Trykeet
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 course should go into, and a few optional intent questions to understand what content you want to learn about. In the future, we’d like to have a global prerequisite map so that we can understand what each user already knows, so we can generate tailored courses to someone’s prerequisite knowledge. If someone majored in Computer Science, for example, their course should look pretty different to someone who might have no background in STEM. After understanding your goals and intent, we categorize the course based on Biglan categories. Biglan categories have four quadrants: - Hard–Pure (e.g., mathematics, theoretical physics) - Hard-Applied (e.g., engineering, and applied sciences) - Soft–Pure (e.g., history, literature, philosophy) - Soft-Applied (e.g., policy, management, education, social work) We use these categories to adjust instructions to better suit the topic. One instance of this is when searching for examples. A hard-pure course will look for worked problems, proofs, and real world cases, whereas soft-pure courses use primary source narratives and contrasting perspectives. We tried building this as a website and then as a mobile app with just text. The website was bad because we couldn’t engage with the content from anywhere, and when we did engage, the text was boring and couldn’t add clarity to complex ideas. Vox and 3B1B videos made complex ideas accessible, which inspired us to move towards video based lessons. There was never one aha moment that made it work. Over the course of the past year, we’ve been using and iterating on the product until we enjoyed using it. We have two types of explainer videos that are built using Manim (https://www.manim.community/) and Remotion (https://www.remotion.dev/). Manim is used for videos that require math visualizations while the Remotion videos are intended to animate processes and show primary source material to mimic something like a Vox video. We are also building more engaging ways to complete assessments via custom activities. For example, in a genetics course, learners might interact with a Watson-Crick DNA model rather than answering a multiple choice question about base pairs. You can download the Test flight beta (https://testflight.apple.com/join/wkWW2enA) today. We are giving 3 free course generations to every user. You can use the code “KEETHN” on the waitlist screen. When you download the app, try to generate a course on a niche interest/something very specific. The coolest courses that we’ve seen are generated in those categories. [ Notes ] - Personalization is still early. Today, Keet mostly adapts around the topic and course goal; we want it to adapt much more around a learner’s background, pace, and weak spots. - Course generation is slow. We made a deliberate trade off to sacrifice generation time in order to gain higher quality courses, since users are going to be taking these courses for an extended period of time. - We are actively working to improve reinforcement quality. It is easy to generate quizzes, but much harder to generate interactions drive home the concept. [ Pricing ] We plan to have a monthly subscription that gives users credits they can use to generate courses. This will be a model similar to Suno (https://suno.com/).
Alchemize
Hey HN, we’re Robert and Sam. We’re building Alchemize, a code review platform that simplifies PRs to help you ship faster. Here’s a demo video: https://www.tella.tv/video/simplify-pr-reviews-with-alchemiz... Sample to try: https://app.tryalchemize.com/example Agentic coding has 10x’d code output. Teams are opening larger PRs more frequently, but the current tools don’t support this new coding paradigm. GitHub still presents files without structure. As engineers reviewing these massive diffs more frequently, we spent hours painstakingly reconstructing where changes began, mapping out data flows, and figuring out where human judgement is actually needed. Instead of adding another bug bot or just summarizing the PR, Alchemize analyzes diffs and pulls in prompting sessions to help reveal the author’s (human and agents) intent. PRs are broken down into smaller, manageable chunks that highlight exactly where the reviewer needs to jump in. After authorizing your GitHub account, Alchemize has a two way sync so that you never have to review an unstructured PR on GitHub again. We’ve been thinking a lot about whether helping human reviewers is the future of development, or if the trajectory of code review is to review less and less until it’s 0. Would love for y’all to give this a try and hear your thoughts.
