I've been building an AI coding agent platform and kept running into this annoying problem: agents would get halfway through a task, hit ...
I've been building an AI coding agent platform and kept running into this annoying problem: agents would get halfway through a task, hit an error or get interrupted, and completely lose context. They'd either start over from scratch or create duplicate files because they forgot what they already did. Tried the usual approaches - longer context windows, better prompts, chat history - but none of it worked reliably. The fundamental issue is that LLM agents are stateless by nature. So I built a state machine that persists everything to DynamoDB: - Which development phase we're in (requirements → frontend → backend → etc) - Granular todos within each phase - What's been completed vs what's pending - Sandbox state (E2B sandboxes can die/restart) - S3 code sync status Now when something goes wrong (and it always does), the agent just resumes from the exact todo it was working on. No context loss, no duplicate work.
Posted by anupsingh123 on Oct 29, 2025, 5:41 AM
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The biggest problem with MOND is that it was hand-crafted with additional parameters simply to fit the data.
(My comment is about detached houses).
The biggest problem with this is that I can't run my company's own internal benchmarks against this API, because they will be trained int...
> steer Claude away from over-engineering parts of the system in response to hypothetical concerns.
Why is there no comparison with FLUX 3 Image?
> our Git monorepo was developing an annoying problem Sentences like this are so common from people using monorepos that I am convinced ...
Why is there no contact form?
The biggest problem with plugging a generator into an outlet is you need a double ended cord.
I've been building an AI coding agent platform and kept running into this annoying problem: agents would get halfway through a task, hit ...
I've been building an AI coding agent platform and kept running into this annoying problem: agents would get halfway through a task, hit an error or get interrupted, and completely lose context. They'd either start over from scratch or create duplicate files because they forgot what they already did. Tried the usual approaches - longer context windows, better prompts, chat history - but none of it worked reliably. The fundamental issue is that LLM agents are stateless by nature. So I built a state machine that persists everything to DynamoDB: - Which development phase we're in (requirements → frontend → backend → etc) - Granular todos within each phase - What's been completed vs what's pending - Sandbox state (E2B sandboxes can die/restart) - S3 code sync status Now when something goes wrong (and it always does), the agent just resumes from the exact todo it was working on. No context loss, no duplicate work.
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