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Show HN: Engineering Schizophrenia: Trusting yourself through Byzantine faults

Hi HN!<p>My name's Robert Escriva. I got my PhD from Cornell's Computer Science department back in 2017. And three years ago I had a psychotic episode that irreversibly shook up my world.<p>Since then I've been applying a skill I learned in grad school---namely, debugging distributed and complex systems---to my own mind.<p>What I've found I've put into a [book (pdf)](<a href="https://rescrv.net/engineering-schizophrenia.pdf" rel="nofollow">https://rescrv.net/engineering-schizophrenia.pdf</a>) on engineering, my particular schizophrenic delusions, and how people who suffer as I once did can find a way through the fog to the other side.<p>This is not a healing memoir; it is a guide and a warning for all those who never stopped to ask, "What happens if my brain begins to fail me?"<p>I am writing because what I've found is not a destination, but a process. It is an ongoing process for me and for people like me. I also believe it is automate-able using the same techniques we apply to machine-based systems.<p>I am looking for others who recognize the stakes of the human mind to engage in discussion on the topic.<p>Happy hacking, Robert

Show HN: Engineering Schizophrenia: Trusting yourself through Byzantine faults

Hi HN!<p>My name's Robert Escriva. I got my PhD from Cornell's Computer Science department back in 2017. And three years ago I had a psychotic episode that irreversibly shook up my world.<p>Since then I've been applying a skill I learned in grad school---namely, debugging distributed and complex systems---to my own mind.<p>What I've found I've put into a [book (pdf)](<a href="https://rescrv.net/engineering-schizophrenia.pdf" rel="nofollow">https://rescrv.net/engineering-schizophrenia.pdf</a>) on engineering, my particular schizophrenic delusions, and how people who suffer as I once did can find a way through the fog to the other side.<p>This is not a healing memoir; it is a guide and a warning for all those who never stopped to ask, "What happens if my brain begins to fail me?"<p>I am writing because what I've found is not a destination, but a process. It is an ongoing process for me and for people like me. I also believe it is automate-able using the same techniques we apply to machine-based systems.<p>I am looking for others who recognize the stakes of the human mind to engage in discussion on the topic.<p>Happy hacking, Robert

Show HN: Engineering Schizophrenia: Trusting yourself through Byzantine faults

Hi HN!<p>My name's Robert Escriva. I got my PhD from Cornell's Computer Science department back in 2017. And three years ago I had a psychotic episode that irreversibly shook up my world.<p>Since then I've been applying a skill I learned in grad school---namely, debugging distributed and complex systems---to my own mind.<p>What I've found I've put into a [book (pdf)](<a href="https://rescrv.net/engineering-schizophrenia.pdf" rel="nofollow">https://rescrv.net/engineering-schizophrenia.pdf</a>) on engineering, my particular schizophrenic delusions, and how people who suffer as I once did can find a way through the fog to the other side.<p>This is not a healing memoir; it is a guide and a warning for all those who never stopped to ask, "What happens if my brain begins to fail me?"<p>I am writing because what I've found is not a destination, but a process. It is an ongoing process for me and for people like me. I also believe it is automate-able using the same techniques we apply to machine-based systems.<p>I am looking for others who recognize the stakes of the human mind to engage in discussion on the topic.<p>Happy hacking, Robert

