Каталог продуктов
Отслеживается продуктов: 465
Postiz
AI-планировщик для соцсетей. Управляйте постами, наращивайте аудиторию, собирайте лиды и растите бизнес с помощью ИИ. Open-source.
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.
Angel Match
База данных из 110 000+ бизнес-ангелов и венчурных инвесторов. Экономьте время на поиске инвесторов — находите подходящих по отрасли, стадии и локации.
SuperX
Растите быстрее в X благодаря скрытым инсайтам и аналитике. Понимайте свою аудиторию, улучшайте контент и ускоряйте рост — всё в одном месте.
DataFast
Аналитика с фокусом на доход. Узнайте, какие маркетинговые каналы приводят клиентов. От первого клика до покупки — понимайте, откуда приходят деньги.
Calendesk
Софт для онлайн-записи. Не тратьте время на согласование встреч — автоматизируйте запись, оплату и управление клиентами. Для терапевтов, коучей, юристов и сферы услуг.
Capgo
Мгновенные обновления для Capacitor-приложений. Выпускайте исправления за минуты, а не недели. Отправляйте OTA-обновления пользователям без задержек App Store.
OpenAlternative
OpenAlternative — каталог open-source альтернатив проприетарному софту. На сайте собраны проекты из разных категорий с информацией о возможностях, стеке технологий и метриках GitHub. Платформа монетизируется через платные размещения и партнёрские ссылки.
Changelogfy
Принимайте лучшие решения и создавайте продукты на основе обратной связи. Единая платформа для сбора фидбека, приоритизации roadmap и публикации обновлений.
Transfer.zip
Самый простой способ отправить файлы. Отправляйте 100 ГБ+ бесплатно — без лимитов, данные зашифрованы. Быстрый и безопасный обмен файлами.
SubSmith
I've been learning Japanese for a few years and kept running into a similar problem. I'd find a video I wanted to learn from, hear a useful sentence, and then realise that turning that sentence into something I could study later was both time consuming and draining at times. I would end up jumping between a video player, subtitles/transcription, a dictionary, screenshots, audio clips and Anki. So I built SubSmith to bring that workflow together. You can drop a video or audio file into it, generate a transcript locally and then use the transcript alongside the media to: * look up words and sentences * replay individual lines * edit the transcript * save useful sentences with their original context/audio * export them as Anki cards The important part for me is that it works with your own media. It isn't tied to a particular streaming service or library, so I can use the random anime episode, podcast, lecture, etc. that I'm actually interested in studying. It's an offline-first desktop app, and transcription happens locally rather than sending the media to a transcription API. I'm sharing it here because I'm now more interested in finding out where this workflow breaks down for other people rather than adding features randomly now that I have solid core/base. For example: * Would you actually save sentences from your own media? * Which part of this process feels like too much work? * Does having the audio/context attached make creating an Anki card more useful? * Would you prefer this to work inside your existing video player/browser? * Is installing a desktop app a significant barrier? * And does requiring an account before starting the free trial make you give up? The current version does require an account to start the trial, and I'm trying to work out whether that's meaningful friction for the people who would actually use this. It's free to try, and I'd particularly appreciate feedback from people who already learn languages through their own videos, anime, films, podcasts or other media. I'm the developer, so I'll be around in the comments to answer questions and discuss how it works. https://subsmith.app
Telem
TL;DR: Web search open router that routes agent web search across providers (Exa, Parallel, Tavily, Brave, SerpAPI etc.), and traces web search results with quality metrics, so you can visualize whether a bad agent run is a web search problem or a reasoning problem. I work in venture capital, and before that I worked in information retrieval. When agents went viral, I built a due diligence agent for my day job. I vibe-coded the first version. It was pretty bad. My first reaction is to blame the model. I tried to change backend models, maybe because Fable doesn't want to help me do people search? Maybe because GPT 5.5 overthinks things? I tried DeepSeek, Kimi, Qwen; I changed prompts again and again, no good. Then I stopped just simply checking the final answer and started reading the actual trajectories: what did the agent search for? What came back? What pages did it read? At what point agents were off the rail? Pretty interesting: Sometimes the agent is stuck and confused because the information provided is irrelevant or even wrong; Sometimes the agent keeps iterate the same query, like "XXX lab UCB CS PhD founder 2026" "2026 XXX lab machine learning systems students startup" because the web search provider is not up-to-date; Sometimes sub-agents give up too early because the agent's intelligence is not enough or too "guardrailed"; Sometimes everything is just slow. A run jeopardized at minute 1 but still ran for another 10 before returning nonsense. That's because of bad searches, but nothing reveals it. So I built two things: 1. Web Search/Fetch router: One gateway for Exa, Parallel, Tavily, Brave, Ceramic, Linkup, Seltz, You, SerpAPI, etc. You or your agent can pick one or query several concurrently, and the responses come back in a homogeneous format. 2. Web Search Observability: Trace every search your agent (and its sub-agents) makes with an evaluator scoring relevance, diversity, and a few other things. Point is to answer one question: is your pipeline broken because the web search is bad? If so, where? Everything is agent-operated. All u need to do is to run this shell script: <curl -fsSL https://docs.telem.ai/alpha_install.sh | sh> Please give it a try, any feedback/suggestion would be great! Especially I’d like to ask: how are you debugging search/retrieval failures nowadays?