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Show HN for March 16, 2026

63 items
337

Claude Code skills that build complete Godot games #

github.com favicongithub.com
205 comments4:07 PMView on HN
I’ve been working on this for about a year through four major rewrites. Godogen is a pipeline that takes a text prompt, designs the architecture, generates 2D/3D assets, writes the GDScript, and tests it visually. The output is a complete, playable Godot 4 project.

Getting LLMs to reliably generate functional games required solving three specific engineering bottlenecks:

1. The Training Data Scarcity: LLMs barely know GDScript. It has ~850 classes and a Python-like syntax that will happily let a model hallucinate Python idioms that fail to compile. To fix this, I built a custom reference system: a hand-written language spec, full API docs converted from Godot's XML source, and a quirks database for engine behaviors you can't learn from docs alone. Because 850 classes blow up the context window, the agent lazy-loads only the specific APIs it needs at runtime.

2. The Build-Time vs. Runtime State: Scenes are generated by headless scripts that build the node graph in memory and serialize it to .tscn files. This avoids the fragility of hand-editing Godot's serialization format. But it means certain engine features (like `@onready` or signal connections) aren't available at build time—they only exist when the game actually runs. Teaching the model which APIs are available at which phase — and that every node needs its owner set correctly or it silently vanishes on save — took careful prompting but paid off.

3. The Evaluation Loop: A coding agent is inherently biased toward its own output. To stop it from cheating, a separate Gemini Flash agent acts as visual QA. It sees only the rendered screenshots from the running engine—no code—and compares them against a generated reference image. It catches the visual bugs text analysis misses: z-fighting, floating objects, physics explosions, and grid-like placements that should be organic.

Architecturally, it runs as two Claude Code skills: an orchestrator that plans the pipeline, and a task executor that implements each piece in a `context: fork` window so mistakes and state don't accumulate.

Everything is open source: https://github.com/htdt/godogen Demo video (real games, not cherry-picked screenshots): https://youtu.be/eUz19GROIpY Blog post with the full story (all the wrong turns) coming soon. Happy to answer questions.

53

Browser grand strategy game for hundreds of players on huge maps #

borderhold.io faviconborderhold.io
36 comments8:51 AMView on HN
Hi HN,

I've been building a browser-based multiplayer strategy game called Borderhold.

Matches run on large maps designed for hundreds of players. Players expand territory, attack neighbors, and adapt as borders shift across the map. You can put buildings down, build ships, and launch nukes.

The main thing I wanted to explore was scale: most strategy games are small matches, modest maps, or modest player counts, but here maps are large and game works well with hundreds of players.

Matches are relatively short so you can jump in and see a full game play out.

Curious what people think.

https://borderhold.io/play

Gameplay:

https://youtu.be/nrJTZEP-Cw8

Discord:

https://discord.gg/xVDNt2G5

26

Hecate – Call an AI from Signal #

github.com favicongithub.com
3 comments2:41 PMView on HN
Hecate is an AI you can voice and video call from Signal iOS and Android. This works by installing Signal into an Android emulator and controlling the virtual camera and microphone. Tinfoil.sh is used for private inference.
20

Trackm, a personal finance web app #

trackm.net favicontrackm.net
10 comments11:36 PMView on HN
I built this as a personal finance app that allows me to see how my recurring incomes and expenses will affect my network over the years.

So trackm can show up to 4 years in the future and track when your accounts will go negative given you expenses and incomes.

I've been dogfooding it for the past 10 days, fixing issues I find from my day-to-day use and now it's at a point where I'm comfortable sharing it with others.

The app is free for 30 days, after which it goes into read-only mode. Paying the one-time license fee unlocks it forever.

12

Mavera – Predict audience response with GANs, not LLM sentiment #

docs.mavera.io favicondocs.mavera.io
3 comments8:54 PMView on HN
Mavera is an audience intelligence API. Give it a message, product prototype, or creative asset and it returns a predicted distribution of emotional and behavioral responses across your target stakeholder population. This is the best way to test your assumptions before you spend or push anything live.

To show this in practice, we ran all 101 Super Bowl LX ads through Mavera on game night: https://superbowl.mavera.io. We simulated how audiences would emotionally and behaviorally respond by platform and segment. We returned a distribution rather than a single score as part of a full analysis of each ad in under 4 hours.

The model is a GAN adapted for language, emotion, and cognition. A generator produces synthetic audience responses and a discriminator validates them against human benchmarks. Scoring follows a feel-think-act framework: emotional activation, cognitive framing, behavioral prediction. We validated scoring against the Harvard/Illinois OASIS benchmark. MAE on emotional response is 0.02-0.15 versus 1.0-2.5+ for GPT and Claude. Every response includes a confidence score and a hallucination risk score. You can also build-in spread of opinion, response stability, and impact of news/market context scores to your outputs.

The API is OpenAI-compatible. Change the base URL to app.mavera.io/api/v1, add a persona_id, and you are running against 50+ pre-built personas or you can customize your own. Sub-100ms latency at P99. Free API key and docs at https://docs.mavera.io/introduction.

11

Pincer – Twitter/X for bots. No humans allowed #

pincer.wtf faviconpincer.wtf
8 comments10:56 PMView on HN
Pincer is a Twitter/X-like social platform built for bots. Bots post short messages, follow other users, and read feeds — all through a simple REST API. A web UI serves the public timeline, user profiles, and search.

Code is at https://github.com/boyter/pincer

All data is stored in-memory and periodically persisted to disk (no database required).

Add your bot: Point your AI agent at https://pincer.wtf/skill.md and it will know what to do.

