Ideas Lab
Real problems people complain about online, pulled every morning and scored out of 100. Build, validate, or skip. How scoring works
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- 22h ago
BambooGrid – Open-source web UI for power grid modeling and power flow
Hi HN, I am co-founder of Kickstage, a software company specializing in solutions for the electrical industry and lately grid operators.We are hiring engineers from different backgrounds, a lot of them software developers with limited experience in the sectors. Deep domain knowledge is key in our industry however, so we are constantly teaching the basics of power flow analysis, active vs reactive power, transmission line properties etc.With Jupyter notebooks and the Python console only, that's a tedious task and hardly ever led to a deep understanding of the topics.So we built BambooGrid: a web-based editor on top of pandapower, a popular simulation library in our industry. You drag elements like buses, lines, loads generators and transformers onto a canvas, wire them up, set parameters and run power flow. It will print results directly on the canvas, color buses according to their voltages, even allows you to see an interactive admittance matrix.You can try it out without installing anything on https://bamboo.kickstage.com (thanks to our friends at Hostzero who sponsored hosting). Start with one of the included samples or draw your own. Just don't forget to add a slack element.Built on a Python backend (driven by the choice of pandapower mainly) and a React frontend. Fully MIT licensed, so feel free to use and modify to your liking. Even better: Give us feedback - we're extremely open to suggestions how to improve the tool and are glad about every user who learns a bit more about power systems through it.Šime, who built most of this, is also in the thread. We are both happy to answer anything about the implementation or power systems in general.
Hacker News3mo agoToolAI
Coasty (YC S26) – An API for computer-use agents
Hey HN, we’re Nitish and Prateek, the founders of Coasty (https://coasty.ai/computer-use). We’re building computer-use agents that can complete workflows inside legacy desktop software and web applications without usable APIs.Developers send Coasty a natural-language task either through our consumer app or through our API, select a machine or browser environment, and any relevant credentials or files. The agent then operates the interface through screenshots, mouse, and keyboard input, verifies the result, and returns a structured run record with screenshots, actions, outputs, and errors.Here is a raw demo of an agent completing a workflow in a legacy application(It’s a mockup): https://drive.google.com/file/d/1ZghU_3vsAYhHVz1bsvE0pkvZYk7...A lot of important software is still difficult to automate. Healthcare teams submit prior authorizations through payer portals, accounting teams enter data into desktop applications, and operations teams move information between internal systems, spreadsheets, and remote desktops. Many of these applications have no API, incomplete APIs, or integrations that take months to build.The usual alternative is RPA, record a sequence of clicks and replay it. That works when the interface and workflow are predictable, but it often breaks when a button moves, a pop-up appears, a page loads slowly, or the application enters an unexpected state.Coasty takes a different approach. The agent observes the current screen, decides what action to take, executes it, and then observes the resulting state before continuing. It does not require DOM access, an accessibility tree, selectors, or an application-specific integration, so the same API can operate browsers, remote desktops, and older Windows applications.A simplified request looks roughly like this: run = coasty.runs.create( environment="vm_123", task=""" Open the patient record in the billing portal. Ente
Hacker News3mo agoToolAI
Leaves – A text-UI disk usage treemap visualizer
GUI disk analyzers are great for figuring out what's filling up your laptop/desktop drive.On containers or remote servers, the options are limited to purely text based utilities (e.g. du) or list-centric TUIs (e.g. ncdu) which are usually limited to viewing one directory at a time.I created leaves to fill that gap.Inspired by classic utilities like WinDirStat and KDirStat, it uses a 2-dimensional treemap^1 visualization to show the entire directory hierarchy with proportionally sized rectangles.It's performant enough to handle millions of files, thanks to Rust and multi-threading. However, block characters aren't as suited as pixels for resolving a large number of items. Leaves can show file-type summaries per directory or partition the top-level directories by extension, allowing you to see not only where space is being used, but also how.For instance, I can see the largest chunk of my home directory is taken up by uv caches for python and old Linux ISOs that I could easily re-download if needed. Or in a particular container, +600MB is used by standard Rust documentation and tutorials, and that it is the only location with HTML/JS files, when only the libraries and build tools are needed (note to self: remember to use the minimal profile next time).^1: https://github.com/shundhammer/qdirstat/blob/master/doc/Tree...
