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Paste in my prompt to Claude Code with an embedded API key for accessing my public readonly SQL+vector database, and you have a state-of-the-art research tool over Hacker News, arXiv, LessWrong, and dozens of other high-quality public commons sites. Claude whips up the monster SQL queries that safely run on my machine, to answer your most nuanced questions.

There's also an Alerts functionality, where you can just ask Claude to submit a SQL query as an alert, and you'll be emailed when the ultra nuanced criteria is met (and the output changes). Like I want to know when somebody posts about "estrogen" in a psychoactive context, or enough biology metaphors when talking about building infrastructure.

Currently have embedded: posts: 1.4M / 4.6M comments: 15.6M / 38M That's with Voyage-3.5-lite. And you can do amazing compositional vector search, like search @FTX_crisis - (@guilt_tone - @guilt_topic) to find writing that was about the FTX crisis and distinctly without guilty tones, but that can mention "guilt".

I can embed everything and all the other sources for cheap, I just literally don't have the money.

Community, All the HN belong to you. This is an archive of hacker news that fits in your browser. When I made HN Made of Primes I realized I could probably do this offline sqlite/wasm thing with the whole GBs of archive. The whole dataset. So I tried it, and this is it. Have Hacker News on your device.

Go to this repo (https://github.com/DOSAYGO-STUDIO/HackerBook): you can download it. Big Query -> ETL -> npx serve docs - that's it. 20 years of HN arguments and beauty, can be yours forever. So they'll never die. Ever. It's the unkillable static archive of HN and it's your hands. That's my Year End gift to you all. Thank you for a wonderful year, have happy and wonderful 2026. make something of it.

I’m Gopal, the guy behind QuickClip.

I built this out of pure frustration. Copying items between my phone and laptop was very painful. Sending notes and links on WhatsApp. Saving random drafts I’d forget about. It was total waste of time.

So I made QuickClip for myself first. A dead simple way to move text, links and images between devices instantly. No setup drama. No thinking. Fully encrypted

I use it every day. Shipping it publicly now to see if anyone else has the same problem.

Would honestly love to hear, how you move stuff between devices today, what’s broken or slow and what would make this actually useful for you

Happy to answer anything and take suggestions. Thanks for checking it out.

I’m building a Unicode reference where each symbol has its own dev-friendly page with all relevant encodings.

Example: [https://fontgenerator.design/symbol/almost-equal-to](https://fontgenerator.design/symbol/almost-equal-to)

Includes Unicode, HTML, CSS, JS, UTF-8/16 bytes, URL encoding, and usage examples.

The same structure is used across thousands of symbols (math, arrows, currency, tech/UI, punctuation).

Built because existing references are fragmented. Feedback welcome.

Hey HN, I built this because the generational gap online is getting wider (and weirder). It’s an LLM-wrapper that translates "Boomer" (normal/corporate English) into "Brainrot" (Gen Alpha slang) and vice versa. It also has an "Image-to-Rot" feature that uses vision to describe uploaded images in slang. It’s mostly for fun, but actually kind of useful for deciphering what your younger cousins are saying. Would love to hear what you think!

Hi HN —

Tidy Baby is a new game made by me and Wyna Liu (of NYT Connections!) that is inspired by the legendary card-based game SET that we assume many of you love (we too love SET).

In SET, you’ve got four dimensions: shape, number, color, and shading, each with three variants.

In Tidy Baby you only have to deal with three dimensions:

- word length (3, 4, or 5 letters) - part of speech (noun, verb, or adjective) - style (bold, underline, or italic)

Like in SET, you are trying to form sets of three cards where, along each dimension, the set is either all the same or all different. If you’ve never played SET there are more details/examples at “how to play” in the game.

The mechanics of Tidy Baby are sort of inspired by a solitaire/practice version of SET I sometimes play where you draw two random cards and have to name the third card that would make a valid set.

In Tidy Baby you are presented with two “game cards” and a grid of up to nine candidates to complete a valid set – your job is to pick the right one before the clock runs out.

Unlike in SET, you get points for “partial” sets where your set is valid on one or two dimensions (but not all three). It’s actually a pretty fun challenge to try to get only sets that are invalid along all three dimensions.

In building the game, we were sort of surprised that the biggest challenge was ensuring that all words were unambiguously one part of speech. You’d be surprised how hard it is to find three-letter adjectives that are not also common verbs or nouns. We did our best!

We’ve got three “paces” in the game: Steady, Strenuous, and Grueling (s/o MECC!)

Let us know what you think!

I used the time over Christmas and in between the years to redesign my website.

