u/roshandxt

When a session gets cut off, whether that's a hard rate limit, a crash, or you just decide to start fresh, what do you actually do to pick back up? do you rely on `--resume`, keep your own notes on the side, commit more often as a checkpoint, or just eat the cost of re-explaining what codex already figured out?

Also curious how people handle the token side of this. do you switch to a cheaper/faster model for the boring parts and save the expensive one for the hard calls, or just run everything on whatever model you started with? and if you do switch models mid task to save tokens, does that cause the same kind of "explain everything again" problem as switching tools entirely, or is it cleaner since it's still technically codex?

The thing i keep coming back to across every sub I've asked this in: it's not really the code that's hard to reconstruct, it's the dead ends you already ruled out and why. does that survive for you across a model switch or a rate limit reset, or is it gone the moment the session ends?

been asking this across a few dev communities and the pattern keeps repeating: people have strong opinions about context management within a session (plan files, /compact, chunking work) but almost nobody has a clean answer for when the session is already gone rate limit, crash, or you're switching tools entirely.

curious what LLM-heavy devs here do. keep notes on the side? commit more often? just re-explain from scratch and eat the cost?

most of what I read here is about building the context retrieval, chunking, what to feed the model and when. curious about the other side: when a coding agent session gets long, hits a limit, or just dies mid-task, what happens to all that carefully engineered context?

in my own experience it just evaporates. the next session starts from zero unless I manually rebuild the state what changed, what failed, what's actually verified vs. what the model assumed.

is anyone treating "context survival across sessions" as its own engineering problem, or is it assumed you just re-engineer it each time?

A lot of the content here is about building good context what to include, how to structure it. curious about the other side: when a long AI coding session gets interrupted or hits a limit, the carefully built context is gone and the next session starts blind. is anyone treating "context survival across sessions" as its own problem, or is re-engineering from scratch just assumed?

Not a tools debate genuinely asking about process. I've been asking this across a bunch of developer communities: when a long AI coding session gets interrupted (hits a limit, crashes, or you just switch tools), what happens to everything that was figured out along the way?

most people either keep
notes on the side, or just re-explain from scratch to a fresh chat. curious if
that matches what people here see, or if there's a cleaner pattern I'm missing.

ran a two week validation pass across dev subreddits before writing code same pain point, asked in different communities, got 140+ replies. reach was decent, engagement was real, but conversion is obviously zero since i wasn't selling anything, just asking questions

trying to figure out how much weight to put on that before actually building. anyone have a read on how well "people replied thoughtfully to my question" predicts "people will actually pay for the thing"? or is that just wishful thinking and the only real signal is watching what happens after launch

Did the standard thing posted a genuine question about a pain point across a bunch of dev communities before writing any code, no pitch, no link, just asking what people actually do. got a real number of thoughtful replies, consistent pattern in the answers.

but i know reddit engagement is basically free and doesn't cost anyone anything to give. curious if anyone here has actually used pre-build validation like this and had it hold up post-launch, or if it just gives you a false sense of confidence and the real signal only shows up once there's something to pay for

not asking which ai is best , more just when a chat in cursor gets too long or starts going in circles, what's your actual move? do you have a rules file, keep notes somewhere, use commits as checkpoints, or just eat it and start explaining everything again

mainly curious about the case where you didn't catch it early. like the chat's already a mess and you don't fully trust it anymore, not the "i proactively kept it clean" version

Spend some time asking dev communities how they deal with ai coding sessions dying or going stale mid workflow before writing any code myself ,Got more replies than expected and the pattern is pretty consistent no one wants a bigger context window but a clean way to handoff as in what changed what failed what's verified and what was a guess one replay called it (handoff becomes the next sessions ground truth) which stuck with me

weird part the sharpest comment was not from devs or a power user . a tax lawyer and a school admin both gave me specific answers they alreadt have their manual workaround , Crowd who probably wont pay for anything since they do it manually

trying to figure if that's a real signal or if I'm reading too much into couple of good comments. anyone here shipped something where the "people who need it most" and "people willing to pay" turned out to be different groups

first few prompts are always clean, then a bunch of iterations later the chat's carrying dead ends, half-changed files, stuff you forgot you asked for. starting fresh helps but then you're stuck re-explaining the whole project

what's your actual process plan file, notes, one-feature-per-chat, or just push through and deal with the mess

kimi k2 seems to be getting used a lot for agentic coding setups lately. curious how people deal with long-running tasks specifically does the context hold up, or do you run into the same drift/staleness thing you get with other models after a while

