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Gemini 3.7 Flash

https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-3-7-fl...
512•thisisauserid•5h ago•308 comments

Accelerating GPT-5.6 Sol Ultrafast

https://www.cerebras.ai/blog/accelerating-gpt-5-6-sol-ultrafast-with-openai
336•pr337h4m•4h ago•133 comments

NP-Overrated

https://gruhn.me/blog/2026-08-13/
83•theanonymousone•2h ago•34 comments

Understanding is the new bottleneck

https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck
113•sebg•3h ago•66 comments

Donkey.bas is 45 Years Old – 131 line of Glory

https://donkeybas.com/
157•jkrauska•4h ago•64 comments

Mistral OCR 4.1

https://docs.mistral.ai/models/ocr-4-1
216•spelk•5h ago•84 comments

Spaghettifying DRAM

https://github.com/xoreaxeaxeax/skitter-creek-bath-salts
453•matt_d•8h ago•131 comments

Choose Boring Technology (2015)

https://mcfunley.com/choose-boring-technology
196•tosh•4h ago•105 comments

How Gödel's Proof Works (2020)

https://www.quantamagazine.org/how-godels-proof-works-20200714/
48•tzury•2h ago•28 comments

How Organizations Use AI: Evidence from ChatGPT [pdf]

https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf
43•malshe•3h ago•21 comments

How Compaction Works in Pi

https://earendil.com/posts/compaction-in-pi/
64•tosh•4h ago•18 comments

Idol Mahjong Final Romance: A Slideshow Disguised as a Video Game

https://nicole.express/2026/more-like-idle-mahjong.html
31•nicole_express•4d ago•6 comments

Finite State Machines in Forth (1994)

https://www.forth.org/literature/noble.html
13•ofalkaed•5d ago•0 comments

Nine PBS sues Iron Mountain over blocked access to archival data

https://current.org/2026/08/nine-pbs-sues-iron-mountain-over-blocked-access-to-archival-data/
184•vinayakborkar•9h ago•90 comments

Smooth Move: Taming Trajectories with Polynomials

https://nick.zoic.org/art/smooth-move-taming-trajectories-with-polynomials/
13•lioeters•3d ago•0 comments

Single log line is 49KB+ (ext4) / 110KB+ (btrfs) of systemd-journald disk writes

https://github.com/systemd/systemd/issues/40262
115•ValdikSS•3h ago•59 comments

Kubernetes on Oxide: How customer needs shaped our integrations

https://oxide.computer/blog/kubernetes-on-oxide
144•stevehipwell•8h ago•62 comments

Tocharian Online

https://lrc.la.utexas.edu/eieol/tokol/0
51•Bluestein•5h ago•8 comments

Launch HN: Bullet (YC S26) – A Faster Coding Agent

https://www.codewithbullet.com
71•adi1•14h ago•45 comments

AI At Home Part 1: A Box Of Scraps

https://jdagostino.github.io/ai-pt1-box-o-scraps/index.html
73•timmmmmmay•6h ago•39 comments

DeepSeek Harness developer preview

https://deepseek.com/harness/en/
519•bjin•9h ago•231 comments

Where did the old web go? We followed 657,607 links to find out

https://0.mk/blog/link-rot
102•tdx•4h ago•69 comments

Choosing an AI model: one prompt, 11 models, different results

https://www.netlify.com/blog/one-prompt-11-models-very-different-results/
162•toddmorey•9h ago•70 comments

Gloomberb

https://gloom.sh/
364•rbanffy•8h ago•182 comments

Come for ENIAC, Stay for UNIVAC and Skeduflo

https://uniqueatpenn.wordpress.com/2026/08/05/come-for-eniac-stay-for-univac-and-skeduflo/
55•cainxinth•2d ago•18 comments

JDK 27 G1/Parallel/Serial GC Changes

https://tschatzl.github.io/2026/08/10/jdk27-g1-serial-parallel-gc-changes.html
41•0x54MUR41•5h ago•13 comments

ATG (YC F25) Is Hiring Member of Technical Staff (Data Platform)

https://atg.science/careers
1•dkobran•10h ago

I built a 500k-domain search engine for makers in a weekend for $10

https://alexmorleyfinch.github.io/marlin/history/v1/article/the_birth.html
136•dreamforever•9h ago•72 comments

How art invented humanity

https://aeon.co/essays/humans-did-not-invent-art-it-was-the-other-way-around
79•prismatic•23h ago•33 comments

GoAccess – Open-source real-time log analyzer and interactive viewer

https://goaccess.io/
44•gregsadetsky•6h ago•9 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.