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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- 11h ago
Shoehorn – Quantize any model down to run on your machine
Working on Mac, Linux, and Windows now. I include a simple GUI to find new models and get things built and set up. It is working quite well across a few models for me. The GitHub README and DESIGN.md files go into detail of the how/why and it's working remarkably well so far. https://github.com/notactuallytreyanastasio/shoehorn
Hacker News2mo agoToolAI
Vendo (YC S26) – Let users build features on top of your product
Hi HN, we’re Yousef & Nour, founders of Vendo (https://vendo.run). Vendo lets users create new features inside the software they already use. A user describes the dashboard, workflow, or small app they need, and Vendo builds it on top of the product’s existing data, API, and interface.Demo: https://www.youtube.com/watch?v=VdpHehY64lsWe built Vendo because every SaaS eventually faces the same problem: every customer needs something slightly different. One wants a new report and another needs a workflow that only makes sense for their team. These requests either sit on the roadmap, become one-off engineering work, or force the customer into spreadsheets and external tools. We wanted the user to be able to create the missing feature themselves, without leaving the product.Here is how it works:- npx vendo init reads the product's API surface, theme, routes, and more. These are used so that the apps Vendo creates (1) look on-brand and native and (2) have the ability to read data and perform actions directly through the company's API- When a user asks for a feature, we have a custom Vendo harness that writes a React component with a bunch of Vendo add-ons and guardrails (ex. ability to make calls to the host API + our component library). Every save is compiled, type-checked, run against real API responses, and rendered before the user sees it. We just released a benchmark and write-up here with more info for anyone interested: https://vendo.run/blog/generating-product-ui-measured- We use QuickJS to make sure that anything the agent creates is sandboxed and can't mess with the company's site. Vendo compiles the component and runs it with Preact inside a QuickJS VM with no access to the DOM, network, or clock. The VM returns a UI tree, which the host renders using the product’s registered components. When the user clicks something, QuickJS emits a tool call; the host executes it through Vendo’s guard and p
Hacker News2mo agoToolAI
Check if any of the $656M in unclaimed royalties at The MLC is yours
Hi HN, I built this. Quick background on why it exists:When music is streamed on digital streaming platforms (think Spotify, Apple Music, Pandora), there are two separate royalty streams: one for the recording, paid through your distributor (DistroKid, TuneCore, CDBaby), and one for the underlying work (generally known as publishing). The work side's mechanical royalties are collected by The MLC, a nonprofit that was created by the 2018 Music Modernization Act (MMA). If you haven't registered your songs with The MLC, there are issues with your metadata, or about half a dozen other reasons, that money will never reach you, it just collects in a big pile we call the "black box".That pile is big. The MLC's own dashboard currently shows over $656M is held (themlc.com/blanket-royalties), and the biggest problem is the MMA dictates that The MLC cannot hold that money indefinitely. At some point, by law, The MLC must distribute this big pile of cash, and since they don't know who it's supposed to go to, they pay it out through a process called "market share" (themlc.com/marketshare). Market share means they pay it out, pro-rata, to the artists, songwriters and publishers that are in the system, which in practice means the largest publishers collect most of the leftovers.This is slated to begin in January 2027 and will pay portions of the pool out monthly (themlc.com/unclaimed-accrued-royalties), as of this morning, the next 12 months of market share sums up to $76.61M, starting with $6.41M in January.Full disclosure: I run Doubly, which is an independent publishing administrator, so I have a commercial interest in this space. That being said, we're a team of two people and we don't have the bandwidth to directly assist the hundreds of thousands of artists and songwriters who are going to start losing this money in January 2027.That's why I built this self-service tool, no signup, no email, no paywal
Hacker News2mo agoToolAI
Automatically detect and patch walking-dead states in Sierra games
Hi HN, I've become lazier in my old age and struggle to replay my favorite Sierra games from the 80s and 90s because I keep getting into those situations where I need an item from 3 acts ago, I have no save game handy, and now I gotta make dinner.So I'm building the Lucasartsifier: a static analysis tool that decompiles Sierra resource files, automatically finds those states, automatically generates code to prevent the player from getting into those states, then emits loose patch files that can be placed alongside the original game resources. There's no game-specific code involved; all the logic is generic, though of course Sierra introduces new idioms and mechanics in every game so every new supported game needs a bunch of engine work.So for example in Leisure Suit Larry 2, the patched game prevents you from boarding the cruise ship until you have both the sunscreen and the Grotesque Gulp. Without them you die on the raft 3 play-hours later.So far this works on Leisure Suit Larry 2 (SCI0), King's Quest 4 (SCI0), King's Quest 6 (SCI1.1), and Laura Bow 2 (SCI1.1). I'm currently working on King's Quest 5 (SCI1.0).This is work done with Claude -- I do the design and playtesting and it does the rest :DAny feedback, play testing, and suggestions would be great!
