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Article URL: https://www.thefountain.me/past-newsletters-the-fountain/fomos-great-deception

Comments URL: https://news.ycombinator.com/item?id=43742920

Points: 1

# Comments: 0

Built a simple app that creates simple icons in black and white and then returns them in SVG format.

Used it to create all the icons on the website and would love to hear your thoughts.

Happy easter :)

Comments URL: https://news.ycombinator.com/item?id=43742871

Points: 1

# Comments: 0

I built a Chrome extension called GithubPin that lets you pin GitHub items (repositories, issues, pull requests, and projects) for easy access later.

It adds a small pin button directly on GitHub pages and stores your pins. You can view and manage everything from a clean, GitHub-like extension's popup or your dashboard.

Key features:

Pin/unpin directly from GitHub UI Organize pins by type (repos, issues, PRs, projects) See metadata like labels and status No login or account required I built it out of personal need and kept it minimal by design. Would love feedback, ideas, and thoughts from fellow devs.

Chrome Web Store: https://chromewebstore.google.com/detail/githubpin/hfedkfpmc...

Comments URL: https://news.ycombinator.com/item?id=43742860

Points: 1

# Comments: 0

Article URL: https://kottke.org/13/06/you-commit-three-felonies-a-day

Comments URL: https://news.ycombinator.com/item?id=43742859

Points: 2

# Comments: 0

Article URL: https://english.elpais.com/opinion/2025-04-20/elon-musks-less-than-original-measures-developing-the-ideas-of-his-grandfather-joshua-haldeman.html

Comments URL: https://news.ycombinator.com/item?id=43742812

Points: 1

# Comments: 0

Article URL: https://utcc.utoronto.ca/~cks/space/blog/sysadmin/TailscaleDNSAndExitNodes

Comments URL: https://news.ycombinator.com/item?id=43742802

Points: 1

# Comments: 0

Article URL: https://www.wsj.com/tech/ai/ai-safe-superintelligence-startup-ilya-sutskever-openai-2335259b

Comments URL: https://news.ycombinator.com/item?id=43742788

Points: 3

# Comments: 1

Article URL: https://bitbbs.bitsoul.xyz

Comments URL: https://news.ycombinator.com/item?id=43742772

Points: 1

# Comments: 1

Article URL: https://www.atlassian.com/blog/design/implementing-typography-at-scale-the-journey-behind-the-screens

Comments URL: https://news.ycombinator.com/item?id=43742756

Points: 3

# Comments: 1

Article URL: https://staysaasy.com/management/2020/07/24/Managing-One-Person.html

Comments URL: https://news.ycombinator.com/item?id=43742735

Points: 1

# Comments: 0

Article URL: https://github.com/ShaojieJiang/n8n-templates/tree/main/n8n_templates/smart_survey

Comments URL: https://news.ycombinator.com/item?id=43742728

Points: 1

# Comments: 0

Article URL: https://onlynv.dev/blog/what-the-hell-is-an-elliptic-curve

Comments URL: https://news.ycombinator.com/item?id=43742722

Points: 1

# Comments: 0

Hi,

I wanted a straightforward way to expose my Fastify server APIs to AI assistants using the Model Context Protocol (MCP) without needing custom wrappers etc., and couldn't find an existing solution that did this automatically.

So I built `@mcp-it/fastify` – a plugin for Fastify that automatically discovers the API routes and exposes them as MCP tools, making them callable by clients like Cursor, Claude Desktop, etc.

It supports: * Automatic route discovery. * SSE and Streamable HTTP transports. * Per-route configuration (e.g., hiding internal routes, custom tool names).

The project is open-source and available on https://github.com/AdirAmsalem/mcp-it.

I'd love to get your feedback! Is this useful? Are there other frameworks you'd like to see supported (NestJS, Express, others)?

Thanks!

Comments URL: https://news.ycombinator.com/item?id=43742712

Points: 1

# Comments: 0

Article URL: https://www.youtube.com/watch?v=M51bMMVWbC8

Comments URL: https://news.ycombinator.com/item?id=43742668

Points: 1

# Comments: 0

Article URL: https://www.whitehouse.gov/lab-leak-true-origins-of-covid-19/

Comments URL: https://news.ycombinator.com/item?id=43742643

Points: 5

# Comments: 2

Article URL: https://medium.com/luminasticity/why-mina-loy-196f859f273b

Comments URL: https://news.ycombinator.com/item?id=43742641

Points: 1

# Comments: 0

Image for day 1: https://i.imgur.com/bQ3Oxc5.png

After I tried to fit DOOM inside a QR code last time (https://news.ycombinator.com/item?id=43729683), I'm trying to continue this "series" to get an actually decent chatbot into a QR code.

