value: armank-dev

value:
Hey! Arman and Rohil here, we built a free, local-first observability tool for developers using the Vercel AI SDK on Next.js:
- Displays all AI SDK generations, tool calls, and failures in real-time
- 100% open-sourced, takes 7 lines of code to set up, and runs fully locally
The goal is to help you understand what your agent is doing without having to leave your local dev environment. Would appreciate feedback from any Next.js devs willing to try this!
// Motivation + background:
Arman used to work at Mintlify, where he spent most of his time optimizing user-facing AI agents built on the AI SDK. Rohil worked on Gmail Intelligence at Google, reducing hallucinations in intent detection across 26B+ daily emails.
Optimizing AI agents is very fun, but debugging them & understanding exactly what they do is annoying lol. Both of us found it especially painful to scrub through logs and find “silent failures” (doom loops, bad tool calls, hallucinations, etc.).
This tool was made to solve that exact issue. Instead of adding `console.log()` everywhere or inspecting hundreds of “UIMessage” objects, we propose a local-first solution that displays all AI SDK generations locally. We care deeply about DX; it’s our single biggest priority.
// Video demo:
https://github.com/The-Context-Company/observatory/raw/main/...
// How this works under the hood:
You can see the full codebase here: https://github.com/The-Context-Company/observatory/
At its core, this is essentially a light-weight sync engine:
- On the server, we initialize a WebSocket server and store AI SDK generations in memory.
- On the browser, we render a lightweight Preact widget that opens a connection with the server to display new runs.
Diagram: https://github.com/The-Context-Company/observatory/blob/main...
We took a lot of inspiration from react-scan, especially when thinking about the DX we want to offer. We internally set these requirements:
- UI must display just the right information and make copying any value easy.
- The widget should not be too obtrusive - must be resizable, movable, and dockable.
- Minimal code changes. This means:

  - Setting up the widget must be as easy as copying and pasting a single <script /> tag.

  - Instrumentation should also be easy to set up and must work alongside other OTel configs (e.g. Sentry) and wrapped functions (e.g. Braintrust).

// What’s next:
We plan on maintaining and improving the tool based on feedback from the Next.js and AI SDK communities by releasing new features and improving the UX.
Both of us are now working full-time on The Context Company, where we’re rethinking context engineering from the ground up, starting with agent observability. AI agents often fail quietly: picking the wrong tools, looping, hallucinating, and more. Our goal is to uncover these silent failures that standard monitoring misses.
// Links:
https://github.com/The-Context-Company/observatory
https://docs.thecontext.company/frameworks/ai-sdk/local
https://thecontext.company/
- Your friendly neighborhood devs, Arman and Rohil

value: Show HN: Local-first observability for AI SDK on Next.js in 10 lines of code

author: armank-dev


value: cpuXguy

value:
The initial question was: what is the minimum needed to gain practical control over the zeta function across the full complex plane, for s ∈ ℂ (including Re(s) < 1 and Re(s) < 0)?
This project is one possible answer. It provides two JavaScript functions, vanilla_zeta() and vanilla_gamma(), aimed at computing the complex zeta and gamma functions with a very simple JS implementation.
You can try both functions directly in the browser via the linked calculator at: www.zeta-calculator.com

value: Show HN: Complex ζ(s) and Γ(s) in pure JavaScript (works for Re(s)<0)

author: cpuXguy


value:
Keep your personal notes truly private with MySecureNote — a fast, lightweight, and fully offline encrypted notepad for Windows.
All data is protected locally using ChaCha20-Poly1305 encryption, ensuring modern, high-performance security without cloud dependencies.
Project is Open Source: https://github.com/mdimec4/Notes
If you want to you can compile it for yourself for free, but if you want to support me, you can buy it in Microsoft Store.
This is my first Windows GUI app, so any suggestions are welcome. Use issues or pull request's on GitHub.

value: Show HN: Keep your personal notes private with MySecureNote


value: addcn

value:
Git AI is a side project I created to track AI-generated code in our repos from development, through PRs, and into production. It does not just count lines, it keeps track of them as your code evolves, gets refactored and the git history gets rewritten.
Think 'git blame' but for AI code. There's a lot about how it works in the post, but wanted to share how it's been impacting me + my team:
- I find I review AI code very differently than human code. Being able to see the prompts my colleagues used, what the AI wrote, and where they stepped in to override has been extraordinarily helpful. This is still very manual today, but hope to build more UI around it soon.
- “Why is this here?” — more than once I’ve giving my coding agent access to the past prompts that generated code I’m looking at, which lets the Agent know what my colleague was thinking when they made the change. Engineers talk to AI all day now…their prompts are sort of like a log of thoughts :)
- I pay a lot of attention to the lines generated for every 1 accepted ratio. If it gets up over 4 or 5 it means I’m well outside the AI’s distribution or prompting poorly — either way, it’s a good cause for reflection and I’ve learned a lot about collaborating with LLMs.
This has been really fun to build, especially because some amazing contributors who were working on similar projects came together and directed their efforts towards Git AI shine. We hope you like it.

value: Show HN: Tracking AI Code with Git AI

author: addcn

title: Show HN: Tracking AI Code with Git AI


value: heavenlxj

value:
Hi HN
I’ve been experimenting with AI image and video models for a while, and one thing I kept running into was creative limits — many tools restrict what can be generated or explored.
So I built a new platform focused on unrestricted creative exploration.
It allows users to experiment with AI-generated images, videos, and audio, with full freedom to test different models, styles, and concepts.
The goal isn’t to shock or provoke — it’s to give creators space to explore ideas that are often filtered out elsewhere, and to study how AI can represent human imagination in its raw, unfiltered form.
We’re starting with a few models and will expand to support more in the future.
Still early days, but if you’re interested in the boundaries of AI media generation, I’d love your thoughts and feedback.

value: Show HN: Exploring the boundaries of AI-generated creativity

author: heavenlxj


value: calebhwin

value:
Hi HN!
a1 is a cost-optimizing AOT/JIT compiler for AI agents. I thought I'd post here and see if folks are interested in early using/contributing/providing feedback.
Paper coming soon.
-- Caleb (PhD student @ Stanford MAST)

value: Show HN: a1 - determinism-maxing JIT compiler for AI agents

author: calebhwin


value: pedalpete

value:
I’m Pete, co-founder and CEO of Affectable Sleep.
I’m a life-long chronic insomniac. My mum says that even when I was a baby, I’d never sleep. One night at 3 a.m. I thought, “I don’t care if I sleep, I just don’t want to be tired anymore.”
That thought led me to dive deep into sleep medicine. At first, everything I found was the same sleep hygiene advice I’d been told since I was a kid. Then I came across phase-targeted auditory stimulation (PTAS), also known as CLAS or SWE, a form of neurostimulation that increases slow-wave activity during sleep without changing total sleep time.
When I’ve mentioned this in earlier HN comments, it’s often met with scepticism, which I completely understand.
So I made a short video showing real EEG data from one of my own sleep nights using our technology. We run stimulation in a within-night stim/sham protocol, and what we see matches (or exceeds) the effects reported in more than fifty published, peer-reviewed studies.
https://youtu.be/HoZvpaOpPu8
Building a consumer-grade wearable EEG that works through the night has been far harder than we expected. We had high standards for comfort, signal quality, and manufacturability, and wanted to do better than previous attempts such as Dreem and Philips or the “fall asleep faster” EEG headbands currently on the market. I made a second video showing the headband itself and some of the design choices we made.
https://youtu.be/4exQXRA0bq0
Though we can measure day to day improvements in biomarkers, and cognitive function, one thing I'm interested in seeing is if we can have a significant impact on longevity. Sleep naturally declines as we age, and it isn't just sleep time, but the slow-wave activity specifically declines. If we begin stimulating slow-wave sleep earlier in life, can we slow the decline in slow-wave sleep, and what impact could that have on health?
Everyone is chasing more data and “insights,” but we’ve had bathroom scales for over a century and society is more obese than ever. More data is not the answer.
We believe the next generation of wearables will go beyond collecting data and displaying graphs. They’ll actively influence our biology, physiology, and neurophysiology to improve health on our behalf. We see Affectable Sleep as the beginning of that shift.
Affective wearables = Affectables.
In the past we’ve had feedback about using a subscription model. We tested a one-time purchase and had zero sales, even when reaching out to people who said they’d buy if there wasn’t a subscription. It isn’t a money grab; it keeps the hardware accessible and gives the company the long-term stability to keep developing the technology. Other companies such as Dreem, Philips, and Frenz all went with one-time pricing and didn’t survive.
We can’t discuss the stimulation protocol or technical implementation freely on a marketing page for regulatory reasons, but Hacker News is a place for discussing the technology and the business itself, so this felt like the right venue to share it openly.
Happy to answer questions about the data, stimulation methods, or hardware.

value: Show HN: Increasing restorative brain activity during sleep (with real EEG data)

author: pedalpete


value: jamesgill

value:
I know, I know: another resume generator.
But this one is simple, easy to customize, and gets to the point: a nicely formatted HTML or PDF document you can share.
Note: Linux and MacOS friendly; not tested on Windows.

value: Show HN: MarkdowntoCV

author: jamesgill

title: Show HN: MarkdowntoCV


value: smnair

value: Hey HN! I built Papiers.ai — a new interface for arXiv papers. It includes an AI-generated wiki, scientific lineage graphs, a mind map, a live Twitter feed, and a research agent to help you explore ideas faster. The motivation is: as scientific progress accelerates, we need tools that augment human cognition so we can keep up.<p>Would love your thoughts!

value: Show HN: Papiers.ai – A new interface for ArXiv

author: smnair


value: joezee

value: Show HN: Klana – AI Design Copilot Plugin for Figma

author: joezee


value:
Hey HN, I’m Matthew — 13 years old, from Nigeria
24 hours ago I published princejs — a tiny Bun-native web framework I built in 3 days on my school Windows laptop.
Real 30-second autocannon benchmarks (same route, same machine):
- princejs → 256,000 requests (8,526 req/sec)
- Hono → 241,000
- Elysia → 286,000 (still fastest but 2 MB+ unpacked)
princejs beats Hono by 6%, is only ~10% behind Elysia, and is *200× smaller* (10 kB total, 178 lines of code, zero deps).
Website + docs: https://princejs.vercel.app
npm: npm i princejs
I’m just a kid with a school laptop and a dream.
Would love your feedback!

