The best Hacker News stories from Show from the past day
Latest posts:
Show HN: In a single HTML file, an app to encourage my children to invest
Show HN: In a single HTML file, an app to encourage my children to invest
Show HN: In a single HTML file, an app to encourage my children to invest
Show HN: HUD-like live annotation and sketching app for macOS
Hey all!<p>I'm happy to announce we've finally released our 2nd macOS app, Draw Over It, a tiny desktop app that enables drawing, highlighting, or annotating directly on top of anything on your Mac.<p>I've always wanted something like this for instant and unobtrusive sketching and annotation for pair programming and demos. I always found the standard web-based diagram and drawing tools a bit too cumbersome. So we built a simple overlay that could appear over any window or app with one shortcut.<p>It doesn't collect any user data and doesn't require any system permissions - it's sandboxed. It all stays on your device. You can export your annotations to a PNG with one click - or just take a screenshot if you need the background too.<p>It offers a slim but functional toolkit for every day tasks:<p>- Global hotkeys, hit a shortcut and start drawing over any app<p>- Multiple tools, pens, shapes, highlighters<p>- Per-screen canvases, each monitor gets its own space<p>- Focus mode, temporarily blur the background to emphasize what matters<p>- Low footprint, no subscriptions, no sign-ups, no data collected<p>- Localization, the app is translated to 14 languages<p>These two reasons make it different from other canvas apps, it's simple, lean and keeps your data on-device only.<p>It’s a one-time purchase ($2.99) on the Mac App Store.<p>I’d love feedback and suggestions for improvements!
Show HN: HUD-like live annotation and sketching app for macOS
Hey all!<p>I'm happy to announce we've finally released our 2nd macOS app, Draw Over It, a tiny desktop app that enables drawing, highlighting, or annotating directly on top of anything on your Mac.<p>I've always wanted something like this for instant and unobtrusive sketching and annotation for pair programming and demos. I always found the standard web-based diagram and drawing tools a bit too cumbersome. So we built a simple overlay that could appear over any window or app with one shortcut.<p>It doesn't collect any user data and doesn't require any system permissions - it's sandboxed. It all stays on your device. You can export your annotations to a PNG with one click - or just take a screenshot if you need the background too.<p>It offers a slim but functional toolkit for every day tasks:<p>- Global hotkeys, hit a shortcut and start drawing over any app<p>- Multiple tools, pens, shapes, highlighters<p>- Per-screen canvases, each monitor gets its own space<p>- Focus mode, temporarily blur the background to emphasize what matters<p>- Low footprint, no subscriptions, no sign-ups, no data collected<p>- Localization, the app is translated to 14 languages<p>These two reasons make it different from other canvas apps, it's simple, lean and keeps your data on-device only.<p>It’s a one-time purchase ($2.99) on the Mac App Store.<p>I’d love feedback and suggestions for improvements!
Show HN: Research Hacker News, ArXiv & Google with Hierarchical Bayesian Models
Hi Hacker News! I’m a Bayesian statistician that has been working on applying hierarchical mixture models (originally developed for genomics) to structure text data, and in the process, used these models to build (what started as a personal) tool for conducting literature reviews and deep research.<p>My literature review process starts with a broad search to find a few key papers/groups, and from there expands along their citation networks. I needed to conduct a few rounds of literature reviews during the course of my research and decided to build a tool to facilitate this process. The tool started as an experimental wrapper over low-level statistical software in C, quickly became a testing/iteration ground for our api, and is now my personal go-to for lit reviews.<p>The tool organizes corpuses of text content, visualizes the high level themes, and enables me to pull up relevant excerpts. Unlike LLMs, this model transparently organizes the data and can train from scratch quickly on small datasets to learn custom hierarchical taxonomies. My favorite part of the tool is the citation network integration: any research paper it pulls up has a button “Citation Network Deep Dive” that pulls every paper that cites or is cited by the original paper, and organizes it for further exploration.<p>I initially built this tool for academic research, but ended up extending it to support Hacker News to mine technical conversation, the top 200 Google results, and earnings transcripts. We have a gallery of ready to explore results on the homepage. If you are kicking off a custom deep dive, it takes about 1-5 minutes for academic search, 3-7 minutes for Hacker News, and 5-10 minutes for Google.
