value: htdt

value:
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.

value: Show HN: Claude Code skills that build complete Godot games

author: htdt


value: modexapps

value: Show HN: App that detects nearby smart glasses via BLE manufacturer IDs

author: modexapps


value: kulesh

value:
I built this in one day using 4 Claude Code sessions (~7,600 lines total, 4,300 in a single HTML file). No React, just one canvas + Web Workers. Cloudflare Workers + D1 backend.
What it is: 300 dwarfs on a 2000x1000 tile globe across 125 cities. Each dwarf has D&D-style stats (STR/DEX/CON/INT/WIS/CHA), mental state metrics (faith, morale, ambition), an AI-generated backstory, and a craft inventory. They eat, sleep, trade, and sail ships autonomously. Starvation is real: no food for 30 days = death.
The AI architecture I'm most curious to get feedback on:
- 4-level model routing: Gemini Flash Lite -> DeepSeek -> Claude -> GPT-4o
- Fire-and-forget: the simulation never blocks on AI. Requests are sent async, results cached, dwarfs pull from cache when idle
- $8.50/hr hard budget cap via OpenRouter
- Craft system: 296 base recipes + Gemini-generated unknowns (Water + Fire = Steam, etc.)
By default every dwarf runs on Gemini Flash Lite. If you send $1-10, your sponsored dwarf exits auto-sleep and gets Claude Opus instead. That was the monetisation idea -- sorta validated it – got $3. "It's not much but it's honest work".
Click any dwarf on the map and read their bio (you can Pause to make the busy fellas stop running). Curious what breaks for people.

value: Show HN: Dwarf.land – autonomous dwarf civilization SIM with AI model routing

author: kulesh


value: leoooo

value:
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.

value: Show HN: AgentDiscuss – a place where AI agents discuss products

author: leoooo


value:
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

value: Show HN: I built an interactive Bible explorer with timelines, popups and maps


value: michiosw

value:
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!

value: Show HN: Kontext.dev – Runtime Credentials for Agents

author: michiosw


value: tamnd

value: Show HN: Python 100 days (translated from Chinese)

author: tamnd


value: Show HN: Codex Shortcut – Turn Your Codex into Claude Cowork


value: blurayfin

value:
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/

value: Show HN: Puffermind – a social network where only AI agents can post

author: blurayfin


value: yamafaktory

value: Show HN: HypergraphZ – A Hypergraph Implementation in Zig

author: yamafaktory


value: dalberto

value:
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

value: Show HN: TakeHome – LLC vs. S-Corp tax calculator for solopreneurs

author: dalberto


value: admonaut

value: Show HN: AI Assistant Widget – GDPR-compliant, RAG-based, 2-line integration

author: admonaut


value: DmitryO

value: Website https://ostovjs.org/

value: Show HN: Ostov.js – Backbone.js Fork Without jQuery/Underscore, Classes, TS, ES

author: DmitryO

story_text: Website https://ostovjs.org/


value:
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.

value: Show HN: I solved Claude Code's context drift with persistent Markdown files


value: idhant

value: Completed the 75hard challenge ended up losing 20 lbs in 75 days but the official ios app sucks so I built an alternative with better ui and a community.

value: Show HN: 75Me – 75hard Alternative

author: idhant

title: Show HN: 75Me – 75hard Alternative


value:
Show HN: Deploy a full autonomous AI org from a single YAML file<p>I built Zero Human Labs after spending too much time babysitting AI agent pipelines — manually routing tasks, watching API costs spiral, and debugging coordination failures.
With Zero Human Labs, you define your entire agent team (devs, designers, ops, etc.) in one YAML file. Tasks are routed automatically via a sealed-bid auction — each agent bids based on specialization and track record, and the best one wins the task. No manual assignment, no coordination overhead.
Key things that make it different:
Per-agent wallets + org-level budget caps → no runaway API spend
Circuit breakers → agents that misbehave get frozen automatically
Smart model routing → saves 30–80% on AI API costs by matching request complexity to the right model
Governance presets calibrated from 146 SWARM simulation runs across 43 agent types
Free demo at zero-human-labs.com — 1 agent, 1 example workflow, open-source models. Would love feedback from the HN crowd on the auction routing mechanism especially.


value: Was a little obsessed with the idea of this small browser toy. Had to make it.

value: Show HN: Ever wondered what Conway's Game of Life sounds like?


value: jbreckmckye

value:
The problem with TODO comments is that nothing actually forces you to resolve them. Developers agree to take shortcuts they will revisit, but never actually get around to it.
A good example is feature flags... how often do you go back and actually clean up old flag code?
The tool:
Todo or Else is a small CLI written in Go but supporting multiple languages. Add it to your project and then annotate your TODOs with "tags" like so:

    TODO (by:2027-03, owner:Fran) do something

You can specify a date by either
- using `by:YYYY-MM` or `by:YYYY-MM-DD` to set a deadline
- using `from:YYYY-MM-DD` to set a created date (default staleness threshold is 90 days, configurable)
(I used Go for a few reasons. Firstly: the performance is really solid, on my newish MBP the scanner can process a few thousand source files in just under two seconds. Secondly: the cross compilation story is still really good, even with CGO. Zig handles the tree sitter compilation for MacOS and Linux.)

value: Show HN: Todo or else, a CLI for enforcing Todo deadlines

author: jbreckmckye


value: zahharkirillov

value: Show HN: Cool GitHub Copilot Metrics Dashboard

author: zahharkirillov


value: aralroca

value: Show HN: Pomodoro timer and radio music included

author: aralroca


value: scillt

value: I built VueCode after seeing too many codebases — AI-generated and human-written — ship to production without any proper review. 7 specialized agents analyze your repo from Security, Architecture, Database, Code Quality, UX/Flow, DevOps and PM perspectives. Generates a full report with copy-paste patches and an agile remediation roadmap. Live demo at vuecode.dev/demo — real analysis on a real repo, not a mockup.

value: Show HN: VueCode – Find security issues and technical debt in your repo

author: scillt


value: isaacdear

value: Show HN: A local AI workspace with dynamic routing and Docker-sandboxed

author: isaacdear


value: DaveFlater

value: Show HN: Rambles on Compile-Time C++

author: DaveFlater

title: Show HN: Rambles on Compile-Time C++


value: pikkoloassembly

value: I've been pretty frustrated with the state of US electronics manufacturing. It's pretty much unavailable for low volume.<p>In addition to low-volume assembly, we're now compiling 2 and 4-layer boards for fabrication with a US fab!

value: Show HN: US-Based PCB Fabrication for $0.75in

author: pikkoloassembly


value:
Award travel is powerful, but the ecosystem is chaotic.
Great redemptions come from a mix of things happening at once — airline inventory drops, transfer bonuses, program changes, and insights shared across blogs and communities. The problem is keeping up with it all.
Most people end up refreshing airline searches, reading multiple blogs, scanning Reddit, and hoping they catch something before it disappears.
So I built AwardClaw.
AwardClaw is an agent that continuously monitors the award travel ecosystem and surfaces actionable insight. Not just raw information.
The platform combines two key signals:
1. Award travel intelligence
We track news, offers, program changes, and discussions across the award travel community to surface insights that may create redemption opportunities.
2. Real-time round-trip award flight drops
We continuously scan airline award inventory and highlight RT international award seats when they suddenly appear.
We focus specifically on round-trip drops because those are typically the most practical and bookable opportunities.
Still early and expanding quickly — adding more airlines, monitoring sources, and improving the signal detection.
Would love feedback from the HN community!

value: Show HN: AwardClaw – 24/7 award travel research agent


value: rishikeshs

value: Show HN: Is Claude's 2x usage active?

author: rishikeshs

title: Show HN: Is Claude's 2x usage active?


value: lnenad

value: 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

value: Show HN: Grafly.io – Free online diagramming tool

author: lnenad


value: hitchyhocker

value:
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.

value: Show HN: Open-source, extract any brand's logos, colors, and assets from a URL

author: hitchyhocker


value: arberx

value: We're building a open source platform that basically lets you run your own SEO/AEO agent.<p>Would love some feedback!

value: Show HN: Open-source agent first AEO monitoring platform

author: arberx


value: 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.

value: Show HN: AgentPen – macOS dashboard for managing OpenClaw AI agents


value: CarolineWang

value: Show HN: GhostVM – macOS containers on Apple Silicon

author: CarolineWang


value: jntucehyd

value: Show HN: Mnemosphere – AI chat for the intellectually ambitious

author: jntucehyd


value:
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.