value: Show HN: Princejs – <10 kB Bun framework by a 13-year-old Nigerian, beats Hono


value: Traclea

value:
In the past month alone, infostealer malware has dominated headlines with breaches exposing billions of credentials—183M Gmail-linked logins dumped in late October (via Synthient's monitoring, loaded to HIBP on Oct 21), Nikkei's Slack histories for 17K+ employees compromised by a single August infection (disclosed Nov 4), and an aggregated 16B credential mega-leak from ongoing stealer campaigns (Cybernews, June but with fresh Oct dumps).
These aren't flashy ransomware hits; they're silent endpoint infections turning your browser, wallet, or Slack into hacker fodder. Vidar 2.0's upgrade (Oct 6) is already spiking campaigns, filling the Lumma void with faster exfil and better evasion.
Traditional breach checkers like HIBP are great for historical dumps, but they miss the real-time flood of stealer logs—raw credential grabs from infected machines, sold cheap on Telegram/dark forums for ATOs and fraud.
That's where Traclea steps in: we scan your emails and usernames across both breach archives and live stealer databases, with platform-specific filters (e.g., Steam/Discord creds, crypto wallets like MetaMask/Binance, socials like IG/TikTok). Alerts hit instantly when your data surfaces, so you can rotate keys before the phishing starts.
Key bits:
Monitors 1000s of sources, including emerging threats like FleshStealer or PhantomVAI loaders.
Username search beats email-only tools—catch exposures on gaming/streaming accounts that slip through.
Simple dashboard, no fluff.
We're bootstrapped, privacy-first (no data resale), and aimed at devs/security folks tired of waiting for breach notifications.
To kick off awareness: First 1000 to join our waitlist get 1 month free on the individual Basic plan.
Head to https://traclea.com/ and sign up—let's make November less breach-y.
Thoughts? Ever dealt with a stealer hit? What's your go-to for credential hygiene?

value: Show HN: Infostealers in Nov 2025: 183M Gmail, 16B Logins, Nikkei Slack

author: Traclea


value:
React Chrono v3.0 is a major release for the popular React timeline component.
Highlights:
- Migrated to Vanilla Extract for zero-runtime CSS (no styled-components)
- New grouped configuration API (props: layout, interaction, content, display, media, animation, accessibility, i18n)
- Per-element Google Fonts control and font caching
- Complete i18n covering 40+ text elements
- Cross-browser fullscreen mode with keyboard shortcuts
- Advanced dark mode (36 customizable theme props, 13 new in v3.0)
- Sticky toolbar, enhanced search
- TypeScript, React 18.2+ support
- Full backward compatibility with v2.x
More details in the README and changelog!

value: Show HN: React Chrono v3 – Zero-runtime CSS,grouped API and fullscreen timelines


value: nicklewers

value:
Hi HN,
I've recently published my first iOS app to the Appstore! Its a photo cleanup app which uses on-device Apple Vision models to cluster similar images together into groups, and then ranks each photo within each group.
Link: https://apps.apple.com/app/ai-photo-cleaner-sorter/id6754172...
How it works:
The best photo from each group gets marked as "Saved" by default, however in the Review stage, you can select others from the group to save if you want or unselect the "Best" one and choose another one to keep. The general concept is "keep the best, remove the rest", and in theory if you agree with the clustering/scoring and didn't want to make any amendments, you could clean up 100's of photos (and MBs) with just a few taps.
Motivation:
I'm one of those people who somehow builds up an insanely bloated camera roll - 15 photos of the same restaurant dish, 10 versions of the same sunset. So I initially built this for myself to help cleanup those 500 photos taken during a 1 week vacation... I've tried a load of the other apps out there, but most seem to have adopted a Tinder style process - swipe left, swipe right, which hardly decreases the workload. I wanted an opinionated way to do a mass cleanup easily.
Things on my roadmap:
- Localisation for major languages
- Improved background processing (so you can clean up 1,000+ photos without keeping the app open)
- User preferences for how the photo scoring is calculated i.e. some people value emotional signals over image sharpness.
Its totally free to use (maybe one day I'll try and monetise it, if I'm ever fully satisfied )), I'd love any feedback - good and bad! (Especially would appreciate feedback on how well the grouping and scoring performs for you - any quirks?)
Thanks!
Nick

value: Show HN: AI Photo Cleaner iOS app (on-device processing)

author: nicklewers


value: leonard-somero

value: I figured I needed a persistent site for my works, where my marketing efforts and long-form musings aren't washed away by social media doomscrolls or group chats.<p>By declaring it's all hand-written HTML files without JS (+1 global CSS file), I have achieved an affordable and super-fast read-only website, which I hope sets an example.

value: Show HN: Static Home Page

author: leonard-somero

title: Show HN: Static Home Page


value: tech-aguirre

value: AI Agents in GoLang, open-source, cloud-native, GitOps-enabled, everything you need to start fast, run it where you want: local, your cluster or ours.

value: Show HN: AgentGoGo

author: tech-aguirre

title: Show HN: AgentGoGo


value: olliepro

value:
A teaching/learning tool to understand popular optimization Algorithms against a roughly textured quadratic landscape
In 2D you can visualize all optimizers at once and see their behaviors around local minima etc.
In 3-15D you can visualize the loss landscape for the first two parameters being optimized. The loss landscape changes as the unobserved parameters change, and because of this, you can only visualize a single optimizer at a time.
This was a quick weekend project vibe coded with the help of codex. Github is here:
https://github.com/olliepro/ND-Optimizer-Arena/tree/main

value: Show HN: ND Loss Optimizer Arena

author: olliepro

title: Show HN: ND Loss Optimizer Arena


value: screemers

value: Show HN: Smart File Organizer, a free app to automatically sort your folders

author: screemers


value: Show HN: I indexed every signature move in basketball


value: sillysideprojs

value: This was a goofy little project I worked on. It's fully open source and you can check it out via the Github link.

value: Show HN: Dice of Sending – I prefer physical dice but need to roll online

author: sillysideprojs


value: I quit my well-paid job in a good company to work on my “next big thing” startup, with ~6 months of runway and the usual plan: ship in a month, then do marketing and SEO. Reality: the launch kept slipping, my runway kept shrinking, and the SEO tools I needed started at ~$200/month subs — which, at that point, was basically “one extra month of life” money. So I asked my wife (also a dev) to vibecode a minimal internal rank tracker for me. Two weeks later it worked well, so in 2 extra weeks we shipped a production-grade product. Looking for feedback.

value: Show HN: There're TOO many SEO tools, so my wife created a new one


value: rodyoversloot

value:
Hi HN,
I built DeepFakeCheck.com a lightweight web tool that lets you upload an image and immediately check if it’s AI-generated or real.
For this test launch I will manually increase the free scans to 25 (instead of the usual 3) after sign up so you can test it thoroughly for free and give feedback.
Please be patient while I check the signups as much as possible and manually increase the credits from 3 to 25 (usually within minutes) unless I am sleeping :)
Try it here: DeepFakeCheck.com
Love to know what you think! Thanks for checking it out.
Rody.

value: Show HN: Deepfakecheck.com – AI deepfake detector (25 free scans for launch)

author: rodyoversloot


value: sReact

value:
I built sReact™, a universal stability metric for large language models.
It detects when AI systems start to drift – quietly, precisely, and in real time.
No data storage. 100% privacy by design.
Built for the upcoming EU AI Act.<p>Would love to get feedback from this community –
especially on use cases and interoperability with existing monitoring tools.

value: Show HN: SReact – AI stability and drift metric (built for EU AI Act)

author: sReact


value: RuehlJohnson

value:
Hey HN, we’re Ruehl, Owen, and Gavin. We’re building Sportfolio (https://sportfolio.gg/), a stock market for sports where fans trade players.
Demo video: https://www.youtube.com/watch?v=KF6bnx9S4J4
We noticed that while sports betting is very popular, it’s mostly you vs. the app and not very social. However, everyone is already watching the same game together, talking about the players, and reacting to every play in real time. We wanted a game with those aspects.
On Sportfolio, you join a weekly tournament, start with the same set of player shares as everyone else, and trade anytime to build the best portfolio. Player performance drives share prices in real time, and there’s a live leaderboard showing how you compare to friends and other users.
Everything is free-to-play right now. This is our first public launch, and we'd love your feedback on the product (UI/UX, trading model, the “fun” aspect, overall engagement).
Thanks for checking it out.

value: Show HN: Sportfolio – Stock market for sports where fans trade players

author: RuehlJohnson


value:
Hi all,
I created Sente, which is an agent that controls remote browsers. It can receive complex tasks like "find me the cheapest tickets from NYC to Tokyo from end of Feb to mid Match" and execute them live, where you can see a stream of the browser it controls in the cloud.

value: Show HN: Sente Controls Remote Browsers

title: Show HN: Sente Controls Remote Browsers


value: Show HN: I made a self-expanding, PXE booting Docker Cluster based on CM5s


value: ruben-davia

value:
Hi HN,
We’re Ruben, Afnan, and Theo, and we’re building Davia to solve a common problem: documenting and explaining large codebases is complex. It takes too long to generate even a first draft of a wiki, visuals are essential to understand the structure, internal docs should be editable in the IDE, and most solutions aren’t open.
Davia is an open source tool. You enter the path of your repo, and it generates a visual wiki you can explore and edit. Diagrams are created automatically, and you can update everything either in your IDE or in a Notion-like editor.
The project is still early, and we’d love to hear feedback, ideas, or experiences from anyone interested in documenting and sharing code internally.
GitHub: https://github.com/davialabs/davia

value: Show HN: Davia – Open source visual, editable wiki from your codebase

author: ruben-davia


value: radiusvector

value:
I built Clarion to fight the sense that staying informed has to feel dreadful. It’s a news app that parses stories from 2,000+ global sources and uses frontier models to surface journalism with clarity, context, and progress — not panic.
Built with Vite + React + TypeScript + Tailwind.
Live at https://clarion.today
 — would love feedback on the curation logic, ranking heuristics, or model tuning ideas.

value: Show HN: Clarion: An AI-powered news curator emphasizing clarity over chaos

author: radiusvector


value: solomonayoola

value:
Hey HN,
We’ve been working on EchoStack, a platform for deployable Voice-AI Playbooks — pre-built, outcome-driven automations that connect directly to business tools like HubSpot, Twilio, or Calendly.
Instead of building from scratch, users define what they want in a Manifest — a simple, declarative blueprint that tells our system:
The goal (e.g., qualify inbound leads, handle after-hours calls)
The tools to connect
The actions to trigger
The orchestrator reads the manifest, provisions the connections, and launches the playbook instantly.
It’s like Terraform, but for business outcomes — instead of spinning up servers, it spins up automations that deliver measurable results.
We’d love feedback on the approach, architecture, or potential edge cases we should think about.

