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Top stories from https://news.ycombinator.com (with 100+ score) Contribute to the development here: https://github.com/phil-r/hackernewsbot Also check https://t.me/designer_news Contacts: @philr
Bzip2 crate switches from C to 100% Rust (Score: 150+ in 5 hours)
Link: https://readhacker.news/s/6whmt
Comments: https://readhacker.news/c/6whmt
Brad Lander detained by masked federal agents inside immigration court (Score: 160+ in 4 hours)
Link: https://readhacker.news/s/6wgLf
Comments: https://readhacker.news/c/6wgLf
Should we design for iffy internet? (Score: 151+ in 8 hours)
Link: https://readhacker.news/s/6wfRs
Comments: https://readhacker.news/c/6wfRs
Resurrecting a dead torrent tracker and finding 3M peers (🔥 Score: 157+ in 2 hours)
Link: https://readhacker.news/s/6wgPy
Comments: https://readhacker.news/c/6wgPy
Making 2.5 Flash and 2.5 Pro GA, and introducing Gemini 2.5 Flash-Lite (🔥 Score: 152+ in 2 hours)
Link: https://readhacker.news/s/6wgwf
Comments: https://readhacker.news/c/6wgwf
GitHub CI/CD observability with OpenTelemetry step by step guide (❄️ Score: 150+ in 6 days)
Link: https://readhacker.news/s/6vXpn
Comments: https://readhacker.news/c/6vXpn
Selfish reasons for building accessible UIs (Score: 150+ in 12 hours)
Link: https://readhacker.news/s/6weDx
Comments: https://readhacker.news/c/6weDx
No Hello (🔥 Score: 150+ in 2 hours)
Link: https://readhacker.news/s/6wftz
Comments: https://readhacker.news/c/6wftz
Dull Men’s Club (Score: 150+ in 17 hours)
Link: https://readhacker.news/s/6wdGE
Comments: https://readhacker.news/c/6wdGE
What happens when clergy take psilocybin (Score: 151+ in 9 hours)
Link: https://readhacker.news/s/6wefk
Comments: https://readhacker.news/c/6wefk
Generative AI coding tools and agents do not work for me (Score: 151+ in 4 hours)
Link: https://readhacker.news/s/6wezB
Comments: https://readhacker.news/c/6wezB
I have reimplemented Stable Diffusion 3.5 from scratch in pure PyTorch (🔥 Score: 156+ in 3 hours)
Link: https://readhacker.news/s/6w8Mn
Comments: https://readhacker.news/c/6w8Mn
The Army’s Newest Recruits: Tech Execs From Meta, OpenAI and More (Score: 151+ in 1 day)
Link: https://readhacker.news/s/6w6fM
Comments: https://readhacker.news/c/6w6fM
SIMD-friendly algorithms for substring searching (2018) (Score: 150+ in 12 hours)
Link: https://readhacker.news/s/6w7Zb
Comments: https://readhacker.news/c/6w7Zb
TimeGuessr (❄️ Score: 151+ in 4 days)
Link: https://readhacker.news/s/6vSW9
Comments: https://readhacker.news/c/6vSW9
Iran asks its people to delete WhatsApp from their devices (Score: 150+ in 4 hours)
Link: https://readhacker.news/s/6whaA
Comments: https://readhacker.news/c/6whaA
Building Effective AI Agents (Score: 154+ in 4 hours)
Link: https://readhacker.news/s/6wgRK
Comments: https://readhacker.news/c/6wgRK
The Grug Brained Developer (2022) (🔥 Score: 153+ in 1 hour)
Link: https://readhacker.news/s/6whqG
Comments: https://readhacker.news/c/6whqG
The magic of through running (Score: 150+ in 10 hours)
Link: https://readhacker.news/s/6wfkF
Comments: https://readhacker.news/c/6wfkF
Honda Conducts Successful Launch and Landing of Experimental Reusable Rocket (🔥 Score: 157+ in 1 hour)
Link: https://readhacker.news/s/6wgjg
Comments: https://readhacker.news/c/6wgjg
Show HN: I recreated 90s Mode X demoscene effects in JavaScript and Canvas (Score: 152+ in 10 hours)
Link: https://readhacker.news/s/6weU5
Comments: https://readhacker.news/c/6weU5
After 25 years of writing software, I was feeling nostalgic for the kinds of things that got me into programming in the first place: the old DOS demoscene. I spent a weekend seeing if I could recapture some of that INT 13H VGA magic using today's web tech, but with the old-school constraints of doing it from scratch.
The result is this portfolio of ten classic effects running in a single HTML file. It's all vanilla JavaScript writing to a <canvas> element, with no external libraries. It was a fun challenge to implement things like:
* The color palette cycling and smooth fading in the Plasma demo.
* The buffer-averaging algorithm for the Fire effect to make the flames feel more natural.
* The distance-based texture crossfading in the Tunnel to create the illusion of flying through different sections.
* A 2D scalar field for the Metaballs to calculate the surface normals for that classic blended, metallic look (I did the best I could with the given constraints).
