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France's homegrown open source online office suite

https://github.com/suitenumerique
379•nar001•3h ago•185 comments

British drivers over 70 to face eye tests every three years

https://www.bbc.com/news/articles/c205nxy0p31o
111•bookofjoe•1h ago•88 comments

Start all of your commands with a comma (2009)

https://rhodesmill.org/brandon/2009/commands-with-comma/
421•theblazehen•2d ago•152 comments

Hoot: Scheme on WebAssembly

https://www.spritely.institute/hoot/
81•AlexeyBrin•5h ago•15 comments

Stories from 25 Years of Software Development

https://susam.net/twenty-five-years-of-computing.html
29•vinhnx•2h ago•4 comments

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

https://openciv3.org/
774•klaussilveira•19h ago•240 comments

Leisure Suit Larry's Al Lowe on model trains, funny deaths and Disney

https://spillhistorie.no/2026/02/06/interview-with-sierra-veteran-al-lowe/
14•thelok•1h ago•0 comments

First Proof

https://arxiv.org/abs/2602.05192
34•samasblack•1h ago•19 comments

Reinforcement Learning from Human Feedback

https://arxiv.org/abs/2504.12501
50•onurkanbkrc•4h ago•3 comments

The Waymo World Model

https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-frontier-for-autonomous-driving-simula...
1021•xnx•1d ago•581 comments

Coding agents have replaced every framework I used

https://blog.alaindichiappari.dev/p/software-engineering-is-back
159•alainrk•4h ago•206 comments

Vocal Guide – belt sing without killing yourself

https://jesperordrup.github.io/vocal-guide/
160•jesperordrup•9h ago•60 comments

Software Factories and the Agentic Moment

https://factory.strongdm.ai/
11•mellosouls•2h ago•11 comments

72M Points of Interest

https://tech.marksblogg.com/overture-places-pois.html
10•marklit•5d ago•0 comments

Unseen Footage of Atari Battlezone Arcade Cabinet Production

https://arcadeblogger.com/2026/02/02/unseen-footage-of-atari-battlezone-cabinet-production/
103•videotopia•4d ago•26 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
17•rbanffy•4d ago•0 comments

StrongDM's AI team build serious software without even looking at the code

https://simonwillison.net/2026/Feb/7/software-factory/
9•simonw•1h ago•3 comments

Ga68, a GNU Algol 68 Compiler

https://fosdem.org/2026/schedule/event/PEXRTN-ga68-intro/
35•matt_d•4d ago•9 comments

Making geo joins faster with H3 indexes

https://floedb.ai/blog/how-we-made-geo-joins-400-faster-with-h3-indexes
152•matheusalmeida•2d ago•42 comments

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

https://github.com/valdanylchuk/breezydemo
261•isitcontent•19h ago•33 comments

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

https://github.com/pydantic/monty
275•dmpetrov•20h ago•145 comments

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

https://github.com/sandys/kappal
15•sandGorgon•2d ago•3 comments

Hackers (1995) Animated Experience

https://hackers-1995.vercel.app/
545•todsacerdoti•1d ago•263 comments

Sheldon Brown's Bicycle Technical Info

https://www.sheldonbrown.com/
417•ostacke•1d ago•108 comments

Show HN: I spent 4 years building a UI design tool with only the features I use

https://vecti.com
361•vecti•21h ago•161 comments

What Is Ruliology?

https://writings.stephenwolfram.com/2026/01/what-is-ruliology/
61•helloplanets•4d ago•65 comments

Show HN: If you lose your memory, how to regain access to your computer?

https://eljojo.github.io/rememory/
334•eljojo•22h ago•206 comments

An Update on Heroku

https://www.heroku.com/blog/an-update-on-heroku/
456•lstoll•1d ago•298 comments

Microsoft open-sources LiteBox, a security-focused library OS

https://github.com/microsoft/litebox
371•aktau•1d ago•195 comments

Google staff call for firm to cut ties with ICE

https://www.bbc.com/news/articles/cvgjg98vmzjo
108•tartoran•2h ago•30 comments
Open in hackernews

Recursive Language Models (RLMs)

https://alexzhang13.github.io/blog/2025/rlm/
135•talhof8•3mo ago

Comments

jgbuddy•3mo ago
This is old news! Agent-loops are not a model architechture
laughingcurve•3mo ago
Everything old is new again when you are in academia
hodgehog11•3mo ago
This feels primarily like an issue with machine learning, at least among mathematical subdisciplines. As new people continue to be drawn into the field, they rarely bother to read what has come even a few years prior (nevermind a few decades prior).
layer8•3mo ago
Loops aren’t recursion?
antonvs•3mo ago
Loops and recursion are fundamentally equivalent.

See e.g. https://textbooks.cs.ksu.edu/cc210/16-recursion/08-recursion...

layer8•3mo ago
Only if you have indexable memory that you can use as a stack, which in the context of LMs isn’t a given.

As another example, a finite-state-machine language can have loops, but it can’t recurse unless there is external memory it has access to in a way that it can serve as a stack. Regular expressions also fall into that pattern; they can loop, but they can’t recurse. For that you need a pushdown automaton: https://en.wikipedia.org/wiki/Pushdown_automaton.

adastra22•3mo ago
I’m confused over your definition of model architecture.
halfmatthalfcat•3mo ago
It broke new ground!
ayazhan•3mo ago
https://arxiv.org/abs/2510.04871 another recursive based model
yorwba•3mo ago
It's a completely different kind of recursion for a completely different (non-language) task.
foolswisdom•3mo ago
I actually came here expecting this to be a language model application of that recursive reasoning paper.
d0mine•3mo ago
> TRM obtains 45% test-accuracy on ARC-AGI-1 and 8% on ARC-AGI-2, higher than most LLMs (e.g., Deepseek R1, o3-mini, Gemini 2.5 Pro) with less than 0.01% of the parameters.
gdiamos•3mo ago
Recursion is so popular in computing that this term “recursive language model” is heavily overloaded

It was even before the rise of LLMs

The authors may want to consider a more specific name

quibit•3mo ago
> Lastly, in our experiments we only consider a recursive depth of 1 — i.e. the root LM can only call LMs, not other RLMs. It is a relatively easy change to allow the REPL environment to call RLMs instead of LMs, but we felt that for most modern “long context” benchmarks, a recursive depth of 1 was sufficient to handle most problems. However, for future work and investigation into RLMs, enabling larger recursive depth will naturally lead to stronger and more interesting systems.