value: Show HN: EchoStack – Deployable Voice-AI Playbooks Powered by a Manifest System

author: solomonayoola


value: jaaamesey

value:
Over the past few weeks, I've been working on a casual multiplayer game called "PictoRally". It's like Pictionary, but limited to 16x16 pixels, and 8 colors of your own choosing.
The core game mode works by passing URLs with another player over some kind of messaging service (e.g. Discord, WhatsApp, and maybe even post?). There's no time limits here, you just exchange drawings and guesses at your own pace, sort of like correspondence chess - that's what I mean by "async".
What's truly cursed is that this mode doesn't rely on a server or database: drawing bitmaps are compressed directly into each share URL. The "live" game mode (for 3 or more players), being a bit closer to a traditional "Jackbox"-style experience, does have a real backend though.
If you're curious, the tech stack is SolidJS, Astro, and Cloudflare Durable Objects. Something even more cursed: every pixel in the editor is actually its own <div>. This was originally a joke that went way too far, but I never got around to switching to something canvas-based and performance remained... fine?
It's designed for phones first, so apologies if you're on a desktop browser - it'll still work there, but there's currently a lot of unused horizontal space.

value: Show HN: An async Pictionary-like, at 16x16 pixels and 8 colors (mobile web)

author: jaaamesey


value: howlanderson

value:
Hi HN,
I built AgentSilex because I was frustrated trying to debug and customize existing agent frameworks. When something went wrong, I'd spend hours digging through thousands of lines of abstraction layers.
AgentSilex is different: the core is ~300 lines of readable Python. You can actually understand how your agents work.
Quick example - a working agent in 20 lines:

    from agentsilex import Agent, Runner, Session, tool

    @tool
    def get_weather(city: str) -> str:
        return "SUNNY"

    agent = Agent(
        name="Assistant",
        model="gpt-4o-mini",  # or any LLM via LiteLLM
        instructions="Help with weather info",
        tools=[get_weather]
    )

    result = Runner(Session()).run(agent, "What's the weather in Paris?")

Features:
- Multi-agent handoffs
- Streaming responses
- MCP protocol support
- Built-in OpenTelemetry tracing
- Works with 100+ LLMs (OpenAI, Anthropic, DeepSeek, Gemini, local models, etc.)
It's designed for:
- Developers who want to understand agent internals
- Companies needing a customizable foundation
- Anyone learning how LLM agents actually work
The trade-off: minimal built-in tools. You'll write more glue code, but you'll know exactly what your system is doing.
I'd love feedback on:
- Is the API intuitive?
- What use cases am I missing?
- Would you actually use this over existing frameworks?
pip install agentsilex
GitHub: https://github.com/howl-anderson/agentsilex
Looking forward to your thoughts!

value: Show HN: AgentSilex – Transparent agent framework you can read in 30 minutes

author: howlanderson


value: barefootford

value: Claude Code skills so it can automatically analyze video footage and build rough cuts or sequences. A Ruby library generates XML for Final Cut or Adobe Premiere.

value: Show HN: Edit Video with Claude Code

author: barefootford

title: Show HN: Edit Video with Claude Code


value: ktaraszk

value:
Most cloud PaaS cost isn’t the CPU you actually use - it’s the CPU you reserve and then sits idle 90% of the time.
Traditional platforms still charge per app, even if each app barely touches the CPU.
We redesigned scheduling: CPU is dynamically shared across your workloads based on real demand. You pay once for the resource, rather than multiple times for idle capacity.
I'm happy to discuss technical details, scheduler design, and the challenges we encountered along the way.

value: Show HN: Fair CPU scheduling to run unlimited apps on one plan

author: ktaraszk


value: juniorlimaivd

value: Show HN: Let me ask AI for you

author: juniorlimaivd

title: Show HN: Let me ask AI for you


value: dogol

value: Elkirtasse is Maktabah Shamilah alike by Qt6, cross-platformm application creator<p>So please help develop further on our own by our own for our own

value: Show HN: Maktabah Shamilah alike for all OS: Elkirtass

author: dogol


value: Show HN: A free Instagram story viewer that lets you watch anonymously


value: withwho

value: Built an accountability agent that tracks user activity so I don't procrastinate. Thought showing a fun simulation of the usability of the app on the web would be cool. Any thoughts greatly appreciated!

value: Show HN: Simulation of a desktop app I built on the web

author: withwho


value: un-nf

value:
The README: https://github.com/un-nf/404/blob/main/README.md
Or the LP: https://404-nf/carrd.co
Or read on...
In a small enough group of people, your TLS-handshake can be enough to identify you as a unique client. Around six-months ago, I began learning about client-fingerprinting. I had understood that it was getting better and more precise, but did not realize the ease with which a server could fingerprint a user - after all, you're just giving up all the cookies! Fingerprinting, for the modern internet experience, has become almost a necessity.
It was concerning to me that servers began using the very features that we rely on for security to identify and fingerprint clients.
- JS - Collection of your JS property values
- Font - Collection of your downloaded fonts
- JA3/4 - TLS cipher-suite FP
- JA4/T - TCP packet header FP (TTL, MSS, Window Size/Scale, TSval/ecr, etc.)
- HTTPS - HTTPS header FP (UA, sec-ch, etc.)
- Much more...
So, I built a tool to give me control of my fingerprint at multiple layers:
- Localhost mitmproxy handles HTTPS headers and TLS cipher-suite negotiation
- eBPF + Linux TC rewrites TCP packet headers (TTL, window size, etc.)
- Coordinated spoofing ensures all layers present a consistent, chosen fingerprint - (not yet cohesive)
Current Status: This is a proof-of-concept that successfully spoofs JA3/JA4 (TLS), JA4T (TCP), and HTTP fingerprints. It's rough around the edges and requires some Linux knowledge to set up.
When there are so many telemetry points collected from a single SYN/ACK interaction, the precision with which a server can identify a unique client becomes concerning. Certain individuals and organizations began to notice this and produced sources to help people better understand the amount of data they're leaving behind on the internet: amiunique.org, browserleaks.com, and coveryourtracks.eff.org to name a few.
This is the bare bones, but it's a fight against server-side passive surveillance. Tools like nmap and p0f have been exploiting this for the last two-decades, and almost no tooling has been developed to fight it - with the viable options (burpsuite) not being marketed for privacy.
Even beyond this, with all values comprehensively and cohesively spoofed, SSO tokens can still follow us around and reveal our identity. When the SDKs of the largest companies like Google are so deeply ingrained into development flows, this is a no-go. So, this project will evolve, I'm looking to add some sort of headless/headful swarm that pollutes your SSO history - legal hurdles be damned.
I haven't shared this in a substantial way, and really just finished polishing up a prerelease, barely working version about a week ago. I am not a computer science or cysec engineer, just someone with a passion for privacy that is okay with computers. This is proof of concept for a larger tool. Due to the nature of TCP/IP packet headers, if this software were to run on a distributed mesh network, privacy could be distributed on a mixnet like they're trying to achieve at Nym Technologies.
All of the pieces are there, they just haven't been put together in the right way. I think I can almost see the whole puzzle...

value: Show HN: Privacy Experiment – Rewriting HTTPS, TLS, and TCP/IP Packet Headers

author: un-nf


value: lexokoh

value:
Hi all, I'm Dante, and I'm building Echos, a platform that gives you pre-built AI agents so you can stop rebuilding orchestrators, database agents, and retry logic every time.
What it does:
- Pre-built agents: Database queries, API calls, web search, data analysis, code generation.
- YAML-based workflows: Define your agent architecture without rebuilding orchestrators.
- Built-in guardrails: SQL injection protection, SSRF blocking, table/domain whitelisting.
- Visual traces: See what happened, where it failed, and how much it cost.
Why I built it:
Every time I build a multi-agent system, I spend 2-3 weeks creating the same infrastructure: orchestrators that route tasks, database agents with SQL guardrails, retry logic, loop limiting, and cost tracking. Then another week of debugging when things break. I wanted to ship features, not plumbing.
Most frameworks are bulky and complex. You just want pre-built components you can compose like AWS services.
What Echos gives you:
- Ship faster: Pre-built agents you compose in YAML.
- Debug in minutes: Visual traces show exactly what happened, where it failed, and how much it cost.
- Prevent disasters: Built-in guardrails (SQL injection protection, SSRF blocking, loop limiting) catch 80% of dangerous operations.
- Control costs: Per-agent spending limits prevent runaway bills.
Try it:
Clone https://github.com/treadiehq/echos or go to https://echoshq.com
import { EchosRuntime } from '@echoshq/runtime';
const runtime = new EchosRuntime({
 apiKey: process.env.ECHOS_API_KEY,
 apiUrl: process.env.ECHOS_API_URL,
 workflow: './workflow.yaml'  // Define agents and routes in YAML
});
// Simple usage
await runtime.run({
 task: 'Analyze customer churn',
 memory: { year: 2024, region: 'north' }
});
Tech:
- NestJS for the backend API: Needed structured DI and middleware for auth.
- Postgres for trace storage: JSON columns for flexible span logs, native SQL performance.
- Resend for magic link authentication: Reliable email delivery without managing SMTP.
- Nuxt 3 for the dashboard: SSR for fast initial load, client-side interactivity for live traces.
- Railway for deployment: Fast deploys. First time trying it. My previous default is Digital Ocean.
What I learned:
- Time saved is the real value: Teams don't want another framework, they want to ship faster.
- Debugging is 50% of the work: Visual traces that show the full execution path are essential.
- Simple guardrails work: Blocking DELETE/DROP and unknown domains catches most disasters.
- YAML > Code for config: Non-engineers can edit workflows without touching code.
Looking for feedback:
- Does this solve a real problem for you?
- Which agents would you use most? database, API calls, web search, data analysis, or code generation?
- Is YAML configuration expressive enough, or do you need more programmatic control?
- What agents should we add next? (GitHub, Slack, email, cloud APIs?)
- Would you use this for autonomous agents, or just one-off tasks?
- Would this save you time on your next multi-agent project?
- What's missing that would make this immediately useful?
Thank you!

value: Show HN: Echos – A lightweight multi-agent AI system with pre-built agents

author: lexokoh


value: sayanarijit

value:
## Why?
- ORMs are magical, but it's not always a feature. Sometimes, we crave for familiar.
- SQLAlchemy Core is powerful but `table.c.column` breaks static type checking and has runtime overhead. This library provides a better way to define tables while keeping all of SQLAlchemy's flexibility. See Table Factory.
- The idea of sessions can feel too magical and opinionated. This library removes the magic and opinions and takes you to back to familiar transactions's territory, providing multiple un-opinionated APIs to deal with it. See Wrappers and Decorators.
## Demos:
- https://github.com/sayanarijit/fastapi-sqla-fancy-core-examp...

