61–70 of 123 posts
As the models have progressively improved (able to handle more complex code bases, longer files, etc) I’ve started using this simple framework on repeat which seems to work pretty well at one shorting complex fixes or new features. [Research] ask the agent to explain current functionality as a way to load the right files into context. [Plan] ask the agent to brainstorm the best practices way to implement a new featur…
It’ll report, “Numbers changed in step 6a therefore it worked” [forgetting the pivotal role of step 2 which failed and as a result the agent should have taken step 6b, not 6a].
Or “there is conclusive evidence that X is present and therefore we were successful” [X is discussed in the plan as the reason why action is NEEDED, not as success criteria].
I _think _ that what is going wrong is context overload and my remedy is to have the agent update every step of the plan with results immediately after action and before moving on to action on the next step.
When things seem off I can then clear context and have the agent review results step by step to debug its own work: “review step 2 of the results. Are the stated results confident with final conclusions? Quote lines from the results verbatim as evidence.”
Earlier quoted context omitted.
I don't do any of that. I find with GitHub copilot and Claude sonnet 4.5 if I'm clear enough about the what and where it'll sort things out pretty well, and then there's only reiteration of code styling or reuse of functionality. At that point it has enough context to keep going. The only time I might clear that whole thing is if I'm working on an entirely new feature where the context is too large and it gets stuck…
Yeah I found that for daily work, current models like Sonnet/Opus 4.5, Gemini 3.0 Pro (and even Flash) work really well without planning as long as I divide and conquer larger tasks into smaller ones. Just like I would do if I was programming myself. For planning large tasks like "setup playwright tests in this project with some demo tests" I spend some time chatting with Gemini 3 or Opus 4.5 to figure out the most i…
As the models have progressively improved (able to handle more complex code bases, longer files, etc) I’ve started using this simple framework on repeat which seems to work pretty well at one shorting complex fixes or new features. [Research] ask the agent to explain current functionality as a way to load the right files into context. [Plan] ask the agent to brainstorm the best practices way to implement a new featur…
I’m uneasy having an agent implement several pages of plan and then writing tests and results only at the and of all that. It feels like getting a CS student to write and follow a plan to do something they haven’t worked on before. It’ll report, “Numbers changed in step 6a therefore it worked” [forgetting the pivotal role of step 2 which failed and as a result the agent should have taken step 6b, not 6a]. Or “there i…
Good read but I wouldn't fully extend the garbage in, garbage out principle to the LLMs. These massive LLMs are trained on internet-scale data, which includes a significant amount of garbage, and still do pretty good. Hallucinations are due to missing or misleading context than from the noise alone. Tech debt heavy code bases though unstructured still provides information-rich context.
As the models have progressively improved (able to handle more complex code bases, longer files, etc) I’ve started using this simple framework on repeat which seems to work pretty well at one shorting complex fixes or new features. [Research] ask the agent to explain current functionality as a way to load the right files into context. [Plan] ask the agent to brainstorm the best practices way to implement a new featur…
As of Dec 2025, Sonnet/Opus and GPTCodex are both trained and most good agent tools (ie. opencode, claude-code, codex) have prompts to fire off subagents during an exploration (use the word explore) and you should be able to Research without needing the extra steps of writing plans and resetting context. I'd save that expense unless you need some huge multi-step verifiable plan implemented. The biggest gotcha I found…
I find it to be a good thing that the code must be read in order to be production-grade, because that implies the coder must keep learning.
I worry about the collapse in knowledge pipeline when there is very little benefit to overseeing the process...
I say that as a bad coder who can and has done SO MUCH MORE with llm agents. So I'm not writing this as someone who has an ideal of coding that is being eroded. I'm just entering the realm of "what elite coding can do" with LLMs, but I worry for what the realm will lose, even as I'm just arriving
Earlier quoted context omitted.
> it seems pretty random as to when it decides to drop that out of context Overcoming this kind of nondeterministic behavior around creating/following/modifying instructions is the biggest thing I wish I could solve with my LLM workflows. It seems like you might be able to do this through a system of Claude Code hooks, but I've struggled with finding a good UX for maintaining a growing and ever-changing collection of…
Wouldn't it be great if we had some kind of deterministic language to precisely and concisely tell a computer what to do
The issues raised in this article are why I think highly-opinionated frameworks will lead to higher developer productivity when using AI assisted coding You may not like all the opinions of the framework, but the LLM knows them and you don’t need to write up any guidelines for it.
I liked the Rust solution a lot, but it had 200+ dependencies vs Bun’s 5 and Rails’ 20ish (iirc). Rust feels like it inherited the NPM “pull in a thousand dependencies per problem” philosophy, which is a real shame.