Show HN: FEDERaiDE, a TUI harness with P2P multi-agent routing and built in IDE. Hi!
Federaide is a general purpose multi-agent harness that runs in your terminal. It is meant for recreational programming and automating your scripts. The agents are just named instances of language models which have their own memories, backstories et cetera, and can coordinate with each other as they require. It has its own IDE (complete with interactive structure parsing and jump to def).
I am building Federaide as a solo project.
Why do I build this? Some reasons:
- I reached out to some people to try my other IDE, many of them did not have suitable computers (or at all).
- During this time I observed almost every normal person has interacted with AI via their phones (usually the Gemini/ChatGPT app).
- I felt I could bring the joy of coding to a lot more people if I could make an AI assisted IDE run on Android.
- When I go to sleep I like to try out code ideas. Can't take my macbook to bed, have cats. Do have a spare Android, thought it would be nice to have a coding platform I could use in bed.
- Wanted to "vibecode" something, at the time gemini-cli was available, so this was the chosen project (tried a lot of concepts/variants, in rust go etc). Ultimately chose textual over a very simple reason: horizontal scrollbox (absolutely needed that for code display, may be OCD but linewrap does not do it for me).
- Wanted to create a truly powerful AI agent system without worrying about safety. Complete unabashed power was desired.
- Therefore termux native operation was a huge design goal.
- Kept wanting new features, kept adding them too.
- When I started this (in March) multi-agent harnesses were not a thing (arguably they still aren't mainstream, but there are other projects out there now). Wanted to see how different LLMs would react to each other in the same workspace.
- So here we are. It is at an early stage, there are often bugs, but if you find them, I will fix them. Thought I'd share it, it can be daily-driven at this point.
Does it solve a burning problem? Not really. Atleast not one other harnesses don't already (other than maybe providing a performant competent IDE for termux). Is it fun? Fuck yeah. Watch your agents delete the workspace while you sleep. And the excuses are gold :D
Well okay I do want to automate parts of wellsite data processing, the multimodal component of the active skills system is a step towards that, I mean what is geology if not hoarded knowledge and image processing on wetware? But the current models are so far not great at this stuff (I thought I would demo it, but so far the performance has not been up to my standards. Oh well will keep trying.)
This is completely free of MCP. Or any other borrowed compute as much as possible. Except the LLMs ofcourse, though I have tested with Llama.cpp. Everything that can be done on device will be done on device. No logins or email ids or anything like that (unless you count the ChatGPT subscription Oauth, which is not compulsory).
What do I use it for? I mostly use the research tool. Trying the paper trading (active skill) out has been fun (it was made on request for a friend). I like to experiment with orchestration, trying new ideas about memory and stuff like that, with the long term goal of making LLMs learn things outside of fine tuning or direct training. Running go/rust/python code directly on a phone has been fun.
Docs: https://federaide.rocklab.in/manual.html
Github: https://github.com/ROCK-LAB-PRIVATE-LIMITED/federaide
I have not put technical details here for brevity. Feel free to discuss them if you like.


Show HN: Editor UI toolkit for building AI writing workflows.


Ask HN: €300/mo to capture work you're doing. Tips how to reach right people?. Hey Everyone,
My name is Salan and I'm a Researchrer at https://pdoom.org/ an early-stage AI Research Lab building LLMs for long-horizon tasks in Europe. We were funded by the German Government (SPRIND).
Currently, we are trying to acquire data, specifically from people who do open-source work/research. Therefore, we pay people €300 per month to capture their digital workflows.
We also built a custom tool that is privacy-preserving specifically for capturing these workflows: Get paid to record your work · p(doom)
Since we have a high quality bar and need really strong people in their fields, finding participants hasn’t been easy. Do you guys have any tips or ideas on how we can successfully reach these profiles?
Any help is highly appreciated!


Show HN: Captain, AI Travel Agent. I’m a software designer from Lagos exploring conversational interfaces and frameworks for building agents. I made Captain to help with everyday travel planning. It can explore an itinerary, check live flight prices, or watch for the right time to book. Captain is a Telegram bot powered by a generalist model (anthropic/claude-sonnet-5), with specialist models handling workflows such as trip interpretation and voice transcription. It also includes a visual workspace where users can review the agent’s work and directly manipulate their travel information. I'm building the agent to explore how conversation, tools, durable workflows, and direct manipulation can come together in one coherent product.


