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GitHub Copilot Coding Agent

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Re: GitHub Copilot Coding Agent

#31

Post by tmpz22 »

twodave wrote: post #23
taurath wrote: post #13

> Copilot excels at low-to-medium complexity tasks in well-tested codebases, from adding features and fixing bugs to extending tests, refactoring, and improving documentation. Bounds bounds bounds bounds. The important part for humans seems to be maintaining boundaries for AI. If your well-tested codebase has the tests built thru AI, its probably not going to work. I think its somewhat telling that they can't share n…

I feel like I saw a quote recently that said 20-30% of MS code is generated in some way. [0] In any case, I think this is the best use case for AI in programming—as a force multiplier for the developer. It’s for the best benefit of both AI and humanity for AI to avoid diminishing the creativity, agency and critical thinking skills of its human operators. AI should be task oriented, but high level decision-making and…

How much of that is protobuf stubs and other forms of banal autogenerate code?

Re: GitHub Copilot Coding Agent

#32

Post by asadm »

In the early days on LLM, I had developed an "agent" using github actions + issues workflow[1], similar to how this works. It was very limited but kinda worked ie. you assign it a bug and it fired an action, did some architect/editing tasks, validated changes and finally sent a PR.

Good to see an official way of doing this.

1. https://github.com/asadm/chota

Re: GitHub Copilot Coding Agent

#33

Post by PretzelPirate »

Scene_Cast2 wrote: post #26
candiddevmike wrote: post #17

Earlier quoted context omitted.

> I also ended up blowing through $15 of LLM tokens in a single evening. This is a feature, not a bug. LLMs are going to be the next "OMG my AWS bill" phenomenon.

Cline very visibly displays the ongoing cost of the task. Light edits are about 10 cents, and heavy stuff can run a couple of bucks. It's just that the tab accumulates faster than I expect.

> Cline very visibly displays the ongoing cost of the task

LLMs are now being positioned as "let them work autonomously in the background" which means no one will be watching the cost in real time.

Perhaps I can set limits on how much money each task is worth, but very few would estimate that properly.

Re: GitHub Copilot Coding Agent

#34

Post by tmpz22 »

Scene_Cast2 wrote: post #8

I tried doing some vibe coding on a greenfield project (using gemini 2.5 pro + cline). On one hand - super impressive, a major productivity booster (even compared to using a non-integrated LLM chat interface). I noticed that LLMs need a very heavy hand in guiding the architecture, otherwise they'll add architectural tech debt. One easy example is that I noticed them breaking abstractions (putting things where they do…

While its being touted for Greenfield projects I've notices a lot of failures when it comes to bootstrapping a stack.

For example it (Gemini 2.5) really struggles with newer ecosystem like Fastapi when wiring libraries like SQLAlchemy, Pytest, Python-playwright, etc., together.

I find more value in bootstrapping myself, and then using it to help with boiler plate once an effective safety harness is in place.

Re: GitHub Copilot Coding Agent

#35

Post by twodave »

tmpz22 wrote: post #31
twodave wrote: post #23

Earlier quoted context omitted.

I feel like I saw a quote recently that said 20-30% of MS code is generated in some way. [0] In any case, I think this is the best use case for AI in programming—as a force multiplier for the developer. It’s for the best benefit of both AI and humanity for AI to avoid diminishing the creativity, agency and critical thinking skills of its human operators. AI should be task oriented, but high level decision-making and…

How much of that is protobuf stubs and other forms of banal autogenerate code?

Updated my comment to include the link. As much as 30% specifically generated by AI.

Re: GitHub Copilot Coding Agent

#36

Post by danenania »

BeetleB wrote: post #29
Scene_Cast2 wrote: post #8

I tried doing some vibe coding on a greenfield project (using gemini 2.5 pro + cline). On one hand - super impressive, a major productivity booster (even compared to using a non-integrated LLM chat interface). I noticed that LLMs need a very heavy hand in guiding the architecture, otherwise they'll add architectural tech debt. One easy example is that I noticed them breaking abstractions (putting things where they do…

> I also ended up blowing through $15 of LLM tokens in a single evening. Consider using Aider, and aggressively managing the context (via /add, /drop and /clear). https://aider.chat/

My tool Plandex[1] allows you to switch between automatic and manual context management. It can be useful to begin a task with automatic context while scoping it out and making the high level plan, then switch to the more 'aider-style' manual context management once the relevant files are clearly established.

1 - https://github.com/plandex-ai/plandex

Also, a bit more on auto vs. manual context management in the docs: https://docs.plandex.ai/core-concepts/context-management

Re: GitHub Copilot Coding Agent

#37

Post by nodja »

I wish they optimized things before adding more crap that will slow things down even more. The only thing that's fast with copilot is the autocomplete, it sometimes takes several minutes to make edits on a 100 line file regardless of the model I pick (some are faster than others). If these models had a close to 100% hit rate this would be somewhat fine, but going back and forth with something that takes this long is not productive. It's literally faster to open claude/chatgpt on a new tab and paste the question and code there and paste it back into vscode than using their ask/edit/agent tools.

I've cancelled my copilot subscription last week and when it expires in two weeks I'll mostly likely shift to local models for autocomplete/simple stuff.

Re: GitHub Copilot Coding Agent

#38

Post by [deleted] »

taurath wrote: post #13

> Copilot excels at low-to-medium complexity tasks in well-tested codebases, from adding features and fixing bugs to extending tests, refactoring, and improving documentation. Bounds bounds bounds bounds. The important part for humans seems to be maintaining boundaries for AI. If your well-tested codebase has the tests built thru AI, its probably not going to work. I think its somewhat telling that they can't share n…

[deleted]

Re: GitHub Copilot Coding Agent

#40

Post by shafyy »

twodave wrote: post #35
tmpz22 wrote: post #31

Earlier quoted context omitted.

How much of that is protobuf stubs and other forms of banal autogenerate code?

Updated my comment to include the link. As much as 30% specifically generated by AI.

I would still wager that most of the 30% is some boilterplate stuff. Which is ok. But sounds less impressive with that caveat.
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