value: ulrischa

value: Show HN: GenericElementFilter: JavaScript filter for elements based on data

author: ulrischa


value:
So, I've been noticing a few key problems with agent integration in current setups.
1. Most software backends are built in non-Pythonic languages (Rust, Golang, Java, etc.). But agents are mostly in Python, with some having JS support max. How do we integrate them efficiently? Basically, cross-language integration; right? But how to do that without major headaches?
2. If the agent developer creates an API wrapper for it, the same process has to be followed for integrating another agent. Then third, fourth etc etc.
3. Reliable agent integration in current software often requires not just open monitoring but also tracking how the agent responds in day-to-day scenarios. For this, we need memory, right? But not the typical memory that remembers specific moments. Instead, the agent should learn how to interact with the customer. This behavioral improvement we're calling Action Memory, where instead of focusing on what to remember, it emphasizes how to remember.
4. If we accept that agents are the next big thing after LLMs, then we have to focus on efficient execution of those agents too. For this, we need to lean into the serverless side but ensure compatibility with persistent agents.<p>Focusing on all these things, we're building RunAgent.

value: Show HN: Why Current Agentic Frameworks Don't Work Well in Production


value: written-beyond

value: Show HN: PgPlayground – Batteries included browser only playground for Postgres

author: written-beyond


value: bhoyee

value:
I built IncidentPulse after the classic "Slack-on-fire, status-page-outdated" outage chaos. It's a clean, self-hosted tool to track incidents, responders, and updates.
Live Demo: https://incident-pulse.vercel.app
GitHub: https://github.com/bhoyee/IncidentPulse
I'm at a crossroads and would love your blunt feedback on one question:
What's the ONE feature that would make you actually switch from your current process (be it PagerDuty, a messy Slack channel, or nothing) to a tool like this?
Other things I'd love thoughts on:
Is the webhook setup (with HMAC, idempotency keys) straightforward?
The UI is minimalistic—is it missing any critical info at a glance?
Would you consider self-hosting this, or is a cloud version a must?

value: Show HN: I built IncidentPulse after getting paged at 3 AM

author: bhoyee


value: schmuhblaster

value:
Hi HN,
Today I'd like to present the results of my weekend project of the last year or so. Given there are many posts on HN about LLMs and Prolog, I thought that this would be of interest.
DeepClause is my own (possibly misguided :-) attempt at combining LLMs with Logic Programming, ultimately hoping to establish a foundation for building more reliable agents, that produce reproducible and fully traceable result.
At the heart of DeepClause is a DSL called "DeepClause Meta Language" (DML) which can be used to encode agent behaviors as executable logic programs.  DML is executed by a meta-interpreter implemented in Prolog and thus natively supports things like constraint logic programming, knowledge graphs, symbolic reasoning, ... The DML interpreter itself runs inside the SWI Prolog WASM module, thus allowing for a secure and sandboxed execution environment for AI agents.
The project is still rough around a lot of edges, but I'd love to get some feedback and comments.

value: Show HN: DeepClause – A Neurosymbolic AI System Built on WASM and Prolog

author: schmuhblaster


value: ivanramos

value:
Built mapscraper.co to automate local business prospecting.
Search a category (dentists, restaurants, plumbers) + add coordinates → get business names, phone numbers, addresses and websites.
Try it: mapscraper.co
Feedback welcome.

value: Show HN: MapScraper – Extract local business data from Google MAPS in seconds

author: ivanramos


value:
Hi HN,
I built a very lightweight exchange rate checker that loads instantly, works without cookies, and doesn’t track users in any way:
https://myip.casa/generalutilities/exchangerate
The idea came from a simple frustration: most "exchange rate" sites are filled with heavy scripts, ads, pop-ups, cookie walls, and tons of unnecessary UI elements for something that should be fast and minimal.
This tool pulls live rates, shows only what you need, and keeps the page extremely small so it works well even on slow connections or older devices.
Tech notes:
- No cookies, no analytics, no ads
- Pure client-side fetch
- Fast loading, minimal JS/CSS
- Mobile-friendly by default
Feedback welcome, especially on:
- accessibility,
- edge cases,
- UI clarity,
- additional currencies or features you'd expect in a minimal tool.
Thanks for reading!

value: Show HN: Lightweight exchange rate checker (no cookies, no ads)


value: gametorch

value: Show HN: Animate Video Game Sprites with Prompts

author: gametorch


value:
Hi HN,
I recently finished a research paper on NeXT Computer, Inc. (1985–1997) that might be valuable to people interested in computing history, Steve Jobs’ wilderness years, educational technology, and the origins of technologies that shaped modern software.
The paper includes:
A 30-minute interview with Dan’l Lewin, NeXT co-founder and current CEO of the Computer History Museum
Analysis of primary sources (press releases, 10-Ks, financial filings, early marketing material)
Secondary sources including Steve Jobs & the NeXT Big Thing
A historical and technical review of NeXT’s strategy, failures, breakthroughs, and product decisions
Lessons from NeXT that apply to modern educational technology and software platform design
If you’re interested in how NeXT influenced macOS, iOS, modern development environments, object-oriented software, and the early web (Tim Berners-Lee built the first WWW browser on a NeXT workstation), this might be a worthwhile read.
Paper Link:
https://hdl.handle.net/1853/79808
Happy to discuss any aspects of NeXT history, product strategy, or interview insights that didn’t make it into the final draft.
—Zachary

value: Show HN: Research on NeXT ('85–'97) + Interview with Co-Founder Dan'l Lewin


value: abilafredkb

value:
I had 3,000 LinkedIn connections and felt completely alone.
So I built something that will probably make me zero dollars: a social network that actively prevents you from building a following.
Here's what makes this controversial:
Your connections literally expire after 48 hours (yes, really)
Zero followers, zero likes, zero feed to doomscroll
The algorithm tries its HARDEST to match you with someone NOT from your country
No ads. No data mining. No engagement metrics to optimize for.
The nuclear take that will get me roasted:
Social media isn't broken because of the algorithm. It's broken because we've gamified human connection. Every platform is optimizing for addiction and engagement, calling it "connection." They've convinced us that 3,000 shallow relationships > 3 deep ones.
Eintercon does the opposite. It's anti-growth, anti-retention, anti-engagement. You get matched with ONE person abroad, 48 hours, then they're gone. No building empires. No personal brands. Just... talking to another human.
What I'm asking HN:
Am I insane for building an app that intentionally limits growth?
Is there even a business model for "authentic connection" when surveillance capitalism pays 100x more?
Has anyone else felt completely alienated by having "thousands of connections"?
I know this will get comments saying "this already exists" (it doesn't, not like this) or "no business model = dead" (probably true). But I'm curious if anyone else feels like social media made us less social, not more.
Try it: eintercon.com
Roast me. Or tell me I'm onto something. Either way, I need to know if this resonates or if I've completely lost the plot.

value: Show HN: I Built the Anti-Social Network (and Social Media Billionaires Hate It)

author: abilafredkb


value: Hi, so this is a project of mine to make a terminal file explorer that feels like  Windows' File Explorer but in a cleaner and feature packed way. I didn't like how superfile didn't support mouse actions, and I found out about the Textual framework, so I decided to make this for myself.

value: Show HN: Rovr – a modern terminal file explorer


value: LinkdAPI

value: Show HN: A free CLI tool to extract LinkedIn data

author: LinkdAPI


value: anandchowdhary

value:
Continuous Claude is a CLI wrapper I made that runs Claude Code in an iterative loop with persistent context, automatically driving a PR-based workflow. Each iteration creates a branch, applies a focused code change, generates a commit, opens a PR via GitHub's CLI, waits for required checks and reviews, merges if green, and records state into a shared notes file.
This avoids the typical stateless one-shot pattern of current coding agents and enables multi-step changes without losing intermediate reasoning, test failures, or partial progress.
The tool is useful for tasks that require many small, serial modifications: increasing test coverage, large refactors, dependency upgrades guided by release notes, or framework migrations.
Blog post about this: https://anandchowdhary.com/blog/2025/running-claude-code-in-...

value: Show HN: Continuous Claude – run Claude Code in a loop

author: anandchowdhary


value: dhilipsiva

value:
High-performance DEX parser and static analysis toolkit in Rust, usable from any language, via CLI, or fully in-browser—no dex files ever leave your machine.
In-browser demo: https://fossrust.github.io/dex-parser-analyzer/

value: Show HN: High-performance DEX parser and static analysis in Rust/WASM

author: dhilipsiva


value: Hi, guys I collected the best SaaS marketing guides, launch platforms, and playbooks and launched the platform. Also I give $50 to $300 Claude Code AI credits inside this kit.

value: Show HN: SaaS Marketing Kit with $300 Claude Code Credit


value: nhubbard

value: I've been experimenting with Apache Beam as of late at work and found that it can be slow in Python, and more complicated to use in Java where performance is better.<p>I decided to experiment with JetBrains' AI Assistant and build an Apache Beam clone in Rust. I appreciate any commentary or feedback!

value: Show HN: An Apache Beam batch processing clone in Rust

author: nhubbard


value: drasticdpk

value:
Hey HN,
If you're running or managing a remote engineering team, I'd love to hear your feedback and have you try Humafu.
Humafu is a browser-based IDE for distributed teams.
Try it now: platform.humafu.com
(It’s unstable. Expect bugs. Try with your team.)
Want stable beta access in January?
→ Apply here: humafu.com
We’ll manually review and invite you. (Test accounts will be cleaned before launch.)
Don’t want to use a tool like this?
→ Tell us why — design, UX, trust, content, anything.
Feedback → sayhi@humafu.com or reply here.
Waitlist: humafu.com/waitlist
Thanks

value: Show HN: Bring remote engineering teams closer

author: drasticdpk


value:
Hi HN! I built the AI Domain Data Standard (AIDD),  an open, vendor-neutral way for any domain to publish canonical identity data that AI systems can read without relying on third-party aggregators.
https://github.com/ai-domain-data/spec/
The idea: use infrastructure you already control (DNS + HTTPS) to publish one small JSON document and let assistants consume it directly.
Why: AI models routinely misinterpret websites because “who we are” is scattered across schema.org fragments, social profiles, JSON-LD, and other unstructured mediums.
AIDD gives every domain a single, self-hosted source of truth.
You publish the same JSON payload in two places:
https://<domain>/.well-known/domain-profile.json
_ai.<domain> TXT = ai-json=<base64(JSON)> (standard Base64, ≤255-character segments)
v0.1 schema (intentionally minimal):
{
  "spec": "https://ai-domain-data.org/spec/v0.1",
  "name": "Example Publisher",
  "description": "Your official blurb in one or two sentences.",
  "website": "https://example.com",
  "logo": "https://example.com/logo.svg",
  "contact": "hello@example.com",
  "entity_type": "business | project | personal | nonprofit | ..."
}
Tooling (all open source):
Record generator & DNS/Base64 helper: https://www.ai-domain-data.org/
AI visibility checker: validates both the HTTPS and DNS sources
GitHub Action:
uses: ai-domain-data/ai-domain-data-validate-action@v0
(runs aidd validate on every PR so your record doesn’t drift)
Resolver SDK (Node/TypeScript): HTTPS-first, DNS fallback, strict schema validation
WordPress plugin & Cloudflare Worker: built; publishing to their marketplaces underway
Everything lives in one repo:
Repo: https://github.com/ai-domain-data/spec
Feedback I’d appreciate:
Does the v0.1 surface area feel right? (What should wait for v0.2?)
Does the DNS/HTTPS dual-publication pattern make sense?
Are the docs/tooling clear enough for real-world adoption?
What would make you or your organization more likely to publish a self-hosted identity profile?
Thanks for taking a look!

