r/compsci

SALT shrinks a long document down to a fixed size before it is sent to a language model, keeping the sentences that carry the most information. It works with any model, produces a shorter plain-text prompt, and cuts the compute, memory, and wait time that long inputs cost. saltChat keeps the theme trie in DRAM across turns, so a document is indexed once and reused for the whole conversation instead of being re-read every message.

First of all, I would like to say that I have no knowledge about computer science.

I recently heard about something called reversible computing. As I understand it, in ordinary computers, erasing information about the intermediate steps of a computation causes energy to be released as heat. But in reversible computing, the information from all the intermediate steps is preserved, so apparently, if we could build a computer capable of doing this, it could theoretically perform computations with essentially no energy consumption.
This made me wonder about something, and I’d love to hear from anyone knowledgeable about physics or computational theory:
If you never erase the information generated during a computation, wouldn’t the amount of storage required keep increasing as the computer performs more and more calculations?
I previously came across a science-related page online that said that an intelligent civilization that has mastered reversible computing wouldn’t need “Dyson’s eternal intelligence” to survive the heat death of the universe.
But wouldn’t such a civilization still need an absolutely enormous amount of storage to preserve all the information generated during its computations?
Or is there some way to preserve the information from the computational process without actually requiring additional physical storage?
I’m especially curious about how this works in the context of extremely long-term civilizations trying to survive the heat death of the universe.

By the way, this sentence was translated from Japanese to English by Chat GPT. Feel free to answer in English. However, I only understand Japanese, so I would really appreciate clear and simple English that is less likely to be mistranslated. Thank you!

A long time ago I went to school for computer science and I remember a big push towards functional programming at the time. I saw a little bit of Scheme and logic programming and I thought it was neat. I can appreciate those different ways of writing code, but I'm still not sure how any of those other styles actually replace object-orientation. I've started to look at Scheme again and I'm noticing that textbooks and libraries will actually build an object-oriented system on top of Scheme using macros. That has pedagogical value, but it seems like we're back at square one?

If you look at chapter 2 in SICP, one of the topics they cover is message passing. They don't use an explicit object-oriented system. Instead, they have an inner dispatch function that operates on local/private data. That seems like the behavior that classes are trying to model in other languages.

Getting to the point... my feeling is this: bundling state and functions seems like a basic thing in programming. This behavior seems to emerge even in systems which don't explicitly call themselves object-oriented. So my question is this: is there a real alternative? Are there large software systems which don't recreate the behavior of classes?

I loved Chapter 5 of Structure and Interpretation of Computer Programs. Building a virtual register machine with an assembler and compiler in Scheme. Are there any other books/online classes or resources that involve building a computing machine (or any machine) from scratch using code?

This one took a while, it's probably the longest thing I've written on this blog. I wanted to do a proper end-to-end walkthrough of cmd/compile: real package names, real data structures, diagrams for the AST and SSA CFG, and the flags you actually need (-m, -m=2, GOSSAFUNC, -S) to observe each phase yourself rather than just take my word for it.

Covers the full pipeline: lexer → parser → type checker → IR lowering → SSA construction → optimization passes (inlining, escape analysis, BCE, nil check elimination, register allocation) → architecture-specific code emission.

Hope it's useful — happy to answer questions or push back on anything that looks wrong.

Once the paper is ready, everyone checks the venue location before the acceptance rate anyway. So I built:https://honestcsrankings.org

It maps ~540 upcoming CORE-ranked conferences, but ranks them by how good the destination actually is. It factors in:

• Weather during the actual conference month (using real climate data)
• Safety (Global Peace Index)
• Cost (World Bank price levels)
• Accessibility & "City Vibe"

I also added an Upsets tab for A* venues in terrible destinations. Great for your CV, bad for your holiday.

You can filter by field, rank, or open deadlines. If you set your home city, you can rank by distance to either maximize that funded long-haul trip or minimize it, your call. You can also export deadlines to .ics and share deep links with coauthors.

ICML/ICLR 2027 are missing because they aren't announced yet, and COLM is missing because CORE hasn't ranked it. The long tail of smaller conferences is scraped from WikiCFP, so there will be some errors.

Anyone interested in Stanford's CS143 class on compilers?
https://web.stanford.edu/class/cs143/
We're organizing a reading group on this, please let me know if you're interested. Thanks.

I've been thinking about this recently.

The existing literature on distributed systems is excellent. It explains the what and the how of mechanisms like replication, consensus, consistency models, logical clocks, sharding, and fault tolerance. Those concepts are essential, and they're taught exceptionally well.

But I wonder if there's room for another pedagogical approach—one that emphasizes why these mechanisms become necessary in the first place.

Imagine starting from an idealized world with instantaneous computation, infinite storage, unlimited compute, a perfect network, a single global clock, and no failures. Then, systematically removing those assumptions and replacing them with the constraints of reality. As each assumption falls away, the need for clocks, replication, consensus, and other mechanisms emerges naturally.

I've been experimenting with building a free crash course (text+video) around this idea. I'm not really looking for views as much as I am for feedback on the framework itself.

If you've learned, taught, or designed distributed systems, do you think this way of building intuition has merit? Where would you expect it to work well, and where do you think it would fall short?

I'm genuinely curious whether this is a useful complement to the way distributed systems are traditionally taught.

Hi y’all, I’m a computer science undergrad going into my third year (planning to graduate May 2028), and have spent the summer working on a few projects / research problems with my research advisor. I’m coming from the math/theory side rather than the programming/engineering side, and I’m starting to worry I have nothing to show and am just wasting everyone’s time, having spent like three months with no meaningful results.

My advisor is great and supportive but unfortunately very hands off — completely unfounded fear but like I don’t want to come off as needy or incompetent and ask her too many questions. She gave me a paper to look at for one of the problems I’m working on and I read it, but like after reading it, I can see the connection but not the takeaway that I’m supposed to get.

How often is reasonable to be in contact with an advisor (especially as an undergrad)? Especially for any mathematicians / CS theorists, how do I get over the feeling that I’m making no progress? Any advice on workflows? I just try to read as much as possible and then write what I think is a reasonable direction, and I feel comfortable with writing proofs, but I haven’t even decided yet what exactly I’m trying to prove. I can’t tell if I made, or am on track to make, real progress