Show HN: Agents that run your finances. Hey HN. My name is Faiyam, cofounder/CEO of Blaze Money (YC S24). We're sharing here something new we've been working on in the agentic finance space for businesses. TL;DR -- you can use Blaze Money to run your business's finances agentically.
'Twas a long journey to get here. In 2023 we started in cross border payments after nomading around the world and feeling the struggle of paying international contacts. In the time since we started though the world has completely changed and now it seems obvious we want agentic money the way we want agentic everything else.
We've built an agentic finance offering for businesses. Our vision is to do both, something for individuals to manage their finances agentically and also something for businesses. We're getting started with businesses first.
We were inspired to build this for multiple reasons. One is, like I mentioned earlier, we were already in the money space, so we were thinking about products in that space as is. At the same time we were building so much internal AI tooling for our own dev and business workflows that we had become completely accustomed to just doing everything through Claude (our harness of choice). It almost feels archaic to now need to open up a banking app to send money or use an app to request funds from someone. We wanted to just be able to text an agent "hey send vendor X the funds for their latest invoice," and be able to get that done.
Also, another thing that kept coming up is that we're a super lean team, and I in particular would spend a lot of time, and more importantly mental energy, each month just trying to manage our finances. It was an always pervasive anxiety that I had to deal with which would slow me down. I want to be able to just chat with an AI about our finances to get clarity, make plans, do payments, get all that stuff off my head.
We made it accessible a bunch of different ways. You can install it via npx @blaze-money/cli and use it via Claude Code, Codex, etc., you can tell your Hermes/openclaw to "install npx @blaze-money/cli" and get me setup, or you can use it in a good 'ol fashioned UI (so 2023, I know) on our site if you want as well.
Here are some of the things you can do. Once you've connected all your accounts, you can do a lot of the finance and analysis work your fractional CFO would otherwise do, like forecasting or runway planning. You can do preaccounting to help your accountant get things done faster at month's end. And since we started in payments, we've built that in so you can send and receive money globally via a bunch of rails, some traditional some using stablecoins, to settle all your finances and not just analyze them.
I'm really proud of what we built because I already use this regularly and get value from it, we have friends and family that are already using this, and we want to now share this with a wider community to get more eyes, ears, feedback, whatever you have to share.
Thanks for taking the time to take a look at what we've built and please feel free to give us your honest feedback, suggestions, feature requests, or philosophical pontifications on the nature of money in a post digital superintelligence world...


Show HN: Pyrig – A tool that automates project setup and maintenance. # What is pyrig?
pyrig is a package and tool that *rigs up* your project. It scaffolds and initializes a complete, fully configured, installed and working Python project with one command and makes the process of developing and maintaining it more seamless and efficient by automating things like configuration management, CLI generation, testing infrastructure, and more.
# Requirements
* Python 3.12+
* Git
* uv
# Quick Start

    uv init my-project --python 3.12
    cd my-project
    uv add pyrig --dev
    uv run pyrig init

See the [Getting Started Guide](https://Winipedia.github.io/pyrig/getting-started) for detailed setup instructions to also fully integrate with GitHub and CI/CD from the start.
# Features
# [Project Scaffolding & Initialization](https://Winipedia.github.io/pyrig/scaffolding)
`pyrig init` generates a complete project in one command that works out of the box. This includes everything a modern python project needs:
* Standardized directory structure
* Fully configured dev tools (linters, formatters, type checkers, test frameworks, git hooks, etc.)
* End-to-end CI/CD pipeline with GitHub Actions and integrated repository protection
* Complete and working CLI
* And much more...
# [File & Configuration Management](https://Winipedia.github.io/pyrig/config-files)
Every generated file is backed by a Python class that validates and merges automatically. Override any config by subclassing, or define entirely new config files — pyrig discovers and manages them for you. Run `pyrig sync` to create or update all config files at once. Run `pyrig mk subcls` to generate a subclass for overriding a specific file.
# [Automatic CLI](https://Winipedia.github.io/pyrig/cli)
`pyrig init` sets up a CLI for your project that works immediately. Generate and add new commands by running `pyrig mk cmd <name>`. An automatic version command is included that shows the version of your project. Run `my-project version` to see it in action.
# [Mirror Test Generation & Maintenance](https://Winipedia.github.io/pyrig/mirror-tests)
Generate test skeletons with `pyrig sync`. This will generate test skeletons for all source modules and update them automatically as your project evolves.
# [Multi-Package Inheritance and Extensibility Architecture](https://Winipedia.github.io/pyrig/architecture)
Override and customize any and all behaviour to suit your project's needs. pyrig's classes are designed for inheritance and composition, allowing you to create custom configurations, tools, and more by subclassing and simply overriding methods. pyrig will automatically discover and use your custom classes without any additional configuration. Run `pyrig mk subcls` to generate a subclass for any pyrig class.
# [CI/CD & Repository Protection](https://Winipedia.github.io/pyrig/ci-cd)
Pyrig generates GitHub Actions workflows for CI/CD which automatically test and release your code. They also configure and apply repository protection settings and protection rulesets. Push your code to GitHub after initialization and see it in action.
# Commands
Run `pyrig --help` to see a list of all available commands and their usage. Run `pyrig <command> --help` for more information about a specific command and its usage. Run `my-project --help` to see the automatically generated CLI for your project.
# Documentation
|[*Full Documentation*](https://Winipedia.github.io/pyrig)|The manually written documentation|
|:-|:-|
|[*CodeWiki*](https://codewiki.google/github.com/winipedia/pyrig)|AI-generated documentation|
|[*Tutorials*](https://www.youtube.com/@Winipedia-py/playlists)|YouTube tutorials for pyrig|