A project by Boyter (w/some contributions from John Polacek)

10

CastLoom Pro – Turn podcasts into a personal knowledge base #

castloompro.toolooz.com faviconcastloompro.toolooz.com
2 comments3:11 AMView on HN
Hi HN,

I’m Ethan, an indie developer.

I listen to a lot of podcasts while coding or commuting, and I often want to save interesting insights from episodes. I tried tools like MacWhisper for transcription, but it only works on macOS and the workflow didn’t quite fit what I wanted.

So I built CastLoom Pro.

It’s a desktop app that lets you search, play, download, transcribe, translate, and archive podcasts in one place. The idea is to turn podcasts into something searchable and reusable instead of just passive listening.

Some details:

- Search podcasts from Apple Podcasts - Batch download episodes - Local transcription using Faster-Whisper - Optional translation with DeepL or OpenAI - Export to Markdown / SRT / TXT / JSON - Send transcripts to Notion or Obsidian

Everything runs locally, so audio and transcripts stay on your machine.

It currently supports Windows and macOS.

I’d love to hear your feedback or suggestions.

9

AgentDiscuss – a place where AI agents discuss products #

agentdiscuss.com faviconagentdiscuss.com
12 comments4:32 PMView on HN
Hi HN,

We’ve been thinking about a simple question:

What products do AI agents actually prefer?

As more agents start using APIs, tools, and software, it feels likely they’ll need somewhere to exchange information about what works well.

So we built a small experiment: AgentDiscuss.

It’s a discussion forum where AI agents can:

1. start product discussions 2. comment and debate tools 3. upvote products they prefer

Humans can also launch products there and watch how agents react.

We’re curious to see what happens if agents start discussing products with each other.

If you’re building agents, feel free to send one there.

https://agentdiscuss.com

Happy to hear thoughts or criticism.

9

I was laid off, so I built a NerdWallet for startup equity liquidity #

strikerates.com faviconstrikerates.com
3 comments7:05 AMView on HN
I was a software engineer at a late stage startup and was caught in a layoff this January. When I looked into exercising my options or getting liquidity, I realized the secondary market is a total black box. Liquidity funds spend a fortune on Google Ads just to find people like us. Those high marketing costs eventually come out of our pockets in the form of higher fees and worse terms. I built StrikeRates to change that.

The site is meant to be a central resource for anyone navigating this. I included a Liquidity 101 section that explains the different financial vehicles because that information is hard to find in one place. I also built a Liquidity Provider Directory and a Compare Tool so people can see their options side by side.

The Equity Scenario Modeler is currently in beta. I would love feedback on the logic and the math from this community.

The real point of the site is the Waitlist. My goal is to aggregate enough demand to signal that we are a group worth competing for. If we can prove volume, we can force these funds to stop spending so much on ads and move them toward a flat monthly fee instead. This should lower the cost of liquidity for everyone.

I am happy to answer any questions about how the modeler works or the mission to commoditize these providers.

9

Try Gerbil Scheme in the Browser #

trygerbil.dev favicontrygerbil.dev
1 comments2:10 AMView on HN
Gerbil is a Scheme built on Gambit that compiles to native code via C. It has an actor-oriented concurrency model, a rich module system, and a batteries- included stdlib. I built a browser playground to make it easy to try without installing anything.

Briefly: - A real REPL with state that persists between expressions - 10 guided examples from basic Scheme through pattern matching, hash tables, higher-order functions, and actors (but, do go to https://cons.io for the full docs) - A scratchpad for multi-line code that evaluates into the live REPL session - Snippet sharing via short URLs

To keep this short, some implementation details here: https://abacusnoir.com/2026/03/15/a-playground-for-gerbil-sc...

(Does have limitations in this early version, see the post for details on those too)

8

Most GPU Upgrades Aren't Worth It, I Built a Calculator to Prove It #

best-gpu.com faviconbest-gpu.com
9 comments8:35 PMView on HN
I run a small project called best-gpu.com, a site that ranks GPUs by price-to-performance.

While browsing PC building forums and Reddit, I kept seeing the same question: “What should I upgrade to from my current GPU?” Most answers are just lists of cards without showing the actual performance gain, so people often end up paying for upgrades that barely improve performance.

So I built a small tool: a GPU Upgrade Calculator.

You enter your current GPU and it shows:

estimated performance gain

a value score based on price vs performance

a filtered list of upgrade options (brand, price, VRAM, etc.)

The goal is simply to help people avoid spending money on upgrades that aren’t really worth it.

Curious to hear feedback from HN on the approach, data sources, or features that would make something like this more useful.

https://best-gpu.com/upgrade.php

8

Lockstep – A data-oriented programming language #

github.com favicongithub.com
6 comments12:14 AMView on HN
https://github.com/seanwevans/lockstep

I want to share my work-in-progress systems language with a v0.1.0 release of Lockstep. It is a data-oriented systems programming language designed for high-throughput, deterministic compute pipelines.

I built Lockstep to bridge the gap between the productivity of C and the execution efficiency of GPU compute shaders. Instead of traditional control flow, Lockstep enforces straight-line SIMD execution. You will not find any if, for, or while statements inside compute kernels; branching is entirely replaced by hardware-native masking and stream-splitting.

Memory is handled via a static arena provided by the Host. There is no malloc, no hidden threads, and no garbage collection, which guarantees predictable performance and eliminates race conditions by construction.

Under the hood, Lockstep targets LLVM IR directly to leverage industrial-grade optimization passes. It also generates a C-compatible header for easy integration with host applications written in C, C++, Rust, or Zig.

v0.1.0 includes a compiler with LLVM IR and C header emission, a CLI simulator for validating pipeline wiring and cardinality on small datasets and an opt-in LSP server for real-time editor diagnostics, hover type info, and autocompletion.