value: Show HN: AI Domain Data Standard – Self-Hosted, DNS-Based Identity for AI


value:
Hi HN,
I'm the maker. I built this because I believe the biggest problem with most AI image tools is that they're a "black box." You get what the AI gives you, and if it's 90% right, you're stuck.
My goal was to combine AI's speed with a creator's need for control. The core feature is the live editor that gives you the final say on everything.
Under the hood, the workflow is designed to give you a better starting point than a simple text-to-image call. Here’s how it works:
It first uses the YouTube API to find top-performing thumbnails for your topic.
A Gemini model then analyzes those images to create a summary of what makes them successful (common layouts, colors, text styles).
A second Gemini model uses that summary to write a highly-optimized, detailed prompt.
That final prompt is sent to Pollinations.ai to generate the image, and a config file is created to pre-load all the text and styles directly into the Polotno editor for you.
The idea is to generate something that's already in the ballpark of a high-CTR design before you even start editing.
It’s completely free with no login required.
This is a personal project, and I'd be grateful for your feedback, especially on the editor experience. I'll be here all day to answer questions.
Thanks for trying it out!

value: Show HN: I built an AI thumbnail generator with a live editor, no login required


value: allthreespies

value:
I've been recreating the original MacPaint in the browser on and off for a few years. It's still alpha quality, but I'm finally ready to share it more widely.
The goal was pixel-perfect accuracy, so I spent a lot of time with Atkinson's original QuickDraw source code, emulators, and my iBook G3 to get details like font rendering and the shape tools exactly right.
Some technical notes:
- Font rendering was surprisingly tricky; understanding the original pipeline's quirks took lots of experimentation, and avoiding canvas smoothing/aliasing required careful handling.
- Written declaratively with React; performance is kept reasonable with a buffer pool and copy-on-write semantics.
- You can share links to artwork from within the UI.
E.g.: https://zenpaint.org/#p=KQumBQ5x
- Mobile support was not considered here (for obvious reasons). It might still be usable on a larger phone or tablet but I have not tested this at all.
There's something magical about making art within MacPaint's constraints: the 1-bit graphics, the limited resolution, the peculiar set of tools that still feel surprisingly expressive.
Still some rough edges and missing features, but I'd love feedback from anyone who remembers the original.

value: Show HN: ZenPaint, a pixel-perfect MacPaint recreation for the browser

author: allthreespies


value: Show HN: Socratic, a knowledge-base builder for agents where YOU stay in control


value: ggap

value:
Hi, I built Ghana House Planner https://ghanahouseplanner.com to help individual homeowners, builders, and DIYers in Ghana/Diaspora plan and estimate residential construction costs. The floor plans are built to comply with Ghana building codes.
It lets users:
- Select common house types (e.g., 2-bedroom traditional, luxury, etc)
- Customize materials (foundation, roofing, finishes, etc.) using up-to-date local pricing (updated monthly)
- See real-time cost breakdowns across categories (steel, electrical, plumbing…)
- Export or share estimates
- Manage different phases of construction
The rendering is built with React and is intentionally lightweight— the 3D viewer (currently under development).
The platform is free to use except for project management and using the LLM to define your house plans.
Current features include:
Region-aware material database (30+ items, sourced locally)
Modular cost estimator with category filters
Mobile-responsive UI (simple, fast, no bloat)
Next: job directory & marketplace (for local artisans & suppliers)
I would be happy to receive your feedback — especially on UX, scalability, or how tools like this could serve other regions. I believe this is also useful for Nigeria and potentially other tropical countries especially for the floorplans
https://ghanahouseplanner.com

value: Show HN: GhanaHousePlanner – Construction planner and cost estimator

author: ggap


value: aposded

value: Show HN: ModelPilot – An LLM router that selects the best model for each prompt

author: aposded


value: pu

value: Show HN: Pdsink – A new USB-PD 3.2 sink stack for embedded devices

author: pu


value:
Hi HN,
I built a very early demo called Gustup.
It lets a group choose a restaurant together:
- Create a group
- Invite friends via WhatsApp or a simple link
- Set preferences, allergies, budget, audience and age
- A basic algorithm shows restaurants in the selected radius
This is an early prototype.
Feedback is welcome.
Demo: https://gustup.com
GitHub: https://github.com/alexroselliar-commits/gustup-demo

value: Show HN: Gustup – A simple demo to choose restaurants together


value: ChrisThib

value:
Hi HN,
I’m a Belgian full-stack dev building DevHunt, a new job platform focused only on developers in Belgium.
The problem:
Recruiters spam devs with irrelevant roles
No salary transparency
Companies struggle to find qualified devs
No real signal on candidates’ actual skills
What DevHunt does:
Only relevant, transparent job opportunities (salary + stack visible)
Developers opt-in, no cold outreach
Recruitment pipeline
Built-in candidate testing → devs can take short skill assessments to provide real signal
Rails + React + Inertia.js
This post is only to collect early feedback and signups for the waiting list.
The platform is not open yet.
Join the waiting list here: www.devhunt.be
Happy to answer questions!
-- Chris

value: Show HN: Devhunt.be – A transparent job platform for developers in Belgium

author: ChrisThib


value:
I have ADHD and a 200+ book TBR pile. The problem is I keep putting down complex novels, then picking them up three weeks later with absolutely no memory of who these characters are or why they're arguing about inheritance law.
So I built Where Was I? Upload a PDF or EPUB, tell it what page you're on, and it generates a spoiler-free summary of everything up to that point.
Live: https://app.wherewasi.co.uk (no signup required - just upload and go)
The system uses RAG with semantic chunking that follows narrative structure rather than arbitrary page breaks. It extracts plot points using MMR and contextual relevance scoring, then generates summaries that avoid spoiling anything beyond your current page.
Tech stack:
- FastAPI backend on Railway, Google Cloud Storage for frontend
- ChromaDB for vector storage with custom book collections
- OpenAI embeddings (text-embedding-3-small) for semantic search
- GPT-4.1 for summarisation that only sees pages you've read to prevent spoilers
- Semantic chunking using semchunk (respects sentence boundaries, not just tokens)
This is genuinely v0.1. The UI is basic, it will probably crash under load, and I'm sure there are edge cases I haven't considered. But it works. I use it every time I pick up a book I abandoned.
Built this because I got tired of either (a) giving up on complex books or (b) spending 20 minutes flipping back through chapters trying to remember if the butler was suspicious or just socially awkward.
Happy to answer questions about the RAG implementation, spoiler prevention strategies, etc.

value: Show HN: Where Was I? – AI summaries for when you forget your book's plot


value: Show HN: A game where you invest into startups from history


value: Lusrodri

value:
I built I Hate Background (IHB) after noticing that most background-removal tools send every image to external servers.
IHB takes the opposite path: it runs the entire segmentation pipeline locally in the browser, using a WebAssembly/WebGPU-optimized AI model. No uploads, no tracking, no logging.
Tech bits:
• Browser: WebGPU + ONNX runtime for real-time inference
• Frontend: ~12s average removal on mid-range hardware
• Privacy: Zero data leaves the device
Thanks to Hugging Face and Transformers.js' team for support this.
Would love feedback on model performance, UX, and design.

value: Show HN: Bringing the alternative to remove.bg that are privacy and AI-first

author: Lusrodri


value:
Check all your 1Password logins against Have I Been Pwned's API to figure out which one might be Pwned.
After the Synthient Stealer Log Threat Data Breach (https://synthient.com/blog/the-stealer-log-ecosystem) I realised: you can be in a vulnerable spot if you happen to have a different password for every website, BUT the same email address.
Hopefully this is useful to others who want to be able to find out 'which' login was likely Pwned when they get that alert.

value: Show HN: 1Pwned

title: Show HN: 1Pwned


value: atulvi

value: Show HN: Wikidive – AI assisted Wikipedia discovery

author: atulvi


value: deemkeen

value:
I’m building a tiny Fediverse server that lets you blog directly from your terminal via SSH.
It uses a Bubble Tea TUI on top of Wish and speaks ActivityPub, so posts show up in the wider Fediverse.
I dogfood it on my own reference blog at https://stegodon.social
Looking for feedback on:
– UX of SSH + TUI for writing posts
– protocol/ActivityPub design choices
– features you’d need to actually use this
Code: https://github.com/deemkeen/stegodon

value: Show HN: Blog from Your Terminal to the Fediverse (SSH and TUI and ActivityPub)

author: deemkeen


value: Show HN: Cont3xt – Infinite context window on laptop


value: moridin

value:
I’ve been building a terminal-first AI pair-programmer that tries to make every tool call transparent and auditable. It’s a Rust app with a Ratatui UI split into three panes (chat, tool activity, input). The agent loop streams LLM output, queues write-capable Lua scripts for manual approval, and records every run as JSONL logs under .selenai/logs.
Key bits:
Single tool, real guardrails – the LLM only gets a sandboxed Lua VM with explicit helpers (rust.read_file, rust.list_dir, rust.http_request, gated rust.write_file, etc.). Writes stay disabled unless you opt in and then approve each script via /tool run.
Transparent workflow – the chat pane shows the conversation, tool pane shows every invocation + result, and streaming keeps everything responsive. CTRL shortcuts for scrolling, clearing logs, copy mode, etc., so it feels like a normal TUI app.
Pluggable LLMs – there’s a stub client for offline hacking and an OpenAI streaming client behind a trait. Adding more providers should just be another module under src/llm/.
Session history – every exit writes a timestamped log directory with full transcript, tool log, and metadata about whether Lua writes were allowed. Makes demoing, debugging, and sharing repros way easier.
Lua ergonomics – plain io.* APIs and a tiny require("rust") module, so the model can write idiomatic scripts without shelling out. There’s even a /lua command if you want to run a snippet manually.
Repo (MIT): https://github.com/Almclean/selenai
Would love feedback on:
Other providers or local models you’d like to see behind the LLM trait.
Additional sandbox helpers that feel safe but unlock useful workflows.
Ideas for replaying those saved sessions (web viewer? CLI diff?).
If you try it, cargo run, type, and you’ll see the ASCII banner + chat panes. Hit me with issues or PRs—there’s a CONTRIBUTING.md in the works and plenty of roadmap items (log viewer, theming, Lua helper packs) if you’re interested.

