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Comment by world2vec | Hacker News Reader

1 billion tokens a day?!! I've done a lot of work these past 2 weeks with GLM-5.3. Like, a lot. And I've just passed 300 million tokens in total.

Can I ask where are you using all those tokens?

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Well, there's essentially two major ways to use these models: Pair programming or fully autonomous fire-and-forget code generation. The second strategy needs essentially zero input, so the number of tokens you can blow is practically only limited by API speed.

There's also a third way that can spend the most tokens: if the AI is used as part of the product, and not just a tool to build the product.

Something like this I guess: https://youtu.be/U-Rqv9dOB1U

This is such a good video. Instant sub. Next to tech bros, we should also put AI-cringe bros.

This was such a gem of a video.

I have 3-5 agent harnesses with large context windows working on different applications concurrently.

Share the resulting code from any one of those please? I've tried so many times to find a setup that facilitates parallel work + high quality results, but it's just impossible regardless of harness or model. Leave the agents alone for too long, and the entire thing just balloons out of control, and next you know you're sitting there with half a million LOC where 80% isn't even needed.

Most of them are not public, but a fun thing I did was a mario cli game - https://github.com/Daviey/mario/ (or `ssh mario.baby`).

I now exclusively use https://omp.sh/ as my harness:

I set it up so it never works in the main branch so subagents etc don't step on each others toes, and only merges back when complete: https://github.com/Daviey/mario/blob/main/.omp/hooks/pre/wor...

A good AGENTS.md is essential: https://github.com/Daviey/mario/blob/main/AGENTS.md

I then provide specifications for what I want, making sure it is unit tested.

That's easy to do with many agents independently told to find bugs in a large codebase.

300M for two weeks is surprisingly low. What are you doing that need so few tokens?

It's not my main model (that would be Fable 5.1 Extra) but it's been doing agent-driven search and optimisation of a cross-trading ranking model (it's for work).

I would suggest you to hook fable or 5.6 to check it regularly and its work because it gets lost easily on stuff it was not trained on. I'm doing some custom inference engine optimization and it's a workhorse but it can easily lose its way and if you don't recheck it you will get wrong answers in the end.

Yeah that's what I already do. Fable writes the plan and checks things at certain milestones. Otherwise it does get lost indeed.

Kind of feels like this applies to every single model, from Astra to Qwen, they all eventually lose track of the plot unless you feed it some human's input that can steer them right every now and then. The only difference is how often you need to do so, and also how often you want to do so heavily influences how good quality the results will be.

you are not false, but there is still difference. its just that the better models are correct more of the time and will better validate its own steps. glm sometimes will understand the plan start implementing and then forget part of it and then say it finished. or then take a wrong turn somewhere and not correct. but they will all happily proclaim they are correct till you question it.

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