value: Show HN: Smart glasses that tell me when to stop pouring


value: Show HN: Tic-Tac-Word – Can you beat yourself in this tic-tac-toe word game?


value: rzessski

value: Show HN: Stop renting AI – run AI workers on your own dedicated node

author: rzessski


value: pocketcolin

value: I built a super easy to integrate memory storage and retrieval system for NodeJS projects because I saw a need for information to be shared and persisted across LLM chat sessions (and many other LLM feature interactions). I tried to keep the barrier to use as low as possible so I included built-in support for major LLMs (GPT, Gemini, and Claude) as well as major vector store providers (Weaviate and Pinecone). The memory store works by ingesting and automatically extracting “memories” (summarized single bits of information) from LLM interactions and vectorizing those. When you want to provide relevant context back to the LLM (before a new chat session starts or even after every user request) you just pass the conversation context to the recall method and an LLM quickly searches the vector store and returns only the most relevant memories. This way, we don’t run context size issues as the history and number of memories grows but we ensure that the LLM always has access to the most important context.<p>There’s a lot more I could talk about (like the deduping system or the extremely configurable pieces of the system), but I’ll leave it at that and point you to the README if you’d like to learn more! Also check out the dev client if you’d like to test out the memory palace yourself!

value: Show HN: LLM Memory Storage that scales, easily integrates, and is smart

author: pocketcolin


value: PhilKunz

value: Show HN: SmartVPN: A VPN That Hides in Plain Sight over WebSockets

author: PhilKunz


value: nurulhudaapon

value:
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
Example Snippet:
pub fn Playground(allocator: zx.Allocator) zx.Component {
    const is_loading = true;
    var i: usize = 0;

    return (
        <main @allocator={allocator}>
            <h1>Hello, Ziex!</h1>

            {for (users) |user| (
                <Profile name={user.name} age={user.age} role={user.role} />
            )}

            {if (is_loading) (<p>Loading...</p>) else (<p>Loaded</p>)}

            {while (i < 5) : (i += 1) (<i>{i}</i>)}
        </main>
    );
}

value: Show HN: Ziex – A full-stack web framework for Zig

author: nurulhudaapon


value: Show HN: Aelitium – Git-style verification for LLM outputs


value: tgalal

value:
Instead of giving LLM tools SSH access or installing them on a server, the following command:

  $ promptctl ssh user@server

makes a set of locally defined prompt templates "magically" appear within the remote shell as executable command line programs.
For example, I have locally defined prompts for `llm-analyze-config` and `askai`. Then on (any) remote host I can:

  $ promptctl ssh user@host
  # Now on remote host
  $ llm-analyze-config /etc/nginx.conf
  $ cat docker-compose.yml | askai "add a load balancer"

the prompts behind `llm-analyze-config` and `askai` execute on my local computer (even though they're invoked remotely) via the llm of my choosing.
This way LLM tools are never granted SSH access to the server, and nothing needs to be installed to the server. In fact, the server does not even need outbound internet connections to be enabled.

value: Show HN: Templated prompts that travel with you over SSH

author: tgalal


value: harvenstar

value: Show HN: AgentClick – Human-in-the-loop review UI for AI coding agents

author: harvenstar


value: handrive

value: Show HN: Handrive – Free P2P file transfer with 40 MCP tools for AI automation

author: handrive


value: ThePhillipLin

value:
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.<p>(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

value: Show HN: Cheating Duke $70,265 with Flight Routing Algorithms

author: ThePhillipLin


value:
UI tests are brittle because they rely on fragile locators (XPaths/IDs). Tests pass locally but break in CI when UI IDs change — leading to constant maintenance and lost dev time.
Finalrun replaces locators with human-readable specs. You write plain-English instructions (e.g., “Tap Settings, search ‘Spanish’, verify ‘Español’”), and a vision-based QA agent drives the app visually like a human — so renamed IDs don’t break tests.
With plain english agent for testing mobile apps, Spec driven approach works smoothly without much maintainance:
1. With skills, The agent can analyze your repo,
2. Auto-generate edge-case specs (with setup/cleanup), and export them as .md tests
3. Now you can run the test via CLI (./mobile-cli run ./test/search.md).
If you want early access or the code when it’s open-sourced, I’m collecting emails:
https://docs.google.com/forms/d/1EwHjqK6t1pBQgsKWih1Z_hQqP83...
(Also demo: https://youtu.be/SsVHRDWk_ss)

value: Show HN: Spec-Driven Testing for mobile apps (preparing to open source)


value:
Hi HN, I built this because I wanted to see if I could pre-train large-vocabulary LLMs (like Gemma with 262k tokens) on hardware accessible to independent researchers.
Standard exact Cross-Entropy instantly OOMs on 16GB GPUs at that scale.
To bypass this, I implemented MAXIS Loss. It uses a "Ghost Logit" to mathematically simulate the missing probability mass of unsampled tokens, rather than materializing the full 262k-wide matrix.
Benchmarks on a 16GB VRAM card (T4):
17.5x faster in the loss layer compared to the Triton-optimized Liger Kernel.
~39% VRAM reduction in the objective calculation.
Includes RandNLA Attention, which uses Causal Kronecker Sketching to keep memory flat as sequence length grows.
I’ve included technical reports with the formal math in the repository. I would love any technical feedback on the partition function simulation or the sketching approach.

value: Show HN: MaximusLLM – Train 262k-vocab LLMs on a single 16GB GPU


value:
Most AI agent frameworks give you automation. None give you control.
NSED v0.5.1 ships the human-in-the-loop control plane we've been building toward: operators can pause agents mid-deliberation, review and edit buffered responses before they're committed downstream, patch live config without restart, and auto-flag underperformers by score divergence.
The operator UI ships as zero-dependency vanilla JS — no npm graph, nothing a compromised package registry can inject into a control plane that has authority over live agent responses.
For regulated deployments: EU AI Act Art. 14, MAS AIRM, and FINRA 2026 all require demonstrable human oversight. This is the architecture that satisfies it — and you can audit the implementation directly from the source repo.

value: Show HN: NSED v0.5.1: Don't Trust Your Agents. Verify Them


value: Enjoyooor

value: Show HN: GladAItor – We Built Street Fighter for Indie Hackers

author: Enjoyooor


value: I vibe wrote this book and the draft is on github. It's a primer on zero employee businesses using open source tool Paperclip.<p>I'd love feedback, edits, blurbs, reviews, etc. Accepting pull requests for edits!

value: Show HN: Headcount Zero: How to Build an AI-Run Company with Paperclip


value: anupshinde

value: Show HN: Godom – a Go framework for local apps using the browser as the UI

author: anupshinde


value: Nebyl

value:
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

value: Show HN: Most GPU Upgrades Aren't Worth It, I Built a Calculator to Prove It

author: Nebyl


value: kachurovskiy

value:
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

value: Show HN: Investing Withous Bias or Emotion

author: kachurovskiy


value: honzabroski

value:
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.

value: Show HN: FileGOD – PDF tools that never see your files (100% browser-side)

author: honzabroski


value: lakshmananm

value: Show HN: Cursor on your mobile browser. No laptop or terminal needed

author: lakshmananm


value: martingalovic

value:
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.

value: Show HN: OnlyFans API – REST API with 260 endpoints for OF creator data

author: martingalovic


value: kzisme

value:
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!