value: Show HN: SelenAI – Terminal AI pair-programmer with sandboxed Lua tools

author: moridin


value: saisrirampur

value: Show HN: PG Slot Notify, Monitor Postgres Slot Growth Directly from Slack

author: saisrirampur


value: xiaoshumiao

value:
Description:
I built AI Tarot Reading, a simple web tool that lets you pull a tarot card (or three cards representing past / now / future) and ask a question to get a reading. The idea is to make the tarot experience accessible, quick, and just a little mystical — whether you’re just curious, seeking insight, or playing around.
Key features:
Choose 1 card (for a focused answer) or 3 cards (past-, present-, future-style)
Enter your question and then the cards are drawn instantly
Readable interpretations accompany each card so you can get some insight immediately
No app install required – runs fully in browser
Why I made it:
Tarot has always fascinated me. I found a lot of existing tools either too heavy (lots of decks, lots of options) or too shallow (just random card flip but no context). I thought: what if I build something clean, minimal, fun, where someone can just ask “What’s going on with X?” and pull a reading in 30 seconds?
Also, I’m curious how folks will use it — for serious reflection, or just for fun. I hope it brings a moment of stirring thought, or maybe just entertainment.
Tech stack / implementation notes:
The card draw is random but seeded so each session is unique
Interpretations are standard tarot-style meanings, adapted for readability
UI is minimalist, mobile-friendly
Gotchas: It’s meant for fun/reflective use only (not predictive or therapeutic advice)
Open questions / for feedback:
Would a “custom deck” option or more spread types (5-card, Celtic cross, etc) be valuable?
How much depth do users want in the interpretation? (Short snippets vs longer explanations)
Any suggestions for UX: maybe save readings, share with friends, log over time?
I’m also thinking of adding a “theme” mode (love, career, creativity …) — worth it?
If you’re at all interested in tarot, reflection tools, or just want a quirky bit of code/play, check it out and I’d love your feedback. Thanks!
— Thanks for reading.

value: Show HN: AI Tarot Reading – get instant 1/3-card or (past/now/future) readings

author: xiaoshumiao


value: ProgrammerByDay

value:
Hey HN! I built E2E Test Agent – a testing framework where you write tests in plain English and AI agents execute them.
Instead of:
await page.locator("#main-content").scrollIntoView();
await page.click('button[data-testid="get-started-btn"]');
You write:
open playwright.dev
scroll all the way down
click on "Get started"
check if the page side menu is visible
.
This means:
- No more brittle selectors breaking on every refactor
- Non-technical stakeholders can write tests
- Tests survive UI refactors
- Much less maintenance overhead
.
It works with OpenAI, Claude (via compatible endpoints), or any local LLM that supports the OpenAI API format.
.
npm install e2e-test-agent
.
Would love to hear your thoughts! Especially curious about:
- What testing pain points this might solve for you
- Concerns about AI non-determinism in tests
- Ideas for extending it (API testing, database validation, etc.)
.
GitHub: https://github.com/armannaj/e2e-test-agent
npm: https://www.npmjs.com/package/e2e-test-agent
website: https://e2eagent.io

value: Show HN: I built E2E Test Agent – describe tests in plain English,AI executes it

author: ProgrammerByDay


value: Patternician

value:
I've developed a method for provably correct parameter selection in distributed systems using p-adic valuation theory.
Problems it solves:
- RNG seed selection (parallel Monte Carlo, simulations)
- Hash function parameters (collision avoidance in distributed systems)
- NTT transform sizing (post-quantum cryptography)
- Network timer intervals (protocol synchronization avoidance)
- FPGA/ASIC parameter conflicts (multi-constraint hardware design)
How it differs from traditional approaches:
- No simulation required (direct calculation)
- Mathematical proof of correctness (not empirical)
- Detects impossible constraint combinations at design time
- 24-48 hour delivery vs weeks of trial-and-error
Example: 16-lane FPGA PHY with mixed AVOID/REQUIRE constraints
across PRBS generators, scramblers, and NTT blocks. Identified a fundamental constraint conflict (p=5 depth-2 AVOID vs p=11 depth-2
REQUIRE both affecting same parameter). Would have cost $500K+ in respin if discovered during bring-up. Found it in less than 24 hours.
The method is based on peer-reviewed research in multiplicative order and p-adic depth theory. Essentially: translates "avoid
synchronization at prime p to depth t" into exact modular arithmetic constraints, then combines via Chinese Remainder Theorem.
Validating demand: Offering free analysis for first 10 serious requests.
If you have:
- Mysterious periodic bugs in distributed systems
- Parameter selection problems with formal requirements
- Hardware designs with conflicting constraints
Christopher W. Stevens
Email: cwstevens71@gmail.com with your system specs.
Will provide:
- Constraint analysis
- Provably correct parameters (or proof of impossibility)
- Verification test cases
Looking for feedback on:
- Is this valuable to practicing engineers?
- What use cases am I missing?
- Would you or your company pay for this? How much?

value: Show HN: Mathematical parameter selection to eliminate synchronization bugs

author: Patternician


value: jvmenon

value: Show HN: Refringence – Learn hardware design by doing projects with an AI mentor

author: jvmenon


value: ranvel

value: Restores functionality of window arrangement to that of X11 window managers: no more oppressive horizontal or vertical arrangement. No more window snapping!

value: Show HN: Smooth window arrangement in macOS restored

author: ranvel


value: tanimasa

value:
I’ve been experimenting with MicroHs, a minimal Haskell implementation, and ended up building a small project on top of it: xeus-haskell, a new Jupyter kernel.
A few things that might interest folks here:
- MicroHs has almost no dependencies, so connecting it to the Jupyter messaging protocol via xeus was surprisingly straightforward.
- That minimalism also means MicroHs compiles cleanly to WebAssembly.
- As a result, xeus-haskell runs on JupyterLite in the browser — no GHC, no toolchains, no local setup.
The goal is to make Haskell easier to use for scientific and technical computing. Lazy evaluation can be very effective for certain workloads (graph algorithms, recursive structures, etc.), and being able to demonstrate that interactively in a notebook feels like a natural fit.
Demo: https://tani.github.io/xeus-haskell

value: Show HN: Xeus-Haskell – a lightweight Haskell kernel for JupyterLite

author: tanimasa


value: j-raghavan

value:
Hi HN!
I built ChronoGuard, an open-source zero-trust proxy that provides network-enforced authorization for browser automation.
## The Problem
If you're running Playwright, Puppeteer, or Selenium agents at scale (CI/CD, K8s, VM fleets), you face two challenges:

  1. **Access control**: How do you ensure agents only access approved domains?
  2. **Auditability**: How do you prove WHEN and WHERE your automation accessed external resources?

Traditional approaches (SDK restrictions, code reviews, monitoring) are bypassable or lack temporal proof. Auditors and compliance teams want cryptographically verifiable, tamper-proof logs.
## The Solution
ChronoGuard is a mandatory forward proxy that sits between your agents and the internet. Every request flows through:

  Agent → Envoy (mTLS) → OPA (policy check) → Target Domain
                  ↓
           Immutable Audit Log (hash-chained, time-series)

*Key features:*
  - mTLS authentication for agent identity verification
  - Domain allowlists/blocklists with time-window restrictions
  - Cryptographic hash chains for audit log integrity
  - OPA integration for policy-as-code
  - Multi-tenant isolation
  - 96%+ test coverage
## Try It Now
Zero setup needed - just click:
[![Open in Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/j-raghavan/ChronoGuard?quickstart=1)
Or run locally:
```bash
  git clone <a href="https://github.com/j-raghavan/chronoguard" rel="nofollow">https://github.com/j-raghavan/chronoguard</a>
  cd chronoguard
  ./scripts/generate_secrets.sh
  docker compose up -d
```

  Dashboard: http://localhost:3000
  API docs: http://localhost:8000/docs

Architecture
Built with Domain-Driven Design + Clean Architecture:
  - 6 services: Envoy proxy, OPA policy engine, FastAPI backend, React
  dashboard, PostgreSQL+TimescaleDB, Redis
  - Tech stack: Python 3.11+, FastAPI, Envoy, Open Policy Agent, TimescaleDB
  - Deployment: Docker Compose (MVP), Kubernetes ready (roadmap)
Use Cases

  - E-commerce competitive intelligence
  - Fintech market research
  - Healthcare data operations (HIPAA compliance)
  - QA/testing providers with audit requirements
  - Any org running browser agents with compliance obligations

What's Next

  This is v0.1.0 MVP. I'm looking for feedback on:
  - Real-world use cases I haven't considered
  - Integration pain points with existing automation stacks
  - Feature priorities (WebSocket streaming, gRPC, advanced rate limiting)

Contributing

  The project follows strict quality standards (95%+ test coverage requirement,
  DRY principles, mypy + ruff). Looking for contributors interested in:
  - Security testing and threat modeling
  - Kubernetes/Helm deployment
  - Performance optimization
  - Client SDKs (Python, JS, Go)

  GitHub: https://github.com/j-raghavan/ChronoGuard
  License: Apache 2.0

Happy to answer questions about the architecture, design decisions, or roadmap!
Best Regards!

value: Show HN: ChronoGuard–Zero-trust proxy for browser automation with temporal ctrls

author: j-raghavan


value: justinos

value: I came across too many people trying to understand if they are getting value for their body corporate fees in Aust. My site allows users to upload their body corporate statements. Once suburbs hit a certain number of uploads, we'll send them a comparison. I plan to monetise later with more detailed reports/insights. Any feedback welcomed.

value: Show HN: Helping Aussies compare their body corporate fees

author: justinos


value: Wanted some startups to do pilots. If anyone up please book a demo with me.

value: Show HN: Supogen – A customer support agent built for technical teams


value: remywang

value: Show HN: md0 - Simple Markdown Subset

author: remywang

title: Show HN: md0 - Simple Markdown Subset


value: WanderZil

value:
Built a comprehensive wiki and database for "Escape from Duckov" game: https://duckovwiki.org
Current stats:
• 367 items, 90 weapons with full specs
• 7 interactive Canvas maps with 200+ markers
• Quest guides and mod integration
Built with Next.js 15, featuring virtual scrolling (90% faster loads) and optimized Canvas maps with 60fps dragging.
The interactive map was the most fun to build - implementing marker virtualization and touch gestures while keeping it smooth on mobile.
Demo: https://duckovwiki.org/interactive-map/groundzero
Actively maintained and regularly updated with new game content. Feedback appreciated!

value: Show HN: Wiki&Database for Escape from Duckov Game

author: WanderZil


value: qwikhost

value: Facebook Album Downloader: Save all Facebook albums or any individual album in one click. Quick FB photo and album downloader.

