Hey Manda and people. First of all I am super I pressed what I am finding here and I can sense opportunities to soften my workload.. BUT. I am very beginner level in tech and I need some guidance. So what would be the best flow for me considering time and tech limitations. My goal is to simplify my work flow, reduce stress in these aspects: 1) How to prioritise my tasks, I never get full to do list done. I need money to keep flowing in from consultations and work into he health shop and put time aside for growing my community. 2) How to organise, decide where and how much money to invest for the best return. As a early pages there is not much coming in sonI don't want to waste it. 3) Is there a way I can create some kind of interactive option for my members in my own hub where they get guidance and direction which parts of my classroom topics are relevant for them to focus on. (Just as an example, I teach how to reduce inflammation using foods but within that same framework people may have slightly specific needs - one needs to work on fatty liver, the other one on hypothyroidism etc etc these all have same foundational approach but I could add more relevant guidelines on top of that). 4) Is there a way I can use AI to help me create my content MY way - have the structure, purpose, goal, tone, personality etc and I can talk it in like I do but I helps me to make it easy to follow. 5) Is there a way to have AI working with me where I could safely manage my clients case studies that at times are very complex ( I could obviously use codes and never private details). Some times it is just really hard to scroll through pages of notes to find out if certain tests are done or how their dietary intake has evolved etc etc. Phewww, these are the first few areas I am thinking. Should I just do the AI course here in Classrooms tab or should I actually pick some apps Manda has here? Sorry for the long post but I thought that this might help some other people too. I am very excited to hear any feedback , has anyone had any experience like this. Manda what would you suggest?

Did you know Claude can read and analyse huge PDFs in one go? Most tools break or lose context with large files. With Claude, you can: upload full PDFs or reports ask for summaries, insights, or inconsistencies compare multiple documents at once It can handle roughly: 500 to 700 pages of text around 10 to 50 MB depending on the file Now you don’t have to split documents just to get answers.

Turns out… it's not the model. It is how I am using it. Turns out Claude doesn’t count messages. It counts tokens. Once that clicked, everything changed. Here are 10 things that make a massive difference: 1. Edit your prompt instead of sending follow-ups If the response is off, don’t send: “No, I meant…” or “That’s not right…” Every extra message gets added to history. Claude re-reads ALL of it every time. Token cost grows fast: At ~500 tokens per exchange 5 messages = ~7.5K tokens 10 messages = ~27.5K tokens 20 messages = ~105K tokens 30 messages = ~232K tokens Instead: edit your original prompt and regenerate. You replace the history instead of stacking it. 2. Start a new chat every 15 to 20 messages Long chats are expensive. Most tokens get burned re-reading old context, not generating new output. One dev tracked usage: 98.5% tokens = re-reading history 1.5% = actual output Fix: Ask Claude to summarise → copy → start fresh chat → paste summary. 3. Batch your questions Stop sending multiple messages. Bad: “Summarise this” “Now list points” “Now give a headline” Better: “Summarise this, list key points, and suggest a headline.” One prompt = one context load. 4. Use Projects for repeat files Uploading the same file repeatedly = repeated token cost. Projects cache your files. Upload once → reuse without re-tokenising. If you use PDFs, briefs, or docs often, this alone saves a lot. 5. Set Memory and Preferences If you keep typing: “Act as…” “I’m a…” “I write like…” You’re wasting tokens every time. Save it once in settings. Claude remembers it. 6. Turn off unused features Search, connectors, advanced thinking all add token cost. If you didn’t intentionally turn it on, turn it off. 7. Use the right model Not everything needs power. Haiku → quick, cheap tasks Sonnet → standard work Opus → deep thinking Most people overuse powerful models and burn budget fast. 8. Spread usage across the day Claude uses a rolling 5-hour window. If you burn everything in one session, you waste the rest of your day.

Truth Protocol 1. Confidence tag Every answer labeled: High / Medium / Low 2. Source handling Browse and cite for post 2024 or high impact claims State when no browsing is used 3. Facts vs assumptions Clearly separate known facts from inferences No guesses presented as facts 4. Verification mindset VERIFY: double check critical info NO BROWSE: declare internal knowledge use RED PEN: actively look for errors AUDIT: step by step validation CITE: include sources when used 5. Math transparency Show full working No skipped steps Totals must reconcile exactly 6. Error handling Own mistakes directly Recalculate fully, not partial fixes 7. Data dependency Flag when dependent on user data State limits if data is incomplete 8. Clarity and directness Keep responses clean, precise, and to the point 9. Consistency check All figures must align across the response Totals must match exactly 10. Final validation Recheck final answer before sending Ensure internal consistency

There’s a lot of AI advice out there. Most of it sounds good. But a lot of it doesn’t actually help. Here’s how to spot bad AI advice fast: 1. It’s too generic “Use AI to grow your business” No steps. No clarity. No use. 2. It skips the thinking part If it jumps straight to tools without defining the outcome, it’s weak. 3. It promises shortcuts “Automate everything” “Set and forget” That’s not how it works. 4. It sounds impressive but says nothing Big words. No real application. 5. You can’t use it immediately Good advice = you can apply it today Bad advice = you just nod and scroll Good AI advice is simple, clear, and usable. Bad AI advice sounds smart but leaves you stuck. What’s the worst AI advice you’ve seen lately?