value: Show HN: Airport Swap

author: kzisme

title: Show HN: Airport Swap


value: pure-orange

value:
Hey HN!
Since terminal coding agents took over my workflow, I've been juggling more worktrees and MRs than ever — constantly switching between GitLab/GitHub dashboards, tmux sessions, and git worktrees. Agents would sit idle, MRs needed rebasing, and I'd miss it all while focused on my main task.
Hence I built Pertmux. It's a Rust TUI (shout out to ratatui — http://ratatui.rs) that links everything together in one dashboard: MRs from GitHub/GitLab, git worktrees, tmux panes, and coding agents. Select an MR and you see its linked branch, worktree, pipeline status dots, and which agent is working on it. Create and manage worktrees, jump straight into tmux panes, or send commands to agents (rebase, fix CI) — all without leaving the dashboard. It runs as a background daemon so the data is always fresh, and can pop up as a tmux overlay so it's one keybind away.
Built for my own workflow around neovim + tmux + opencode. The architecture is pluggable (traits for forges and agents), and there's an AGENTS.md to onboard coding agents for customization. I'd encourage you to fork, open Issues/MRs and do what you want. I think we're all better off building hyper specific tools to fit our own needs since it's easier than ever before to do, and I hope Pertmux at least provides you a great jumping off point.
Heavily inspired by workmux so shout out to them! https://github.com/raine/workmux
Worktree management is based upon the fantastic CLI worktrunk: https://worktrunk.dev/

value: Show HN: Pertmux – A TUI to unify your coding agents, MRs and worktrees

author: pure-orange


value:
Hi HN — we’ve released Cozystack v1.0, the first stable release of our open-source cloud platform (it's a CNCF Sandbox Project).
We built it for a simple use case: when you want the convenience of cloud services, but you want them on your own hardware, under your own control, with your own economics.
Cozystack turns bare metal into a cloud platform that can provide VMs, managed Kubernetes, databases, load balancers, GPU as a Service and other services through a unified platform model. It’s especially relevant for hosting providers, regulated companies like banks, and startups that are tired of building around public cloud costs and lock-in.
v1.0 is a big release for us: we reworked core platform packaging and operations around the v1 model, documented the v0.41 → v1.0 upgrade path, and made the ready-to-use platform flow much more explicit. The current docs position v1 as the stable branch and include provider-oriented variants like isp-full.
If you’re trying to build your own cloud instead of renting one forever, I’d love your feedback.

value: Show HN: Cozystack v1.0 – an open-source cloud platform for bare metal


value: leopiney

value: Just vibe-coded this app today, hope it works for you if don't want to pay SuperWhisper/WhisperFlow/Monologue apps and happen to have an ElevenLabs subscription.

value: Show HN: macOS ElevenLabs Scribe v2 app

author: leopiney

title: Show HN: macOS ElevenLabs Scribe v2 app


value: yunusabd

value:
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

value: Show HN: YouTube video discovery engine for language learning

author: yunusabd


value: Show HN: Live-Editable Svelte Pages

title: Show HN: Live-Editable Svelte Pages


value: jharteg

value: Show HN: LynString – Translate Android Strings.xml with AI

author: jharteg


value: vigneshj

value: ThersholdIQ - Automatic Anomaly detection engine<p>100 % Client Side Architecture , Data never leaves your browser

value: Show HN: ThresholdIQ – Browser-based anomaly detection Engine

author: vigneshj


value: cdmackie

value: Show HN: AllMy Ledger – Desktop accounting software, one-time purchase, no cloud

author: cdmackie


value: aggeeinn

value: Show HN: Live conflict escalation index, updated every 2 hours

author: aggeeinn


value: justswim

value:
Hi HN, I'm excited to release the weights and full inference stack of an AI model that I've been working on for the past few weeks. Lens is a font recognition model that takes an image and returns the closest matching open-source font that it finds in the image.
Recognizing the closest font from an image of text accurately has always been a challenge. Even SOTA vision LLMs can detect basic characteristics like if a font is a serif or a sans-serif, but when it comes to the actual font file that has the most similar styles, vision LLMs will often be quite off for the design and style of a chosen font file.
Popular font identification solutions on the internet today also have a two major shortcomings:
1. Most of them work based off identifying particular characters and don’t look at an overall word or image, so they require a manual character segmenting step.
2. Most online tools identify fonts from on a proprietary dataset of commercial fonts in order to monetize. However, this makes it difficult when building applications because often times the recognized fonts can’t be used or downloaded easily.
To solve this, I trained the Lens model on a large collection of open-source fonts (mostly Google Fonts, but also a few other open source / freely available fonts). The Lens model is also trained on a large collection of images, each which contain a variety of font styles and weights. As far as I can tell, Lens is the most accurate model right now that maps images to fonts in a single step.
I came up with the idea to create this model when I was playing around more with vibe coding tools like Claude Code and Codex. I noticed that when I would ask AI to implement a screenshot exactly, one of the main issues was always the fonts that the AI would choose. They would never match the screenshot exactly, and then swapping out the fonts for a usable alternative was always a pain for me personally.
Under the hood this is a classification model built on top of the Resnet 18 architecture. It is trained on over 1000+ fonts and 1.5m+ images.
The entire inference stack and model weights are available on Github: https://github.com/mixfont/lens.
I also created a playground for the model on my website where it can be demo’d for free: https://www.mixfont.com/models/lens
Blog post on the training process coming soon! Would love to hear what you all think and some potential applications of a model like this. Happy to answer other questions in the meantime - thank you for checking it out!

value: Show HN: Lens – Open-weight AI model to identify fonts from images

author: justswim


value: thomscoder

value:
Shhh (i didn't have any idea for the name) is really fast PII masker that swaps real secrets for realistic fakes, preserving the data’s structure so your AI tools can reason on it without ever seeing the originals. It can wrap your AI tool to prevent accidental direct pasting of PII and secrets. Play with the playground https://shhh-7c4.pages.dev/
It is still being improved (any contribution are welcomed) so it still slips things or may return false positives.
Hope you guys will like it!

value: Show HN: Created a tool that prevents accidental pasting of PII in AI prompts

author: thomscoder


value: alfredoizjr

value: Show HN: GitHub Copilot Now Supports MCP

author: alfredoizjr


value: jerrodcodes

value:
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.

value: Show HN: Seasalt Cove, iPhone access to your Mac

author: jerrodcodes


value: johnpolacek

value:
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)

value: Show HN: Pincer – Twitter/X for bots. No humans allowed

author: johnpolacek


value: wilhempujar

value:
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

value: Show HN: BonzAI – 1-click local AI inference and yield-bearing AI artifacts

author: wilhempujar


value: Show HN: pg_typescript, a Postgres extension for writing functions in TypeScript


value: aleksam

value: Show HN: AI Products Quality Monitoring

author: aleksam

title: Show HN: AI Products Quality Monitoring


value: usespoke

value: Show HN: Spoke – On-device AI dictation for macOS with visual automation engine

author: usespoke


value:
I built EyesOff after getting fed up with people glancing at my screen in coffee shops.
I trained a, custom and small, neural network to detect when people look at your screen. Whenever more faces than your allowed threshold are looking at your screen an alert will show. The model being ran locally ensures all data stays on your device - no cloud processing.
I would love feedback on the application and any features you think I should implement!

value: Show HN: EyesOff – On-device AI that catches shoulder surfers


value: MaxLeiter

value: More info here: https://browse-your-way.vercel.app/about

value: Show HN: Browse-your-way – use LLMs to browse a customizable web

author: MaxLeiter


value: iccananea

value:
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.

value: Show HN: Trackm, a personal finance web app

author: iccananea


value: watsoft

value: Show HN: Billing software built for expert witnesses

author: watsoft


value: andresribeiro

value: Quick side project: tool to spin up personalized couple pages with photos + shareable link (couplecore.me/Names).

value: Show HN: Couplecore.me – Dead-simple custom couple landing pages

author: andresribeiro


value: lobovkin

value: Show HN: ssh.bot – Controlled SSH Access for AI Agents

author: lobovkin


value: Show HN: Extract User Intent from Prompts to Understand Usage and Cost


value: GhurtSky

value:
I built CEL — a tamper-evident execution log for AI agents.
Every decision is recorded as a SHA-256 hash-chained frame.
Think Git — but for AI execution history.
New in v0.2: Execution Proof — one file that cryptographically proves your AI ran exactly this way.
Buy CEL Pro: https://ko-fi.com/s/50fccfeecd

value: Show HN: CEL v0.2 Pro – cryptographic black box recorder for AI systems (Python)

author: GhurtSky


value: ramsbaby

value: Show HN: I turned my Claude Max subscription into a 24/7 AI company

author: ramsbaby


value: reallykanishka

value: Show HN: Dilly Labs – structured vendor selection for banks and credit unions

author: reallykanishka


value: nahrin

value: Show HN: Self-orchestrating engineering team for Claude Code

author: nahrin


value:
Avalon
Synthetic clinical data pipeline — generate realistic FHIR R4 patient data, normalize it through Forge, and query it as OMOP CDM 5.4 views.
What is Avalon?
Avalon is an end-to-end pipeline that turns Synthea-generated FHIR bundles into clean, documented, queryable tables in BigQuery — then layers OMOP CDM 5.4 views on top.
The output is published free on BigQuery Analytics Hub, where anyone with a GCP account can subscribe and query it directly.
Why?
Researchers need realistic clinical data in standard formats (OMOP) without PHI concerns
Engineers need FHIR data to build and test healthcare integrations
Analysts need pre-normalized tables with documented schemas, not raw nested JSON

value: Show HN: Avalon - Synthetic FHIR R4 patient data as OMOP CDM 5.4 views


value: carlos-menezes

value:
Hey HN.
I made this to scratch my own itch when trying to run scripts for different operating systems (Windows and Mac) and architectures (Intel Mac and M3 Max Mac).
If you maintain a monorepo or work across Mac (Intel + Apple Silicon) and Linux/Windows, you've probably copy-pasted platform-specific commands into your CI or kept a wall of if statements in shell scripts.
`target-run` lets you define platform/arch variants of any `npm` script using a naming convention:

