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We Mourn Our Craft

https://nolanlawson.com/2026/02/07/we-mourn-our-craft/
125•ColinWright•1h ago•93 comments

Speed up responses with fast mode

https://code.claude.com/docs/en/fast-mode
24•surprisetalk•1h ago•26 comments

Hoot: Scheme on WebAssembly

https://www.spritely.institute/hoot/
121•AlexeyBrin•7h ago•24 comments

Stories from 25 Years of Software Development

https://susam.net/twenty-five-years-of-computing.html
62•vinhnx•5h ago•7 comments

U.S. Jobs Disappear at Fastest January Pace Since Great Recession

https://www.forbes.com/sites/mikestunson/2026/02/05/us-jobs-disappear-at-fastest-january-pace-sin...
124•alephnerd•2h ago•81 comments

OpenCiv3: Open-source, cross-platform reimagining of Civilization III

https://openciv3.org/
829•klaussilveira•21h ago•249 comments

Al Lowe on model trains, funny deaths and working with Disney

https://spillhistorie.no/2026/02/06/interview-with-sierra-veteran-al-lowe/
55•thelok•3h ago•8 comments

The AI boom is causing shortages everywhere else

https://www.washingtonpost.com/technology/2026/02/07/ai-spending-economy-shortages/
109•1vuio0pswjnm7•8h ago•139 comments

Brookhaven Lab's RHIC Concludes 25-Year Run with Final Collisions

https://www.hpcwire.com/off-the-wire/brookhaven-labs-rhic-concludes-25-year-run-with-final-collis...
4•gnufx•41m ago•1 comments

The Waymo World Model

https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-frontier-for-autonomous-driving-simula...
1060•xnx•1d ago•611 comments

Reinforcement Learning from Human Feedback

https://rlhfbook.com/
76•onurkanbkrc•6h ago•5 comments

Start all of your commands with a comma (2009)

https://rhodesmill.org/brandon/2009/commands-with-comma/
484•theblazehen•2d ago•175 comments

I Write Games in C (yes, C)

https://jonathanwhiting.com/writing/blog/games_in_c/
10•valyala•2h ago•1 comments

Vocal Guide – belt sing without killing yourself

https://jesperordrup.github.io/vocal-guide/
210•jesperordrup•12h ago•70 comments

SectorC: A C Compiler in 512 bytes

https://xorvoid.com/sectorc.html
9•valyala•2h ago•0 comments

France's homegrown open source online office suite

https://github.com/suitenumerique
559•nar001•6h ago•257 comments

Coding agents have replaced every framework I used

https://blog.alaindichiappari.dev/p/software-engineering-is-back
222•alainrk•6h ago•343 comments

A Fresh Look at IBM 3270 Information Display System

https://www.rs-online.com/designspark/a-fresh-look-at-ibm-3270-information-display-system
37•rbanffy•4d ago•7 comments

Selection Rather Than Prediction

https://voratiq.com/blog/selection-rather-than-prediction/
8•languid-photic•3d ago•1 comments

History and Timeline of the Proco Rat Pedal (2021)

https://web.archive.org/web/20211030011207/https://thejhsshow.com/articles/history-and-timeline-o...
19•brudgers•5d ago•4 comments

72M Points of Interest

https://tech.marksblogg.com/overture-places-pois.html
29•marklit•5d ago•2 comments

Unseen Footage of Atari Battlezone Arcade Cabinet Production

https://arcadeblogger.com/2026/02/02/unseen-footage-of-atari-battlezone-cabinet-production/
114•videotopia•4d ago•31 comments

Where did all the starships go?

https://www.datawrapper.de/blog/science-fiction-decline
76•speckx•4d ago•75 comments

Show HN: I saw this cool navigation reveal, so I made a simple HTML+CSS version

https://github.com/Momciloo/fun-with-clip-path
6•momciloo•2h ago•0 comments

Show HN: Look Ma, No Linux: Shell, App Installer, Vi, Cc on ESP32-S3 / BreezyBox

https://github.com/valdanylchuk/breezydemo
273•isitcontent•22h ago•38 comments

Learning from context is harder than we thought

https://hy.tencent.com/research/100025?langVersion=en
201•limoce•4d ago•111 comments

Show HN: Kappal – CLI to Run Docker Compose YML on Kubernetes for Local Dev

https://github.com/sandys/kappal
22•sandGorgon•2d ago•11 comments

Monty: A minimal, secure Python interpreter written in Rust for use by AI

https://github.com/pydantic/monty
286•dmpetrov•22h ago•153 comments

Making geo joins faster with H3 indexes

https://floedb.ai/blog/how-we-made-geo-joins-400-faster-with-h3-indexes
155•matheusalmeida•2d ago•48 comments

