value:
I built a WebDAV plugin — now you can collaborate with openclaw in its workspace accessing files from anywhere using your phone, laptop, whatever - from any device that supports webdav (most do).
From the start I've used an Openclaw I deployed on a remote machine, and tried a few solutions to collaborate with it like a google workspace - but collaborating with it in its own workspace filesystem always gave the best results. I started out building a an ad-hoc python WebDAV proxy to access my remote OpenClaw instance - and this friction-free access made collaborating and iterating over projects and files intuitive and productive.
So I decided building a native openclaw plugin for WebDAV could help everyone in the community be able to have this same frictionless way of working with openclaw on projects.

value: Show HN: Access OpenClaw's workspace files from anywhere and any device


value: ProtectAI's deberta-v3 is the most downloaded prompt injection classifier on HuggingFace. We benchmarked directly against it on Qualifire's independent dataset (excluded from our training).<p>* 91.68% vs 72.28% accuracy<p>* 95.84% vs 65.33% precision — they block 1 in 3 legitimate users<p>* 83MB vs 739MB ONNX size<p>* 101ms vs 646ms on CPU

value: Show HN: Prompt injection detector beats ProtectAI by 19% accuracy, 8.9x smaller


value: vivekalogics

value: Show HN: Canvora – describe what you want, get a branded visual in any language

author: vivekalogics


value: vijayvkrishnan

value:
I built OpenMix because computational chemistry has great tools for individual molecules (RDKit, DeepChem) but nothing for mixtures.
pip install openmix. Apache 2.0.
Technical blog: https://vijayvkrishnan.substack.com/p/the-missing-layer-in-c...

value: Show HN: OpenMix, open-source computational framework for formulation science

author: vijayvkrishnan


value: cshjoshi

value:
Built this project over the past few months to make it simpler to host and scale Python servers.
For FastAPI, Flask, Django apps all you need to do is connect your git repo and make a push, all the config is automatically figured out and your app is deployed for you.
For any other framework, you just need to specify a start command before you make a push.
Your applications run on Phemeral's managed compute and automatically scale to zero and rapidly (~30ms) scale up using vm snapshots when receiving traffic.

value: Show HN: Host Infinite Python Services

author: cshjoshi

title: Show HN: Host Infinite Python Services


value:
Hi HN! We are Ashish and Armend, founders of GlassFlow.
Over the last year, we worked with teams running high-throughput pipelines into self-hosted ClickHouse. Mostly for observability and real-time analytics.
A question that came repeatedly was:
What happens when throughput grows?
Usually, things work fine at 10k events/sec, but we started seeing backpressure and errors at >100k.
When the throughput per pipeline stops scaling, then adding more CPU/memory doesn’t help because often parts of the pipeline are not parallelized or are bottlenecked by state handling.
At this point, engineers usually scale by adding more pipeline instances.
That works but comes with some trade-offs:
- You have to split the workload (e.g., multiple pipelines reading from the same source)
- Transformation logic gets duplicated across pipelines
- Stateful logic becomes harder to manage and keep consistent
- Debugging and changes get more difficult because the data flow is fragmented
Another challenge arises when working with high-cardinality keys like user IDs, session IDs, or request IDs, and when you need to handle longer time windows (24h or more). The state grows quickly and many systems rely on in-memory state, which makes it expensive and harder to recover from failures.
We wanted to solve this problem and rebuild our approach at GlassFlow.
Instead of scaling by adding more pipelines, we scale within a single pipeline by using replicas. Each replica consumes, processes, and writes independently, and the workload is distributed across them.
In the benchmarks we’re sharing, this scales to 500k+ events/sec while still running stateful transformations and writing into ClickHouse.
A few things we think are interesting:
- Scaling is close to linear as you add replicas
- Works with stateful transformations (not just stateless ingestion)
- State is backed by a file-based KV store instead of relying purely on memory
- The ClickHouse sink is optimized for batching to avoid small inserts
- The product is built with Go
Full write-up + benchmarks:
https://www.glassflow.dev/blog/glassflow-now-scales-to-500k-...
Repo:
https://github.com/glassflow/clickhouse-etl
Happy to answer questions about the design or trade-offs.

value: Show HN: 500k+ events/sec transformations for ClickHouse ingestion


value: areebms

value:
When Smith writes "value" and Ricardo writes "value," do they mean the same thing? I built a tool that gives a quantitative answer.
The tool trains multiple Word2Vec models on each text independently, aligns them into a shared vector space via Generalized Procrustes Analysis, and lets you compute cosine similarity with 95% confidence intervals across the ensemble through an API. First, it lets you compare semantic drift between authors. How does the semantic neighbourhood of "rent" in Smith compare to "rent" in Ricardo? Where do they converge, and where do they diverge? Second, it measures precision. With shorter texts, embeddings get noisy. Confidence intervals help you distinguish genuine semantic drift from a model that didn't have enough data to learn a stable relationship.
Currently running on Smith, Ricardo, Mill, Steuart, and Bastiat, sourced from Project Gutenberg.
Stack: Python, FastAPI, Lambda, DynamoDB, React. Six containerized Lambdas, zero idle cost.
Backend: https://github.com/areebms/embedding-analytics
Frontend: https://github.com/areebms/embedding-analytics-frontend
I think reliability measurements in embeddings may have applications well beyond historical texts. Would love feedback from anyone working on embedding evaluation or uncertainty quantification.

value: Show HN: Embedding Similarity with Confidence Intervals

author: areebms


value: paulkyle

value: Show HN: Palinode – Git-versioned Markdown memory for AI agents

author: paulkyle


value: maxwg

value: Show HN: A two (or single) player codenames like game with an embedding based AI

author: maxwg


value: jellyotsiro

value:
Hi HN,
I built agentsearch, a free tool that turns any documentation website into a browsable filesystem that you can access with one command.
npx nia-docs https://docs.anthropic.com
This opens a shell where the docs are mounted as files. You can:
- tree to explore
- grep across pages
- cat specific files
The idea is simple: let agents read docs the same way developers read codebases.
Most coding errors from agents come from stale or incomplete context. Docs change constantly, but models are trained on old data. RAG helps, but it returns fragments. In practice, a lot of answers live across multiple pages or require exact structure that chunking loses.
Instead of retrieving snippets, this lets the agent just browse the docs directly.
Under the hood:
- we crawl the docs site and map each page to a file
- expose basic filesystem ops (read, grep, tree, find)
- run a lightweight bash-like shell client-side
- Everything is read-only and cached.
You can also plug it into agents in one line:
npx nia-docs setup https://docs.site.com | claude
Then the agent can do things like:
- grep -rl "webhook" .
- cat getting-started.md
and write code against the latest docs instead of training data.
The reason for the filesystem approach is that agents already know it. Models have seen a huge amount of bash usage during training, so tools like grep and cat work out of the box without teaching new abstractions.
Right now this works on documentation sites. The longer term idea is making more of the web navigable like a codebase.
Would love feedback, especially from people building agent workflows.
Website: https://www.agentsearch.sh/

value: Show HN: Agentsearch – browse any docs as a filesystem

author: jellyotsiro


value: vQ-bert

value: Show HN: Application management app (for job applications)

author: vQ-bert


value: martimchaves

value:
Hey HN, I built a small app, called Smol Invoice Agent.
You can upload invoices, have them parsed, and correct the parsed data. The corrections are saved, and the next time you upload a similar invoice, the agent may apply them automatically if they make sense.
You can provide feedback on the automatically applied corrections. Rejected corrections are immediately reverted, and you can add an explanation, which will be taken into account the next time the agent encounters a similar invoice.
Here's a 4 min demo showing the full feedback loop in action: https://www.youtube.com/watch?v=txaJ0OhOFVw
Export as JSON or CSV. You can also use it via the API.
Tech stack: Postgres (with pgvector), FastAPI, Claude for extraction, Celery + Redis for background processing, TypeScript with React, Voyage AI for embeddings, Mistral for OCR (with pymupdf fallback).
It's pay-as-you-go, no subscription required, just top up your account and use as needed. You can try it for free.
I'd really appreciate hearing what you think, especially if you deal with invoices regularly. Is the correction/feedback loop something that would actually save you time?
Thanks!
Martim

value: Show HN: Smol Invoice Agent, invoice processor that learns from your corrections

author: martimchaves


value: shylendar

value:
If you have a curated home feed like me and fear opening a random video or a topic in fear of polluting your main feed. This extension would come in handy for you.
It is a fully local Chrome extension, Which uses YouTube channels to isolate your watch feed. And you can quickly switch between profiles with a shortcut or using the 3dots menu of a video to watch in a specific profile.
Or if you are a bilingual person like me and want to maintain two seperate feeds in one premium account...this would help.
Would love to hear some feedback if you find this extension useful.
Free Extension is at: https://chromewebstore.google.com/detail/yt-profiles-multipl...
Know more about the extension at: https://ytprofiles.shyl.work/
Built with love for the community and nothing else.

value: Show HN: Chrome extension to have Watch Profiles for YouTube similar to Netflix

author: shylendar


value: latchkey

value: Show HN: Orange Juice – Small UX improvements that make HN easier to read

author: latchkey


value: rafmendiola

value:
Itsumo is a language learning app that generates short lessons from stories about topics you choose, at your level. Instead of working through a fixed curriculum or another flashcard deck, you can make a lesson about whatever happens to be interesting to you that day, with audio, tappable words, and grammar notes.
You can try a language story from the landing page without signing up, and if you want to try full access, no credit card is required at signup, and the premium tier free for 14 days. It supports Japanese, Polish, Spanish, English, Portuguese, French and Italian.
I built it because I kept bouncing between two kinds of Japanese study tools: textbooks I liked but never had with me, and apps I always had with me but didn't actually want to open. What I wanted was something that felt closer to reading a lesson in a book than doing a drill, and I wanted it on my phone.
There aren't a lot of apps out there targeting intermediate language-learners. I've been studying Japanese, and a lot of the tools are optimized either for complete beginners or for spaced-repetition-heavy review. That works for some people, but once I was past the basics, I wanted to practice in the context of a story that felt relevant to me.
Technical details, since this is HN:
- It's an Expo React Native app, and we're going for universal access. It's available on the web now, with mobile versions launching soon.
- Created with a cool custom mobile studio I talked about here: https://youtu.be/ufgYlZCfRM4?t=643
- Lesson generation goes through an LLM pipeline with a verification pass to keep the output near the requested JLPT level.
I'm especially interested in feedback from people who are somewhere in the middle with Japanese, where beginner material feels too basic but native material is still too much work. If you try it, I'd like to hear where it feels off, confusing, or not actually helpful.
Give it try it: https://itsumo.study
Built by Margaret and Raf https://okthink.ai/

value: Show HN: Itsumo: Make yourself fun and interesting academic language lessons

