- Built-in text type recognition (URLs, emails, IPs, timestamps, programming/natural languages, etc.)
- Multiple trigger methods with independent rule configurations
- Extensible via regex, scripts, or local AI integration
- Customizable toolbar icons
Feedback and contributions welcome!

value: Show HN: TextGO – A text selection popup tool (alternative to PopClip/SnipDo)


value: Show HN: A Fizzy to Telegram webhook handler


value: lukasschwab

value:
Spotify's "Account privacy" dashboard lets you bulk-export an "extended streaming history" (i.e. everything you've ever listened to).
I built this static tool for analyzing the resulting piles of data:
- Who are my top artists in December, across all years?
- Objectively speaking, what is actually my favorite Big Thief song?
- Why won't the cowards at Spotify admit what I know in my heart is the truth (that I am this year's top listener of the Masonna/Prurient split 'Annihiliationism')?
Your data never leaves your browser. Full source is on GitHub.[^1]
Spotify might take a day or two to generate your export; in the mean time, you can try the tool with my depersonalized demo data (link under the upload widget).
Enjoy!
[^1]: https://github.com/lukasschwab/spotify-explore

value: Show HN: Explore your own Spotify history

author: lukasschwab


value: ambitiouz

value:
Build a complete website for 10 cents
No Hosting Fees
You own the code
Add custom domain free
No React just pure HTML, CSS, JS
Goolge loves it...

value: Show HN: The App SEO Pros Are Quietly Switching To

author: ambitiouz


value: kenryu

value: Show HN: A Claude Code plugin that auto-export plans for multi-model workflows

author: kenryu


value: Show HN: Atlas – AI YouTube Dubbing and Enhancement


value: sueszli

value: built a tiny pytorch clone in c after going through prof. vijay janapa reddi's mlsys book: mlsysbook.ai/tinytorch/<p>perfect for learning how ml frameworks work under the hood :)

value: Show HN: Autograd.c – A tiny ML framework built from scratch

author: sueszli


value: kwyishuai

value: Show HN: Incremark – 2-10x faster Markdown parsing for AI streaming

author: kwyishuai


value: DannyHeng

value: Hi HN, We just launched our MVP for AI Agents, would love for you try us out and get some feedback!

value: Show HN: Omni Channel AI Agents with Conversational Onboarding

author: DannyHeng


value: Show HN: I built a webapp to inject Exif/XMP GPS metadata into video files


value: quasibyte

value: Show HN: Vector Search for the Bible

author: quasibyte

title: Show HN: Vector Search for the Bible


value: yamsasson

value: Show HN: Cellular Automata Studio – A visual synthesizer for exploring 2D CA

author: yamsasson


value: debarshri

value: Show HN: Footywhoops – MIDI Sequencer Software

author: debarshri


value: mononykus

value:
ngDiagram is an open-source Angular library for building graphs and diagramming solutions.
It lets you design any diagram interface – e.g., node-based editors, flow charts, network and schematic diagrams – with draggable nodes, customizable connections, and groups. Built for Angular using signals for performance, with vast look & feel customization options with Angular templates. Supports Angular 18+ versions.
We've been developing it for several months and now have a stable version ready for testing. We'll be happy to hear your feedback, feature suggestions, and issue submissions.

value: Show HN: NgDiagram: open-source graph and diagramming library for Angular

author: mononykus


value: omnarayan

value:
Devices in Bangalore office + SF office + home = one unified lab.
Your APK/IPA, test data, network calls - all flow directly between
your machines via WebRTC P2P (DTLS encrypted). We only handle
signaling (which device is where).
Setup:

  # Machine with phones
  curl -fsSL https://app.devicelab.dev/device-node/KEY | sh

  # CI runner
  curl -fsSL https://app.devicelab.dev/test-node/KEY | sh

NAT traversal via Google/Cloudflare STUN. Fallback relay via
Cloudflare TURN (~15% of cases) - still E2E encrypted, they
see packets not content.
What this isn't for:
- "Test on 100 models" - use BrowserStack
- 100% air-gapped (TURN breaks this)
Works with Appium, Maestro, Espresso, XCUITest.
Also open sourced Maestro iOS real device support:
github.com/devicelab-dev/maestro-ios-device
https://devicelab.dev | $99/device | First one free forever
Anyone else running multi-location device labs? What broke first?

value: Show HN: DeviceLab – Turn phones across offices into one device lab

author: omnarayan


value: idham

value: A 100% free, open-source link shortener deployable with a one-click install on Cloudflare, featuring full functionality and working on your existing domain with Cloudflare routing.

value: Show HN: The All-in-One Open Source Serverless URL Shortener on Cloudflare

author: idham


value: rajvarkala

value:
Hi HN,
I’m one of the builders behind Valmi Value.
After building AI products for a while, we kept seeing the same mismatch: we bill for tokens and usage, while customers care about results.
Valmi is billing and payments infrastructure built for AI agents.
It supports outcome-based and hybrid pricing, with open-source SDKs and a full software stack that’s free to deploy (available under Elastic License).
We are live on Product Hunt too (Upvote helps!).
https://www.producthunt.com/products/valmi-io?launch=valmi-v...
Links:
https://github.com/valmi-io/value
https://value.valmi.io
We’re early and would appreciate feedback and feature requests from people building agentic or usage-based products.
Would be very happy to answer questions.
I wrote more about my hypothesis here: https://www.valmi.io/blog/an-imperative-for-ai-agents-outcom...
-Raj

value: Show HN: Valmi: Outcome-Billing and Payments for AI Agents (Open Source SDK)

author: rajvarkala


value: wadudu

value: I built PhysicsAI.chat to help students get step-by-step reasoning (not only final answers). It supports photo problems and tries to show clear derivations + units checks. I’d love feedback on: (1) explanation clarity, (2) wrong-answer failure modes, (3) what would make you trust it more.