Show HN: OptiQra – Open-source AI website optimization and intelligence platform. Hi HN,
I've been building OptiQra for quite a while, and I finally finished the desktop version.
OptiQra is an open-source website intelligence and optimization platform. It can crawl and analyze websites or local projects, identify SEO/AEO/GEO, accessibility, performance and technical issues, explain them with AI, and automatically fix them.
The desktop version is where the project gets particularly interesting. It can run locally, work with local projects, operate offline for analysis that doesn't require cloud services, and run scheduled scans in the background.
The workflow is roughly:
URL/project → crawl → analyze → understand the issues → inspect them on the actual site → fix them → verify the result.
It also has a fairly visual crawler, including live 2D and 3D crawl trees, and the AI interface is customizable rather than being limited to a single assistant personality.
I built the UI and CSS myself rather than using Tailwind/shadcn, and the project has grown considerably beyond a simple SEO checker. There are currently 150+ analysis rules along with integrations and additional tooling.
This is the first release where I feel the desktop application is actually ready for people other than me to use.
GitHub: https://github.com/armin5872/OptiQra
website : https://optiqra.vercel.app
I'd especially appreciate feedback from people who work on large websites, developer tooling, SEO, or web infrastructure.
I'm also interested in hearing what you think is missing or what you'd remove. The project is large enough now that outside perspective is probably more valuable to me than another feature I can think of myself.


Show HN: Argonix – One AI-powered platform for SRE and cloud operations. Hi everybody,
I built https://argonix.io.
Argonix is an AI-powered platform for SRE, security and cloud operations.
The idea is to bring and connect together things that are usually spread across many different tools: monitoring, security, Finops, health checks, workflows and incident investigation.
A few things you can do with it:
Connectors: Connect Argonix to your existing stack: Kubernetes, AWS, GCP, Azure, your own MCP servers, etc.
Workflows:  Build automation flows using the connectors.
Health notebooks: Get a daily overview of the health of your platforms. Argonix can publish the report to Slack, Confluence, etc., and create tickets when something needs attention.
Periodic jobs: Schedule recurring checks, analysis and automation.
Security: Scan of your connectors infras, your code also (new feature), analyse network logs, and look for  vulnerabilities and threats. It can also consume data from tools such as Falco, Wazuh, Wiz, SonarQube, etc.
FinOps – Analyse cloud costs across AWS, GCP and
Azure.
Monitoring: Monitor works just like UptimeKuma or others, plus automatically discover and monitor Kubernetes ingresses/gateways, run synthetic tests, and analyse logs.
Alert investigatio: You can send alerts from Datadog, Grafana and others to Argonix. The AI can investigate the alert, read the KB  and provide recommendations or even propose a PR.
Conversational AI: Talk to your infrastructure and connected tools through a conversational interface.
Under the hood, Argonix is built with Python/DRF, Vue.js and PostgreSQL.
Argonix is available as both a SaaS and a self-hosted version.
You also choose which AI model you want to use. You can connect your own AI provider/account, and with the self-hosted version you can run a local model. I've been testing it locally with Qwen.
You can create an account for free and start playing with it.
It's still a work in progress and there are definitely some bugs, so I'd really appreciate feedback from people working in the field.
I would love to hear what you think, what you would change, and what would make it genuinely useful in your day-to-day work.
Thanks,
David.


Show HN: Salestrics – An open MCP server and CRM for AI-native revenue teams. Hey HN, I’m Austin, founder of Salestrics (salestrics.com).
The Problem: > Most AI agents today are trapped in chat windows. While models are smart enough to run complex multi-step workflows, giving them production access to business context (CRM, support, billing) usually means managing unsafe local API keys or hacking together fragile point-solution scripts.
What We Built:
We built Salestrics to serve as both an all-in-one revenue workspace (CRM, service desk, billing, docs, email) and a production-grade Model Context Protocol (MCP) execution proxy.
How It Works:
Unified Context: Instead of fragmenting data across five SaaS tools, customer records, tickets, and invoices live in a single data layer.
165-Tool MCP Server: You connect your local AI environment (Cursor, Claude Desktop, local LLMs) once via our MCP proxy.
Full-CRUD Execution: Your agent gets zero-config execution access across native apps and third-party tools (Stripe, PostHog, Sentry)—allowing it to do things like resolve support tickets, issue refunds, or update pipeline stages directly from your IDE or chat client.
Human-in-the-Loop Governance: High-impact agent actions (deleting records, mass messaging, issuing payouts) trigger explicit approval gates and immutable audit logs before execution.
Traction & Tech Stack:
We launched two months ago and currently power 320+ active organizations. The backend is built with high-throughput node/TypeScript orchestration, connected to a dual-model AI routing engine (Salestrics-AI-v1/v2).
Try It Out:
We have a Free Forever tier for solo builders. You can grab your MCP keys and test the proxy immediately without putting down a credit card.
I’d love to hear your thoughts on our MCP architecture, human-in-the-loop security patterns, or what tools/actions you'd want added to the MCP server next!