This time I decided to make it in the theme of an evil Sith Lord that commands the Galactic Cookie Empire, because I found my previous cookie consent game a bit boring after a while.

Here's the website's welcome page and the cookie consent game: https://cookie.engineer/index.html

(the cookie consent game isn't started on any other page of my website, only on the welcome page)

I also made a "making of" weblog article series, in case you're interested in the development process and how I implemented it and what kind of troubles I went through already:

- Making of the Game: https://cookie.engineer/weblog/articles/making-of-my-website...

- Making of the Avatar: https://cookie.engineer/weblog/articles/making-of-my-website...

- Debuggers to toy around with: https://cookie.engineer/design/consent/index.html

It "should" work on modern browsers. I tested it on Firefox on Linuxes, Chrome/Chromium on Linuxes, and Safari on Macbook. Don't have an iPhone so I can't test that, but my two old Android phones were also working fine with the meta viewport hack (I can't believe this is still the "modern" way to do things after 15 years. Wtf).

Best experience is of course with a bigger display. On smaller screen sizes, the game will use a camera to zoom around the game world and follow the player's spaceship. Minimum window width is 1280 pixels for no camera, and I think 800 pixels to be playable (otherwise the avatar gets in the way too much in the boss fights).

Oh, there's also a secret boss fight that you can unlock when you toy around with the Dev Tools :)

What's left to do on the avatar animation side:

- I have to backport CMUdict to JavaScript / ECMAScript. That's what I'm working on right now, as I'm not yet satisfied with the timings of the phonemes. Existing tools and pipelines that do this in python aren't realtime, which leads to my next point.

- I want to switch to using the "waveform energy detection" and a zero cross rate detector to time phonemes more correctly. I believe that changes in waveforms and their structures can detect differences in phonemes, but it's a gut feeling and not a scientific fact. Existing phoneme animation papers were kind of shit and broken (see my making of article 2). The phoneme boundary detector is highly experimental though and is gonna need a couple weeks more time until it's finished.

That's it for now, hope you gonna enjoy your stay on my website and I hope you gonna have fun playing the Cookie Consent Game :)

Oh, also, because it might not be obvious: No LLMs were used in the making of this website. Pretty much everything is hand-coded, and unbundled and unminified on purpose so visitors can learn from the code if they want to.

~Cookie

Airbnb is missing out on the biggest rental opportunity of 2026 so I built the solution.

Need to open Microsoft Teams and your other favorite Electron app? Get a temporary increase of memory

16 GB ought to be enough for everybody but in case it's not, you can rent more

I got tired of Claude Code forgetting all my context every time I open a new session: set-up decisions, how I like my margins, decision history. etc.

We built a shared memory layer you can drop in as a Claude Code Skill. It’s basically a tiny memory DB with recall that remembers your sessions. Not magic. Not AGI. Just state.

Install in Claude Code:

  /plugin marketplace add https://github.com/mutable-state-inc/ensue-skill
  /plugin install ensue-memory
  # restart Claude Code

What it doesn’t do: - it won’t read your mind - it’s alpha; it might break if you throw a couch at it

Repo: https://github.com/mutable-state-inc/ensue-skill

If you try it and it sucks, tell me why so I can fix it. Don't be kind, tia

I work with perfetto traces for ML/GPU optimization a lot. Copy pasting trace info to LLM is not easy. As usually the traces are gzip compressed, have very large file sizes. Also there is no way to select a certain section and sending it to LLM easily (can maybe write a query to do this but not ergonomic). So vibe coded this tool to quickly select the kernels/slices/threads I want to ask about and one click to get a text/json/markdown in my clipboard to paste for LLMs.

I think an MCP server for this might also be useful (tried a few but all miss certain things i wanted so chose to just build this quickly)

The article discusses the creation of the BrainKernel project, which aims to develop an open-source, cross-platform, deep learning framework for brain-computer interface applications. The project focuses on building a modular and scalable system to facilitate research and development in the field of brain-computer interfaces.

The article discusses the LLMRouter, a tool that enables efficient routing and management of large language models (LLMs) on modern hardware. It highlights the LLMRouter's ability to optimize model execution and resource utilization, improving performance and scalability for large-scale AI applications.

TL;DR explanation (go to https://github.com/Sakura-sx/Aroma?tab=readme-ov-file#tldr-e... if you want the formatted version)

This is done by measuring the minimum TCP RTT (client.socket.tcpi_min_rtt) seen and the smoothed TCP RTT (client.socket.tcpi_rtt). I am getting this data by using Fastly Custom VCL, they get this data from the Linux kernel (struct tcp_info -> tcpi_min_rtt and tcpi_rtt). I am using Fastly for the Demo since they have PoPs all around the world and they expose TCP socket data to me.