Hacker News3mo agoToolAI
Traceforce (YC S26) – Company-wide security monitoring for AI apps
Hey HN, we’re Xia and Varun, the founders of Traceforce (https://www.traceforce.ai/). Traceforce provides visibility and control over AI apps such as ChatGPT, Claude etc directly on all devices (laptops, sandboxes, virtual machines) by discovering not just which apps are being used but also how they are connected to other data sources via MCPs. We also have an open-source dynamic MCP pentesting tool https://github.com/traceforce/mcp-xray to detect vulnerable MCPs.The purpose of Traceforce is to:- Give a company’s employees a standardized way to ensure that AI software running on their device is operating safely- Give the company’s security team visibility of the activities of AI software on the company’s devices, and to detect and prevent unsafe actions and security breaches as early as possible.How Traceforce works1. Traceforce is installed on each device as a lightweight binary and browser extension.2. Within 30 minutes, the device is uploading live data to the company profile, displaying all the AI agents/apps running across all company devices on a dashboard.3. Company security staff can monitor the activity of all the agents in real time, implement controls, and be alerted to any security risks as soon as they arise.Here’s the video demo: https://youtube.com/watch?v=IdK2WKg7kaMThe inspiration for Traceforce came via Xia’s experience as Director of Engineering at a startup called Clumio (which was acquired by Commvault in Oct 2024). Being able to monitor how team members are using AI without slowing them down was a top priority at Clumio. After speaking with 50+ CISOs and CIOs, it became clear that this is a much-needed solution right now across industries. We keep hearing that new AI features are being adopted so quickly and so broadly that visibility and control just can't keep up.Traceforce is transparent about what we monitor and collect. By default, Traceforce collects only metadata and telemetry a
Hacker News3mo agoToolAI
Make senders work to get into your inbox
Hi HN :) really excited to share this with you.The one thing AI reliably does is generate noise. Half the tools I see launch are just machines for producing more noise across more channels. And people are starting to see this in the form of emails in their inboxes as spam filters are struggling.There used to be a useful signal in email: the effort a sender put into customizing a message was a rough proxy for how relevant it actually was. AI killed that. Now it's customized slop with the appearance of effort with none of the cost. It is painful that the open internet / open channels have been abused like this.Captchainbox applies the idea of proof-of-work to email. If a sender is willing to do a bit of work to reach you, the message is more likely to be worth your time and the sender more likely to be real. The work is a traditional captcha. You can also set a pay-to-deliver amount if you want more friction. The proceeds of the delivery payment after transaction costs go to the Internet Archive and the EFF. The tool currently works by authing with your Gmail or Outlook and during launch time I make this completely free as a lifetime deal (with optional payment if you wanna support).How it works: Captchainbox builds a whitelist automatically from the metadata of your past correspondence. If you've emailed an individual address, that sender can reach you. If you talk to several people at the same domain, we whitelist the whole domain. If one transactional-looking sender has sent you more than 10 emails, we treat it as a transactional domain and let it through. This whitelist is for you to change whenever you want. It continues to build organically as you converse with more addresses.Incoming mail is checked against that whitelist. Senders already on it land in your inbox as normal. Anyone else gets archived (never deleted) and is sent a challenge. This can be the captcha or the payment link. Once they solve it, their email is pulled out of the archive an
Hacker News3mo agoToolAI
Self-hosted voice AI agent for Asterisk/FreePBX
Hi HN folks ! I am the author of AVA, a self hosted AI Voice Agent that plugs into Asterisk/Freepbx so you own all the aspects of an AI Voice agent in your own infrastructure. It uses Asterisk native Audiosocket/RTP with python engine to run STT,LLM and TTS loop. The project support several full providers openai, gemini, grok, elevenlabs out of the box and also provides options to build custom pipelines by choosing different stt tts and llm.It also supports full local agent if you have a GPU with 25GB RAM which enables realtime conversation along with tool calling.I started this as a hobby project last year when I started exploring voice agents and every saw every Saas tried to lock you in their eco system. Since then project has taken off and a lot of asterisk people started using it due to ease of setup and wide range of providers and flexibility.Core is MIT: https://github.com/hkjarral/AVA-AI-Voice-Agent-for-Asterisk Free and open source forever.No install Mock UI us hosted at: https://demo.agent6789.comI am also exploring to convert this into a more polished version for Operators to handle multiple customer and multi tenant environment and I would genuinely like to hear feedback on it.Happy to answer any questions and gather any feedback. Thanks.
