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Tailscale didn't stop the Hugging Face intrusion

https://tailscale.com/blog/hugging-face-intrusion
422•bluehatbrit•5h ago•163 comments

Elevators

https://john.fun/elevators
850•Jrh0203•9h ago•214 comments

qm

https://github.com/yc-software/qm
432•tosh•6h ago•93 comments

Twenty-five years ago it was cryptography, today it's model weights

https://weeraman.com/because-we-can/
138•aweeraman•3d ago•51 comments

The Absurdity of Albert Camus

https://www.historytoday.com/archive/portrait-author-historian/absurdity-albert-camus
39•apollinaire•1d ago•23 comments

Progressive Web Components

https://arielsalminen.com/2026/progressive-web-components/
75•hosteur•14h ago•11 comments

Big Food vs. the People

https://www.lighthousereports.com/investigation/big-food-vs-the-people/
188•jruohonen•8h ago•123 comments

June in Servo: real world compat, media queries, SharedWorker, and more

https://servo.org/blog/2026/07/31/june-in-servo/
97•iamnothere•6h ago•30 comments

DeepSeek V4 Flash 0731 Intelligence, Performance and Price Analysis

https://artificialanalysis.ai/models/deepseek-v4-flash
529•theanonymousone•17h ago•288 comments

Demystifying DRAM Read Disturbance: RowHammer and RowPress Phenomena

https://arxiv.org/abs/2607.28233
28•Jimmc414•4h ago•16 comments

Anime Professions

https://github.com/elmiram/anime-professions
12•Gray0Ed•5d ago•5 comments

Let's make the worst Htmx

https://zserge.com/posts/worst-htmx-ever/
60•RebelPotato•19h ago•18 comments

Run Kimi K3 using 29 GB of RAM at 0.50 tok/s

https://github.com/sqliteai/waste
140•marcobambini•10h ago•57 comments

Loops (YC W22) Is Hiring a Product Educator

https://www.ycombinator.com/companies/loops/jobs/zqUnwqB-product-educator-technical-content-creator
1•chrisfrantz•3h ago

Golang proposal: container/: generic collection types

https://github.com/golang/go/issues/80590
119•jabits•6h ago•72 comments

Getting 25 Gbps Thunderbolt Ethernet on My Mac Studio

https://www.jeffgeerling.com/blog/2026/getting-25g-ethernet-mac-thunderbolt/
126•speckx•8h ago•77 comments

A past and future of trade secrets

https://www.cabinetmagazine.org/issues/70/kofen.php
12•Hooke•1d ago•1 comments

How JPEG works: Interactively explore JPEG's lossy compression methods

https://cgjennings.ca/articles/jpeg-compression/
101•at1as•4d ago•11 comments

Termixer (TUI DJ Mixer)

https://github.com/l00sed/termixer
50•l00sed•6h ago•35 comments

The most official water costs $120k a gallon

https://signoregalilei.com/2026/07/26/the-most-official-water-costs-120000-a-gallon/
131•surprisetalk•10h ago•108 comments

Dubious research tied to Red Bull has shaped energy drink policy

https://www.theexamination.org/articles/red-bull-funded-research-energy-drinks-alcohol
112•Jimmc414•9h ago•172 comments

The First Transatlantic Telegraph Cable Was a Bold, Beautiful Failure

https://spectrum.ieee.org/the-first-transatlantic-telegraph-cable-was-a-bold-beautiful-failure
15•sparsesignal•2d ago•3 comments

Using the railway network as a flatbed scanner [video]

https://media.ccc.de/v/emf2026-74-1-using-the-railway-network-as-a-flatbed-scanner
48•Jimmc414•6h ago•25 comments

Authorize, don't authenticate

https://blog.marcua.net/2026/07/31/authorize-dont-authenticate.html
48•marcua•10h ago•12 comments

Is AI reasoning right for the wrong reasons?

https://www.quantamagazine.org/is-ai-reasoning-right-for-the-wrong-reasons-20260731/
112•retupmoc01•9h ago•145 comments

