Каталог продуктов
Отслеживается продуктов: 190
boringBar
Hi HN! I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together. I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine). I’ve been dogfooding it for a few months now, and it finally felt polished enough to share. It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile. I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else. P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
Oberon System 3 runs natively on Raspberry Pi 3 (with ready SD card)
Show HN: Oberon System 3 runs natively on Raspberry Pi 3 (with ready SD card)
I'm organizing a vibe coding game dev competition
Hi everyone, I just saw a vibe coded game on HN, and thought maybe I should post about this here. I'm organizing a vibe coding game dev competition called Vibe Jam. Last year we did it too and there was 1000+ games submitted. This year the deadline is May 1 and you can submit your games until then. There's $35,000 in prizes with the Gold prize being $20,000. Let me know what you think! -Pieter
Keyboard First Email Client
My email clients/inbox really fu*ing annoyed me. Tallyman is what happened next: a keyboard driven email client on top of Gmail and Outlook. Your vim muscle memory works (j/k, gg, relative line numbers, counts, ...) 39 rebindable shortcuts, command palette, email templates, themes ... No migration. OAuth only. Verified by Microsoft and live now. Google verification is under review. 30 day free trial, $9/mo per inbox after that. Write me an email if you need an extended trial: contact@tallyman.io
I built a site that shows every world event you lived through
Show HN: I built a site that shows every world event you lived through
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
FluidCAD
Hello HN users, This is a CAD by code project I have been working on on my free time for more than year now. I built it with 3 goals in mind: - It should be familiar to CAD designers who have used other programs. Same workflow, same terminology. - Reduce the mental effort required to create models as much as possible. This is achieved by: - Provide live rendering and visual guidance as you type. - Allow the user to reference existing edges/faces on the scene instead of having to calculate everything. - Provide interactive mouse helpers for features that are hard to write by code: Only 3 interactive modes for now: Edge trimming, Sketch region extrude, Bezier curve drawing. - Implicit coding whenever possible: e.g: There are sensible defaults for most parameters. The program will automatically fuse intersecting objects together so you do not have to worry about what object needs to be fused with what. - It should be reasonably fast: The scene objects are cached and only the updated objects are re-computed. I think I have achieved these goals to a good extent. The program is still in early stages and there are many features I want to add, rewrite but I think it is already usable for simple models. Update to add more details: This is based on Opencascade.js WASM binding. So you get all the good things that come with any brep kernel. Fillets, chamfers, step import and export... The scene is webview but the editing is in your local file. You use your own editor and the environment you are familiar with. One important feature that I think make this stand out among other code based cad software is the ability to transform features not just shapes. More here: https://fluidcad.io/docs/guides/patterns You can see it in action in the lantern example: https://fluidcad.io/docs/tutorials/lantern
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.
Hindsight Simulator
Show HN: Hindsight Simulator – Go back in time and get rich
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!
Control your X/Twitter feed using a small on-device LLM
We built a Chrome extension and iOS app that filters Twitter's feed using Qwen3.5-4B for contextual matching. You describe what you don't want in plain language—it removes posts that match semantically, not by keyword. What surprised us was that because Twitter's ranking algorithm adapts based on what you engage with, consistent filtering starts reshaping the recommendations over time. You're implicitly signaling preferences to the algorithm. For some of us it "healed" our feed. Currently running inference from our own servers with an experimental on-device option, and we're working on fully on-device execution to remove that dependency. Latency is acceptable on most hardware but not great on older machines. No data collection; everything except the model call runs locally. It doesn't work perfectly (figurative language trips it up) but it's meaningfully better than muting keywords and we use it ourselves every day. Also promising how local / open models can now start giving us more control over the algorithmic agents in our lives, because capability density is improving.
41 years sea surface temperature anomalies
Show HN: 41 years sea surface temperature anomalies