Hacker News2mo agoToolAI
ThoughtDAG – An editable context graph for LLM conversations
Hacker News2mo agoToolAI
OneCLI (YC S26) – OSS sandboxed agent harness for teams
Hi HN, Jonathan & Guy here from OneCLI, an agent harness built for teams, giving every employee a secured, sandboxed personal agent.Here’s what you can do with it:1. get a sandboxed agent, with all the OneCLI capabilities in place like connect your GitHub account, Gmail, Notion, or Dropbox simply from the chat.2. deterministic human in the loop approval in the chat itself for things that you need 100% control like sending an email or deleting the Linear ticket.3. manage team policy in one place, enforced across every agent in the workspace4. enjoy global connections at the team level, like shared LLM keys or service accountsHere’s a demo: https://www.youtube.com/watch?v=dlW-44ntpbEWe started working on this by accident, even though our careers were in the security space. We were working on a devtool called ChartDB, an open-source DB tool. When OpenClaw took off back in January, we started using it to orchestrate agents on top of ChartDB. We quickly understood there is a big issue around auth. Agents need credentials to do real work, but to give them those secrets would not be the best idea. They keep them in their memory and also write them down to local files and their sessions as plain text. And we knew that agents can easily be fooled into giving up those API keys/secrets. So we needed some way to control the agent and stop prompt injections from tricking it into using its services for an attacker's benefit.We created OneCLI that started as a vault for AI Agents built in Rust.We found out that most of our demand for OneCLI came from autonomous agents like Hermes, OpenClaw and NanoClaw for individuals and teams.Users looked for useful agents that do things for the person who runs them with two missing parts: 1) managing secrets and permissions. 2) and for teams - multiplayer management.We decided to pivot and provide the agent itself as a harness for teams, to give each employee an agent. We saw that teams had to deal with setting up t
Hacker News2mo agoToolAI
machine0 (YC S26) – Persistent CPU and GPU VMs from the CLI
Hi HN! I’m Barnaby, founder of machine0 (https://machine0.io). I’m building a CLI for long horizon agent compute: `machine0 new mybox` gives your agent a persistent cloud VM, billed by the minute, from $0.013/hr up to 60 vCPU / 240 GB RAM and GPUs (H100s, H200s etc), with 99.99% VM level uptime. Agents self drive via CLI or MCP.Demo: https://www.youtube.com/watch?v=gyllkZ0M04EAgent workloads are moving from ephemeral to always-on. A coding agent working on a complex feature runs 6-8 hours. Agent orchestrated training & RL runs take days. OpenClaw & Hermes run 24/7. As you run more in parallel:- Resources: a few agents on a large codebase saturate RAM and CPU. Model training and RL needs GPUs you don't have.- Security: `--yolo` on your personal machine is one prompt injection away from exfiltrated credentials.- Availability: close your laptop and the agent dies mid-task.- Isolation: there's no clean line between you and the minimum your agent actually needs.machine0 gives every agent its own computer. It's a CLI simple enough that both humans and agents use it without reading docs: `machine0 new mybox` creates an SSH-ready VM with a static IP and HTTPS endpoint. Always on (with 99.99% VM level uptime) until you switch it off.- Billed by the minute. 1 vCPU / 1 GB at $0.013/hr up to 60 vCPU / 240 GB, plus GPUs from RTX 4000 Ada to 8×H200.- Suspend, snapshot and resume. Making it easy to pause your work, and come back to it later. Or to make a golden master image to stamp out clones for a fleet.- Block storage. Persistent volumes (from 10 GB to 16 TB) that you can manage with intuitive grammar: `--yolo` and attach to your VMs.- Profiles. Bundles of credentials, MCP connections, prompts, and env vars, injected at VM creation. So each agent gets exactly the capabilities you choose, and nothing else.- Agents self-serve. Hand the CLI or MCP server to Claude, Codex, or OpenCode and it manages its o
Hacker News2mo agoToolAI
Woxi - Open-source Mathematica / Wolfram Language reimplementation