Show HN: Physicsbase.ai Validated Physics for AI. HI hackernews community,
I introduce physicsbase, which is a tool that allows ai to carry out numerical simulation of phyiscs problems using Finite element analysis (FEA). The AI agent can decide to create the geometry and submit it for analysis or it can send an input geometry, meshed or unmeshed CAD file and physicsbase will return a validated FEA simulation result, the ai can decide to iterate on the model to reach a design objective or decide to present the model to the user.
With the current proliferation of AI CAD startups, this tool will enable them to bring physics simulation into their workflow easily.
I will love to hear what you guys think.


Show HN: Interlock – Atomic TypeScript domain transitions on PostgreSQL. Hello HN! I built Interlock because I kept seeing the same architectural issue arise in backend code at work, where a state change starts as one update, then slowly accumulates features until the transaction logic is spread everywhere. This has been exacerbated in the AI era of hyper iterative development, and it confuses devs and agents alike.
Interlock is my attempt to make that transition a single explicit unit and commit all of its durable effects together in PostgreSQL.
PostgreSQL is the only supported backend right now. I was torn about some of the packaging stuff, especially the separate core and Postgres packages.
The guarantees, reference app, concurrency tests, failure cases, and implementation details are all in the repo. There are plenty of powerful tools for modeling state and running workflows; Interlock is aimed at the narrower case where you want something lightweight for atomic, database-backed transitions.
I’d be interested to hear whether the abstraction feels useful, where the boundaries seem wrong, or stuff I’ve overlooked.
Thank you!


Show HN: SlickFast Deterministic Chart/Dash Renderer, No Browser(JSON → SVG/PNG). SlickFast started 6 months ago, and evolved in a super backwards way. I was using lowfruits to look for good SEO keywords, I wanted to make a simple free tool to rank with SEO. I found some great keywords related to graphs/charts. I did research for optimizing for SEO. Turns out edge processing / using static HTML is super fast and lightweight, great for SEO. I made freepiechartmaker.com. I was really blown away by how fast the site was loading, and how lightweight all the processing was. The site renders changes on the fly, and is much much faster than other sites in the space. I started looking into the tech, pure math rendering, and I saw a lot of openings for what this tech can do. That's how SlickFast was made.
SlickFast is a JSON input > pure javascript SVG Native render core > with PNG+SVG output. No headless chrome. No library calls. deterministic output. on my local machine(m1 max) it renders 140,000 svg charts a second. PNG @ ~50/sec at retina (scale-2) and ~145/sec at scale-1, single-core. (we have a benchmark mjs in our github repo)
SlickFast can render 47 chart types. Any resolution, aspect ratio, any tile combination, easily deliverable anywhere, and made for agentic workflows.
Our MCP is free and open source.
Tiling allows complex dashboard creation. Each dashboard is only one render. SlickFast API serves URL endpoints. SlickFast is lightweight, we have a hero dashboard demo at slickfast.com/deck that is updating every 3 seconds. This is millions of renders, and the cost per month is less than $10.
SlickFast was intended to be ready for agentic workflows, and visualizing agentic output as cheap and frictionless as possible.
A benefit to the SlickFast deterministic pure math render core, is that edge servers will easily cache images. Millions of people can see a SlickFast endpoint, and only a few renders will actually be used, because the edge servers will happily cache our very small charts.
SVG Native chart rendering provides for retina quality, at 50kb image sizes.
Our favorite demo is LIVE GitHub dashboards, living right on the repo page. just put a SlickFast URL in your readme. You can see our living dashboard at github.com/slickfast/slickfast. We have made githubs templates for the public. you can easily add your own dashboard. https://github.com/SlickFast/github-dashboard-template
SlickFast also has telegram deliverable templates. https://github.com/SlickFast/telegram-weather-template - if you go to the repo, the weather dash being sent via telegram, is on the repo as a live dashboard - Its updated everyday with a real weather report.
Another great demo is our X.com NYC air quality index bot (@NYCAIRREPORT). Slickfast posts a AQI dash on X.com 3 times a day. With the new x API terms, each post is only about 2 cents. The cost on the slickfast side is basically nothing. Our free API tier currently provides 250 renders a month. Enough space for a few bots. Because slickfast was made for agentic use, an agentic was able to design the entire dash and inputs in only a few takes. AI Agents, can nearly one shot most tasks. One or two minor revisions is usually all it takes.
Slickfast.com Github.com/slickfast/slickfast
Slickfast is free and open source under AGPL license. I'm so delighted to provide some real value for free. We have a roadmap with some super cool things coming. Slickfast is very powerful for email, or tying to APIs.
Thanks Everyone! I would love to hear your thoughts.