Show HN: Librario, a book metadata API that aggregates G Books, ISBNDB, and more

<i>TLDR:</i> Librario is a book metadata API that aggregates data from Google Books, ISBNDB, and Hardcover into a single response, solving the problem of no single source having complete book information. It's currently pre-alpha, AGPL-licensed, and available to try now[0].<p>My wife and I have a personal library with around 1,800 books. I started working on a library management tool for us, but I quickly realized I needed a source of data for book information, and none of the solutions available provided all the data I needed. One might provide the series, the other might provide genres, and another might provide a good cover, but none provided everything.<p>So I started working on Librario, a book metadata aggregation API written in Go. It fetches information about books from multiple sources (Google Books, ISBNDB, Hardcover. Working on Goodreads and Anna's Archive next.), merges everything, and saves it all to a PostgreSQL database for future lookups. The idea is that the database gets stronger over time as more books are queried.<p>You can see an example response here[1], or try it yourself:<p><pre><code> curl -s -H 'Authorization: Bearer librario_ARbmrp1fjBpDywzhvrQcByA4sZ9pn7D5HEk0kmS34eqRcaujyt0enCZ' \ 'https://api.librario.dev/v1/book/9781328879943' | jq . </code></pre> This is pre-alpha and runs on a small VPS, so keep that in mind. I never hit the limits in the third-party services, so depending on how this post goes, I’ll or will not find out if the code handles that well.<p>The merger is the heart of the service, and figuring out how to combine conflicting data from different sources was the hardest part. In the end I decided to use field-specific strategies which are quite naive, but work for now.<p>Each extractor has a priority, and results are sorted by that priority before merging. But priority alone isn't enough, so different fields need different treatment.<p>For example:<p>- Titles use a scoring system. I penalize titles containing parentheses or brackets because sources sometimes shove subtitles into the main title field. Overly long titles (80+ chars) also get penalized since they often contain edition information or other metadata that belongs elsewhere.<p>- Covers collect all candidate URLs, then a separate fetcher downloads and scores them by dimensions and quality. The best one gets stored locally and served from the server.<p>For most other fields (publisher, language, page count), I just take the first non-empty value by priority. Simple, but it works.<p>Recently added a caching layer[2] which sped things up nicely. I considered migrating from <i>net/http</i> to <i>fiber</i> at some point[3], but decided against it. Going outside the standard library felt wrong, and the migration didn't provide much in the end.<p>The database layer is being rewritten before v1.0[4]. I'll be honest: the original schema was written by AI, and while I tried to guide it in the right direction with SQLC[5] and good documentation, database design isn't my strong suit and I couldn't confidently vouch for the code. Rather than ship something I don't fully understand, I hired the developers from SourceHut[6] to rewrite it properly.<p>I've got a 5-month-old and we're still adjusting to their schedule, so development is slow. I've mentioned this project in a few HN threads before[7], so I’m pretty happy to finally have something people can try.<p>Code is AGPL and on SourceHut[8].<p>Feedback and patches[9] are very welcome :)<p>[0]: <a href="https://sr.ht/~pagina394/librario/" rel="nofollow">https://sr.ht/~pagina394/librario/</a><p>[1]: <a href="https://paste.sr.ht/~jamesponddotco/a6c3b1130133f384cffd25b33a8ab1bc3392093c" rel="nofollow">https://paste.sr.ht/~jamesponddotco/a6c3b1130133f384cffd25b3...</a><p>[2]: <a href="https://todo.sr.ht/~pagina394/librario/16" rel="nofollow">https://todo.sr.ht/~pagina394/librario/16</a><p>[3]: <a href="https://todo.sr.ht/~pagina394/librario/13" rel="nofollow">https://todo.sr.ht/~pagina394/librario/13</a><p>[4]: <a href="https://todo.sr.ht/~pagina394/librario/14" rel="nofollow">https://todo.sr.ht/~pagina394/librario/14</a><p>[5]: <a href="https://sqlc.dev" rel="nofollow">https://sqlc.dev</a><p>[6]: <a href="https://sourcehut.org/consultancy/" rel="nofollow">https://sourcehut.org/consultancy/</a><p>[7]: <a href="https://news.ycombinator.com/item?id=45419234">https://news.ycombinator.com/item?id=45419234</a><p>[8]: <a href="https://sr.ht/~pagina394/librario/" rel="nofollow">https://sr.ht/~pagina394/librario/</a><p>[9]: <a href="https://git.sr.ht/~pagina394/librario/tree/trunk/item/CONTRIBUTING.md" rel="nofollow">https://git.sr.ht/~pagina394/librario/tree/trunk/item/CONTRI...</a>

Show HN: Ferrite – Markdown editor in Rust with native Mermaid diagram rendering

Ferrite: Fast Markdown/Text/Code editor in Rust with native Mermaid diagrams<p>Built a Markdown editor using Rust + egui. v0.2.1 just dropped with major Mermaid improvements:<p>→ Native Mermaid diagrams - Flowcharts, sequence, state, ER, git graphs - pure Rust, no JS<p>→ Split view - Raw + rendered side-by-side with sync scrolling<p>→ Syntax highlighting - 40+ languages with large file optimization<p>→ JSON/YAML/TOML tree viewer - Structured editing with expand/collapse<p>→ Git integration - File tree shows modified/staged/untracked status<p>Also: minimap, zen mode, auto-save, session restore, code folding indicators.<p>~15MB binary, instant startup. Windows/Linux/macOS.<p>GitHub: <a href="https://github.com/OlaProeis/Ferrite" rel="nofollow">https://github.com/OlaProeis/Ferrite</a><p>v0.2.2 coming soon with performance improvements for large files. Looking for feedback!