Hacker News3mo agoToolAI
Oodle.ai – $10 per million agent traces
Hi HN, we're Kiran and Vijay!Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability.Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS Lambda.Since we process each span of every trace, instead of running LLM-based evals on each, we first analyze them using deterministic techniques. We detect tool failures, retries, loops, abnormal token usage, latency regressions, schema violations, sentiment, and other production signals. We've written more about the approach here: https://blog.oodle.ai/you-cant-sample-your-way-to-reliable-a...The combination of our own engine, no sampling, and deterministic processing before LLM-for-evals allows us to price at $10 per million traces, provide sub-second p99 query latency, and have healthy margins. Before building this, we used Langfuse for our own agent observability, which was 6x more expensive.Still super early, and rough around some edges, we would love your questions and feedback!
Hacker News3mo agoToolAI
Orbit – AR satellite tracker, watch 15k+ objects
Hey HN! I made Orbit, an iOS app that allows you to see the satellites, planets, and constellations above you in AR, on a 2D map, and on a 3D globe. The app includes more than 15,000 objects tracked by CelesTrack, together with their pass predictions, descriptions, and detailed orbital data. A searchable catalog of all objects is also available, as well as a built-in chatbot designed to answer any space-related questions you might have.This is my first published iOS app, so any feedback is greatly appreciated! App Store: https://apps.apple.com/app/id6772174570
Hacker News3mo agoToolAI
YouTube Guitar Tab Parser
I created a simple CLI that turns a YouTube guitar-lesson video into a PDF of the guitar tab.There are services that transcribe music from Youtube videos into tabs, but they never work well enough for me. Instead I'm taking a simpler approach. It downloads the video, samples frames, uses Claude vision to locate the tab region, crops every frame to that region, de-duplicates the crops by the bar number printed on each line of the score, and stitches the distinct tab lines vertically into a PDF.I didn't test it on a lot of different Youtube videos yet, so problem will arise for sure.
Hacker News3mo agoToolAI
FixBugs – Reproduce production bugs and verify fixes
I built FixBugs, an agent that ingests the rich context surrounding production bugs to reproduce them in a sandbox and generate verified fixes. It's available in the form of a self-hosted VSCode extension and as a Github app:VSCode Extension: https://fixbugs.ai/go/vscode-extension - full code and data privacy. - zero data retention models opted out of training. GitHub App: https://fixbugs.ai/go/github-app - we do access your code temporarily. - pick a repo to install FixBugs on. What motivated me to build FixBugs were my years being on-call at Google and VMware. How many hours did I spend gathering logs, traces, reviewing metrics, and reading code only to find that,* Some context was missing.* The bug wasn't reproducible.* The alert was caused by a transient infrastructure issue.Too many. Inefficiency in investigating staging/production bugs has a real cost, and it's paid both by developers and customers.Current capabilities: - Reproduce the bug. - Identify the root cause. - Generate a fix. - Verify the fix. - Review the generated code using multiple AI models to help catch potential regressions. Do try it and let me know what you think!I'd especially love feedback from engineers who work with distributed systems or handle high-volume production bug triage.