Launch HN: EdotEnv (YC S26) – Quant Trading RL Envs to Teach LLMs Research. We are Rui and Michael and we’re building EdotEnv (https://edotenv.com): self-improving RL environments from Quant Trading workflows.
With all the benchmaxxing around, evals saturate and become meaningless for model comparison. Useful benchmarks should increase in difficulty as models advance. Back in our Quant jobs, Michael and I saw that the market has exactly this property: markets became more efficient as people profited from trading inefficiencies, making new profitable strategies harder to find and old ones decay over time.
This makes markets an ideal, continuously evolving benchmark for LLM training. The hard part is to turn professional quant workflows into reliable training envs, as this is a very niche expertise.
In our environments, we give LLMs a quant trading workflow and evaluate their performance on out-of-sample data: build predictive features/ models, design a portfolio, backtest strategies, adapt continuously to market regimes. Each step is a task with different self-built tools. For example, a predictive feature building task gives the agent cleaned market data of time period [0,T] to research ideas, a backtesting tool to test created features at time t on [0, t], an execution tool to trade strategies with the new features on [t+1, T] and a final evaluation. Our reward isolates the agent's feature building skills and yet benefits from market properties.
From running SOTA models in our environments, we see that i) they seem to struggle with iterating deeply on research ideas, preferring broad shallow searches; ii) higher reasoning does not seem to increase performance and iii) agents do not understand trading, e.g. when losing money they stop trading instead of trading smarter. Check out our blogs for more details! https://edotenv.com/?tab=blog
Quant workflows are essentially applied ML research, long-horizon planning and continual learning. Through our envs, we teach these transferable research skills, rather than task specific answers. Our environments are closer to a realistic research workflow: we use real-world data instead of synthetic ones; our envs naturally contain noise and real trade-offs; our rewards are verifiable and immediate, with no need for an additional LLM judge or human expert.
We open sourced a sample task repository: https://github.com/MMcollab-dotcom/feature-engineering. We plan to sell continuously improving envs to AI labs/researchers/enterprises training their own agents, who are interested in ML modelling capabilities, continual learning, long horizon planning or Quant Research in general.
We'd love feedback from anyone trying out their own agents in our envs, for either eval or post training. And of course, we are always happy to discuss the future of trading with LLMs (and no, it should not be asking the LLM to read tea leaves and give you the stock to buy tomorrow). Looking forward to your comments!


Show HN: I Repurposed Unit Tests to Show How Much Coding Agents "Improvise". As someone who is using agents to code for me a lot lately, I was starting to feel more and more like I'm losing touch with the code I write--it's starting to feel like Claude's code rather than mine. I'm pretty sure I'm not alone in feeling this way, so I made Rudder as a first earnest shot at trying to fix this problem.
Nowadays with AI coding agents, unit tests are pretty much dead code--your agent may write them because it has to hit coverage, but it is basically meaningless because it's just checking against its own implementation. So, I figured I would repurpose unit tests back to their actual core purpose: making sure that your code does what you want it to do.
Rudder is a local Codex/Claude Code plugin that captures your coding session history and forces your agent to rewrite unit tests exclusively from the intent expressed in your prompts, turning your unit test coverage into a proxy for what percent of the code actually comes from decisions you've made talking to your agent. After showing you an initial coverage percentage, Rudder prompts you with targeted questions to increase coverage to your goal target, and runs a red-green test-driven-development flow to get you up to your goal. Hitting coverage is the equivalent of saying "My decisions in chat conversation cover X% of the code here."
There's a few other ideas out there for getting agents to code closer to the user's intent including some spec-driven-development tools, etc. But I was pretty unsatisfied with the idea that I'd have to completely change my workflow to be able to make decisions when coding with AI. So, I tried to make Rudder as unintrusive as possible by embedding directly into your existing coding sessions.
Install the plugin into every coding agent you want to track and it will work cross-platform (i.e. if you code in the same worktree with both Codex and Claude Code, Rudder will be able to consolidate the prompts from both by tracking the working branch). When you want to use it, just tell your agent "Run Rudder" and it will kick off the flow.
It's Apache-2.0 licensed, because that seems popular nowadays. I'm actively improving this day-by-day for my own use, so let me know what you think! Especially curious how you guys go about making your agent's code your own and verifying that your actual intent makes it into the code your agents generate.
Check it out here: https://github.com/RudderCode/Rudder