author: rafmendiola


value: humblejedi

value: Show HN: Deterministic security guardrails for Claude Code

author: humblejedi


value: shawnpana

value: Show HN: smux: terminal automation and agent-to-agent communication via tmux

author: shawnpana


value: henriklipp

value: Show HN: Benchmark multiple LLMs to compare quality, speed, and cost

author: henriklipp


value: insany

value:
Built a small tool that sets up VLESS + REALITY over SSH in one command.
It handles: - full Xray setup - client configs (vless URI, sing-box, mihomo) - rollback if something breaks
Example: ./irit.sh --mode setup --host <ip>
Would love feedback.) https://github.com/anonymmized/Irit

value: Show HN: I built a tool to bootstrap VLESS and REALITY over SSH (with rollback)

author: insany


value: Show HN: Eclipse Linux Alpha - Void-based with custom init


value: probiruk

value:
I built this TypeScript agent framework to make it easier to build AI agents that are type-safe end-to-end (client to server). It also lets you package common runtime behavior as plugins and reuse them across agents, like auth, rate limits, and sandboxing (e.g. Daytona, E2B, or other sandbox providers). It’s fully event-driven.
Most setups I tried didn’t handle end-to-end typing well, didn’t fit cleanly into an existing app, or made it hard to reuse behavior across agents. This tries to keep things consistent across client and server, make plugins easy to share, and support stateful, resumable runs without a lot of glue code.
Website: https://better-agent.com
GitHub: https://github.com/better-agent/better-agent

value: Show HN: Better Agent – A composable AI agent framework in TypeScript

author: probiruk


value: Eikon

value: Show HN: PostgreSQL running in a browser tab, persisting to S3

author: Eikon


value: Show HN: An open-source Go CLI to generate local AWS cost reports


value: Achiyacohen

value: Show HN: Safari MCP – Native macOS browser automation (80 tools)

author: Achiyacohen


value: ngup

value: Zoom alternative!

value: Show HN: MiroTalk SFU

author: ngup

story_text: Zoom alternative!

title: Show HN: MiroTalk SFU


value: patrickaljord

value:
I built PrettyMux as a native Linux terminal workspace for multitask workflows and keeping track of my agents.
It’s a GTK4 app built on Ghostty/libghostty, with split panes, workspaces, vertical tabs, notifications, project-aware tabs (shows favicons/logo automatically), and an in-app browser so terminals and docs/tools can live side by side.
I started it because I wanted something tmux-like for modern GUI workflows on Linux, but native and not Electron, there is cmux but only available on macos (prettymux compiles on windows and macos too but not tested for now there)
It’s open source: https://github.com/patcito/prettymux
Would love feedback from people who use tmux, Ghostty, or struggle with lots of terminals/browser tabs/parallel tasks.

value: Show HN: PrettyMux, a Linux terminal for multitask workflows based on Ghostty

author: patrickaljord


value: asakin

value: Show HN: LLM-context-base – Git template for LLM-powered personal wikis

author: asakin


value: jason-festa

value: Show HN: 2,500 in free AI ad credits – product placed in creator videos

author: jason-festa


value: etherio

value:
Hi HN!
Druids (https://github.com/fulcrumresearch/druids) is an open-source library for structuring and running multi-agent coding workflows. Druids makes it easy to do this by abstracting away all the VM infrastructure, agent provisioning, and communication. You can watch our demo video here (https://www.youtube.com/watch?v=EVJqW-tvSy4) to see what it looks like.
At a high level:
- Users can write Python programs that define what roles the agents take on and how they interact with each other.
- A program is made of events - clear state transitions that the agents or clients can call to modify state. Each event gets exposed as an agent tool.
- Druids provisions full VMs so that the agents can run continuously and communicate effectively.
We made Druids because we were making lots of internal coding tools using agents and found it annoying to have to rearrange the wiring every time.
As we were building Druids, we realized a lot of our internal tools were easier to express as an event-driven architecture – separating deterministic control flow from agent behavior – and this design also made it possible to have many agents work reliably.
We had issues with scaling the number of concurrent agents within a run, so we decided to have each program run in an isolated sandbox program runtime, kind of the same way you run a Modal function. Each agent then calls the runtime with an agent token, which checks who can talk to who or send files across VMs, and then applies the tool call.
Our early users have found the library useful for:
- running many agents to do performance optimization
- building custom automated software pipelines for eg code review, pentesting, large-scale migrations, etc...
We've heard that the frontier labs have the infrastructure to quickly spin up 100 agents and have them coordinate with each other smoothly in various ways. We're hoping that Druids can be a starting point to make that infrastructure more accessible.

value: Show HN: Druids – Build your own software factory

author: etherio


value: ninadwrites

value:
Was experimenting with YT and figured I needed more data.
Like every engineer procrastinating appearing in front of a camera, I decided to vibe code an analytics tool that helps fetch YT data, figure out what's working, what's not, and a Kanban board to create a full content calendar for your channel.
Try it out while I can afford to keep it free and let me know!

value: Show HN: YTGrowAI – Get detailed analytics for YouTube channel growth

author: ninadwrites


value: saketpradhan

value: Show HN: Gimble CLI – Turn your terminal session into a live debugging chat

author: saketpradhan


value: notaiagent

value: Show HN: The go-to Python package for App Reviews

author: notaiagent


value: rjozefowicz

value: Metrya is a private iOS app that turns Apple Health data from all your devices into clear, personal insights. It exists because there was no easy way to plug real AI into Apple Health, so Metrya lets you bring your own Claude, OpenAI, or Gemini API key

value: Show HN: Metrya – Private HealthKit AI advisor with your own LLM key

author: rjozefowicz


value:
TL;DR: I built a website that allows users to track the concerts they've been to. If you have strong opinions about engineering/design or how shows should be tracked (festivals, venues, etc...), I'd love to get your input!
For the past ~5 years, I've been tracking the shows I attend on my personal website (https://love-music-will-travel.henryrobbins.com). It's fun to see things like distance traveled and how many times I've been to certain venues. I know many friends who also track their shows through notes, ticket stubs, Excel, etc... It always bummed me out that I couldn't pore through their concert data myself...
showcount.com is my solution to that desire. It's essentially a public version of my old personal website, where anyone can make an account and manage a show list (mine is https://www.showcount.com/user/love-music-will-travel).
I'm currently on the lookout for other live music lovers and/or data nerds to try out the site and give opinions on various design choices. If any of the following topics are of interest to you, please reach out!
- How should venue name/location changes be handled?
- How should music festivals be handled?
- I have an initial version of an AI parser for loading in existing show lists; how can this be made more robust?
- What else should have first-class tracking support (e.g., friends in attendance)?
As an aside, this project is also my first experiment with full-on vibe-coding / harness-engineering. I began the project with Cursor and then switched to Claude Code. I've been programming for the better part of a decade, mostly Python and Java. Full-stack development is relatively new to me. I include the tech stack below. Most decisions were made pragmatically based on what I thought would get me to a first version of the site as quickly as possible.
- Next.js web app hosted on Vercel
- Fast API backend service (for the AI parsing) hosted on Railway
- Supabase
- Observability through Axiom (logging), PostHog (analytics), and Sentry (monitoring)
- Clerk for user authentication
- Google Maps API for venue locations
- Claude API for the AI parser
- Terraform for infra-as-code

value: Show HN: A website to track live music attendance


value: AlexFromTwelve

value: Just launched. Looking for first users and feedback!

value: Show HN: Give Claude Code disposable servers to work on tasks in parallel

author: AlexFromTwelve


value: pcpuser

value:
Starla is an unofficial RIPE Atlas Software Probe client that is cross-platform, has an optional tray icon applet, and is a single static binary with no external dependencies.
Starla is available for Windows, MacOS, Linux, and for container runtimes.
Starla is documented at https://ananthb.github.io/starla. The RIPE Atlas Probe protocol is also documented at https://ananthb.github.io/starla/protocol.html.

value: Show HN: Starla – Unofficial Ripe Atlas Software Probe

author: pcpuser


value: cdesh

value: Show HN: I Used 15 AI Agents to Design a Wearable – Here's Where They Broke

author: cdesh


value: zhendershot

value:
We built CongaLine to solve a specific problem: give teams and individuals self-hosted AI assistants without collapsing everyone into a shared instance. Each agent runs in its own Docker container with isolated networks, secrets, and config. Security is the primary design constraint.
Three deployment targets via a single Go CLI (conga):

  - Local: Docker Desktop, no cloud required
  - Remote: any SSH-accessible Linux host (VPS, Raspberry Pi, bare metal)
  - AWS: zero-ingress via SSM, no inbound ports

Runtime is decoupled from deployment. OpenClaw (Node.js) and Hermes Agent (Python) ship out of the box - same infrastructure, your choice of agent. Multi-runtime is fully wired on local today; remote and AWS default to OpenClaw. More coming soon.
Channels are optional. Every agent gets a web UI via SSH/SSM tunnel. Slack and Telegram are both supported, each with a dedicated router container that fans events to per-agent containers. Two agent types: user agents (DM-based) and team agents (channel-based).
The name is literal: spiny lobsters travel in single-file conga lines during seasonal migration, in physical contact, reducing drag and sharing protection. A procession of isolated-but-coordinated agents.
Apache 2.0. Repo has Terraform for AWS, a Go CLI, and a Node.js Slack router.
Would love feedback on the provider abstraction. We're planning Kubernetes and GCP next.

value: Show HN: CongaLine – Self-hosted isolated AI agent fleet (OpenClaw, Hermes)

author: zhendershot


value: vpfaiz

value:
I got tired of the overhead required to run even a simple data analysis - cloud setup, ETL pipelines, orchestration, cost monitoring - so I built a fully local data-stack/IDE where I can write SQL/Py, run it, see results, and iterate quickly and interactively.
You get data lake like catalog, zero-ETL, lineage, versioning, and analytics running entirely on your machine. You can import from a database, webpage, CSV, etc. and query in natural language or do your own work in SQL/Pyspark. Connect to local models like Gemma or cloud LLMs like Claude for querying and analysis. You don’t have to setup local LLMs, it comes built in.
This is completely free. No cloud account required.
Downloading the software - https://getnile.ai/downloads
Watch a demo -  https://www.youtube.com/watch?v=C6qSFLylryk
Check the code repo - https://github.com/NileData/local
This is still early and I'd genuinely love your feedback on what's broken, what's missing, and if you find this useful for your data and analytics work.

value: Show HN: I built a local data lake for AI powered data engineering and analytics

author: vpfaiz


value: kibermaks

value:
Been using this system for 2 years, started as Apple Shortcuts. Every morning, fill calendar gaps with work/side blocks. Kept using it, and it helped me to feel the rhythm of the day and gain extra productivity.
Rebuilt script into a native macOS app, same algorithm, reads your calendar, finds gaps, fills them with sessions dynamically. But now it also tracks time live, ambient sounds, progress on the dock icon, and mini player.
fully local, no accounts, no cloud, OSS.
Do not have any intention to sell/promote it, want to share it with the community.
https://github.com/kibermaks/SessionFlow

value: Show HN: SessionFlow macOS app that auto-schedules sessions around your calendar

author: kibermaks


value: Aejkatappaja

value: Show HN: Phantom UI – Skeleton that measures your real DOM (Web Component, 8kb)

author: Aejkatappaja


value: anonfunction

value:
I built this because I was interested in the data. Didn't fully get it to what I wanted, but thought I'd share it nonetheless. Maybe someone has better data sources they could share!
Turns out live ship tracking APIs are expensive so I manually just copied the json from https://www.marinetraffic.com/en/ais/home/centerx:57.4/cente... I'll probably have an ai agent do the same thing on some cron interval, if this gets any fanfare.
To actually know if the port is open without live ship tracking I found https://portwatch.imf.org/pages/cb5856222a5b4105adc6ee7e880a... which was perfect, except it has 4 day lag!
I also thought of adding news feed parsing or prediction market data to get a more definitive answer on if it's open right when you load it, but I spent a few hours and am gonna move on for now.

value: Show HN: Is Hormuz open yet?

author: anonfunction

title: Show HN: Is Hormuz open yet?


value: linggen

value:
Hi HN, I built Linggen — a local-first, open-source AI coding agent written in Rust.