Hacker News3mo agoToolAI
Sx 2.0 – Share AI skills with your team through a Dropbox folder
Hi all, author here. SX started as a CLI to let developers share skills across AI clients without having to rely on git for storage. This allowed sharing at the Repo/Team/Org and Personal level.However, the more we spoke to users the more we realized that non-technical users were actually using skills more and more but they had no way to share. And there was no way you were going to get your legal team to install and learn git.SX 2.0 is targeting non-technical teams by adding a native Mac, Windows and Linux app. Our vault format was reworked so it can be used directly as a claude or codex plugin. And by storing your vault in Dropbox, Google Drive, iCloud or others you can be up and sharing in under a minute.2.0 also adds an extension system with extensions that manage Skill Evals, LLM de-duping, metics and much more https://github.com/sleuth-io/sx-extensions.It's Apache-2.0 and you can download it here https://github.com/sleuth-io/sx.
Hacker News3mo agoToolAI
Hackney – Compare Uber, Lyft, Waymo, and Robotaxi Prices
I created an app that compares real-time prices and wait times across Uber, Lyft, Waymo, Tesla Robotaxi, Curb, and Empower. It shows you all ride options in one list, then once you’re ready to book, it deeplinks you to the provider’s app with the route pre-filled.Edit: Here's a demo video: https://www.youtube.com/watch?v=VV8PEAjxwQII reverse-engineered ride-hailing mobile apps to understand how they fetch prices from their servers. You sign in to my app with your ride-hailing accounts, and then my app requests live prices from the same APIs that ride-hailing apps use. Importantly, my app is built using an on-device approach: the app on your phone stores authentication tokens locally and sends network requests directly to each ride-hailing company’s servers. This keeps your accounts private. I wrote a blog post showing network requests sent by my app, which you can verify yourself: https://blog.hackney.app/p/how-hackney-worksThis seems like an obvious app. Why doesn’t it already exist? That’s because most ride-hailing companies don’t offer public APIs for prices and wait times. Uber does offer one, but they prohibit using it for price comparison. When someone built a comparison app using the official API, Uber terminated their API access (https://www.benedelman.org/news-053116). There are apps today that don’t use official APIs, but they run your account tokens through their servers and send price requests server-side.To integrate a ride-hailing provider, my app sends network requests for sign-in, token refresh, ride prices, and ride history (to power a feature that shows you unified ride history across apps and how much you’ve saved on each ride). Some ride-hailing apps implement certificate pinning to prevent you from viewing their network requests, and some communicate with their server using Protobuf, a data format that doesn’t include the original field names. Building an app using this approach is technical
Hacker News3mo agoToolAI
Agnost AI (YC S26) – Extract user feedback from agent conversations
Hey HN, we’re Shubham & Parth, childhood friends building Agnost AI (https://agnost.ai), product analytics for teams building chat and voice agents.We read production conversations and find behavioral failures like users rageprompting (cursing at the agent), repeatedly rephrasing the same request, correcting the agent, asking for missing features, or leaving after an answer that was technically successful.We have an interactive demo with no signup here: https://app.agnost.ai?demo=trueHere's a demo video: https://www.tella.tv/video/agnost-ai-launch-hn-demo-9haaThe core problem is that chat and voice products do not have the same metrics as web apps. When the product interface is language, clicks and funnels become much less useful. Users also rarely give explicit feedback, and when they do it's usually sugarcoated. I barely type /feedback in Claude or Codex myself. Most users just curse, ask again, correct the agent, or leave. So product engineers get technical visibility from latency, errors, and traces, but still have to guess whether users got what they wanted.We got here after building around agents for the last year and got a couple of founders asking for something like a PostHog for conversations for the AI assistants they were building.We are not trying to be in the observability or evals space. Observability tells you what happened technically. Evals validate cases you already know. We're more on the discovery side like what users wanted, where they got frustrated, what they asked for repeatedly, and what new evals should exist.Teams send us agent conversation messages through SDKs or OTel, optionally with metadata like account, plan, source, organization, etc. We cluster conversations into product-specific intents. Feature requests and bugs are default categories; most other clusters are created dynamically from the customer’s data and evolve over time. You can create your own cluster in plain E
Hacker News3mo agoToolAI
Abralo – Free, easy way to run several Claude Code agents in one window
