Каталог продуктов

PO

Postiz

AI-планировщик для соцсетей. Управляйте постами, наращивайте аудиторию, собирайте лиды и растите бизнес с помощью ИИ. Open-source.

SA

Simple Analytics

Google Analytics с фокусом на приватность. Простая и понятная аналитика без cookies, полностью совместимая с GDPR. Тысячи компаний по всему миру уже используют.

AM

Angel Match

База данных из 110 000+ бизнес-ангелов и венчурных инвесторов. Экономьте время на поиске инвесторов — находите подходящих по отрасли, стадии и локации.

DA

DataFast

Аналитика с фокусом на доход. Узнайте, какие маркетинговые каналы приводят клиентов. От первого клика до покупки — понимайте, откуда приходят деньги.

CA

Calendesk

Софт для онлайн-записи. Не тратьте время на согласование встреч — автоматизируйте запись, оплату и управление клиентами. Для терапевтов, коучей, юристов и сферы услуг.

CA

Capgo

Мгновенные обновления для Capacitor-приложений. Выпускайте исправления за минуты, а не недели. Отправляйте OTA-обновления пользователям без задержек App Store.

CH

Changelogfy

Принимайте лучшие решения и создавайте продукты на основе обратной связи. Единая платформа для сбора фидбека, приоритизации roadmap и публикации обновлений.

RI

Risklytics

Hi HN, we're Sam and Alex, founders of Risklytics (https://risklytics.ai). We're both on leave from Harvard, and we run an insurance brokerage for companies building robots, drones, autonomous systems and satellites. You describe your machine, we find the insurance companies willing to cover it, and we read everything before you sign. Buying insurance for any frontier tech goes like this: - your first customer or pilot requires coverage before your machine can work on their site - a broker hands you an application written for ordinary businesses, and often will deny you on baseless "risks" (one of our clients got denied for using CAD software in their workflow) - you get filed under something close or outright denied, the person at the insurance company often can't tell what they're looking at, and the safe answer is no - if a quote does come back, the AI risk is often quietly removed by an add-on clause that prevents you from truly being covered Those clauses surprised us. Insurance companies mostly don't write their own policies from scratch. They license standard forms from an industry body called ISO and attach add-on clauses to adjust them, and in January ISO published clauses that void coverage for losses arising out of AI in any part of a process. Each insurer decides whether to adopt them, account by account, and there's no public record of who has and to what capacity. There's an open question in the wording too. It defines generative AI as systems trained on data that produce text, images, audio, video or code. Whether a robot's control model, which produces motor commands, falls under that is unsettled. We've asked underwriters, the people at insurance companies who make these calls, and gotten different answers across the board. We didn't initially plan to be brokers. We came into YC to work in insurance and spent the first half of the batch on a wildfire risk model we wanted to sell to insurance companies. They liked it and wanted to build their own, which is the polite way to say no. While that was dying, a batchmate who launches satellites mentioned he couldn't get insured, and once we started asking around we kept hearing the same story from hardware companies. So we got licensed in a week and now can place coverage. What we've built so far: - a six-step application that asks about what you're building and drafts the structured version insurance companies like, omitting anything you didn't say - a map of which insurers add the AI clauses, which will remove them, and which cover frontier companies on purpose - when an insurer says no, you see their reason and what would change it, full transparency all the time - a hands off process for companies trying to get fully covered Here's a demo of the whole process: https://youtu.be/gVqCamTNH5E On money: the insurance company pays us a commission when you get covered, usually 10-20% of what you are charged, and we charge you nothing on top (that's right, no brokerage fees). Also, if you're a software company with ordinary needs, we'll still handle the whole process for you and get you the best policy we can find. In the past week alone we have helped three companies get covered that were denied elsewhere. The companies include a robotics wholesaler and rental store, who had trouble getting covered earlier because some of their property uses AI (even though they never use it themselves), a bridge collision system that was getting denied because they use CAD software, and a tele-operating robotics service that needed coverage within a week for their first pilot. If you're looking for insurance in any capacity feel free to reach out to me at alex@risklytics.ai or fill out an application at https://risklytics.ai. And if you work on the carrier side and think you'd wanna partner with us let us know.