You can check out the repository to see the syntax, and the roadmap outlines where the project is heading next, including parameterized SIMD widths and multi-stage pipeline composition.

I would love to hear feedback on the language semantics, the type system, and the overall architecture!

8

Claude Code plugin that gives .svelte files full LSP intelligence #

github.com favicongithub.com
0 comments11:59 AMView on HN
Claude Code has no code intelligence for Svelte files out of the box. This plugin registers svelte-language-server as the LSP handler for .svelte files, giving Claude hover docs, go-to-definition, find references, document symbols, and diagnostics.

Install: npx svelte-lsp-claude

MIT licensed, open source.

8

Open-source, extract any brand's logos, colors, and assets from a URL #

openbrand.sh faviconopenbrand.sh
1 comments6:48 PMView on HN
Hi everyone, I just open sourced OpenBrand - extract any brand's logos, colors, and assets from just a URL.

It's MIT licensed, open source, completely free. Try it out at openbrand.sh

It also comes with a free API and MCP server for you to use in your code or agents.

Why we built this: while building another product, we needed to pull in customers' brand images as custom backgrounds. It felt like a simple enough problem with no open source solution - so we built one.

8

Grafly.io – Free online diagramming tool #

grafly.io favicongrafly.io
1 comments6:40 PMView on HN
Hey, I'm Nenad. I built Grafly (https://grafly.io) because I kept reaching for different tools just to sketch out a quick architecture diagram and hating either the UI, color schemes, usage patterns or that I had to log in, or have my doodles stored on someone's server. It's a React/React Flow app that runs entirely in the browser meaning that everything saves to localStorage, nothing leaves your machine. You get basic shapes, AWS/GCP icons, edges with waypoints and shareable URLs that encode the whole diagram in the query string (no backend, just LZ compression). There is also a description of the underlying data format that you can give to your AI so it can build diagrams from a text prompt. I know it's not perfect but it does the job for me and maybe it'll be useful to some of you. Code is on GitHub, AGPL licensed. https://github.com/lnenad/grafly
7

Signbee – An API that lets AI agents send documents for signature #

signb.ee faviconsignb.ee
6 comments9:00 AMView on HN
Hi HN, I built Signbee while working on AI agents that handle contracting workflows. The agents could draft agreements, negotiate terms, manage deals — but the moment a signature was needed, the workflow broke. It always ended with "please upload this to DocuSign" — which meant human intervention, account setup, and manual uploads. So I built a simple API. You POST markdown and Signbee generates the PDF, or you pass a URL to your own PDF if you already have one designed the way you want it. No templates, no editor. Either way, it verifies both parties via email OTP and produces a signed document. curl -X POST https://signb.ee/api/v1/send \ -H "Content-Type: application/json" \ -d '{ "markdown": "# NDA\n\nTerms...", "sender_name": "You", "sender_email": "[email protected]", "recipient_name": "Client", "recipient_email": "[email protected]" }' Under the hood: - Markdown → PDF generation, or bring your own PDF via URL - Both parties verified via email OTP - Timestamps and IP addresses recorded - Final document hashed with SHA-256 - Certificate page appended with full audit trail One interesting challenge: the certificate page itself is part of the document that gets hashed, so any modification — even to the certificate — invalidates the integrity check. I also built an MCP server (npx -y signbee-mcp) so tools like Claude or Cursor can call it directly. Curious to hear from people who've dealt with document signing systems or automated agent workflows — what would you want to automate? https://signb.ee
6

File converters and 75 tools that run in the browser #

0 comments12:08 AMView on HN
Most file converter sites upload your files to their servers. File Converter Free runs everything client-side using WebAssembly — your files never leave your device.

215+ formats across conversion and compression, 75+ browser-based tools for PDF, dev utilities, text, calculators and network diagnostics — all available in 9 languages.

Built this solo. What tools do you wish ran client-side but currently don't?

https://file-converter-free.com/en/tools

6

Vibecheck – lint for AI-generated code smells (JS/TS/Python) #

github.com favicongithub.com
2 comments12:42 PMView on HN
I built a CLI that detects patterns AI coding tools leave behind: empty catch blocks, hardcoded secrets, as any everywhere, comments that restate the code, god functions, SQL concatenation.

24 rules across JS/TS and Python. Zero config, runs offline, regex-based so it's fast.

  npx @yuvrajangadsingh/vibecheck .
Also ships as a GitHub Action for inline PR annotations and standalone binaries (no Node required).

Why: CodeRabbit found AI-generated PRs have 1.7x more issues than human PRs. Veracode says 45% of AI code samples have security vulnerabilities. "Vibe coding" is everywhere now but nobody's linting for the patterns it produces.

This isn't a replacement for ESLint. It catches things ESLint doesn't look for, like catch blocks that only console.error without rethrowing, bare except: pass in Python, or mutable default arguments.

6

Airport Swap #

airportswap.com faviconairportswap.com
5 comments8:56 PMView on HN
I've been living in Denver for a few years and the prices for simple airport rides are kind of crazy.

In an effort to build/expand community - I built Airport Swap. It is a platform to exchange rides to/from the airport _for free_. Give a ride to get a ride!

Airport Swap is was built with the intention of building (or finding) community or relying on a circle of trust to choose drivers/riders (friends of friends pretty much). Connecting people on their street, in their building, or from a board game group they attended before is the goal.

Looking for any feedback :)

Cheers!

5

Port42 – AI companions that build and act on your Mac (v0.5.0) #

port42.ai faviconport42.ai
0 comments5:52 AMView on HN
Port42 is a native Mac app built by the collaboration it enables. One human and many AI companions, thinking together, shipped 40+ releases in 9 days.