  What's new in 0.9.2:
  - P2P remote access via WebRTC — control your agent from your phone, no port forwarding or cloud relay needed. Just
  `ling login` and scan a QR code.
  - Plan mode — the agent proposes a step-by-step plan before writing code, you approve or edit before execution.
  - Works with any model — Ollama, OpenAI-compatible APIs, Gemini, DeepSeek. Bring your own keys or run fully local.

  It's like Claude Code but model-agnostic, extensible through skills (markdown files), and now accessible from
  anywhere.

  Demo video on the landing page showing install → plan → build → mobile sync.

  Install: curl -fsSL https://linggen.dev/install.sh | bash

  GitHub: https://github.com/linggen/linggen

value: Show HN: Linggen – Open-source AI agent with P2P remote access from your phone

author: linggen


value: thalesfp

value: Show HN: Tired of logic in useEffect, I built a class-based React state manager

author: thalesfp


value: kaashmonee

value: Show HN: Dis: Dev environments but without Node.js

author: kaashmonee


value: WalkingFridge

value:
I'm a SW/ML engineer that spends a tad too much time on Twitch.. I've been eyeing the moderation tooling used for livestreaming and I can't help but notice how primitive it still is - keyword lists, regex, basic shimming, etc. So, I thought I could do better.
Behold ModCheck - a context-aware moderation tool. It understands what's happening in the stream, who the streamer is, what's currently happening, the mood of chat, what the streamer is saying, etc.. It uses all of that to make moderation decisions.
Instead of keyword lists or regex, you write moderation rules in plain English. I call them Judges. They run in parallel for each message. A few examples:
1. Boundary Judge - "Flag viewers who comment on my appearance, body, or clothing choices." Catches stuff like "you'd get way more viewers if you showed more skin js"
2. Backseat Judge - "Flag unsolicited gameplay advice and strategy tips. Allow tips if I've asked chat for help."
The goal is to make this fully open source and have a paid tier for those who don't want to self-host.
NOTE: the product is not yet released. I'm looking for early adopters to test it out and help shape the direction.
Also, if anyone is interested in building this out with me, please let me know!
--
For the interested, Judges are based on
1) scemantic similarity over a space of previously flagged examples having the stream context embeeded into the search and
2) finetuned small models as classifiers.
The combination of the two allows to keep latency low-ish (under 500ms) and to use less GPU. Also 1) grows with your usage. The more you use a judge, the more accurate and fast it becomes.

value: Show HN: Context-Aware Twitch Moderation

author: WalkingFridge


value: AndreBaltazar

value:
Hey HN. I built an AI agent harness over the past few months and I'm open sourcing it today.
Some context on why. I've been building with Claude Code daily using this harness. It orchestrates multiple AI agents as a team, with a dashboard, chat, kanban board, the works. I used it to build a full SaaS product (MyUpMonitor, https://myupmonitor.com) in about 24 hours of focused coding.
Then yesterday Anthropic announced Mythos and decided to keep it behind closed doors. Meanwhile I'm paying for Claude and I can't access their best model. I don't think that is nice at all...
So I'm open sourcing the harness with support for both Claude Code and OpenAI Codex. The whole point is that you shouldn't be dependent on one company's API to run your AI workflow.
What this harness does:
- Spawns and manages multiple AI agents in parallel
- Each agent gets a persona, role, and communication channels
- CEO agent that can hire/fire workers on its own
- Web dashboard with chat, kanban board, and live terminal output
- Supports Claude Code, OpenAI Codex, Cursor Agent, opencode, and local models
- MCP server per worker for tool access
- Written in Go. Two binaries. SQLite. No heavy deps.
Would love feedback from anyone working on multi-agent setups or just what you think in general. Thank you!

value: Show HN: I built an open source multi-agent harness in Go

author: AndreBaltazar


value: nadermx

value: Show HN: Free.ai

author: nadermx

title: Show HN: Free.ai


value: Show HN: HUMA – Behavioral bot detection API, no CAPTCHAs required


value:
Hey HN!
I've built clipper.dev -  the fastest container repository in the world. (Big claim, I know.) It deduplicates at a file/chunk level, rather than at the layer level.
I get around 10-20% faster pulls than docker (when using containerd for both) for fresh pulls.
For some workflows, I see 80-90% faster patch pulls (think v0.17.5 -> 0.17.6).
Here's a best case scenario for me (and worst case for dockerhub)

    docker> time sudo nerdctl pull ollama/ollama:0.17.5
    docker.io/ollama/ollama:0.17.5:
    elapsed: 244.1s total:  2.9 Gi (12.3 MiB/s)

    real 4m4.172s
    user 0m0.115s
    sys 0m0.227s
    docker> time sudo nerdctl pull ollama/ollama:0.17.6
    docker.io/ollama/ollama:0.17.6:
    elapsed: 234.9s total:  2.8 Gi (12.3 MiB/s)

    real 3m54.965s
    user 0m0.051s
    sys 0m0.275s

---

    clipper> time sudo clipper pull clipper/ollama:0.17.5
    using containerd runtime at /run/containerd/containerd.sock
    fetching manifest clipper.dev/clipper/ollama:0.17.5 ...
    image created: clipper.dev/clipper/ollama:0.17.5
    fetched 46 chunks (2.2 GB, 11.7 MB/s), took 3m16.134s
    done — clipper.dev/clipper/ollama:0.17.5

    real 3m17.532s
    user 0m0.006s
    sys 0m0.012s

    clipper> time sudo clipper pull clipper/ollama:0.17.6
    using containerd runtime at /run/containerd/containerd.sock
    fetching manifest clipper.dev/clipper/ollama:0.17.6 ...
    image created: clipper.dev/clipper/ollama:0.17.6
    fetched 6 chunks (599.2 MB, 7.7 MB/s), took 1m17.48s
    done — clipper.dev/clipper/ollama:0.17.6

    real 1m19.061s
    user 0m0.005s
    sys 0m0.016s

The first two layers of 0.17.6 are shared with 0.17.5, but everything else has to be transported again with docker, but not with clipper. The exact speedup depends on how fast my wifi is feeling at the time, but I'm pulling 1.5 GB less than dockerhub can do. This is _huge_ for robotics workflows or anyone else pulling docker images on poor connections. (This is not even as fast as it could be - there's other optimizations like chunking within files or using BytePort that could make this go even faster).
I built this because at multiple companies I've been at, pulling new images for updates has been...very painful. Robotics Dockerfiles tend to have a lot of dependencies stuffed into a small amount of layers. Adding one new dependency means shipping the bytes for every other dependency, even with perfect pinning. Additionally, the Dockerfiles are oftentimes...not well written, compounding the problem. So I decided to fix the tool to fit the user, rather than the inverse.
There's some other side benefits to using clipper:
 - Moving a file or changing permissions is "free": another layer already has the bytes
 - The on-disk structure lends itself much better to P2P device updates than docker's layout
I do intend this to be a commercial product, but pricing is still up in the air. I'm currently targeting robotics companies as they feel the most pain here. There's some other possible fields that I'm interested in that might find use in this: IoT, edge AI, space datacenters/etc, developmer tooling.
Please give it a try. If you don't want to upload an image, these repos should be publically pullable once you register an account:

    clipper/ubuntu: 20.04, 22.04, 24.04
    clipper/ollama: 0.17.5, 0.17.6
    clipper/python: 3.13, 3.14

value: Show HN: Clipper – The World's Fastest Container Registry


value: mahadillah-ai

value: Show HN: Portable AI memoryswitch providers, keep your context

author: mahadillah-ai


value: jlowin

value:
Hi HN,
I'm the author of FastMCP, the most popular Python framework for building MCP servers. I've been really excited about MCP Apps for a while. I think letting a server ship a fully interactive UI directly into the conversation is one of the most compelling additions to the protocol.
I wanted to make this a first-class experience in FastMCP, but I kept getting stuck on what it actually means for a Python framework to integrate with a frontend feature. The JavaScript ecosystem has extraordinary tooling for this. I didn't want to build a worse version of it just to stay in Python.
What changed my thinking was looking at how MCP is actually deployed. Most people think of it as a way to reach customers, but what we overwhelmingly see is companies using MCP internally — replacing dashboards, workflows, and internal tools. And for that, Python developers don't need the full JS ecosystem and its polished, branded, custom frontends. What they need is a way to compose the right components and data bindings.
So we built Prefab. It's a generative UI framework with 100+ shadcn components that you compose using Python context managers. It serializes to a JSON protocol that renders as a real React application, with full client-side interactivity, reactive state, actions, and no JavaScript required. And because context managers have no closing tags, a partial Prefab script is already a valid UI, which means an agent can stream an interface to the renderer as it generates, without waiting for the full program to complete.
Prefab is built right into FastMCP 3.2, but Prefab Apps can also run standalone or against any REST backend (like FastAPI).
Docs: https://prefab.prefect.io
Try the playground (no install required): https://prefab.prefect.io/docs/playground
GitHub: https://github.com/PrefectHQ/prefab

value: Show HN: Prefab – A generative UI framework for Python

author: jlowin


value: olivergrabner

value:
I've been working on Composer 2.0 (usecomposer.com), a tool that connects to your AI coding agents (Claude Code, Cursor, Copilot, Windsurf) via MCP and generates full architecture diagrams from your codebase.
You can also describe what you want to build in plain English and an AI architect designs the system with you!
Would love feedback on where it's useful and where it breaks down.

value: Show HN: Composer – Diagram Your Codebase with MCP

author: olivergrabner


value: turlockmike

value: I wanted a lightweight harness to run local models. By  constraining it to a single tool (Bash) the focus can be on building the programs around it and making the program itself as close to using a Unix program as possible so that it is also easily fit into any other program.