The score is calculated by doing tcpi_min_rtt/tcpi_rtt. It's simple but it's what worked best for this with the data Fastly gives me. Based on my testing, 1-0.7 is normal, 0.7-0.3 is normal if the connection is somewhat unstable (WiFi, mobile data, satellite...), 0.3-0.1 is low and may be a proxy, anything lower than 0.1 is flagged as TCP proxy by the current code.

Hi HN,

Every year, we ask thousands of readers (and authors) to share their 3 favorite reads of the year.

Now you can enter a book/author you love and see what books readers loved who also loved that book/author.

Try it here: https://shepherd.com/bboy/2025

This goes wide and doesn't try to limit itself to the genre, so you get some interesting results.

What do you think?

Background:

I want better recommendations based on my reading history. I'm incredibly frustrated with what is out there.

This system is based on 5,000 readers voting on their 3 favorite reads from 2023 to 2025. So, this covers ~15,000 books and is a high-quality vote. We wanted to keep the dataset small for now while we play with approaches.

We are building a full Book DNA app that pulls in your Goodreads history and delivers deeply personalized book recommendations based on people who like similar books (a significant challenge).

You can sign up to beta test it here if you want to help me with that:

https://docs.google.com/forms/d/1VOm8XOMU0ygMSTSKi9F0nExnGwo...

The first beta is coming out in late January, but it's pretty basic to start.

Past Show HNs as we've built Shepherd:

https://news.ycombinator.com/item?id=40084193

https://news.ycombinator.com/item?id=38600246

https://news.ycombinator.com/item?id=26871660

Thanks, looking forward to your comments :)

Ben

How small can a language model be while still doing something useful? I wanted to find out, and had some spare time over the holidays.

Z80-μLM is a character-level language model with 2-bit quantized weights ({-2,-1,0,+1}) that runs on a Z80 with 64KB RAM. The entire thing: inference, weights, chat UI, it all fits in a 40KB .COM file that you can run in a CP/M emulator and hopefully even real hardware!

It won't write your emails, but it can be trained to play a stripped down version of 20 Questions, and is sometimes able to maintain the illusion of having simple but terse conversations with a distinct personality.

--

The extreme constraints nerd-sniped me and forced interesting trade-offs: trigram hashing (typo-tolerant, loses word order), 16-bit integer math, and some careful massaging of the training data meant I could keep the examples 'interesting'.

The key was quantization-aware training that accurately models the inference code limitations. The training loop runs both float and integer-quantized forward passes in parallel, scoring the model on how well its knowledge survives quantization. The weights are progressively pushed toward the 2-bit grid using straight-through estimators, with overflow penalties matching the Z80's 16-bit accumulator limits. By the end of training, the model has already adapted to its constraints, so no post-hoc quantization collapse.

Eventually I ended up spending a few dollars on Claude API to generate 20 questions data (see examples/guess/GUESS.COM), I hope Anthropic won't send me a C&D for distilling their model against the ToS ;P

But anyway, happy code-golf season everybody :)

I've been working on a little open source [1] search engine, nilch. I noticed that nearly all well known search engines, including the alternative ones, tend to be run by companies of various sizes with the goal to make money, so they either fill your results with ads or charge you money, and I dislike this because search is the backbone of the internet and should not be commercial, so it runs in a not-for-profit style and aims to survive on donations. Additionally I'm personally really sick of AI in my search results so I got rid of that, and I wanted DuckDuckGo bangs so it supports all of them. Like many alternative search engines, it is fully private.

Sadly, it currently does not have its own index but rather uses the Brave search API. Once I'm in a financial position that it's possible, I would absolutely love to build a completely new index from the ground up which is open source, as well as an open source ranking and search algorithm, to back it.

I posted on Reddit and got an amazing amount of feedback which I implemented a number of feature requests, so I would really like your ideas, critiques, and bug reports as well. Thank you and sorry for the long post!

[1] https://github.com/UnmappedStack/nilch

Hi HN, I’m Oscar, a Year 8 student from Australia who enjoys messing around with computers and AI.

I recently built an AI detector to build on my skills in computer science. I entered the prototype into the Oliphant Science Awards which is a local science competition (writing a 4000-word report on the methodology) and ended up winning, so I decided to polish it into a real web service that the world can make use of.

I noticed that schools and businesses are rushing to use AI detectors, but most commercial tools require you to send full, plaintext documents to cloud servers. For researchers or IP-sensitive work, sending data unencrypted to a third party (who might use it for training) is a major privacy risk. Additionally, current commercial AI detectors aren't very transparent and are unable to tell you why they come to a conclusion. I wanted to build something that helps people make informed decisions based on as much information as possible, not tell them a simple percentage with nothing to back it up.