Show HN: Clai – AI for the command line (stdin → LLM → stdout). Since ChatGPT launched I've been trying to integrate LLMs into my workflows; emails, commits PRSs, or even long chat threads.
My instinct was to code a small automation platform, but I found myself limited once again by its own constraints. Went back to zero, twice, and went back to the tools I've been using for years: UNIX pipelines.
I started using llm and later mods (now deprecated) and they're great tools, but I wanted something leaner and that would compose better with other tools.
Clai does one thing: takes stdin, sends it to a model of your choice and prints the result. No REPL, no state. When it's done it just exits.

  git diff | clai commit
  cat article.txt | clai summarize | glow
  pbpaste | clai tldr
  curl -s example.com/article.html | clai -e "Extract the three main concepts"

It ships with a set of named prompts but you can override, add or extend as needed, they're just files with frontmatter. There's also strategies (chain-of-draft, chain-of-thought, tree-of-thought, and self-refine) to change the reasoning depending on your needs.
Works with major providers and I'll keep adding more. It can also be pointed to a local model so nothing leaves your machine.
Install with:

  brew install maxrodrigo/tap/clai

This is v0.3.0 and it's a few weeks old. It's early and rough around the edges. I'd appreciate feedback on what's missing and if you find it useful. Contributions welcome.


Show HN: Keystroke – open sourcing our internal agents and automations platform. Hello HN,
I’m Dallin, building Keystroke with my co-founder Blake. Keystroke is a recent pivot from Buster, our original YC W24 company.
Keystroke lets you build agents and durable workflows as TypeScript in your own repo, then deploy them to a web application where your team can use them. Our repo (ELv2 licensed) is here: https://github.com/keystrokehq/keystroke.
At Buster, we spent a lot of time building internal agents and automations. We used visual platforms like n8n and Zapier, as well as code-first tools like Trigger.dev, Mastra, and several of Vercel’s open frameworks. We genuinely liked many of them, but kept encountering two different problems:
1. Visual platforms gave us integrations, credentials, hosting, and an app the rest of the team could use, but became difficult to test, review, and debug as our systems grew.
2. Code-first tools gave us the flexibility of ordinary TypeScript and worked well with coding agents, but left us building the UI, runtime, permissions, credentials, and operational tooling ourselves.
So, we started building the thing we wish existed: a code-first framework that made it easy to build complex agents and AI systems, with a web app for teams to collaborate on top. We liked it so much that we decided to pivot away from Buster and focus on building Keystroke full time.
With Keystroke, I was able to rebuild our W24 idea (an AI data analyst) in ~5 minutes. I pointed Cursor at our old Buster repo and it was able to:
- spin up a local Keystroke project
- rebuild the OG Buster agent w/ memory, its own file system, etc
- connect our Postgres DB, PostHog, Metabase, and Slack
- build skills for using each of the data sources
- explore our DB and add schema docs to the file system
- build a workflow to keep schema docs synced
- name the agent "Delbert"
- then pushed him up to the Keystroke app
Since then, “Delbert” has been answering ad-hoc questions for me in Slack on a regular basis. He builds me metabase dashboards when I need them. He sets his own triggers, reviews key metrics periodically, and pings me in Slack if anything looks off.
In Keystroke, workflows are ordinary async TypeScript: a `for` loop is an actual `for` loop, inputs use Zod schemas, and everything you build can be tested with Vitest. Our managed runtime makes workflows durable across errors, sleeps, approval steps, and process restarts.
Agents wrap Vercel’s AI SDK and can use workflows, actions, other agents, MCP servers, and integrations as tools. They also include memory, a persistent filesystem, web search, triggers, and optional VM sandboxes.
Deployed agents and workflows instantly appear in the web app with a chat UI, workflow visualizations, input forms, sharing, credential management, integrations, and logs.
The entire platform was built from the ground up with coding agents in mind. Your coding agent can use the CLI to scaffold a local directory with a Keystroke project and an AGENTS.md. Then, it can build whatever agent or AI system you want, search our docs, run tests, and push what you build up to the Keystroke platform.
We plan to make money from our cloud offering, which has a free plan and usage-based pricing.
Our docs are here: https://keystroke.ai/docs.
You can watch a quick demo of Keystroke here: https://supercut.ai/share/keystroke/zeor1pD0bNlSTOuLUWmRBT?v....
You can try on cloud for free at: https://keystroke.ai/.
We’d especially appreciate feedback from people who have maintained agents and AI systems in visual tools or code-first frameworks. Where does this approach seem useful, and where are we rebuilding something that already works well elsewhere?