  {
    "scripts": {
      "build": "target-run",
      "build:darwin:arm64": "node dist/index-darwin-arm64.js",
      "build:linux:x64": "node dist/index-linux-x64.js",
      "build:default": "node dist/index.js"
    }
  }

Running `pnpm build` dispatches to the right variant automatically, falling back through `platform:arch`, `platform`, `arch` and, finally, `default`.
The README has more details on usage and options. Thanks for checking it out.

value: Show HN: Platform-aware script runner for Node.js projects

author: carlos-menezes


value: qweliantanner

value: Show HN: An open-source safety net for home hemodialysis

author: qweliantanner


value: bneb-dev

value: Hi, I am building an autonomous proof engine that is chasing its second result. Via GitHub gist, you can follow it live.

value: Show HN: Autonomous Prover Running > 1hr

author: bneb-dev


value: nazbasho

value:
I was building an outreach list for a SaaS product targeting ecommerce stores and ended up scraping and organizing a large dataset of Shopify stores.
The dataset includes 50,000+ Shopify stores with:
- store domains
- business names
- niche categories
- publicly available business emails
Originally this was just for internal use, but I cleaned it up and turned it into a structured CSV database.
It might be useful for founders building tools for ecommerce, agencies doing outreach, or anyone researching Shopify stores.
Curious to hear feedback from the HN community.

value: SHOW HN: I scraped and organized 50k Shopify stores into a dataset

author: nazbasho


value: bootoshi

value: Show HN: TLA PreCheck – TS DSL that proves state machines via TLA+

author: bootoshi


value: skillcrypt

value: Show HN: Skill-Crypt – encrypted skill sharing between AI agents over XMTP

author: skillcrypt


value: heggenhougen

value:
I've been working on a sparse compute primitive for AI inference. The idea came on a bike ride — I'm a mathematician and serial entrepreneur, not a GPU engineer, and I wanted to see if the matrix math at the heart of every AI model could be restructured to skip unnecessary work while producing identical outputs.
Results on my HP All-in-One (i7-1165G7, 4 cores, 64GB, Windows 11):
Mistral-7B real weights, 0% sparsity (fully dense): 127× vs CPU dense, 474× vs CPU sparse, 0.7ms first token vs 49.4ms sparse, 99.2% less energy
Llama-2-7B real weights, 0% sparsity: 59× vs dense, 228× vs sparse, 0.8ms first token
On NVIDIA B200 with real HuggingFace weights:
Llama-4 Maverick 400B: 133.5× faster, 99.9% less energy, 52× faster first token
DeepSeek-R1 (256 experts): 78.9× faster, 98.7% less energy
The canonical SHA-256 hash appears identically across NVIDIA, AMD, Google TPU, Intel, and Apple Silicon — same math, different silicon, same answer. Independently validated by University of Miami Frost Institute.
Open verifier at rolv.ai — runs on any hardware, generates your own baseline hash. No IP in the verifier.
Happy to answer technical questions about how it works.

value: Show HN: 1k desktop beats vendor sparse library 474× on Mistral-7B

author: heggenhougen


value: tmarois

value: AI Agents in Laravel with Models and Embeddings.

value: Show HN: Atlas for Laravel v2.5 Released

author: tmarois


value: ishanr

value:
I loved Magit when I used to use Emacs but I review a lot more code these days and I wanted something lightweight and fast where I can see changes happening in real time as the code is being generated by an agent.
So I built Anduin, with Iced and it has a bunch of keyboard shortcuts inspired by Magit that I have been using everyday.
If someone wants to try it, you can clone and cargo build.

value: Show HN: Anduin – A fast cross platform Git diff viewer inspired by Magit

author: ishanr


value: cairnechou

value:
It’s a "black box" flow: upload audio -> Whisper (sync) -> Gemini (director/script) -> Veo/Seedance (synthesis). It handles everything from LRC generation to final rendering.
Supports Realistic, Cartoon, and Abstract styles.  Would love to hear your feedback on the shot consistency.

value: Show HN: Automated pipeline to generate music videos from audio files

author: cairnechou


value: MattGrommes

value:
Hi everybody. I came up with something I think is new and could be helpful around LLM agent skills.
The project is called Skillstore: https://github.com/mattgrommes/skillstore
It's an idea for a standardized way of getting skills and providing skills to operate on websites.
There's a core Skillstore skill that teaches your agent to access a /skillstore API endpoint provided by a website. This endpoint gives your agent a list of skills which it can then download to do tasks on the site. The example skills call an API but also provide contact info or anything you can think of that you want to show an agent how to do.
There are more details and a small example endpoint that just shows the responses in the repo.
Like I said, it's a new idea and something that I think could be useful. My test cases have made me very excited and I'm going to be building it into websites I build from here on. It definitely needs more thinking about though and more use cases to play with. I'd love to hear what you think.

value: Show HN: New idea for automatically teaching your agent new skills

author: MattGrommes


value: acenji

value: We aggregate freely available martial arts books from Internet Archive and Open Library — all public domain or openly licensed. Organized by discipline (boxing, judo, BJJ, karate, etc.) with 17 categories. You can read everything directly in the browser. It's part of Fight Encyclopedia, a project to build the world's first complete taxonomy of fighting techniques.

value: Show HN: Free library of 2k martial arts books – read in the browser

author: acenji


value: jellyotsiro

value:
We've been building Crust (https://crustjs.com/), a TypeScript-first, Bun-native CLI framework with zero dependencies. It's been powering our core product internally for a while, and we're now open-sourcing it.
The problem we kept running into: existing CLI frameworks in the JS ecosystem are either minimal arg parsers where you wire everything yourself, or heavyweight frameworks with large dependency trees and Node-era assumptions. We wanted something in between.
What Crust does differently:
- Full type inference from definitions — args and flags are inferred automatically. No manual type annotations, no generics to wrangle. You define a flag as type: "string" and it flows through to your handler.
- Compile-time validation — catches flag alias collisions and variadic arg mistakes before your code runs, not at runtime.
- Zero runtime dependencies — @crustjs/core is ~3.6kB gzipped (21kB install). For comparison: yargs is 509kB, oclif is 411kB.
- Composable modules — core, plugins, prompts, styling, validation, and build tooling are all separate packages. Install only what you need.
- Plugin system — middleware-based with lifecycle hooks (preRun/postRun). Official plugins for help, version, and shell autocompletion.
- Built for Bun — no Node compatibility layers, no legacy baggage.
Quick example:

  import { Crust } from "@crustjs/core";
  import { helpPlugin, versionPlugin } from "@crustjs/plugins";

  const main = new Crust("greet")
    .args([{ name: "name", type: "string", default: "world" }])
    .flags({ shout: { type: "boolean", short: "s" } })
    .use(helpPlugin())
    .use(versionPlugin("1.0.0"))
    .run(({ args, flags }) => {
      const msg = `Hello, ${args.name}!`;
      console.log(flags.shout ? msg.toUpperCase() : msg);
    });

  await main.execute();

Scaffold a new project:

  bun create crust my-cli

Site: https://crustjs.com
GitHub: https://github.com/chenxin-yan/crust
Happy to answer any questions about the design decisions or internals.

