Posted by hansonw 12 hours ago
I gave it a shot last month but I did not enjoy it due to the lack of a proper planning mode and being able to accept each edit independently, has it improved?
Wouldn't the model automatically do that using attention techniques? Why do you need to do it at the token layer and not leave it to the model to automatically decide which tokens are worth paying attention to?
Exactly. Standard Multi-Head Attention uses a matrix that grows to 4B parameters for a 64K sequence as a starting place. FlashAttention v2 helps slightly, but as you grow to 128K context length, you still need over 1TB/s memory bandwidth to stay compute-bound in practice even with this optimization.
So there has been a lot of research in this area and model architectures released this year are showing some promising improvements. Sliding windows lose context fidelity and if you go fully linear, you sacrifice math, logic, and long multi-turn (agentic) capabilities, so everyone is searching for a good alternative compromise.
MiniMax-M1 had lightning attention to scale up to 1M context lengths. It's "I/O aware" via tiling and calculates attention two ways block-wise (intra-block traditional attention and inter-block linear attention), thereby avoiding the speed-inhibiting cumulative summation.
DeepSeek V3.2 uses DeepSeek Sparse Attention (DSA), which is sub-linear by only computing "interesting" pairs. For example, in 128K context lengths this requires only 10-20% of attention pairs to be materialized.
Both Qwen3-Next and Kimi Linear adopt a Gated DeltaNet, which is borrowed from Mamba2. In Qwen3-Next it alternates three Gated DeltaNet (linear attention) layers for every one gated [full] attention. The speedup is from a delta rule, which basically amounts to caching in a hand-wavy way.
There's no universally-adopted solution yet, as these are all pretty heavy-duty compromises, but the search is going strong right now for linear or better attention mechanisms that still perform well.
You don't know how an LLM works and you are operating on flawed anthropomorphic metaphors.
Ask a frontier LLM what a context window is, it will tell you.
For example, DeepSeek 3.2, which employs sparse attention [1], is not only faster with long context than normal 3.1, but also seems to be better (perhaps thanks to reducing the noise?).
[1] It uses still quadratic router, but it's small, so it scales well in practice. https://api-docs.deepseek.com/news/news250929
With that out of the way, parent was wondering why compaction is necessary arguing that "context window is not some physical barrier but rather the attention just getting saturated". We're trying to explain that 3+2=2+3 and you people are sitting in the back going "well, actually, not all groups are abelian".
In practice, when training a model, people select a context window so that during inference, you know how much GPU memory to allocate for a prompt and reject the prompt if it exceeds the memory limit.
Of course there's also degrading performance as context gets longer, but I suspect memory limit is the primary factor of why we have context window limits.
I'm a very happy Codex customer, but everything turns to disgusting slop if I don't provide:
(1) Up-to-date AGENTS.md and an excellent prompt
(2) A full file-level API with function signatures, return types and function-level guidance if it's a complex one
(3) Multiple rounds of feedback until the result is finely sculpted
Overall it's very small units of work - one file or two, tops.
I've been letting the above standards go for the last couple of weeks due to crunch and looking at some of the hotspots of slop now lying around has me going all Homelander-face [1] at the sight of them.
Those hotspots are a few hundred lines in the worst cases; I'm definitely not ready to deal with the fallout of any unit of work that takes even more than 20min.
[1] https://i.kym-cdn.com/entries/icons/original/000/050/702/ab7...
It seems to pick a lot up from my code base. I do have an Agents.md with some basics on how to run stuff and what to do that seems to help it going off on a wild goose chase trying to figure out how to run stuff by doing the wrong things.
I think from first using codex around July to now has been quite a journey where it improved a lot. It actually seems to do well in larger code bases where it has a lot of existing structure and examples of how things are done in that code base. A lot of things it just does without me asking for them just because there's a lot of other code that does it that way.
After recent experiences, I have some confidence this might work out well.
http://github.com/agentify-sh/10x
does minimal overhead with agent orchestration (its just a bash/typescript) as its main focus was adding enhancements to codex like double redundant checkpoint via git and jj (lessons learned from codex being git reset --hard happy), something like claude skills (just a bunch of mds that steer it towards specific activity like think, plan, execute), timeout wrappers (to get you unstuck if codex waits a long time), blacklist commands during yolo (rm -rf, git reset banned even if it by small chance run it) MIT licensed
you can work sequentially (subagents launch one after the other) or parallel (worktrees) but tbh sequentially is better because you understand what is going on with parallel it might be best for dealing with tests and UI.
its been essential to my workflow as well
i use both jj and git and jj is great for just creating a snapshot that i can revert to incase it fails
im still exploring it to see what else i can do with it for agentic use
"I wasn’t able to finish creating the new base homepage module template and updating every module to inherit from it within the available time. I did not make any changes or commits."
Told it to get back to work. Let's see how that goes.