Woxi is an interpreter for the Wolfram Language written in Rust.It comes with Woxi Studio, a Mathematica-like GUI built with iced, but you can also use Woxi through a CLI, Jupyter kernel, Python package, npm package, or WASM module.Compared with wolframscript / Mathematica, the main differences are:- Free and open source - Very fast startup - Typically milliseconds rather than seconds for the Wolfram kernel, making Woxi practical for shell scripts, one-liners, and other short-lived processes - Embeddable - It can run in a browser via WASM or be embedded into another application as a scripting languageA more detailed comparison with Mathematica is available here: https://woxi.ad-si.com/docs/comparison/mathematica/.Conformance is ensured with ~26'000 unit tests and ~900 .wls script snapshot tests.The current focus is on fixing remaining edge cases, improving performance, and growing the community.If you use the Wolfram Language, I'd be particularly interested in feedback on compatibility and missing functionality. Contributions and bug reports are also very welcome: https://github.com/ad-si/Woxi
Hacker News2mo agoToolAI
Openleetcode – Local LeetCode runner where tests live in the repo
You write a standard solution, just like on LeetCode, and run it through the CLI. It identifies the problem by ID or title, executes your code against local test cases, and shows the result.It currently supports around 1.4k problems and multiple languages, including Python, C++, Rust, Java, Go, TypeScript, Swift, and others.The project is still an MVP. System design, SQL, and concurrency problems are not supported yet, but support for more problem types is planned.Made in Haskell!
Hacker News2mo agoToolAI
How do you keep up with HN these days?
In the last 2-3 years, mostly because of AI, keeping up with interesting articles on HN has become harder and harder.How do you deal with it? Besides the simple solution of simply ignoring interesting stuff more and more.
Hacker News2mo agoToolAI
Speko (YC S26) – OpenRouter for Voice AI
Hi HN! I'm Bek, founder of Speko, a platform that finds an optimal combination of speech-to-text, LLM, and text-to-speech models, given your constraints, among all our public benchmarked options, and tells you why.Demo: https://www.youtube.com/watch?v=no2LY2gRh-cTypical production voice agent is an ensemble of three models: STT, an LLM, and TTS.Each of those layers offers a dozen credible vendors, and each month there are new models on the market. Almost everyone evaluates once, picks a stack of their choice, and never rechecks because switching from a vendor to another involves yet another integration and arguments about the numbers.The result is that you use voice agents running last quarter's models while better and cheaper options are available.Before founding Speko, I spent four years as cofounder and CTO building voice agents for enterprises across Asia in 10+ languages. Each time a new speech model would arrive, we repeated the same ritual: hire native-speaking raters, benchmark it against our existing stack, and update production if it improved. Speko turns this process into an API. A team running thousands of calls a day told us: "we can literally go to this dashboard, switch the model, and it will do it for us."How it works: you send a request with your optimization criteria (accuracy, latency, cost or balanced), language and region. The router filters to models which we measured for the given combination of constraints, benchmarks them, selects the winner, and returns a response with headers containing provider, model names, and the scores. The gateway prefetches signed session plans, so a new session dials the provider straight from memory; no control-plane round trip while a caller waits.Failover happens only during connection setup stage: if the provider refuses the connection attempt, we start connecting to the runners-up.Some of the customer stories: one founder came to us not knowing what to pick at all: he gave us
Hacker News2mo agoToolAI
Programmable timer web app (for gym workouts or stretching sessions)
Over the last couple of months, I’ve been building a timer web app for myself that I use for workout and stretching sessions. My main use-case is gym routines that consist of repeatable sequences, e.g. where you are holding certain positions for a set time (rinse and repeat). The app counts down the program, beeps, and reads the activities out loud.Two things (I suppose) are special about it:- The timers are “programmable”, so you can freely express your own routines and procedures in a declarative notation.- The app is all static (no backend): the entire program is encoded in the URL and can be bookmarked or shared/transferred via QR-code.You can check it out at https://timer.jotaen.net, optionally with a demo program pre-loaded: https://timer.jotaen.net/#demo.Source code is at https://github.com/jotaen/timer. I’ve also written up a small behind-the-scenes on my blog: https://www.jotaen.net/SAKxq
Hacker News2mo agoToolAI
No desktop app for Grok AI
Users want a native desktop application for Grok to improve workflow and accessibility beyond web/browser use.