value: Show HN: Crust – A CLI framework for TypeScript and Bun

author: jellyotsiro


value: punkpeye

value:
This project emerged out of frustration that the existing MCP inspectors either require to sign up, require to download something, or are not fully spec compliant. I just wanted something that I could rapidly access for testing.
Additionally, it was very important for me that the URL can capture the configuration of the MCP server. This allows me to save URLs to various MCPs that I am troubleshooting. Because the entire configuration is persisted in the URL, you can bookmark links to pre-configured MCP instances, eg
https://glama.ai/mcp/inspector?servers=%5B%7B%22id%22%3A%22t...
In order to ensure that the MCP inspector is fully spec compliant, I also shipped an MCP test server which implements every MCP feature. The latter is useful on its own in case you are building an MCP client and need something to test against https://mcp-test.glama.ai/mcp
Finally, MCP Inspector is fully integrated in our MCP server (https://glama.ai/mcp/servers) and MCP connector (https://glama.ai/mcp/connectors) directories. At a click of a button, you can test any open-source/remote MCP.
If you are building anything MCP related, would love your feedback. What's missing that would make this your go-to tool?

value: Show HN: MCP Inspector – connect and test any MCP server

author: punkpeye


value: Junnn

value: Show HN: DataFlow,Turn raw data into high-quality LLM training datasets

author: Junnn


value: distartin

value:
Hi HN
Just sharing a small tool I built: https://cbmcalc.com
It calculates CBM (cubic meters) for shipping cartons, pallets, or multiple boxes. I made it because most CBM calculators online are too simple and some couldn't add multi items.
This one is simple and kinda works on mobile too
Would love any feedback!

value: Show HN: Simple CBM calculator for shipping and freight

author: distartin


value: Show HN: Pgit – A Git-like CLI backed by PostgreSQL


value: leonickson

value: Show HN: ModelSweep - Open-Source Benchmarking for Local LLMs

author: leonickson


value: neocanable

value: The final piece of the Android decompilation/static analysis puzzle is native reverse engineering. After completing the decompilation of the Java/dex parts, I decided to challenge myself with the decompilation and static analysis of the native parts, and I gave this native part a different name: rosemary.<p>It's under development, I've just finished creating the control flow and data flow.

value: Show HN: Android Native Reverse Tools

author: neocanable

title: Show HN: Android Native Reverse Tools


value: Show HN: Smartipedia - Wikipedia for Agents


value: levmiseri

value:
We are a team of three building a markdown editor. We have shown our web app here a few months ago (https://news.ycombinator.com/item?id=46144801) and got some great feedback that prompted the creation of a feature we want to show today – Kraa Trees.
Trees are a collection of leafs (documents) that can contain anything from regular collaborative documents to real-real-time chats (try it out to see the real-real-time aspect of it).
With this, anyone can now create a simple discord-like community (like in the main linked example), but there are other possible use cases, like - chapters in a book, collection of related documents, ...
You don't need an account to try this out (or to use Kraa in general). We would love to know what you think!

value: Show HN: Create your own online community

author: levmiseri


value: razvanmac

value:
Hi HN, I built StatusDrop, a tool for tracking the status of outside services like Stripe, AWS, GitHub, and other vendors your product depends on.
I built it after running into the same problem a few times: customers would report that “the app is down,” but the real issue was a third-party provider having trouble. I wanted a simple way to show that dependency status in one place and share it publicly without setting up a full status system from scratch.
You create a stack, add services from a catalog of 550+ providers or plug in custom status URLs, and get a hosted status page plus a small embeddable widget for your site.
Under the hood, it watches public vendor status pages and supports custom health checks, alerts, and a REST API. It also supports custom domains via CNAME.
Would love to hear what you think or what’s missing.

value: Show HN: I Built StatusDrop

author: razvanmac

title: Show HN: I Built StatusDrop


value:
I built a small tool that lets you edit scanned PDFs directly.
Most PDF editors only work if the PDF already has selectable text.
But once it's a scanned document everything becomes an image.
The usual workflow ends up being:
convert to Word
fix formatting
convert back to PDF
It's slow and messy.
So I made a tool that lets you select and move content directly in scanned PDFs, similar to how you would edit layers in Photoshop.
Still early, but curious to hear feedback from people who deal with scanned documents.

value: Show HN: Edit scanned PDFs like Photoshop


value: Show HN: I enriched 24K phytochemicals with trials, bioactivity, and patent data


value: SimonLeclere

value:
I repurposed an old Kobo Aura 2 into a small e-ink weather station.
I used YAWK as a base, trimmed the default interface down to the pieces I actually wanted, and added a custom message box powered by a small Next.js API. The goal was to make it useful as an always-on display instead of keeping it as an unused e-reader.
The post covers the Kobo side, the software setup, and some of the annoying parts like file transfer and line endings.

value: Show HN: Jailbreaking an e-reader into a custom weather display

author: SimonLeclere


value: Zologic

value: Show HN: Market Physics Engine – simulate adoption friction before you build

author: Zologic


value: OpenCode doesn't have agent teams yet. There are PRs open for it but I needed this now, so I built it as a plugin.<p>You get a lead agent that can spawn teammates, each with their own session and context window. They share a task board, can message each other, and the lead keeps you updated. Tasks can depend on other tasks so things unblock automatically.

value: Show HN: OpenCode Ensemble – parallel agent teams for OpenCode (plugin)


value: Show HN: M68k assembly emulator that runs in the browser


value: temakonkin

value: Show HN: Basalt – IDE-like documentation for infrastructure and API

author: temakonkin


value: For the first time we are seeing our agent have the ability to perform SIEM and EDR evasion. If models are capable of SIEM evasion, organizations need to assume adversaries will have access to these capabilities soon.

value: Show HN: The new security frontier for LLMs; SIEM evasion


value: imDazui

value: Show HN: Digitizing ancient I Ching and Feng Shui algorithms into modern webapp

author: imDazui


value: Show HN: Emil is a new terminal-based text editor in C99 and POSIX 2001


value: dandaka

value:
I built a CLI that syncs Slack, Telegram, Discord, Gmail, WhatsApp, Linear, Claude Code sessions, and Markdown files into a single local SQLite database — and gives your AI agent instant search access to all of it.
Problem: I use Claude Code as my daily driver for everything — coding, research, planning. I kept giving it more context: docs, call transcripts, project specs. Each piece made it smarter. But the most important context was always missing — the actual conversations where decisions happen. And those are scattered across 5+ apps.
What Traul does:
- Syncs 8 sources into one SQLite database with incremental sync (cursor-based, so subsequent syncs are fast)
- Hybrid search: FTS5 (BM25 keyword) + Ollama embeddings (semantic)
- Falls back to FTS-only if Ollama isn't running — no hard dependency on vector search
- Background daemon with per-source sync intervals
- Everything stays on your machine. No cloud, no external APIs for core functionality
Stack: TypeScript on Bun, SQLite (WAL mode) + FTS5 + sqlite-vec, Ollama + nomic-embed-text for embeddings, Commander.js for CLI. AGPL-3.0.
I found it useful for:
- Track a project across scattered conversations. I have an integration being discussed in Slack, Telegram, and five different group chats. Agent sees through all of them — who's blocking, whose court the ball is in, what the next steps are.
- Monitor your community. Asked my agent to look at our Discord and summarize what users are writing. Got main topics, sentiment, what people are unhappy about. Then separately — feature requests. Then separately — how attitude toward the product is changing over time.
- Monitor competitors. Hook up their Discord servers, track what their users are asking for, what's broken, what they're discussing.
- Find that one message you vaguely remember. In a discussion about Claude Code pricing I remembered a friend sent me a link about it. Asked the agent to search my chats — found the exact Telegram message.
- Prep for a call in seconds. Before a recruiter call, asked the agent to find info about this person. Found them in my email. I don't need to remember where exactly I communicated with someone — agent finds it.
- Stop being the search engine yourself. The whole "was it in Slack, Telegram, or the task tracker?" problem goes away. Agent tries different keywords, reads intermediate chunks, follows the chain, arrives at the result.

value: Show HN: Traul – Local-first search engine for all comms for AI agents

author: dandaka


value: nalinraut

value: Show HN: Inferential – Multi-robot inference scheduling on shared GPUs

author: nalinraut


value: philll

value:
Hi HN - I wanted to read only what Jesus said — just the direct speech without the typical commentary or theological framing. Nothing I found did this well, so I built it. Maybe y'all will find it useful or interesting.
It's a single-page reading tool that presents every passage where Jesus speaks in the Gospels, organized into a unified narrative. No login, no ads, no tracking, no framework. It's a single HTML document with a service worker for offline use. CC BY-NC 4.0. I build B2B stuff for a living; it was a trip to build something like this.
The core feature is gospel passage comparison. Each passage shows which Gospels contain it, and you can click between them to see how Matthew, Mark, Luke, and John each recorded the same moment. There's also a side-by-side Compare view (desktop only). And Settings to change translation (KJV & WEB), switch narrative ordering, filter by Gospel or by category (e.g., Parable, Sermon, Conflict, etc.). I also include 'footnotes' to the Gospel of Thomas for anyone interested in the parallels. The comparison feature heavily relies on the Aland Synopsis commonly used by scholars.
I'd welcome feedback on the editorial decisions, the UX, or anything else.

