Real problems people complain about online, pulled every morning and scored out of 100. Build, validate, or skip. How scoring works
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 News1mo agoToolAI
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 News28d agoToolAI
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 News28d agoToolAI
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 News29d agoToolAI
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 News1mo agoToolAI
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 News1mo agoToolAI
Users want a native desktop application for Grok to improve workflow and accessibility beyond web/browser use.
X29d agoToolAI
Hacker News1mo agoToolAI
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 News1mo agoToolAI
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 News1mo agoToolAI
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 News1mo agoToolAI
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.
X1mo agoToolAI
Hacker News1mo agoToolAI
Line9 is a new rendering engine for Mermaid flowchart diagrams that aims to remove the need for manual adjustment of layout. Mermaid is a popular text-based language for describing multiple types of diagrams.Several Mermaid rendering engines already exist. Most, like the original mermaid.js, use Dagre or ELK graph drawing libraries to automatically lay out their diagrams. I’ve used Mermaid for the past four years but often recreated my flowcharts in a diagram editor so that I could modify the layout to something that better suited my needs.Use Line9 when you want an automatic flowchart arrangement that optimises for clear communication. It seeks to layout flowcharts with regular grid spacing, closer positioning of related nodes, shorter, straighter edges and targets a page-like aspect ratio. The Compare button in the /diagram page (share panel on the left) shows you the Line9 and mermaid.js renders next to each other.Use for free, no account required. A CLI tool can also be downloaded for Mac, Windows and Linux. Free-use diagrams carry a watermark. Commercial use of the CLI requires a paid account and you also get diagrams without watermarks. Line9 is not open source (I need to create an income and the business model is being tested).I’ve tried to encode and automate the layout principles I use when creating flowcharts by hand. All rendering takes place in the browser, so no private data is sent to the web service. The CLI is written in Rust and embeds the renderer so you get a single binary install and all data and processing remains local. It’s currently a little slower than mermaid.js, due to the difficulty of solving node positioning and edge routing simultaneously to find the layout that best meets the design principles.Output is produced in SVG or PNG formats. Diagrams can also be shared via a URL with a fragment that embeds the Mermaid text.Things I know still need improvement: positioning and rotating subgraphs, folding graphs to hit good aspect ratios
Hacker News1mo agoToolAI
Users need an AI phone agent that can take defined goals like disputing bills or booking appointments, navigating phone trees and holds autonomously until resolution.
X1mo agoToolAI
Hey HN, we're Advaith and Akash from Discovered Materials ( https://discoveredmaterials.com/ ). We build AI agents that discover new materials for the semiconductor industry.GPUs today have a heat problem. Nvidia & AMD are almost doubling the TDP (Thermal Design Power) in every chip they release - the H100 (released 2022) has a TDP of 700W, Blackwell (2024) gives out 1.2 kW and Rubin (2026) gives out at 2.3 kW of heat. This trend is expected to continue, and getting rid of this heat is one of the major reasons datacenters consume so much power and water today - they need it to keep chips cool during operation.The amount of heat produced by a chip and its ability to dissipate it are both influenced by the materials used to make it. For example, we could reduce the energy per bit required to move data between logic and memory by 10-50x by 3D packaging chips (placing HBM memory stacks directly on top of logic chips, instead of placing them beside logic on a 2D circuit board). However, we're unable to do this today because the dielectric material used in HBM (such as SiO2) is a very poor thermal conductor, trapping heat between logic and memory and causing drastic temperature rise during operation. Similarly, there's many other materials in the GPU that are being re-evaluated today - 2 more examples are thermal interface materials and substrates. However, getting a new material into a fab takes years and hundreds of millions of dollars of research - the infamous "lab-to-fab valley of death".At Discovered Materials, we're optimistic that AI agents can reduce the timeline and cost required to introduce new materials into semiconductor chips. We're seeing glimpses of this already - we tested 7 models from Anthropic, OpenAI and Kimi, and found that they're all able to computationally discover new materials that are dynamically stable and possess promising properties. This was surprising to us - it would generally t
Hacker News1mo agoToolAI
