frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

# [derive(Clone)] Is Broken

https://rgbcu.be/blog/derive-broken/
74•RGBCube•3d ago•41 comments

New sphere-packing record stems from an unexpected source

https://www.quantamagazine.org/new-sphere-packing-record-stems-from-an-unexpected-source-20250707/
324•pseudolus•14h ago•145 comments

Epanet-JS

https://macwright.com/2025/07/03/epanet-placemark
87•surprisetalk•3d ago•7 comments

Mercury: Ultra-fast language models based on diffusion

https://arxiv.org/abs/2506.17298
454•PaulHoule•20h ago•199 comments

The chemical secrets that help keep honey fresh for so long

https://www.bbc.com/future/article/20250701-the-chemical-secrets-that-help-keep-honey-fresh-for-so-long
143•bookofjoe•3d ago•81 comments

The New York Times wants your private ChatGPT history – even the deleted parts

https://thehill.com/opinion/technology/5383530-chatgpt-users-privacy-collateral-damage/
20•isolli•1h ago•10 comments

LookingGlass: Generative Anamorphoses via Laplacian Pyramid Warping

https://studios.disneyresearch.com/2025/06/09/lookingglass-generative-anamorphoses-via-laplacian-pyramid-warping/
79•jw1224•10h ago•14 comments

What Microchip doesn't (officially) tell you about the VSC8512

https://serd.es/2025/07/04/Switch-project-pt3.html
99•ahlCVA•3d ago•20 comments

I used o3 to profile myself from my saved Pocket links

https://noperator.dev/posts/o3-pocket-profile/
382•noperator•20h ago•145 comments

Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

183•bhaktatejas922•18h ago•143 comments

The Miyawaki Method of micro-forestry

https://www.futureecologies.net/listen/fe-6-5-the-method
151•zeristor•3d ago•28 comments

The Two Towers MUD

https://t2tmud.org/
96•astronads•2d ago•59 comments

Adding a feature because ChatGPT incorrectly thinks it exists

https://www.holovaty.com/writing/chatgpt-fake-feature/
891•adrianh•18h ago•324 comments

What is going on in Unix with errno's limited nature

https://utcc.utoronto.ca/~cks/space/blog/unix/ErrnoWhySoLimited
40•ingve•4d ago•16 comments

ChatGPT testing a mysterious new feature called 'study together'

https://techcrunch.com/2025/07/07/chatgpt-is-testing-a-mysterious-new-feature-called-study-together/
15•Bluestein•1h ago•9 comments

When Figma starts designing us

https://designsystems.international/ideas/when-figma-starts-designing-us/
245•bravomartin•1d ago•110 comments

My first verified imperative program

https://markushimmel.de/blog/my-first-verified-imperative-program/
147•TwoFx•15h ago•68 comments

François Chollet: The Arc Prize and How We Get to AGI [video]

https://www.youtube.com/watch?v=5QcCeSsNRks
189•sandslash•4d ago•165 comments

Why are there no good dinosaur films?

https://briannazigler.substack.com/p/why-are-there-no-good-dinosaur-films
96•fremden•3d ago•220 comments

Berry Script: lightweight embedded scripting language for microcontrollers

https://berry-lang.github.io/
7•hasheddan•2d ago•1 comments

CU Randomness Beacon

https://random.colorado.edu/
27•wello•2d ago•6 comments

Man of Glass: Boccaccio: A Biography

https://literaryreview.co.uk/man-of-glass
5•Thevet•3d ago•0 comments

Show HN: NYC Subway Simulator and Route Designer

https://buildmytransit.nyc
159•HeavenFox•18h ago•18 comments

Integrated photonic source of Gottesman–Kitaev–Preskill qubits

https://www.nature.com/articles/s41586-025-09044-5
9•gnabgib•3d ago•1 comments

Lightfastness Testing of Colored Pencils

https://sarahrenaeclark.com/lightfast-testing-pencils/
146•picture•3d ago•38 comments

SIMD.info – Reference tool for C intrinsics of all major SIMD engines

https://simd.info/
21•pabs3•7h ago•5 comments

Analysing Roman itineraries using GIS tooling

https://link.springer.com/article/10.1007/s12520-025-02175-w
28•diodorus•3d ago•3 comments