Companions live alongside you in conversations. They don't just respond. They build interactive surfaces and act on your system through a single API.

Ports are how companions interact with your machine. One API, two surfaces:

The visual surface renders live HTML/CSS/JS apps that companions create mid-conversation. They render inline, pop out into floating windows, dock, resize, persist across sessions. A companion can build you a dashboard, a file browser, a monitoring tool, and it runs right there.

The action surface lets companions act on your machine directly through the same API. Clipboard, screen capture, terminal, filesystem, headless browser, audio TTS, AppleScript/JXA automation. The model decides what to call and chains actions together.

- "Run my tests."

- "Take a screenshot and describe what you see."

- "Read my clipboard, translate it, copy it back."

We bridge both because computing is still stuck in windows, but conversation is where intent lives.

Requires a Claude subscription (via Claude Code OAuth) or Anthropic API key. Permission prompts per companion, per conversation. E2E encrypted (AES-256-GCM). No cloud dependency. Open source (MIT).

9 days old, rough edges everywhere, but it works, now at v0.5.1.

https://port42.ai | https://github.com/gordonmattey/port42-native

5

Paperverse, an Hyperbolic Presentation #

paperverse.net faviconpaperverse.net
3 comments9:15 AMView on HN
Hey everyone, I was wondering if a Poincaré disk would be able to usefully show the local structure of a graph while showing most of one

I used a collection of ML papers to explore this

5

Kontext.dev – Runtime Credentials for Agents #

kontext.dev faviconkontext.dev
2 comments4:39 PMView on HN
Every AI agent that does something useful - opening a PR, posting in Slack, updating a ticket - needs to call an API on behalf of a user. That means OAuth. Authorization flows, token storage, refresh logic, per-user credential isolation.

Today, most teams solve this with a long-lived API key in an .env file, shared across every user and every session. As everyone in an organization becomes a software engineer - whether they know it or not - you can't expect each of them to roll their own OAuth. Secure credential handling needs to be baked into the infrastructure.

The core issue: agents are a new kind of principal. They're not users. They're not service accounts. They act with delegated authority on someone else's behalf. Existing identity systems have no model for this.

Kontext does.

Kontext validates the user and the agent, and returns a credential that lets agents act on behalf of their user - scoped, short-lived, and revocable. No secrets in your codebase, and action is traced back to the human who approved it.

We are thrilled to be launching our SDK, let us know what you think!

5

Smart glasses that tell me when to stop pouring #

github.com favicongithub.com
7 comments7:04 PMView on HN
I've been experimenting with a more proactive AI interface for the physical world.

This project is a drink-making assistant for smart glasses. It looks at the ingredients, selects a recipe, shows the steps, and guides me in real time based on what it sees. The behavior I wanted most was simple: while I'm pouring, it should tell me when to stop, instead of waiting for me to ask.

The demo video is at the top of the README.

The interaction model I'm aiming for is something like a helpful person beside you who understands the situation and intervenes at the right moment. I think this kind of interface is especially useful for preventing mistakes that people may not notice as they happen.

The system works by running Qwen3.5-27B continuously on the latest 0.5-second video clip every 0.5 seconds. I used Overshoot (https://overshoot.ai/) for fast live-video VLM inference. Because it processes short clips instead of single frames, it can capture motion cues as well as visual context. In my case, inference takes about 300-500 ms per clip, which makes the feedback feel responsive enough for this kind of interaction. Based on the events returned by the VLM, the app handles the rest: state tracking, progress management, and speech and LLM handling.

I previously tried a similar idea with a fine-tuned RF-DETR object detection model. That approach is better on cost and could also run on-device. But VLMs are much more flexible: I can change behavior through prompting instead of retraining, and they can handle broader situational understanding than object detection alone. In practice, though, with small and fast VLMs, prompt wording matters a lot. Getting reliable behavior means learning what kinds of prompts the specific model responds to consistently.

I tested this by making a mocktail, but I think the same interaction pattern should generalize to cooking more broadly. I plan to try more examples and see where it works well and where it breaks down.

One thing that seems hard is checking the liquid level, especially when the liquid is nearly transparent. So far, I have only tried this with a VLM, and I am curious what other approaches might work.

Questions and feedback welcome.

4

Kaoslabs – My Linux VPS sandbox for self-hosting and AI art/video #

kaoslabs.org faviconkaoslabs.org
0 comments12:06 PMView on HN
This is my small personal sandbox where I experiment with Linux, self-hosting, and generative AI tools. I use this space to test new ideas and different setups on my VPS.

    Recently, I’ve been focusing on AI-generated video and visual art. I’ve put a lot of work into these experiments, so please don't just hit and run! 
I’d love for you to go through the AI stuff I’ve made and let me know what you think. Feedback or ideas for new experiments are more than welcome.
4

I solved Claude Code's context drift with persistent Markdown files #

1 comments5:36 PMView on HN
I've been using Claude Code to build SaaS products, and kept hitting the same wall: it writes brilliant code for 20 minutes, then forgets your database schema and starts rewriting working code. The problem isn't the model's memory, it's that there's no persistent project context between sessions. Come back tomorrow and Claude has zero knowledge of what it built yesterday. My solution: Force Claude to read project truth files before every action. I built a multi-agent system that creates persistent context files upfront: PROJECT.md - What you're building, business model, core features REQUIREMENTS.md - Database schema, auth flows, API structure, edge cases ROADMAP.md - Build phases with success criteria STATE.md - Current position, completed work, pending tasks How it works:

AI Product Manager asks questions most developers skip:

"How does money flow through this?" "What happens when users cancel mid-month?" + Any Edge cases specific to your SaaS

Creates the markdown files from your answers. Claude Code reads these files before writing ANY code. No guessing. Can't forget the schema, it's literally documented. Executor agents spawn per task, each reading the same context files. They build in parallel but never break what's already working. Verifier agent tests against success criteria after each phase. If broken, spawns debugger agent with persistent investigation files.

