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Show HN: qqqa – A fast, stateless LLM-powered assistant for your shell
I built qqqa as an open-source project, because I was tired of bouncing between shell, ChatGPT / the browser for rather simple commands. It comes with two binaries: qq and qa.<p>qq means "quick question" - it is read-only, perfect for the commands I always forget.<p>qa means "quick agent" - it is qq's sibling that can run things, but only after showing its plan and getting an approval by the user.<p>It is built entirely around the Unix philosophy of focused tools, stateless by default - pretty much the opposite of what most coding agent are focusing on.<p>Personally I've had the best experience using Groq + gpt-oss-20b, as it feels almost instant (up to 1k tokens/s according to Groq) - but any OpenAI-compatible API will do.<p>Curious if the HN crowd finds it useful - and of course, AMA.
Show HN: qqqa – A fast, stateless LLM-powered assistant for your shell
I built qqqa as an open-source project, because I was tired of bouncing between shell, ChatGPT / the browser for rather simple commands. It comes with two binaries: qq and qa.<p>qq means "quick question" - it is read-only, perfect for the commands I always forget.<p>qa means "quick agent" - it is qq's sibling that can run things, but only after showing its plan and getting an approval by the user.<p>It is built entirely around the Unix philosophy of focused tools, stateless by default - pretty much the opposite of what most coding agent are focusing on.<p>Personally I've had the best experience using Groq + gpt-oss-20b, as it feels almost instant (up to 1k tokens/s according to Groq) - but any OpenAI-compatible API will do.<p>Curious if the HN crowd finds it useful - and of course, AMA.
Show HN: I scraped 3B Goodreads reviews to train a better recommendation model
Hi everyone,<p>For the past couple months I've been working on a website with two main features:<p>- <a href="https://book.sv" rel="nofollow">https://book.sv</a> - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews<p>- <a href="https://book.sv/intersect" rel="nofollow">https://book.sv/intersect</a> - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: <a href="https://book.sv/remove-my-data" rel="nofollow">https://book.sv/remove-my-data</a>)<p>Technical info available here: <a href="https://book.sv/how-it-works" rel="nofollow">https://book.sv/how-it-works</a><p>Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results. If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page.<p>Note 2: This is uncommon, but if you get an unexpected non-English titled book in the results, it is probably not a mistake and it very likely has an English edition. The "canonical" edition of a book I use for display is whatever one is the most popular, which is usually the English version, but this is not the case for all books, especially those by famous French or Russian authors.
Show HN: I scraped 3B Goodreads reviews to train a better recommendation model
Hi everyone,<p>For the past couple months I've been working on a website with two main features:<p>- <a href="https://book.sv" rel="nofollow">https://book.sv</a> - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews<p>- <a href="https://book.sv/intersect" rel="nofollow">https://book.sv/intersect</a> - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: <a href="https://book.sv/remove-my-data" rel="nofollow">https://book.sv/remove-my-data</a>)<p>Technical info available here: <a href="https://book.sv/how-it-works" rel="nofollow">https://book.sv/how-it-works</a><p>Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results. If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page.<p>Note 2: This is uncommon, but if you get an unexpected non-English titled book in the results, it is probably not a mistake and it very likely has an English edition. The "canonical" edition of a book I use for display is whatever one is the most popular, which is usually the English version, but this is not the case for all books, especially those by famous French or Russian authors.
Show HN: I scraped 3B Goodreads reviews to train a better recommendation model
Hi everyone,<p>For the past couple months I've been working on a website with two main features:<p>- <a href="https://book.sv" rel="nofollow">https://book.sv</a> - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews<p>- <a href="https://book.sv/intersect" rel="nofollow">https://book.sv/intersect</a> - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: <a href="https://book.sv/remove-my-data" rel="nofollow">https://book.sv/remove-my-data</a>)<p>Technical info available here: <a href="https://book.sv/how-it-works" rel="nofollow">https://book.sv/how-it-works</a><p>Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results. If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page.<p>Note 2: This is uncommon, but if you get an unexpected non-English titled book in the results, it is probably not a mistake and it very likely has an English edition. The "canonical" edition of a book I use for display is whatever one is the most popular, which is usually the English version, but this is not the case for all books, especially those by famous French or Russian authors.
