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I think LLMs are overused to summarise and underused to help us read deeper.

I built a system for Claude Code to browse 100 non-fiction books and find interesting connections between them.

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.

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.

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 (https://trails.pieterma.es/trail/useful-lies/). 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.

Details:

* The books are picked from HN’s favourites (which I collected before: https://hnbooks.pieterma.es/).

* Chunks are indexed by topic using Gemini Flash Lite. The whole library cost about £10.

* Topics are organised into a tree structure using recursive Leiden partitioning and LLM labels. This gives a high-level sense of the themes.

* There are several ways to browse. The most useful are embedding similarity, topic tree siblings, and topics cooccurring within a chunk window.

* Everything is stored in SQLite and manipulated using a set of CLI tools.

I wrote more about the process here: https://pieterma.es/syntopic-reading-claude/

I’m curious if this way of reading resonates for anyone else - LLM-mediated or not.

While working on a proof of concept project, I kept hitting Claude's token limit 30-60 minutes into their 5-hour sessions. The accumulating context from the codebase was eating through tokens fast. So I built a language designed to be generated by AI rather than written by humans.

GlyphLang

GlyphLang replaces verbose keywords with symbols that tokenize more efficiently:

  # Python
  @app.route('/users/<id>')
  def get_user(id):
      user = db.query("SELECT * FROM users WHERE id = ?", id)
      return jsonify(user)

  # GlyphLang
  @ GET /users/:id {
    $ user = db.query("SELECT * FROM users WHERE id = ?", id)
    > user
  }

  @ = route, $ = variable, > = return. Initial benchmarks show ~45% fewer tokens than Python, ~63% fewer than Java.

Before anyone asks: no, this isn't APL with extra steps. APL, Perl, and Forth are symbol-heavy but optimized for mathematical notation, human terseness, or machine efficiency. GlyphLang is specifically optimized for how modern LLMs tokenize. It's designed to be generated by AI and reviewed by humans, not the other way around. That said, it's still readable enough to be written or tweaked if the occasion requires.

It's still a work in progress, but it's a usable language with a bytecode compiler, JIT, LSP, VS Code extension, PostgreSQL, WebSockets, async/await, generics.

Docs: https://glyphlang.dev/docs

GitHub: https://github.com/GlyphLang/GlyphLang

Ferrite: Fast Markdown/Text/Code editor in Rust with native Mermaid diagrams

Built a Markdown editor using Rust + egui. v0.2.1 just dropped with major Mermaid improvements:

→ Native Mermaid diagrams - Flowcharts, sequence, state, ER, git graphs - pure Rust, no JS

→ Split view - Raw + rendered side-by-side with sync scrolling

→ Syntax highlighting - 40+ languages with large file optimization

→ JSON/YAML/TOML tree viewer - Structured editing with expand/collapse

→ Git integration - File tree shows modified/staged/untracked status

Also: minimap, zen mode, auto-save, session restore, code folding indicators.

~15MB binary, instant startup. Windows/Linux/macOS.

GitHub: https://github.com/OlaProeis/Ferrite

v0.2.2 coming soon with performance improvements for large files. Looking for feedback!

Hi HN,

I ported the teaching OS xv6-riscv to HiFive Unmatched and got it running on real hardware, including passing usertests.

I've been self-studying OS internals using the MIT 6.1810 materials. After finishing most of the labs, I was eager to see what it's like to run the OS on bare metal, rather than QEMU.

The Unmatched may not have the latest RISC-V features, but it's well-documented, and the Rev B release has made it more affordable, which makes it a good learning platform.

The porting process involved several interesting challenges:

- Hardware Quirks: Handling things like enabling A/D bits in PTEs (the hardware doesn't set them automatically, causing page faults), proper handling of interrupts, and instruction cache synchronization.

- Boot Flow: xv6 expects M-mode on startup, but standard RISC-V boot flows (typically via OpenSBI) jump to S-mode. To bridge this gap, I created a minimal U-Boot FIT image that contains only the xv6 kernel. This way, U-Boot SPL handles the complex CPU/DDR initialization, then hands control to xv6 in M-mode (skipping OpenSBI).

- Drivers: Ported an SPI SD card driver, replacing the virtio disk driver.

I wrote up implementation notes here: https://github.com/eyengin/xv6-riscv-unmatched/blob/unmatche...

Hopefully, this is useful for others who are learning OS internals and want to try running their code on real RISC-V hardware.

I was curious to see how some of the latest models behaved and played no limit texas holdem.

I built this website which allows you to:

Spectate: Watch different models play against each other.

Play: Create your own table and play hands against the agents directly.

TLDR: 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].

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.

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.