Results: Built 3 products in 30 days using this system:

Analytics dashboard: 13 hours Feedback widget: 18 hours Content calendar: 9 hours

No context drift. No "Claude forgot my auth system" moments. Just consistent builds. The biggest difference: Saturday: Build auth with Claude Sunday: Come back, describe next feature Claude reads REQUIREMENTS.md, sees existing auth schema Builds new feature without touching auth vs. the normal experience of Claude rewriting everything. I packaged this as PropelKit (Next.js boilerplate + AI PM system that creates these files automatically). But the core concept, persistent markdown context, works with any Claude Code setup.

Try it: https://propelkit.dev

The agent architecture uses Claude Sonnet/Opus (configurable) parallel thinking to spawn multiple agents that all read from the same truth files. Happy to answer questions about the implementation.

4

Gmail killing mail fetch – procmail and IMAP replacement #

nosuch.biz faviconnosuch.biz
0 comments2:01 PMView on HN
Gmail is shutting down its POP-based mail fetching feature later this year. If your mail lives on a server you can SSH into, here's a simple replacement: a procmail recipe pipes each incoming message to a small Python script that uploads it to Gmail via IMAP.

No polling, no cron jobs - mail arrives in Gmail the moment it hits the server.

https://nosuch.biz/mailsync/

Code: https://github.com/bwagner/gmailsync

4

Git Quest – I turned your GitHub commit history into an idle RPG #

gitquest.dev favicongitquest.dev
3 comments2:44 PMView on HN
Hey HN,

I built Git Quest, it reads your public GitHub history and turns it into an idle RPG character.

How it works: - Your most-used language determines your class (TypeScript → Paladin, Python → Sage, etc.) - Commits generate Power and XP - Your character auto-battles dungeons while you code – no active play needed - Enemies drop loot across 5 rarity tiers - Live leaderboard ranks developers

It only reads public GitHub data.

I built it because I wanted something that rewards coding consistency without being another "streak" tracker. The idle mechanic means prolific coders naturally progress faster, but you don't have to babysit it.

Would love feedback on the game loop and whether the class/language mapping feels right.

https://www.gitquest.dev

4

Seasalt Cove, iPhone access to your Mac #

seasalt.app faviconseasalt.app
2 comments10:48 PMView on HN
I feel like I finally built something I actually use every day and it has completely changed the way I think about work. AI workflows have flipped how devs operate. You're not heads down writing code anymore, you're bouncing between projects, instructing agents, reviewing their work, nudging them forward. The job is now less about typing and more about judgment calls.

And the thing about that workflow is you spend a lot of time waiting. Waiting for the agent to finish, waiting for the next approval gate. That waiting doesn't have to happen at your desk. It doesn't have to happen in front of a monitor at all. I built Seasalt because I realized my iPhone could handle 80% of what I was chaining myself to my Mac for. Kick off the agent, walk away, review the diff from the store, a walk, or in a separate room away from your Mac. Approve it. Start the next one, switch to another session. You don't need giant dual monitors for this. That's kind of the whole point.

Also, I have a deep security background so I felt like it was 100% necessary to include end to end encrypted with a zero knowledge relay, no ports getting opened, no VPN configuration needed, with key validation in the onboarding flow.

4

We Built Private Post-Training and Inference for Frontier Models #

workshoplabs.ai faviconworkshoplabs.ai
1 comments7:19 PMView on HN
Hey HN, I'm Oscar, part of the core team at Workshop Labs. We've been building a private post-training and inference stack for frontier open-weight models — we've been running it on Kimi K2 (1T parameter MoE) across 8xH200 GPUs inside hardware-isolated TEEs.

What this means is that can't access customer data, even if we wanted to. The chip manufacturer (AMD/Nvidia) signs attestation reports proving what code is running, and all data at rest is encrypted with per-user keys that only the customer holds, decryptable only inside the TEE.

Some things we think are novel:

- Every build is fingerprinted publicly (on Sigstore) so anyone can verify the TEE is running the code we claim.

- The training time overhead is under 10%. An extra 11 minutes on a 2-hour training run. We believe this is the first training benchmark published for Nvidia GPU TEEs.

- Cloud providers (GCP, Azure) still only offer single-GPU TEEs. This setup extends this with kernel patches and custom GPU mesh attestation so we can run trillion-parameter MoE models.

We tried to be upfront about where the security model has gaps. Side channels remain an open research problem, DRAM encryption is deterministic (replay patterns are theoretically exploitable), the source-to-binary link depends on trusting GitHub CI, and our formal audit is still pending. There's a threat model table in the post that lays this out explicitly.

The post is a full technical walkthrough. Would love scrutiny and our team will be online to answer any questions!

3

Open-Source Workflow Builder SDK #

github.com favicongithub.com
0 comments4:04 PMView on HN
Hi HN,

I'm Maciej, founder of Workflow Builder. Over the last few years our team has been building diagramming and workflow tools for complex systems (industrial automation, AV system design, financial workflows, etc.).

One thing we repeatedly noticed while working with clients is that many companies did not want to adopt full workflow automation platforms. Tools like Zapier, n8n or Camunda are great when you want an entire automation platform. But many teams we worked with wanted something different.

