71–80 of 194 posts
Not a fan of their context compaction and I feel like 1M-token context should minimum today. Each day I see how GPT 5.5 and 5.6 struggle a bit after each compaction before they get to the full speed, sometimes focusing too much on some older steering message that made it into the compacted context.
This was tweeted about when it happened, with some explanation from Tibo here: https://x.com/thsottiaux/status/2076543065045795309
Codex is open source. You can build it yourself. You don't have to use upstream's parameter choices, compaction strategies, subagent heuristics, or whatever. Strange how people act like tweaks to these parameters are mandates when they're really just suggestions relayed to you via git. For example: upstream recently changed the effort level hotkeys so M-. would stop at xhigh, not max. If you want max, upstream, you g…
I know a lot of people like to say that compaction makes this moot, but the level of detail you lose across compaction is wildly too much for most things that I do, unfortunately. Perhaps if your plans don't have as much detail, or if you're not, for example, having a discussion with a lot of nitty-gritty then it's fine? The lack of long context is the main reason that I still end up using Anthropic. The worst is whe…
I use GLM5.2 and gpt-5.5 and 5.6 and never noticed any quality degradation near the limits of the context or due to compaction.
This was tweeted about when it happened, with some explanation from Tibo here: https://x.com/thsottiaux/status/2076543065045795309
To see replies: https://xcancel.com/thsottiaux/status/2076543065045795309 The linked tweet is an unofficial reply to Tibo's official info and Tibo makes a correction in a reply.
Earlier quoted context omitted.
I agree. Compaction sucks, so I made tools that let the LLM selectively delete (and recall if needed) chunks of its context. You might want to try context bonsai if you're routinely hitting the auto-compaction wall. https://github.com/Vibecodelicious/context-bonsai-agents
I've implemented a similar approach – although I'm surprised not to see mention of cache prefix busting in there!
I know a lot of people like to say that compaction makes this moot, but the level of detail you lose across compaction is wildly too much for most things that I do, unfortunately. Perhaps if your plans don't have as much detail, or if you're not, for example, having a discussion with a lot of nitty-gritty then it's fine? The lack of long context is the main reason that I still end up using Anthropic. The worst is whe…
OpenAI compaction is very different than Anthropic compaction, especially in 5.6 I use GLM5.2 and gpt-5.5 and 5.6 and never noticed any quality degradation near the limits of the context or due to compaction.
that's because you and your workloads probably fall into a bucket of users where compaction is tuned well.
but the reality is that those processes need to pick what to carry over, and that's a tough thing to get right for everyone, so for me compaction is a signal to restart the session or to tell the LLM to reference existing design docs lest we go off the rails.
large context limits is one of the things that make the open model competitors very attractive to me.
I know a lot of people like to say that compaction makes this moot, but the level of detail you lose across compaction is wildly too much for most things that I do, unfortunately. Perhaps if your plans don't have as much detail, or if you're not, for example, having a discussion with a lot of nitty-gritty then it's fine? The lack of long context is the main reason that I still end up using Anthropic. The worst is whe…
Curious, what kinds of tasks do you do that require such a large context window? Anything specific?
it's hard to modularize a monolith without keeping large chunks in context at first pre-delegation; the orchestrator -- however you implement it -- needs to carry as much of the original thing as possible into context and big monoliths make this task heavier.
so, tl;dr : I use a lot of tokens re-writing legacy codebases that were constructed with very little CS training in some huge spaghetti fashion by random people around the world.
I know a lot of people like to say that compaction makes this moot, but the level of detail you lose across compaction is wildly too much for most things that I do, unfortunately. Perhaps if your plans don't have as much detail, or if you're not, for example, having a discussion with a lot of nitty-gritty then it's fine? The lack of long context is the main reason that I still end up using Anthropic. The worst is whe…
Earlier quoted context omitted.
That seems quite different from my design process. I write a plan.md that goes through multiple revisions. The plan is the memory. Restarting and reading the plan again to do another review is a good way to get a different perspective.
I have these plan files as well, but it depends on the scope and scale of the things you're executing on, I think. However much detail gets put into the plan, it still doesn't help if part of what the model needs to understand is the fine-grained / perfect detail of a large surface area.