To demonstrate the process, I put together a video walkthrough of a short literature review I conducted on AI hallucinations: <a href="https://www.youtube.com/watch?v=OUmDPAcK6Ns" rel="nofollow">https://www.youtube.com/watch?v=OUmDPAcK6Ns</a><p>I host this tool on my company’s website, free for personal use. I’d love to know if the HN community finds it useful (or to hear what breaks)!
Show HN: Research Hacker News, ArXiv & Google with Hierarchical Bayesian Models
Hi Hacker News! I’m a Bayesian statistician that has been working on applying hierarchical mixture models (originally developed for genomics) to structure text data, and in the process, used these models to build (what started as a personal) tool for conducting literature reviews and deep research.<p>My literature review process starts with a broad search to find a few key papers/groups, and from there expands along their citation networks. I needed to conduct a few rounds of literature reviews during the course of my research and decided to build a tool to facilitate this process. The tool started as an experimental wrapper over low-level statistical software in C, quickly became a testing/iteration ground for our api, and is now my personal go-to for lit reviews.<p>The tool organizes corpuses of text content, visualizes the high level themes, and enables me to pull up relevant excerpts. Unlike LLMs, this model transparently organizes the data and can train from scratch quickly on small datasets to learn custom hierarchical taxonomies. My favorite part of the tool is the citation network integration: any research paper it pulls up has a button “Citation Network Deep Dive” that pulls every paper that cites or is cited by the original paper, and organizes it for further exploration.<p>I initially built this tool for academic research, but ended up extending it to support Hacker News to mine technical conversation, the top 200 Google results, and earnings transcripts. We have a gallery of ready to explore results on the homepage. If you are kicking off a custom deep dive, it takes about 1-5 minutes for academic search, 3-7 minutes for Hacker News, and 5-10 minutes for Google.
To demonstrate the process, I put together a video walkthrough of a short literature review I conducted on AI hallucinations: <a href="https://www.youtube.com/watch?v=OUmDPAcK6Ns" rel="nofollow">https://www.youtube.com/watch?v=OUmDPAcK6Ns</a><p>I host this tool on my company’s website, free for personal use. I’d love to know if the HN community finds it useful (or to hear what breaks)!
Show HN: Learn German with Games
I just started learning German, and it has been a frustrating experience, to say the least. There are so many seemingly arbitrary rules that make pattern recognition very difficult. Therefore, I have been looking for ways to make memorization a bit easier and fun. So, I came up with a bunch of games to make learning German a bit more engaging. Hope you find it useful as well!
Show HN: Learn German with Games
I just started learning German, and it has been a frustrating experience, to say the least. There are so many seemingly arbitrary rules that make pattern recognition very difficult. Therefore, I have been looking for ways to make memorization a bit easier and fun. So, I came up with a bunch of games to make learning German a bit more engaging. Hope you find it useful as well!
Show HN: Learn German with Games
I just started learning German, and it has been a frustrating experience, to say the least. There are so many seemingly arbitrary rules that make pattern recognition very difficult. Therefore, I have been looking for ways to make memorization a bit easier and fun. So, I came up with a bunch of games to make learning German a bit more engaging. Hope you find it useful as well!