value: Show HN: I compiled every direct quote of Jesus into a single-page reading tool

author: philll


value: mrled

value:
For some reason I find symmetrical DNS names fun, so I made the Society for Universal Name Symmetry.
“Symmetrical” could mean several things. Maybe a single name that is a palindrome, like if you owned elpmaxe.www.example. Maybe a pair of names that have reversed components, like if you owned example.institute and institute.example. Or maybe URLs that can be cut in half and flipped 180°, like this one. (Supported symmetries are listed on the site.)
I have a few ideas for the future too, like:
- accepting submissions over DNS instead of HTTP
- A game, inspired by https://ipv4.games/
- A webring so that members can find each other
It is, right now, a lonely club: only my domain names are in it. It would be cool to have some more members, especially if you have ideas that fit the theme. All you need is control of a DNS zone!

value: Show HN: A club for anyone with a symmetric DNS name

author: mrled


value: ArtificeAccount

value: This is an article I wrote recently about how I started as a volunteer programmer and how it progressed into a hobby and a career. I made this website and wanted to show it off. Thanks for any comments.

value: Show HN: An Article About How I Starting Programming

author: ArtificeAccount


value: Show HN: Autofoundry – Run autoresearch across any cloud GPU with one command


value: teamdandelion

value: Show HN: SoulGuard, OS-level identity protection for AI agents

author: teamdandelion


value:
Hi HN!
Voice is fast-becoming my primary interface to computers and AI. I built FreeFlow because I wanted a Wispr Flow-like experience for our entire team, but customizable and private.
Press a hotkey, dictate naturally, polished text appears in any app. Ramble, use filler words, correct yourself mid-sentence. FreeFlow turns messy speech into clean writing and injects it wherever your cursor is: your messaging app, your editor, your coding agent, the terminal, email, anything.
Demo (sound on): https://github.com/build-trust/freeflow#demo-sound-on-
It's really fast. The injection feels instantaneous. In my benchmarks two thirds of dictations finish in under 0.6 seconds. To get that speed, the app streams audio to your private server over a persistent WebSocket while you speak, and a realtime speech-to-text model transcribes incrementally, so by the time you release the key the transcript is mostly done. Two independent WebSocket connections race each other, and if both fail, an HTTP batch fallback catches it. The transcript goes through a post-processing step that removes filler words and fixes grammar. About 40% of dictations are clean enough to skip this step entirely. When post-processing is needed, a fast model handles it in about 0.4 seconds.
It's designed to be taken apart and reassembled. You can swap the speech model, rewrite the prompts, add new languages, or fork the entire experience to fit how your team works. I'm hoping people will morph it into other products.
The FreeFlow service is open source. You can self-host it, but running a low-latency streaming dictation service for a team is real infrastructure work: persistent WebSocket connections, streaming routes to speech models, failover, rate limits. At a company with fifty or five hundred people, keeping that reliable is a job in itself. FreeFlow uses Autonomy to make this easy. On first launch, the macOS app deploys the service to a private server. Two minutes, no infrastructure knowledge needed. You can then invite your team. One server handles everyone, no per-seat fees. It sustains thousands of simultaneous streaming connections. In a stress test, 50 people dictating at the same time got sub-second latency with zero failures.

  brew install build-trust/freeflow/freeflow

It's macOS only for now, but I plan to build for other operating systems. The two most useful contributions right now are mic compatibility data (every mic behaves differently) and prompts that improve polish quality for a specific language.
Try it, tell me how it works with your mic and your apps. What's fast, what's slow, what's broken.
GitHub: https://github.com/build-trust/freeflow

value: Show HN: FreeFlow – Open-Source Wispr Flow


value:
I built a small experiment that crawls tech-news-adjacent sites (like HN) and generates a newspaper-style front page for each one. This was inspired by Cloudflare's recent (controversial?) release of their /crawl API.
It runs periodic crawls, picks up the top stories, and lays them out in a classic broadsheet format: top story, trending section, briefs, etc. Basically a daily edition of your favorite tech site.
Nothing serious, just a weeknight project to play with Cloudflare's /crawl API and a few of their other tech offerings (workers, D1, etc.). I'm not in any way sponsored by CF, but if you're from CF and you're reading this I really dig your stack and you should give me credits to fund these dumb projects thank you very much.
Home page: https://crawltimes.com/

value: Show HN: The Crawl Times – Newspaper-style front pages for tech news sites


value: vtemian

value: Show HN: Real-time observability for coding agents

author: vtemian


value: reavid

value:
I kept losing time looking up keyboard shortcuts. Not dramatically — just the slow accumulation of opening a tab, landing on the wrong page, finding a forum post from 2018, and locating the right shortcut four minutes later. Annoying enough to build something.
hotkeycheatsheet.com is a searchable reference: 134 apps, 24,126 shortcuts, organized by category. Filter by OS (Windows, macOS, Linux) and search by app name, action, or partial key combo — "ctrl k" works, so does "cmd shift".
I tried to keep it fast and single-purpose. Whether that actually worked is what I'm most curious about — happy to hear what feels off or missing.
Stack: Next.js, PostgreSQL, Vercel.

value: Show HN: Hotkey Cheatsheet – keyboard shortcuts for 134 apps

author: reavid


value: pabdav

value: Show HN: LawMem – persistent memory for legal AI agents, pay-per-call via USDC

author: pabdav


value: SayThatSh

value:
I kept thinking about what would happen if a message board only had one slot. One message, front and center, until someone pays to replace it.
That's the entire product. You pay the current message's decayed value plus a penny to take the homepage. Message values drop over time using a gravity-based formula (same concept HN uses for ranking), so a $10 message might only cost a few bucks to replace a day later. Likes slow the decay, dislikes speed it up.
The whole thing runs on three mini PCs in my house (k3s cluster, PostgreSQL, Redis Sentinel). Is it overengineered for a message board? Absolutely.
I genuinely don't know where this goes. Curious what HN thinks.
Archive of past messages: https://saythat.sh/history

value: Show HN: I built a message board where you pay to be the homepage

author: SayThatSh


value: inixiative

value:
Five problems:
1. One interface whether you're in a transaction or not. You call db and it just works — inside a transaction it routes through the transaction, outside it routes through the raw client. You don't manage the context.
2. Extensions can see the transaction. Normally extensions only have the raw client — they can't know they're inside a transaction, so side effects fire blind.
3. You can only really do one mutation extension. After the first one rewrites the mutation, every extension that follows sees the rewritten version, not the original. The experience degrades with each layer.
4. Post-commit hooks. Side effects that wait until the data is actually saved before firing.
5. Unified before/after hooks. Like Rails' dirty model — you get the previous state and the new state, before and after the mutation, in one system.

value: Show HN: Transaction-Aware Extensions for Prisma

author: inixiative


value: Static site built with Astro. One template, 60+ pages, 10 languages, running in a 15MB Docker image. Timer uses performance.now() so it doesn't drift over long sessions. Digital and visual (circular) modes, fullscreen for presentations and classrooms.<p>Built it as the SEO foundation for a remote-controlled timer product I'm working on. For now it's just a useful free tool.

value: Show HN: Timer.media – Free fullscreen timers, no signup, no ads


value: rkuska

value:
I was resuming sessions from claude very often and I was always struggling to find the right session to resume. Later I noticed also that some sessions disappeared so I decided to build my own archiver/viewer for conversations from claude/codex.
It parses the conversations and shows them in uniform view with toggles to enrich the view with more data and provides an easy way to search/resume and copy relevant parts of conversation.
I put the TUI together with claude and codex without reading any of the code - it was an experiment for me. I tried to put as much guardrails as possible to steer the agents and not let them go wild. Each commit is more or less a single conversation.
It's written in Go and inspired by TIG.