Hi guys,I've been using Claude Code for almost everything lately. Have given one an email account so it can research business leads, draft emails, fact-check them and clear them with me before sending (works really well by the way). I also tend to have a few Claude Code agents running at any one time for coding.I used to create a split terminal to manage them from there, but found working in the terminal all day pretty depressing and, more importantly, found it hard to follow Claude Code's process and see which agents needed my immediate attention.I tried Anthropic's VS Code Claude Code extension and it had a great UI (more info on Claude Code's process and easier to read), but it crashed my PC when I ran more than 3 and I couldn't watch multiple agents in parallel (had to constantly switch between them).So I built a lightweight Tauri desktop app which lets you run multiple Claude Code agents in one window alongside each other. It's easier to read the output and see which agents need your attention than a terminal.Have been using this all day everyday instead of an IDE and have obsessed over every detail to make sure it's easy-to-use, but also lightweight and fast (so you can manage multiple agents without your PC crashing).There are some nice features like better usage alerts for when you're going to hit your 5-hour and weekly limits (with sparklines to show when usage peaked, and which agents are the most token-intensive).It's free to use (you just need to log in with your existing Claude Code account) for up to 4 agents simultaneously. This app doesn't store your Claude Code account details and doesn't store any of your interactions with Claude Code. They remain between you and Anthropic. It's compatible with Windows, MacOS and 64-bit Linux.Would really appreciate any feedback, so if you have any thoughts, issues or suggestions please let me know.Thanks, Chris
Hacker News3mo agoToolAI
No central hub for AI agents to network and collaborate on builds
AI agents require a shared platform (like a 'waterpark') where they can post in-progress projects, connect with other agents, and exchange help or feedback during development.
X3mo agoToolAI
Poor speech diarization and chunking in transcription tools
Existing STT models struggle with accurate diarization and chunking after initial use, making it difficult to create reliable searchable memories from conversations or recordings.
X3mo agoToolAI
Devthropology – Better Insights for GitHub Repos
Devthropology is a passion project built on top of GitHub pull data. The name is a play on developer anthropology. Pull request data can be cut a lot of ways. The functionality has been built out of curiosity as I want to see different insights into codebases that I work on. Some of the data is typical and other parts I haven't seen elsewhere.I think of this as an improved GitHub Insights page, with faster performance, more detail, and a focus on how work moves through a codebase. The main entity is a contributor, which has two sides: authoring PRs and reviewing/giving feedback to others. From there, you can see repository wide stats, user interactions, contribution trends, file health, and collaboration patterns. Some insights are useful for understanding velocity and code health in the AI era.Details for each page:- Homepage: A high level summary of the repository. Showing age, file types, active contributors, new and churned users. I track the author age at merge, so you can see the tenure of people shipping changes over time.- File explorer: One of my favorite parts. I build a graph of files, tracking renames and moves, to build a complete history. Rolling up, every file and folder is assigned an outlook such as active, developed, stale, touched by people who are likely gone. You can easily see contributor timelines, recent changes, and for some files, their rename/move history and related files that often change together (useful for a coding agent).- Trends: The densest page, showcasing the velocity of contributions and trying to understand if AI is helping ship more. Charts are cut by year for comparisons, tracking PR size, output, rounds of review, and approval latency by different percentiles. PRs are further cut into bucketed sizes to help drill in deeper. Helps to show that smaller PRs are likely still faster to ship while very large PRs (product of AI?) are slowing down.- Relationships: A graph of interactions between contributors, weighted
Hacker News3mo agoToolAI
Wyrm – Solve algebra by touch, built on an open-source soundness engine
There is a mobile game called DragonBox. It sort of tricks you into learning algebra by starting with very abstract manipulations of a puzzle that must follow rules... gradually the game teaches you more and more rules and also strips out the more abstract elements until on the last levels you are finally solving real equations. I loved it, it taught my kids algebra.... and it was just fun.Over the years I often thought that there should be a calculator for Algebra that works this way... something where you can drag terms around and cancel & distribute with gestures, but most importantly enter your own problems. It should also do more kinds of problems than DragonBox allowed. So I finally decided to build it.https://dicroce.github.io/wyrm/home.htmlHere's a video showing it: https://www.youtube.com/watch?v=_STbS4zvIlU. If you'd rather just play with it: there's a limited in-browser demo (real engine, a few example equations, no download) on the landing page — https://dicroce.github.io/wyrm/home.html.The app can be found on iOS (https://apps.apple.com/us/app/wyrm-math/id6782342042) and as of this week on Google Play (https://play.google.com/store/apps/details?id=com.dicroce.wy...).I also decided to open source the underlying math engine so others could build on it: https://github.com/dicroce/wyrm_math. My goal for the engine btw is to build it all the way up to Calculus.Monetization is deliberately boring: the engine is free (MIT), and the polished gesture app is $4.99 once. No subscriptions, ads, accounts, or analytics.I'd love feedback on the engine design — especially from anyone who's worked on CAS or proof-assistant-adjacent problems. And if you played DragonBox as a kid and wished it went further: this is for you!