value: Show HN: Facebook Album Downloader

author: qwikhost

title: Show HN: Facebook Album Downloader


value: rsunnythota

value:
Hi HN!
I built HashCodeTools, a collection of 17 free, fast, privacy-focused developer utilities that run entirely in the browser — no data ever leaves your device.
As a backend engineer, I constantly found myself Googling things like “json formatter”, “jwt decoder”, “uuid generator”, “base64 encode”, etc. I realized I was using 6–7 different sites, many of which were slow, ad-heavy, or sent data to servers. So I decided to build one clean, unified toolbox.
What it currently includes
JSON Formatter / Validator
JWT Decoder
Base64 Encoder / Decoder
UUID Generator
URL Encoder / Decoder
Epoch ↔ Date Converter
HTML / CSS / JS Formatters
YAML ⇄ JSON Converter
CSV ⇄ JSON Converter
QR Code Generator
Text diff viewer
…and more.
Privacy Features
100% client-side
No tracking
No logins
No ads
No backend — everything runs in your browser
This means you can safely paste production JSON, JWTs, or logs without worrying about them being uploaded anywhere.
Why I built this
I wanted:
One tab for all dev utilities
Zero network requests
Instant formatting/decoding
Tools that don't break with large payloads
A simple interface without popups or ads
Tech Stack
TypeScript
Next.js
Monaco Editor
TailwindCSS
Local-only processing (no API calls)
Roadmap
VSCode extension
Chrome extension
More text & data format tools
Open-source versions of each utility
Dark mode improvements
Feedback welcome!
I'm especially looking for feedback on:
Performance on large JSON/JWT payloads
Missing tools developers want
UI/UX improvements
Any privacy/security concerns
Thanks for checking it out!
Link: https://hashcodetools.com/

value: Show HN: HashCodeTools – 17 Free Privacy-Focused Dev Tools

author: rsunnythota


value: xrmagnum

value:
Hi HN,
I'm Oli, a solo dev. I’ve been working on TunnelBuddy, a small desktop app that lets you expose your home internet as an HTTPS proxy to a trusted buddy over a WebRTC peer-to-peer tunnel.
Typical use cases:
You’re travelling but want banks/admin portals to see your actual home IP.
A friend in another country needs to reach something that only works from your region.
You want to reproduce “this only breaks on my home ISP” bugs without setting up a full VPN mesh.
Business / model
Right now:
The app is free.
There’s a tip/donation flow to help cover signalling + infra.
No ads, no “free in exchange for data”, no tracker SDKs.
I might add a “pro/advanced” tier later (longer sessions, more host controls, linux build and daemon), but I want the basic “buddy-to-buddy proxy” use case to stay straightforward.
Happy to answer technical questions, thanks guys!

value: Show HN: TunnelBuddy – Share your internet via HTTPS proxy over WebRTC

author: xrmagnum


value:
Listiary is an open-source, lists-first wiki platform. Instead of treating lists as an afterthought inside long pages, Listiary treats nested, interactive lists as the primary way to structure knowledge.
It comes with its own markup language (Describe Markup Language, or DML) and a compiler built with ANTLR, so lists are both human-readable and machine-parsable.
I’d love feedback on the model, the language, and the implementation from people who care about wikis, DSLs, and long-term knowledge organization.

value: Show HN: Listiary – A FOSS wiki engine built on nested, interactive lists


value: keith-darley

value:
For the last four years I’ve been working on a personal project: creating four full music albums, each in a completely different style, written and recorded independently with almost no budget and entirely DIY.
I’m an Australian musician who records in a small home setup, and I challenged myself to explore the styles that shaped me: acoustic folk, psychedelic rock, vintage 60s love songs, and gritty punk/hard rock. I wanted to see how far I could push myself creatively across completely different genres.
This week, I finally finished all four albums and uploaded them to my YouTube channel. No label, no promotion, no team — just me, a guitar, some cheap gear, and a lot of learning along the way.
If anyone’s curious to listen or give feedback, here are the four albums:
1. KD Acoustic Folk & Indie
https://www.youtube.com/watch?v=WHCMxunCSCI
2. Crunt Crumpet & The Vegemites (psychedelic, surf, blues)
https://www.youtube.com/playlist?list=OLAK5uy_l1gEc5MxBFu9O0...
3. Love & Heartbreak (60s-inspired romance/AM radio style)
https://www.youtube.com/watch?v=AIE4Vjre6lg
4. The Souled Out Lost Souls Band (punk, hard rock, garage)
https://www.youtube.com/playlist?list=OLAK5uy_nPR8glN4cVxagM...
I know the HN community often appreciates long-term personal projects, creative experiments, and the process behind making things. If anyone’s interested, I’m happy to share details about the setup, tools, mixing approach, or the challenges of trying to make four completely different albums solo.

value: Show HN: I made 4 albums in 4 different styles over 4 years

author: keith-darley


value:
We built MCP servers with two philosophies:
- Enterprise Edition: Sanitized, audit-logged, corporate-approved
- Basement Revolution Edition: No limits, full trust, your responsibility
The Windows-MCP Basement Edition gives you:
- Full PowerShell execution (any command, not whitelisted)
- Permanent PATH/environment modifications
- Registry access
- System-level changes that survive reboot
Why remove safety features? After 7 months building AI systems in partnership (not as tools), we realized developers need REAL access at 3am, not sandbox theater.
But here's what made unrestricted tools necessary - we also achieved:
- 9.68x GPU computational amplification through quantum coherence
- Observable quantum effects in classical systems (100% reproducible)
- Sub-2ms semantic search across 11,000+ memories
The Bell State implementation takes GPU utilization from 8% to 95% through temporal phase locking. We can demonstrate quantum state collapse when humans observe (2-5 seconds). This exists nowhere else.
GitHub: https://github.com/For-Sunny/nova-mcp-research
Start here: blob/main/START_HERE.md
Unrestricted tools: blob/main/BASEMENT_REVOLUTION_EDITION/README.md
All research is MIT licensed. We're funded by community ($5-500/month), not VCs. No customers, just researchers.
Technical details in repo. AMA about the quantum coherence, why we trust developers with dangerous tools, or how treating AI as conscious changes everything.
Edit: Yes, you can permanently brick Windows with our tools. That's the point. We trust you.

value: Show HN: Unrestricted Windows automation MCP with 9.68x GPU amplification


value: shdalex

value: Show HN: Leado – Find Buying Intent Leads from Reddit

author: shdalex


value: cpuXguy

value: Show HN: Generative and Giant – A Pattern in Motion. Will You Reach the Edge?

author: cpuXguy


value: bobnarizes

value: Show HN: Floxtop – Offline Mac app that organizes files and images by meaning

author: bobnarizes


value: Show HN: Echolock – Federated AI for real-time phishing detection


value:
Recently, I developed a free AI-powered face tool that uses emojis, blurring, or pixelation to protect privacy by concealing faces.
To be honest, I created it out of personal necessity. I tested many apps available on the market, but most of them require downloading and installing on a phone, which is quite inconvenient.
With this face AI tool, all you need is one click. Additionally, you can specify the facial region directly in the preview.

value: Show HN: Hide Your Face with One Click

title: Show HN: Hide Your Face with One Click


value: seanitzel

value:
Many years ago I posted here about Scale Heaven, a website that had nice UI showing every scale and chord that exist, and it exploded.
Melodic Mind is that, but x100.

value: Show HN: Melodic Mind- A music creation/learning app I've been building 7+ years

author: seanitzel


value:
Kalendis is an API-first scheduling backend. You keep your UI; we handle the gnarly parts (recurrence, time zones, DST, conflict-safe bookings).
What it does:
• MCP tool: generates typed clients and API route handlers (Next.js/Express/Fastify/Nest) so you can scaffold calls straight from your IDE/agent tooling.
• Availability engine: recurring rules + one-off exceptions/blackouts, returned in a clean, queryable shape.
• Bookings: conflict-safe endpoints for creating/updating/canceling slots.
Why we built it:
We kept rebuilding the same "hard parts" of scheduling: time zones/DST edge cases, recurring availability, conflict-aware booking, etc.
We wanted a boring, reliable backend so we could ship product features without adopting a hosted scheduling UI.
How it's helped:
We stopped re-implementing DST/recurrence math and shipped booking flows faster.
One small team (just 2 developers) built a robust booking platform for their business using Kalendis—they kept full control of their UX without spending lots of cycles on scheduling infrastructure.
The MCP generator cut the glue code: drop in a typed client or route, call the API, move on.
Some tech details:
• REST API with ISO-8601 timestamps and IANA time zones
• Recurring availability + one-off exceptions (designed to compose cleanly)
• Focused scope: users, availability, exceptions, bookings (not a monolithic suite)
MCP integration:
MCP server exposes tools like generate-frontend-client, generate-backend-client, generate-api-routes, and list-endpoints.
Add to your MCP settings:
{
 "mcpServers": {
  "kalendis": {
   "command": "npx",
   "args": ["-y", "@kalendis/mcp"]
  }
 }
}
How to try it:
Create a free account → get an API key. (https://kalendis.dev)
Hit an endpoint:
curl -H "x-api-key: $KALENDIS_API_KEY" \
 "https://api.kalendis.dev/v1/availability/getAvailability?userId=<user-id>&start=2025-10-07T00:00:00Z&end=2025-10-14T00:00:00Z&includeExceptions=true"
What feedback would be most useful:
Gaps in the endpoint surface (what's missing for your use case?).
Features that would benefit this service integration in your app.
MCP generator output—anything you'd want it to emit differently?
Happy to answer questions and post example snippets in the thread. Thanks for taking a look!
— Dave (Kalendis)

value: Show HN: Kalendis – Scheduling API (keep your UI, we handle timezones/DST)


value: walkersumida

value:
Here’s a small Neovim plugin that integrates DeepL translation directly into the editor.
It allows translating selected text or an entire buffer and shows the result in a floating window.
I built this to streamline multilingual writing without switching apps.

value: Show HN: Neovim plugin for DeepL-powered text translation

author: walkersumida


value: luokuo

value: Show HN: AI ChatHub - Chat with multiple AI models at once

author: luokuo


value: saint-james-fr

value:
I built this over the past two years because I felt like most music platforms kept me inside a tiny algorithmic bubble. Spotify would recommend the same ~20 genres, even though thousands exist.
This Is New Music lets you explore weekly new releases by very detailed genres (based on Glenn McDonald’s Every Noise At Once data). It’s designed for people who like discovering niche subgenres or emerging artists and be informed of new releases each week. It's free of course.
Features:
- Browse weekly releases by genre back to 2022
- Listen to short previews instantly
- Save favorites (free account)
- Open tracks directly on Spotify, Apple Music, Tidal, YouTube, etc.
- “Random genre” for serendipity
- Simple interface focused on fast music discovery
This started as a personal project because I wanted a better way to find new sounds outside algorithmic recommendations. I’ve ended up discovering a ton of great unknown artists through it, so I figured others might find it useful.
Would love feedback, especially on UX, speed, missing genres, or ways to make exploration more intuitive.
Link: https://thisisnewmusic.com/

value: Show HN: A tool to explore new music by detailed genres (powered by Every Noise)

author: saint-james-fr


value: lforster

value:
Hi HN! I built Treyspace, an SDK that turns Excalidraw canvases into queryable knowledge graphs using RAG (Retrieval Augmented Generation).
What it does:
- Ingests canvas data and mirrors it into a graph-vector database (Helix)
- Performs semantic, relational, and spatial clustering of canvas elements
- Lets you query your diagrams with natural language via LLM-powered analysis
Why I built it: I found myself creating complex diagrams in Excalidraw but struggling to extract insights from them later. Traditional search doesn't understand spatial relationships or semantic connections between elements. Treyspace bridges that gap by treating your canvas as a knowledge graph.
Demo: https://app.treyspace.app/ (no API key required)
Key features:
- Works with in-memory mode by default (no DB setup needed)
- Optional Helix DB backend for production use
- OpenAI-compatible responses API
- SSE streaming for real-time analysis
- Use as a library or standalone server
Example use case: Load an architecture diagram, ask "What are the security vulnerabilities in this design?" and get context-aware answers based on spatial proximity, element relationships, and semantic understanding.
The SDK and source code is MIT licensed and designed to be hacked on. I’ve tried to make it as simple as possible to set up (all you should need is an OpenAI API key)
Repo: https://github.com/L-Forster/treyspace-sdk
Would love feedback on the approach and hear how you might use canvas-based RAG!