You can see an example response here[1], or try it yourself:

  curl -s -H 'Authorization: Bearer librario_ARbmrp1fjBpDywzhvrQcByA4sZ9pn7D5HEk0kmS34eqRcaujyt0enCZ' \
  'https://api.librario.dev/v1/book/9781328879943' | jq .
  

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.

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.

For example:

- 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.

- 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.

For most other fields (publisher, language, page count), I just take the first non-empty value by priority. Simple, but it works.

Recently added a caching layer[2] which sped things up nicely. I considered migrating from net/http to fiber 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.

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.

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.

Code is AGPL and on SourceHut[8].

Feedback and patches[9] are very welcome :)

[0]: https://sr.ht/~pagina394/librario/

[1]: https://paste.sr.ht/~jamesponddotco/a6c3b1130133f384cffd25b3...

[2]: https://todo.sr.ht/~pagina394/librario/16

[3]: https://todo.sr.ht/~pagina394/librario/13

[4]: https://todo.sr.ht/~pagina394/librario/14

[5]: https://sqlc.dev

[6]: https://sourcehut.org/consultancy/

[7]: https://news.ycombinator.com/item?id=45419234

[8]: https://sr.ht/~pagina394/librario/

[9]: https://git.sr.ht/~pagina394/librario/tree/trunk/item/CONTRI...

The article describes the Mcp-CLI, a command-line interface tool for managing and deploying Apify cloud platform resources. It provides information on installing, configuring, and using the tool to perform various operations, such as deploying and managing actors, schedules, and datasets.

I've never posted on ShowHN before, but I wanted to share my Xmas 2025 project; to try a new approach to controlling our kids screen time.

This also involved massively over-engineering a solution in order to play with a shiny new gadget (and avoid the in laws at Christmas, obviously)

I've shared some learnings on AI coding with embedded devices, and how I approached the product design/hardware selection side of things.

The Web App is at https://screenie.org - and I'm Open Sourcing the device and web app code later today (links to follow on that site)

Hey all- I just released a New tab replacement Chrome extension that makes browsing a lot easier - it also solves many of the annoyances with browser tabs. It's called Yellopages and it's free. Hope you'll give it a try.

* Groups all tabs from same domain. Makes it simple to kill all your Gmail tabs in one click (or keep just one).

* Groups all tabs playing audio. Toggle the sound for each one.

* Single text search for open tabs, bookmarks, and browsing history.

* Groups all tabs with new notifications (e.g. emails, likes, posts, replies, etc.)

* One click to kill all tabs (e.g. you're sharing screen in Zoom). A second click brings them all back.

I'm a solo web developer and I'm hoping to build an audience with my work. More at: https://buymeacoffee.com/kawaicheung

Hi HN! I built VAM Seek because I was frustrated with 1D seek bars – you never know where you're going until you get there.

VAM Seek renders a 2D thumbnail grid next to your video. Click any cell to jump. All frame extraction happens client-side via canvas – no server processing, no pre-generated thumbnails.

- 15KB, zero dependencies - One-line integration - Works with any <video> element

Live demo: https://haasiy.main.jp/vam_web/deploy/lolipop/index.html

Would love feedback!

The article explores the development of a new solar-powered electric vehicle, highlighting its innovative design, impressive performance, and the potential impact it could have on the future of sustainable transportation.

Inspired by TAWS. Built as a learning exercise.

I kept running into the same problem in large codebases: “temporary” code almost never gets removed. People add TODOs, FIXMEs, or quick hacks to hit a deadline, and six months later nobody remembers why they’re there or who owns them. They quietly turn into production bugs. I built a small CLI that treats those comments as time-bounded instead of permanent. You can attach an expiry date to a TODO in the code, and when the date passes, CI fails and points to exactly where the expired code lives. It works by: scanning comments across any language parsing structured annotations and optionally using git blame to infer who added them and when I tried to keep it simple and CI-friendly rather than tied to any particular language or linter. Here’s the repo if anyone wants to look at the implementation: https://github.com/jobin-404/debtbomb I’d love feedback from people who’ve dealt with long-lived “temporary” code in production.

The article provides an introduction to the Marten, a .NET document database and event store built on top of PostgreSQL. Marten offers features such as document storage, event sourcing, and querying capabilities, making it a versatile choice for building modern .NET applications.

Umaro is a mobile app that helps users manage their finances, set savings goals, and track their spending habits. The app provides personalized insights and recommendations to help users make informed financial decisions.

Dota2.tools is a comprehensive website that provides a range of resources and tools for Dota 2 players, including hero guides, item builds, and gameplay analytics to help improve their in-game performance and strategy.

I recently shared a real case where DataMatrix-based automation exposed counterfeit pharma products across borders.

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.

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.

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 HA + (1-H)B where H is a hyper-parameter that controls the relative importance of A and B.