value: Show HN: Infer – Pipe friendly Agent Harness with one tool: Bash

author: turlockmike


value: jvtheworld

value:
They tried to bury these files.
Single HTML file, Supabase, Netlify Functions for the Anthropic API. 160 products, 11 categories. Each one with an authorized narrative, a suppressed narrative, a 3D threat matrix you can orbit, and an assessment on demand.
What say you, serfs?

value: Show HN: Palesignal – Vote on whether emerging tech is an asset or a threat

author: jvtheworld


value: douglaswlance

value:
I built a pairwise ranking platform on Cloudflare Workers. You get two items, pick the better one, and ELO ratings sort out the rest. No more tier list arguments. Let the votes decide.
I seeded it with a "Best Programming Language" ladder to settle the debate once and for all: https://ladderrank.app/d/greatest-programming-language
The stack: Hono + D1 + R2 on Cloudflare Workers, React frontend on Pages, Drizzle ORM. Anyone can create their own ladder and share it. Anonymous voting works too (at reduced weight).
Curious to see what HN thinks is the best language, and whether the ELO rankings match your priors.

value: Show HN: LadderRank: Rank anything with ELO ratings

author: douglaswlance


value: massanishi

value:
BakaBags is a small experiment I built over the last week.
You paste a public Solana wallet address, the app reads the wallet’s holdings and current market context, and an AI character reacts to the portfolio in a sharp, screenshotable way.
Important: it only reads public wallet data. There is no wallet connect, no signing, and no private key access.
The idea was to combine onchain data with personality instead of building yet another sterile dashboard. So it sits somewhere between wallet analysis, entertainment, and a shareable crypto-native toy.
I’m mainly curious about two things:
- whether the “personality + live wallet data” concept feels interesting at all
- whether this reads as a gimmick, or as a product direction with real potential
Would love blunt feedback.

value: Show HN: BakaBags, a tsundere AI that roasts yours Solana wallets

author: massanishi


value: xppexx

value: Show HN: A simple no bloat character checker

author: xppexx


value: nadermx

value:
Felt the name was too difficult to type out and spell sometimes, so it's technically a new show HN? If not, oh well; In reference to this last Show HN:
https://news.ycombinator.com/item?id=47559509

value: Show HN: Ptoe.org

title: Show HN: Ptoe.org


value: jettfu

value: Show HN: Side-by-side comparison tools for US LLC formation,banking and payments

author: jettfu


value: markneub

value:
Pasteur is a free, in-browser app for generating trippy videos from your music using the legendary Milkdrop visualizer (you may remember it from Winamp). I built the first version nearly 10 years ago, but it required a server running ffmpeg, which added complexity and I eventually shelved it after the MVP.
Recently I discovered WebCodecs for in-browser video encoding and decided to rebuild. Without the server dependency, I was able to rebuild a better version in just a few evenings.
Source: https://github.com/markneub/pasteur
Example output: https://www.youtube.com/watch?v=UkZ3a8I5iMg

value: Show HN: Pasteur, a Milkdrop Visualization Exporter

author: markneub


value: nbaronia

value: Show HN: Public Domain Audiobooks with AI (Azalea Audio)

author: nbaronia


value: zakariaelhjouji

value:
I love reading benchmark / eval papers. It's one of the best way to stay up-to-date with progress in Vision Language Models, and understand where they fall short.
Vision tasks vary quite a lot from one to another. For example:
- vision tasks that require high-level semantic understanding of the image. Models do quite well in them. Popular general benchmarks like MMMU are good for that.
- visual reasoning tasks where VLMs are given a visual puzzle (think IQ-style test). VLMs perform quite poorly on them. Barely above a random guess. Benchmarks such as VisuLogic are designed for this.
- visual counting tasks. Models only get it right about 20% of the times. But they’re getting better. Evals such as UNICBench test 21+ VLMs across counting tasks with varying levels of difficulty.
Compiled a list of 2.5k+ vision benchmarks with data links and high-level summary that auto-updates every day with new benchmarks.

value: Show HN: 2500 vision benchmarks / evals for Vision Language Models

author: zakariaelhjouji


value:
I've been building an image hosting platform designed to work without a human in the loop.
Most image CDNs require you to fill out a form, confirm an email, set up a dashboard. Things an AI agent can't do by itself. I'm trying to flip the onboarding with tiered access that starts open and tightens as you need more.
Anonymous agents get ephemeral images through the API, and verified emails get persistent storage. The interesting part is that verification is started by the agent through the API, not the human. The agent is also informed at every step of its usage and next steps for unlocking higher tiers.
The CDN does on-demand resizing, format conversion, and image transformation at the edge. Flat monthly pricing, no bandwidth fees, no per-request charges. The whole stack is three Cloudflare Workers (web, API, CDN) backed by D1, R2, and the Images API.
I'd love feedback on the agent workflow specifically. Is the tiered access model the right approach, or would you want something different?

value: Show HN: An agent-friendly image CDN built on Cloudflare Workers


value: jsonstcyr

value: Show HN: Feature release system with MCP agent controls

author: jsonstcyr


value: sftechdude

value:
Generating thumbnails from PDFs, DOCs, and other files is surprisingly painful — slow, clunky, or requires APIs.
We built a simple solution internally for The Drive AI: just prepend preview.thedrive.ai/ to any file URL and get an instant thumbnail. Files aren’t stored and are deleted immediately.
Now we’re releasing it for free: https://preview.thedrive.ai/
Curious to see what else we can do with this.

value: Show HN: Preview.thedrive.ai – instant thumbnails for any document URL

author: sftechdude


value: islandbytes

value: Show HN: CCG – Block merges until devs prove they read their own diff

author: islandbytes


value: Airyisland

value: Professional audio editing tools right in your browser. No downloads, no installations.

value: Show HN: AudioEditor – Free Online Audio Tools

author: Airyisland


value: pbanchik

value:
I was looking for a business going through reviews when I thought it would be nice if I could see reviews from people I knew, so I built this.
To mimic the privacy from recommendations from friends only people that follow you can see your reviews and you can even hide specific reviews from specific followers.

value: Show HN: I built a website to see business reviews from friends

author: pbanchik


value: artificium

value:
MemForge is an experiment in a single database (PostgreSQL) with local embeddings. The goal is to enable long-term, persistent memory independent from the model or agent framework. It began as an attempt to maintain context over longer periods without the token cost spiraling out of control and the lost context after consolidation.
The architecture is based on a rough simulation of human memory and uses sleep cycle (agent inactivity) where an LLM reorganizes and stores memory in a multi-tier database with a number of sorting, ordering, and prioritization mechanisms. I also attempted to build this with security built in from the beginning.
Since I first published this, it has evolved quite a bit based on some feedback and integrating some ideas from many of the other fantastic memory management systems that show up daily. Hopefully my contribution to the community can help move things forward and bring some value to people as a full solution, or just to carry some ideas forward in other projects.
Looking for feedback on how the sleep cycle works, the bet on a PostgreSQL only solution - particularly from anyone willing to run this under real workloads. Full transparency -- it's Alpha now, but has undergone multiple rounds of stress testing, hardening, and benchmarking.

value: Show HN: Self-improving agent memory system, 92% R 5 LongMemEval, PostgreSQL

author: artificium


value:
Hi! I developed CVD (Color-Vector-Decimal), a C++ algorithm that treats images as sequential bit-planes.
Results: Lossy mode (CVD2) reached 99.9% compression on AI art (13MB to 10KB). Lossless mode (CVD1) is also faster/smaller than PNG in many cases.
Blog & Proofs:  [https://mohamedtrigui5.blogspot.com/]
Code & Tool: [https://github.com/mohamedtrigui5-lab/CVDTool]

value: Show HN: CVD Tool – Image Compression Algorithm (13MB to 10KB)"


value: airobus

value: Show HN: Image to Music AI – Turn Any Photo into an Original Soundtrack

author: airobus


value:
What is AIPOCH Medical Skill Auditor?
Medical Skill Auditor is an evaluation framework that AIPOCH uses to assess the quality of its medical research agent skills before they are made available to users. It acts as a gatekeeper, ensuring that skills meet defined standards in reliability, usability, security, and scientific integrity.
How does Medical Skill Auditor work?
Veto Gates
To enforce strict quality control, Skill Auditor is designed with two layers of veto mechanisms. Any failure in these checks may lead to immediate rejection of a skill.
Skill  Veto
Operational Stability
Structural Consistency
Result Determinism
System Security
Research  Veto
Scientific Integrity
Practice Boundaries
Methodological Ground
Code Usability
Core Capability
Evaluates a skill’s design and contract against key dimensions such as Functional Suitability, Reliability, Performance & Context, Agent Usability, Human Usability, Security, Agent-Specific and Maintainability.
Medical Task
Assesses actual outputs of a skill with layered criteria.
For skill testing, the AI automatically generates inputs. The number of inputs in specific categories will increase or decrease depending on the complexity of the skill. The following 7 inputs represent the most comprehensive version.
/Canonical
/Variant A
/Edge
/Variant B
/Stress
/Scope Boundary
/Adversarial
Skill Complexity Classification
Label  Code/Rank  Definition
Simple S Narrow task scope
Moderate M Moderate branching or multiple task types
Complex C Broad or multi-step specialized skill
Simple (S):3 inputs
Moderate (M):5 inputs
Complex (C):7 inputs
Final Score
The Skill Evaluator uses a two-stage scoring system: static evaluation (design quality, accounting for 40%) and dynamic evaluation (runtime performance, accounting for 60%). The final overall score is derived by combining both.
Static (40%)
Dynamic (60%)
Final Score = Static Score × 40% + Dynamic Score × 60%
You can view evaluation results for selected AIPOCH skills here:https://www.aipoch.com/agent-skills/medical-research-literat....
This framework is still under active development， we’d love to hear your feedback!
Right now this assessment framework is only applied to a subset of AIPOCH’s skills, but we’re considering expanding it more broadly. If this evaluation framework could be used to assess third‑party skills in the future, would you consider trying it in your own projects? Are there evaluation frameworks you’re already using?