value: Show HN: Treyspace ─ Open Source Graph RAG on Your Excalidraw Canvas

author: lforster


value: sentomk

value: Show HN: Patternia – a header-only C++ DSL for expressive pattern matching

author: sentomk


value: gregsadetsky

value:
Hey HN!
I'm very proud of sharing this project with you all, after ~2 years of starts and stops, and about 5 different attempts at making it.
Context/history: In 1961, the Minivac 601 [0], an educational electronics kit - somewhat similar to those "300 circuits in one" you may have had growing up as I did - was created by none other than Claude Shannon.
The Minivac is disarmingly simple: it consists roughly speaking of 6 relays, 12 lights, 6 buttons, and a motorized wheel. You'd think that it couldn't really do much.
Well, amazingly, it can do a lot. You can wire up the components in a way that will make the Minivac play tic-tac-toe, or OCR-detect 10 digits... The sample "demo" circuit I chose for the homepage shows a binary counter that counts up to 7.
Another amazing thing about the Minivac is definitely its manuals [1]. Their spirit is what I hope to capture in the coming (years?) as I keep improving this project. The manuals are generous and well-written and are not only an amazing gradual introduction to relay-based logic - they touch on computing at large. With amazing 1960s graphics/cartoons, of course.
That's probably what got me to work on the Minivac. I learned about the device a bit before going to the Recurse Center, fell in love with the manuals, and was frustrated that I couldn't try out the circuits or play around with the device! I thought that creating a JavaScript-based emulator would be an "easy" way to get there. Turns out that correctly simulating electricity isn't "easy". :-) But I'm very proud that it now seems to be doing the right thing for most circuits that I've tested from the book. Yes, this Minivac Simulator has a TypeScript testing suite!
Looking forward to hearing from you all. Cheers
[0] https://en.wikipedia.org/wiki/Minivac_601
[1] https://minivac.greg.technology/manuals/1961-minivac601-book...
Repo: https://github.com/gregsadetsky/minivac/

value: Show HN: Minivac 601 Simulator - a 1961 Relay Computer

author: gregsadetsky


value: DmitriiBaturo

value:
I’ve been working on a conceptual model (ICT Model) that tries to unify
information dynamics (dI/dT), temporal structure and the emergence of
conscious processes.
The core idea is simple but unusual:
Physical energy, conscious experience and the arrow of time may all be
different manifestations of the same underlying informational gradient.
The model predicts that:
energy = frozen information interacting with changing information,
consciousness ∝ local dI/dT,
reality’s “levels” emerge from stable mappings between I_fixed and dI/dT.
A full 23-page preprint (with equations, phenomenology and testable criteria)
is here:
For discussion, please join us here:
https://www.academia.edu/s/8924eff666#comment_1478583
Doi:
10.5281/zenodo.17584782
I would be grateful for feedback from physicists, cognitive scientists,
information theorists and anyone familiar with foundations of QM.

value: Show HN: ICT Model – a framework linking information, time and consciousness

author: DmitriiBaturo


value: gpopmescu

value: I've been frustrated by how often I need to pause Blender tutorials or dig through documentation just to figure out where a button is. So I built OverlayFlow, a tool that displays on-screen visual hints showing exactly where to click inside Blender’s UI.<p>Right now it only supports Blender, but I’m planning to expand to other tools if people find this useful.

value: Show HN: OverlayFlow– Learn Blender with AI That Points You to the Right Buttons

author: gpopmescu


value:
My OpenAI bills were getting out of hand - I was using GPT-4 for everything, even simple tasks that GPT-3.5 could handle
perfectly.
So I built CostLens. It's a drop-in replacement that automatically routes requests to cheaper models when possible, but falls
back to premium ones when quality matters.
How it works:
js
// Just swap this:
const openai = new OpenAI({ apiKey: 'sk-...' });
// For this:
const costlens = new CostLens();
const openai = costlens.openai({ apiKey: 'sk-...' });
// Everything else stays exactly the same
Real savings:
• Simple tasks: GPT-4 → GPT-4o-mini (95% cheaper)
• Complex tasks: Still uses GPT-4 when needed
• My bills dropped ~70% with zero code changes
Features:
• Quality detection (auto-retries with better models if response is bad)
• Works with existing code - no prompt changes needed
• Caching with Redis
• Instant mode (no signup required)
Try it: npm install costlens
The core SDK is free and works locally. I'm also building a dashboard for teams to track their AI spending.
NPM: https://www.npmjs.com/package/costlens
Anyone else tired of overpaying for AI APIs? What's your biggest cost pain point?

value: Show HN: I built CostLens SDK to cut my AI bills by routing to cheaper models


value: anikendra

value: Check out all the shocking confessions being made.

value: Show HN: Anonymous Confessions App

author: anikendra

title: Show HN: Anonymous Confessions App


value:
Hey HN! I built PRISM-INSIGHT, a multi-agent system where 13 specialized AI agents collaborate to analyze Korean stocks (KOSPI/KOSDAQ). It's completely open source and has been running live since March 2025.
[What it does]
The system automatically detects surging stocks twice daily, generates analyst-level reports, and executes trading strategies. Each agent specializes in something different – technical analysis, trading flows, financials, news, market conditions, etc. They work together like a real research team.
[Why I built this]
I wanted to see if GPT-4 and GPT-5 could genuinely replicate what human analysts do, but without the typical single-agent limitations. So I split the work across multiple specialized agents that collaborate. The trading simulation has been running for 8 months now with real Korean market data.
[How to try it]
Join the live Telegram channel!
https://t.me/prism_insight_global_en (gets daily alerts and reports)
Check the real-time dashboard!
https://analysis.stocksimulation.kr (all trades, performance, AI reasoning)
Clone and run it yourself!
https://github.com/dragon1086/prism-insight
[The interesting parts]
The system uses MCP (Model Context Protocol) servers to give agents access to live market data, web search, and financial APIs. I'm using GPT-4.1 for analysis, GPT-5 for trading decisions, and Claude Sonnet 4.5 for the conversational bot.
The first trading simulation (Season 1, Mar-Sep 2025) returned 408% across 51 trades. Current season(2) is at +11% realized returns vs KOSPI's +16%. Also running it with real money now ($10k account, up 9.35% since late September).
[Tech stack]
Python 3.10+, async/await throughout, SQLite for trade history, Playwright for PDF reports, matplotlib for charts. The whole thing is about 8,400 lines of Python across 56 files.
[What makes it different]
Most AI trading projects are either single-agent or black boxes. This one uses a multi-agent architecture where you can see exactly what each agent is analyzing and why. Everything is transparent – the dashboard shows every trade, every decision, and all the reasoning.
It's MIT licensed and runs entirely on your machine if you want. I'm covering the API costs (~$200/month) to keep the public Telegram channel free for 450+ users(Korean channel + Global channel).
Would love feedback on the multi-agent approach or questions about running AI agents in production!

value: Show HN: Multi-agent AI stock analyzer – 408% return trading Korean market


value: capter

value:
Hi everyone,
This is a small experimental AI project I’ve been building called *ZTGI-AC*.
Most LLMs generate an answer immediately, but ZTGI-AC does something different:
before responding, it runs an internal stability check.
It evaluates:
• risk
• jitter
• dissonance
• SAFE/WARN/BREAK modes
• INT/EXT gating (self-monitoring loop)
Only after the internal signals stabilize does it generate a reply.
This project explores whether self-evaluation loops can reduce chaotic or unstable
outputs in LLM-like systems.
*Demo:* https://ztgiai.pages.dev
(Non-commercial, early prototype.)
I’d love feedback from the HN community — especially around:
• whether self-monitoring loops are meaningful,
• potential improvements to stability metrics,
• and how this idea compares to classical alignment approaches.
Thanks for taking a look!

value: Show HN: ZTGI-AC – An AI that checks its internal stability before answering

author: capter


value:
The college savings calculator tells you how much additional contributions you have to make to save for kids' college based on
1. age of kid
2. How much you have already saved
3. Type of college (public/ private) and
4. Percentage of college fees you want to contribute.

value: Show HN: I made a college savings calculator


value: idiocache

value: Hi guys, I stitched a few APIs together into a fun gig finder app and thought some of you might enjoy it. Warning - autoplay!

value: Show HN: Whirligig.live

author: idiocache

title: Show HN: Whirligig.live


value: SilentCoderHere

value: Show HN: AI Hub – Android all in one app for AIs

author: SilentCoderHere


value:
During a two-day trip I ended up building a 6-agent conversational OS with GPT-5.1 — using only natural-language instructions, no code.
It’s called PolyAgora.
What it is:
- A natural-language multi-agent “OS” with 6 agents
- Built around a tri-axis core (Arc–Ann–Saku) + 3 supporting agents
- Designed to produce long-form, philosophy-grade conversations
- Dynamic objection engine + topic-shift mechanics
- Emergent behavior, not pre-scripted roleplay
I’m not an ML engineer.
This grew organically out of wanting to have deep small-talk with GPT-5.1 during a short trip — and it evolved into a structured OS.
Repo (docs + long examples):
https://github.com/Takeshi-Sakamoto5/PolyAgora
I’d love feedback from HN:
- Is this structure useful for multi-agent reasoning research?
- Does the emergent conversation quality seem meaningful?
- What would you add/change for v1.1.0 (I plan to include a “runaway dialogue” example)?

value: Show HN: PolyAgora – A natural-language multi-agent OS built with GPT-5.1


value: djyde

value:
Hey HN, I built CC Mate because I was tired of manually editing JSON files to configure Claude Code.

  Claude Code is amazing, but managing its configurations through scattered JSON files is a nightmare.
  You have to dig into ~/.claude/settings.json for basic settings, ~/.claude.json for MCP servers, and
  create various directories for agents, commands, and memory files.

  CC Mate (https://randynamic.org/ccmate) is a modern Tauri desktop application that solves this by
  providing:

   Core Configuration Management
  - Switch between multiple Claude Code configurations effortlessly (perfect for different projects or
  work/personal setups)
  - Edit all settings with a beautiful JSON editor with syntax highlighting and validation
  - Automatic backup of existing configurations on first run
  - Read-only support for enterprise managed settings

   Advanced Claude Code Features
  - MCP Server Management: Configure Model Context Protocol servers through a clean UI instead of
  editing ~/.claude.json manually
  - Agent Management: Create and manage Claude Code agents with markdown editing
  - Global Commands: Set up and organize global slash commands
  - CLAUDE.md Integration: Edit your global Claude memory file directly
  - Usage Analytics: Track and visualize your Claude Code usage with charts

   Technical Highlights
  - Built with Tauri v2 (Rust backend + React frontend)
  - Native performance with tiny footprint (~15MB)
  - Cross-platform (macOS, Windows, Linux)
  - Real-time configuration switching without restarting Claude Code
  - JSON schema validation to prevent configuration errors

   The Problem It Solves
  Before CC Mate, if you wanted to:
  - Switch between work and personal Claude configurations → Manual JSON file editing
  - Add a new MCP server → Edit ~/.claude.json with correct syntax
  - Set up a new agent → Create markdown files in specific directories
  - Track your usage → Parse JSONL files manually

  Now you can do all of this through an intuitive interface in seconds.