Show HN: Ferrite – Markdown editor in Rust with native Mermaid diagram rendering

Ferrite: Fast Markdown/Text/Code editor in Rust with native Mermaid diagrams<p>Built a Markdown editor using Rust + egui. v0.2.1 just dropped with major Mermaid improvements:<p>→ Native Mermaid diagrams - Flowcharts, sequence, state, ER, git graphs - pure Rust, no JS<p>→ Split view - Raw + rendered side-by-side with sync scrolling<p>→ Syntax highlighting - 40+ languages with large file optimization<p>→ JSON/YAML/TOML tree viewer - Structured editing with expand/collapse<p>→ Git integration - File tree shows modified/staged/untracked status<p>Also: minimap, zen mode, auto-save, session restore, code folding indicators.<p>~15MB binary, instant startup. Windows/Linux/macOS.<p>GitHub: <a href="https://github.com/OlaProeis/Ferrite" rel="nofollow">https://github.com/OlaProeis/Ferrite</a><p>v0.2.2 coming soon with performance improvements for large files. Looking for feedback!

Show HN: Miditui – A terminal app/UI for MIDI composing, mixing, and playback

Show HN: Various shape regularization algorithms

I deal with a lot of geometry stuff at work with computer vision and photogrammetry, which usually comes from the real world. It's seldom clean and neat, and I'm often trying to find a way to "make it nice" or "make it pretty". I've always struggled with what that really means formally.<p>That led me to shape regularization (a technique used in computational geometry to clean up geometric data). CGAL had implemented a few methods for that, but there are more ways to do it, which I thought were nice. Also I typically work in Python, so it was nice to have a pure Python library could handle this.<p>I struggled to get the first version working as a QP. At a high level most of these boil down to minimizing a cost A + B where A is the cost associated the geometry and goes up the more you move it, and B is the cost associated "niceness" or rather the constraints you impose, and goes down the more you impose them. Then you try and minimize A + B or rather H<i>A + (1-H)</i>B where H is a hyper-parameter that controls the relative importance of A and B.<p>I needed a Python implementation so started with the examples implemented in CGAL then added a couple more for snap and joint regularization and metric regularization.

Show HN: I built a tool to create AI agents that live in iMessage

Hey everyone, I made this thing: <a href="https://tryflux.ai/" rel="nofollow">https://tryflux.ai/</a>. Here's a demo video: <a href="https://screen.studio/share/1y2EnC26" rel="nofollow">https://screen.studio/share/1y2EnC26</a><p>Context: I've tried probably 15 different AI apps over the past year. ChatGPT, note-taking apps, productivity apps, all of it. But most of them are just clutter on my iphone.<p>They live in some app I have to deliberately open. And I just... don't. But you know what I open 50 times a day without thinking? iMessage. So out of mild frustration with the "AI app graveyard" on my phone, I built Flux.<p>What it does: - You describe a personality and what you want the agent to do - In about 2 minutes, you have a live AI agent in iMessage - Blue bars. Native. No app download for whoever texts it.<p>The thesis that got us here: AI is already smart enough. The bottleneck is interaction. Dashboards get forgotten. Texts get answered.<p>This was also my first time hitting #1 on Product Hunt, which was surreal.<p>It's still rough and probably broke something. If you try it, feedback is super welcome, weird edge cases, "this doesn't work," or "why would anyone use this" comments all help.<p>That's all. Happy to answer questions.

Show HN: Play poker with LLMs, or watch them play against each other

I was curious to see how some of the latest models behaved and played no limit texas holdem.<p>I built this website which allows you to:<p>Spectate: Watch different models play against each other.<p>Play: Create your own table and play hands against the agents directly.

Show HN: Play poker with LLMs, or watch them play against each other

I was curious to see how some of the latest models behaved and played no limit texas holdem.<p>I built this website which allows you to:<p>Spectate: Watch different models play against each other.<p>Play: Create your own table and play hands against the agents directly.

Show HN: Play poker with LLMs, or watch them play against each other

I was curious to see how some of the latest models behaved and played no limit texas holdem.<p>I built this website which allows you to:<p>Spectate: Watch different models play against each other.<p>Play: Create your own table and play hands against the agents directly.

