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Asynchrony is not concurrency

https://kristoff.it/blog/asynchrony-is-not-concurrency/
151•kristoff_it•4h ago•104 comments

How to write Rust in the Linux kernel: part 3

https://lwn.net/SubscriberLink/1026694/3413f4b43c862629/
21•chmaynard•1h ago•0 comments

Ccusage: A CLI tool for analyzing Claude Code usage from local JSONL files

https://github.com/ryoppippi/ccusage
14•kristianp•47m ago•4 comments

Shutting Down Clear Linux OS

https://community.clearlinux.org/t/all-good-things-come-to-an-end-shutting-down-clear-linux-os/10716
12•todsacerdoti•25m ago•2 comments

Silence Is a Commons by Ivan Illich (1983)

http://www.davidtinapple.com/illich/1983_silence_commons.html
57•entaloneralie•2h ago•8 comments

Broadcom to discontinue free Bitnami Helm charts

https://github.com/bitnami/charts/issues/35164
80•mmoogle•4h ago•43 comments

Wii U SDBoot1 Exploit “paid the beak”

https://consolebytes.com/wii-u-sdboot1-exploit-paid-the-beak/
61•sjuut•3h ago•7 comments

EPA says it will eliminate its scientific reseach arm

https://www.nytimes.com/2025/07/18/climate/epa-firings-scientific-research.html
57•anigbrowl•1h ago•20 comments

Multiplatform Matrix Multiplication Kernels

https://burn.dev/blog/sota-multiplatform-matmul/
44•homarp•4h ago•16 comments

lsr: ls with io_uring

https://rockorager.dev/log/lsr-ls-but-with-io-uring/
291•mpweiher•11h ago•151 comments

Valve confirms credit card companies pressured it to delist certain adult games

https://www.pcgamer.com/software/platforms/valve-confirms-credit-card-companies-pressured-it-to-delist-certain-adult-games-from-steam/
139•freedomben•8h ago•140 comments

Meta says it wont sign Europe AI agreement, calling it growth stunting overreach

https://www.cnbc.com/2025/07/18/meta-europe-ai-code.html
82•rntn•6h ago•117 comments

Trying Guix: A Nixer's impressions

https://tazj.in/blog/trying-guix
132•todsacerdoti•3d ago•38 comments

Replication of Quantum Factorisation Records with a VIC-20, an Abacus, and a Dog

https://eprint.iacr.org/2025/1237
57•teddyh•5h ago•14 comments

AI capex is so big that it's affecting economic statistics

https://paulkedrosky.com/honey-ai-capex-ate-the-economy/
180•throw0101c•4h ago•196 comments

Show HN: Molab, a cloud-hosted Marimo notebook workspace

https://molab.marimo.io/notebooks
61•akshayka•5h ago•8 comments

Mango Health (YC W24) Is Hiring

https://www.ycombinator.com/companies/mango-health/jobs/3bjIHus-founding-engineer
1•zachgitt•5h ago

CP/M creator Gary Kildall's memoirs released as free download

https://spectrum.ieee.org/cpm-creator-gary-kildalls-memoirs-released-as-free-download
226•rbanffy•13h ago•118 comments

The year of peak might and magic

https://www.filfre.net/2025/07/the-year-of-peak-might-and-magic/
68•cybersoyuz•6h ago•34 comments

Sage: An atomic bomb kicked off the biggest computing project in history

https://www.ibm.com/history/sage
10•rawgabbit•3d ago•0 comments

Show HN: I built library management app for those who outgrew spreadsheets

https://www.librari.io/
42•hmkoyan•4h ago•27 comments

Cancer DNA is detectable in blood years before diagnosis

https://www.sciencenews.org/article/cancer-tumor-dna-blood-test-screening
152•bookofjoe•5h ago•94 comments

A New Geometry for Einstein's Theory of Relativity

https://www.quantamagazine.org/a-new-geometry-for-einsteins-theory-of-relativity-20250716/
71•jandrewrogers•8h ago•1 comments

Show HN: Simulating autonomous drone formations

https://github.com/sushrut141/ketu
12•wanderinglight•3d ago•2 comments

How I keep up with AI progress

https://blog.nilenso.com/blog/2025/06/23/how-i-keep-up-with-ai-progress/
165•itzlambda•5h ago•85 comments

Benben: An audio player for the terminal, written in Common Lisp

https://chiselapp.com/user/MistressRemilia/repository/benben/home
45•trocado•3d ago•3 comments

Making a StringBuffer in C, and questioning my sanity

https://briandouglas.ie/string-buffer-c/
25•coneonthefloor•3d ago•13 comments

Hundred Rabbits – Low-tech living while sailing the world

https://100r.co/site/home.html
214•0xCaponte•4d ago•60 comments

How to Get Foreign Keys Horribly Wrong

https://hakibenita.com/django-foreign-keys
49•Bogdanp•3d ago•23 comments

When root meets immutable: OpenBSD chflags vs. log tampering

https://rsadowski.de/posts/2025/openbsd-immutable-system-logs/
126•todsacerdoti•15h ago•41 comments
Open in hackernews

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

https://github.com/em-llm/EM-LLM-model
113•jbotz•2mo ago

Comments

MacsHeadroom•2mo 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•2mo 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•2mo ago
do you have references to

> TTT, cannon layers, and titans

najarvg•2mo 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•2mo ago
is titans replicated? I feel like lucidrains couldn't replicate.
logicchains•2mo 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•2mo 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•2mo 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•2mo 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•2mo 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.