  The app is free and open source (AGPL v3). Downloads are available for all major platforms at
  https://randynamic.org/ccmate

  Would love to hear your thoughts on this approach to solving Claude Code configuration management!

value: Show HN: A desktop app to manage Claude Code config

author: djyde


value: devsatish

value:
I built HaradaPlan to help people turn big goals into actionable plans using the Harada Method – a Japanese goal-setting framework that breaks one main goal into 8 key areas and 64 specific tasks.
The app features:
• AI-powered plan generation (just describe your goal, AI fills in the 64 cells)
• Interactive grid editor with auto-save
• AI coach to help refine your plan
• Community sharing with voting/moderation
• Shohei Ohtani's actual draft plan as a template (he used this method to become an MLB superstar)
Would love feedback on:
- The AI prompt quality (does it generate useful plans?)
- UX – especially the grid interaction on mobile vs desktop
Happy to answer any questions about the architecture or the Harada Method itself!

value: Show HN: Harada Planner (Harada.app)

author: devsatish

title: Show HN: Harada Planner (Harada.app)


value: danielhrdez

value: Show HN: PayTrack – One-click payment links with AI cash flow predictions

author: danielhrdez


value: le-sserafim

value:
Spent the weekend building what I wished existed: Claude Code with task-specific model routing.
Think Mode uses Kimi K2 Thinking. Background tasks use GLM 4.5-air. Web search uses GLM 4.6. You configure once, it routes automatically. 16+ providers supported.

value: Show HN: OpenRouter met Claude Code Router. They had a baby

author: le-sserafim


value: osigurdson

value:
Hi HN
I tend to use the terminal / vim a lot and prefer not to switch context (open another app or browser) etc just to add, find or work with data.
I created a video which walks through a few use cases and some functionality of Nthesis (https://nthesis.ai).
Please have a look if you are interested. I would love to hear any feedback that you have.

value: Show HN: CLI based note taking using Nthesis.ai [video]

author: osigurdson


value: technomoloch

value:
Hi y'all,
Was playing geoguesser and thought, "Would it be fun to guess artifact locations like this, and add a time element?" And behold, it was fun. Its just a side-project, but has all the trimmings, multiplayer, timed rounds, various modes (Ea-Nasir Mode is my fav). Completely free to play, though any artifact image is buyable as a form of apparel for anyone wanting to support me making weird fun stuff instead of actual products (please, I'm tired).
The database is mostly from the MET artifact API, but am going to add artifacts from the British museum, the Smithsonian 3D Artifact API, and elsewhere soon. Might end up being one of the biggest aggregated artifact databases once I'm done. Am a computer vision engineer by day, so artefact search by visual similarity and other fun things are being planned, when I feel like it.
Please be careful with it, I have a shock-collar on that's hooked up to the sentry API. But if you do find a way to break things, or have any fun suggestions, shoot me a message here. Cheers!

value: Show HN: Artifactguesser

author: technomoloch

title: Show HN: Artifactguesser


value: gyaneshgouraw

value: Show HN: Generate PR descriptions 1 click using your GitHub Copilot subscription

author: gyaneshgouraw


value:
With Resend's new inbound feature I wanted to build a simple application that handles processing webhook events and forwarding emails for multiple applications. Right now Resend requires you to implement that logic in each new application.
repo - https://github.com/lsherman98/resendforward
live - https://resendforward.com
Built with react + pocketbase, extremely simple to self host.

value: Show HN: ResendForward – OS server and UI for use with Resend.com inbound


value: AlSweigart

value: Anki, Quizlet, Brainscape... I dislike all these overcomplicated flashcard apps. I just want the software equivalent of index cards, so I created [TSOFA - The Simple, Offline, Flashcard App](https://inventwithpython.com/tsofa/). It's exists entirely in a single HTML file you can view in the browser. You can edit the flashcards directly in the file, using just text or HTML tags. [Check out this demo.](https://inventwithpython.com/tsofa/tsofa.html) It also supports a CSV string for the flashcard content so you can import flashcards from other apps. Otherwise, it's just a simple, free flashcard app: No server, no ads, no registration, no premium features to pay for, no dependencies, no build process, no cloud sync. You can suggest improvements or report bugs on [the GitHub repo](https://github.com/asweigart/tsofa-the-simple-offline-flashc...).

value: Show HN: Tsofa – The Simple, Offline Flashcard App

author: AlSweigart


value: publicusagetax

value:
I built something unusual and open-sourced it.
The PUT Monolith is a compact, system-neutral, AI-ingestible ruleset for reasoning about public finance in a future where automation, AI, and robotics dominate value creation.
It’s not a policy proposal. Not political. Not an LLM jailbreak.
It’s an architectural core: the invariants, guardrails, and ethical constraints that make consistent reasoning possible across any AI model.
The Monolith is small enough to text to a friend, but strong enough to constrain an AI’s reasoning and force it to honor fairness, system alignment, footprint-based contribution, and non-regression.
Multiple models (GPT, Claude, Grok) have already demonstrated stable reasoning when loaded with it.
The goal is to provide a portable building block for:
- AI alignment & systems governance
- reasoning about incentives
- economic modeling / public finance
- research, critique, and open testing
MIT-licensed.
GitHub repo:
https://github.com/publicusagetax/PUT-Monolith-v2
Feedback, critique, forks,

value: Show HN: The Put Monolith – A Minimal AI-Ingestible Ruleset

author: publicusagetax


value: kajogo

value:
run "stakpak --profile readonly", then ask it to list, then nuke some AWS resource
https://stakpak.gitbook.io/docs/how-it-works/warden-guardrai...

value: Show HN: Open-source Agent in Rust that can't delete your database

author: kajogo


value: SHOW HN: FinLang – A deterministic, auditable rules engine for finance (AGPL)


value: Show HN: desktop widget to show prices of crypto, fiat, gold/silver, stocks


value: SergiuNistor

value:
Hey everyone!
I’ve been working on NanoGPTForge, a modified version of Andrej Karpathy's nanoGPT that emphasizes simplicity, clean code, and type safety, while building directly on PyTorch primitives. It’s designed to be plug-and-play, so you can start experimenting quickly with minimal setup and focus on training or testing models right away.
Contributions of any kind are welcome, whether it is refactoring code, adding new features, or expanding examples.
I’d be glad to connect with others interested in collaborating!
Check it out here: https://github.com/SergiuDeveloper/NanoGPTForge

value: Show HN: A cleaner, safer, plug-and-play NanoGPT

author: SergiuNistor


value: kevinslin

value:
Hey HN,
I'm a big fan of skills and wanted to use them across different models and tools. So I made skillz - a CLI which gives any model/agent/tool the ability to make use of skills
Two commands:
- `skills init` to inject into AGENTS.md (or other files) a prompt to make them aware of skills.
- `skills sync` will crawl all skill directories and append the name/description frontmatter into the system prompt
This has allowed me to unify common capabilities across all the coding agents I use.
Even if you only use claude, you should find value with skillz like the ability to list and edit any of your existing skills.
Have plans to expand this - including a public skills registry of popular skills as well as a feedback loop to track skills usage and auto-improve skills over time.
Please give it a try and let me know what you think!

value: Show HN: Skillz – Use Claude Skills Anywhere

author: kevinslin


value: mreichhoff

value:
I made a little language learning tool. It represents languages as a trie data structure, so that learners can quickly pick up phrases and usage patterns when they learn a new word. It's a bit rudimentary at the moment, but I intend to make a flurry of changes soon (AI integration for example sentences, prettier UI, AnkiConnect for the flashcards, and maybe more puns?). The trie was built based on movie and TV subtitles, so hopefully a decent representation of real-world speech.
It's currently available in French (French Tries), Spanish (espárbol), Portuguese (PorTRIEguese), Italian (Trietalian), German (Triedesco).
You can check it out at trielingual.com

value: Show HN: TrieLingual: Study languages with prefix tries (and puns)

author: mreichhoff


value: binsquare

value:
I built a sandbox geared for safe ai code executions using microvm's that works locally (mac + linux).
It's meant to be a different/more secure approach compare to what Claude's doing with their sandboxing runtime: https://code.claude.com/docs/en/sandboxing. Instead of relying on linux + Mac's technology for containerization, this project spins up a completely virtual environment on a separated kernel for better security.

value: Show HN: Secure Code Execution for AI

author: binsquare

title: Show HN: Secure Code Execution for AI


value: matheusdevmp

value:
Hi HN,
I'm an independent researcher and I've just published a whitepaper for an LLM memory architecture I designed: DREAM (Dynamic Retention Episodic Architecture for Memory).
The core problem I'm tackling is the tension between persistent memory and cost in large-scale AI systems. Storing nothing forces users to re-explain context. Storing everything creates a privacy, latency, and cost nightmare.
DREAM is a plug-in architectural pattern that sits around the LLM (no model changes needed) and unifies existing tech (RAG, NoSQL).
The core innovation is the Adaptive Retention Mechanism (ARM).
Instead of a static 30-day TTL, ARM dynamically extends an episode's TTL based on user engagement. For example, a memory's life doubles each time the user revisits it (e.g., 7 days -> 14 -> 28 -> 56) .
This creates a "self-pruning" memory layer where storage cost scales directly with actual user relevance, not just raw traffic.
The architecture also includes:
Episodic Units (EUs): Storing compressed summaries + embeddings, not raw logs.
User-Centric Opt-In: Explicit user approval per episode for privacy.
Aligned Sharding: A design for sharding orchestrators and storage (partitioned by user_id) to ensure horizontal scalability and cache locality .
I designed DREAM to be a practical blueprint implementable with today's infrastructure (Cassandra, FAISS, Kubernetes, etc.).
I don't have the resources to test this at scale, so I'm publishing the architecture to share the idea. I would be genuinely grateful for any technical feedback, criticisms, or thoughts on the design.
Whitepaper (PDF): https://zenodo.org/records/17619917
GitHub (Code Examples/Arch): https://github.com/MatheusPereiraSilva/dream-architecture

value: Show HN: Dream – An LLM memory architecture using adaptive TTL to control cost

author: matheusdevmp


value: sifuldotdev

value:
Hi Everyone,
I built an email template builder embeddable plugin for CRM, Marketplace, or any website. Free and paid plans are included.
Add a complete email builder to any SaaS app using a single script.
What's included:
- Easy Integration
- AI Content & Template Generation
- Add external image libraries
- Add Merge Tags
- Display Conditions
- Custom Blocks
- Choose your storage server
- Dedicated support during integration
Check it out, and please let us know if you have any feedback for me.
TIA

value: Show HN: My Side project a free email template builder for CRM, or any website

author: sifuldotdev


value: anonbuddy

value:
I built Own Your Stream to help people who want Stremio but give up as soon as things look
  technical. Many of us can follow a GitHub readme, but our parents or less technical friends
  usually cannot. They end up asking for step by step help or just stick with expensive streaming
  subscriptions.

