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Bitchat – A decentralized messaging app that works over Bluetooth mesh networks

https://github.com/jackjackbits/bitchat
62•ananddtyagi•1h ago•36 comments

Nobody has a personality anymore: we are products with labels

https://www.freyaindia.co.uk/p/nobody-has-a-personality-anymore
144•drankl•3h ago•88 comments

Intel's Lion Cove P-Core and Gaming Workloads

https://chipsandcheese.com/p/intels-lion-cove-p-core-and-gaming
70•zdw•3h ago•0 comments

Building the Rust Compiler with GCC

https://fractalfir.github.io/generated_html/cg_gcc_bootstrap.html
89•todsacerdoti•4h ago•4 comments

Show HN: I wrote a "web OS" based on the Apple Lisa's UI, with 1-bit graphics

https://alpha.lisagui.com/
253•ayaros•7h ago•89 comments

There's a COMPUTER inside my DS flashcart [video]

https://www.youtube.com/watch?v=uq0pJmd7GAA
25•surprisetalk•1h ago•0 comments

Jane Street barred from Indian markets as regulator freezes $566 million

https://www.cnbc.com/2025/07/04/indian-regulator-bars-us-trading-firm-jane-street-from-accessing-securities-market.html
258•bwfan123•11h ago•145 comments

Data on AI-related Show HN posts

https://ryanfarley.co/ai-show-hn-data/
223•rfarley04•2d ago•133 comments

I extracted the safety filters from Apple Intelligence models

https://github.com/BlueFalconHD/apple_generative_model_safety_decrypted
259•BlueFalconHD•6h ago•159 comments

Centaur: A Controversial Leap Towards Simulating Human Cognition

https://insidescientific.com/centaur-a-controversial-leap-towards-simulating-human-cognition/
13•CharlesW•2h ago•5 comments

Opencode: AI coding agent, built for the terminal

https://github.com/sst/opencode
131•indigodaddy•8h ago•31 comments

Get the location of the ISS using DNS

https://shkspr.mobi/blog/2025/07/get-the-location-of-the-iss-using-dns/
258•8organicbits•13h ago•76 comments

Functions Are Vectors (2023)

https://thenumb.at/Functions-are-Vectors/
150•azeemba•10h ago•79 comments

I don't think AGI is right around the corner

https://www.dwarkesh.com/p/timelines-june-2025
155•mooreds•5h ago•180 comments

Backlog.md – Markdown‑native Task Manager and Kanban visualizer for any Git repo

https://github.com/MrLesk/Backlog.md
79•mrlesk•5h ago•16 comments

Lessons from creating my first text adventure

https://entropicthoughts.com/lessons-from-creating-first-text-adventure
29•kqr•2d ago•1 comments

Crypto 101 – Introductory course on cryptography

https://www.crypto101.io/
28•pona-a•4h ago•2 comments

Evaluating the factuality of verifiable claims in long-form text generation

https://aclanthology.org/2024.findings-emnlp.552/
3•gone35•2d ago•0 comments

Corrected UTF-8 (2022)

https://www.owlfolio.org/development/corrected-utf-8/
39•RGBCube•3d ago•28 comments

Mirage: AI-native UGC game engine powered by real-time world model

https://blog.dynamicslab.ai
22•zhitinghu•1d ago•16 comments

The Broken Microsoft Pact: Layoffs and Performance Management

https://danielsada.tech/blog/microsoft-pact/
44•dshacker•2h ago•22 comments

Hannah Cairo: 17-year-old teen refutes a math conjecture proposed 40 years ago

https://english.elpais.com/science-tech/2025-07-01/a-17-year-old-teen-refutes-a-mathematical-conjecture-proposed-40-years-ago.html
349•leephillips•10h ago•76 comments

A non-anthropomorphized view of LLMs

http://addxorrol.blogspot.com/2025/07/a-non-anthropomorphized-view-of-llms.html
93•zdw•3h ago•85 comments

Metriport (YC S22) is hiring engineers to improve healthcare data exchange

https://www.ycombinator.com/companies/metriport/jobs/Rn2Je8M-software-engineer
1•dgoncharov•8h ago

Curzio Malaparte's Shock Tactics

https://www.newyorker.com/books/under-review/curzio-malapartes-shock-tactics
5•mitchbob•3d ago•2 comments

Swedish Campground: "There are too many Apples on the screen!"

https://www.folklore.org/Swedish_Campground.html
22•CharlesW•2h ago•6 comments

Async Queue – One of my favorite programming interview questions

https://davidgomes.com/async-queue-interview-ai/
95•davidgomes•9h ago•82 comments

Toys/Lag: Jerk Monitor

https://nothing.pcarrier.com/posts/lag/
47•ptramo•11h ago•37 comments

Collatz's Ant and Σ(n)

https://gbragafibra.github.io/2025/07/06/collatz_ant5.html
25•Fibra•8h ago•3 comments

Attabotics CEO on devastating week that brought bankruptcy

https://retailtechinnovationhub.com/home/2025/7/6/attabotics-ceo-scott-gravelle-opens-up-about-devastating-week-that-saw-bankruptcy-and-tesco-deal
7•bcantrill•48m ago•0 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.