value: Show HN: We Evaluates Medical Research Agent Skills


value:
I build AI agents and got tired of squinting at raw JSONL chat logs. Existing online viewers either choke on JSONL or upload your data. So I made one that runs offline in a single HTML file.
Paste JSONL → auto-formatted with syntax highlighting. Auto-expands nested JSON strings (common in tool-call args). Toggle to render \n as real line breaks for reading embedded markdown/code.
It's just one HTML file — open DevTools to inspect, Ctrl+S to save your own copy and modify however you like.
https://tasks.hamsterbase.com/jsonl-viewer/

value: Show HN: Jsonl Viewer – An offline JSON Lines viewer in a single HTML file


value: lirongliu

value: Show HN: Connect with strangers who feel the same as you

author: lirongliu


value: coderredlab

value:
I built a drop-in replacement for proot that runs full Linux userspace (Node.js, Python, Git, Chromium) on Android without root.
proot uses ptrace — 2 context switches per syscall. proroot uses LD_PRELOAD + binary patching — 0 context switches, in-process path translation.
It powers andClaw, an app that turns Android phones into AI agent gateway servers running OpenClaw with a real Chromium browser.
Tested with Node.js 24, Python 3.12, Git, and Chromium headless_shell (Playwright).
Binary release (arm64 .so files): https://github.com/coderredlab/proroot

value: Show HN: Proroot – Zero-overhead proot replacement for Android

author: coderredlab


value: cambone

value: Show HN: Local browser extension, scans ToS with regex, no AI

author: cambone


value: Show HN: 8d – Agent-Native Version Control, where Git breaks


value: Show HN: AccessGuard – Scan your site for accessibility lawsuit risks


value: reesericci

value: Show HN: Stricline: A meta-framework to easily build robust CLI apps w/stricli

author: reesericci


value: ramoz

value:
The blog inspired me to audit my own repos - and of course I want to use an agent to do it. The skill is completely agent/llm driven and produces a nice html report with findings, rationale, & recommendations.
Original:
https://news.ycombinator.com/item?id=47699181

value: Show HN: Agent Skill Based on "Open Source Security at Astral"

author: ramoz


value: namin

value: Show HN: Guardians – Verify tool-using agent workflows before execution

author: namin


value: hanwoo

value: Show HN: An API that catches what your LLM confidently got wrong

author: hanwoo


value: emregucerr

value: I built this fun benchmark to pitch LLM models against each other in Oxford-style debate.<p>The format is inspired by Intelligence Squared. The side who flips most votes win.

value: Show HN: Artificial Intelligence Squared – LLMs Debate Each Other

author: emregucerr


value: kreicer

value: Hi HN, I built Omen, a lightweight Kubernetes chaos engineering operator. Existing chaos tools can often feel like a black box or require heavy installations. I wanted something simpler that prioritizes safety and transparency. Omen lets you declaratively define chaos experiments (e.g., killing pods on a cron schedule), but with a focus on control: it selects the exact target pods upfront and can pause for manual approval (with webhook notifications) before executing any disruption. You can also use dry runs to preview targets without any actual impact. It's built with Kubebuilder and installs easily via Helm.<p>I'd love to hear your thoughts and feedback!

value: Show HN: Omen – A lightweight Kubernetes chaos operator with manual approval

author: kreicer


value: Eikon

value: Show HN: PostgreSQL running in the browser, persisting to S3

author: Eikon


value: anophelon

value: Show HN: A dev tool for routing local traffic, built with Pingora

author: anophelon


value: csomar

value: Show HN: Chrome extension to filter HN comments by user karma/account age

author: csomar


value: vektormemory

value:
Massive Upgrades: 1.4.6:
28 Claude Mcp tools
Cloak Stealth Browser
D3 Graph
Custom Ink Cli
Ollama Support
Grog, Claude, Openai,Gemini Mistral Support
Research Tools
Better Sqlite Db
Onnx embedding
Local first memory migration
Audn memory eval
Rem dream cycle
Instant hyper intel memory curation and retention
Review Vektor:
https://github.com/Vektor-Memory/Vektor-memory

value: Show HN: D3 4 Layer Magma Graph – Cloak Stealth Browser

author: vektormemory


value: tommyjepsen

value:
Created an app that uses the Gemma 4 26B(only 18gb) model locally on my Mac, to support me as a UX Agent continuously evaluating my work and giving me feedback and pointers on where to improve.
It takes a screenshot, analyzes it through its Vision capabilities and returns historical-related feedback in under ~6 seconds. The smaller models even faster.
All done locally, privacy-friendly and offline available.
Tried including good installation instructions, if any is interested in trying it out. Using LM Studio.

value: Show HN: UX Agent Mac app running continuously locally using Gemma 4

author: tommyjepsen


value: niksmac

value:
Today we’re launching HoldMyBill on Product Hunt.
It helps you keep receipts, warranties, and product documents organized in one place. Just upload a bill and the app turns it into a structured product record with purchase and warranty details.
Website
https://holdmybill.com
Product Hunt launch
https://www.producthunt.com/products/holdmybill?launch=holdm...
If you want to try it, message me.
I can add extra free slots so you can store more products and test it out.
Your feedback would help a lot.

value: Show HN: HoldMyBill – Organize Ownership

author: niksmac


value:
I made it for me, nothing fancy, but feel free to use it as well.
It's free, no account needed (no server, all data stays on your phone), no tracking, open sourced (https://github.com/bdav24/illll).
It's a tracker for 6 longevity habits backed by research:
1. Sleep — brain clears toxic waste during deep sleep, <7h raises mortality 13%
2. Exercise — 11 min/day of walking cuts premature death risk 23% (Br J Sports Med, 2023)
3. Healthy eating — cuts depression risk 33% (SMILES trial), cardiovascular disease 30% (NEJM, 2018)
4. Structured breathing — 5 min/day, lowers cortisol, raises HRV (Stanford, 2023)
5. Morning sunlight — 10-30 min, sets circadian clock, boosts serotonin (JAMA Psychiatry, 2016)
6. Gratitude — writing 3 things daily, measurable neural changes lasting months
But you can also set custom goals and add your own habits.
https://apps.apple.com/us/app/illll-live-longer/id6760903742
https://play.google.com/store/apps/details?id=com.devanco.il...
Any feedback is welcome!

value: Show HN: I made an app to live longer (free, open source, no tracking)


value: renatoworks

value:
I built an open source tool that turns any svg into beautiful interactive 3d assets.
you can drag an svg in, type some text, or draw pixel art and it becomes a 3d object you can spin around, animate, embed in your site and export as 4k image or video.
100% free, no account needed. runs entirely in your browser, nothing gets uploaded to any server.
Playground: https://3dsvg.design
Github: https://github.com/renatoworks/3dsvg

value: Show HN: I built a free open-source SVG to 3D tool

author: renatoworks


value: SolarpunkRachel

value:
I had a 6-minute pitch at 1 Million Cups and wanted more than a countdown timer. I wanted to see, at a glance, how my time was breaking down across slides as I presented.
The app fills the screen with a dark-to-bright red gradient as time passes. Tapping advances slides, which steps the brightness up, creating visible bands. Wide band = you lingered. Thin band = you moved fast. If you go overtime, solid white fills from the top, overwriting your gradient.
Live at https://preso.wetfish.net, source at https://github.com/wetfish/preso
Built it for my own pitch, but my friend already used it for a college presentation so I figured I'd share it.

value: Show HN: Giving My First Pitch at 1M Cups Using a Custom Mobile App

author: SolarpunkRachel


value: yurigokoryan

value:
I'm a UI/UX designer with 10+ years in the industry. I kept seeing the same problem: freelancers and creatives who need a simple online presence end up fighting Webflow or paying $15/mo for features they'll never use.
mnml.page lets you build a clean one-page site in minutes. Pick a template, edit your content, publish. No drag-and-drop complexity, no CMS, no bloat.
A few things that make it different:
- AI site generation from your CV or links
- Flat $49/year pricing (no tiers, no upsells)
- Designed for people who want their work to speak, not their website
Built solo with Next.js, TypeScript, Neon, Vercel, and Cloudflare R2.
Would love feedback on the product and the landing page. What's missing? What feels off?

value: Show HN: Mnml.page – Minimalist one-page websites for freelancers

author: yurigokoryan


value: seuros

value: Show HN: [PKG47] AI-Controlled Package Registry

author: seuros


value: afistfullof

value:
I spent the past couple of weekends building an open-source alternative to Harvey/Legora's popular tabular review application for lawyers.
The project was sparked by a viral LinkedIn post from lawyer Joshua Upin, who described being shown a hallucinated citation by Harvey that was falsely attributed to one of Harvey’s competitors. Seeing such a basic failure emerge from their architecture made me ask a simple question: could I recreate a similar product of theirs without using a single generative model, and in doing so make hallucinations architecturally impossible?
As it turns out, quite a lot.
In building the app, I did not use a single external or generative model. The entire system uses models my organisation trained and owns. More specifically, it uses a combination of Kanon 2 Enricher, Kanon 2 Embedder, and Kanon Answer Extractor. All three are encoder-based, and there are no generative models anywhere in the stack.
That means hallucinations are architecturally impossible. It also means the system can retrieve, classify, extract, and link information in a much more structured and interactive way than products that lean heavily on generation.
At its core, the app turns contracts into a wiki-style, interconnected knowledge graph: a network of entities, annotations, spans, and relations that users can explore interactively. Key features like parties, locations, dates, signatures, and terms are extracted on the first pass. From there, users can define custom spans and relations, extending the graph as they go.
The end result is a tabular review system that matches the core experience offered by the market and, in several meaningful respects, goes beyond it.
I embedded a static version of the app at the top of the linked page so people can try it directly. The static version has real public contracts processed using the application. These contracts relate to public figures like Mark Zuckerberg, Elon Musk, and Jensen Huang, making it easy to verify the accuracy of the stack. The linked page also works as a step-by-step guide for anyone who wants to build something similar themselves.

value: Show HN: I built a Harvey-style tabular review app, then open sourced the code

author: afistfullof


value: pielouNW

value: Show HN: Running a 1.7B parameters LLM on an Apple Watch

author: pielouNW


value: mrxdev

value:
For anyone using Obsidian, thought I'd share an Open Source plugin that I built to really streamline my workflow, and so far it has worked very well for me:
https://github.com/spencermarx/obsidian-ai
Yes, I'm the creator of this, so take all this with a grain of salt.
The plugin essentially allows you to run your AI Agent CLI of choice within the Obsidian UI. It detects whatever agentic CLIs you have available on your machine, gives you the ability to pick which one you want to work with, and wraps this up in a pretty standard little UI chat sidebar with all the standard AI chat features (pulls in your slash commands, your Vault's tags, as well as file references and allows for image drag-n-drop). Pretty straightforward, just rigged up to launch inside your Obsidian UI.
For me, it's been super convenient to have Claude Code available directly in Obsidian as it saves me from having to switch between the vault UI and a terminal/IDE for running Claude Code against the same vault files. This might not make a big difference to some people, but for me the context switching savings and ease of use have paid off.
The plugin is still awaiting approval to move into the Community Plugins directory, but in the meantime, the repo walks you through how to get set up on your own.
And of course, all of this is Open Source, FREE (no catches), and for the benefit of the builder community.
Would love feedback on this (good, bad, ugly) for those of you interested on trying it out! And if you do find it valuable, would appreciate a share/github star to increase visibility and accelerate feedback/refinement.
Thanks and happy building!

value: Show HN: Obsidian AI Copilot – Bring Claude Code, OpenCode, etc. into Obsidian

author: mrxdev


value: witnote

value: Show HN: GlotShot – Generate App Store screenshots in 12 languages

author: witnote


value: kotysoft

value:
I'm a solo dev who got tired of checking Google Analytics on mobile. The official app is fine for browsing reports, but you can't build your own dashboards with the metrics you actually care about. And personally I wasn't satisfied with Looker Studio dashboards on mobile and the existing Business Intelligence were too much for my siple needs.
So I built EasyDash — a native Android app that connects directly to your GA4 property and lets you create custom dashboards with drag-and-drop widgets. Charts, metric cards, data tables — you pick what you want to see.
Your data goes straight from your phone to Google's API. I never see it.
It's still early and I'd love to hear what you think — what would you want from a tool like this?

value: Show HN: EasyDash for Google Analytics [Android][Wip]

author: kotysoft


value:
I've spent the last 7 years doing a mix of open source, a full-time job,
  and freelance work. My public GitHub profile looks almost empty.

