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Kimi K3: Open Frontier Intelligence

https://www.kimi.com/blog/kimi-k3
1385•vincent_s•14h ago•843 comments

How Has Roman Concrete Lasted for Millennia? 1,900-Year-Old Latrine Offers Clues

https://www.smithsonianmag.com/smart-news/how-has-roman-concrete-lasted-for-millennia-a-1900-year...
32•divbzero•1h ago•14 comments

An Engineer's Guide to USB Typе-С (2024)

https://www.ti.com/lit/eb/slyy228/slyy228.pdf?ts=1759892558029
70•gregsadetsky•6d ago•1 comments

Microsoft Comic Chat is now open source

https://opensource.microsoft.com/blog/2026/07/16/microsoft-comic-chat-is-now-open-source/
606•jervant•13h ago•136 comments

The Human-in-the-Loop Is Tired

https://pydantic.dev/articles/the-human-in-the-loop-is-tired
115•haritha1313•5h ago•58 comments

GrapheneOS recommended for domestic abuse victims

https://privacypros.com.au/privacy-hub/articles/dv-safe-phone-australia/
42•aussieguy1234•3h ago•21 comments

Decoy Font

https://www.mixfont.com/experiments/decoy-font
475•ray__•13h ago•112 comments

LM Studio Bionic: the AI agent for open models

https://lmstudio.ai/blog/introducing-lm-studio-bionic
197•minimaxir•9h ago•71 comments

$100 AI Music Video: Claude Fable 5 vs. GPT-5.6 Sol

https://www.tryai.dev/blog/ai-music-video-arena-claude-vs-gpt-5.6
198•hershyb_•9h ago•232 comments

Solod: Go can be a better C

https://solod.dev
88•koeng•3d ago•20 comments

The Little Book of Reinforcement Learning

https://github.com/alxndrTL/little-book-rl/
87•mustaphah•6h ago•10 comments

M 3.9 Experimental Explosion – 147 Km ENE of Ponce Inlet, Florida

https://earthquake.usgs.gov/earthquakes/eventpage/us7000t13l/executive
50•hnburnsy•4h ago•20 comments

NotebookLM is now Gemini Notebook

https://blog.google/innovation-and-ai/products/gemini-notebook/notebooklm-gemini-notebook/
277•xnx•13h ago•141 comments

Old Icons

https://leancrew.com/all-this/2026/07/old-icons/
25•zdw•5d ago•1 comments

'Likweli': A new monkey species discovered in the Congo Basin

https://news.yale.edu/2026/07/15/meet-likweli-new-monkey-species-discovered-congo-basin
65•gmays•7h ago•11 comments

Simulating everything, sort of: The promise and limits of world models

https://arstechnica.com/ai/2026/07/simulating-everything-sort-of-the-promise-and-limits-of-world-...
22•LorenDB•3d ago•1 comments

Mathematics of Data Science

https://arxiv.org/abs/2607.11938
124•Anon84•8h ago•3 comments

How Our Rust-to-Zig Rewrite Is Going

https://rtfeldman.com/rust-to-zig
450•jorangreef•17h ago•236 comments

Detecting LLM-Generated Texts with “Classical” Machine Learning

https://blog.lyc8503.net/en/post/llm-classifier/
175•uneven9434•12h ago•119 comments

Immersive Linear Algebra Book with Interactive Figures (2015)

https://immersivemath.com/ila/
189•srean•13h ago•26 comments

Helium escaping from atmosphere of nearby rocky exoplanet in a habitable zone

https://www.science.org/doi/10.1126/science.aea9708
85•anyonecancode•9h ago•23 comments

Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning

https://arxiv.org/abs/2607.12395
45•binyu•7h ago•15 comments

Show HN: Clx – Compile Lua to Native Executables Through C++20

https://github.com/samyeyo/clx
102•_samt_•5d ago•11 comments

Pseudpocalypse

https://dynomight.net/pseudpocalypse/
103•surprisetalk•2d ago•61 comments

What loss.backward() actually does

https://oraziorillo.com/blog/what-loss-backward-actually-does/
6•oraziorillo•5d ago•1 comments

Pebble Mega Update – July 2026

https://repebble.com/blog/pebble-mega-update-july-2026
4•crazysaem•1h ago•0 comments

Abstracting Effects with Continuations

https://crowdhailer.me/2026-07-15/abstracting-effects-with-continuations/
54•crowdhailer•18h ago•2 comments

How to Train a Gen AI Kick Drum Model on Your Old Linux Desktop with 6GB VRAM

https://www.zhinit.dev/blog/training-a-kick-drum-diffusion-model
115•zhinit•14h ago•56 comments

CD sales growth outpaced vinyl in the first half of 2026

https://consequence.net/2026/07/the-cd-revival-is-getting-hard-to-ignore/
81•speckx•12h ago•90 comments