Software factories and the agentic moment

https://factory.strongdm.ai/
71•mellosouls•4h ago•75 comments
Open in hackernews

Scaling Latent Reasoning via Looped Language Models

https://arxiv.org/abs/2510.25741
84•remexre•1mo ago

Comments

kelseyfrog•1mo ago
If you squint your eyes it's a fixed iteration ODE solver. I'd love to see a generalization on this and the Universal Transformer metioned re-envisioned as flow-matching/optimal transport models.
kevmo314•1mo ago
How would flow matching work? In language we have inputs and outputs but it's not clear what the intermediate points are since it's a discrete space.
Etheryte•1mo ago
One of the core ideas behind LLMs is that language is not a discrete space, but instead a multidimensional vector field where you can easily interpolate as needed. It's one of the reasons LLMs readily make up words that don't exist when translating text for example.
kevmo314•1mo ago
Not the input and output though, which is the important part for flow matching modeling. Unless you're proposing flow matching over the latent space?
cfcf14•1mo ago
This makes me think it would be nice to see some kinda child of modern transformer architecture and neural ODEs. There was such interesting work a few years ago on how neural ode/pdes could be seen as a sort of continuous limit of layer depth. Maybe models could learn cool stuff if the embeddings were somehow dynamical model solutions or something.
the8472•1mo ago
Does the training process ensure that all the intermediate steps remain interepretable, even on larger models? Not that we end up with some alien gibberish in all but the final step.
oofbey•1mo ago
Training doesn’t encourage the intermediate steps to be interpretable. But they are still in the same token vocabulary space, so you could decode them. But they’ll probably be wrong.
the8472•1mo ago
token vocabulary space is a hull around human communication (emoji, mathematical symbols, unicode scripts, ...), inside that there's lots of unused representation space that an AI could use to represent internal state. So this seems to be bad idea from an safety/oversight perspective.

https://openai.com/index/chain-of-thought-monitoring/

oofbey•1mo ago
What is a bad idea? Allowing reasoning to happen in continuous space instead of discrete token space? This paper can be seen as a variant of the Coconut models (continuous chain of thought). Continuous reasoning is certainly more efficient when it works. Lack of interpret ability makes certain safety systems harder to enforce. Is that your point?
the8472•1mo ago
Yes. Coconut has the same issue. See also: a joint statement by researchers from several labs about CoT monitorability: https://arxiv.org/abs/2507.11473
oofbey•1mo ago
Interesting. Thanks for the reference!

It's hard to know which way this will go. Discrete/text reasoning has many advantages. Safety as you note. Interpretability, which is closely related. Interoperability - e.g. the fact that you can switch models mid-discussion in Cursor and the new model understands the previous model's CoT just fine, or the ability to use reasoning traces from a larger model to train a smaller model to reason.

Continuous latent reasoning is a big hassle, but wins on efficiency, and in some situations I'm sure people will decide that benefit is worth the hassle. Because efficiency is fighting physics, which is hard to argue with on small devices with batteries. So my guess is that we'll see some of each approach in the future - with most cloud stuff being discrete, and a few highly-tuned edge applications being continuous.

Safety is a multi-faceted problem. I think it's easy to over-index on it because the impacts can be so huge. But there are so many different ways to approach the problem, and we must not rely on any one of them. It's like cyber-security - you need to use defense in depth. And sometimes it makes sense to sacrifice one kind of protection in order to get some convenience. e.g. if you decide to use continuous reasoning, that probably means you need to write a custom classifier to detect mal-intent rather than relying on an off-the-shelf LLM to analyze the reasoning trace. So I wouldn't ever take a position like "nobody should ever use continuous reasoning because it's too dangerous" - it just means that kind of safety protection needs to be applied differently.

lukebechtel•1mo ago
so it's:

output = layers(layers(layers(layers(input))))

instead of the classical:

output = layer4(layer3(layer2(layer1(input))))

oofbey•1mo ago
Yeah if layers() is a shortcut for layer4(layer3(layer2(layer1(input)))). But sometimes it’s only

output = layers(input)

Or

output = layers(layers(input))

Depends on how difficult the token is.

remexre•1mo ago
Or more like,

    x = tokenize(input)
    i = 0
    do {
      finish, x = layers(x)
    } while(!finish && i++ < t_max);
    output = lm_head(x)
oofbey•1mo ago
That’s closer still. But even closer would be:

    x = tokenize(input)
    i = 0
    finish = 0
    do {
      p, x = layers(x)
      finish += p
    } while(finish < 0.95 && i++ < t_max);
    output = lm_head(x)
Except the accumulation of the stop probabilities isn’t linear like that - it’s more like a weighted coin model.