Changelogfy
Принимайте лучшие решения и создавайте продукты на основе обратной связи. Единая платформа для сбора фидбека, приоритизации roadmap и публикации обновлений.
Calendesk
Софт для онлайн-записи. Не тратьте время на согласование встреч — автоматизируйте запись, оплату и управление клиентами. Для терапевтов, коучей, юристов и сферы услуг.
Angel Match
База данных из 110 000+ бизнес-ангелов и венчурных инвесторов. Экономьте время на поиске инвесторов — находите подходящих по отрасли, стадии и локации.
I made a free list of 100 places to promote your SaaS
It’s a curated list of directories and launch platforms where you can submit your product and actually get traffic, backlinks, and early users I included useful data for each one so you don’t have to guess where it’s worth posting Features; 100+ directories and platforms to promote your SaaS; SEO data like domain rating and traffic; Info on whether links are dofollow or nofollow; Organized and easy to go through; Saves hours of searching and manual research; Great for getting early users and backlinks; If you’re launching or growing a SaaS and don’t know where to promote it, this should help
Dull
I kept deleting and redownloading Instagram because I couldn't stop watching Reels but needed the app for DMs. Tried screen time limits, just overrode them. So I built this. Dull loads Instagram, YouTube, Facebook, and X and filters out short-form content with a mix of CSS and JS injection. MutationObserver handles anything that lazy-loads after the page renders, which is most of the annoying stuff since these platforms love to load content dynamically. The ongoing work is maintaining the filters. Platforms change their DOM all the time, Instagram obfuscates class names, YouTube restructures how Shorts appear in the feed, etc. It's a cat-and-mouse thing that never really ends. Also has grayscale mode, time limits, and usage tracking. Happy to answer questions.
Contrapunk
Show HN: Contrapunk – Real-time counterpoint harmony from guitar input, in Rust
A cartographer's attempt to realistically map Tolkien's world
Show HN: A cartographer's attempt to realistically map Tolkien's world
Relvy
Hey HN! We are Bharath, and Simranjit from Relvy AI (https://www.relvy.ai). Relvy automates on-call runbooks for software engineering teams. It is an AI agent equipped with tools that can analyze telemetry data and code at scale, helping teams debug and resolve production issues in minutes. Here’s a video: [[[https://www.youtube.com/watch?v=BXr4_XlWXc0]]] A lot of teams are using AI in some form to reduce their on-call burden. You may be pasting logs into Cursor, or using Claude Code with Datadog’s MCP server to help debug. What we’ve seen is that autonomous root cause analysis is a hard problem for AI. This shows up in benchmarks - Claude Opus 4.6 is currently at 36% accuracy on the OpenRCA dataset, in contrast to coding tasks. There are three main reasons for this: (1) Telemetry data volume can drown the model in noise; (2) Data interpretation / reasoning is enterprise context dependent; (3) On-call is a time-constrained, high-stakes problem, with little room for AI to explore during investigation time. Errors that send the user down the wrong path are not easily forgiven. At Relvy, we are tackling these problems by building specialized tools for telemetry data analysis. Our tools can detect anomalies and identify problem slices from dense time series data, do log pattern search, and reason about span trees, all without overwhelming the agent context. Anchoring the agent around runbooks leads to less agentic exploration and more deterministic steps that reflect the most useful steps that an experienced engineer would take. That results in faster analysis, and less cognitive load on engineers to review and understand what the AI did. How it works: Relvy is installed on a local machine via docker-compose (or via helm charts, or sign up on our cloud), connect your stack (observability and code), create your first runbook and have Relvy investigate a recent alert. Each investigation is presented as a notebook in our web UI, with data visualizations that help engineers verify and build trust with the AI. From there on, Relvy can be configured to automatically respond to alerts from Slack Some example runbook steps that Relvy automates: - Check so-and-so dashboard, see if the errors are isolated to a specific shard. - Check if there’s a throughput surge on the APM page, and if so, is it from a few IPs? - Check recent commits to see if anything changed for this endpoint. You can also configure AWS CLI commands that Relvy can run to automate mitigation actions, with human approval. A little bit about us - We did YC back in fall 2024. We started our journey experimenting with continuous log monitoring with small language models - that was too slow. We then invested deeply into solving root cause analysis effectively, and our product today is the result of about a year of work with our early customers. Give us a try today. Happy to hear feedback, or about how you are tackling on-call burden at your company. Appreciate any comments or suggestions!