Hi HN! We’re Zack and Tommy the Co-Founders of Keet (https://trykeet.com). We are building a mobile app that generates courses on any topic, with short videos for explanation and games for reinforcement. Courses mirror a real curriculum to help you learn over an extended period of time.Tommy and I met in linear algebra class in college and spent the next 4 years taking classes together. Our friendship was formed around learning new things. In school, someone else designs the curriculum, delivers the content, and writes the assessments. All you have to do is show up. Learning without this structure was frustrating, and there was a lot of friction to get started.Zack has a coffee obsession and struggled to assemble youtube videos, conversations with ChatGPT, and the books he was reading into a coherent understanding of all of the different variables that go into brewing a cup of coffee. His attempts gave him the freedom to follow his curiosity, but the instructional design was difficult. Every piece of content either presumed some prerequisite knowledge or none at all.Keet is our attempt at providing the autonomy to teach yourself anything while adding a structure conducive to learning. It finds a custom starting point and sequences lessons in a logical order.We have found Keet most useful in the following scenarios:- You have a subject matter interest that you enjoy passively learning about. (i.e. you really enjoy learning about medieval history and generate courses on medieval engineering)- You want to explore a niche topic of a subject area you already know a lot about. (i.e. You know a lot about biology but want to explore how migratory animals sense Earth’s magnetic field.)- You see a really niche topic get mentioned somewhere you want to explore more (i.e History of Penny Universities, Double Entry Book keeping or Robert Moses and the creation of the BQE)When you create a course we ask some questions about how difficult it should be, how much depth the
Hacker News1mo agoToolAI
TL;DR I'm looking for advice regarding browser-based frontend development from people with practical e-ink UI experience. My specific device is a Bigme Hibreak Pro BW but I'm aiming for relatively broad compatibility.I've recently switched to a black-and-white e-ink smartphone with the motivation of withdrawing from the attention economy somewhat and it's a genuinely cool piece of hardware. While the majority of my needs are met by this device there are a few things I'd like to have which don't work terribly well with the e-ink screen. I'm planning to implement a couple of projects to fill these gaps, at the moment I'm planning a Lemmy frontend and an OpenRouter frontend specifically for e-ink. Both are to be browser-based rather than native, to maximise compatibility and because I'm much more familiar with the web than Android development.I would like to study the principles of sound e-ink UI design before approaching these projects to avoid creating unusable slop, in particular I am not entirely sure how to approach treating the refreshes as a first-class aspect of the design when I can't control them from the browser, and how to apply comprehensible UI conventions when a greyscale, high-contrast display is the target.Some specific problems I have out of the gate are:* Streaming LLM output to the screen is basically the worst-case scenario for e-ink, I need to buffer it and paint it in chunks without this becoming horrible to use.* Ghosting is a serious problem, browsing HN on the device is a particularly obvious example. Ideally I want to avoid scrolling as far as possible and rely on pagination instead, which I feel has the potential to become annoying if not done well.* Given I must rely exclusively on layout and type to carry the UI, what design languages emphasise these qualities best? My gut says the early Mac OS versions wouldn't be a bad place to start, this seems relevant given the display constraints
Hacker News1mo agoArticleAI
Hi HN, we’re Eren, Berat and Kaan. We’re building Stoa (https://www.stoaexchange.com), a marketplace for new and used GPUs and AI servers.GPUs are the collateral in the data center buildout. Today, financing terms mostly depend on the offtaker, meaning the company that has committed to use the compute.If that company is a hyperscaler, the financing can look investment grade. If it’s a smaller cloud or startup, terms get expensive fast, even with the same hardware as collateral.The lender’s problem is pretty reasonable. If the borrower defaults and we need to sell these servers, what can we actually get for them? There isn’t a good answer today.We started brokering GPU deals to understand why. It was much more manual than we expected. The hardware is still traded through phone calls, forwarded spreadsheets and long email threads.One week, a seller quoted us $200k for a server node and another quoted $240k for what looked like the same thing. Neither was necessarily wrong. They had different information and could only see their own corner of the market.Before we could compare the quotes, we had to sort out the configuration, condition, warranty, location and delivery terms. It’s the same information Kelley Blue Book attaches to a used-car price through the year, trim, mileage and condition. “An H100 server” isn’t enough information to know what something is worth, just as “a used BMW” isn’t.This is also just a bad way to buy or sell hardware. A buyer looking for the best price shouldn’t have to contact several brokers and dealers separately, repeat the same request and then untangle a pile of different quotes. Sellers shouldn’t have to search for demand one buyer at a time. A market of this size deserves better liquidity.Stoa puts the request into one format and sends it to dealers that have gone through know-your-business (KYB) checks. We verify the company, who owns it and who is allowed to trade for it. Before the request goes out, the buyer confirms the
Hacker News1mo agoToolAI