value: Show HN: Typesafe async friendly unopinionated enhancements to SQLAlchemy Core

author: sayanarijit


value:
Hi HN! This is my second submission today about *FateGuide*. My project, *suanmingzhun.com*, is an attempt to use *TypeScript* and *AI* not just to calculate, but to model the interpretation logic of complex Chinese Metaphysics systems.
The core engineering challenge here is converting *qualitative wisdom into quantitative data*—a problem I think many engineers dealing with unstructured data can relate to.
1.  *Modeling Interpretation Logic:* How do we turn the qualitative rules of *Bazi* (Five Elements interaction) and *Ziwei Doushu* (star combinations across 12 Palaces) into clean, deterministic TSX code? We built a modular system where each traditional rule is represented as a data object, allowing the *AI algorithm* to traverse complex logic trees consistently.
2.  *Consistency vs. Nuance:* The system ensures that if one user and another user input the exact same birth data, the "interpretation" logic generated for *Career, Wealth, or Marriage* is identical. This removes human bias while still preserving the multi-dimensional analysis required by these systems.
3.  *Why TSX?* TypeScript’s strict typing was crucial for managing the hundreds of interconnected rules (stars, palaces, flying stars, etc.) in *Ziwei Doushu* without runtime errors.
We are actively working on open-sourcing the non-proprietary parts of our Bazi/Ziwei data structure for the community.
(Find the technical deep-dive on our GitHub project here: [suanmingzhun.com].

value: Show HN: Data Modeling Ancient Chinese Logic (Bazi/Ziwei Doushu) with AI


value: EGreg

value: This is part of the Q.js framework that I'm putting out. Which itself replaces Angular, React, Vue, etc. while being smaller than React even without ReactDOM.<p>But the Handlebars.js one posted here is just a drop-in replacement.

value: Show HN: Drop-in replacement for Handlebars template engine, weighing 5KB

author: EGreg


value: GuiShou

value:
Hi HN!

  I built Nano Banana 2, a SaaS platform for AI-powered image using Google's Gemini 2.5 Flash.

You can try it for free.Would love your feedback!

value: Show HN: Nano Banana 2 – AI image generation SaaS powered by Google Gemini

author: GuiShou


value: spullara

value: I was on a United flight with an especially bad Viasat connection and I kept having to recheck it while trying to do some work. Since I had ollama and gpt-oss:120b on the machine I asked it to write a small app for me. Interactions with computers like this I think will soon be the norm. Most software will be requested by end users and built just in time by AI much like this app was built.

value: Show HN: Mac menu bar app to monitor your internet connection

author: spullara


value: kanodiaayush

value:
I'm building out Kerns, as an AI environment for research. You can seed a space with a topic and multiple source documents, and complete your research completely in one space. There's interactive mindmaps for exploration, podcast mode, powerful source readers with original plus chapter level summaries that let you zoom into source on demand, a powerful chat agent that lets you control context and cite refs, and AI assisted note taking.
My goal is to have one place to do research on any topic which minimizes manual context engineering, and jumping around between chat/notes/readers.
Please let me know if you try it and have feedback!

value: Show HN: Kerns, an AI interface to understand anything, better than NotebookLM

author: kanodiaayush


value: kuneosu

value:
A web-based 3D Rubik's Cube built with Three.js and React. I just added an English version to share it here on HN!
Features:
- QWEASD keyboard shortcuts for fast solving
- 16 camera angles
- Speed cubing timer with online rankings
- Undo/redo
I wanted to bring speed cubing to the web so people could compete online. Built this with Claude Code after learning Three.js. It takes a bit to get used to compared to a physical cube, but feedback welcome!
My best time: 1:14:361
 https://rubiks-cube-sandy.vercel.app/

value: Show HN: 3D Rubik's Cube Speed Cubing in the Browser

author: kuneosu


value: Show HN: Comprehensive token usage analysis and cost tracking for opencode


value: Show HN: PhantomCollect – Open-Source Web Data Collection Framework in Python


value: lymanli

value: I’ve been working on a small browser puzzle game called Block Sweep, and I just released it on the MiniPlay platform. The idea mixes the logic of 3 Tiles with the clearing mechanics of classic Block Puzzle. You pick blocks from stacked layers and place them onto a grid to complete lines and sweep the board clean. Since many blocks are hidden beneath others, you need to think ahead and plan your sequence before you run out of space.<p>I created 48 handcrafted levels for the first version, each getting progressively trickier as new layouts force you to rethink your strategy. The whole thing runs directly in your browser, no downloads or sign-ups required, and works on both desktop and mobile.

value: Show HN: I built a puzzle game that combines 3 Tiles and Block Puzzle

author: lymanli


value: arlindb

value:
Hey HN
I’ve been building Mapnitor, a lightweight server monitoring platform focused on speed, simplicity, and no bloat.
It’s designed for small teams, sysadmins, and hosting providers who just want quick visibility — without setting up Zabbix, Grafana, or a huge stack.
What it does:
Uptime & latency checks (Ping, TCP, HTTP)
Clean dashboard with per-node performance view
Lightweight agent (optional) — or just add targets directly
Instant history and analytics view
What it doesn’t do:
No complex dashboards, plugins, or configs.
No heavy setup or long onboarding — just add IPs and go.
Why I built it:
I manage multiple servers across clients and hosting environments — I was tired of setting up full-stack monitoring tools for small cases.
Mapnitor started as a personal script, then evolved into a minimal SaaS that can handle 20+ devices from different companies in seconds.
It’s still in early beta, so I’d love feedback from sysadmins, hosting engineers, or anyone dealing with uptime monitoring.
https://mapnitor.com
Would love to hear:
What features matter most for small infra setups?
Should I focus next on alerting (email/Slack) or integrations?

value: Show HN: Mapnitor – Lightweight server monitoring for Linux and Windows

author: arlindb


value: Aplikethewatch

value: Hey HN! This is a tool I built to evaluate system prompts using the metrics proposed from Anthropic's effective context engineering blog post. It determines how vague or specific your system prompt is, and provides suggestions for improvement so that it is 'just right'!

value: Show HN: Promptometer – feedback on your AI Agent system prompts

author: Aplikethewatch


value: Show HN: Open-source OAuth2 server Ory Hydra 25.4 ships OAuth2.1 and Device Auth


value: gamosoft

value:
Hey HN! I built NoteDiscovery - a self-hosted note-taking app that stores everything as plain markdown files, and most importantly, free! :-)
Why I made this: I do like Obsidian and been using it for quite some time, but having to install in all different computers to use it in, plus the hassle of syncing notes made me think about a new alternative.
Key features:
- Docker
- Plain markdown files
- Modern web UI with live preview
It's 100% free and MIT licensed.
Website: https://www.notediscovery.com
GitHub: https://github.com/gamosoft/NoteDiscovery
Please bear in mind it's in VERY early stages so it doesn't get even close to what the excellent Obsidian can do, but for the time being it's more than enough to suit my personal needs, so depending on my availability I may be adding more features. ;-)
I really hope you like it!

value: Show HN: NoteDiscovery – Free Self-Hosted Alternative to Notion/Obsidian

author: gamosoft


value: iaroo

value: Show HN: Autogenerate efficient backward kernels for Triton

author: iaroo


value: lone-cloud

value:
Gerbil is an open source app that I've been working on for the last couple of months. The development now is largely done and I'm unlikely to add anymore major features. Instead I'm focusing on any bug fixes, small QoL features and dependency upgrades.
Under the hood it runs llama.cpp (via koboldcpp) backends and allows easy integration with the popular modern frontends like Open WebUI, SillyTavern, ComfyUI, StableUI (built-in) and KoboldAI Lite (built-in).
Why did I create this? I wanted an all-in-one solution for simple text and image-gen local LLMs. I got fed up with needing to manage multiple tools for the various LLM backends and frontends. In addition, as a Linux Wayland user I needed something that would work and look great on my system.

value: Show HN: Gerbil – an open source desktop app for running LLMs locally

author: lone-cloud


value: wiso

value: Show HN: High-Performance .NET Bindings for the Vello Sparse Strips CPU Renderer

author: wiso


value: Vonng

value: Show HN: PostgreSQL Extension Cloud

author: Vonng

title: Show HN: PostgreSQL Extension Cloud


value: mustaphah

value:
A small tool to explore and build GraphQL queries visually.
I built it for fun, but I think it has real utility for navigating complex, deeply nested GraphQL schemas like GitHub's, which can contain hundreds of interconnected object and interface types.
Under the hood, it works by recursively introspecting the schema based on user selections.
Everything runs locally in the browser.
Source code: https://github.com/mhadidg/gqlvis

value: Show HN: Visual GraphQL Query Builder

author: mustaphah

title: Show HN: Visual GraphQL Query Builder


value:
Hey HN,
I've been working on an Java alternative to Streamlit in the last 4 months: Javelit.
I've explored interactive programming and malleable code principles for a long time, and I've always felt that this kind of tool was missing in the Java ecosystem. I think this will be useful to the Java community for presentations, experimentation, talks, learning, data viz apps and small back office apps.
You can try a few apps in your browser in the playground: https://javelit.io/playground
(note this is not compatible with Safari).
Under the hood the project is a bit different from Streamlit, as it is possible to integrate Javelit apps in existing Java systems. There are 2 main ways to run and build Javelit: "standalone", as a CLI, with a great hot-reload experience and a single click deploy with railway, or "embedded", as a library in an existing system.
The API to build custom components is also simpler, as there is no difference between official components and user components.
If you are curious about my build process, I maintain a dev log here:
https://world.hey.com/cdecatheu/

value: Show HN: I Built Streamlit for Java

title: Show HN: I Built Streamlit for Java


value: sifulweb

value:
I create a text-to-email template generator.
I built a small tool that converts plain-text email drafts into responsive HTML email templates.
How it works:
Type a prompt → get a responsive email instantly.

value: Show HN: I create a Free Text to Email template generator

author: sifulweb


value: Show HN: Visualizing 8k+ LLM papers with t-SNE (awesome-LLM-papers.github.io)


value: robjampar

value:
I've built an open source library that can take a Python GraphQL service and expose it as an MCP service.
It supports using Strawberry, Adriane, Graphene or anything that uses graphql-core.
Just interested in any general feedback or seeing if this is useful for anyone!

value: Show HN: Graphql-MCP Python Library

author: robjampar

title: Show HN: Graphql-MCP Python Library


value: hauxir

value: Show HN: Query ClickHouse Databases from ChatGPT Custom GPTs via OpenAPI

author: hauxir


value: andupotorac

value:
I built a prototype that lets users send voice commands to your site, as long as you expose some core functionality through MCP. Think of it as Siri for your website.
If you scroll down, there’s a quick demo game where you move a ball into the goal using your voice. You can even chain actions like “one square down, two left”, depeding on the maze you get.
This could be useful for products with complex workflows, or e-commerce sites where screen space is focused on a single CTA. The YT video also shows it running on an actual website.
There are still bugs to iron out & I haven’t tested on mobile or tablet yet, so please use a desktop for now. :-)
Note: this isn’t for customer support, but for user actions.