Show HN: Dlog – Journaling and AI coach that learns what drives wellbeing (Mac)
Hi HN! I’m Johan. I built Dlog, a journaling app with an AI coach that tracks how your personality, daily experiences, and well-being connect over time. It’s based on my PhD research in entrepreneurial well-being.<p>Edit: here's a video demo so you can see it before downloading: <a href="https://www.youtube.com/watch?v=74C4P8I164M" rel="nofollow">https://www.youtube.com/watch?v=74C4P8I164M</a> - it's unvarnished but I'm told that's how people like it here :)<p>How Dlog works<p>- Journal and set goals/projects; Dlog scores entries on-device (sentiment + narrative signals) and updates your personal model.<p>- A built-in structural equation model (SEM) estimates which factors actually move your well-being week to week.<p>- The Coach turns those findings into specific guidance (e.g., “protect 90 minutes after client calls; that’s when energy dips for you”).<p>- No account; your journals live locally (in your calendar). You decide what, if anything, leaves the device.<p>The problem<p>- Generic AI coaches give advice without understanding your personality or context.<p>- Traditional journaling is reflective but doesn’t surface causal patterns.<p>- Well-being apps rarely account for individual differences or test what works for you over time.<p>What my research found (plain English)<p>In my PhD I modeled how Personality, Character, Resources, and Well-Being interact over time. The key is latent relationships: for example, Autonomy can buffer the impact of low Extraversion on social drain, while time/energy constraints mediate whether “good advice” is actionable. These effects are person-specific and evolve—so you need a model that learns you, not averages.<p>The solution<p>Dlog pairs on-device journaling analytics with an SEM that updates weekly. You get a running estimate of “what moves the needle for me,” and the Coach translates that into concrete suggestions aligned with your goals and constraints.<p>Early stories (anonymized from pilot users)<p>- A founder saw energy dips clustered after external calls; moving deep work to mornings reduced “bad days” and improved weekly mood stability.<p>- A solo designer’s autonomy scores predicted well-being more than raw hours worked; small boundary changes (client comms windows) helped more than time-tracking tweaks.<p>Tech & security<p>- Platform: macOS (Swift/SwiftUI). Data: local storage + EventKit calendar for entries/timestamps.<p>- Analytics: on-device sentiment + narrative features; SEM computed locally; weekly updates compare to your baseline.<p>- AI Coach: uses an enterprise LLM API for reasoning on derived features/summaries. By default, raw journal text does not leave the device; you can opt-in per prompt if you want the Coach to read a specific passage.<p>- Why 61 baseline variables? The SEM needs multiple indicators per construct (Personality, Character, Resources, Well-Being) to estimate stable latent factors without overfitting; weekly check-ins refresh those signals.<p>What I’ve learned building this<p>- Users value clarity with depth: concise recommendations paired with focused dashboards, often 5–10 charts, to explain the “why” and trade-offs.<p>- Cold start matters: a solid baseline makes the first week of insights credibly useful.<p>- Privacy UX needs to be explicit: users want granular control over what the Coach can read, per request.<p>I’m looking for feedback on:<p>- Onboarding (baseline survey and first-week experience)<p>- Coach guidance clarity and usefulness<p>- Analytics accuracy vs. your lived experience<p>- Edge cases, bugs, and performance<p>Download: <a href="https://dlog.pro" rel="nofollow">https://dlog.pro</a><p>If you hit token limits while testing, email me at johan@dlog.pro<p>Background<p>PhD (Hunter Center for Entrepreneurship, Strathclyde), MBA (Babson), BComm (UCD). I study solo self-employment and well-being, and built Dlog to bring that research into a tool practitioners can use.<p>Note: The Coach activates after your first scored entry. If you haven’t written one yet, you’ll see a hold state—add a quick journal entry and it unlocks.<p>Appearance: On a few Macs the initial theme can render darker than intended. If you see this, switch to Light Mode as a temporary workaround; a fix is incoming.<p>Edit: For general users it's free for 14 days with 10K free tokens; then its 1.99 per month at the moment.
However, for HN readers that DM me or email me with the email they register with, I'll give a free perpetual license so there's no monthly fee; and add 1 million tokens.
Show HN: Apache Fory Rust – 10-20x faster serialization than JSON/Protobuf
Serialization framework with some interesting numbers: 10-20x faster on nested objects than json/protobuf.<p><pre><code> Technical approach: compile-time codegen (no reflection), compact binary protocol with meta-packing, little-endian layout optimized for modern CPUs.
Unique features that other fast serializers don't have:
- Cross-language without IDL files (Rust ↔ Python/Java/Go)
- Trait object serialization (Box<dyn Trait>)
- Automatic circular reference handling
- Schema evolution without coordination
Happy to discuss design trade-offs.
Benchmarks: https://fory.apache.org/docs/benchmarks/rust</code></pre>
Show HN: Apache Fory Rust – 10-20x faster serialization than JSON/Protobuf
Serialization framework with some interesting numbers: 10-20x faster on nested objects than json/protobuf.<p><pre><code> Technical approach: compile-time codegen (no reflection), compact binary protocol with meta-packing, little-endian layout optimized for modern CPUs.
Unique features that other fast serializers don't have:
- Cross-language without IDL files (Rust ↔ Python/Java/Go)
- Trait object serialization (Box<dyn Trait>)
- Automatic circular reference handling
- Schema evolution without coordination
Happy to discuss design trade-offs.