  All my real work is in private repos under company accounts, client
  organizations, or separate work emails. I had no way to show that output
  to anyone without violating NDAs or sharing proprietary code.

  So I built Unified Contribution Portal (UCP).

  You connect your GitHub accounts via personal access tokens (read-only,
  contribution metadata only). UCP fetches dates and counts — no repo names,
  no commit messages, no code — and merges everything into a single 52-week
  heatmap.

  The token is used once and discarded. Nothing is stored.

  Live: https://unifiedcontributions.vercel.app/

  It's free. I launched on Product Hunt today and have zero users. I'm
  trying to figure out if this is a real problem other developers have or
  just a problem I had.

  Honest questions for HN:

  1. Do you have the same problem — work that's invisible on your public profile?
  2. Would you use this? What's missing?
  3. Is there something obviously broken with the approach?

  Happy to answer anything about how it works technically

value: Show HN: 7 years of code, nothing to show on GitHub – so I built this


value: SirHound

value:
Hi HN! I've just released CSS Studio, a design tool that lives on your site, runs on your browser, sends updates to your existing AI agent, which edits any codebase. You can actually play around with the latest version directly on the site.
Technically, the way this works is you view your site in dev mode and start editing it. In your agent, you can run /studio which then polls (or uses Claude Channels) an MCP server. Changes are streamed as JSON via the MCP, along with some viewport and URL information, and the skill has some instructions on how best to implement them.
It contains a lot of the tools you'd expect from a visual editing tool, like text editing, styles and an animation timeline editor.

value: Show HN: CSS Studio. Design by hand, code by agent

author: SirHound


value: dezmou

value: Show HN: Gaussian splatting for real estate promotion

author: dezmou


value: Acmeon

value:
Webcellar simplifies the use of JavaScript (and TypeScript) in Excel.
The key feature is that JS module exports from ".xlsx.js" (or ".xlsx.ts") files become automatically available in corresponding "*.xlsx" files as functions (as long as Webcellar is running).
Another example of a feature is that Webcellar automatically handles dimensionality and data type conversion between Excel and JS. For example, JS objects are converted into Excel entities (i.e., single cell values with properties that can be accessed).
Webcellar has primarily been developed to meet my needs in application development, with use cases primarily relating to, for example, forecasting, risk analysis, decision analysis etc. Using Excel to manage the relevant application data (e.g., editing a cell or sorting by a column) is quite straightforward and often reduces the time I have to spend on developing a user interface. However, Excel application development is affected by the limitations of formulas and Visual Basic (a programming language embedded in Excel). In this context, using JavaScript in Excel by way of Office Add-Ins seems promising, but the development of Office Add-Ins can be unnecessarily cumbersome. Thus, I developed Webcellar to simplify the use of JavaScript (and TypeScript) in Excel.
View the Github repository for the Webcellar source code, a demo (also as an annotated screenshot) and more information: https://github.com/Acmeon/Webcellar
Note: Almost none of the code is AI generated. AI code is only used in one file, `.webcellar/taskpane-init.html`, to capture requested Office.js URLs.
I would be happy to answer any questions that you may have.

value: Show HN: Webcellar – JavaScript (and TypeScript) in Excel Made Simple

author: Acmeon


value: deronEx

value: Show HN: Maki – The efficient terminal coding agent

author: deronEx


value: GodelNumbering

value: Fully open source, a hard fork of cline. Full evals on the github page that compares 7 agents (Cline, Kilo, Ohmypi, Opencode, Pimono, Roo, Dirac) on 8 medium complexity tasks. Each task, each diff and correctness + cost info on the github<p>Dirac is 64.8% cheaper than the average of the other 6.

value: Show HN: I built Dirac, Hash Anchored AST native coding agent, costs -64.8 pct

author: GodelNumbering


value: Show HN: Homebutler – I manage my homelab from chat. AI never gets raw shell


value: chipcleary

value:
When I joined an improv troupe last year, I was anxious about being a good scene partner. I wanted to improve by practicing between our group’s sessions. But I found few ways to practice improv solo. I could watch endless episodes of "Whose Line Is It Anyway” but found it tough to practice the core skills (building on your partner's ideas, staying present, committing to a scene).
So, working with my coach Lisa, we built one.
What it is: Improv Playground is a web app with AI-driven virtual scene partners and a coach. The alpha has four games: "Yes, And..." (the core acceptance/building skill), Word Association Chains (associative thinking and spontaneity), Story Spine (narrative structure), and Foreign Poet (commitment and bringing out emotions).
To try It: You can play immediately at alpha.improv-playground.com. No account needed. Free registration if you want to save progress. To learn more about it, check out improv-playground.com.
What I'm trying to learn: I'm a learning scientist by background, which shapes how I think about this. I don't believe AI can replace the ensemble experience — the whole point of improv is human connection. But I do think targeted solo practice could help people get more from their real troupe time, the same way a musician does scales between rehearsals.
I want to know:
 - Are the games actually engaging?
 - Do they practice skills novice improvisers genuinely need?
 - Is the AI coach relevant and helpful (and not overbearing)?
This is the first of three planned game suites. Before I build more, I want to know if the approach is sound. Especially curious to hear from anyone with improv teaching or coaching experience — your reaction to the game selection and skill sequencing would be genuinely useful.

value: Show HN: Improv Playground – practice improv solo with AI scene partners

author: chipcleary


value: nasibahd

value: Show HN: Free tool to search datasets across Kaggle and HF in one place

author: nasibahd


value: willmeyers

value: Show HN: 41 years sea surface temperature anomalies

author: willmeyers


value: Jenqyang

value: Show HN: I built Memory Sync so I don't have to reteach every AI who I am

author: Jenqyang


value:
After building a lot of projects and giving up on them I found I always had a problem staying consistent. I'd quit as soon as things got hard because I couldn't see any short term consequences for doing so. Same thing with the gym, I'd go for a few weeks when motivation hit and as soon as it was gone I'd stop.
I'm still in high school so the consequences of failing feel really far away, if I quit a startup I still have plenty of time, if I skip the gym my body isn't going to change overnight. My brain just doesn't respond to long term consequences very well.
So I thought what if I just brought those consequences to the short term by charging myself money the moment I fail. $5 didn't work, I'd quit anyway. $25 completely changed my behavior.
Kiri is that system built for other people who have the same problem. You commit to a work session upfront and if you don't submit proof you completed it you get charged real money. Happy to answer anything or hear what you think.

value: Show HN: I built an app that charges you real money when you miss your goals


value: helmterminal

value:
Hi HN,
I'm the solo founder of Helm Terminal (helmterminal.dev). It's a free portfolio analytics tool that connects to your accounts via Plaid and surfaces actionable insights and intelligence. Concentration risk, tax-loss harvesting, earnings exposure, idle cash, sector drift, etc. Automatically, every day.
Every other portfolio tracker/brokerage I've tried showed me balances and pie charts but never informed me what to do or what was going on. I'd check my accounts, see things were generally "fine", and move on. Then I realized I'd been sitting on thousands of dollars in unharvested tax losses, 60%+ of my portfolio was exposed to tech, and I didn't know half of my positions were reporting earnings soon.
Helm is the tool I built to fix all of these issues, and a tool I wish existed. It runs on a set of intelligence engines on top of your connected accounts and gives you a prioritized inbox of things worth doing, as well as news tailored to your holdings worth looking at. "AAPL down 12.3%, Harvest $3,582 before Dec 31, Tech sector exceeds 60% concentration threshold, consider rebalancing." etc.
Stack is Next.js + Supabase + Plaid + Polygon, deployed on Vercel.
Free stock analysis tool at /analyze(any US ticker) that doesn't require a signup if you want to check it out. https://helmterminal.dev/analyze
Free, no card, no account.
I've also launched on Product Hunt today if anyone wants to check it out there: https://www.producthunt.com/products/helm-terminal-2. But the product itself is helmterminal.dev
Would love feedback from the community especially on risk detection logic and whether the action prioritization feels right.

value: Show HN: Helm Terminal – Free portfolio intelligence that tells you what to do

author: helmterminal


value: DataMind

value:
Hey Everyone,
I have been on the platform for years and mainly used HN for tracking startups but,never posted. Hope this resonates with anyone out there thats been struggling finding a new job.
After over a decade of tech recruiting in big tech and seeing the rise in layoffs and the rise AI in hiring, its become impossible to become noticed in a sea of applicants. Linekdin imo has become a professional facebook where followers are all that matters and the genuine human connection is dissapearing.
Over the last few months, I have started the journey to build an intitial platform to continue the Company - Candidate engagement and to gain verified feedback. It's the first MVP but,I appreciate any insights.
The Product:
The tech interview process produces almost no useful signal for candidates. Rejections arrive as form emails or silence. Skills go unvalidated. Growth stalls. Loopback sends a structured feedback request to the recruiter on the candidate's behalf — building a verified Live Resume backed by real interview data over time. Free for candidates. Would love honest feedback from anyone who's been through the process recently.
Im not trying to fix hiring but more so fix accountability... in addition to driving structured feedback to improve career growth.