Hymn to Babylon, missing for a millennium, has been discovered

https://phys.org/news/2025-07-hymn-babylon-millennium.html
192•wglb•4d ago•82 comments

Solving Wordle with uv's dependency resolver

https://mildbyte.xyz/blog/solving-wordle-with-uv-dependency-resolver/
159•mildbyte•2d ago•14 comments

The era of exploration

https://yidingjiang.github.io/blog/post/exploration/
90•jxmorris12•17h ago•8 comments
Open in hackernews

EM-LLM: Human-Inspired Episodic Memory for Infinite Context LLMs

https://github.com/em-llm/EM-LLM-model
113•jbotz•1mo ago

Comments

MacsHeadroom•1mo ago
So, infinite context length by making it compute bound instead of memory bound. Curious how much longer this takes to run and when it makes sense to use vs RAG.
zfountas•1mo ago
Hi MacsHeadroom, first author here. Thanks for the great questions about compute/memory trade-offs.

The quick take: To give you an example of processing speed, with a 7B model on an NVIDIA V100, EM-LLM processes (or generates) about 326 tokens/sec with a 51.2K context window (which is quite competitive for these old GPUs).

More broadly, EM-LLM is designed to make ultra-long contexts (memory-prohibitive for standard O(n^2) attention) computationally tractable. The Appendix C of our paper https://openreview.net/pdf?id=BI2int5SAC details how: significantly better attention scaling, efficient O(nm) memory formation, and large KV cache management via CPU/disk offloading. While there's a slight per-chunk overhead compared to the simplest retrieval methods initially, the crucial part is our ability to handle sequences at scales infeasible for full-context models. For instance, we're successfully using 8B models with 10M token contexts on a single GPU without prohibitive delays.

Regarding RAG in particular, EM-LLM often shows significant gains on tasks needing deep understanding of a single, long, coherent context. A key reason is that EM-LLM allows each layer to retrieve and integrate relevant information from different "episodes" of the context independently, offering more nuance than a typical single RAG step, for similar overall resource use.

mountainriver•1mo ago
TTT, cannon layers, and titans seem like a stronger approach IMO.

Information needs to be compressed into latent space or it becomes computationally intractable

searchguy•1mo ago
do you have references to

> TTT, cannon layers, and titans

najarvg•1mo ago
This was the nearest reference I could find. Links to an unofficial pytorch implementation on Github are also linked in the threads somewhere - https://www.reddit.com/r/LocalLLaMA/comments/1i0q8nw/titans_...
vessenes•1mo ago
is titans replicated? I feel like lucidrains couldn't replicate.
logicchains•1mo ago
I think something like Titans explains Gemini's excellent long context performance. That would explain why the Titan team hasn't released the training code or hyperpameters used even though they said in the paper that they would, and why soon after that it came out that DeepMind would be holding off publishing new results for 6 months to avoid giving away competitive advantages.
p_v_doom•1mo ago
Interesting. Before there even was attention I was thinking that the episodic memory model offers something that could be very useful for neural nets, so its cool to see people testing that
killerstorm•1mo ago
Note that this works within a single sequence of tokens. It might be consistent with "episodic memory" metaphor if we consider a particular transformer run as its experience.

But this might be very different from what people expect from "memory" - i.e. ability to learn vast amounts of information and retrieve it as necessary.

This is more like a refinement of transformer attention: instead of running attention over all tokens (which is very expensive as it's quadratic), it selects a subset of token spans and runs fine-grained attention only on those. So it essentially breaks transformer attention into two parts - coarse-grained (k-NN over token spans) and fine-grained (normal).

It might be a great thing for long-context situations. But it doesn't make sense when you want millions of different facts to be considered - making them into long context is rather inefficient.

yorwba•1mo ago
It would be inefficient if you had to do it from scratch for every query, but if you can do it once as a preprocessing step and reuse the prepared context for many queries, it might start to become more efficient than a shorter context that includes only some documents but has to be reprocessed because it's different every time.
killerstorm•1mo ago
Yes, I think it might be a good solution where you have a context up to 10M of tokens and you do a lot of requests with that context. It might be relevant for agentic stuff which tends to produce long chat logs - especially with some gadgets on top, e.g. some 'episodes' might be completely removed as obsolete.

But I don't think it's a good solution for bigger amounts of data - as in that case it's more beneficial if that can be formed into independent memories.