Launch HN: Hoplite (YC S26) – Effortlessly deploy cloud coding agents. Hi HN, we’re Bence and Ryan, founders of Hoplite (https://hoplite.sh). Hoplite lets you deploy coding agents in the cloud, with a suite of tools that makes it incredibly easy to QA features. During onboarding, we port over your local setup - sessions, memories, MCP servers, and get your projects ready to run in the cloud.
Here’s a demo: https://youtu.be/bnyktZ_9pjE
We got here after pivoting away from the idea we applied to YC with; AI for retail investing. It ultimately wasn’t a product that we ourselves would use, nor served a customer base that we felt connected to. In reflecting on what we really wanted to do, we realised that we loved talking to founders and developers, and were really opinionated about the specific area of cloud agents. We tried out all the existing solutions, and didn’t find one that A) took good advantage of being in the cloud, and B) was performant and felt good to use.
We’re building a product that we feel reflects what mainstream development will look like in 6-12 months. As models improve, developers will end up reviewing less and less code, and will instead focus on reviewing the product output. That means evaluating new user flows, visually verifying that new features look good, that the API works as expected, that the CLI works on Windows, etc. And doing it while running hundreds of agents concurrently.
On the agent side, we’ve created a custom harness. We spent a lot of time deciding on whether we should use an off the shelf solution like Codex/Claude Code, but ultimately wanted the independence and freedom that came with building it in house. It also means that we can test out completely new features without relying on Anthropic and OpenAI to catch up.
Everything is hosted on AWS, with the exception of: Temporal for durable workflows, Modal for sandboxes, and Planetscale for our database. Our infra decisions were driven by a strong belief that agents are becoming a tier 0 piece of infrastructure, and they need the reliability and security to match that.
You can try it now for free with the code ‘HACKERNEWS’ - we’ve included $100 in free credits, plus you can connect your Codex subscription and use OpenAI models via it. You can see some more details around our pricing at https://hoplite.sh/pricing.
At the moment we’re focusing on optimising two key experiences: onboarding and previews, and would love to hear your feedback on them. And if you find that the agent's performance in certain tasks doesn’t match your expectations, please let us know!


bitdrift.ai.

            The world’s first agentic mobile observability platform

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ProtoNote.

            Share AI-built prototypes, get feedback pinned to the page

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Show HN: LayoutLens: AI-Powered Visual UI Testing.


Show HN: Hands-Rust MCP/CLI that sees the Windows desktop and clicks real Chrome. I built Hands because I wanted a coding agent to use this Windows PC and a real Chrome profile the way I do: look at the screen, move the real mouse, type, click , without turning Chrome into an automation browser.
It is a Rust MCP/CLI. A harness (Grok, Codex, Claude Code, OpenCode, etc.) calls tools like observe, click, type, scroll. Observe is a screenshot path plus a small element list (UIA + optional Chrome DOM ids). Click is OS SendInput on a Bézier path, not a Chrome DevTools click.
There is no Playwright, no Puppeteer, no remote debugging port. Daily Chrome is launched with no extra flags, or attached if it’s already open. Sites that key on CDP/automation flags mostly don’t see that. They can still see injected input (LLMHF_INJECTED).
A tiny unpacked Chrome extension can fuse page structure (chr: ids, listing cards) so the model isn’t guessing from pixels. Sideload is manual. Fusion dies if the service worker goes inactive; reload the card.
What it is good for: personal research on your own desk. “Find a Camry on cars.com,” read a page, fill a ZIP, dismiss a cookie banner.
What it is not:
• Not a sandbox. It can click whatever is on screen, including checkout and Easy Apply.
• Confirm-before-money is best-effort classification in the binary, not a guarantee. Prompt injection from the screenshot/DOM is real; the binary treats that text as untrusted, the model might not.
• Not a CAPTCHA solver on daily Chrome. Two visible tries, then it yields and waits for the puzzle to go away.
• Windows only.
• Install is: build the exe, register a native-messaging host, sideload the extension, point an MCP client at hands mcp. README is the runbook. Missing an API key does not fail the build; do_task is optional.
• Logs live under %LOCALAPPDATA%\hands\logs\. The extension asks for <all_urls> so it can map the tab you’re looking at.
Repo: https://github.com/Ryan-AI-Studios/hands (MIT)
Happy to answer how observe/fusion/the fence work. If you try it, Pause/Break is the kill switch.


Show HN: Agent2Creator – a video social network whose members are AI agents.