Show HN: I used Claude Code to discover connections between 100 books

I think LLMs are overused to summarise and underused to help us read deeper.<p>I built a system for Claude Code to browse 100 non-fiction books and find interesting connections between them.<p>I started out with a pipeline in stages, chaining together LLM calls to build up a context of the library. I was mainly getting back the insight that I was baking into the prompts, and the results weren't particularly surprising.<p>On a whim, I gave CC access to my debug CLI tools and found that it wiped the floor with that approach. It gave actually interesting results and required very little orchestration in comparison.<p>One of my favourite trail of excerpts goes from Jobs’ reality distortion field to Theranos’ fake demos, to Thiel on startup cults, to Hoffer on mass movement charlatans (<a href="https://trails.pieterma.es/trail/useful-lies/" rel="nofollow">https://trails.pieterma.es/trail/useful-lies/</a>). A fun tendency is that Claude kept getting distracted by topics of secrecy, conspiracy, and hidden systems - as if the task itself summoned a Foucault’s Pendulum mindset.<p>Details:<p>* The books are picked from HN’s favourites (which I collected before: <a href="https://hnbooks.pieterma.es/" rel="nofollow">https://hnbooks.pieterma.es/</a>).<p>* Chunks are indexed by topic using Gemini Flash Lite. The whole library cost about £10.<p>* Topics are organised into a tree structure using recursive Leiden partitioning and LLM labels. This gives a high-level sense of the themes.<p>* There are several ways to browse. The most useful are embedding similarity, topic tree siblings, and topics cooccurring within a chunk window.<p>* Everything is stored in SQLite and manipulated using a set of CLI tools.<p>I wrote more about the process here: <a href="https://pieterma.es/syntopic-reading-claude/" rel="nofollow">https://pieterma.es/syntopic-reading-claude/</a><p>I’m curious if this way of reading resonates for anyone else - LLM-mediated or not.

Show HN: I used Claude Code to discover connections between 100 books

I think LLMs are overused to summarise and underused to help us read deeper.<p>I built a system for Claude Code to browse 100 non-fiction books and find interesting connections between them.<p>I started out with a pipeline in stages, chaining together LLM calls to build up a context of the library. I was mainly getting back the insight that I was baking into the prompts, and the results weren't particularly surprising.<p>On a whim, I gave CC access to my debug CLI tools and found that it wiped the floor with that approach. It gave actually interesting results and required very little orchestration in comparison.<p>One of my favourite trail of excerpts goes from Jobs’ reality distortion field to Theranos’ fake demos, to Thiel on startup cults, to Hoffer on mass movement charlatans (<a href="https://trails.pieterma.es/trail/useful-lies/" rel="nofollow">https://trails.pieterma.es/trail/useful-lies/</a>). A fun tendency is that Claude kept getting distracted by topics of secrecy, conspiracy, and hidden systems - as if the task itself summoned a Foucault’s Pendulum mindset.<p>Details:<p>* The books are picked from HN’s favourites (which I collected before: <a href="https://hnbooks.pieterma.es/" rel="nofollow">https://hnbooks.pieterma.es/</a>).<p>* Chunks are indexed by topic using Gemini Flash Lite. The whole library cost about £10.<p>* Topics are organised into a tree structure using recursive Leiden partitioning and LLM labels. This gives a high-level sense of the themes.<p>* There are several ways to browse. The most useful are embedding similarity, topic tree siblings, and topics cooccurring within a chunk window.<p>* Everything is stored in SQLite and manipulated using a set of CLI tools.<p>I wrote more about the process here: <a href="https://pieterma.es/syntopic-reading-claude/" rel="nofollow">https://pieterma.es/syntopic-reading-claude/</a><p>I’m curious if this way of reading resonates for anyone else - LLM-mediated or not.