value: Show HN: Siri for Sites Using MCP

author: andupotorac

title: Show HN: Siri for Sites Using MCP


value: Show HN: Chess960^2 is now open source

title: Show HN: Chess960^2 is now open source


value: nicohayes

value: Show HN: Nano Banana 2 – Next-Generation AI Image Editor and Generator

author: nicohayes


value: pandaupup

value:
Hi:
I built WordToTime.org, a free browser-tool that helps you convert word-counts, character-counts or page-counts into estimated time for speaking, reading, voice-over or audiobook delivery (all processed locally in your browser).
Why I built it
When working on talks, podcasts or video scripts, I kept asking: “How many minutes will it take to read this aloud?” Simple word-to-time tools exist but often ignore realistic pause modelling, language differences, or character/ page-based inputs. This tool addresses those gaps.
Key features
Paste text or input count → estimate duration (HH:MM:SS) for various delivery modes
Select mode (speech / silent reading / voice-over / audiobook) +  WPM
Local processing only: your text never leaves your device; no upload or server-storage. (See our privacy policy)
WordToTime.org
Useful for content creators, educators, presenters, authors, voice-over artists
Why you might find it interesting
Lightweight front-end engine with configurable pacing and language presets
Bridges the gap between raw word-count and actual delivery time
Embeddable future widget/API planned for script-tools, editor integrations
Would love to hear your feedback:
What delivery modes do you use (live talk vs video vs voice-over)?
What WPM or character-per-minute benchmarks do you personally track?
Any features you wish such timing tools had?
Thanks for checking it out!

value: Show HN: A tool to estimate how long your script/speech will take to read aloud

author: pandaupup


value: Martibis

value: Show HN: Boosterpack – Create full website from a business name

author: Martibis


value:
I kept missing incidents until it was too late. By the time my monitoring alerted me, servers/nodes were already unrecoverable.
So I built Linnix. It watches your Linux systems at the kernel level using eBPF and tries to catch problems before they cascade into outages.
The idea is simple: instead of alerting you after your server runs out of memory, it notices when memory allocation patterns look weird and tells you "hey, this looks bad."
It uses a local LLM to spot patterns. Not trying to build AGI here - just pattern matching on process behavior. Turns out LLMs are actually pretty good at this.
Example: it flagged higher memory consumption over a short period and alerted me before it was too late. Turned out to be a memory leak that would've killed the process.
Quick start if you want to try it:

  docker pull ghcr.io/linnix-os/cognitod:latest
  docker-compose up -d

Setup takes about 5 minutes. Everything runs locally - your data doesn't leave your machine.
The main difference from tools like Prometheus: most monitoring parses /proc files. This uses eBPF to get data directly from the kernel. More accurate, way less overhead.
Built it in Rust using the Aya framework. No libbpf, no C - pure Rust all the way down. Makes the kernel interactions less scary.
Current state:
- Works on any Linux 5.8+ with BTF
- Monitors Docker/Kubernetes containers
- Exports to Prometheus
- Apache 2.0 license
Still rough around the edges. Actively working on it.
Would love to know:
- What kinds of failures do you wish you could catch earlier?
- Does this seem useful for your setup?
GitHub: https://github.com/linnix-os/linnix
Happy to answer questions about how it works.

value: Show HN: Linnix – eBPF observability that predicts failures before they happen


value:
Hey HN,
I’ve been building CyBox Security a platform that acts like a virtual security team for developers.
It combines multiple security scanners into one unified dashboard, covering SAST, SCA, IaC, and Secrets in a single workflow.
The idea came after seeing how many small dev teams and startups don’t have dedicated security staff, yet still need continuous scanning and clear remediation guidance.
CyBox integrates with GitHub and stores only scan results (no source code).
It’s still early (MVP stage), but it already works, you can sign up, connect a repo, and see scan results in minutes.
Would love your feedback, use cases, and what matters most for you when using security tools.
Thanks

value: Show HN: CyBox Security – Your Virtual Security Team for the AI Era


value: lifenautjoe

value:
Hey HN, Joel here, co-founder of Venturu.
Imagine trying to buy a house before Zillow. That’s what buying a local business is like today. It's a massive market, but it's completely fragmented and stuck in the 90s.
My co-founder, Luis, discovered this firsthand by knocking on doors to buy six of his own businesses. I saw it at industry conferences, where at 29, I’m usually the youngest person in the room. The system is built on gatekept information and a wall of fees designed to keep people out.
For a small business owner, it starts with a gut punch: you have to pay thousands of dollars just to get an idea of what your life's work is worth. Then, you face thousands more in listing fees just to get it seen on an outdated platform.
This broken model forces brokers to be gatekeepers. The high costs mean they can only list a fraction of their clients' businesses, hiding the rest on thousands of separate, clunky websites.
We’re trying to fix this by building the single, open, and free platform this market needs. We got rid of the scary upfront fees by offering free, instant valuations, and unlocked the hidden market by making all listings free.
It’s one place for owners, buyers, and brokers to finally connect efficiently.
It seems to be working. We’ve welcomed over 1,300 brokers who have listed 3,800+ businesses across all 50 states.
It's still early days, but our goal is to build this into the definitive marketplace for local businesses, creating the first real source of truth for valuations and making the entire process, from discovery to closing, more straightforward.
We’re building in the open and would love your feedback.
Ask us anything.

value: Show HN: Venturu – Zillow for the market of local businesses

author: lifenautjoe


value: Show HN: Grizzly – Memory-Efficient Zip Viewer for macOS with Quick Look Preview


value: courtcircuits

value:
Last weekend, I built a TUI to download crackmes.one challenges from my terminal because
come on, who leaves his terminal in 2025 ?<p>(btw the cli is written in rust)

value: Show HN: Cracked – a TUI to download crackmes.one challenges easily

author: courtcircuits


value: kinderpingui

value: Create a localhost API with FastAPI + GPT-5 tool calling to compute emotion weights from user prompts. Includes code, curl I/O, and built-in safety checks.

value: Show HN: Emotion-Weighting API with GPT-5

author: kinderpingui


value:
Hey HN,
I've been frustrated that every time I switch AI tools (ChatGPT, Claude, Gemini, etc.), I lose all context about my preferences, projects, and past conversations. So I built a portable memory system that maintains context across different AI platforms. This became the #1 product of the day on product hunt
How it works:
A browser extension that lets you store highlights, conversations, file uploads etc. and then adds inline context to any of your query on any agent
Current state:
- Works with all major AI platforms (ChatGPT, Claude, Grok, Gemini, Perplexity etc.).
- Can create n different contexts and select which one you want to use when
- implementing TEEs on the backend for privacy
What I'm unsure about:
I'm trying to figure out if this is actually valuable enough for people to pay for, or if it's just solving my own niche problem. Questions for this community:
Do you find context-switching between AI tools painful enough to pay to solve it?
What would make you trust a third-party service with your AI conversation context?
Is this something you'd expect to be free, or does $12/month seem reasonable?
Would love honest feedback - especially if you think this is a terrible idea or I'm missing something obvious.
PH Launch: https://www.producthunt.com/products/ai-context-flow
AI Context Flow extension: https://chromewebstore.google.com/detail/ai-context-flow-imp...

value: Show HN: Built a portable memory layer that works across AI tools


value: tikazyq

value: Show HN: LeanSpec - Built in 10 days using the specs it defines

author: tikazyq


value: Kerenm

value:
Hi HN,
After 15 years in product management, I realized there’s nowhere for Product Managers to actually practice, most learning focuses on theory, not doing.
I built The Product Sandbox, a platform where PMs can practice real-world product scenarios, make decisions, and strengthen core skills through daily missions and challenges. It’s designed for aspiring to senior PMs who want to keep improving through real practice.
I’d love feedback from this community on how you think PMs (or teams) can best build real-world experience outside of their day jobs.
https://sandbox4pm.com

value: Show HN: The Product Sandbox – Practice real-world product challenges

author: Kerenm


value: Show HN: Hypalink – Web Component for organizing links on websites and web apps


value:
Server Compass gives you the same Vercel's UX on a $5 VPS - no terminals, no YAML, just ship.
Connect your GitHub repo and deploy with one click. Automatic SSL certificates, domain management, env variables, real-time logs, and server monitoring - all through a clean desktop app. No control panel bloat on your server, no subscription fees. Deploy Next.js apps like you're using Vercel, but pay $19 once.
Own your infrastructure & stop paying platform premiums for simple deployments.

value: Show HN: Self host your apps, domains, SSL. Vercel UX on your VPS


value: jackemerson

value:
Hey HN,
I'm Jack, and I built SpotScribe because I was constantly frustrated trying to reference specific quotes from podcasts I'd listened to. I'd remember hearing something insightful but couldn't find it without scrubbing through entire episodes. Spotify doesn't provide transcripts for most podcasts, so I built this tool to extract them automatically.
Beyond transcription, I added AI summarization to quickly understand key points, and a chat feature so you can ask questions about the episode content and get instant answers. It's been useful for my own research and note-taking, and I'm curious to hear if others find it helpful too.
Happy to answer any technical questions about the implementation.
Link: https://spotscribe.io

value: Show HN: I built a Spotify podcast transcript generator with summaries and chat

author: jackemerson


value: Show HN: Vector-logic, a lightweight rules engine from first principles


value: Did you make the list?

value: Show HN: World Ranking of Games for Slacking Off at Work

story_text: Did you make the list?


