7/

Be careful with the activity.

Does it drain you? Then do it just a little every day (like 30 min max)

The rest, you can batch as usual 🙌🏼

6/

✅ My main learning: if you want to stay productive, you DON’T have to batch everything

5/

I do like (even love) everything else, but the last bit of it — video editing — was very draining

And when I batched tasks, I batched that too, which left me completely burned out by the end of the cycle

4/

Here I found my answer: editing videos drains my energy

3/

I was trying to discover what the problem was 🤔

Overall, I liked the activity I was doing (content creation). Then why did I suddenly stop after just a few weeks?

2/

I know batching repeating tasks is the best way to get more things done, and I've always been doing that

But sometimes I just stop doing everything for a week or so. It’s like I suddenly burn out 😶‍🌫️

6. Refine & iterate 🔄

Combine data + insights → update hypotheses, product design, or messaging

Then repeat the cycle with fresh data

Which step do you find hardest: analyzing data or talking to users? 👀

5. Ask directly (qualitative research) 🧐

Talk to users:

👂 User interviews

👀 Contextual inquiry

✍️ Open-ended survey questions

Numbers ≠ motivations

4. Design and run tests 🧪

Validate your assumptions through:

✅ A/B testing

✅ Prototype tests

✅ Usability sessions

✅ Think-alouds

3. Generate hypotheses 💭

Turn patterns into assumptions about why it happens

Example:

🔹 “Form feels too long”

🔹 “CTA label is unclear”

2. Analyze results 🔍

Look for anomalies, bottlenecks, or unexpected behaviors

Example:

❌ “Users drop at Step 3”

❌ “Avg. time on task is unusually high”

1. Collect quantitative data 📊

Analytics, funnels, heatmaps, event tracking, scaled surveys

🎯 Goal: spot what’s happening → drop-offs, errors, or usage patterns

4️⃣ Provide real context or verified info

Example:
“Based on this 2023 WHO report that says X, explain why…”

This anchors the model to real data and helps it build a better response around that info

Tell what you do when you're not sure you can trust an AI response? 👇🏼

3️⃣ Give permission to say ‘I don’t know’

Say:
– “Only answer if you’re at least 80% confident”
– “If you’re not sure, just say that”

Most models are tuned to sound confident, even when unsure

Telling them it’s ok not to know makes them more honest 💃🏻

2️⃣ Narrow the question

Instead of:
“Tell me about quantum physics”

Try:
“Explain what a photon is, in 2–3 sentences, for a high school student”

Broad = wild guesses
Specific = grounded answers

1️⃣ Ask for sources or evidence

Say:

– “List your sources and make sure they’re real”
– “Cite real publications with dates”

Why it helps: The model cross-checks internally instead of pulling random stuff from memory

🟡 Bonus: You can verify the sources yourself