Каталог продуктов
Отслеживается продуктов: 51
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.
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
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.
I built a local data lake for AI powered data engineering and analytics
I got tired of the overhead required to run even a simple data analysis - cloud setup, ETL pipelines, orchestration, cost monitoring - so I built a fully local data-stack/IDE where I can write SQL/Py, run it, see results, and iterate quickly and interactively. You get data lake like catalog, zero-ETL, lineage, versioning, and analytics running entirely on your machine. You can import from a database, webpage, CSV, etc. and query in natural language or do your own work in SQL/Pyspark. Connect to local models like Gemma or cloud LLMs like Claude for querying and analysis. You don’t have to setup local LLMs, it comes built in. This is completely free. No cloud account required. Downloading the software - https://getnile.ai/downloads Watch a demo - https://www.youtube.com/watch?v=C6qSFLylryk Check the code repo - https://github.com/NileData/local This is still early and I'd genuinely love your feedback on what's broken, what's missing, and if you find this useful for your data and analytics work.
An interactive map of Tolkien's Middle-earth
An interactive map of Tolkien’s Middle-earth, with events from across the legendarium plotted as markers. I have been commuting a fair bit between the East and West coast, and thanks to American Airlines' free onboard WiFi, I was able to vibe-code a full interactive map of Middle-earth right from my economy seat at the back of the bus. It's rather amazing how much an LLM knows about Tolkien's work, and it was fun to delve into many of the nooks and crannies of Tolkien's lore. Some features: - Plot on the map the journey of the main characters in both The Hobbit and The Lord of the Rings. - Follow a list of events in the chronological Timeline - Zoom in on the high-def map and explore many of the off-the-main-plotline places - Use the 'measure distances' feature to see how far apart things are. I also had a lot of fun learning about tiling to allow for efficient zooming. If you are anything like me, this should provide a fun companion to reading the books or watching the movies (note that on this site, I followed the book narrative, and did not include Peter Jackson's many departures) If you get the chance to check it out, I would love more feedback, and if there is demand, I might do the same for Game of Thrones.
Output.ai
Show HN: Output.ai - OSS framework we extracted from 500+ production AI agents
Per-user isolated environments for AI agents
Show HN: Per-user isolated environments for AI agents
I Built Paul Graham's Intellectual Captcha Idea
PG has posted about improving social networks using something like an "intellectual CAPTCHA" many times [1][2][3][4] - "Make users pass a test on basic concepts like the distinction between necessary and sufficient conditions before they can tweet." I felt the same way. So I built one using a mix of simple math, logic, and Twitter/X Community Noted posts. Try sample questions here - https://mentwire.com/sample - without signing up. - Invites are temporarily open to HN users. - Onboarding test + one daily question before accessing feed, post or reply. - Posts authors are anonymous until upvoted or downvoted, forcing evaluation of content on merit. - Face ID (on-device only) to post/reply, pangram checks for AI text. Sourcing good questions turned out to be much harder than I thought. If you have suggestions to scale this, I would love to hear. Eventually, could be gated across disciplines/topics to get a competence × interest graph instead of the pure interest graph of today's social networks. [1] https://x.com/paulg/status/1235949761359904768 [2] https://x.com/paulg/status/1576517990182359040 [3] https://x.com/paulg/status/1514979883948126209 [4] https://x.com/paulg/status/1505842647319126016 Repost from https://news.ycombinator.com/item?id=47577829. This link contains a full quiz and linked directly to sample to try without signing up.
Cabinet
Hi HN, for quite some time I've been thinking how LLMs are missing the knowledge base, where I can dump CSVs, PDFs, and most important, inline web app. running on Claude Code (bring your own agent) with agents with heartbeats and jobs https://runcabinet.com It runs locally and is installable via npm. GitHub (open source): https://github.com/hilash/cabinet This is still very early. I put the first version together quickly after seeing a post by Andrej Karpathy about LLM knowledge bases, which matched closely with what I’d been building. Some people have already started trying it and opening PRs, which has been encouraging (got 374 stars in 2 days :] ) If useful: Waitlist for a hosted version: https://runcabinet.com/waitlist Discord (small, but growing): https://discord.gg/rxd8BYnN Would really appreciate feedback: does this “KB + agents” model make sense? what would you expect from a system like this? where does this fall apart? Happy to answer anything. Hila
Ray
I've been using this daily for 4 months and figured others might find it useful. This is my first open source project so would love any feedback. Ray connects to your bank via Plaid, stores everything in an encrypted local SQLite database, and lets you ask questions about your finances in natural language. No cloud, no account, your data is stored on your machine. Before anything reaches the LLM, all PII is stripped — your name, companies, transaction details are redacted and replaced with tokens, then rehydrated locally in the response. The AI never sees who you are.
Sycamore
Show HN: Sycamore – next gen Rust web UI library using fine-grained reactivity
Cerno
Show HN: Cerno – CAPTCHA that targets LLM reasoning, not human biology