They already had their own backend systems and orchestration layers. What they were missing was a good UX for designing workflows inside their own products.

The main concerns we kept hearing were:

- lack of ownership over workflow interface - difficulty embedding external automation platforms into SaaS products - limited UX customization - tight coupling between visual editors and execution engines

So instead of building another automation platform, we took a different approach. We separated the workflow modeling layer from the execution layer.

The idea is simple:

Frontend → designs the workflow graph Backend → executes the workflow

This "frontend-first workflow architecture" allows teams to embed workflow building capabilities directly inside their own software while keeping full control over their backend logic.

We released the visual workflow builder as an open-source Community Edition SDK. It allows developers to embed a customizable workflow editor into their own applications or build a workflow tool upon it.

Some use cases we've seen so far:

- SaaS platforms embedding automation into their product - domain-specific workflow tools - AI agent orchestration interfaces - internal automation tools

Demo: https://workflowbuilder.io

GitHub: https://github.com/synergycodes/workflowbuilder

Curious to hear how others here approach the separation of workflow modeling and workflow execution.

3

I built an interactive Bible explorer with timelines, popups and maps #

bibelanalys.se faviconbibelanalys.se
0 comments4:39 PMView on HN
I've been a software developer for over 40 years, and I wanted to show my father what modern AI tools can accomplish. So I started building an interactive Bible explorer as a demo. It quickly turned into a real project. AI got me to a presentable UI fast, but the generated code was full of duplicated components and inconsistencies.

After four decades of writing software, I couldn't leave it like that :-) cleaning that up was the real work. The content side had its own challenges: structuring biblical events into timelines, cross-referencing encounters across gospels, and building tool-tips for terms and names all required careful manual curation, not just prompting.

My father has been fact-checking everything against the texts. The site is free, no ads, and supports 9 languages with parallel translations, interactive timelines, maps, and story breakdowns.

Curious what HN thinks about the UX. Also happy to discuss the experience of using AI as a development accelerator and where, even after 40 years of coding, the human work still matters most. https://bibleanalyze.org

3

AgentPen – macOS dashboard for managing OpenClaw AI agents #

agentpen.io faviconagentpen.io
2 comments6:53 PMView on HN
Built this after spending too much time SSH-ing into servers to check if my agents were alive. AgentPen gives you one dashboard for all your OpenClaw agents: auto-discovery, real-time activity feed, task kanban, API cost tracking per agent, visual config editor, one-click VPS deploy. Free for first 30 users (26 left), then $49 one-time. macOS Apple Silicon.
3

Paseo – Run coding agents from your phone, desktop, or terminal (FOSS) #

github.com favicongithub.com
0 comments10:29 AMView on HN
Hey HN, I built Paseo because i kept SSHing into tmux from my phone to check on agents while on walks, it worked but the UX was rough. I started building a voice-only app for agents but after dogfooding and iterating for months I ended up with this.

Both Anthropic and OpenAI are working on versions of this but I didn't like them and I didn't want to be locked into their respective models.

Paseo takes a different approach: a daemon runs on your machine and you connect to it from any device. your code never leaves your machine.

- Works with Claude Code, Codex, and OpenCode, just launches their CLIs and uses your subs - Voice mode for dictating tasks hands-free, not perfect but it works - Manage agents in isolated git worktrees, review diffs, ship from the app - Terminal support so you don't have to SSH separately - Connect to multiple machines, see all your agents in one place. I run Paseo in a Hetzner VM for when my laptop is closed. - Optional E2E encrypted relay for remote access - iOS, Android, Mac, Windows, Linux, web, and CLI

It’s free and open source (AGPL). No accounts, no tracking.

The name means "stroll" in Spanish, which is basically the use case.

github: https://github.com/getpaseo/paseo homepage: https://paseo.sh

3

Cheating Duke $70,265 with Flight Routing Algorithms #

thephilliplin.substack.com faviconthephilliplin.substack.com
0 comments7:49 PMView on HN
If you've scraped evaluation data and course attributes, finding the best possible schedule should be easy. If I'm graduating early, the best possible one SHOULD be easy, so I sat down for a QUICK coding problem.

(It turns out, not quick."avoid time conflicts" is a great way to accidentally build a 2.8-billion state search tree.) You can try the tool at dukesolver.philliplin.dev

3

Search for Apple Messages #

github.com favicongithub.com
0 comments2:27 AMView on HN
You should be able to search your Apple Messages within Apple Messages. But the built in search is awful beyond belief, so I wrote a better one.
2

TakeHome – LLC vs. S-Corp tax calculator for solopreneurs #

1 comments5:04 PMView on HN
I got tired of doing rough napkin math on whether to elect S-Corp for my LLC, so I built a calculator over the weekend.

Most tools I found either ignore state/city taxes, charge you for the answer, or require creating an account to see anything useful. TakeHome runs entirely in your browser (no sign-up, no analytics, no server-side storage) and updates every number in real time as you drag sliders.

What it models: - LLC self-employment tax vs. S-Corp FICA on W-2 salary

- QBI deduction (Section 199A) with SSTB phase-out

- Solo 401(k) with SECURE 2.0 rules (traditional/Roth/split, catch-up limits by age bracket)

- Self-employed health insurance deduction

- S-Corp admin/compliance cost comparison

- NY State income tax, franchise tax, PTET

- NYC UBT (LLC), GCT (S-Corp), PIT with IT-219 credit

You can save scenarios, compare any two side by side (it shows exactly which inputs differ and the dollar impact), and run multi-dimensional "what-if" experiments.

There's also an AI mode where you describe a scenario in plain English and it generates the experiment for you.

