Show HN: Bribes.fyi – Know before you go. New feature added https://ift.tt/IXtW5m6

Show HN: Bribes.fyi – Know before you go. New feature added https://ift.tt/zv0shDQ July 26, 2026 at 12:36AM

Show HN: I made some transistor animations https://ift.tt/WxwQtjh

Show HN: I made some transistor animations Hi HN, I made some animations of the most important kinds of transistors using my semiconductor simulation, details of which are on the page. I tried to make the visuals as realistic as possible while also aiming for clarity. If you want to go beyond the charge carriers and look at, for example, the electric field, you can do so in the simulation software. The desktop software also has less common devices like IBGTs and SCRs that have similar animations. The last thread about my software was posted here about a year ago: https://ift.tt/mv2GXz8 https://ift.tt/57dc0rG July 25, 2026 at 12:07AM

Show HN: Sourceminder.org - token-efficient code search https://ift.tt/1FeXwuU

Show HN: Sourceminder.org - token-efficient code search Hey HN, The code indexing tools released in December now have added capabilities (Rust, Perl support), better token efficiency, easier install method, and a website! The website has a wasm port of the query tool, qi, so you can try it out in the browser. Let me know what you think. Thanks! https://ift.tt/i41sOrm July 24, 2026 at 10:28PM

Show HN: A representation of a chapter of your life through music https://ift.tt/7zYZJxF

Show HN: A representation of a chapter of your life through music https://www.cuecard.live July 24, 2026 at 11:22PM

Show HN: Advanced Coffee Search Covering Over 17,000 coffees https://ift.tt/CPTDxJH

Show HN: Advanced Coffee Search Covering Over 17,000 coffees https://ift.tt/gA0G7Ni July 24, 2026 at 02:59AM

Show HN: Notebooker.ai – NotebookLM alternative, your own models, keys, storage https://ift.tt/T6MyYGV

Show HN: Notebooker.ai – NotebookLM alternative, your own models, keys, storage Hi HN. This is a personal side project I've been building for about six months on top of the open-source Open Notebook platform ( https://ift.tt/c4hCWuU ). Notebooker saves the stuff you'd otherwise lose in tabs and bookmarks - links, PDFs, audio, video - and makes it useful later: chat with a notebook and get answers that cite the exact source, turn a reading list into a podcast episode (private RSS feed, works in any player), or generate study material from your own sources. The part I've had the most fun with is a plugin engine for creation types - flashcards, charts, infographics, mindmaps, textbooks, essays, slideshows, timelines, wikis are each plugins, and new ones can be added without touching the core. Everything I've built on top of open-notebook has either been submitted upstream as PRs or is open source at https://ift.tt/0nJiTMx . Decisions this crowd might care about: - Bring your own AI keys (OpenAI, Anthropic, local/OpenAI-compatible endpoints) or use the built-in defaults. I enjoy testing Cloudflare Worker AI models. Nothing you save is used for training. You can deploy you own OpenAI compatible endpoint to play with at https://ift.tt/tgq5Lcz - Bring your own S3-compatible storage (R2, Spaces, AWS) if you want your files in a bucket you control. - Webhooks in and out — anything that can POST JSON can trigger a workflow, and workflows can POST anywhere. Notebook also processes incoming RSS feeds and generates its own - Export everything or delete your account with a click. There's a view-only demo notebook at https://ift.tt/Qvh7Tau . Happy to answer any questions. I'm expecting some hiccups in releasing, feel free to report bugs or feedback. https://notebooker.ai/ July 24, 2026 at 12:32AM

Show HN: Trifle – Open-source analytics that stores answers, not events https://ift.tt/y0navJd

Show HN: Trifle – Open-source analytics that stores answers, not events Trifle is an open-source time-series analytics library that aggregates nested counters instead of storing raw events. All in the database you already have. After rebuilding it twice over 10 years, it now tracks ~1B events a day at my day job. It started in 2015 as my own Rails APM. I plugged into ActiveSupport::Notifications, got a few small users, and one bigger one whose scraping app broke everything. That sparked the core idea: aggregate counters into pre-defined time buckets, so a single write increments multiple buckets at once. The APM eventually faded away without much traction. Later in 2021 I needed analytics at my day job. Instead of going for something out there I revised the idea of Trifle as a more generic analytics library, borrowing some data warehouse ideas. First used Redis, then Postgres, eventually MongoDB. Hence why Trifle::Stats comes with multiple drivers that keep the DSL unified while storage layer changes with your needs. In our case (huge write volume, some reads) PG read faster but slowed on large writes. The nested values are the whole trick here. Single: Trifle::Stats.track( key: 'requests::aws::s3_uploads', values: { count: 1, status: { request.response_code => 1 }, size: payload.bytes, duration: { sum: request.duration, count: 1 } } ) builds up counts for requests, success rate, result status codes, duration for multiple time buckets at once. Single bucket from 2am then looks like: { count: 14, status: { 200: 12, 500: 2 }, size: 5628341, duration: { sum: 43, count: 14 } } If request.duration is in seconds, then sum stored under duration would be in seconds as well. Success rate is never stored, but it is calculated by dividing 200s over total number of requests. Same with average duration: sum over count. You ask for a metrics key, granularity and timeframe and you get back aggregated values at each point. Ready for charts or to answer "Average response time over last 30 days". There's a Series wrapper for aggregating and formatting values for charts in a simple call. And as building dashboards is not as much fun for other devs as I thought, I built Trifle App - a visual layer with dashboards, scheduled digests and alerts. It's written in Elixir, so I ported the library to Elixir too. And later to Go for a CLI. All three are compatible, write in one and read in another. Today we track activity from over 100M background jobs a day which turns into about 1B events. It runs surprisingly cheap when you're willing to trade some safety away (turn off journaling and write concerns in Mongo). 3-node Hetzner MongoDB cluster where the primary does 20% utilization costs us around $1k/month. It has its limitations. Payloads can't hold tens of thousands of keys. Documents becomes too large to update efficiently. Some planning ahead is needed. And then there are no dimensions. Sometimes you can nest them (country - there are only so many countries), sometimes it's better to have dedicated metrics key per dimension (customer - growing forever). That multiplies tracked events, hence 1B events from 100M jobs. The libraries are MIT. The App is source-available under ELv2 - free to self-host and paid cloud if you want it managed. I build this on the side with no investor money to burn on a free service. Happy to answer anything about architecture, storage models, my failures or why I didn't give up on this yet. https://trifle.io/ July 22, 2026 at 08:09PM