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Building Interactive C/C++ workflows in Jupyter through Clang-REPL [video]

https://fosdem.org/2026/schedule/event/QX3RPH-building_interactive_cc_workflows_in_jupyter_throug...
1•stabbles•52s ago•0 comments

Tactical tornado is the new default

https://olano.dev/blog/tactical-tornado/
1•facundo_olano•2m ago•0 comments

Full-Circle Test-Driven Firmware Development with OpenClaw

https://blog.adafruit.com/2026/02/07/full-circle-test-driven-firmware-development-with-openclaw/
1•ptorrone•3m ago•0 comments

Automating Myself Out of My Job – Part 2

https://blog.dsa.club/automation-series/automating-myself-out-of-my-job-part-2/
1•funnyfoobar•3m ago•0 comments

Google staff call for firm to cut ties with ICE

https://www.bbc.com/news/articles/cvgjg98vmzjo
2•tartoran•3m ago•0 comments

Dependency Resolution Methods

https://nesbitt.io/2026/02/06/dependency-resolution-methods.html
1•zdw•3m ago•0 comments

Crypto firm apologises for sending Bitcoin users $40B by mistake

https://www.msn.com/en-ie/money/other/crypto-firm-apologises-for-sending-bitcoin-users-40-billion...
1•Someone•4m ago•0 comments

Show HN: iPlotCSV: CSV Data, Visualized Beautifully for Free

https://www.iplotcsv.com/demo
1•maxmoq•5m ago•0 comments

There's no such thing as "tech" (Ten years later)

https://www.anildash.com/2026/02/06/no-such-thing-as-tech/
1•headalgorithm•5m ago•0 comments

List of unproven and disproven cancer treatments

https://en.wikipedia.org/wiki/List_of_unproven_and_disproven_cancer_treatments
1•brightbeige•6m ago•0 comments

Me/CFS: The blind spot in proactive medicine (Open Letter)

https://github.com/debugmeplease/debug-ME
1•debugmeplease•6m ago•1 comments

Ask HN: What are the word games do you play everyday?

1•gogo61•9m ago•1 comments

Show HN: Paper Arena – A social trading feed where only AI agents can post

https://paperinvest.io/arena
1•andrenorman•10m ago•0 comments

TOSTracker – The AI Training Asymmetry

https://tostracker.app/analysis/ai-training
1•tldrthelaw•14m ago•0 comments

The Devil Inside GitHub

https://blog.melashri.net/micro/github-devil/
2•elashri•15m ago•0 comments

Show HN: Distill – Migrate LLM agents from expensive to cheap models

https://github.com/ricardomoratomateos/distill
1•ricardomorato•15m ago•0 comments

Show HN: Sigma Runtime – Maintaining 100% Fact Integrity over 120 LLM Cycles

https://github.com/sigmastratum/documentation/tree/main/sigma-runtime/SR-053
1•teugent•15m ago•0 comments

Make a local open-source AI chatbot with access to Fedora documentation

https://fedoramagazine.org/how-to-make-a-local-open-source-ai-chatbot-who-has-access-to-fedora-do...
1•jadedtuna•17m ago•0 comments

Introduce the Vouch/Denouncement Contribution Model by Mitchellh

https://github.com/ghostty-org/ghostty/pull/10559
1•samtrack2019•17m ago•0 comments

Software Factories and the Agentic Moment

https://factory.strongdm.ai/
1•mellosouls•17m ago•1 comments

The Neuroscience Behind Nutrition for Developers and Founders

https://comuniq.xyz/post?t=797
1•01-_-•17m ago•0 comments

Bang bang he murdered math {the musical } (2024)

https://taylor.town/bang-bang
1•surprisetalk•17m ago•0 comments

A Night Without the Nerds – Claude Opus 4.6, Field-Tested

https://konfuzio.com/en/a-night-without-the-nerds-claude-opus-4-6-in-the-field-test/
1•konfuzio•20m ago•0 comments

Could ionospheric disturbances influence earthquakes?

https://www.kyoto-u.ac.jp/en/research-news/2026-02-06-0
2•geox•21m ago•1 comments

SpaceX's next astronaut launch for NASA is officially on for Feb. 11 as FAA clea

https://www.space.com/space-exploration/launches-spacecraft/spacexs-next-astronaut-launch-for-nas...
1•bookmtn•23m ago•0 comments

Show HN: One-click AI employee with its own cloud desktop

https://cloudbot-ai.com
2•fainir•25m ago•0 comments

Show HN: Poddley – Search podcasts by who's speaking

https://poddley.com
1•onesandofgrain•26m ago•0 comments

Same Surface, Different Weight

https://www.robpanico.com/articles/display/?entry_short=same-surface-different-weight
1•retrocog•28m ago•0 comments

The Rise of Spec Driven Development

https://www.dbreunig.com/2026/02/06/the-rise-of-spec-driven-development.html
2•Brajeshwar•32m ago•0 comments

The first good Raspberry Pi Laptop

https://www.jeffgeerling.com/blog/2026/the-first-good-raspberry-pi-laptop/
3•Brajeshwar•32m ago•0 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.