It feels a little disingenuous to call it a Recursive Language Model when the recursive depth of the study was only 1.

yandie•3mo ago
This isn't just context optimization. Not much different from agent-to-agent workflow IMO.
cs702•3mo ago
Briefly, an RLM wraps an existing language model (LM) together with an environment that can dynamically manipulate the prompt that will be fed into the LM.

The authors use as an environment a Python REPL that itself can call other instances of the LM. The prompt is programmatically manipulated as a Python variable on the REPL.

The motivation is for the LM to use Python commands, including commands that call other LM instances, to figure out how best to modify the context at inference time.

The results from early testing look impressive at a first glance: An RLM wrapping GPT-5-mini outperforms GPT-5 by a wide margin on long-context tasks, at significant lower cost.

I've added this to my reading list.

NitpickLawyer•3mo ago
A comparison to dSPY would be nice. cmd+f in the provided link doesn't bring any results tho...
cs702•3mo ago
An RLM is like a language model using DSPy plus all of Python to manipulate its prompt.
integricho•3mo ago
Sounds like unforgivable overhead for very questionable benefits, this whole LLM space is an overengineered slop, and everyone is jumping in building layers on top of layers of slop.
nowittyusername•3mo ago
My existing project is very similar to this with some other goodies. I agree with the author that focus on systems versus LLM's is the proper next move. Orchestrating systems that manage multiple different llms and other scripts together can accomplish a lot more then a simple ping pong type of behavior. Though I suspect most people who work with agentic solutions are already quite aware of this. What most in that space haven't cracked yet is the dynamic self modifying and improving system, that should be the ultimate goal for these types of systems.
ipnon•3mo ago
Hopefully this can solve the problem of Claude needing to compact itself every 10 minutes, blocking execution. It would be better if it was always compacting in the background. But that requires perhaps more compute than is realistic.
wild_egg•3mo ago
Tell it to use subagents more. I often say something like "you're banned from taking direct actions, use subagents for everything" and it can run easily for 60-90 minutes before a compaction.
rancar2•3mo ago
For that issue, try Codex until Claude catches up to your style.
behnamoh•3mo ago
in today's news: MIT researchers found out about AI agents and rebranded it as RLM for karma.
rf15•3mo ago
or: found out about RNNs with extra steps.
fizx•3mo ago
I read the article, and I'm struggling to see what ideas it brings beyond CodeAct (tool use is python) or the "task" tool in Claude code (spinning off sub-agents to preserve context).
nathanwh•3mo ago
This reminded me of ViperGPT[1] from a couple of years ago, which is similar but specific to vision language models. Both of them have a root llm which given a query produces a python program to decompose the query into separate steps, with the generated python program calling a sub model. One difference is this model has a mutable environment in the notebook, but I'm not sure how much of a meaningful difference that is.

[1] https://viper.cs.columbia.edu/static/viper_paper.pdf

UltraSane•3mo ago
Extending this so that the Root LLM can choose the best option from many other LLMs seems pretty powerful.
ttul•3mo ago
This is what Codex is doing. The LM has been trained to work well with the kinds of tools that a solid developer would use to navigate and search around a code repository and then to reason about what it finds. It’s also really competent at breaking down a task into steps. But I think the real magic - watching this thing for at least 40 of the last 50 working hours - is how it uses command line tools to dig through code quickly and accurately.

It’s not relying on the LM context much. You can generally code away for an hour before you run out of context and have to run a compression step or just start fresh.

lukebechtel•3mo ago
this doesn't appear to bring anything new to the table.

please correct me if I'm wrong..this is just subagent architecture?

sophia_james•3mo ago
I’m not sure if I understood this correctly:

1.Recursion is used to break down the large context and dispatch to different LLM calls to get the useful context.

2.This may lead to longer test-time execution on large contexts (even with parallelism in deep recursion), and the monetary cost may increase rapidly.

I think it’s a different idea from using RAG or manually maintaining a context window

correct me if I'm wrong

pontusrehula•3mo ago
If you would setup an RLM, would you set a higher temperature for the root LLM calls and a lower temperature for LLM calls deeper in the recursion?
patcon•3mo ago
Just wanted to say that I really like this question. Very thought-provoking :)

EDIT: makes me think of many computation systems in various substrates, and how they work. Focus vs distraction/creativity. ADHD workers in hierarchies of capitalism, purpose of breadth vs depth of exploration at various levels of the stack, who's at the "top" and why, etc etc

Weaver_zhu•3mo ago
IMO the author is a little over-claiming this work by naming 'recursive'. Quote from this blog:

> Lastly, in our experiments we only consider a recursive depth of 1 — i.e. the root LM can only call LMs, not other RLMs.

> but we felt that for most modern “long context” benchmarks, a recursive depth of 1 was sufficient to handle most problems.

I don't think a size 2 call stack algorithm should be regarded as 'recursive'.

vrighter•3mo ago
a model calls another (not self) model, which in turn returns without calling anything else. What you'ce discovered is called a function call.

It simply hopes two drunks are more coherent than one drunk.