Benchmarks: https://fory.apache.org/docs/benchmarks/rust</code></pre>
Show HN: ISS in Real Time – 25 Years Aboard the International Space Station
Today my collaborator and I are releasing issinrealtime.org, a multimedia project that plays back every day onboard the ISS. Feedback welcomed.<p>Here's an article that was just released about it: <a href="https://www.collectspace.com/news/news-102725a-iss-in-real-time-25-years-continuous-human-occupancy-space-station.html" rel="nofollow">https://www.collectspace.com/news/news-102725a-iss-in-real-t...</a><p>I also wrote a "making of" post about it here:
<a href="https://benfeist.com/posts/iss-in-real-time/" rel="nofollow">https://benfeist.com/posts/iss-in-real-time/</a>
Show HN: ISS in Real Time – 25 Years Aboard the International Space Station
Today my collaborator and I are releasing issinrealtime.org, a multimedia project that plays back every day onboard the ISS. Feedback welcomed.<p>Here's an article that was just released about it: <a href="https://www.collectspace.com/news/news-102725a-iss-in-real-time-25-years-continuous-human-occupancy-space-station.html" rel="nofollow">https://www.collectspace.com/news/news-102725a-iss-in-real-t...</a><p>I also wrote a "making of" post about it here:
<a href="https://benfeist.com/posts/iss-in-real-time/" rel="nofollow">https://benfeist.com/posts/iss-in-real-time/</a>
Show HN: ISS in Real Time – 25 Years Aboard the International Space Station
Today my collaborator and I are releasing issinrealtime.org, a multimedia project that plays back every day onboard the ISS. Feedback welcomed.<p>Here's an article that was just released about it: <a href="https://www.collectspace.com/news/news-102725a-iss-in-real-time-25-years-continuous-human-occupancy-space-station.html" rel="nofollow">https://www.collectspace.com/news/news-102725a-iss-in-real-t...</a><p>I also wrote a "making of" post about it here:
<a href="https://benfeist.com/posts/iss-in-real-time/" rel="nofollow">https://benfeist.com/posts/iss-in-real-time/</a>
Show HN: Bash Screensavers
A github project to collect a bunch of bash-based screensavers/visualizations.
Show HN: Bash Screensavers
A github project to collect a bunch of bash-based screensavers/visualizations.
Show HN: Bash Screensavers
A github project to collect a bunch of bash-based screensavers/visualizations.
Show HN: Git Auto Commit (GAC) – LLM-powered Git commit command line tool
GAC is a tool I built to help users spend less time summing up what was done and more time building. It uses LLMs to generate contextual git commit messages from your code changes. And it can be a drop-in replacement for `git commit -m "..."`.<p>Example:<p><pre><code> feat(auth): add OAuth2 integration with GitHub and Google
- Implement OAuth2 authentication flow
- Add provider configuration for GitHub and Google
- Create callback handler for token exchange
- Update login UI with social auth buttons
</code></pre>
Don't like it? Reroll with 'r', or type `r "focus on xyz"` and it rerolls the commit with your feedback.<p>You can try it out with uvx (no install):<p><pre><code> uvx gac init # config wizard
uvx gac
</code></pre>
<i>Note: `gac init` creates a .gac.env file in your home directory with your chosen provider, model, and API key.</i><p>Tech details:<p><i>14 providers</i> - Supports local (Ollama & LM Studio) and cloud (OpenAI, Anthropic, Gemini, OpenRouter, Groq, Cerebras, Chutes, Fireworks, StreamLake, Synthetic, Together AI, & Z.ai (including their extremely cheap coding plans!)).<p><i>Three verbosity modes</i> - Standard with bullets (default), one-liners (`-o`), or verbose (`-v`) with detailed Motivation/Architecture/Impact sections.<p><i>Secret detection</i> - Scans for API keys, tokens, and credentials before committing. Has caught my API keys on a new project when I hadn't yet gitignored .env.<p><i>Flags</i> - Automate common workflows:<p><pre><code> `gac -h "bug fix"` - pass hints to guide intent
`gac -yo` - auto-accept the commit message in one-liner mode
`gac -ayp` - stage all files, auto-accept the commit message, and push (yolo mode)
</code></pre>
Would love to hear your feedback! Give it a try and let me know what you think! <3<p>GitHub: <a href="https://github.com/cellwebb/gac" rel="nofollow">https://github.com/cellwebb/gac</a>