I built Veredict to be secure and private: 1. The browser generates a one-time AES-256 key and encrypts the text locally using the Web Crypto API. 2. This AES key is encrypted using the server’s RSA public key. 3. The encrypted payload is sent to my backend (Python/FastAPI running on Modal serverless GPUs). 4. We decrypt in memory only for the split-second of inference. The plaintext is never saved to a database.

The detection logic uses an ensemble of 4 models (including statistical analysis of perplexity/burstiness and a fine-tuned BERT model) to output a confidence score.

A note on the login: I know HN prefers demos without sign-ups. Since I am a student paying for the GPU compute out of a limited budget, I strictly require Google Auth to prevent bots from draining my credits. I hope you understand.

The app provides a free daily quota (250 words) so you can test the architecture.

Link: https://veredictlabs.com

I'd really appreciate feedback on basically anything regarding my project :)

This article discusses the importance of implementing a safety net for AI language models like Claude, which can help prevent unintended behaviors or outputs. It highlights key considerations, such as model oversight, prompt engineering, and the need for comprehensive testing and monitoring.

A small web puzzle game where you guess coordinates in n-dimensional space. Tutorial and manual are available. Available in 17 languages.

Play here: https://euclidle.com/ Note: Google Analytics and AdSense are used.

Tutorial: https://docs.euclidle.com/en/tutorial.html Manual: https://docs.euclidle.com/en/manual.html

Bluesky: https://bsky.app/profile/euclidle.com

Hi everyone, I built FuseCells, a minimalistic logic puzzle game where every level is handcrafted (no procedural generation). It started as a personal challenge: design a clean rule-set and scale it to thousands of puzzles without losing difficulty balance.

What’s unique: - 2,500 handcrafted levels across multiple grid sizes - Deterministic logic, no guessing required - A rule system inspired by constraint-solving and path-finding concepts - Daily Challenge + leaderboards (I added these to give the game a “reason to come back” without turning it into a grind) - Built end-to-end as a solo dev project

A small data point: after adding Daily Challenge + leaderboards, the game quickly reached ~400 active players (depending on how you define active), and I’m trying to understand what makes them return: competition, habit, or simply puzzle pacing.

Technical notes (if you’re curious): - My internal tools validate solvability using a custom constraint solver - Difficulty is approximated via solver step count - Optimized to run smoothly on low-end devices - Designed for iOS, fully adapted for iPad

I’d love feedback from puzzle lovers, game designers, and anyone into handcrafted logic design - especially on difficulty curve, onboarding clarity, and whether daily + leaderboards add value or feel distracting.

Title says all. I kept searching for a Metronome app that would have everything I need as I learn to play faster on guitar and just couldn't find one so I just built my own instead. Sharing with everyone else in case they have a need for something like this :)

Has a few cool nifty features like:

* Keybinds for everything. I hate my mouse

* Progressive mode where the bpm increases every x bars by y bpm

* Speed burst training switches between 2 speeds either at a preset amount of bars or by pressing a key

It's almost New Year's and here's an original way to show the countdown using Tetris. I'm sure something similar has been done before, but I had a lot of fun building this (TDD style) using Claude Code.

After New Year's it can also function as a regular clock:

https://tetris-time.koenvangilst.nl/

For the impatient, it's possible to increase the speed using url parameters:

https://tetris-time.koenvangilst.nl/?to=2025-12-31T23:00:00....

I am currently working on a larger project that needs a short-lived HTTP "auth" based on a separate, out-of-band authentication process. Since every allowed IP only needs to be allowed for a few minutes at a time on specific server names, I created this project to solve that. It should work with any Redis-compatible database. For the docker-compose example, I used valkey.

This is mostly useful if you have multiple domains that you want to control access to. If you want to allow 1.1.1.1 to mywebsite.com and securesite.com, and 2.2.2.2 to securesite.com and anothersite.org for certain TTLs, you just need to set hash keys in your Redis-compatible database of choice like:

1.1.1.1:

  - mywebsite.com: 1 (30 sec TTL)

  - securesite.com: 1 (15 sec TTL)

  - securesite.com: 1 (3600 sec TTL)

  - anothersite.org: 1 (never expires)

An in-process cache can also be used, but is not enabled unless you pass --enable-l1-cache to kvauth. That makes successful auth_requests a lot faster since the program is not reaching out to the key/value database on every request.