This is, of course, not as easy as the former. I could always cheat and make a rule-based ELIZA style chatbot (that I actually dabbled with earlier) but I want to make something actually somewhat useful. I know quite little about how LLMs and Transformers fundamentally work so this will also teach me a lot about AI (also, will be public and Open Source when it actually turns into something somewhat cool)

Here's our limitations: The largest standard QR code (Version 40) holds 2,953 bytes (~2.9 KB). This is very small—a Windows sound file of 1/15th of a second is 11 KB! PLUS, we can't directly dump HTML/JS into the QR code, we need to compress it to BASE64 (or BigInt) which takes up 0.1-0.15Kb as well, so we have about 2.7Kb for the entire thing, yikes!

Here's what I did for day 1:

The first version (v0) was incredibly basic - a simple pattern-matching chatbot with predefined responses:

``` const V = "you,I,is,are,do,what,how,why,,...e".split(","); const P = [ [5,2,0,8], // what is you like [5,4,0,8], // what do you like.... [0,8,15,9] // you like me think ]; ```

(v1) added better CSS (still light theme), topic memory, sentiment analysis and transition patterns, but all this made the file size a bit over 4kb.

(v2) was v1 with more compression, lost features but shrank to 2.8kb.

(v3) added a retro UI because it seemed fitting, ASCII art and simplified text formatting with newlines, but it was still extremely dumb. (v4) and (v5) added more cuts to barely get it below the limit (2.85kb).

So I changed the approach for (v6) and went for a trie data structure for response lookups: ``` const t={h:{e:{l:{l:{o:["Hello! How can I help you today?","Hi! What's on your mind?"]}}}}}; ```

This allowed for prefix matching under our constraints AND there was no need for pattern matching.

(v7) was trying to optimise it, but it still ended up being around 3.3kb, better than before but still not very "intelligent".

For (v8), I took a lot of time and switched to a very basic implementation of a 2 layered neural network: ``` const network = { embeddings: new Float32Array(c.vSize * c.eDim), hidden: new Float32Array(c.eDim * c.hSize), output: new Float32Array(c.hSize * c.oSize), hiddenBias: new Float32Array(c.hSize), outputBias: new Float32Array(c.oSize) }; ```

This gives us a 582 char neural network that's 8 bit quantized but, as you would expect, this was huge, about 11kb.

(v9) and (v10) were basically minifying this further, down to about 3.2kb, not bad!

The last version I worked on today was (v10.5). I used word level processing instead of character level with 4D vectors, template responses with context awareness, better state tracking and 8 output dimensions. Also added a repetition penalty (currently a little broken) but is actually kind of good... 5.3kb good.

For Day 2, I'm thinking: 1. Implement better context handling 2. Optimize the neural architecture further (maybe a tiny transformer?) 3. Maybe find a way to compress it even more?

Resources: https://www.youtube.com/watch?v=aircAruvnKk https://www.youtube.com/watch?v=zhxNI7V2IxM&t=275s https://github.com/rasbt/LLMs-from-scratch https://github.com/lionelmessi6410/Neural-Networks-from-Scra...

Comments URL: https://news.ycombinator.com/item?id=43742639

Points: 1

# Comments: 0

Article URL: https://deepfake-total.com/spot_the_deepfake/

Comments URL: https://news.ycombinator.com/item?id=43742625

Points: 1

# Comments: 1

Hi HN, I’m Krishil Rohit Sheth, and over the last 4 years I’ve developed a new algorithm (RPF) for squaring large numbers — and it outperforms Karatsuba, and even FFT-based methods for numbers under 800 digits.

Raw performance: RPF beats Karatsuba in execution time and scales better with input size. With GMP enhancements: Even after both are optimized with GMP, RPF still maintains a performance edge. Better than FFT for mid-size inputs: Up to 800 digits, RPF is also faster than FFT-based multiplication, which usually kicks in beyond this range.

I’ve attached benchmark charts and comparisons here: -https://drive.google.com/file/d/1aZ-JR0Oq5KnY4xKd2tAPEvr1wFPowhSt/view?usp=drive_link This has applications in:

Cryptography (modular exponentiation)

Blockchain & ZK systems

Financial simulations

Any compute-heavy big-number operations

I’ve filed a provisional patent and I’m looking to either license the algorithm, collaborate, or sell the IP outright.

Would love feedback from devs, researchers, and cryptographers here! Also happy to talk if you work on libraries like GMP, OpenSSL, Java BigInteger, Libgcrypt, etc.

Thanks! —Krishil Sheth -krishilsheth@gmail.com -+91 9372677245

Comments URL: https://news.ycombinator.com/item?id=43742624

Points: 2

# Comments: 0

Article URL: https://www.theverge.com/news/651786/subaru-solterra-ev-2026-facelift

Comments URL: https://news.ycombinator.com/item?id=43742618

Points: 1

# Comments: 0