value: Marceltan

value:
Hey HN, I'm Marcel from Tusk. We’re launching Tusk Drift, an open source tool that generates a full API test suite by recording and replaying live traffic.
How it works:
1. Records traces from live traffic (what gets captured)
2. Replays traces as API tests with mocked responses (how replay works)
3. Detects deviations between actual vs. expected output (what you get)
Unlike traditional mocking libraries, which require you to manually emulate how dependencies behave, Tusk Drift automatically records what these dependencies respond with based on actual user behavior and maintains recordings over time. The reason we built this is because of painful past experiences with brittle API test suites and regressions that would only be caught in prod.
Our SDK instruments your Node service, similar to OpenTelemetry. It captures all inbound requests and outbound calls like database queries, HTTP requests, and auth token generation. When Drift is triggered, it replays the inbound API call while intercepting outbound requests and serving them from recorded data. Drift’s tests are therefore idempotent, side-effect free, and fast (typically <100 ms per test). Think of it as a unit test but for your API.
Our Cloud platform does the following automatically:
- Updates the test suite of recorded traces to maintain freshness
- Matches relevant Drift tests to your PR’s changes when running tests in CI
- Surfaces unintended deviations, does root cause analysis, and suggests code fixes
We’re excited to see this use case finally unlocked. The release of Claude Sonnet 4.5 and similar coding models have made it possible to go from failing test to root cause reliably. Also, the ability to do accurate test matching and deviation classification means running a tool like this in CI no longer contributes to poor DevEx (imagine the time otherwise spent reviewing test results).
Limitations:
- You can specify PII redaction rules but there is no default mode for this at the moment. I recommend first enabling Drift on dev/staging, adding transforms (https://docs.usetusk.ai/api-tests/pii-redaction/basic-concep...), and monitoring for a week before enabling on prod.
- Expect a 1-2% throughput overhead. Transforms result in a 1.0% increase in tail latency when a small number of transforms are registered; its impact scales linearly with the number of transforms registered.
- Currently only supports Node backends. Python SDK is coming next.
- Instrumentation limited to the following packages (more to come): https://github.com/Use-Tusk/drift-node-sdk?tab=readme-ov-fil...
Let me know if you have questions or feedback.
Demo repo: https://github.com/Use-Tusk/drift-node-demo

value: Show HN: Tusk Drift – Open-source tool for automating API tests

author: Marceltan


value:
Hey HN,
So, I wanted to share something I've been building called Struxs.
The whole idea for this came from my old job as a consultant. I was building systems for all sorts of clients, and it felt like every other project involved scraping data from images—passports, receipts, driver's licenses, you name it. It was always a huge pain. I'd either be hunting for some super-specific OCR API for one document type, or I'd be stuck using generic OCR and then trying to duct-tape it together with a mountain of fragile regex. It was a maintenance nightmare and I was juggling a dozen different API subscriptions.
The final straw, funny enough, came from my wife. She's an accountant and was complaining about having to manually check invoice numbers from PDFs to see if they'd already been paid. She just needed one field, the invoice number. All the existing solutions were crazy expensive enterprise tools that did way too much.
It just clicked. Why isn't there a tool where I can just point at what I want from any document and get a simple API for it?
So I built Struxs.
The workflow is pretty straightforward: you upload a sample image or PDF. Then, in the editor, you don't draw boxes or anything complicated—you just click on the text you want and give it a key (like invoice_number or patient_name). You can define nested objects or lists for line items, whatever structure you need. The moment you hit save, it's a live, production-ready API endpoint.
And because I've spent years dealing with flaky third-party services, I knew this thing had to be fast and reliable. So I spent a ton of time on the backend building a custom orchestration layer to manage the GPU workloads. My whole goal was to build the kind of rock-solid API that I would have wanted to use for my own clients back in the day.
Anyway, I'm putting this out here to see if this is just a "me" problem, or if other devs have been banging their heads against the same wall. How do you all handle this kind of stuff right now?
I'd honestly love to hear any feedback you have, good or bad. The product is still early, and any thoughts would be super valuable.
There's a free plan with 200 credits to play around with, no credit card needed. You can build a couple of templates and see if it's useful.
You can check it out here: https://struxs.com/

value: Show HN: I built a tool to create custom OCR APIs in minutes, no training needed


value: upwithme

value: Show HN: An open-source, runtime-aware AI debugger to solve "AI Trust Paradox"

author: upwithme


value: sadafnajam

value:
Hi HN, I'm a Senior SDET at Plotly. My company just launched Plotly Studio, a new tool that uses AI to build data visualizations and analytics apps.
My job was to answer the big question: does it actually work with real, messy data? When I first started testing it against our collection of 100+ diverse datasets, our success rate was around 30%.
The problem I faced was that you can't just unit-test an AI that generates code for a desktop app. You have to test the full, end-to-end user experience.
So, I led the effort to build our own internal benchmark system to validate performance at scale. Every day, our CI (GitHub Actions) kicks off a job that:
Generates a full data app from each of our 100+ test datasets
Launches each app in a real browser using Playwright
Asserts that the app loads without any Python or JavaScript errors
Takes screenshots to verify the visual output
Runs each test 3 times to detect "flakiness" (inconsistent results)
This gave me and the rest of the team a clear, actionable metric. The dev team used the failure reports to improve the backend, and we just hit a 100% success rate on our latest test run.
I wrote an article about the architecture of this benchmarking system. We're now expanding it with user-donated datasets to make it even more challenging.
I'd love to hear your feedback. You can read my full technical write-up here:
Link: https://plotly.com/blog/chasing-nines-on-ai-reliability-benc...

value: Show HN: I benchmarked our AI tool from 30% to 100% success

author: sadafnajam


value: jmpavlec

value:
Hi all, I’ve been working on this project for a while but haven't shared it properly on Hacker News.
It is a casual gaming platform focused on simple multiplayer games that can be played in person with a central screen (like a TV) or remotely via video chat. You can also play on your smart Android based TVs via the app: https://play.google.com/store/apps/details?id=com.gametje (it was just released recently so could be buggy). It is also available directly in Discord: https://discord.com/discovery/applications/12153230008666071... as an embedded activity.
It is playable in 9 languages and doesn’t require any downloads. Most games revolve around creativity in some shape or form. They can be played by just about anyone whether or not you consider yourself a “gamer”. If you can text, you can play these games.
Why did I create it?
Some of you may see the resemblance to Jackbox games. I have been a huge fan of them for 10+ years and enjoyed playing their games a lot. However, I found their support for other languages a bit lacking. While living in the Netherlands, I have encountered quite a few non-native English speakers and wanted to help them have a similar experience. Jackbox also has some fragmentation issues between app stores. I own their games on PC and PS4 but I can’t share a “license” between them. They also come out with a pack every year with 5 games. You never know if the game(s) will be fun, or if you should try to buy a previous pack with the one killer party game in it.
I designed Gametje with these issues in mind. It is playable in multiple languages with more being added regularly (feel free to request one). You can play it from any device with a web browser. There is no need to install it via Steam or a game console. All games are available in one place with no “packs” to buy.
What’s up with the name?
I have been living in the Netherlands for some years and part of my original motivation stems from wanting to give my friends here a game to play in their native language. It's way easier to be witty/funny in your mother tongue after all! Because of that, I wanted to incorporate something Dutch into the site's name. The suffix ‘-tje’ is one of the diminutive endings in Dutch and is meant to soften a word or make it "smaller". Game + tje = Gametje, or a little game. I have been informed by native Dutch speakers that it should have been ‘Gamepje’ to be "correct" but I liked the way Gametje sounded better.
Where can I try it?
Go here: https://gametje.com/
You can test it out as a guest without signing up in order to get a feel for the games. Clicking into each game gives a short explanation and a small example of the gameplay. When creating a game room, you can choose to host via a central screen, host and play from a single device (like a phone) or cast the main screen to a Chromecast. There is also an Android TV app available that was just recently released: https://play.google.com/store/apps/details?id=com.gametje
After creating a game room, you can join from another browser window or device. You can also add AI players if you want to try it out on your own, although it is a lot more fun with real people. I also created a discord channel: https://discord.gg/7jrftHuHp9 where you can find other users to play with. If you sign up for an account, you can opt-in as an alpha tester and see the new games as they are developed. It’ll also keep track of all your previous games and make sure not to duplicate content. You can review previous games as well and relish in your past victories.
What am I looking for?
I am interested in feedback about the whole concept and also the gameplay. Is it fun? What could be improved? Interested in helping out? Let me know!
Happy to share the more technical details as well for those that are interested. You can also read a bit about the platform and games in my blog:
https://blog.gametje.com/
Thanks!

value: Show HN: Gametje – A casual online gaming platform

author: jmpavlec


value: htieu

value:
We’ve been working on Scout QA, a tool that uses AI to catch subtle bugs that usually slip past manual testing and reviews.
Instead of focusing on test automation, Scout analyzes your app’s UI, flows, and logs to surface tiny inconsistencies — things like broken states, unclear error messages, or small regressions that humans might ignore because “it still works.”
The goal isn’t to replace testers but to act like an extra pair of curious eyes — pointing out issues that affect real users’ experience.
We’re exploring questions like:
- How can an AI develop an intuition for “something feels off”?
- What kind of feedback loop helps it learn from real user bugs?
Would love feedback from developers, QA engineers, and indie makers:
what small-but-painful bugs do you wish tools could catch automatically?

value: Show HN: Scout QA – AI that finds small bugs people often overlook

author: htieu


value: cle-b

value:
Hi HN,
I recently updated my pytest plugin based on httpdbg to include the HTTP traces directly in the Allure reports. As with httpdbg, the idea is to have nothing more to do than to add an argument to your command line: --httpdbg-allure.
For example:
pytest examples/pytest_demo.py --alluredir=./allure-results --httpdbg-allure
For each test, all HTTP requests will be recorded and saved in the Allure report under a step named httpdbg.
You can check the README in the repository to see how it looks:  https://github.com/cle-b/pytest-httpdbg?tab=readme-ov-file#c... (the compact mode is quite simple, but the full mode is identical to the httpdbg UI).
I hope this will be helpful for some of you :) Any feedback is welcome.
If you enjoy using httpdbg, don’t hesitate to check out the Git repository to discover new features — and give it a star to help make it more visible.
httpdbg: https://github.com/cle-b/httpdbg  -- https://pypi.org/project/httpdbg/
pytest-httpdbg: https://github.com/cle-b/pytest-httpdbg -- https://pypi.org/project/pytest-httpdbg/
documentation: https://httpdbg.readthedocs.io/en/latest/test/
a blog post: https://medium.com/@cle-b/python-rest-api-tests-enhance-your...

