This might work only if you have “infinite” compute and infinite tokens. As someone that used the $20 plan, this pure agentic approach is impossible to do because I’d hit the limit fast and I would end up with less outcome. What I found that work incredibly well was to provide a human written code as reference, and ask it to extend it. So I scaffold the entire thing, architect it, write few samples code (controllers, services, models, components, database schema, how auth works, etc) so the LLM can have a headstart on their attention (pun intended) I usually wrote a stub with a lot of details on how to implement it. Something like a higher abstraction pseudo code. Then ask the LLM to implement it. When it fails, it is often better to undo the whole changes, adjust the stub so it catches what fails before, and try again. Or, commit the changes, and use a new fresh context and only address what went wrong. - Whenever I tried this agentic from scratch approach, I always end up disappointed; both on the outcome and on the limit that I hit before an hour even passed.

You are not going anywhere with $20 plan Upgrade to $200/month and you should see more usage but even for a hardcore user for me, one can never have enough. I'm still very jealous of those guys that got 200x usage simply by RSVP'ing to openai party

Yep thats true pre grounding is very much worth it, if you just feed the agent a quick environment brief upront instead of making it spam tool calls to figure out where it is, you save a lot of tokens.

What I still can't understand is why is massive amount of code generated is a flex? I don't feel that software has gotten a lot better in past 3 years, only sloppier. It's surprising to me that people who know about reward hacking choose a simple objective like lines of code generated as a signal for quality. I'd argue you have to optimize for less lines generated as possible while secondary optimization should be readability for humans. I suspect it's not seen as a problem by providers because more lines generated means more tokens used and hence more billing put out on customers. And if I am working on an existing codebase then isn't a good commit often a negative sum between added and removed lines? I don't want to bloat my codebase but make it more polished and elegant. After reading that I wonder if what they have done could have been accomplished for a far fewer LoC budget.

I also can't help but notice they didn't mention how many tokens were burned, or how much that translates to in terms of cost over the 5 months at enterprise AI prices. I'm going to guess this wasn't a cheap demo.

I think the telling part is in this line: > Because the repository is entirely agent-generated, it’s optimized first for Codex’s legibility I asked a question from a perspective of a human engineer, as in, I will have to read the code and understand, fix it once it breaks. OpenAI approach is opposite, even if it is breaking it is the agent that will be doing the fixing, millions of lines and inelegant designs don't matter because human readability doesn't matter. In any case you use more tokens so you fork over more money. I will say, however, that IMHO there is objectively bad and good code in terms what it can do and performance, if I can do the same thing in 50 lines as opposed to 1000 lines, this difference still matters for the model. Smaller context usage, better approach that informs downstream generation.

>I suspect it's not seen as a problem by providers because more lines generated means more tokens used and hence more billing put out on customers. I have also grown skeptical of token usage in order to run up my bill! But since I feel like it takes me MORE effort to write LESS lines of code myself, I'd expect a quick and dirty AI-generated solution to be MORE lines of code and cost LESS to generate than a concise/elegant solution in LESS lines of code.

> I suspect it's not seen as a problem by providers because more lines generated means more tokens used and hence more billing put out on customers. To generate elegant code with more restrictions, it means more thinking tokens and more stronger adherence to instructions. So tha naive view that they are doing it for billing is wrong.

It is a very valid question. My intution (no grounding) is to the model training. Optimizations traditionally have worked well in human wrote software with either experience of the developer , usage of architectural patterns or a second ir third pass of fine tuning. In case of model written code - (e/p one token at a time), only possible orchitectural optimization is either with a strict guardrail on patterns to use for a specific implementation OR by giving a second or third optmization path. All of which burns more tokens, but can lead to better software.

I’d be interested to know two things: 1. What’s the job satisfaction like day to day being an engineer on this project? How have they adapted to this way of working? 2. How much did it cost? Work is being done whilst the engineers sleep but if that 6 hours overnight task cost $300 and could have been done by a person in 2 hours is it a real saving?

ok, but you had 1x token to generate , then more 1x to review locally, 1x for the agent local, 1 x for the cloud. then ???x until all bots are satisfied. You end-up spending at least 5x the amount of tokens for maybe prediction machine to find a discontinuity? I would say a way better approach is 1.123x to generate code + tests + passing analysis tools + human review + 1x "simplify as much as possible", than letting the snake its own tail without boundaries.

Step 1: Be rich.

I for one am not protesting because I know that this is bullshit marketing nonsense. Look at reliability metrics of OpenAI, they’re terrible. Everyone knew a long way ahead that it’s a scam, now they’re cranking up pricing and trying to rug pull. There will be a lot of developers who will come out very well once the stock tanks. That’s my two cents