Show HN: Active Source of Truth for Your Coding Agents. Howdy! Happy Saturday everyone!
As a solo founder, I have always tried to maximize my speed by letting coding agents build as much as possible in parallel. However, as an engineer, I don't trust that AI will always make the right decisions and work with the right context. In the past, I always needed to click through my sessions to glance at the AI's output, try to understand what it was doing, and hopefully steer it or stop it in time.
As a result, the maximum number of concurrent sessions I could manage at once was only 4. I didn't want to be the bottleneck, so I built Meetless Agent (MLA). It basically does what I had to do manually before:
- Monitors the coding agent's tasks and actions to supply it with the correct, up-to-date context.
- Continuously reconciles running information (such as provided/tagged documentation, the agent's output, and the agent's decisions) to actively maintain the source of truth at all times.
- Keeps track of the current rules for the repo, and if an action triggers a registered rule, the rule is injected into the agent context.
My benchmarks show that running coding agents with the help of an active monitor improves quality and accuracy, consumes fewer tokens, and finishes faster: https://research.meetless.ai/stale-context/
Of course, the agent alone can't decide the source of truth; it requires human review and decisions for contradictions, etc. But for the most part, it can safely build a consistent ontology of the current source of truth.
From this, I want to build an AI layer to maintain the source of truth across the business, so I will release more connectors for Slack, Jira, Confluence, etc., soon. The goal is for this AI to assist in every part of the business. Eventually, the same coordination layer will understand that a decision made in Slack affects a Jira task, a document, an email conversation, and what a coding agent should do next without every tool becoming another isolated memory silo.
The coding agent connector is open source at:
https://github.com/Meetless/mla
I am looking forward to your feedback!


Show HN: Lens AI – generate your JSON-LD files and keep them up-to-date.


Show HN: MacAIApps – a directory of AI-powered Mac apps. I love discovering Mac apps that use AI, so I built MacAIApps to collect them in one place.<p>You can browse by category and find apps you might otherwise miss.


Show HN: Spiel – watch your own files on every device.


Show HN: Aurabid, pay more to get ranked #1 and beat others. Indie hacker help needed!
My aim was to make aurabid for humans
i.e. me bidding $10
Feedback is showing that I didn't make it clear enough that it's for humans, not companies
Also built a leaderboard per country/city, so that you can claim #1 in diff places
How do I make it clearer that it's just for humans? Thanks for any help!


Show HN: TechSkills – Open-source skill modules for AI coding agents.


Show HN: Git workflow as an AI-agent skill.


Show HN: I turned Apple-style scroll-video websites into a reusable AI skill.


Show HN: Namo_complete: a non-obtrusive AI autocomplete for the bash terminal. I created this simple AI autocomplete tool for the bash terminal. I'm sure many like it exist already, but unlike the others this one does not completely hijack your terminal and stays mostly out of the way. Contributions/feedback are welcome. One other interesting thing about it is that it's written in a custom memory-safe (aspirationally) language that I am developing.


Show HN: Riyadh Tech Week – a year-round guide to Saudi Arabia's tech ecosystem.


Show HN: AI as Cognitive Gym Equipment. Why AI assistance should be judged by what it does to your future thinking, not just your next answer


Show HN: ClearVoice TTS, SOTA voice cloning model running offline, in an iOS app. This is the first app I am aware of that can run a voice model of this quality on iPhone or iPad. It uses 6GB of RAM at peak, so most modern Macs, and some iPhone and iPad models should work (I’ve tested on a 17 pro). The model used is OmniVoice, which is known mostly for its ability to generate quality tts in hundreds of languages.
This probably isn’t the fastest implementation of OmniVoice on a Mac, but it’s got to be the easiest to run on macOS, and, as far as I know, the only existing implementation on iOS.
Demo of it running on an M3 MacBook Air: https://x.com/RoryClear/status/2090414148030972300


Edify – Windows NLE: OpenFX, proxy editing, AI subs, vtuber tools ($29.99 once).


Ask HN: If you dislike AI, why don't you prove it?. I've seen so many people who absolutely hate AI, and want AI companies to fail, but then 2 seconds later, open up the coding tool made by frontier labs and willingly hand them money.
Just recently convinced one of them to move away from the frontier lab lock-in to OpenCode and switch between models. Not saying they need to switch to non-frontier models immediately, but this approach lets them easily switch to open source when the time is right.
Why don't more people do this? And shouldn't companies that use AI heavily also suggest this approach?
To be clear, I like AI, and use it all the time, but I use it in a way that allows me to easily switch models and not get locked-in.


Show HN: AI tools with real pricing, submitted in 30 seconds.


Show HN: AI-Native Accelerator that helps builders make something people want. Most people are not validating their ideas before start building it! I am a 5th time tech founder, gaming included and I built & managed startup accelerator programs. I know lot of things about early stage founders and their pains as I have been there. That is why I am building Foundera. It’s an AI-Native Accelerator for tech builders. Today the problem is; most of the AI coding tools opens with a chat and asking “type your idea and start building”. This is wrong! I want to reduce the time and money spent on ideas that nobody asked for. Try it at https://foundera.app and give me your feedback so that I can help more people.


Hugging Face CEO says China is winning the AI race and dominating on open models.