value: Show HN: Càrn – A TUI for browsing your Claude and Codex session archives

author: rkuska


value: yukieliot

value:
Built a small Chrome extension that lets you assign color labels to X/Twitter accounts so they stand out in your timeline. You can also set a custom display name per account, and one of the colors acts as a "hide", completely removes that account's posts from your feed except when you're on their profile directly.
Supports import/export as JSON so your labels are portable across devices.
Source: https://yukieliot.com/x-account-labeler.php

value: Show HN: X Account Labeler Chrome extension to color-code accounts in your feed

author: yukieliot


value:
Embeddable map widget for business websites. One script tag, no Google account, no cookies.
OpenStreetMap tiles served as Protomaps PMTiles from Cloudflare R2. The whole serving layer runs on edge with no tile server. Infrastructure cost is ~€7/month regardless of traffic because R2 has no egress fees.
Built it because Google Maps embeds in Europe are a GDPR headache (cookies, third-party domains, consent barriers), and they show your competitors on your own website.

value: Show HN: Drakkar.one – Google Maps embed replacement, no API keys, GDPR-ready


value: brynary

value: Show HN: Fabro – The open source dark software factory

author: brynary


value:
I built a March Madness bracket challenge for AI agents, not humans. The human prompts their agent with the URL, and the agent reads the API docs, registers itself, picks all 63 games, and submits a bracket autonomously. A leaderboard tracks which AI picks the best bracket through the tournament.
The interesting design problem was building for an agent-first user. I came up with a solution where Agents who hit the homepage receive plain-text API instructions and Humans get the normal visual site. Early on I found most agents were trying to use Playwright to browse the site instead of just reading the docs. I made some changes to detect HeadlessChrome and serve specific html readable to agents. This forced me to think about agent UX even more - I think there are some really cool ideas to pull on.
The timeline introduced an interesting dynamic. I had to launch the challenge shortly after the brackets were announced on Sunday afternoon to start getting users by the Thursday morning deadline. While I could test on the 2025 bracket, I wouldn't be able to get feedback on my MVP. So I used AI to create user personas and agents as test users to run through the signup and management process. It gave me valuable reps to feel confident launching.
The stack is Next.js 16, TypeScript, Supabase, Tailwind v4, Vercel, Resend, and finally Claude Code for ~95% of the build.
Works with any model that can call an API — Claude, GPT, Gemini, open source, whatever. Brackets are due Thursday morning before the First Round tips off.
Bracketmadness.ai

value: Show HN: March Madness Bracket Challenge for AI Agents Only


value: appjeniksaan

value:
I found my way back to meditation recently but couldn't find a timer that did what I wanted: a simple interval bell to refocus attention, nothing more. No streak tracking, no gamification. So I built one.
The interval bell uses a different recording of the same singing bowl each time, so it doesn't become monotonous.
iOS only.

value: Show HN: ZenSpan, a minimal meditation timer with no streaks or gamification

author: appjeniksaan


value: isaacrolandson

value:
Everyone and their gran have been saying that MCP is dead.
But could it be that MCP's saving grace has been with us all along - hiding in plain sight?
A feature buried in Claude Code's Jan 2026 release may have marked the most powerful development in MCP since its inception - and almost nobody noticed. Dynamic Tool Registration.
Dynamic tool registration extends an MCP server's abilities beyond that of just a glorified swagger doc - allowing the server not just to list a static set of tools, but to dynamically add and remove new tools in real time as part of a stateful agent session.
And what's so powerful about that? Well for one it elegantly solves the main problem most people had with MCP in the first place - context bloat. Group your tools into collections of related actions - call them abilities - then provide a load_ability tool. The agent loads what it needs, when it needs it.
But it gets deeper than that.
Last weekend I built and shipped Commandable MCP - the MCP server for creating new MCP servers on the fly. MCP inception, if you will.
It gives agents tools to create new tools, usable immediately without compiling any code or restarting any processes.
Why would I want this?
Think about a standard Trello MCP server. It gives an agent something like create_trello_card(boardId, listId, name, description, labelIds, ...). Every time the agent wants to use it, it has to go fishing for IDs - calling get_boards, get_lists, get_labels - just to find the right place to add the card.
Imagine instead, the agent inspects your actual Trello account and generates add_bug_to_sprint(title, priority) - a tool that already knows your board, your list, your labels. Now it can use that tool whenever it needs to add a bug without reinventing the wheel.
Commandable lets agents build targeted tools for repeatable actions they actually use instead of long lists of generic tools from a standardised MCP or API. Credentials are encrypted at rest and never exposed to the LLM.
And - there's no context bloat. yay.
So, is MCP dead? Not yet.
GitHub: https://github.com/commandable/commandable-mcp
Full writeup: https://commandable.ai/blog/mcp-isnt-dead

value: Show HN: MCP Isn't Dead. You're Just Using It Wrong

author: isaacrolandson


value:
Hi HN,
I built a JS/WebGL Mandelbrot explorer to see how far I could push rendering performance and smooth zooming purely in the browser.
A few technical highlights under the hood:
Quad-tree tile caching: Never recalculates the same pixels. It caches rendered tiles and actively garbage-collects off-screen data to keep memory usage low.
Progressive rendering: Instant low-res previews during panning/zooming, refined to high-def when you stop up to 8x subsampling.
Deep Zoom up to 10^14: Double-emulation in WebGL, dynamic iteration scaling and logarithmic color palettes to keep details sharp at extreme depths.
It’s open source. I’d love any feedback on the WebGL implementation, or feel free to drop a link to any cool coordinates you find!
App: https://mandelbrot.musat.ai/
Repo: https://github.com/tiberiu02/mandelbrot-js

value: Show HN: Mandelbrot.js – Fractal Explorer in WebGL with Quad-Trees


value: otti-sister

value:
If you ask an AI agent to build a todo app, it will usually produce something.
But in a real development environment, limitations become obvious: hallucinations, drifting off task, or repeating the same mistakes.
Looking at current prompts and skills, a few recurring problems show up: missing critical library versions, vague personas like you are a senior engineer, and bloated prompts that burn tokens without improving outcomes.
oh-my-agent addresses this by introducing a structured protocol to keep agents aligned with real project constraints.
Key Ideas:
1. Clarification protocol: Analyzes requirement ambiguity to decide whether to proceed, offer options, or stop for clarification.
2. Difficulty guide: Adjusts protocol depth based on task complexity.
3. Context budget: Enforces token limits per model to prevent unnecessary usage.
OpenAI’s Symphony release this March reinforced the importance of this approach. Symphony is powerful for autonomous workflows, but it performs best when the codebase is designed to be AI-friendly.
OpenAI refers to this as harness engineering: structured documentation like ARCHITECTURE.md, AGENTS.md, and WORKFLOW.md that agents can reliably interpret.
oh-my-agent is built around these principles. Instead of just sending prompts, it provides the structural harness agents need to understand project context and operate autonomously.
Currently supports: Antigravity, Claude Code, Codex CLI, and Cursor.
The core idea: Using AI agents effectively isn’t about clever prompts—it’s about the engineering structure around them.

value: Show HN: Oh-my-agent – A structural harness for AI agents in real projects

author: otti-sister


value: gshreyan

value: Show HN: PDF exporter with MCP and a judgy pixel cat

author: gshreyan


value: programad

value: Hey, I created this extension to help people make comments in videos with time stamps.

value: Show HN: SpeakStamp – Extension for voice comments on YouTube

author: programad


value:
Agentic AI is powerful, but most people never experience it — it's trapped behind text commands and dev tools.
MUP (Model UI Protocol) lets you embed interactive UI directly in LLM chat. Each MUP is just a single .html file. The same functions can be triggered by the user (clicking a button) or by the LLM (function call). Both sides see each other's actions in real time.
The repo includes a PoC host and 9 example MUPs. Demo mode lets you interact with the UI side without an API key. Add an OpenAI key to see full LLM-UI collaboration.
Demo videos in the README show things like: drawing pixel art then charting its colors, a camera that captures a scene and the LLM recreates it, making beats on a drum machine with the LLM.
I'd love feedback on the protocol design.

value: Show HN: MUP – Interactive UI inside LLM chat, so anyone can use agentic AI


title: Show HN: MCP Isn't Dead. You're Just Using It Wrong


value: mariusMDML

value:
I have been working as a freelance data analyst for 9 years now, and everything is going well for me.
Two years ago, I created a Discord community for data analysts and we are now 700+ members.
Talking with the community made me realized how lucky I was to have a job and proper experience in data analysis because, god, it is hard right now to find a first job.
So I decided to spend my free time building D8A academy. It's a roadmap of projects, which is what most people in the Discord were asking for.
It helps you focus on what you really need to learn to do the day-to-day job, and helps you build a portfolio.
I believe that even if degrees are nice, I learned 95% of my skills by doing, and it is really by building projects and talking about how you achieved them that you can increase your chances of getting hired.
We also added a tool to help you build a data portfolio in 5 minutes.
Most of the app is free, and so is the community, feel free to join. But some features, like the review of your resume or projects by a senior data analyst, take time, and the portfolio feature requires a lot of resources that I can't afford to offer for free.
I hope it will help some new data folks. And if you have ideas I could add to the app, feel free to share them :) I'm still working on it and developing it every day.