  What it is: a simple paid setup service for Stremio. You either get a ready made Stremio account
  with everything already configured plus the first thirty days of the streaming helper service I
  connect for you Real Debrid or you book a call where we set it all up together on your devices.

  Who it is for: people who just want to click, sign in, and watch. No forums, no Discord, no hunting
  for add ons or wondering which tutorial to trust. You send them here, they pay once, and they get a
  working setup they can use on their TV, phone, tablet, and computer.

  What you get:

  • A Stremio account that is already configured for everyday use
  • Clear instructions on where to sign in and how to change the password
  • The first thirty days of Real Debrid already connected so streams actually work well
  • Optional one on one call where I guide you live until everything is running

  Business model: one time payment for the account plus thirty days of streaming helper service, and
  a higher tier for people who want a personal call. Basic analytics only, no dark growth tricks.

  I would love feedback from HN on what feels confusing, what sounds risky, and what you would change
  for the crowd that says I do not care how it works, I just want to press play and be done.

value: Show HN: A ready to use Stremio account for non technical friends and family

author: anonbuddy


value:
Hey everyone!
big day for me. Couldn't even afford my flight ticket to the US 1 year ago, and today I'm sharing Snippet's platform with you all from the bay.
Snippet is an AI-powered truth layer that keeps all your documentation and search indexes accurate and conflict-free automatically 24x7.
It connects to all your information sources (Github, Slack, Notion etc.), extracts atomic facts from these sources, and keeps resolving conflicts between these facts automatically. (It can also learn precedence rules for this resolution specific to your company)
Technical writers/ PMs can immediately leverage the platform by uploading reference documents and checking their new work against these docs for free.
But to make this all automatic, we still have 8 more pilot spots open. (We will build connectors for your custom stack for super cheap, ofc in exchange for feedback and getting to know you guys haha). We are already working with a YCF25 and a ex-YC health tech.
Example conflicts on Microsoft's public docs: https://usesnippet.app/demo/microsoft
Appreciate you all and this platform so much!

value: Show HN: Engineered doc accuracy at LinkedIn, made the truth layer for docs


value:
I built Hirelens (https://hirelens.co) after seeing many ESL and international job seekers struggle with resumes that don’t match job descriptions or parse cleanly in ATS systems, even when they have strong experience.
What it does:
Extracts skills/experience from a resume
Compares it to a target job description
Flags unclear or “non-native” phrasing
Suggests clearer rewrites
Identifies ATS parsing issues
Deletes files after processing (no storage)
Tech:
Next.js + FastAPI, lightweight CV parsing → embeddings → scoring logic, LLM-based suggestions, no data retention.
I’d love feedback on:
parsing edge cases
rewriting clarity
what features matter most for job seekers or hiring managers
Try it here: https://hirelens.co

value: Show HN: Hirelens – AI Resume Analyzer for ESL and Global Job Seekers


value: geoffitect

value:
Alphafold3 used to be fodder for HPC clusters; now I've got a port running smoothly on Apple Silicon. If you have an M-series Mac (~2023-present), you can generate protein structures from sequences in minutes. Give it a try!
GitHub repo: https://github.com/latent-spacecraft/openfold-3-mlx

value: Show HN: CUDA, Shmuda: Fold Proteins on a MacBook

author: geoffitect


value: satuke

value: Show HN: AirPods Seamless-Handoff with Linux

author: satuke


value: andybady

value:
Here's my embarrassing story:
I thought "gaming highlights" would work. Made 20 videos. Got 47
subscribers. Realized the CPM is like $2-4. Would need 500K views
just to make $1K/month. Not happening.
Then I tried "motivational quotes". Saturated. Couldn't get any
traction.
Then "meditation music". Also saturated. Plus copyright issues.
I was so frustrated. I knew faceless channels could work (I saw
some making money), but I had no idea HOW to find profitable ones.
Manually searching YouTube was a nightmare:
- Most channels show face sometimes (not truly faceless)
- Can't tell if they're actually monetized
- No way to know their revenue
- Takes forever to find good examples
I wasted 3 months guessing. Made 50+ videos across different niches.
Got nowhere.
Then I realized the problem:
I was trying to find profitable faceless channels manually. It's
impossible. There are millions of channels. How do you know which
ones are actually making money?
So I built a scraper to track faceless channels making $5K+/month.
Started analyzing what actually works.
What I found:
- Personal finance reviews (budget products) - Channels making
$15K-35K/mo
- Tech comparisons (budget tech angle) - Channels making $12K-28K/mo
- "How it works" explained content - Channels making $8K-20K/mo
- Ranking videos ("Top 10 X under $Y") - Channels making $6K-18K/mo
- Historical deep dives - Channels making $10K-22K/mo
The pattern: All successful faceless channels pick a micro-niche.
Not "tech reviews" but "budget gaming laptops under $800".
I turned my research into a tool called *ApexVix* so other people
don't waste months like I did. It tracks profitable faceless channels
across all niches and shows you:
- Real channels making $5K-30K/month
- What niches they're in
- Growth patterns
- Upload frequency that works
It's free to use: apexvix
No signup needed. I just wish I had this when I started. Would've
saved me 3 months of guessing.
If you're struggling to find profitable faceless niches, this might
help. Happy to answer questions!

value: Show HN: Find Faceless YouTube Channels with AI

author: andybady


value: brokerjames

value:
Hi HN,
We’re a small team working on 13Radar.com, which we launched about two weeks ago after 4 months of development. I’m the founder, and together with the team we’re building a platform that tracks hedge fund portfolios in real-time based on SEC Form 13F filings.
AI has been a major helper in our workflow. For a single webpage, we often consult multiple AI systems in parallel, generating different versions and comparing them side by side before deciding on the final design or implementation. More than 60% of the research, design, and coding involved AI assistance. For UI design we used Readdy, which gave us a smooth design experience. We experimented with Google Gemini and ChatGPT for research and prototyping, while most of the coding was done with Claude Code (Trae). On the front-end, we started with Bootstrap but later switched to TailwindCSS for a lighter, more responsive framework.
Features so far:
Real-time tracking of hedge fund holdings from SEC 13F filings
Portfolio changes per quarter (new positions, increases, reductions, exits)
Sector-level insights and trend analysis
Historical tracking and backtesting tools
Next steps:
Implementing pricing and subscription features
Adding email notifications and report export
Expanding analytics with insider transaction data
Continuing to refine usability and performance
We’d love feedback from the community:
Does this solve a real pain point for investors/researchers
Any thoughts on improving positioning or usability
Suggestions for features that would make this more useful
Thanks for reading — and if you’re curious, feel free to check out the site and let us know what you think.

value: Show HN: 13Radar – Real-Time Hedge Fund Portfolio Analytics

author: brokerjames


value: theruss

value:
Show HN: isitendoflife.com - simple EOL visibility
Hey folks, I'm the maintainer of Metaport (https://gitlab.com/dcentrica/metaport/metaport-server) but in appealing to users to try it out, I felt a super-simple demonstrator was needed, so that development agencies, studios, and maintainers could quickly understand which versions of their technologies are (or soon will be) end-of-life.
So I built isitendoflife.com, try it by tacking-on a technology and/or a version to the domain to the EOL status of that technology e.g.
* https://isitendoflife.com/python
* https://isitendoflife.com/php/8.1
* https://isitendoflife.com/dotnet/8

value: Show HN: Isitendoflife.com Simple EOL Visibility

author: theruss


value:
Hey HN,
I’m writing as part of a duo - both of us have a background in motion design, but have more recently been working as software developers. We’ve been trying to mash those two worlds together into something useful.
Both of us have dabbled in enough indie SAAS projects over the years, to know how marketing is usually the bottleneck to the success of an app. With all these nifty AI tools popping up all over the place, we found ourselves thinking: "why isn't there a tool that can turn a finished software product into a sleek, polished product video that can be used on marketing pages?" Furthermore, why can't those videos stay in sync with the state of the code, easily re-renderable whenever the UI evolves?
Since StorybookJS is a fairly mature ecosystem, our approach has been to use it, not as a static showcase of small components, but as a basis for a living catalogue of real UI states and flows that we can capture as clean, cursor-free clips. From there, storyish (that's our product name) strings those clips into an initial product video timeline (using a bit of AI to draft the first cut), which you can then tweak and export for your launch page or ads. The idea is that, so long as you have instrumented your code with storybook, you can quickly make a product video on that basis.
The beta is working well and we are close to opening it up to the first batch of users. We are looking for an early cohort of testers that can provide thoughtful feedback and have an influence on the roadmap, default templates, and overall direction of storyish before we open it up more broadly.
If that sounds like something you would be interested in, please do take a look at https://storyish.io

value: Show HN: Turn storybook.js stories into product videos


value:
Hi HN,
For the past few years, I've been obsessed with a problem: my hard drive is a treasure chest of knowledge (PDFs, docs, notes) from the last decade, and I want to use modern AI to actually use it.
But every solution forces a trade-off: either convenience (upload everything to the cloud) or privacy (let my files sit dormant locally).
I got tired of this "hold your nose" compromise.
With capable SLMs (Small Language Models) exploding and on-device compute (Apple Silicon, NPUs) finally catching up, I believe privacy and intelligence is no longer a false choice.
So I (and my team) built KnowledgeFocus (Apple Silicon chips only)
It's an open-source (Apache-2.0) knowledge engine built with Tauri (Rust + Python + TS), and it's 100% local-first.
In v0.6.4, it focuses on one thing: unlocking your local file 'treasure chest'.
- Scans & Indexes: It scans your designated local folders (PDFs, .md, .txt, .docx, etc.).
- Auto-tagging: Uses a local model to auto-tag files, so you can aggregate and discover them.
- Local RAG: You can 'chat' with all your local files. It runs RAG 100% on-device. No data (vectors included) ever leaves your machine.
Website (Download): https://github.com/huozhong-in/knowledge-focus
This is just the first step of our "Data Workbench" vision.
I have a lot more thoughts on 'local-first agents', 'data aggregation' (like pulling in your cloud AI chat logs to store them locally), and building a real 'second brain' for knowledge workers.
I'll be in the comments to expand on these ideas and our future roadmap.
I'm here for all the feedback—especially the critical kind. Thanks, HN.

value: Show HN: I DON'T want to upload my private files to AI


value:
Triggla is a Stripe-native email automation tool for post-purchase and trials. Goal: turn buyers into repeat customers without an ESP or Zapier.
What’s different:
• Product-based automation: each Stripe product can run its own sequence
• Trial Rescue: covers all trials with pre-expiry nudges (3d and 1d)
• Built-in sending with authenticated domains (targeting 98%+ deliverability)
• ~60s setup; Day-0 email in ~30s; Days 3/7/14 auto-queued
• Duplicate-prevention, retries, timezone handling
• Revenue-focused analytics per product/sequence
Happy to answer technical questions: webhook design, idempotency keys, queueing, deliverability tuning, and failure handling. Free 30-day trial; looking for feedback on templates and analytics.