if you do hit that wall, what's the actual recovery move

been seeing more people mention using grok for coding stuff lately. curious how it holds up on longer tasks specifically does it stay coherent, or does it start losing the plot the same way other models do after enough back and forth

if it does get messy, what's your move? fresh chat and re-explain, or some kind of running notes

mostly curious how people using deepseek for coding handle the same problem everyone else has session gets long, starts dragging old context around, eventually you can't really trust it anymore

do you keep notes on the side, restart and re-explain, or is there something deepseek-specific that makes this less annoying than it is with other models. genuinely don't know if this is a universal problem or if certain setups handle it better

curious what people are doing here specifically, kiro's still pretty new so not sure what the common wisdom is yet.

when a task runs long and the session starts accumulating old attempts / stale assumptions, do you keep some kind of spec or plan file on the side, lean on kiro's own context handling, or just restart and re-explain when it gets bad. also curious what happens if you need to bail out of kiro mid-task and pick it up somewhere else — is that something people actually do

not fishing for "which ai is best," genuinely asking about process.

when a chat's been going a while and starts carrying baggage — old attempts, wrong turns, half-finished stuff — what do you actually do? keep some kind of running notes/plan file, rely on the tool's own summarize/compact feature, screenshot important bits, or just push through until it falls apart and start explaining everything from scratch

mainly interested in the case where you didn't catch it early enough and the chat's already kind of unreliable. that's the part i haven't seen a clean answer for yet

had an annoying moment last week — had claude walk me through restructuring part of a ci pipeline, made a few calls along the way (why we skip caching on this stage, why the deploy step is ordered like it is) and then the session died before i wrote any of it down anywhere.

six months from now someone's gonna open that yaml and have zero idea why it's set up like that. same problem we already deal with for regular infra decisions except now it's buried in a chat transcript instead of a runbook or a pr description

curious how people handle this when infra/pipeline changes come out of an ai session. do you make a habit of writing a real changelog/comment right after, or does the reasoning mostly just evaporate unless something breaks and you go dig for it

we've all got some version of an ADR process for human decisions — a doc explaining why we picked postgres over mongo, why we went with a queue instead of sync calls, whatever.

now that a chunk of my codebase gets written by claude/copilot mid-conversation, i'm running into the same problem except worse. the ai makes a real call inside a chat (skip the caching layer here, do it this way not that way) and unless someone manually writes that down afterward, it's just gone. buried in a session log nobody's ever gonna reread.

been messing around with treating the chat transcript itself as a raw adr source — pulling the actual reasoning out and tying it to the diff it produced, so later you can look at a module and see the decision trail, not just git blame telling you who touched it last.

curious if anyone here has an actual process for this already, or if most teams just quietly accept that ai-made decisions go undocumented unless a human bothers to formalize them after the fact

I’ve been looking at how people restart Claude / Claude Code sessions when a coding task gets too long, stale, or full of old debugging paths.

A normal summary feels too weak. It often keeps the wrong things: old attempts, vague decisions, and noisy transcript history.

The better pattern seems closer to a handoff skill that preserves only the useful working state:

- current goal

- files touched

- git diff / changed files

- decisions made

- what failed

- what is known true

- what not to retry

- tests or verification status

- next smallest action

For people building or using Claude skills: what would you want a handoff skill to include?

And just as important, what should it deliberately leave out so the next session starts clean?

I’ve been asking around about what people do when AI coding chats get too long, stale, or full of old debugging paths.

A plain summary seems too weak. It often misses the details that actually matter for continuing the work.

The handoff format people keep mentioning looks more like:

- current goal

- files touched

- git diff / changed files

- what failed

- what is known true

- decisions made

- what not to touch

- tests or verification

- next smallest action

For people using AI to code regularly: what do you include when you restart a chat or move work to another tool?

And what do you deliberately leave out so the next session does not inherit all the noise?

For people using OpenClaw on longer coding tasks: how do you keep the next session grounded when the current one gets messy?

I mean the point where the chat has old attempts, half-abandoned ideas, files that changed, and things the agent should not retry.

Do you keep a plan.md, git diff, commits, scratchpad, repo notes, or some kind of handoff prompt?

The handoff format I keep seeing from other AI coding users is something like:

- current goal

- files touched

- what failed

- what is known true

- decisions made

- what not to touch

- next smallest test

Curious what OpenClaw users actually do in practice.