Product Catalog
16 products tracked
Angel Match
A curated database of 110,000+ angel investors and venture capitalists. Save time searching for investors — find the right ones for your startup with filters by industry, stage, and location.
Calendesk
Appointment scheduling software. Don't waste time arranging meetings with clients — automate bookings, payments, and client management. Built for therapists, coaches, lawyers, and service businesses.
Changelogfy
Take better decisions and build impact products from user feedback. All-in-one platform to capture and organize feedback, prioritize your product roadmap, and announce updates.
Superlog (YC P26)
Hey HN, we’re Nico and Arseniy, co-founders of Superlog (https://superlog.sh). We're building a self-installing, self healing observability tool meant not to be opened. It has a wizard that daily sets up proper logging and an agent that investigates errors and opens PRs. Super short demo: https://www.youtube.com/watch?v=xFhU9Mk247M. In our earlier startups, we tried Sentry, Datadog, Grafana, Dash0, and nothing was good enough. Proper telemetry and alerting still requires a ton of manual setup. We struggled with adding good logs, so debugging was tough, especially as codebases grow at a faster pace. Meanwhile, the Datadog/Dash0 bill kept climbing, and we still spent engineering hours to learn, configure, and maintain our observability tooling. With Sentry, we found ourselves flooded by a stream of alerts into our Slack channel, most were duplicates or lacked context, so alert fatigue/constant interrupts were a real pain. The #ops notification is consistently the worst feeling on a Saturday morning We’ve seen too many times servers run out of memory and disk, and three AWS metrics giving us three different values. Half of the graphs on dashboards are normally empty or outdated, and manually clicking through UIs, especially when the team is small, seems like a huge waste of time. At some point we realized that solving this problem would be more valuable than the things we had been working on, and we had the expertise to do it, since Arseniy had spent years at Datadog, getting paged during the night to debug production incidents. So we decided to build a platform that would just work: agent-first, MCP-native, zero-setup. Here’s how Superlog works: we have a wizard that scans your repo, and automatically instruments it with well-structured logs, traces and metrics via OpenTelemetry. We make sure to highlight main failure modes, endpoint performance, usage per tenant, and LLM/upstream cost (by callsite, tenant and model). Errors get fingerprinted and grouped into incidents, so you see one issue, not a thousand duplicates. When you get a notification from Superlog, you see a clear failure summary, its inferred severity and impact upfront. Then the agent investigates and tries to solve the issue. If it has enough context, it produces a concise and tested PR. If it doesn't, it posts its findings for the investigating team, and automatically pulls in the engineers that could contribute more context based on documentation, previous investigations and Slack threads. Either way the output is one clean PR per incident, posted in Slack, that you can merge, ignore, or open as a Claude Code session and modify. Three things we think are different from other observability vendors: (1) We solve the setup pain. The wizard will instrument everything with native OTel SDKs, respecting the semantic conventions, with proper service and environment tagging. We’re also working on native automatic dashboards and alerts, so that you can see what’s going on in a glance and don’t miss subtle failure modes. (2) Our telemetry doesn’t decay. The wizard runs daily, and keeps adding logs, alerts and dashboards where it’s needed. You don't have to remember to instrument new features. The next time something breaks, the data you need to debug it is already there. (3) Our goal is to solve alert fatigue. We use agents to merge similar errors and refine the summaries, giving you relevant information upfront. We have a custom evaluation setup that makes sure that our summaries are dense and correct, and severity and impact is on point. We also give you confidence scores for every LLM-enhanced metric so that wrong guesses don’t get boosted. Important: superlog telemetry is vendor-neutral, so you keep all the logs/metrics/traces we install. Pricing is on the site. We're early, so expect rough edges and please tell us when you find them. You can try it at https://superlog.sh. We'd love to hear what you're using today, what's broken about it, and whether the "one mergeable PR per incident" model sounds useful or terrifying. Especially keen to hear from folks running integration-heavy products, anyone who's rolled their own observability, and anyone who has tried Sentry / Datadog MCPs and given up. Comments and feedback welcome!
Vibe Coding a $20k /Year Enterprise Logistics Platform
Show HN: Vibe Coding a $20k /Year Enterprise Logistics Platform
Live Sun and Moon Dashboard with NASA Footage
Show HN: Live Sun and Moon Dashboard with NASA Footage
CatchAll
Hey HN, Artem and Maksym from NewsCatcher here. Some of you know us as we started six years ago as two freshly graduated economics students who decided to build the best news API product. We started NewsCatcher thinking the market for news APIs was so big that we could build a self-serve platform and get millions of $29 users. Obviously, it was a wrong assumption. We pivoted to serve enterprises and had success with it. But we are hackers at heart, and we want to serve hackers. We haven't used our Launch HN yet, so consider this our smoke test. We're looking for feedback and power users rather than revenue. So, happy to provide enough credits for any HN user who finds CatchAll useful. CatchAll is built for one thing: retrieving every matching event from the web. The use cases that fit it are ones where missing events have real consequences — funding and M&A monitoring, regulatory and compliance feeds (FDA approvals, SEC filings, policy changes), cybersecurity incident tracking, supply chain signals. If your pipeline consumes structured records and the answer to your query is "find all of them," that's where it works. It's not the right tool for small, bounded queries that return 5 high-precision results. The 15-minute job time is a direct consequence of the pipeline depth: analyze, fetch, cluster, validate, extract, deduplicate. You're not getting a ranked list of links; you're getting a verified record set. Our latest benchmark run: https://newscatcherapi.com/blog-posts/web-search-api-benchma...
Data-driven GEO and marketing agent platform
Show HN: Data-driven GEO and marketing agent platform
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!
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
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.
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!