value: Show HN: PhysicsAI.chat – step-by-step physics solver with diagrams

author: wadudu


value: vladimiras

value:
Hey all! I am excited to show off Hugity - a documentation CMS that lets you write docs in a Notion-style block editor and publish them without touching Git, YAML or CI configs.
I built Hugity to manage my own Hugo websites. I had multiple Hugo websites deployed on Github and I found editing raw markdown in VS Code frustrating. Hugity removes that friction, you can create a static docs website and start editing within a couple of clicks.
I'd love to hear your feedback about the UX, ease of use and any edge cases I may have missed!

value: Show HN: Hugity – Notion-style static docs editor with GitHub/Hugo auto-deploy

author: vladimiras


value: halilbugol

value:
Hi HN,
We’re a small team at Thyris working on open-source AI, with a focus on making AI systems safer and more practical to run in production.
While integrating LLMs into real-world applications, we kept running into the same issues:
- sensitive data leaking into prompts or model outputs,
- unsafe or non-compliant responses,
- and brittle structured outputs (e.g. invalid JSON) breaking downstream systems.
To explore these problems, we built TSZ (Thyris Safe Zone), an open-source guardrails and data security layer that sits between applications and external AI or API systems.
At a high level, TSZ:
- detects and redacts PII and secrets before data leaves your environment,
- applies rule-based and semantic (AI-assisted) guardrails,
- validates structured outputs against predefined schemas,
- and returns clear signals (redacted output, metadata, blocked flag) so applications can decide how to proceed.
The project is fully open source (Apache 2.0) and self-hosted. We’re sharing it mainly to learn from others building LLM systems in production and to get feedback on what works and what doesn’t.
Repo: https://github.com/thyrisAI/safe-zone
Docs: https://github.com/thyrisAI/safe-zone/tree/main/docs
Happy to answer questions or discuss alternative approaches people are using.

value: Show HN: TSZ – Open-source guardrails and data security layer for LLM pipelines

author: halilbugol


value: SlavomirDurej

value: I kept hitting Claude limits and built a small Windows desktop widget to show session and weekly usage in real time. Fully local, no telemetry, MIT licensed. Feedback welcome.

value: Show HN: A Windows widget that shows Claude usage limits

author: SlavomirDurej


value: SidDaigavane

value: Show HN: ToneFit AI – strength workouts generated from goal, time and equipment

author: SidDaigavane


value: Show HN: Spec-AGENTS.md – A tiny Doc-Driven "spec" for AI coding tools


value: rogimatt

value:
Hi HN,
I kept forgetting things shortly after learning them, so I built a small tool to visualize memory decay over time.
The idea is simple: instead of reviewing blindly, you can see when a memory is about to drop and review at the right moment.
You can add content manually or from links (articles, videos), and the tool schedules reviews automatically.
This is an early beta and I’m mainly looking for feedback, especially on the learning model and UX.
Link: https://flashmind-app.vercel.app/
Happy to answer any questions or explain the technical choices.

value: Show HN: Visualizing when you forget what you learn

author: rogimatt


value: shobankr

value: A Desktop app that turns meeting recordings or transcripts into visual, actionable insights like decision flows, mind maps, accountability matrices, sentiment arcs, and participation heatmaps.

value: Show HN: Visualize Meeting Transcripts into Flows and Mind Maps (Offline)

author: shobankr


value: jamesrandall

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value: WoWSaaS

value:
Vect AI is the world's first Autonomous Marketing Operating System (OS).
We built Vect because most AI marketing tools suffer from the same fatal flaw: Subjectivity. Writers and Chatbots guess what to write based on old training data, often producing generic content that lands in the "Red Ocean" (saturated markets).
Vect replaces this guesswork with a state-aware "Operating System" that searches, plans, and executes autonomously.
The Killer Feature: Market Signal Analyzer (Real-Time Grounding) The heart of our difference is the Market Signal Analyzer. Unlike standard LLMs that are cut off from the live web, this tool connects directly to real-time search data to find "Blue Ocean" opportunities before your competitors do.
Real-Time Radar: It performs live Google searches to identify exactly what users are asking right now, not 6 months ago.
The "Untapped Angle": It analyzes the top search results to detect Content Gaps—specifically highlighting what your competitors are missing so you can take a contrarian or superior stance.
Buzz Score: It rates sub-topics by "Velocity" (0-10), helping you identify viral trends before they peak.
The Ecosystem: From Insight to Execution The Market Signal Analyzer is just the brain; the OS handles the hands. Once a signal is found, the Signal-to-Asset Pipeline allows you to instantly generate assets using our other tools:
Campaign Builder: Turn a signal into a full 3-phase marketing campaign (Tease, Launch, Sustain) in under 60 minutes. It builds the strategy and allows for one-click generation of all assets (Email, Social, Blog).
Veo-Powered Video: Generate physics-based, commercial-grade video ads (1080p, 60fps) to capture attention on the trend.
Autonomous Agents: Deploy "Digital Employees" (e.g., a Social Media Manager) to monitor these signals and draft content in the background without human intervention.
State-Aware & Brand Consistent Vect AI is State-Aware. You define your Brand Voice, Target Audience, and Product DNA once in your Global Settings. Every tool—from the Market Signal Analyzer to the Video Generator—inherits this context. You never have to copy-paste a prompt about who you are ever again.
Our Ask We want to move marketing from "Random Acts of Content" to "Data-Backed Precision."
We'd love for you to try the Market Signal Analyzer to see what "Unfair Advantage" topics exist in your niche right now.
Link: https://vect.pro

value: Show HN: Vect AI – Autonomous Marketing OS with Real-Time Market Signal Analyzer

author: WoWSaaS