A week ago I posted the usual reasons usage stays high after you set up ICM. Most of the replies zeroed in on #5 (model choice) and #6 (loops and retries). I promised a deeper post. Here it is, and a lot of it is your ideas. I wrote #5 the day it burned me. Polished new process, felt careful, and Opus and Fable crushed my account usage before I noticed. Lesson: a polished process is not a cheap process. #5: the model leak *The shop.* Haiku is the parts runner, Sonnet is the mechanic, Opus is the foreman. You do not pay the foreman to fetch a filter, and you do not ask the runner to decide if the engine is worth saving. *What I changed.* My setup now picks the model by the job: Haiku for mechanical work (search, extract, rename, format), Sonnet for routine building and writing, Opus only for judgment (planning, review, hard debugging, anything a client sees). Max four Opus agents in any one run, so an overnight job can not shred the account. Fable only when I name it for that job. *Subagents are not a discount.* @Eddie Barry nailed this one. Handing a stage to a subagent protects your main conversation's context, but it is the same meter. The saving only shows up when the subagent runs on a lighter model. So my rule now: a subagent never inherits the parent's heavy model. @Mofedul Alam Joy Joy hands big reads to a subagent that sends back only a summary. Great move, and pair it with Eddie's point: put that reader on the light model. *Cost is not value.* @Pascal Pollack asked the real question: what are we getting for the tokens? Twice the tokens for a clearly better result can be the cheap choice. Half the tokens for worse work is not a win. Before you move a stage down a model, decide what good looks like for that stage, then check the lighter one still hits it. #6: the loop leak *Run it small first.* @Gabriel Azoulay 's rule, now mine: run any new process once on a small input and look at the usage before you let it loop or leave it overnight. This is the one that would have saved me.

Title: My First Structured Prompt: Making Complex Financial informations Easy to read digest I just applied the five-part prompt framework to a real project: creating a financial dashboard for a condominium general meeting. My original prompt was simple: "Can you create a clear dashboard for a condominium general meeting so that property owners can easily understand the different expense categories and make financial discussions more efficient? What would be the most appropriate tool to build it?" The intention was clear, but I hadn't defined the audience, functionality, constraints, or expected deliverables. Here's how I restructured it using the five-part framework. 1. Identity / Act as a financial data visualization specialist and UX/UI designer with experience in condominium management, financial reporting, and making complex financial information accessible to non-experts. Your approach should combine financial accuracy, visual clarity, and intuitive information design. 2. Task / Design a clear, interactive financial dashboard for a condominium general meeting. The dashboard must help property owners understand how their money is spent, identify major expenses, compare budgets against actual spending, and make informed decisions during the meeting. Evaluate and recommend the most appropriate tool or technology to build and present this dashboard. 3. Context / The audience consists of condominium property owners with different levels of financial literacy. Most not accounting experts. During meetings, financial discussions become lengthy and traditional financial statements are difficult to interpret. The dashboard should transform accounting tables into accessible visual information. The source data will come from accounting spreadsheets, annual budgets, expense reports, and financial statements provided by the property management company. 4. Constraints - Prioritize simplicity and clarity over visual complexity. - Use a clean, minimalist design inspired by Swiss editorial and information design. - Avoid unnecessary animations, decorative graphics, or excessive colors. - Clearly distinguish approved budgets, actual expenses, and financial variances. - Ensure financial calculations remain accurate and traceable to their source data. - Avoid technical jargon and explain financial terms when necessary. - Prefer affordable, maintainable solutions that do not require advanced technical knowledge. - Do not invent missing financial data. - Protect confidential financial information.

Ask a model to turn an essay into a carousel and you get a different carousel every time, with a few lines the essay never said. This one is a converter, not a writer. One kind of essay goes in: a real incident, the first guess, what it turned out to be, the lesson. A fixed 8-slide carousel comes out, with the same roles in the same order on every run. Two rules hold the shape: - Every filled slide cites the exact paragraph and sentence it came from. - A slide with nothing to fill says "not in source". It never gets padded. A short Python script then checks every cited line against the exact spot in the essay it claims to come from. The window is narrow enough that a real quote from the paragraph next door still fails. That case came from an earlier build of mine that got caught by exactly that. The repo also plants 337 faults into real outputs (a swapped word, a quote cut short, a citation moved one paragraph) and counts how many the checks catch. It lists what the script can't judge: whether a sentence was put in the right role. That's still a reader's call. Try it yourself: run your repurposing prompt twice on the same piece. If the two outputs have a different shape, you have a writer. If you need a converter, fix the shape first. Question for the comments: what are you repurposing, and does the output come back the same shape twice? Free, MIT, nothing to sign up for: https://github.com/NFTYoginis/essay-carousel-translator

Sounds a bit random but just wondering if anyone wanted to catch up and chat AI. It would be great to connect with others and collaborate or just meet in real life with similar people who are using the folder structure. (Sometimes it is hard talking to others who have no idea). No pitch, no expectations and I am happy to buy the coffee. No drama either way Cheers Nick (Apologies @Jake Van Clief if this is against the rules)

What would you do with it? Would you become 50x more productive? Or would you just waste 50x more resources? If you’re already wasting your current AI quota on poor planning, repeated prompts, lost context and unfinished projects, why would 50x more suddenly change that? You’re not scaling productivity. You’re scaling inefficiency. Now imagine you had to build a system capable of managing that 50x quota intelligently. Persistent memory. Structured workflows. Task prioritization. Agent orchestration. Verification loops. Resource allocation. And it needs to scale to 100x or 200x without falling apart. Now comes the real twist. What happens when you take everything you’ve learned and apply it to your regular 1x account? Same structure. Same intelligence. Same efficiency. Suddenly, you can accomplish far more with the exact same quota. The real challenge was never how to use 50x more AI. It was learning how to get more out of the 1x you already had. So let me ask you: Do you actually need a bigger AI quota, or do you need a better system to manage it? That's the whole point: learn how to get more out of the 1x you already have. That's how you turn 1x into 50x, not 50x into 1x.