Show HN: I scraped 3B Goodreads reviews to train a better recommendation model
Hi everyone,<p>For the past couple months I've been working on a website with two main features:<p>- <a href="https://book.sv" rel="nofollow">https://book.sv</a> - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews<p>- <a href="https://book.sv/intersect" rel="nofollow">https://book.sv/intersect</a> - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: <a href="https://book.sv/remove-my-data" rel="nofollow">https://book.sv/remove-my-data</a>)<p>Technical info available here: <a href="https://book.sv/how-it-works" rel="nofollow">https://book.sv/how-it-works</a><p>Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results. If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page.<p>Note 2: This is uncommon, but if you get an unexpected non-English titled book in the results, it is probably not a mistake and it very likely has an English edition. The "canonical" edition of a book I use for display is whatever one is the most popular, which is usually the English version, but this is not the case for all books, especially those by famous French or Russian authors.
Show HN: Zee – AI that interviews everyone so you only meet the best
Hey HN, I'm Dave one of the co-founders of Zeeda.<p>After scaling my last company (Voxpopme) from 0 to $10M ARR and hiring 100+ people, I lived through the worst part of hiring: posting a role on LinkedIn, getting 200+ applications, and drowning in resumes where 80% are completely unqualified.<p>So we built Zee - an AI that conducts actual 20-30 minute first-round interviews (not just screening) with every applicant, then gives you a shortlist of the 5-10% actually worth your time.<p>We're also solving stakeholder misalignment too before Zee interviews any applicants he talks to key stakeholders about the role to go beyond basic job descriptions.<p>Would love HN's feedback, especially from fellow founders who've felt this pain.
Show HN: I was in a boring meeting so I made an encyclopedia
Show HN: I was in a boring meeting so I made an encyclopedia
Show HN: I got fired so I built a bank statement converter
I recently got fired and decided to channel my energy into something productive. Over two weeks, I spent 16-hour days building a tool that converts Australian bank PDFs into clean, reliable CSVs, tailored specifically for Aussie banks.<p>Most Aussie banks only provide statements as a PDF, and generic converters often fail: columns drift, multi-line descriptions break parsing, headers shift. Existing tools don’t handle it well and I wanted a tool that just works.<p>To get started, I used my own bank statements to build the initial parsers. There was a "duh" moment when I realised how hard it is to get more realistic test data. People don't just hand over their financial ledgers. This solidified my core principle: trust and privacy had to be the absolute top priority.<p>I initially tried building everything client-side in JavaScript for maximum privacy, but performance and reliability were poor, and exposing the parsers on the front-end would have made them easy to copy.<p>I settled on a middle ground: a Python and FastAPI backend on Google Cloud Run. This lets me balance reliability with a strict privacy architecture. Files are processed in real-time and the temp file is deleted immediately after the request is complete. There is no persistent storage and no logging of request bodies.<p>My technical approach is straightforward and focused on reliability:<p>- I use pdfplumber to extract text, avoiding complex and error-prone OCR.<p>- I apply a set of bank-specific regex patterns to pinpoint dates, amounts, and descriptions.<p>- A lookahead heuristic correctly merges multi-line transactions. Each parser is customised to its bank's unique PDF layout quirks.<p>The project is deliberately focused. Instead of supporting hundreds of banks with mediocre results, I'm concentrating on a small set to get them right. It currently supports CommBank, Westpac, UBank, and ING, with ANZ and NAB next. The whole thing is deployed on Cloudflare Pages and outputs clean CSVs ready for Excel, Google Sheets, Xero, or MYOB.<p>It was a fun challenge in reverse-engineering messy, real-world data.<p>Try it out here: <a href="https://aussiebankstatements.com" rel="nofollow">https://aussiebankstatements.com</a><p>I'd love to hear feedback. If it breaks on your statement, a redacted sample would be a huge help for improving the parser.<p>I'm also curious to hear how others here have tackled similar messy data extraction challenges.
Show HN: sudocode – manage specs, tasks, and context-as-code for coding agents
sudocode is a lightweight context management system for coding agents that lives in your repo. It helps organize the chaos of human-AI collaboration by capturing user intent as durable specs and tracking agent activity as issues, all version-controlled with Git. This "context-as-code" approach reduces agent amnesia and accelerates development on long-horizon tasks.