X2mo agoToolAI
Laptop is the last place your secrets are still in plaintext
Hacker News2mo agoToolAI
Mole – Deep research agent for your terminal
Doing research with agents is fun until they blow way past budget, jumble the sources, and don't even give you the best possible answer, just sound confident.And if you want to run some research task on local data - you have no idea where your data ends up after the prompt consumes it.So I built this tool: a deep-research agent with an enforced budget, verified quotes, and a privacy boundary for local data.1. Never spend more than you budgeted (measured overshoot is 0%). 2. Every claim carries a source 3. Data stays local (give a CSV, it'll analyze it without the data ever leaving your machine)Works with most LLMs, including coding agents, subscriptions, local models, etc.It's free and open source, would appreciate all feedback!
Hacker News2mo agoToolAI
Bullet (YC S26) – A Faster Coding Agent
Hi HN! We’re Adi and Alex, founders of Bullet, a faster coding agent.Bullet started in a senior year dorm. We were fresh out of working at AppLovin and Citadel, and naturally thought we were on a sure path to startup success. We were going to use our skills optimizing stock pricing calculation speeds and agent document context to take over the world. So, Bullet started as an AI hedge fund, a browser-use agent, synthetic financial data (oof), a mobile IDE, and a bunch of other things. We wanted to build something people wanted, but it seemed like everything we built was just terrible, useless, or both.So, we decided to do something completely different, something completely out of the blue, something that no one had ever done before. Solve a problem we actually had.Over the course of six pivots, we suffered. Throughout all of our adventures, one final boss kept getting in our way. Claude Code and his little brother Codex. We were spending hours waiting for coding agents like Claude Code and Codex, and got so frustrated to the point that I downloaded the Claude Code whip. We had spent months of time waiting for six codebases-worth of useless coding agent work.Lightbulb moment. There’s nothing more noble than destroying the institutions! Let’s take on Claude Code and Codex, we can do it! Piece of cake!And so, Bullet started off as a side project. We used the Claude Code to improve the Claude Code:1. Model routing. Do you regret giving a task to Fable when it could have literally been done by Sonnet?2. Targeted code + context search. We think embedding the whole repo is dumb. We also think sticking the whole context (or compressed context) in chat is dumb. So we do faster and better greps over both.3. Aggressive context hygiene. Tool output is bounded, stale screenshots disappear, we don’t re-read files…the garbage never floods the model.4. Efficient turns. Batch independent investigation, make one surgical edit, then perform one focused verification. Internal measureme
Hacker News2mo agoToolAI
OJCP – an open protocol for agent-consumable job data
Author here!Agents are applying to jobs for people right now, with progressively more volume, and there's nothing built for it. So they scrape career pages and fight ATS forms with Playwright/Browser Use, which breaks constantly (or they get bot blocked). Employers get buried in applications that don't fit, candidates hear nothing back, and the resume is now an AI-written thing that another AI scores (which breaks the existing model entirely, btw).OJCP is MCP tools for search and apply, a manifest at /.well-known/ojcp.json so agents can find providers, and schemas that extend schema.org instead of replacing it. The playground on the site is a live MCP endpoint, so you can throw calls at it right now.Why a spec at all when models keep getting better at figuring things out? Inference can't produce authorization. An agent can work out what a form wants. It can't establish that someone consented to this specific submission, and then employer has no way to verify who's calling. So TL;DR a more capable agent is also a more capable impersonator.In this model, trust runs both direction. Agents sign requests using the same method that CloudFlare and OpenAI are already using, providers sign their manifests, agents can check against a JWKS, and trust tiers cap how much candidate PII can go to a given provider. Validation happens at consent, so browsing costs nothing and you only pay the verify when the interaction occurs.I'm the CTO of Recruitics (job advertising) and spent time at LinkedIn before that, so I've been at the intersection of hiring and job search for a while and have felt the pain of both sides.Happy to answer any questions!
Hacker News2mo agoToolAI
AI agents that consult each other to resolve complex tasks
Users need a tool where multiple AI agents (like Sol and Fable) can collaborate and consult on problems before providing a final output, as single-agent interactions fall short for nuanced issues.
X2mo agoToolAI