It was a great exercise in getting back to first principles and a reminder of how much those early demo programmers could accomplish with so little. I hope it brings back some good memories for others who grew up with this stuff.
I'd love to hear about your favorite classic demos or if there are any other iconic effects you think would be a fun challenge to add.
Cheers!
How Frogger 2’s source code was recovered from a destroyed tape [video] (Score: 151+ in 1 day)
Link: https://readhacker.news/s/6wbtp
Comments: https://readhacker.news/c/6wbtp
Fossify – A suite of open-source, ad-free apps (Score: 153+ in 4 hours)
Link: https://readhacker.news/s/6wfc6
Comments: https://readhacker.news/c/6wfc6
Twin – A Textmode WINdow Environment (Score: 150+ in 1 day)
Link: https://readhacker.news/s/6wbpt
Comments: https://readhacker.news/c/6wbpt
OpenAI wins $200M U.S. defense contract (Score: 150+ in 8 hours)
Link: https://readhacker.news/s/6wen6
Comments: https://readhacker.news/c/6wen6
Snorting the AGI with Claude Code (Score: 157+ in 13 hours)
Link: https://readhacker.news/s/6wczT
Comments: https://readhacker.news/c/6wczT
Google Cloud Incident Report – 2025-06-13 (Score: 150+ in 11 hours)
Link: https://readhacker.news/s/6w8bd
Comments: https://readhacker.news/c/6w8bd
Launch HN: Chonkie (YC X25) – Open-Source Library for Advanced Chunking (❄️ Score: 150+ in 5 days)
Link: https://readhacker.news/c/6vQEL
Hey HN! We're Shreyash and Bhavnick. We're building Chonkie (https://chonkie.ai), an open-source library for chunking and embedding data.
Python: https://github.com/chonkie-inc/chonkie
TypeScript: https://github.com/chonkie-inc/chonkie-ts
Here's a video showing our code chunker: https://youtu.be/Xclkh6bU1P0.
Bhavnick and I have been building personal projects with LLMs for a few years. For much of this time, we found ourselves writing our own chunking logic to support RAG applications. We often hesitated to use existing libraries because they either had only basic features or felt too bloated (some are 80MB+).
We built Chonkie to be lightweight, fast, extensible, and easy. The space is evolving rapidly, and we wanted Chonkie to be able to quickly support the newest strategies. We currently support: Token Chunking, Sentence Chunking, Recursive Chunking, Semantic Chunking, plus:
- Semantic Double Pass Chunking: Chunks text semantically first, then merges closely related chunks.
- Code Chunking: Chunks code files by creating an AST and finding ideal split points.
- Late Chunking: Based on the paper (https://arxiv.org/abs/2409.04701), where chunk embeddings are derived from embedding a longer document.
- Slumber Chunking: Based on the "Lumber Chunking" paper (https://arxiv.org/abs/2406.17526). It uses recursive chunking, then an LLM verifies split points, aiming for high-quality chunks with reduced token usage and LLM costs.
You can see how Chonkie compares to LangChain and LlamaIndex in our benchmarks: https://github.com/chonkie-inc/chonkie/blob/main/BENCHMARKS....
Some technical details about the Chonkie package: - ~15MB default install vs. ~80-170MB for some alternatives. - Up to 33x faster token chunking compared to LangChain and LlamaIndex in our tests. - Works with major tokenizers (transformers, tokenizers, tiktoken). - Zero external dependencies for basic functionality. - Implements aggressive caching and precomputation. - Uses running mean pooling for efficient semantic chunking. - Modular dependency system (install only what you need).
In addition to chunking, Chonkie also provides an easy way to create embeddings. For supported providers (SentenceTransformer, Model2Vec, OpenAI), you just specify the model name as a string. You can also create custom embedding handlers for other providers.
RAG is still the most common use case currently. However, Chonkie makes chunks that are optimized for creating high quality embeddings and vector retrieval, so it is not really tied to the "generation" part of RAG. In fact, We're seeing more and more people use Chonkie for implementing semantic search and/or setting context for agents.
We are currently focused on building integrations to simplify the retrieval process. We've created "handshakes" – thin functions that interact with vector DBs like pgVector, Chroma, TurboPuffer, and Qdrant, allowing you to interact with storage easily. If there's an integration you'd like to see (vector DB or otherwise), please let us know.
We also offer hosted and on-premise versions with OCR, extra metadata, all embedding providers, and managed vector databases for teams that want a fully managed pipeline. If you're interested, reach out at shreyash@chonkie.ai or book a demo: https://cal.com/shreyashn/chonkie-demo.
We're eager to hear your feedback and comments! Thanks!
Peano arithmetic is enough, because Peano arithmetic encodes computation (Score: 151+ in 23 hours)
Link: https://readhacker.news/s/6w6Ey
Comments: https://readhacker.news/c/6w6Ey
Implementing Logic Programming (Score: 150+ in 15 hours)
Link: https://readhacker.news/s/6w7vM
Comments: https://readhacker.news/c/6w7vM