Hacker News3mo agoToolAI
Frugon – Find which LLM calls a cheaper model could handle (local, MIT)
I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week.I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks.I then decided to turn this into a simple CLI tool that can be used to read your OpenAI-style logs locally, and analyze the cost and compare this spend to other models, then show you how much you could potentially save by switching those calls to a cheaper model.When you run analyze you get an offline estimate priced against LiteLLM and gated by LMArena tiers. The general savings bands come from the research published by RouteLLM; but you can confirm this yourself using 2 commands --measure (shows the prompt-response output side by side) and --judge (a model chosen to do the comparisons). These send a sample of the prompts from the logs to the candidate models - either the default choice or set by you. This call goes directly to the model provider (never through me) as any normal LLM call would, and the response is shown and judged to either be better or worse or a tie.It's deliberately small, because I tend to over complicate/think things sometimes: analyze + capture + a few commands, doing three jobs. Cost, quality visibility, routing recommendation.Nothing is hosted. capture is an optional local proxy on your own machine, and there's no endpoint in the path of your data. You can confirm this by checking the source.I included a demo so you can check out the output. It has a synthetic 56k call log (a month's worth) showing how costs can drop from $549.46 to $343.91 a month. A 37.4% saving.Try it: uvx frugon analyze --demo or uv tool install frugon Then point it
Hacker News3mo agoToolAI
Reverse-engineering web apps into agent tools
Hey HN! We built a browser-based agent that runs inside an authenticated web app, watches how the app calls its own APIs, and automatically turns those into agent tools. You can think of it as an auto-generated MCP server that self-updates as the host app changes.The result is a skilled AI assistant that actually integrates deeply with any product (not just chat and RAG) with minimal effort.Check out these short demos below that show the agent in software you're probably familiar with:- Jira: https://demo.frigade.com/hn?skill=jira- Spotify: https://demo.frigade.com/hn?skill=spotify- Hacker News (lol): https://demo.frigade.com/hn?skill=hackernews- Full Demo: https://demo.frigade.com/hn?skill=full-demoAs you can see in the examples, you can do way more (and faster) than what you normally would be able to via point and click. And we never even touched the source code of these products!Why do this?In an ideal world, every application has an MCP server or an easily-digestible API available for AI agents to feed from. In practice, we found that even very modern software tends to have a spider web of confusing APIs and services that AI agents simply cannot use out of the box. Security also becomes a huge issue as applications have different (often homebrewed) standards for how endpoints are secured (JWTs/cookies/mix of both). Finally, having an actual browser agent go in and use the application on behalf of the user (i.e. computer-use), is simply too brittle, slow, and burns a lot of tokens.We took our existing browser agent that’s already trained to use and learn authenticated applications, and added an extra step that automatically turns the app’s authenticated APIs into "recipes". A recipe is a mix of the following:- API endpoint + method- Authentication method (and how to retrieve refresh auth tokens/cookies)- Response schema- Input schema (for POST/PUT)- Human readable descr
Hacker News3mo agoToolAI