Why not juat write the code. Its not that much faster to produce good code with AI. I spend nearly identical amoubts of time reviewing AI code as it would take to write. Im honestly not convinced there is all that much of a productivity boost, maybe 20% faster.
I was mostly joking, but to answer your question, the productivity boost entirely depends on your concern for code quality. Sometimes is it faster to just write the code than to try to explain in the prompt what to do. But if you don't care or don't need to understand what it is creating, you can get a ton of functionality fast.
for claude, it's response headers contain available usage, you can get it there. My deepseek harness plugin auto stops asking things when I am at 80% to allow inflight prompts to finish.
last 7 days I've used ~4.8B tokens. most of that was during a 2 day span when codex was using one of my harnesses to iterate over a codebase to improve performance of some parts of it - fully automated.
Take a 2 million line of code repo and ask it to convert from programming language A to B and statically verify every logic flow with an AST for each lang, and then get it to write a test harness that hits 100% code coverage, screenshots every major state and compares screenshots for each state between the old and new. Request it uses an agent team with up to 20 agents. You will use that in a few hours easily.
23 em-dashes in the span of a single README. I gotta hand it to Anthropic. They seriously managed to find a completely legal way to sell crack to crack addicts using other crack addicts as their unpaid sales force.
Of course, it's impossible to know for sure what was LLM processed or not, but some of your posts (like this one) have been getting classified that way.
Pretty cool tool! Congrats!
Not sure if I'm missing something but this tells you where your tokens went, breakdown per day/tool.
So the "why" is still a question left for the user to answer. Can be something like "Because I was missing a good CLAUDE.md file so it had to explore the whole repo before doing any work" or anything else. Just my take.
I made an "amateur hour" error with Codex. Given Anthropic's recent reliability problems I thought I'd take a little time to try Codex with their $20/mo plan. So I downloaded it and gave it a whirl, not realizing that the default model was gpt-5.6-sol. Well after just an hour or two, I blew through my entire week's quota. Whoops! It would be cool if these harnesses could all graphically display your quota usage on the screen at all times.
The only time I've ever managed to burn through a quota that fast (on the cheap plan) was with an open-ended request to check a codebase for any defects or deficiencies. It dispatched five Fable subagents.
Yeah. I like the way Claude Design handles this: if you come back to a chat after the cache expires, the chat UI shows a message like "start a new chat to save 300k tokens" or whatever. It's pretty nice if you're working in multiple design sessions and are quota-constrained.
I just used Claude to write a plug-in which changed the bottom of the CLI to say:
( ) Usage: 34% (resets in 2h 47m) Context: 56% [Opus 4.6]
auto mode on (shift+tab to cycle)
That way if my usage starts shooting up, it's very easy to notice (and the color of "( )", which is a circle that I couldn't paste here, changes when it gets high, ensuring I don't miss it).
I coupled that with a hook that watches for usage spiking (basically when I've been talking too long or did something to add a ton of context, so suddenly every turn sends a ton of context back, using up a ton of usage).
Between the two I haven't hit usage caps in weeks.
B) Warning everyone, it slowly eats tokens - If you're like me and on the pro plan, perhaps not a good idea to have its (albeit slow) drain on the tokens.
I have something like this persistent in my waybar, with session, weekly, and fable usage, with little color coded percentage bars for each. Super helpful, highly recommend it.
Great idea on the hook alert.
I feel like the advent of LLMs has made a super customizable OS like Linux so, so much better than the closed source alternatives, and so much better than they were before.
I just checked the repo. Nice work
A suggestion:
1. Adding which repo/project was where my most tokens were consumed. I checked the image in the repo README, didn't see that graph.
I genuinely don't even understand how people run out of tokens with the basic subscription. I had Claude write an entire app that has completely replaced Adobe Illustrator for my specific usecases (mostly inking sketches, and embellishing generated SVGs). It does everything exactly the way I want and has much better ergonomics (blender hotkeys ftw) and the whole thing took me like 700k tokens over the course of a couple days.
Do people just let Opus ultra extra max thinking run amok on their whole codebase with a single context? I don't even think I could even design sensible features faster, I'm the limiting factor here, the robot pretty much oneshots everything I ask for.
> Is it safe to start a big refactor now, or should I wait for my window to clear?
I typically do one context per feature.
Of course, it's impossible to know for sure what was LLM processed or not, but some of your posts (like this one) have been getting classified that way.
Surprised they haven't brought the same UX to CC.
Suggests either starting from a summary, a new conversation, or keep going.
I coupled that with a hook that watches for usage spiking (basically when I've been talking too long or did something to add a ton of context, so suddenly every turn sends a ton of context back, using up a ton of usage).
Between the two I haven't hit usage caps in weeks.
B) Warning everyone, it slowly eats tokens - If you're like me and on the pro plan, perhaps not a good idea to have its (albeit slow) drain on the tokens.
Great idea on the hook alert.
I feel like the advent of LLMs has made a super customizable OS like Linux so, so much better than the closed source alternatives, and so much better than they were before.
Do people just let Opus ultra extra max thinking run amok on their whole codebase with a single context? I don't even think I could even design sensible features faster, I'm the limiting factor here, the robot pretty much oneshots everything I ask for.
That avoids surprises