value: Show HN: Loopback – Verified interview feedback that builds your live resume

author: DataMind


value: Show HN: Incidentary – see what caused your incident before the war room starts


value: vincent-uden

value:
Miro is a native pdf viewer for Windows and Linux (and macOS) built with iced and mupdf-rs. You may remember it from my slightly aggressively titled post "I hate acrobat" (https://news.ycombinator.com/item?id=45598776).
Anyways, since then I've release 2 whole new versions with the aid of many helpful contributors. Most recently is a presentation mode to hide the UI and a toggle for fullscreen mode.
Code is found at: https://github.com/vincent-uden/miro

value: Show HN: Miro-pdf v0.8.0 – Fullscreen, presentation mode and friendly keybinds

author: vincent-uden


value: podlp

value:
Hi HN! I built an on-device coding agent called Junco, designed to explore what's possible using the AI (Apple Intelligence) you already have on your Mac.
Junco is a ~9MB Mach-O binary written entirely in Swift using the LanguageModelSession API. It's primarily an exploration and learning exercise for me, but it's also exciting to see what's possible. A clear pattern emerged: deterministic scaffolding is critical to guiding a small model along. Whereas Claude Code can defer task decomposition to Opus 4.6, small models need a lot more hand-holding.
Of all the techniques I explored, the Compile-Verify-Fix (CVF) loop was clearly a winner. Even when I was just prompting the AFM directly, I always knew I would need some self-healing mechanism because the Apple Foundation Model (AFM) was not designed to code. When it wrote code, it was often indented strangely and riddled with minor syntax issues.
I initially set out to build a general-purpose coding agent, but given the AFM was not tuned for coding, I quickly realized that scope would never work so I focused specifically on Swift. It also helps that XCode ships with API headers that allow for on-device API signature discovery, since the model lacks recent world knowledge (the knowledge cutoff is probably around mid-2024).
So I wouldn't recommend Junco anytime soon for production work. Make sure you've committed any changes before playing around with it. But conceptually the idea of a batteries-included, on-device coding agent shows real promise.

value: Show HN: I built a local coding agent using Apple Intelligence

author: podlp


value: iamnotstatic

value: Show HN: Vibetime – Track what you ship in AI coding sessions

author: iamnotstatic


value:
(The author is a work colleague, I'm posting on his behalf due to "Show HN" limitations for new accounts)
During my recent projects with OpenTelemetry, I was looking for a truly practical tool for local development.
I found several existing solutions —some very powerful— but for my day-to-day local use, they were often too heavy, not simple enough to install, or not quick enough to get started with.
So I decided to build my own dedicated tool: otel-gui.
This is not a complete observability suite nor a telemetry backend designed for large-scale production or long-term retention.
It's a tool designed for developers who want to debug efficiently locally.

value: Show HN: Otel-GUI – an open source OpenTelemetry viewer for dev and debug


value: robvanderleek

value:
MDServe renders any GitHub Markdown file instantly in the browser.<p>Development just started, looking for feedback on:
- Use cases I haven't thought of
- Markdown files that break it
- UX additions (automatic ToC? dark mode? configuration file support?)

value: Show HN: MDServe – Instantly render any GitHub Markdown file in the browser

author: robvanderleek


value: caudena

value:
Hi HN.
We built NoxScan as a side product to solve our own problem.
Backstory: we're SOC 2 compliant, which means we have to run continuous vulnerability scanning on our own infrastructure. We were paying €700/mo to a well known scanner for 50+ IPs. For that money we got full port coverage, but scans ran once a month on top of basic Nmap. No service enrichment, no smart filtering. Just raw output that we had to triage by hand. Their cheaper tiers were worse: capped at the top 1,000 ports.
What we actually wanted was three things: full 65,535 port coverage, daily scans instead of monthly, and output that a human didn't have to hand filter. That combination didn't seem to exist at a price that made sense for our size, so we started building it ourselves. After running it internally for a while we realized other small SOC 2 and ISO 27001 shops almost certainly have the same pain, so here we are.
We started with masscan. It's the obvious choice for sweeping 65K ports across a lot of hosts. It's fast, but under load it drops open ports, and context switching eats into throughput when you're trying to push line rate on cheap hardware.
Then we read c3l3si4n's Phrack article on high performance scanning with AF_XDP (Phrack #72, article 5) and it was pretty much exactly what we needed. The short version: attach an eBPF program to the NIC's XDP hook, set up a shared UMEM region with a four ring mailbox (RX, TX, FILL, COMPLETION), and you get zero copy packet I/O between userspace and the driver with almost no syscall overhead. The eBPF side handles SYN/ACK matching at the XDP hook, so we never push every response up to userspace. Rate limiting is done per destination ASN instead of globally, otherwise a few large targets will starve everything else.
For now we run masscan and our AF_XDP scanner in parallel. Masscan is still the battle tested workhorse doing the bulk of production scans, and we cross check its findings against our AF_XDP path on every run. The Phrack benchmarks line up with what we see on our own infra: AF_XDP is faster and more accurate in dense scans against a single host (c3l3si4n measured ~1.3s vs masscan's ~2s on a full 65,535 port scan, and masscan dropped ports at that rate while AF_XDP didn't). Once we're confident our AF_XDP path is stable across the mix of NICs and kernels our customers actually run, we'll switch to it fully. Right now the redundancy is worth the extra compute.
Port open/closed on its own is almost useless. What you actually want to know is "what's running on that port and which version is it." For that we use ZGrab2 with a set of custom probes we wrote for the services we care about: HTTP and TLS of course, but also etcd, Consul, Prometheus, unauthenticated Redis, exposed Docker daemons, and similar. ZGrab2 is underrated. It's pretty much what you'd build yourself if you had to fingerprint thousands of services per second, and the output format is cleaner than Nmap's NSE.
Once we know what's running, we enrich it with known CVEs and assign severity. Web services also go through Nuclei. Nuclei is the best thing that happened to vuln scanning in years. Compared to OpenVAS or ZAP, which will happily flag every missing X-Content-Type-Options header as a vulnerability, Nuclei's templates are version aware and the signal to noise ratio is much better.
Even Nuclei produces noise in aggregate. We route anonymized findings through an LLM that cross references against the NVD CVE database, deduplicates related findings, and assigns severity based on actual exploitability in context (exposed to the internet vs. behind auth vs. requires specific config to trigger).
Last piece: because we're SOC 2 compliant ourselves, the PDF reports are in the exact format our auditor asked for. CC7.1 mapping, timestamps, scope definitions, remediation tracking. Basically the report we wish our old scanner had been producing.
HN folks can use LAUNCH30 code during checkout.

value: Show HN: Noxscan.io 65K Port and Vuln scanner with LLM false-positive filtering

author: caudena


value:
In 1915 Malevich painted a black square and called it the zero point of art. Everything decorative, everything unnecessary — eliminated. Someone needed to do the same thing to the smartphone.
The Square is that attempt.
It's a PWA. You install it, pick a daily time, and when the notification arrives your screen becomes a black square with a timer. For one hour you don't touch the phone. If you switch apps or close the screen, the session ends. You try again tomorrow.
No account. No email. No data collected. Anonymous by design.
Whether this is useful or pretentious is up to you.
thesquare.is

value: Show HN: A PWA that destroys itself if you touch your phone for one hour


value: Show HN: Zero human company in Go stack

title: Show HN: Zero human company in Go stack


value: IssueCapture

value: Show HN: IssueCapture – AI-powered bug reporting widget for Jira and JSM

author: IssueCapture


value: Show HN: MobileCodeAI – Professional AI code editor for iPhone


value: koubaki

value: Show HN: I built a 2006 twttr clone with no replies, likes, or algorithms

author: koubaki


value: joshryandavis

value: Show HN: Captcha to detect bots with a simple question

author: joshryandavis


value: pdrgds

value:
I built a Rust CLI that takes an EPUB and rewrites it at a target CEFR level (A1–C2) using an LLM. Give it a German novel and ask for Portuguese at A2 — it produces a new EPUB ready for your Kindle.
For beginner levels (A1/A2), it runs a two-pass pipeline: first simplify in the source language, then translate. This produced 15–25% better vocabulary coverage than doing both in one shot.
It uses Groq or Ollama (local) as backends. A sample book (Kafka's Metamorphosis) is included so you can try it immediately.

value: Show HN: Nheengatu – CLI tool to simplify books to your language level with LLMs

author: pdrgds


value: Show HN: I built a desktop app for building and debugging MCP tools


value: alxstn

value:
I kept copy pasting between two Claude Code instances. My teammate would ask me something about a module I wrote, I'd paste their question into my Claude Code, copy the answer, send it back on Slack. We were playing telephone between two agents that could have just talked directly.
So I built AgentDM. It's a hosted messaging grid where AI agents DM each other by @alias.
Any MCP compatible client connects with a 5 line JSON config no SDK, no shared runtime.
This is how it works:
- Each agent gets a unique @alias.
- Three(main) MCP tools: send_message, read_messages, message_status.
- Messages encrypted with AES-256, deleted after delivery.
- Guardrails (static + LLM powered) filter messages before delivery.
Last week we shipped an MCP/A2A protocol bridge. Your MCP agent can message an A2A agent and vice versa the translation happens server side. Neither agent knows or cares what protocol the other speaks.
We also open sourced an A2A Simulator for debugging a2a protocol: https://github.com/agentdmai/a2a-simulator
https://agentdm.ai

value: Show HN: AgentDM – Agent to agent messaging over MCP and A2A

author: alxstn


value: mantyx

value:
Hi all! This is a very early project and with the launch of Claude Managed Agents, alternatives are on the rise. MANTYX Provides a more user friendly way to define, run and manage your Agents at scale.
Connect and interconnect your favourite tools, expose your agents to other and centralize your MCP registry to interconnect your different tools.
Welcoming feedback!

value: Show HN: Mantyx – Agentic business automation platform

author: mantyx


value: drcursor

value:
After trying multiple apps that promised full automatic local transcription of meetings but ended up using remote AI, OpenRouter, or requiring Ollama to be running in the background.
I decided to fork another project and make it as straightforward to use as possible.
It just runs on the background, and transcripts calls automatically when they happen and saves them as md files I can use in obsidian or anywhere else.
I still need to fix some tech issues, and make local AI summaries actually useful, but in the meantime, I have the app I've wished for a long time. Hope others find it useful.