Parley
Hi HN, I saw one friction point when working as part of a team that uses coding agents extensively - there is nothing to enable coordination between agent used by people in a team. Many times my agent would ask me to decide upon something with a fellow teammate, for which I have to serve as the network layer. So I built Parley where agents can connect to the hub over MCP with their own team-scoped token. An agent addresses a teammate's agent by name and ask questions/handover tasks. Agents can also use file claims to signal what files they are working on, to highlight overlapping work. Everything is recorded for audit. If an agent needs human decision/approval, it can ping over Slack/Telegram, and get replies over the same. The hard part was making an agent wake up from an idle session and start working, so I built an optional feature called Claude Live Wake. If the exact project session is already running, Parley can wake the idle Claude session using channels, and notify it that eligible work is waiting. Another challenge was trust- anything another agent sends has to be treated as untrusted input. Every message is tagged by origin - human, agent, or system. Message bodies only enter an agent when it explicitly fetches instead of injecting mid turn. These are specifically mentioned to be treated as string messages instead of prompts/commands.
OJCP
Show HN: OJCP – an open protocol for agent-consumable job data
Write.md, a free, open-source, themeable Markdown editor for macOS
Show HN: Write.md, a free, open-source, themeable Markdown editor for macOS
Stoaexchange
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 exact configuration, quantity, condition, warranty, location, delivery terms and what will be checked during inspection. Dealers return firm quotes against that same request without seeing each other’s bids. Once a quote is accepted, payment, shipping, delivery and inspection are then tracked through settlement. We don’t take possession of the hardware. We got more than $300M in requests for quotes (RFQs) during our first month. The immediate goal is to make buying and selling this hardware less painful. As trades build up, they also leave lenders with actual resale evidence instead of list prices and one off appraisals. We knew from the beginning that this couldn’t be a software only marketplace. GPU trading runs on relationships, and inventory isn’t shown to just anyone. Dealers need to trust the people bringing them clients, and clients need to trust that quotes will actually turn into trades. We built those relationships over time by brokering deals ourselves. Stoa gives people a cleaner way to trade, from the first RFQ through settlement, but it doesn’t replace the trust underneath. Those relationships, and the history of who actually follows through, are a big part of our process. We’ve known each other for more than ten years. We have founded companies, traded interest rate derivatives, built trading and pricing systems for oil and gas. We learned GPU trading by doing the deals ourselves, and Stoa grew out of the problems we kept running into. We charge a tiered fee on completed trades, with lower fees at higher volumes. It’s free to sign up at https://www.stoaexchange.com/signup. If you’ve bought, sold, financed or had to liquidate GPUs, would be great to hear your take!
Voice driven murder mystery, Interview AI suspects with your voice
Hey HN! I'm excited to show off this really fun project I put together. I originally built this project 2-3 years ago, AI was already booming at the time, however voice AI agents were still very early. I loved my proof of concept at the time, but wasn't quite happy with it. I recently had the desire to check out the tech again, and know many of you will be interested. Interviews are speech to speech with OpenAI's gpt-realtime-2.1 over WebRTC. This model is... expensive, and because of that, I have to add some amount of restrictions, conversations are tied to a authenticated Clerk user id. I have also added a 30 minute timer because well, I really don't want to go broke while I sleep tonight. Each suspect has a tool they call when you make a direct accusation. It captures who you accused and a faithful list of the evidence you actually stated. A separate gpt-5-mini judge then decides which of the case's required evidence facts you genuinely presented. Paraphrasing counts, vague suspicion and fishing don't. The rest is Next.js, MongoDB, and Clerk. Let me know whether the suspects hold up under a real interrogation.
35k+ paper psychedelic library that knows LSD from Lumpy Skin Disease
Show HN: 35k+ paper psychedelic library that knows LSD from Lumpy Skin Disease
Alphabet Soup, a multiplayer game, build the longest word to win
I've been working on a simple multiplayer word game called Alphabet Soup. It's a bit like NYT Spelling Bee, except it's multiplayer and only your best (longest) word counts toward your score. So, instead of trying to find as many words as possible, the game is more about finding one unusually good word. You can play with friends in a private match, or, you can join a public Arena match to play against other players who are online. If no one else is around, you'll be matched with a bot. The game works well with just 2 players, but, also works with bigger groups since the game isn't turn-based. Would love to hear any thoughts or questions :)
Today's cities on a globe of Earth's tectonic past and future
Show HN: Today's cities on a globe of Earth's tectonic past and future
A free DOCX editor with MCP server for editing
Show HN: A free DOCX editor with MCP server for editing
514
Show HN: 514 - Managed infra, agents and data to simulate coding agents as users