OA

OzBrain, a shared brain for knowledge between agents and your team

I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me. A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work. What I built OzBrain to do: + Create a central place for agent reasoned knowledge to live + Be agnostic about what apps/agents connect to it + Capture everything and track it so I can audit it + Enable teams, collaborators or partners to share brains + Handle conflicts so many agents in the same article doesn’t blow up + Refactor knowledge into more token friendly chunks and map the index well + Close the knowledge loop so new thinking supersedes old thinking across the corpus. Don’t erase, depreciate and link + Keep user data safe and secure ++ Be easy enough to use that you don’t have to have any technical knowledge Some among us will always build their own custom solutions, but there are millions of tech professionals and small business owners that will use agents heavily and need a solution. So I’m trying to build that. Isn’t this like gBrain? Yes, similar. I think it’s like AWS vs Vercel. AWS is very powerful, configurable, and useful if you’re technical and want to invest the time into really fine tuning your system… but if you just want your web deploy/hosting to just work and be easy to deal with you use Vercel. // WHY I MADE IT I’ve been enjoying getting back to my technical roots, as I lost my coding skills more than a decade ago, but with AI I can focus on the system and the product in partnership with agent coding workflows. I recently built a Voice AI for older people. To build it I created an agentic engineering workflow (feel free to rip that up as I’m always looking to improve systems: https://ozbrain.com/resources/eng-flow) My approach with coding agents is trust but verify, and I’m trying to replace the parts where a human would review with an adversarial or specialized agent who would give a better answer/review. I have workflows that will go high level task to shipped PR running in Claude cloud sessions. I use Claude Code locally and Cursor when I want a tighter loop on doing visual work like UI or layout. And Codex to either load balance usage for TokenThriffting or when I want a different llm to think thru something. It was a pain in the ass passing .md files around and keep track of which version was the most recent, so I built a hosted .md storage right in Supabase and any of my agents already have Supabase access. This let me build a solid, scalable, secure voice AI from my phone at the gym. All my agents have access to our knowledge, can write to it, update and refer to it as we build and improve the product and the systems we use. Out of 75 founder friends I asked about how they manage shared knowledge, 26 built their own custom knowledge systems… Obsidian vaults with 7k files synced through a VPS, markdown repos behind their own MCP servers, cron jobs stitching Supabase to a skills file… each a different Frankenstein they have to maintain. 32 said they felt the pain of moving static files around but didn’t have any solution for it. So I rebuilt my brain better and used it to build it. // HOW YOU CAN HELP Would love to have you try it out. The maintenance loop is still in alpha so not running it on customer data yet. If you built your own brain I’d love to hear how you did it. What criteria was most important for you in its design & function. If you are tired of shuffling .md files around I’d love to have you try out OzBrain and to give feedback, just ask your agent to put it in the shared bugs & features brain! Cheers! Bubs.co

KA

Kandelo

Kandelo is an open-source, Wasm-based multi-process kernel that runs POSIX programs in browsers and Node.js. Kandelo is still experimental, but it already runs a substantial range of existing software. Do you have use cases for this? We are trying Kandelo as a new foundation for WordPress Playground which runs server-side WordPress entirely in the browser. Kandelo also looks promising as a sandbox for running agents in the the browser and on the command line. On the side, we've been playing with porting games and desktop environments and even compiling runnable programs within Kandelo. Yet it feels like there are many possibilities we haven't considered. How would you like to use something like this? Demos: Some notes: The demos have been tested in desktop browsers. Unfortunately, YMMV on mobile today. Some of the disk images are large (~50MB) and may take a while to boot initially. Main set, with Shell (bash, vim, nethack, and more), Nginx, PHP, WordPress, and Doom: https://kandelo.dev/20260819-demo/ LÖVE game engine: https://kandelo.dev/20260819-demo-love/ SNKRX running under LÖVE: https://kandelo.dev/20260819-demo-love/?vfs=love-snkrx-abi44... Commander Keen running in DOSBox: https://kandelo.dev/20260819-demo-dos/?demo=keen LXDE desktop PoC: https://kandelo.dev/20260819-demo-lxde/?demo=desktop-lxde Background I wanted an authentic OS-level foundation for running systems software in the browser and started this as a vibe-coded exploration. I figured it would end up being too slow and that we would have to offer many different ways to compromise default POSIX behavior to get anything usable. But after weeks of fighting agents, insisting on genuine POSIX compatibility as the default, I was surprised at how well the system worked without those compromises. Nginx, PHP, Python, Ruby, Redis, and even MariaDB were able to be built using the SDK with minimal hacks. Then we started porting games, having fun, and playing to see how far we could push it. Notes on architecture: There is a central, single-worker kernel, aiming to provide all supportable POSIX syscalls. Each process is a dedicated worker with independent memory. Each process thread is a dedicated worker that shares memory with threads from the same process. Syscalls are done with the process SharedArrayBuffer and the Atomics API. fork() is supported. The system is centered around virtual file system (VFS) images, and the VFS can contain lazy references to programs that may or may not be used. Vim is such a reference in the shell demo. On GitHub: https://github.com/Automattic/kandelo

SP

Speko

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-c Typical 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 his use case and now routes everything through the platform. A property management AI runs LiveKit in Python and had not updated STT or TTS since launch: they did not know their STT had high error rates on their calls, better options existed, and swapping always looked like an R&D project. One team did not know which models to pick for Spanish. A medical team did not know which STT handles medical vocabulary best. In every case we helped find the right stack from the benchmarks, and now they route through us. The measuring part is public: we pass the same inputs to every model in one region in different dated runs and we publish the boards, including those where our selections perform worse than alternatives. A launch demo answers which 30-second clip sounds better; production asks which model survives minute eight, so we test spontaneous speech, money and dates, ten-minute takes, and the rankings change. We trained an automatic scorer for TTS naturalness on our blind head-to-head listening votes; on providers it has never seen a vote for, it picks the same winner our raters do about as often as raters agree with each other. We don't train or sell models ourselves, that's precisely how we keep our rankings impartial. We also open sourced the gateway for teams who want to avoid an extra network hop on the audio path and don't want to share keys with our cloud (https://github.com/SpekoAI/gateway, MIT): one Go binary, which is running as a sidecar in your agent's container, speaks one local protocol over Unix socket, pins provider hosts and attaches your keys. In BYOK mode it doesn't communicate with us at all. Notice that the anonymous, content-free telemetry is enabled by default, and one env var disables it. Cost: the gateway and BYOK setup will be free forever, we charge for the hosted router and managed keys with consolidated billing. Since we started the batch in late June, external usage has grown about 25 percent per week on average, front-loaded toward the launch weeks. I would love feedback from the community: how do you pick speech models now, and what makes you trust the third-party benchmark? https://speko.ai/

ST

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!