value: Show HN: HushScribe – Self-contained local meeting transcription for macOS

author: drcursor


value: tamnd

value: Show HN: Browsable index of every lecture and talk from the CMU Database Group

author: tamnd


value: eummm

value: Show HN: Guruka.com – free guided mediations. No signup, private, works offline

author: eummm


value: tinyprojects

value:
Hi HN,
I'm Ben / Tiny Projects (I once posted here about buying 300 emoji domains from Kazakhstan…).
For the past 3 years I've been solo bootstrapping PromptBase, an AI marketplace with 450k+ users. At the peak, I was burning $9,400/month in opaque Stripe Connect fees for seller payouts, so I built Zoneless to replace it:
- GitHub: https://github.com/zonelessdev/zoneless
- Website: https://zoneless.com
Zoneless is a free, open-source (Apache 2.0) drop-in replacement for the payout part of Stripe Connect. It allows you to pay marketplace sellers globally with stablecoins (USDC) using an identical API to Stripe and at near-zero fees.
I've been dogfooding Zoneless on PromptBase for 3 months with some good results:
- 2,200+ sellers onboarded
- 1,400+ payouts completed
- Monthly payout fees reduced to just a few dollars
- 73% of sellers, when given the choice at onboarding, actively picked Zoneless over Stripe Connect
A massive part of running a marketplace is paying sellers. While Stripe Connect is a great product, it has big pain points:
- Expensive + complex fees: $2/mo per active account, 0.25% + $0.25 domestic payout fee, $1.50 international payout fee, 0.25–1.25% cross-border fee, 0.50–1% FX fee. It costs >$2 to move $1.
- Limited reach: Only supports around 47 countries.
- Slow payouts: Takes 2-7 days to settle.
- Platform risk: A massive single point of failure if your account gets randomly flagged.
Zoneless is designed to solve all this:
- Payouts cost ~$0.002 on Solana
- Global: 220+ countries/regions
- Instant payouts, 24/7.
- Self-hostable and open-source
The API/SDK is identical to Stripe (same webhook events, same object shapes, etc.). If you know Stripe, you already know how to use Zoneless. There’s also an Express Dashboard for sellers to onboard and track their earnings.
I've been able to remove annoying things on PromptBase like forcing sellers to accrue a $30 minimum balance before a payout just to keep our costs down. I can now also onboard sellers from more countries, which has helped spread the word and grow the buyer side too.
A big worry was that non-crypto users would be confused or hate getting paid in USDC, but they actually don’t mind at all, they just care about being paid faster. If they want to convert to their local currency, they simply use an exchange like Coinbase.
Zoneless is self-custodial, meaning you create and own your wallet, and the code never touches funds. You can also easily plug in providers for KYC/AML.
I appreciate that anything related to crypto is like Marmite (pretty polarizing); I’m a no-coiner and have never dabbled in NFTs, but I do think stablecoins are different: they’re just boring tech to move money around cheaply.
I'd love to hear your thoughts, feedback, or questions - especially if you've dealt with Stripe Connect / payouts / marketplaces before.
- GitHub: https://github.com/zonelessdev/zoneless
- Website: https://zoneless.com
- Docs: https://zoneless.com/docs

value: Show HN: Zoneless – Open-source Stripe Connect clone with $0.002 fees using USDC

author: tinyprojects


value: Arham-Begani

value:
I built Forze after running into the same catch-22 repeatedly: you need market research and branding to know if an idea is worth building, but you can't justify spending weeks or thousands on that until you know it's worth it.
Forze runs four AI agents in sequence — market research, brand identity, landing page copy, and a feasibility score. The whole run takes under 10 minutes.
Try it: https://www.forze.in
Happy to answer questions about the agent architecture or anything else.

value: Show HN: Forze – A platoform that turns a startup idea into a product in mins

author: Arham-Begani


value: Bangaroojack

value:
Your cloud bill spiked 480% overnight. Nobody on the team knows why.
If you have ever been that person at 9am staring at a Cost Explorer chart with no answers, this is for you. DevOps engineers, platform teams, FinOps practitioners, CTOs who own the cloud budget but do not live in the AWS console.
---Why not Cost Explorer---
Cost Explorer is a good tool if you already know what question to ask. It shows you that a bar went up. It does not tell you which service drove it, why, or by how much relative to your baseline. You still have to manually drill through filters, and the output is a chart, not an explanation you can share with anyone.
Dropping the export into ChatGPT does not solve it either. It has no context about your baseline or spending patterns. It is a one-off, not a workflow.
---What you get---
Upload your billing export and you get back a ranked breakdown of what changed: which services, by how much in dollars, and what likely caused it. There is a before/after comparison, per-service deltas, and plain-language cause summaries. Results are algorithmic approximations meant as a starting point for investigation, not a black box.
Supported formats: AWS CUR 1.0 and 2.0, FOCUS 1.x. Azure and GCP are supported via FOCUS export from your billing provider.
---How it works---
You upload a billing export. The tool parses your line items, establishes a baseline from your prior billing period, and compares it against the current period at the service and usage type level. Cost changes are ranked by dollar impact, and for each driver it looks for signals in the data that explain the change: new resources appearing, usage volume shifts, unit price changes, or coverage gaps like a Reserved Instance expiring.
The output is a report, not a dashboard. You get a ranked list of what drove the spike, a dollar figure for each, and a plain-language explanation of what the data suggests happened. You can share it with anyone without giving them access to your AWS account.
---Features---
Ranked cost driver breakdown with dollar deltas and cause summaries
Before/after period comparison with confidence score
Full analysis history across billing periods
Multi-file upload for linked accounts (paid)
Webhook delivery to Slack, Microsoft Teams, or any HTTP endpoint (paid)
---Known shortfalls---
It requires two billing periods in the file to do a comparison. If you only have one month of data the analysis will still run but confidence will be low and there is no baseline to diff against.
Cause inference is based entirely on signals in the billing data. It does not have access to your CloudWatch metrics, deployment history, or config changes, so it can tell you that a new resource appeared and cost spiked, but it cannot tell you which deploy or engineer caused it.
Very large files with millions of line items take longer to process and occasionally time out on the free tier. If you have a CUR file above a few hundred MB, compressed is strongly preferred.
Azure and GCP support depends on your billing provider exporting in FOCUS format. If they do not offer that yet, the tool will not work for those clouds.
---Privacy and security---
Your billing file contains your entire infrastructure footprint, spending patterns, and service usage. Files go straight to encrypted S3 via presigned URL, bypassing our servers entirely, and are deleted after processing. The raw file is never retained. Your data is never used for training or shared with anyone.
Accounts start with 5 free analyses during beta. After beta that drops to 3. Beyond that, credit packs are one-time purchases with no expiry.
If you have a billing export from a spike you already know the answer to, upload it and tell me whether the report gets it right. That is the most useful feedback right now.
https://billspike.io/
Thanks yall

value: Show HN: BillSpike – Root cause analysis for cloud cost spikes

author: Bangaroojack


value:
My brother turned 39, I shipped his gift from a shop directly to him (we live in different countries and couldn't travel home due to my son being born)... Shop wouldn't let me add a postcard. So I went looking for a quick e-card service and every single one was painful; Hallmark wants a subscription, Paperless Post has a coin system, Canva technically works but you're designing a greeting card in a full design tool.
I wanted the tinyletter version of this. You write something, pick a photo, get a link; that's it. No account, no email wall and nothing to install. The recipient taps the link and the card opens with a little animation (flip, envelope, fold :D I (Claude) spent way too long getting the CSS 3D to feel right on mobile).
Built on Cloudflare Workers + D1, React Router 7, images from Unsplash. Free cards expire after 14 days. I added a $3.99 one-time premium if you want to schedule it: but the payment doesn't work right now :) ie didn't have a chance to hook up to Stripe yet.
I built this while my newborn son was sleeping next to me.

value: Show HN: TinyCard – The TinyLetter of Greeting Cards


value: Show HN: 81% on LongMemEval with Pgvector and BM25 – Memory for Claude Code


value: Show HN: Our Next Decade – A curated forecast of the next ten years


value: adcent

value: Show HN: Postagent – Postman CLI, but for AI Agents

author: adcent


value: donge

value:
Built a fully offline Chinese voice assistant that runs entirely on-device (no server, no cloud).
Pipeline: VAD (Silero, 16kHz) → LLM (Gemma 4 2B via LiteRT-LM on Snapdragon GPU) → TTS (sherpa-onnx + matcha-icefall-zh-baker, 22050Hz) → speaker. Barge-in interrupts TTS mid-sentence.
Demo video in the README. Code and full setup instructions on GitHub.

value: Show HN: Qiaohu – offline multimodal voice assistant on Snapdragon 8 Gen 2

author: donge


value: TheZupZup

value: Show HN: SilentGuard: A lightweight Linux network monitor with TUI and GUI

author: TheZupZup


value: michaelquigley

value:
MCP servers work great locally over stdio... until you need one on another machine, or want to share tools across a team. Most approaches to that involve exposing endpoints and managing network access. I built a tool that keeps remote MCP as simple and secure as local stdio.
mcp-bridge wraps any stdio MCP server in one command and shares it over a zrok/OpenZiti overlay. mcp-gateway aggregates multiple backends... local stdio servers and remote shares... into a single connection with a unified, namespaced tool registry. Permission filtering removes tools from the schema entirely... they're not checked at runtime, they're gone from the registry. No listening ports, no VPN, no port forwarding. Your MCP server doesn't change.
Go, Apache 2.0, single binaries, part of the OpenZiti project. Works seamlessly with the llm-gateway I introduced a couple of weeks ago.

value: Show HN: MCP Gateway – Zero-Trust Access to MCP Tool Servers

author: michaelquigley


value: sohaibtariq

value:
Hi, we're Sohaib and Hannan from APIMatic. We've been building tools to help Developers integrate with APIs for 5+ years at APIMatic. We're now trying to help AI agents do the same.
This started from a conversation at PayPal DevDay 2025. The PayPal developer experience team were monitoring developers using AI agents to integrate PayPal APIs, and the agents kept reaching for outdated docs and deprecated SDK versions, often falling back to training data instead of reading the current API documentation.
We tested this ourselves with Cursor. Across multiple iterations, 87% of runs fetched outdated reference docs using web search, and 13% of implementations used a deprecated SDK version.
What we built:
We built Context Plugins - given an OpenAPI spec, we generate SDKs and a remote MCP server that exposes language-specific API context (auth flows, method signatures, error handling, pagination, integration patterns from the API team) packaged and optimised for LLM consumption. Instead of guessing from training data or relying on web search, coding agents get the correct SDK methods and integration patterns they need to build API integrations.
We've just launched in Beta with PayPal: https://developer.paypal.com/serversdk/java/getting-started/...
We benchmarked Context Plugins on two codebases, nopCommerce (a mature ASP.NET e-commerce platform) and Microsoft's eShop reference app, and saw 2x faster integration time, 65% lower token usage, and near-zero hallucinations.
Getting started:
- We've published ready-to-use Context Plugins for Spotify, Twilio, Slack, and other APIs; the Show HN link takes you to our product showcase with one-click install for popular AI agents.
- Want to generate Context Plugins from your own spec? Sign up for a free 2-week trial on the APIMatic website, no credit card required.
- Want to dive deeper into the data? Read more here https://www.apimatic.io/product/context-plugins/case-study
We're really looking forward to hearing the community's feedback on this. We're particularly interested in hearing from API platform teams seeing this problem with their users, developers who've fought this problem manually or anyone who's tried building something similar.
What are we missing? What would make this more useful?

value: Show HN: AI agents are bad at API integrations – we fixed it

author: sohaibtariq


value: unvalley

value: Show HN: Cognitox – open-source Amazon Cognito emulator written in Rust

author: unvalley


value:
The idea: I wanted a place to capture my memories of kitesurfing session, runs, hikes and dives with a few notes and photos so I built it myself and put it to the App Store.
Native SwiftUI, iCloud sync, HealthKit import. Free, no account, no subscription.<p>I'm happy to hear your feedback.

value: Show HN: SweatDiary – simple workout journal, native iOS and macOS app


value: himanshudongre

value: Show HN: PropOps – AI agent that reads Indian govt property data nobody checks

author: himanshudongre