Adaptional (YC S25) Is Hiring

https://www.ycombinator.com/companies/adaptional/jobs
1•acesohc•12h ago
Open in hackernews

The Fastest Way yet to Color Graphs

https://www.quantamagazine.org/the-fastest-way-yet-to-color-graphs-20250512/
62•GavCo•1y ago

Comments

tonyarkles•1y ago
In case you haven't looked at the article, this is looking specifically at the Edge Coloring problem and not the more commonly known Vertex Coloring problem. Vertex Coloring is NP-complete unfortunately.
erikvanoosten•1y ago
You can convert edge coloring problems into vertex coloring problems and vice versa through a simple O(n) procedure.
meindnoch•1y ago
Wrong. You can convert edge-coloring problems into vertex-coloring problems of the so-called line graph: https://en.m.wikipedia.org/wiki/Line_graph

But the opposite is not true, because not every graph is a line graph of some other graph.

erikvanoosten•1y ago
Indeed. Thanks, I stand corrected.
tonyarkles•1y ago
Hrm... right. It's been a while. And it looks like both Vertex Coloring and Edge Coloring are both NP-complete (because of the O(n) procedure you're talking about and the ability to reduce both problems down to 3-SAT). I've started looking closer at the actual paper to try to figure out what's going on here. Thanks for the reminder, I miss getting to regularly work on this stuff.

Edit: thanks sibling reply for pointing out that it's not a bidirectional transform.

mauricioc•1y ago
For the edge-coloring problem, the optimal number of colors needed to properly color the edges of G is always either Delta(G) (the maximum degree of G) or Delta(G) + 1, but deciding which one is the true optimum is an NP-complete problem.

Nevertheless, you can always properly edge-color a graph with Delta(G) + 1 colors. Finding such a coloring could in principle be slow, though: the original proof that Delta(G) + 1 colors is always doable amounted to a O(e(G) * v(G)) algorithm, where e(G) and v(G) denote the number of edges and vertices of G, respectively. This is polynomial, but nowhere near linear. What the paper in question shows is how, given any graph G, to find an edge coloring using Delta(G) + 1 colors in O(e(G) * log(Delta(G))) time, which is linear time if the maximum degree is a constant.

Syzygies•1y ago
Yes. The article ran through this point as follows:

"In 1964, a mathematician named Vadim Vizing proved a shocking result: No matter how large a graph is, it’s easy to figure out how many colors you’ll need to color it. Simply look for the maximum number of lines (or edges) connected to a single point (or vertex), and add 1."

I keep wondering why I ever read Quanta Magazine. It takes a pretty generous reading of "need" to make this a correct statement.

JohnKemeny•1y ago
phkahler•1y ago
Is this going to lead to faster compile times? Faster register allocation...
john-h-k•1y ago
Very few compilers actually use vertex coloring for register allocation
isaacimagine•1y ago
Totally. The hard part isn't coloring (you can use simple heuristics to get a decent register assignment), rather, it's figuring out which registers to spill (don't spill registers in hot loops! and a million other things!).
NooneAtAll3•1y ago
and this post isn't even about vertex coloring
DannyBee•1y ago
No.

In SSA, the graphs are chordal, so were already easily colorable (relatively).

Outside of SSA, this is not true, but the coloring is still not the hard part, it's the easy part.

Not really. Coloring a graph is almost always talking about proper coloring, meaning that things that objects that are related receive different colors.

If you read the introduction, you'll also read that the goal is to "color each of your lines and require that for every point, no two lines connected to it have the same color."

Ps. "How many colors a graph needs" is a very well established term in computer science and graph theory.

mockerell•1y ago
I think the comment referred to the phrase „a graph needs X (colors or whatever)“. For me, this can be read two ways: 1. „a graph always needs at least X colors“ or 2. „a graph always needs at most X colors“.

Personally, I would interpret this as option 1 (and so did the comment above I assume). In that case, the statement is wrong. But I’d prefer to specify „at most/ at least“ anyways.

Or even better, use actual vocabulary. „For every graph there exists a coloring with X colors.“ or „any graph can be coloured using X colors“.

PS: I also agree with the sentiment about quanta magazine. It’s hard to get some actual information from their articles if you know the topic.

JohnKemeny•1y ago
What about this statement:

No matter how large a car is, it is easy to figure out how much money you'll need to buy it. Simply look at the price tag.

(From: No matter how large a graph is, it’s easy to figure out how many colors you’ll need to color it. Simply look for the maximum ...)

mauricioc•1y ago
Parent's point is that sometimes (but not always) the store is perfectly fine selling you a car for $1 less than what the "price tag" of Delta(G)+1 dollars asks for, so "need" is a bit inaccurate.