Everyone is building LLM routers, we deprecated ours

https://manifest.build/blog/why-we-deprecated-our-llm-router/
85•brunaxLorax•6h ago•44 comments

Predictive Speculative KV Replication for Bursty LLM Inference

https://jwlabs.vercel.app/post/biting-the-bullet
23•shreybirmiwal•5h ago•2 comments

Algorithms on billion-scale graph using 10GB RAM: I love DataFusion

https://semyonsinchenko.github.io/ssinchenko/post/datafusion-graphs-cc-2/
95•speckx•9h ago•31 comments

Severance

https://lcamtuf.substack.com/p/severance
199•surprisetalk•7h ago•62 comments

Britain's New World of Tobacco (2017)

https://www.historytoday.com/archive/feature/britains-new-world-tobacco
10•benbreen•2d ago•0 comments
Open in hackernews

Computational Complexity of Air Travel Planning [pdf] (2003)

http://www.demarcken.org/carl/papers/ITA-software-travel-complexity/ITA-software-travel-complexity.pdf
76•rochoa•1y ago

Comments

buildsjets•1y ago
This is well over 20 years old and is based on pre 9/11 flight data. I would suspect that a lot has changed since then. So proceed with no caution at all.
gwern•1y ago
Since these sorts of things usually only get more and more complex over time, I would guess that it's all still true, but much more so.
throw0101b•1y ago
(2003)
throw0101b•1y ago
The PDF was produced by ITA, which famously used Common Lisp:

* https://en.wikipedia.org/wiki/ITA_Software

From 2001, a message from the same author as the linked paper:

> (Here's an email Carl de Marcken of ITA Software sent to a friend, describing their experiences using Lisp in one of the software industry's most demanding applications.)

* https://www.paulgraham.com/carl.html

Qem•1y ago
Are there any public, open, comprehensive datasets on flights?
dieselerator•1y ago
> Are there any public, open, comprehensive datasets on flights?

Airlines and commercial aviation operators schedule their own flights. That is a dynamic schedulle. So, perhaps there is no "comprehensive data set".

However, FlightAware makes publicly available scheduled and completed flight data over many routes in the USA. You can search by route and get a list of flights.

Flight information includes filed departure time, route of flight, and speed. For completed flights actual time, altitude, and route is shown. For example, a search on the route Dallas/Fort Worth to Austin lists 45 flights.

I hope that helps.

foundart•1y ago
A very interesting dive into, as the title says, the computational complexity of air travel planning. Graph algorithms with lots of complexity added due to the wide variety of fare conditions that airlines have dreamt up over the years.

The article may be from 2003 but I would call it an evergreen. While I imagine some of the details have changed since then, I suspect that the complexity has only grown since then.

foundart•1y ago
It makes me wonder: Would an airline that drastically simplified its fares be more likely to appear in flight search results?

Simplifying the fares would make it less computationally expensive and, in theory, could take fewer steps to answer a flight planning query.

Imagine a flight search planner that, say, fanned out N airline-specific workers when handling a planning query and then displayed to the user whatever results it got back within some time limit. If FooAir had simple fares, the FooAir searcher would likely run faster than searchers for other airlines. Thus it would be more likely to return results for more queries, assuming the deadline is fairly tight because of usability metrics. (People don't tend to stick around waiting for slow results.)

sjburt•1y ago
At least a few years ago (~2014), the fare search was actually nearly instant, but all major airfare search sites added a delay because customers had the impression they were getting a better deal when they had to wait. It seems like the delay has been dialed back lately.
teleforce•1y ago
This is a very popular article that get submitted every now and then (nearly every year) [1].

I think this kind of problem would be a very nice for logic, optimization and constraint programming that probably can be solved with modern tools like Google OR-Tool or Monash University MiniZinc [1],[2],[3].

[1] Past:

https://hn.algolia.com/?query=Computational%20Complexity%20o...

[2] Logic, Optimization, and Constraint Programming: A Fruitful Collaboration - John Hooker - CMU (2023) [video]:

https://www.youtube.com/live/TknN8fCQvRk

[3] Google OR-Tools:

https://developers.google.com/optimization

[4] MiniZinc:

https://www.minizinc.org/