Show HN: sudocode – manage specs, tasks, and context-as-code for coding agents
sudocode is a lightweight context management system for coding agents that lives in your repo. It helps organize the chaos of human-AI collaboration by capturing user intent as durable specs and tracking agent activity as issues, all version-controlled with Git. This "context-as-code" approach reduces agent amnesia and accelerates development on long-horizon tasks.
Show HN: A living wall of life goals and deathbed regrets
Answer the question "Before I die, I want to..." and post it to the wall.<p>I built this as part of a journaling app I’m working on as a side project.
Personally I find life goals fascinating - they're the things we think will make us feel the happiest and the most fulfilled, yet often people think they won't achieve them.<p>I have tried to make it feel atmospheric with the goal of making you stop and think about your own life for a second.
Show HN: A living wall of life goals and deathbed regrets
Answer the question "Before I die, I want to..." and post it to the wall.<p>I built this as part of a journaling app I’m working on as a side project.
Personally I find life goals fascinating - they're the things we think will make us feel the happiest and the most fulfilled, yet often people think they won't achieve them.<p>I have tried to make it feel atmospheric with the goal of making you stop and think about your own life for a second.
Show HN: Centia.io – Open PostgreSQL/PostGIS back end for developers
Built a developer-friendly BaaS around PostgreSQL + PostGIS. Instant APIs, real-time updates, self-hostable Docker image. Feedback welcome
Show HN: An AI to match your voice to songs and artists you should sing
30 second test, don't need to be a singer to try. Feedback please!
Show HN: MyTimers.app offline-first PWA with no build step and zero dependencies
Hello,<p>For quite some time, I've been unsatisfied with the built-in timers on both Android and iOS; especially for workouts, when I needed to set up a configurable number of series with rest periods in between. That's when I started thinking about building something myself. It was just a timer and I said to myself "how hard could it be?", I had no idea.<p>The first iteration of the project worked "just fine", but the UI was an eyesore (even more than it is now), and the UX was quite awful as well. As you can probably guess, I'm not versed in design or front-end development. In fact, my last real experience with front-end work was back when jQuery was still a thing.<p>However, I knew what I wanted to build, and over the last few days (and with the help of the infamous AI) I was able to wrap up the project for my needs. It required quite a lot of "hand holding" and "back and forth", but it helped me smooth out the rough edges and provided great suggestions about the latest ES6 features.<p>The project is, as the title states, an offline-first PWA with zero dependencies; no build step, no cookies, no links, no analytics, nothing other than timers. It uses `Web Components` (a really nice feature, in my opinion, though I still don't get why we can't easily inherit styles from the global scope) and `localStorage` to save timers between uses.<p>I'd appreciate any comments or suggestions, since I just want to keep learning new things.<p><a href="https://mytimers.app/" rel="nofollow">https://mytimers.app/</a>
Show HN: MyTimers.app offline-first PWA with no build step and zero dependencies
Hello,<p>For quite some time, I've been unsatisfied with the built-in timers on both Android and iOS; especially for workouts, when I needed to set up a configurable number of series with rest periods in between. That's when I started thinking about building something myself. It was just a timer and I said to myself "how hard could it be?", I had no idea.<p>The first iteration of the project worked "just fine", but the UI was an eyesore (even more than it is now), and the UX was quite awful as well. As you can probably guess, I'm not versed in design or front-end development. In fact, my last real experience with front-end work was back when jQuery was still a thing.<p>However, I knew what I wanted to build, and over the last few days (and with the help of the infamous AI) I was able to wrap up the project for my needs. It required quite a lot of "hand holding" and "back and forth", but it helped me smooth out the rough edges and provided great suggestions about the latest ES6 features.<p>The project is, as the title states, an offline-first PWA with zero dependencies; no build step, no cookies, no links, no analytics, nothing other than timers. It uses `Web Components` (a really nice feature, in my opinion, though I still don't get why we can't easily inherit styles from the global scope) and `localStorage` to save timers between uses.<p>I'd appreciate any comments or suggestions, since I just want to keep learning new things.<p><a href="https://mytimers.app/" rel="nofollow">https://mytimers.app/</a>
Show HN: Yourshoesmells.com – Find the most smelly boulder gym
A crowdsourced map for ranking Boulder gym stinkiness and difficulty. Get a detailed view of the gym. “Is there toprope in the gym?” “Any training boards?”
Show HN: I built a local-first daily planner for iOS