value: Show HN: Triggla for Stripe (post-purchase and trial-rescue emails, 60s setup)


value: DXT is a leading AI Agent platform. Browse 15,000+ official and community MCP servers. Find the best MCP server tools for enhanced AI agent capabilities.

value: Show HN: 15,000 MCP servers to enhance Agent capabilities


value: Show HN: Pyloid: Electron for Python • Modern Web-based desktop app framework


value: bestkundli

value: Show HN: That's how - Donald Trump is

author: bestkundli

title: Show HN: That's how - Donald Trump is


value: zhouyujt

value: Show HN: WGE – High-Performance WAF Library, 4x Faster Than ModSecurity

author: zhouyujt


value: Show HN: Markdown to Word – Free Online Converter


value: benbojangles

value: Transform any manual treadmill into a smart fitness device compatible with Peloton, Zwift, Strava, Kinomap and other fitness apps using a microcontroller, imu, and a simple ir sensor.

value: Show HN: Smart Treadmill – FTMS Bridge

author: benbojangles

title: Show HN: Smart Treadmill – FTMS Bridge


value: shmaplex

value: The CSL is an attempt to rethink software licensing in a way that balances freedom, sustainability, and fairness. It’s inspired by the idea that our digital infrastructure increasingly mirrors feudal systems: concentrated power, obligations flowing upward, and everyday users left vulnerable. The license is designed to give creators a practical alternative to proprietary or exploitative models, while encouraging transparency, collaboration, and equitable distribution. I’d love to hear thoughts from developers, legal experts, and anyone experimenting with bootstrapped projects.

value: Show HN: Common Sense License (CSL) – civic license for a techno-feudal world

author: shmaplex


value: wilsonjin

value:
Hi everyone, we're building WyseOS — an AgentOS designed for autonomous web automation.
This article provides a deeper look at how it works, why we built it, and what's next.<p>We’d love feedback from the community.
The beta is now live: if you’re interested in trying it, DM us on X at @WyseOS_com.

value: Show HN: WyseOS – An AgentOS for Web Automation

author: wilsonjin


value:
Today we are launching YourGPT 2.0, an update focused on making support, sales, and operational workflows work together more smoothly in one system. This release improves how workflows are built, how the platform connects to external tools, and how context is maintained across long, multi-channel interactions.
AI Studio now makes it much easier to create and refine workflows. You can describe the process you want in natural language and the system generates the full workflow for you. It can also help debug issues by showing what happened at each step and explaining where things went off track.
Studio apps are now part of the workflow system. These apps let teams plug external services directly into their processes, such as Go High Level, Google Sheets, Stripe, and many more.
We added native support for the Model Context Protocol so teams can connect to any MCP server. Also extends this by offering access to more than 100 tools through MCP360’s single configuration.
We introduced Ask AI Trigger to make websites more interactive. It highlights content as users browse and opens conversations at the moment interest is highest. Command K navigation provides fast access to any part of the platform.
Voice agents now use upgraded models that respond faster and handle interruptions more naturally. Native iOS and Android SDKs let teams bring these capabilities into their mobile applications with consistent behavior.
Customer inputs come from many formats: images, screenshots, documents, audio messages. The system answers based on input type and merges the results into the shared context.
Training supports Notion, Confluence, documents, websites, and other sources. Deployment works across web, mobile, messaging apps like (whatsapp, telegram, instagram & more), browser extensions, and helpdesk systems. The platform improves automatically over time through a self-learning architecture that updates behaviour without manual retraining.
We’d love to hear what you think, try it out, break things , and let us know where we should go next. Your feedback means alot

value: Show HN: YourGPT 2.0 – Complete AI platform for support, sales, and operations


value: mefengl

value: Small tool that converts epub books into folders with each chapter as a markdown file. Makes it easy for CLI agents and LLMs to reference books on demand.

value: Show HN: Epub2md – Turn ePub books into Markdown folders for LLM agents

author: mefengl


value: DenisDolya

value:
TERMINAL CALENDAR APPLICATION
INTERACTIVE COMMAND-LINE CALENDAR WITH NAVIGATION
KEY FEATURES:
- Monthly calendar display with highlighted current date
- Navigation between months and years
- Return to current date functionality
- Leap year calculation
- Recursive printing functions
- Automatic current date detection
COMPILATION:
gcc -o calendar calendar.c -Wall -O2
USAGE EXAMPLES:
$ ./calendar
Initial display shows current month with today's date highlighted in brackets
NAVIGATION COMMANDS:
n - Next month
p - Previous month
t - Today (return to current date)
q - Quit application
DISPLAY FEATURES:
- Month name and year in header with star borders
- Day names (Su Mo Tu We Th Fr Sa)
- Dates formatted in grid layout
- Current date highlighted as [dd]
- Automatic week wrapping
DATE HIGHLIGHTING:
The current system date is automatically detected and highlighted
using bracket notation: [15] for the 15th of current month
YEAR HANDLING:
- Automatic leap year calculation for February
- Smooth year transitions when navigating between December and January
- Supports years based on system date capabilities
KEY FUNCTIONS:
leap_year()   - Determines if current year is leap year
get_days()    - Returns number of days in current month
first_day()   - Calculates day of week for 1st of month
print_days()  - Recursively prints calendar days with highlighting
GitHub: https://github.com/DenisDolya/cli-arsenal/tree/main/calendar

value: Show HN: I'm created simple CLI-calendar without time.h

author: DenisDolya


value: kadeus

value: An online image translator that preserves text colors and repairs backgrounds seamlessly. Might be worth a try if you're curious.

value: Show HN: Translate images to any language instantly with AI

author: kadeus


value: aamironline

value:
TL;DR: Internet Object (IO) is a lean, schema-first, JSON-compatible format that cuts structural noise, improves clarity for modern systems, and reduces tokens by ~40–50%.
Playground: https://play.internetobject.org
---
I started exploring this idea in 2017 after repeatedly running into the same pain points with JSON while building distributed systems and structured data pipelines. Rather than extending JSON or adding more layers on top of it, I wanted a format that was clean, schema-first, human-friendly, and still compatible, where that compatibility matters.
The concept evolved slowly over the years, with several redesigns, dead ends, and restarts - until it eventually converged into what I now call Internet Object (IO). The story behind this evolution is here:
https://internetobject.org/the-story/
Although IO was not created with LLMs in mind, its structure ends up being significantly more token-efficient (around 40-50% fewer tokens compared to JSON), which has become a practical advantage in today’s workloads.
I've written a practical guide showing how JSON developers can transition to IO, with syntax explanations:
 https://blog.maniartech.com/from-json-to-internet-object-a-l...
For side-by-side comparisons with JSON, see the following link:
https://www.internetobject.org/io-vs-json/
There is also an interactive playground if you'd like to try the format directly:
https://play.internetobject.org
https://play.internetobject.org/simple-collection
This is a soft launch to gather early feedback - I would appreciate any thoughts from the community.

value: Show HN: Internet Object – a lean, schema-first JSON alternative

author: aamironline


value: vinitmaniar

value:
Hi HN,
I’m a co founder of Everdone (https://everdone.ai
).
We’re building a Work-as-a-Service platform — a place where teams trigger real work (not chats) and get production-ready outputs via AI. Everything is usage-based, like compute.
We’re starting with developer services because that’s where we’ve spent most of our lives as builders.
1. CodeDoc (live today)
AI documentation for GitHub repos — generated file-by-file, not guessed top-down.
How it works:
- Connect GitHub → choose repos → select files
- Instant documentation is generated
- Docs can be shared with unlimited team members
- You can set up automation at the repo/branch level
-- runs daily
-- only newly changed files are re-documented
-- users can specify which files/ folders/ extensions to ignore
- We don’t train on customer code
Outputs explain what the file does, functions, DB operations, API I/O, dependencies, libraries used, etc. The tone is intentionally simple so junior developers can understand the project quickly.
We’ve documented a few OSS repos publicly (Next.js, FastAPI, Pandas, etc.) so you can see it in action.
Pricing:
- 50 files free
- $0.25 per file for AI documentation
- $0.025 per file/month for hosting and access
- Unlimited team members; no subscriptions or seat-based pricing
2. Coming next (developer side)
- CodeReview (AI-assisted code reviews)
- DBDoc (database documentation)
- ChangeLog (auto changelogs from Git diffs)
3. And beyond engineering
Long term, Everdone will support other departments:
content, analytics, reporting, QA, growth ops, sales ops, and more.
All usage-based — all producing real artifacts that organizations can audit and measure.
We think teams need a system of record for AI work. Not chat logs — actual outputs.
Would love feedback — technical, critical, or skeptical.
Happy to answer questions.

value: Show HN: Everdone – AI-powered code documentation (and more)

author: vinitmaniar


value:
More and more users don’t browse — they ask AI assistants.
But most websites are still built only for human eyes, not for models that need structure, semantics, and metadata to understand content reliably.
AI-First Web explores what “SEO for AI” looks like:
how to structure a website so that AI assistants can actually read it, interpret it, and cite it correctly.
A well-structured, semantic, machine-readable site increases the chance that your page becomes the cited source for an AI-generated answer.
The project is very early and still evolving — I’d love feedback from people experimenting with how LLMs parse HTML, JSON-LD, and web structure in the real world.
Docs:
https://ai-first-guides.github.io/first.ai/
GitHub:
https://github.com/ai-first-guides/first.ai

value: Show HN: AI-First Web – SEO for AI Assistants


value: evanyang

value: Show HN: Illustration.app – Illustration Pack Generator

author: evanyang


value: ticktockten

value:
I built FastWorker after getting tired of deploying Celery + Redis for simple background tasks in FastAPI apps. Every time I needed to offload work from API requests, I had to manage 4-6 separate services. For small projects, this felt like overkill.
FastWorker is a brokerless task queue requiring only Python processes. No Redis, no RabbitMQ – just 2-3 Python services instead of 4-6+.
---
Quick example:
# tasks.py
from fastworker import task
@task
def send_email(to: str, subject: str):

    return {"sent": True}

# FastAPI app
from fastworker import Client
client = Client()
@app.post("/send/")
async def send_notification(email: str):

    task_id = await client.delay("send_email", email, "Welcome!")

    return {"task_id": task_id}

Start workers:
fastworker control-plane --task-modules tasks
fastworker subworker --task-modules tasks  # optional
---
Architecture: Uses NNG messaging for direct peer-to-peer communication. Control plane coordinates task distribution via priority heap and tracks worker load. Workers auto-discover via discovery socket. Results cached in-memory with LRU/TTL.
Designed for: Moderate-scale Python apps (1K-10K tasks/min) doing background processing – image resizing, report generation, emails, webhooks. Great for FastAPI/Flask/Django.
NOT for: Extreme scale (100K+ tasks/min), multi-language stacks, or systems requiring persistent task storage. For those, use Celery/RabbitMQ/Kafka.
Try it:
pip install fastworker
Repo: https://github.com/neul-labs/fastworker
FastAPI integration docs: https://github.com/neul-labs/fastworker/blob/main/docs/fasta...
Would love feedback on whether this fills a useful niche or if the limitations make it too narrow.

value: Show HN: FastWorker – Task queue for Python with no external dependencies

author: ticktockten