value: Show HN: Pytest-httpdbg – a simple way to include HTTP traces in Allure reports

author: cle-b


value: vinserello

value:
I'm one of the creators of Datastripes, a visual data analysis builder. We've been building out our platform and kept running into a recurring pain point: we wanted to quickly prototype workflows that involved AI models from the Hugging Face ecosystem, but it always meant dropping out of our visual editor to write a Python script or a JS client to call the Gradio API. It killed the "flow" of building.
So we decided to solve it by building a "Hugging Face Space" node directly into our tool.
You give it the URL of any public, Gradio-based Hugging Face Space (e.g., user/space-name), and the node does the rest.
If you wanna try it: https://datastripes.com

value: Show HN: We built a node to use Hugging Face Spaces without writing API code

author: vinserello


value: a-kgeorge

value: Show HN: dspx — Serverless-friendly DSP for Node.js (native C++ + Redis state)

author: a-kgeorge


value: asbebe

value:
Hello Hacker News,
I'm Mack and I'm sharing a Chrome Extension I built to solve a huge bottleneck in data implementation: manually verifying if collected analytics data (GA4, Amplitude, etc.) matches the predefined log specifications. This QA process is time-consuming and prone to errors for developers, product managers, and data analysts alike.
Introducing Analytics Data QA
Analytics Data QA is a Chrome DevTools extension that automatically validates your collected events against your uploaded log definition files. It turns hours of manual checking into seconds of automated verification.
Key Features
Automated Log Validation: Instantly alerts you if an event parameter is missing, mismatched, or if the event structure deviates from your standards.
Visual Event Tracking: Organizes events by page and automatically captures a screenshot of the page where the events occurred, providing critical context for QA.
Recording & Diagnosis: Records the user actions that led to the event firing, making it simple to reproduce and diagnose issues.
Efficiency: Significantly reduces the time required for data implementation quality assurance.
Why Does it Require a Login? (Important Note)
I understand the Hacker News community prefers tools without sign-up barriers. However, the tool requires a quick login for the following reasons:
The login is necessary for data security and efficient project management, as the tool handles sensitive log definition files and organizes verification results across different client projects.
Try it Out Instantly!
To make testing easy, you can use the following temporary test account:
Temporary Test Email: test1@test.com
Password: 1q2w3e4r
Please note: This is a shared test account for immediate feature testing. To secure your own project files, a dedicated sign-up is recommended later.
Chrome Web Store Link: https://chromewebstore.google.com/detail/icopicdkpapbeojapna...
This is an early stage project, and any feedback—positive or critical—on the usability, features, or overall approach to data QA is highly appreciated. Thank you!

value: Show HN: Auto-Compare GA4/Amplitude Data Against Log Definitions

author: asbebe


value: tabpref

value:
Hey HN, Aaron here, founder of TabPref.com
.
A few months ago, I shared TabPref as a social network for the people who keep the hospitality world running: bartenders, servers, barbacks, vendors, and venue owners.
Since then, it has evolved far beyond networking.
TabPref is now an all-in-one platform for the service and hospitality industry that connects professionals, establishments, and vendors while powering real business operations.
It includes:
• Multi-profile switching for professionals, establishments, vendors, and consumers
• Business Hub with scheduling, time-clock, vendor catalogs, and POS integrations such as Toast, Square, and 7shifts
• Tab Chat for real-time team communication
• Groups and Events for community and discovery
• Jobs to help professionals find shifts and venues fill roles quickly
We are still in public beta, and user feedback continues to shape what comes next.
I recently submitted TabPref to Y Combinator to accelerate growth and bring more value to the people who keep hospitality alive.
https://tabpref.com
If you have ever worked behind the bar, managed a venue, or supplied one, I would appreciate your thoughts and feedback.
Cheers,
Aaron Horne
Founder, TabPref

value: Show HN: TabPref – The platform for professionals and businesses in hospitality

author: tabpref


value:
Working on pocket wise. Yet another personal finance tracker :)
I started with double entry accounting using hledger and it was always great financial realization but I would stop using it after a couple of weeks. So I started writing a web interface for my ledger file, with the intent to make it simple enough that I actually stick with it. This became pocket wise over time.
Pocketwise does not need need any bank credentials, instead you do the data entry.
But to make it easier you can type (or say) natural language descriptions of your expenses like - "Chipotle $15 cash" or "Netflix 19.89 Apple Card" and it will store them as double entries like

    2025-11-11 Chipotle
      Expenses:Food & Dining                    $15.00
      Assets:Cash                               $-15.00

    2025-11-11 Netflix
      Expenses:Subscriptions                    $19.89
      Liabilities:Credit Card:Apple Card        $-19.89

It has a 7 days free trail, if you decide to use it, I would love to hear your feedback. Thanks you. Thank you. Thank you. :)

value: Show HN: PocketWise – a simple, natural-language double-entry finance tracker


value:
Hi HN,
We're Davide, Gianluca and Marco from INAYA. We’re building autonomous AI analysts that help manufacturing procurement plan and buy under commodity price volatility.
We applied to YC W26 and are waiting for feedback. We previously led AI/Data programs at a Big Four with manufacturing clients; that vantage point made the procurement wedge obvious.
THE PROBLEM (AND WHY NOW)
Procurement is being asked to manage more risk with fewer people and tighter budgets. Price swings collide with slow approvals, expiring supplier quotes, and data scattered across tools. The result: missed hedging windows, re-sourcing under pressure, and margin leakage that finance sees only after month-end.
Recent CPO surveys show risk management and resilience have moved to the top of the agenda; teams need earlier signals, clearer explanations, and faster decisions — volatility didn’t end; slack did.
- Sunflower oil +39% (Q1 ’25)
- Aluminum premiums +250% (Oct ’25)
- Cocoa +85% (Apr ’25)
WHAT WE BUILT FIRST (BEFORE AGENTS)
To validate the need, we shipped four apps that teams use today:
1) Global OSINT Engine
Configurable and fully parametric. Define query templates, select countries and languages, and capture local-language signals (policy, logistics, trade) across 80+ languages and 230+ countries. It often surfaces indicators before market feeds.
2) Predictive Core
Uses market, macro, and sentiment features (500+ signals) to generate price forecasts up to 18 months.
On commodities currently released in our platform, backtests show 90–95% accuracy, outperforming the tools our pilot manufacturers use today. (We can share evaluation details—splits, metrics, baselines—in the comments.)
3) Spot & Forward Prices
Combines quoted and non-quoted spot prices with forward curves from major exchanges to give a full time view and a simple way to compare forecasts to market structure.
4) Multimodal What-If
Runs instant simulations to quantify exposure and show how price shifts flow into unit costs and margins (useful for make/buy timing and hedging).
Together, these apps give procurement comparable views (signals ↔ forecasts ↔ forward curves ↔ P&L impact) so decisions aren’t made in the dark or too late.
WHAT WE'RE ADDING ON
We’re adding an agent that uses these apps together, so teams can work conversationally instead of hunting through dashboards.
Ask:
- “Why did copper rise this week?”
- “What happens to our cost base if gas is +10%?”
- “Simulate Chinese export limits on copper.”
The agent runs OSINT → Forecasts → Spot/Forward → What-If, cross-checks the results, and returns a sourced explanation in plain language (in the app or via Teams/Slack). The goal isn’t to replace judgment but to shorten the path to a defensible decision.
WHERE WE ARE
Early access with manufacturers in the EU and US.
Next milestones: proactive alerts, deeper ERP/P2P integrations, and public notebooks for long-horizon forecast evaluation.
WHAT WE’D LIKE FEEDBACK ON
- Better ways to evaluate long-horizon forecast quality (beyond MAPE/hit-rate).
- How to design explanations users can trust (data traces, competing hypotheses, ablations).
- First integration points you’d want (ERP, P2P, Teams/Slack workflows).
- Any additional valuable feedback is more than welcome.
LINK
Website: https://www.inaya.ai
Thanks for reading — happy to go deep on the technical details.

value: Show HN: Inaya – AI agent for manufacturers managing commodity volatility


value: mushgev

value:
I built SpecMind, an open source developer tool for spec driven vibe coding. It keeps architecture and implementation aligned from the first commit instead of letting them drift apart.
With AI assistants writing more of our code, projects move faster but architectural consistency is often lost. Each developer or AI can introduce new patterns, and after a few sprints, the structure becomes fragmented. SpecMind helps prevent that by generating and maintaining living architecture specs directly from your code.
It works in three steps: 1. analyze – scans your codebase and generates .specmind/system.sm with architecture diagrams and relationships, 2. design  – creates a spec describing how the feature will change the system, 3. implement  – applies the spec, updates diagrams, and logs what changed
All specs are plain text files with Markdown and Mermaid diagrams, stored alongside your code. A VS Code extension lets you preview them visually.
Supports TypeScript, JavaScript, Python, and C#, with Go and Rust coming next. Works with Claude Code and Windsurf today, Cursor and Copilot soon.
Demo: https://www.youtube.com/watch?v=4-9gQxw8DQU
Repo: https://github.com/specmind/specmind
Would love to get feedback from engineers working on large or complex codebases.

value: Show HN: SpecMind – AI architecture tool for vibe coding

author: mushgev


value:
I just wanted independent temperature control for each room — a simple home project.
But after a few iterations of YAML automations and manual file uploads to my Home Assistant, I realized I needed better tooling.
So I built a workflow that connects *Cursor IDE* directly to *Home Assistant* using an MCP integration and an on-board Agent.
Now I can write, deploy, and version automations right from the IDE — no SSH, no web UI.
In the video I show how it works and how to install it.
Both components are open-source and available on GitHub.
Demo: https://youtu.be/xocbWonWdoc
 Installation guide: https://youtu.be/RZNkNZnhMrc
 Code:
• https://github.com/Coolver/home-assistant-cursor-agent
• https://github.com/Coolver/home-assistant-mcp
Full write-up with story and screenshots:
https://coolver.github.io/home-assistant-cursor-agent/

value: Show HN: I just wanted to fix my heating but built a Cursor agent instead


value: wsxiaoys

value:
Though parallel agents already exists, we built this out differently in Pochi. Most other tools operate inside a single editor tab. That means there is at most one conversational state to interact with.
Parallel Agents in Pochi use separate Git worktrees, so each task has its own working directory, branch, chat history, and terminal environment. Task state stays isolated.
From a UX standpoint, the important bit is how it surfaces in the editor: each agent is a separate tab, each tab bound to its own worktree. You can diff, commit, discard, or merge worktrees independently. You can run two model-generated solutions side-by-side and compare outcomes without the branches stepping on each other.
Under the hood it’s git worktrees with orchestration to bind each worktree to its own agent state. If you're familiar with git worktree add you’ll recognize what’s happening. We expose the worktrees directly through VS Code (Source Control + Pochi tabs), so there’s no manual git worktree management - each agent just is a worktree with its own runtime context. Goes to show how some simple changes dramatically changes the UX.

value: Show HN: Run parallel agents in VSCode tabs

author: wsxiaoys


value: signumflow

value:
Hi HN,
I’m building SignumFlow — an API-first way to upload documents and run workflows.
Most workflow + signature tools are UI-first. SignumFlow is for developers:
you call the API, bring your own UI, and keep users inside your product.
Working today
• Upload documents
• Start workflows (sequential / parallel routing)
• Retrieve workflow + document state
• Approvals
• Developer portal with API keys + docs
In progress
• Webhooks
Docs + quickstart:
https://docs.signumflow.com
If you’ve built signature or workflow integrations before:
I’d love feedback on what’s confusing / missing or any must-have features.
Happy to answer questions!