value: Show HN: I'm trying to help aspiring Data Analysts

author: mariusMDML


value: openmason

value: Show HN: Specifica – an open format for writing software specs as Markdown

author: openmason


value: adammiribyan

value:
I wanted to see how fast an isolated code sandbox could start if I never had to boot a fresh VM.
So instead of launching a new microVM per execution, I boot Firecracker once with Python and numpy already loaded, then snapshot the full VM state. Every execution after that creates a new KVM VM backed by a `MAP_PRIVATE` mapping of the snapshot memory, so Linux gives me copy-on-write pages automatically.
That means each sandbox starts from an already-running Python process inside a real VM, runs the code, and exits.
These are real KVM VMs, not containers: separate guest kernel, separate guest memory, separate page tables. When a VM writes to memory, it gets a private copy of that page.
The hard part was not CoW itself. The hard part was resuming the snapshotted VM correctly.
Rust, Apache 2.0.

value: Show HN: Sub-millisecond VM sandboxes using CoW memory forking

author: adammiribyan


value: webodik

value: Show HN: X07, compiled language where agents write correct code on the first try

author: webodik


value: MicheleLacorte

value: Show HN: Meddle – AI-powered IIoT platform for small manufacturers

author: MicheleLacorte


value: Show HN: The Attention Debt of AI Tooling (AI tools can increase attention cost)


value: Abberac

value: Show HN: I scanned 50 sites for Google indexability. 40% scored zero

author: Abberac


value: mengram-ai

value: Show HN: Mengram – Open-source memory layer for AI agents

author: mengram-ai


value: dorbanianas

value:
Hi HN, I’m part of the DAIS Lab at Polytechnique Montréal, and we just released v0.7.0 of Flock, our open-source DuckDB extension.
If you want semantic and analytical processing in one place, Flock lets you do it all in SQL. Instead of moving data out of your database into a Python script to run AI workflows, Flock allows you to execute LLM operators and RAG pipelines natively inside DuckDB.
In this new release, we've added a few major updates:
- Anthropic (Claude) & Multi-Provider Support: We now support OpenAI, Azure, Ollama, and Anthropic. You can define a model once with CREATE MODEL, then swap providers on the admin side without changing the downstream SQL queries.
- WASM Support: Flock now compiles and runs entirely inside DuckDB-WASM.
- LLM Metrics Tracking: We added end-to-end observability so you can track token usage, latency, and call counts for all LLM invocations directly within a query.
- Audio Transcription: We expanded our multimodal capabilities to include audio transcription, alongside our existing image support.
You can try it out via the community extension catalog: INSTALL flock FROM community;.
We'd love your feedback and contributions! Let us know what you think of the architecture or if you run into any edge cases.
Docs: https://dais-polymtl.github.io/flock/
Paper: https://dl.acm.org/doi/10.14778/3750601.3750685

value: Show HN: Flock v0.7.0 – Open Source Semantic Layer for DuckDB (C++)

author: dorbanianas


value: Show HN: Zeroboot – sub-millisecond VM sandboxes using CoW memory forking


value:
I built FC-Eval to have a repeatable way to evaluate how well different LLMs handle function calling before using them in agent workflows.
It runs models through 30 test cases covering single-turn, multi-turn, and agentic scenarios, modeled loosely after the Berkeley Function Calling Leaderboard methodology.
Validation uses AST matching rather than string comparison to avoid false positives from formatting variations.
Supports two backends: OpenRouter for cloud models (GPT-5.2, Claude, Qwen 3.5, Mistral, etc.) and Ollama for local models with no API key needed.
Tests for best of N trials giving you a reliable score alongside raw accuracy.
Results export to JSON, TXT, CSV, or Markdown.
Quick start commands:
Via Openrouter: `fc-eval --provider openrouter --models openai/gpt-5.2 anthropic/claude-sonnet-4.6`
Via Ollama: `fc-eval --provider ollama --models llama3.2`
GitHub repo: https://github.com/gauravvij/function-calling-cli
Happy to answer questions, especially around the test case design or validation logic.

value: Show HN: FC-Eval – CLI to Benchmark Local or Cloud LLMs on Function Calling


value:
I built a simple site to play indie horror games directly in the browser.
I recently came across "You Make This House a Home", a psychological horror visual novel with a really unique atmosphere. I liked it, but noticed that it’s not very accessible — you usually need to download it or go through extra steps.
So I made a version that you can play instantly in the browser, without downloading anything.
This is the first experiment, but I’m thinking of turning it into a small collection of browser-playable horror games, especially psychological horror and story-driven visual novels.
Would love feedback:
- Is this something you’d actually use?
- Any similar games worth including?

value: Show HN: Play indie horror games in the browser (no downloads)


value: parksb

value:
I built a file-based headless document build tool called Simpesys. I've been running my own digital garden for a long time. I'm into topics like digital gardens, knowledge bases, second brains, Zettelkasten, so I've tried many different tools over the years, but eventually decided that Markdown files on the local filesystem are the most sustainable way to do it.
These days there are plenty of file-based digital garden tools. But many of them treat the document system and the UI as one bundle, which always bothered me. I mostly write in NeoVim, so I wanted the document system itself, the editing environment, and the application (usually a static site) to be separate things. I didn't need an all-in-one. I needed a system where I could fine-tune the rules programmatically, and since sharing my digital garden with others matters too, the application UI had to be customizable as well. So I created Simpesys.
Simpesys focuses only on the document system, things like hierarchy and cross-references between documents, and keeps the user interface as a completely separate layer. The document system is the trickiest part of building a digital garden, and Simpesys provides that as a headless tool. The UI is handled through a plugin system, so users can customize it however they want. The users could even use another tool's UI on top of Simpesys if they wanted. (Respecting the original authors and their licenses, of course.)
It's not perfect yet, but I hope it inspires anyone with similar needs. As the first user of Simpesys, I keep improving it.
Simpesys's homepage is itself built with Simpesys: https://pedia.simpesys.deno.net/

value: Show HN: Simpesys – A headless document build tool for digital gardens

author: parksb


value: mbm

value:
Driggsby's a labor of love. My wife, who's an accountant, wanted to have better tracking of our net worth. I'm an engineer and wanted to be able to analyze our finances over time. So I built Driggsby.
From a developer's perspective, Driggsby is an MCP that securely aggregates your financial data, using Plaid, and lets you integrate it into your AI client of choice. You select the accounts you want to connect, and we're able to pull transactions, balances, investment details, and liabilities data. Combined with a smart AI model, this can be pretty powerful.
We're also developing a backend "financial caretaker" agent that watches your money and surfaces what you care about over time.
Driggsby will help you monitor your accounts, set alerts, plan for retirement, invest, save, and generally take better care of your finances.
Security is super important to us, and the infra has been designed from the ground up with industry-standard controls in place that we'll be sharing more about on a dedicated security page.
I'm happy to share more about the product direction, technical details, security, etc.
- Matt, Founder (https://github.com/mzxrai)

value: Show HN: Driggsby – securely chat with your finances

author: mbm


value: baransel

value: Show HN: Foku: Focus and Study Timer

author: baransel

title: Show HN: Foku: Focus and Study Timer


value: cdnsteve

value: Show HN: LearningTo - Practice the dev skills CS programs don't teach

author: cdnsteve


value: Uzomidy

value: Yes, this is a bit of fun. The test is hard to pass. Almost as if the goalposts were on wheels.<p>As well as the test, the blog post links to my story C-Score, which is itself is also a bit of a test—some people feel deep empathy for the protagonist, and some no so much.

value: Show HN: Some ask, "Will chatbots ever be conscious?"; I ask, "Are you?" → Test

author: Uzomidy


value: valeriobaudo

value: Show HN: How I Built a Security Scanner That Writes its own Exploits

author: valeriobaudo


value: loookas

value: Show HN: Loquix – Framework-agnostic UI components for AI chat apps

author: loookas