I didn't do any hardcore profiling on this but did enable the chi logger middleware to see how long requests generally took:

kvauth-1 | 2025/12/30 21:32:28 "GET http://127.0.0.1:8888/kvauth HTTP/1.0" from 127.0.0.1:42038 - 401 0B in 300.462µs # disallowed request

nginx-1 | 192.168.65.1 - - [30/Dec/2025:21:32:28 +0000] "GET / HTTP/1.1" 401 179 "-" "curl/8.7.1"

kvauth-1 | 2025/12/30 21:32:37 "GET http://127.0.0.1:8888/kvauth HTTP/1.0" from 127.0.0.1:40160 - 401 0B in 226.189µs # disallowed request

nginx-1 | 192.168.65.1 - - [30/Dec/2025:21:32:37 +0000] "GET / HTTP/1.1" 401 179 "-" "curl/8.7.1"

# IP added to redis allowlist

kvauth-1 | 2025/12/30 21:34:02 "GET http://127.0.0.1:8888/kvauth HTTP/1.0" from 127.0.0.1:54032 - 200 0B in 290.648µs # allowed, but had to reach out to valkey

kvauth-1 | 2025/12/30 21:34:02 "GET http://127.0.0.1:8888/kvauth HTTP/1.0" from 127.0.0.1:54044 - 200 0B in 4.041µs

nginx-1 | 192.168.65.1 - - [30/Dec/2025:21:34:02 +0000] "GET / HTTP/1.1" 200 111 "-" "curl/8.7.1"

kvauth-1 | 2025/12/30 21:34:06 "GET http://127.0.0.1:8888/kvauth HTTP/1.0" from 127.0.0.1:51494 - 200 0B in 6.617µs # allowed, used cache

kvauth-1 | 2025/12/30 21:34:06 "GET http://127.0.0.1:8888/kvauth HTTP/1.0" from 127.0.0.1:51496 - 200 0B in 3.313µs

nginx-1 | 192.168.65.1 - - [30/Dec/2025:21:34:06 +0000] "GET / HTTP/1.1" 200 111 "-" "curl/8.7.1

IP allowlisting isn't true authentication, and any production implementation of this project should use it as just a piece of the auth flow. This was made to solve the very specific problem of a dynamic IP allow list for NGINX.

Spacelist is an open-source project that allows users to create interactive lists and dashboards. It provides a lightweight and customizable platform for organizing and visualizing information, making it useful for project management, task tracking, and data presentation.

OP here.

Most Deep Learning approaches for TSP rely on pre-training with large-scale datasets. I wanted to see if a solver could learn "on the fly" for a specific instance without any priors from other problems.

I built a solver using PPO that learns from scratch per instance. It achieved a 1.66% gap on TSPLIB d1291 in about 5.6 hours on a single A100.

The Core Idea: My hypothesis was that while optimal solutions are mostly composed of 'minimum edges' (nearest neighbors), the actual difficulty comes from a small number of 'exception edges' outside of that local scope.

Instead of pre-training, I designed an inductive bias based on the topological/geometric structure of these exception edges. The agent receives guides on which edges are likely promising based on micro/macro structures, and PPO fills in the gaps through trial and error.

It is interesting to see RL reach this level without a dataset. I have open-sourced the code and a Colab notebook for anyone who wants to verify the results or tinker with the 'exception edge' hypothesis.

Code & Colab: https://github.com/jivaprime/TSP_exception-edge

Happy to answer any questions about the geometric priors or the PPO implementation!

The article explores the process of creating a virtual bookshelf using the Claude language, a new AI-powered programming tool. It discusses the challenges and techniques involved in building an interactive and visually appealing bookshelf application.

Repo: https://github.com/valdanylchuk/xcc700

Hi Everyone! I just wrote my first compiler!

- single pass, recursive descent, direct emission

- generates REL ELF binaries, runnable using ESP-IDF elf_loader

- very basic features only, just enough for self-hosting

- treats the Xtensa CPU as a stack machine for simplicity, no register allocation / window usage

- compilable on Mac, probably also Linux, can cross-compile for esp32 there

- wrote for fun / cyberdeck project

Sample output from esp32:

    xcc700.elf xcc700.c -o /d/cc.elf
    
    [ xcc700 ] BUILD COMPLETED > OK
    > IN  : 700 Lines / 7977 Tokens
    > SYM : 69 Funcs / 91 Globals
    > REL : 152 Literals / 1027 Patches
    > MEM : 1041 B .rodata / 17120 B .bss
    > OUT : 27735 B .text / 33300 B ELF
    [ 40 ms ] >> 17500 Lines/sec <<

Cuecard.dev is an open-source web application that provides a modern, user-friendly interface for managing and organizing personal notes, tasks, and other information. The platform offers features like note-taking, task management, and integration with popular productivity tools.