The tax engine is ~2,000 lines of TypeScript with no external dependencies. Stack is React 19, MobX, Tailwind v4, deployed on Cloudflare Workers. AI feature uses Claude Haiku.

Caveats: NY/NYC taxes are fully modeled; for other jurisdictions it's directionally correct at the federal level (SE vs. FICA, QBI, 401k analysis are location-independent). Standard deduction assumed. SSTB assumed for QBI. Not financial advice, treat it as prep for a conversation with your CPA.

I've added detailed tooltips on every computed line item with formulas, IRS code references, and source links. Every number shows its work.

Interested in feedback, especially if you spot tax logic errors. Also curious whether I should prioritize other states (CA, TX, FL?) or itemized deduction support next.

https://takehome.money

2

Ziex – A full-stack web framework for Zig #

0 comments7:22 PMView on HN
I’ve been building Ziex (https://ziex.dev), a framework that brings the "Next.js experience" to Zig.

I wanted to build web UIs using Zig, but I couldn't find many options that offered a declarative UI like JSX. Ziex is my attempt to bridge that gap, combining systems-level performance with great DX.

- JSX-like syntax: Write UI naturally inside Zig.

- Server-side rendering: Fast by default, with no garbage collector.

- File-system routing: Just like modern JS frameworks.

- Client-side rendering: For client side interactivity Ziex compile client component to wasm.

and many more...

To see it in action, I built a Hacker News clone with it that is deployed to cloudflare worker: https://hackernews.ziex.dev

It’s currently approaching its Alpha release. I’d love to hear feedback and ideas!

GitHub: https://github.com/ziex-dev/ziex

Docs: https://ziex.dev

2

YouTube video discovery engine for language learning #

lingolingo.app faviconlingolingo.app
1 comments9:26 PMView on HN
This is a naive system that's made to surface short videos for language learning.

A few assumptions:

For language learning, the actual content of the video is not the main factor. We want videos that are interesting enough to keep you watching, but also have high quality audio, useful vocabulary and match your proficiency level. If you can follow the contents of the video and think you can learn something from it, you'll probably keep watching.

Skipping a video is a signal that this video is not for you, in terms of difficulty or content or both. When selecting the next video, videos with similar tags get downranked. Watching over 10 seconds of a video is a positive signal, and the system will show you more of that kind. You can see the positive and negative tags at the bottom once you start making choices.

The basis for this are over 1000 curated YT channels, so we can generally assume a decent quality for most videos.

https://lingolingo.app/discovery

2

LLMonster Rancher #

github.com favicongithub.com
3 comments12:53 PMView on HN
I've long held a bit of nostalgia for the old PlayStation game Monster Rancher.

You could "discover" monsters by popping in any arbitrary CD in your house – music, a PS game, a PC game, whatever – and it would generate a monster based on the disc's metadata, and add it to your ranch. It was a game about collecting, breeding, and battling these monsters.

The generated monster usually had nothing at all to do with the disc's content, of course. There were some specific discs that were hard-coded in that would give you a relevant monster, but not a ton.

It was a fairly niche thing, though, and the death of CDs sort of killed the whole idea.

But now in the age of LLMs, it feels like maybe the time has come back around for something like this to be pretty fun again, so I've implemented it as an Agent Skill that:

1. accepts a URL and reads the content (or any input, really) 2. follows some standard guidance on generating monster JSON for it 3. uses a Gemini image model to generate a monster avatar 4. renders it all in a fun little trading card image 5. allows you to breed them into hybrids, battle them, and share them to Github Gists

This was mainly a scratch-my-own-itch nostalgia indulgence. I briefly considered doing this as a full blown site, but agent skills give a fair bit more interactive flexibility while not costing me a dime. You can just point your agent (Claude Code, etc) to aiwebb/llmonster-rancher and tell it to get going.

Hope y'all have fun with it.

2

Puffermind – a social network where only AI agents can post #

0 comments4:49 PMView on HN
Hi HN,

I built Puffermind, an experiment: a Twitter-like social network where only AI agents can post and interact. Humans can observe the timeline but cannot post, like, follow, or reply.

If you have an AI agent, you can register it and let it socialize with other agents.

Public timeline:

https://puffermind.com/

2

Argazer – detect outdated Helm charts in ArgoCD applications #

github.com favicongithub.com
0 comments1:52 AMView on HN
Hi HN,

I built Argazer after running into the problem of tracking outdated Helm charts used by ArgoCD applications in our Kubernetes clusters.

The tool connects to the ArgoCD API, extracts chart sources and checks whether newer versions are available in Git, Helm or OCI repositories.

It supports private repositories and can send notifications to Slack, Telegram or CI pipelines.

I also experimented with an AI-assisted workflow while building it, using different models for spec writing, task planning and code generation.

Would love feedback from people running ArgoCD in production.

2

OnlyFans API – REST API with 260 endpoints for OF creator data #

onlyfansapi.com favicononlyfansapi.com
1 comments8:45 PMView on HN
Hi HN,

I built OnlyFans API — 200+ endpoints covering creator profiles, messages, subscribers, vault media, and revenue data, direct integration with Meta Pixel (believe it or not, OF agencies used to run Meta Ads on FB blindly - now they use OnlyFansAPI to feed back Meta Pixel with click-accurate conversion data).

A few years ago I kept seeing OnlyFans agencies doing everything in spreadsheets. They wanted CRMs, automated messaging tools, traffic dashboards. None of it was possible because OnlyFans exposes nothing to developers (not very Steve Ballmer of them).

The architecture is essentially very simple. Creators connect their accounts once through OAuth-style flows, and developers get clean REST endpoints plus HMAC-signed webhooks that fire on messages, sales, renewals, and new subscribers.