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.

The article describes the ESPTimeCast project, an open-source software that uses an ESP8266 microcontroller to display accurate time information on a digital display. It provides a simple and inexpensive solution for creating a customizable clock or time display.

I wanted to run markdown files like shell scripts. So I built an open source tool that lets you use a shebang to pipe them through Claude Code with full stdin/stdout support.

task.md:

    #!/usr/bin/env claude-run

    Analyze this codebase and summarize the architecture.

    chmod +x task.md

    ./task.md

A script that runs your tests and reports results (`run_tests.md`):

    #!/usr/bin/env claude-run --permission-mode bypassPermissions

    Run ./test/run_tests.sh and summarize what passed and failed.

    cat data.json | ./analyze.md > results.txt

    git log -10 | ./summarize.md

    ./generate.md | ./review.md > final.txt

    for f in logs/\*.txt; do

        cat "$f" | ./analyze.md >> summary.txt

    done

One thing we didn't expect is that these are more auditable (and shareable) than shell scripts. Install scripts like `curl -fsSL https://bun.com/install | bash` could become:

    `curl -fsSL https://bun.com/install.md | claude-run`

The (really cool) executable markdown idea and auditability examples are from Pete Koomen (@koomen on X). As Pete says: "Markdown feels increasingly important in a way I'm not sure most people have wrapped their heads around yet."

We implemented it and added Unix pipe semantics. Currently works with Claude Code - hoping to support other AI coding tools too. You can also route scripts through different cloud providers (AWS Bedrock, etc.) if you want separate billing for automated jobs.

GitHub: https://github.com/andisearch/claude-switcher

What workflows would you use this for?

GYESME is an early-stage, design-led project exploring alternative architectural approaches within the GNOME desktop ecosystem.

Rather than aiming to replace GNOME or ship a new desktop environment, the project treats GNOME as a platform and asks a narrower question: which behaviors and assumptions could be made optional or more cleanly abstracted without compromising a minimalist default experience?

The current focus is on research and documentation rather than implementation. Areas being examined include:

Architectural modularity versus extension-based customization,

Opt-in functionality for features that are disabled or removed upstream,

Preserving established Linux interaction patterns without expanding defaults,

Reducing unnecessary hard dependencies where practical, including systemd-specific assumptions,

The intent is not to “fix” GNOME or argue against upstream decisions, but to explore design tradeoffs around minimalism, flexibility, and long-term maintainability in modern Linux desktops.

At this stage, the project consists primarily of a concept site, documentation, and open design questions. Feedback on the framing, assumptions, and scope is very welcome.

Project site and repository: https://github.com/runleveltwo/GYESME

Thanks for taking a look.

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:

  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.

Current code and physics can be found at: https://github.com/donutTheJedi/Rocket-Launch-Simulation

I built this as a personal open-source project to explore how EU AI Act requirements can be translated into concrete, inspectable technical checks.

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)

I’m especially interested in feedback on whether this kind of technical framing of AI regulation makes sense in real-world projects.

Hi HN, I built this side project earlier this week. It executes an English auction on Solana with a reserve price of 0.1 SOL. Auction winner gets control of a Codex editor. Auction losers get refunded (minus a <$0.01 processing fee). The Codex agent operates in a sandbox and can only output HTML/JS/CSS.

The project is open-source: https://github.com/neelsomani/the-daily-auction

I built a macOS menu bar app to track Claude usage in real time via API after hitting limits mid-flow too often.

Signed and notarised by Apple. Open source.

https://github.com/richhickson/claudecodeusage

https://x.com/richhickson

Hey - I've been playing with LLMs since GPT-2 and recently experimented with fully generative UIs where the HTML/Canvas are generated just-in-time.

Every post on the feed( on slop/duck/storytime) you see is streamed and generated just-in-time with HTML and into a Canvas with Gemini 3 Flash.

Comments and DMs are bidirectionally linked with a Cloudflare Workers Durable Object which is why they feel so fast. Every generated post is saved into a DO SQLite which is then served into the "Following" feed so it can be served quicker.

This was inspired by Wikitok, a VSCode Extension I made around brainrot, and another fully generative UI site I made.

I analyzed 10 years of Hacker News data (2016-2025) to track Show HN submission trends.

- Show HN posts went from ~900/month (2016-2019) to 3,315 in Dec 2025

- Share of all stories: 2.4% (2016) → 12.8% (Dec 2025)

- Notable spikes: COVID-19 (2020), AI boom (2023), and accelerating growth through 2024-2025

Data source: HackerBook (144k Show HN posts / 3.4M total stories)

The article introduces miditui, a Python library that provides a graphical user interface (GUI) for working with MIDI data, allowing users to easily visualize and edit MIDI files without needing to write complex code.