Show HN: I used Claude Code to discover connections between 100 books

I think LLMs are overused to summarise and underused to help us read deeper.<p>I built a system for Claude Code to browse 100 non-fiction books and find interesting connections between them.<p>I started out with a pipeline in stages, chaining together LLM calls to build up a context of the library. I was mainly getting back the insight that I was baking into the prompts, and the results weren't particularly surprising.<p>On a whim, I gave CC access to my debug CLI tools and found that it wiped the floor with that approach. It gave actually interesting results and required very little orchestration in comparison.<p>One of my favourite trail of excerpts goes from Jobs’ reality distortion field to Theranos’ fake demos, to Thiel on startup cults, to Hoffer on mass movement charlatans (<a href="https://trails.pieterma.es/trail/useful-lies/" rel="nofollow">https://trails.pieterma.es/trail/useful-lies/</a>). A fun tendency is that Claude kept getting distracted by topics of secrecy, conspiracy, and hidden systems - as if the task itself summoned a Foucault’s Pendulum mindset.<p>Details:<p>* The books are picked from HN’s favourites (which I collected before: <a href="https://hnbooks.pieterma.es/" rel="nofollow">https://hnbooks.pieterma.es/</a>).<p>* Chunks are indexed by topic using Gemini Flash Lite. The whole library cost about £10.<p>* Topics are organised into a tree structure using recursive Leiden partitioning and LLM labels. This gives a high-level sense of the themes.<p>* There are several ways to browse. The most useful are embedding similarity, topic tree siblings, and topics cooccurring within a chunk window.<p>* Everything is stored in SQLite and manipulated using a set of CLI tools.<p>I wrote more about the process here: <a href="https://pieterma.es/syntopic-reading-claude/" rel="nofollow">https://pieterma.es/syntopic-reading-claude/</a><p>I’m curious if this way of reading resonates for anyone else - LLM-mediated or not.

Show HN: EuConform – Offline-first EU AI Act compliance tool (open source)

I built this as a personal open-source project to explore how EU AI Act requirements can be translated into concrete, inspectable technical checks.<p>The core idea is local-first compliance: – risk classification (Articles 5–15, incl. prohibited use cases) – bias evaluation using CrowS-Pairs – automatic Annex IV–oriented PDF reports – no cloud services or external APIs (browser-based + Ollama)<p>I’m especially interested in feedback on whether this kind of technical framing of AI regulation makes sense in real-world projects.

Show HN: EuConform – Offline-first EU AI Act compliance tool (open source)

I built this as a personal open-source project to explore how EU AI Act requirements can be translated into concrete, inspectable technical checks.<p>The core idea is local-first compliance: – risk classification (Articles 5–15, incl. prohibited use cases) – bias evaluation using CrowS-Pairs – automatic Annex IV–oriented PDF reports – no cloud services or external APIs (browser-based + Ollama)<p>I’m especially interested in feedback on whether this kind of technical framing of AI regulation makes sense in real-world projects.

Show HN: Rocket Launch and Orbit Simulator

I (17y/o) have been developing a rocket launch simulation that allows the user to explore what it's like launching a rocket from earth and putting it into orbit. This idea originally started as an educational simulation but as i've gone more down the rabbit hole the more i've wanted to make it realistic. The problem is that I've never had a formal orbital mechanics class or anything like that so I don't know what I'm missing, what I currently have implemented is:<p><pre><code> Variable gravity Variable Atmospheric drag (US Standard Atmosphere 1976) Multi-stage rockets Closed-loop guidance / pitch programs (works well within ranges 350km to 600km) Orbital prediction and thrusting options to change your orbit. </code></pre> The feedback I'm looking for is: UI improvements and possible future physics implementations that I can work on.<p>Current code and physics can be found at: <a href="https://github.com/donutTheJedi/Rocket-Launch-Simulation" rel="nofollow">https://github.com/donutTheJedi/Rocket-Launch-Simulation</a>

Show HN: Rocket Launch and Orbit Simulator

I (17y/o) have been developing a rocket launch simulation that allows the user to explore what it's like launching a rocket from earth and putting it into orbit. This idea originally started as an educational simulation but as i've gone more down the rabbit hole the more i've wanted to make it realistic. The problem is that I've never had a formal orbital mechanics class or anything like that so I don't know what I'm missing, what I currently have implemented is:<p><pre><code> Variable gravity Variable Atmospheric drag (US Standard Atmosphere 1976) Multi-stage rockets Closed-loop guidance / pitch programs (works well within ranges 350km to 600km) Orbital prediction and thrusting options to change your orbit. </code></pre> The feedback I'm looking for is: UI improvements and possible future physics implementations that I can work on.<p>Current code and physics can be found at: <a href="https://github.com/donutTheJedi/Rocket-Launch-Simulation" rel="nofollow">https://github.com/donutTheJedi/Rocket-Launch-Simulation</a>

Show HN: Similarity = cosine(your_GitHub_stars, Karpathy) Client-side

GitHub profile analysis - Build your embedding from your Stars - Compare and discover popular people with similar interests and share yours - Generate a Skill Radar - Recommend repositories you might like