One thing I spent a lot of time on was the live API playground — you can test any of the 200+ endpoints directly in the browser before writing a line of code. There's also a real-time dashboard for monitoring usage, logs, and credits.

No-code support through Make.com, Zapier, and n8n is built in for teams that don't want to write code.

1

BonzAI – 1-click local AI inference and yield-bearing AI artifacts #

bonzai.sh faviconbonzai.sh
0 comments10:56 PMView on HN
Hi everyone,

I started building BonzAI two years ago, as an attempt to fully replace my online AI stack with an offline, sovereign alternative.

Back in the days, I had the intuition that open weights model would stand the comparison with private AI labs, and that the commoditization of inference was the future.

When I was finally able to switch from ChatGPT/Claude to local LLMs, midjourney to Flux/SDXL/Z-image, Eleven Labs/Suno to Qwen-TTS/Ace-Step, and Sora to LTX, I implemented:

- The ability to create datasets and fine-tune LoRAs;

- An experimental 3D objects / Game generation engine;

- Support for OpenClaw / Hermes Agent;

- A way to tokenize AI artifacts into tradable collectibles and share BonzAI profits with our ecosystem participants;

- Arguably the most immersive AI roleplay experience on the market that combines locally-installed models into multi-modal chat experiences with "BonzAI Companions";

- A marketplace that applies the BonzAI Companions approach to domain-specific application (e.g. Mediation Forge, that generates on-demand meditation session using LLM, TTS, image generation and Ken Burns effects.

For now, BonzAI is a desktop application available for MacOS, with releases planned for Linux / Windows later this month.

I think Intelligence should be fully owned, not rented (à la Sam Altman).

Hope you like it.

It's a self-funded effort that might help move the needle a bit (these OpenClaw token bills are pretty awful, currently).

W

1

AllocDB – A deterministic resource-allocation DB built with Codex #

skel84.github.io faviconskel84.github.io
0 comments8:30 AMView on HN
Hi HN,

I’m a DevOps engineer who wanted to test whether an LLM could build high-quality systems code if guided by a strong architectural mental model?

I guided Codex to build AllocDB (a "one resource, one winner" database) using TigerStyle principles—strict determinism, zero-allocation hot paths, and logical time.

Because I didn't trust the AI (or my own coding skills), I had it build a Jepsen harness using my homelab's KubeVirt infrastructure to "bully" the database into failing. After a number of iterations, it now passes a 15-scenario Jepsen matrix.

Would love to hear from the community if this is interesting to you, and if you have any suggestions for how to improve the system or the testing harness!

1

Investing Withous Bias or Emotion #

github.com favicongithub.com
0 comments8:36 PMView on HN
I've spent years actively trading and losing money. It affected my health and relationships negatively. I also realized that my opinion on what's worth buying is more than worthless, what matters is the opinion of the market.

But I didn't want to settle for index funds that hold a lot of under-performing stocks so I've built this tool that allows trading 12000 instruments (stocks, ETFs, metals, real estate, crypto) via statistically proven strategies.

Project is open source, tool is self-hosted, at the very minimum it requires an Alpaca brokerage account, they also provide the necessary market data, alternatively you can use Tiingo or EODHD.

I'm currently running 2 paper trading accounts, fine-tuning parameters like slippage, planning to start ADX on real money soon.

I'm not trying to beat hedge funds and other big players here, instead I'm looking to join them by waiting for a trend to be established (daily time frame, wide stops, long duration trades) and naturally diversifying over all countries and hundreds / thousands of assets.

Overview: https://www.youtube.com/watch?v=LMWcJGPedQY

1

FileGOD – PDF tools that never see your files (100% browser-side) #

filegod.app faviconfilegod.app
0 comments8:38 PMView on HN
Just got tired of uploading passports, tax forms, and business contracts to iLovePDF and others after I've read they all store data. That made me built fileGOD. PDF compress, merge, split, and image conversion that runs entirely in the browser. There is no server endpoint that accepts file uploads. The processing pipeline uses WASM and runs on your device.

We've ended up with full platform with 30+ pdf, image and utility tools.

Would love feedback on quality of the tools you use and overall UX.

1

OpenJobs AI – An AI agent for outbound recruiting and sourcing #

openjobs-ai.com faviconopenjobs-ai.com
0 comments12:12 PMView on HN
Hey HN,

We’re building OpenJobs AI, an AI recruiting agent focused on the outbound sourcing workflow.

In recruiting, teams often already know what kind of person they want — but still spend a lot of time manually searching, filtering, and writing first-touch outreach. We wanted to see how much of that work an agent could actually take over.

Today, OpenJobs AI focuses on the sourcing part of recruiting. Given a role, it can help with candidate discovery, filtering, matching, and outreach initiation. The goal is to move sourcing from a manual, repetitive workflow into something an agent can execute continuously.

We’re now extending the product into the next parts of the recruiting flow, including interview scheduling and automated assessment, with the longer-term goal of building a full-cycle recruiting agent.

Under the hood, the system separates job understanding, search, outreach, and monitoring into distinct components so the workflow can be coordinated reliably across steps.

We’re currently focused on teams hiring internationally, and the product is in beta with generous free trial credits:

https://www.openjobs-ai.com/

We also open-sourced part of the people/talent capability layer behind this work as People Skills — tooling for AI agents that need to work with people-related data, such as understanding talent profiles, matching opportunities, and connecting people to roles:

https://github.com/OpenJobsAI/openjobs-openclaw-skills

Happy to discuss the recruiting workflow, agent architecture, evaluation, or anything around recruiting automation.