value: Show HN: SignumFlow – API-first document routing and workflow engine

author: signumflow


value: meshcoder

value:
Hi HN,
I’d like to introduce HelloTriangle, an online platform for Python-based 3D modelling, mesh generation and analysis. You can write a few lines of code, generate or import a mesh, manipulate it, analyse it, and instantly share it via a link.
I built HelloTriangle because I often ran into barriers when doing advanced, mesh-based 3D modeling: steep learning curves, sometimes tricky installations, juggling multiple tools, or using expensive software.
Another frustration was sharing results: 3D insights often get lost when all you can send are 2D screenshots, since others don’t have the software to view your models, or because you don't want to share the underlying files.
You can try it here: https://www.hellotriangle.io
I’d really love your feedback — especially on use cases or missing features you’d want for a smoother Python-based 3D workflow.

value: Show HN: HelloTriangle – Python-based online 3D modeling and sharing platform

author: meshcoder


value: Jintin

value: Show HN: MockK Under the Cover

author: Jintin

title: Show HN: MockK Under the Cover


value: BASSAMej

value: Show HN: The Startup and Business Vocabulary for anyone building startups

author: BASSAMej


value: Show HN: Reversing a Cinema Camera's Peripherals Port


value: DepthSight

value: Show HN: I have zero dev experience and built a 220k LOC fintech SaaS with AI

author: DepthSight


value: haniehz

value:
Hey HN!
We built a cloud platform that lets you deploy MCP servers (agents, tools, ChatGPT apps) to production instantly. Think Vercel, but for agents using the Model Context Protocol (MCP).
The problem: MCP servers are great locally, but running them in production is a pain. You need hosting, handle auth, manage secrets, deal with long-running processes, and connect everything securely.
With mcp-c, it's: uvx mcp-agent deploy
You get a production URL that works with Claude Desktop, ChatGPT, Cursor, or any MCP client.
What makes this different:
- Durable execution (agents can run for hours without timing via Temporal)
- Built-in secrets management
- Full MCP spec support (sampling, notifications, logging)
- Free during beta
Try this hosted ChatGPT Pizza example:
Pizza ordering agent: https://pizzaz.demos.mcp-agent.com/sse
We're the team behind mcp-agent (8K GitHub stars), and this is our answer to "how do I actually ship this?"
Open beta, free to use. Would love your feedback on what would make this more useful.
Link to docs: https://docs.mcp-agent.com/get-started/cloud

value: Show HN: Durable cloud hosting for MCP servers

author: haniehz


value: bitbuilder

value:
I started building this over a year ago to help plan routes for a social bike ride I help lead in St. Louis. I got annoyed with existing tools either being clunky to use, routing us down arterials, or making it hard to share routes. Then I let feature creep take over and it became something more general for getting around cities by bike, foot, or train.
What I think is cool about it:

  - Custom routing profiles that try to find safe and fun ways through the city. The route line can be colorized based on traffic safety or steepness.
  - Drop pins to mark cool spots you find or safety hazards. Currently anonymous submissions are allowed (not sure how long that will last).
  - Deep OSM integration for both safety ratings and querying features along routes.
  - Simple public profiles so you can find friends or a ride leader on the map during group rides.
  - Train integration for multi-modal trips. Bike to a station, ride partway, continue biking. Pretty rough around the edges stil, and depends on having decent data from HERE.
  - Way more map customization than anyone asked for (crosshatch patterns, color themes, etc). I just like making maps.
  - Simple A to B routes, or you can plot a route with some pretty solid route editing tools (insert waypoint, move, delete, etc).
  - Gratuitous LLM integration for route safety and fun analysis, if you toggle it on in preferences.
  - Built with MapLibre GL, Alpine.js, and FastAPI. Intentionally simple tech stack. Maybe 50% of the code written with LLM assistance.

The mobile experience is designed to feel app-like. Native apps are on the roadmap.
No monetization yet, though I'll have to figure that out here soon. Any features related to safety will always be free.

value: Show HN: Grid Mapper – a routing tool for finding safe, fun ways through cities

author: bitbuilder


value: calebhwin

value: Show HN: A1 – An optimizing JIT compiler for AI agents

author: calebhwin


value:
Hi HN, I’m Sandhya and we have built Visual DB — a web front end for databases. It lets you create data-entry forms, spreadsheet-like grids, and reports directly on top of your existing relational database.
Here’s a quick walkthrough: https://youtu.be/4zv_HQKdKeI (13 minutes)
WHAT PROBLEM IS THIS SOLVING?
Building CRUD apps with master-detail forms, transactions, and proper concurrency controls typically requires weeks of custom development and ongoing maintenance. Visual DB lets you build these applications in hours. Developers can focus on backend business logic while Visual DB handles the UI layer.
THE FLAW IN DATABASE-SPREADSHEET HYBRIDS
Many organizations turn to database-spreadsheet hybrids (such as Airtable) as an alternative to custom development. However, these tools all share the same flaw: they abandon database fundamentals at the application layer.
Developers choose Postgres for strong consistency, data integrity, and ACID transactions—fundamental requirements for mission-critical data. Yet even when built on Postgres, these tools use spreadsheet semantics—letting users overwrite each other’s changes, failing to treat records as atomic units, and mixing concurrent edits. The result: the very inconsistencies databases are designed to prevent.
If you're a software developer, you'd never accept a version control system where users constantly overwrite each other's changes. And yet this is what database-spreadsheet hybrids do with your data. Simply using Postgres as storage won't give you ACID guarantees if your UI implements spreadsheet semantics.
HOW VISUAL DB FIXES THIS
Visual DB is a traditional ACID-compliant database application, not a database-spreadsheet hybrid. It implements database semantics with proper concurrency control to prevent the "lost update" and "write skew" problems that plague database-spreadsheet hybrids. When conflicts occur, users get explicit notifications and a visual merge interface. Changes are never silently overwritten. More details: https://visualdb.com/concurrencycontrol
As a database-focused tool, Visual DB includes features you won't find in database-spreadsheet hybrids: query builders and query parameters for server-side filtering (loading subsets, not full tables), AI assistance for query building, and full support for Row-Level Security (RLS).
See the full feature comparison matrix here: https://visualdb.com/comparison
TECH STACK
Since this is HN, many readers will be interested in our tech stack. We are using Kubernetes in the backend, hosted on Azure. To provide free Postgres instances we have integrated with Neon’s claimable database feature. UI is built using React and TypeScript.
CHANGES SINCE LAST UPDATE
We introduced Visual DB to HN over a year ago and received lots of feedback (see https://news.ycombinator.com/item?id=41503251). We have incorporated all of the feedback. Some of it required major overhaul: Allowing Visual DB to be self-hosted required significant re-architecting. In addition we have added a ton of new features including master-detail forms, input validation in sheets, visual conflict resolution, ability to review and revert changes, row-level security, SQLite support, and many, many more features.
FEEDBACK REQUESTED
We’d love your feedback! To test drive go to https://visualdb.com At a high level, did we strike the right balance between power and ease of use? This is always one of the hardest decisions, especially for a database front end. Visual DB exposes queries and query parameters in the UI, while most products in this space hide them. In this and many other ways, Visual DB is closer to Microsoft Access than to database-spreadsheet hybrids.

value: Show HN: Visual DB – Web front end for your database (update)


value: Gillinghammer

value: Read Grokipedia articles relevant to your current browser tab.

value: Show HN: Grokipedia Sidekick Chrome Extension

author: Gillinghammer


value: git-quick-stats

value: Show HN: Git Quick Stats – The Easiest Way to Analyze Any Git Repositor

author: git-quick-stats


value: aretecodes

value:
Hey HN,
I’m Arete, a designer and frontend developer who recently launched Reframe Labs a software design and development agency that helps startups and scale-ups build high-quality products, from MVPs to enterprise-grade applications.
At Reframe Labs, we specialize in:
Product Design – clean, modern UI/UX that turns ideas into intuitive experiences
Web Development – fast, scalable apps built with modern tools
AI Automations – integrating AI tools to streamline workflows and enhance productivity
Growth & Optimization – helping products scale and convert better post-launch
I’ve spent the past eight years working with startups and agencies across multiple industries, helping them go from rough concepts to polished, revenue-generating products. Now, we’re opening up a few new client slots this month and would love to connect with founders who want to move fast and build something great.
https://reframelabs.co
I’d love your feedback on the site, messaging, and focus areas, and happy to answer any questions about our stack, design process, or client workflows.

value: Show HN: Reframe Labs – We Build, Scale and Grow Your Startup

author: aretecodes


value: orbanlevi

value: Show HN: Cactoide – Federated RSVP Platform

author: orbanlevi


value: rh-app-dev

value: Show HN: What if every photo/video could prove itself?

author: rh-app-dev


value: cotreasoner

value:
Hi HN,
I’m an engineer-turned-founder. I kept seeing smart people (me included) lose listeners in the first 60 seconds of a pitch.
Not because the idea was bad, but because the opener was rambling, vague, or missing a clear ask.
So I built a tiny tool to pressure-test that first minute.
What it does
- You hit record in the browser (no signup).
- Speak your pitch (up to ~60 seconds).
- You get: a score out of 100, and three surgical edits (e.g. clarify who it’s for, add proof, tighten ask).
Why I’m posting
- To see if this is actually useful beyond my own use case.
- To get feedback on:
    - Does the scoring feel fair or gimmicky?
    - Are the edits specific enough to be actionable?
    - What would you want before using this with a real pitch?
Thanks for checking it out! Happy to answer questions in the comments.

value: Show HN: Enact – 60-second pitch analyzer (score/100 and 3 edits, no signup)

author: cotreasoner


value: eportet

value:
I built a tool that lets you ask questions about all previous World Cups! The dataset includes info on teams, players, matches, groups, and tournaments. You can just ask questions in plain English and get responses sourced by data.
The dataset was curated by Fjelstul and is available at https://github.com/jfjelstul/worldcup
To play with it login and click "Select a data source" and choose "FIFA World Cup" (we're experimenting with a few others too).
It's early and probably a bit buggy. I'd appreciate feedback on the UX, bugs, and what questions you'd want it to handle better. Happy to share details on data sources, parsing, and how I translate queries under the hood.
If you want to kick the tires, try prompts like:
"How did Argentina perform vs France across their last three World Cups?"
"Show me Messi's assists in knockout rounds (2010–2022, extra time included)"
"Have any goalkeepers scored in the world cup?"
Or choose one of the generated questions that show up when you select the dataset.

value: Show HN: I built a tool that lets you ask questions on previous World Cups

author: eportet


value: scarletkc

value: Vexor is a small CLI that lets you search files by meaning – like grep, but semantic.

value: Show HN: Vexor – A semantic grep that finds files by meaning, not by text

author: scarletkc