GitByBit
GitByBit is an interactive course that teaches you Git by practice right in your code editor. You follow bite-sized instructions, run real Git commands in the terminal or click through your editor’s Git interface, and the course verifies what happened. When something breaks, it tells you why and how to get unstuck. It's well-designed and illustrated.
A WYSIWYG word processor in Python
Hi all, Finding a good data structure for a word processor is a difficult problem. My notebook diaries on the problem go back 25 years when I was frustrated with using Word for my diploma thesis - it was slow and unstable at that time. I ended up getting pretty hooked on the problem. Right now I’m taking a professional break and decided to finally use the time to push these ideas further, and build MiniWord — a WYSIWYG word processor in Python. My goal is to have a native, non-HTML-based editor that stays simple, fast, and is hackable. So far I am focusing on getting the fundamentals right. What is working yet is: - Real WYSIWYG editing (no HTML layer, no embedded browser) with styles, images and tables. - Clean, simple file format (human-readable, diff-friendly, git-friendly, AI-friendly) - Markdown support - Support for Python-plugins Things that I found: - B-tree structures are perfect for holding rich text data - A simple text-based file format is incredibly useful — you can diff documents, version them, and even process them with AI tools quite naturally What I’d love feedback on: - Where do you see real use cases for something like this? - What would be missing for you to take it seriously as a tool or platform? - What kinds of plugins or extensions would actually be worth building? Happy about any thoughts — positive or critical. Greetings
Zatanna
Hey! I am Alex and together with my co-founder Tarun built Kampala (https://www.zatanna.ai/kampala). It’s a man-in-the-middle (MITM) style proxy that allows you to agentically reverse engineer existing workflows without brittle browser automation or computer use agents. It works for websites, mobile apps, desktop apps. Demo: https://www.youtube.com/watch?v=z_PeostC-b4. Many people spend hours per day in legacy dashboards and on-prem solutions reconciling data across platforms. Current attempts at automation use browser automations or computer use agents which are brittle, slow, and nondeterministic. I come from a web reverse engineering background and spent the last 7-8 years building integrations by hand for sneaker/ticket releases, sportsbooks logins, and everything in\ between. During that time I consulted for several companies and brought them off of browser based infrastructure into the requests layer. When we started Zatanna (that’s our company name) we worked in dental tech, which meant we had to deal with tons of insurance payer dashboards and legacy dental-practice solutions. Our superpower (as a fairly undifferentiated voice agent/front desk assistant company) was that we could integrate with nearly any system requested. During this time we built extensive tooling (including what we’re now calling Kampala) to allow us to spin up these integrations quickly. Existing MITM proxies and tooling didn’t work for a few reasons: (1) They manipulated the TLS and HTTP2 fingerprint over the wire which was detected by strict anti-bots. (2) They had bad MCPs which did not adequately expose necessary features like scripts/replay. (3) They did not allow for building workflows or actions given a sample or sequence of requests. As the tools we built got more powerful, we began to use them internally to scrape conference attendees, connect to external PMS systems, and interact with slack apps. I even sent it to my property manager mom, who (with a lot of help from me lol), automated 2-3 hours of billing information entry in Yardi. At that point we realized that this wasn’t really about dentistry :) Because Kampala is a MITM, it is able to leverage existing session tokens/anti-bot cookies and automate things deterministically in seconds. You can either use our agent harness that directly creates scripts/apis by prompting you with what actions to make, or our MCP by manually doing a workflow once, and asking your preferred coding agent to use Kampala to make a script/API to replicate it. Once you have an API/script, you can export, run, or even have us host it for you. We think the future of automation does not consist of sending screenshots of webpages to LLMs, but instead using the layer below that computers actually understand. Excited to hear your thoughts/questions/feedback!
Data-driven GEO and marketing agent platform
Show HN: Data-driven GEO and marketing agent platform