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Google DeepMind Releases AlphaGenome Atlas

https://blog.google/innovation-and-ai/models-and-research/google-deepmind/alphagenome-atlas/
440•utiiiD•7h ago•106 comments

On the Navier–Stokes Millennium Prize Problem

https://openai.com/index/navier-stokes-solution/
957•tedsanders•5h ago•777 comments

Kimi K3 (2.8T) at 1 token/s on a MacBook Pro, streamed from four SSDs

https://github.com/argonautlabsai/deltafin
160•Argonautlabs•2h ago•72 comments

Navier-Stokes – Tristan Buckmaster [pdf]

https://cims.nyu.edu/~tristanb/statement.pdf
949•procedurecall•16h ago•415 comments

DaVinci Resolve 21.1

https://www.blackmagicdesign.com/media/release/20260908-03
317•tosh•8h ago•141 comments

Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses

https://quesma.com/blog/qwen38-27b-quantizations-benchmarked/
192•stared•7h ago•97 comments

Large Language Models Develop Novel Social Biases Through Adaptive Exploration

https://openreview.net/challenge?redirect=%2Fforum%3Fid%3Dpc7fqaOcAH
9•paimapi•26m ago•1 comments

Mercury 2.5

https://www.inceptionlabs.ai/blog/introducing-mercury-2-5
76•Topfi•1h ago•8 comments

Muse: Meta's personal AI agent, features and capabilities

https://ai.meta.com/muse/
193•yks•2h ago•184 comments

Implementation of GCC's Nested Functions (vs. C++ Lambdas)

https://uecker.codeberg.page/2026-09-05.html
43•uecker•3d ago•3 comments

I-have-ADHD: A skill to stop coding agents from burying the answer

https://github.com/ayghri/i-have-adhd
253•domhudson•8h ago•200 comments

Animation in Bevy: The Big Picture

https://glocq.com/en/blog/20260827/
23•ibobev•2h ago•1 comments

Show HN: LLM Attention Visualization

https://ishamf.dev/p/llm-attention-visualizer/
95•ifz•5h ago•18 comments

Trey Parker and Matt Stone Are Changing the Name of South Park to South America

https://twitter.com/SouthPark/status/2097364141237539116
92•HelloUsername•2h ago•34 comments

Tracing np.add, all the way down

https://blog.veitheller.de/numpy.html
18•luu•4d ago•2 comments

The Helicopter with Radioactive Blades

https://hackaday.com/2026/09/07/the-helicopter-with-radioactive-blades/
136•zdw•1d ago•33 comments

Replacing a Rust Enum with a 64-Bit Word Made My Interpreter 17% Faster

https://pointersgonewild.com/2026-08-25-replacing-a-rust-enum-with-a-64-bit-word/
61•metrofun•3d ago•25 comments

Reverse Engineering an ASIC

https://kjartanvandriel.github.io/asic/
7•burekqueen•1d ago•0 comments

Show HN: Copperhead – Hardware as Fast as Software

https://copperhead.sh/
189•animeshchouhan•8h ago•76 comments

C*: Unifying Programming and Verification in C

https://arxiv.org/abs/2504.02246
64•rramadass•6h ago•38 comments

The 92-Year-Old Mathematician and the Teenage Apprentice

https://www.nytimes.com/2026/09/06/science/92-year-old-mathematician-apprentice.html
111•robinhouston•1d ago•9 comments

The two Christian saints who are the Buddha

https://signoregalilei.com/2026/08/30/the-two-christian-saints-who-are-secretly-the-buddha/
191•surprisetalk•7h ago•132 comments

Connecting the Machines

https://herdr.dev/blog/connecting-the-machines/
67•collinmanderson•5h ago•24 comments

Function Arguments Are Not Function Colors

https://jerf.org/iri/post/2026/func_args_are_not_colors/
23•ingve•3h ago•8 comments

ZX Spectrum: Experimenting with 1-Bit Sound

https://bumbershootsoft.wordpress.com/2026/09/05/zx-spectrum-experimenting-with-1-bit-sound/
87•ibobev•7h ago•26 comments

AlphaGenome Atlas predictive map of every DNA letter change in the human genome

https://deepmind.google/blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-chan...
73•fady0•7h ago•9 comments

FreeBSD 14.5-Release

https://www.freebsd.org/releases/14.5R/announce/
100•joshcsimmons•10h ago•18 comments

Getting your hands dirty is good for you

https://www.bbc.com/future/article/20260904-how-getting-your-hands-dirty-boosts-your-health-withi...
205•HatchedLake721•12h ago•170 comments

Y Combinator Early Access Network

https://events.ycombinator.com/yc-early-access-fall-26
70•tosh•5h ago•53 comments

ChatGPT Images 2.5

https://openai.com/index/introducing-chatgpt-images-2-5/
251•vertigoruntime•3h ago•313 comments
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.