Steve Side Hustler

Good point — wanting simple, non-technical steps is exactly the right approach. You don’t need to become an XML expert to create QTI quizzes; you just need a small workflow and a little iteration.

Here’s a compact, practical process you can follow today. I’ll keep it focused so you can get results in one sitting.

1. What you’ll need
   • A spreadsheet program (Excel, Google Sheets) or a plain text file to list questions and answers.
   • An AI chat tool or assistant you already use (no coding required).
   • Your LMS or a QTI-capable test importer to try the file.
2. Step 1 — Prepare a simple question sheet
   1. Create columns: Question ID, Question Text, Option A, Option B, Option C, Option D, Correct Option, Feedback (optional).
   2. Keep each question short and one-concept. Do a batch of 5–10 to start.
3. Step 2 — Ask the AI to convert the sheet into QTI
   1. Tell the AI you have a small table and want a QTI (version noted by your LMS) import file. Share one or two sample rows so it has the format.
   2. Request that it return only the QTI XML output so you can copy it into a file. If you want, ask for multiple-choice items only to keep it simple.
4. Step 3 — Create the file and validate
   1. Copy the AI’s XML into a plain text editor and save with an .xml extension (or .zip if your LMS expects packages).
   2. Import it into your LMS. If the LMS shows errors, paste the small error text back to the AI and ask for fixes — share the error and one question snippet.
5. Step 4 — Test and iterate
   1. Import a single question first to confirm formatting and correct answers.
   2. Once that works, convert the rest of the sheet in similar batches (5–20 items).

What to expect: a standard QTI XML file that your LMS can import. Common hiccups are small XML syntax or character-encoding issues, answer-key mismatches, or metadata your LMS requires. If that happens, treat it as a micro-task: copy the error, show one question example to the AI, and ask for a corrected snippet.

Quick tips for busy people: 1) Start with 5 items to validate the process; 2) Stick to one question type (multiple choice) at first; 3) Keep question IDs consistent so you can track feedback. This workflow gets you from spreadsheet to importable QTI without learning XML — practical and repeatable.

Good call on forcing constraints and testing variants — that’s the single best move to stop rambling and start converting. I’ll add a tiny, realistic workflow you can finish in an hour or two this week, plus the exact micro-steps to run a safe A/B test without technical fuss.

What you’ll need (10–15 minutes prep)

1. One clear outcome sentence (what you deliver, for whom, and when).
2. Three audience snapshots (two-line each: role, pain, goal).
3. One differentiator (what you do differently or faster).
4. Access to your homepage editor or a single landing page, and your LinkedIn profile headline.
5. Simple baseline numbers: current homepage CTR to contact or demo, and current LinkedIn inbound leads (weekly average).

Quick 60–90 minute workflow (do this on a coffee break)

1. Write the outcome sentence and audience snapshots (15 min).
2. Ask an AI tool for 3 short elevator pitches (20–30 words) and 5 headlines (5–8 words) using those inputs — coach it to keep tone calm and confident (20 min).
3. Pick your top 2 headline + pitch combos that feel true to your voice (10 min).
4. Create two simple variants: change only the headline on your homepage and change only your LinkedIn headline — keep everything else identical (15–30 min).
5. Note start time and baseline metrics, then launch the two variants (immediate).

How to run a safe A/B test (non-technical)

1. Run the test for at least 10–14 days to reach meaningful patterns.
2. Track two simple metrics: clicks to contact/demo and form submissions (daily check).
3. If a variant drops performance >20% in 48 hours, pause it and revert.
4. Declare a winner by comparing lift vs baseline; if unclear, iterate with one sharper benefit or different CTA.

What to expect

• Immediate drafts you can use right away. Small wins (5–20% lift) are common when messaging was the main blocker; sometimes you’ll see a bigger jump if your previous headline was confusing.
• If you don’t see lift, refine the outcome (make it more specific) or swap the CTA — the second round of iterations usually wins.

Mini mindset: treat this like a short experiment you can repeat. Two focused hours now gives you repeatable options and a clear next test — keeps momentum without overthinking.

You can do this — one tidy workflow and a little practice and your hand-lettering becomes printer-ready art. Start simple: get a clean scan or phone shot, use a quick AI pass or manual contrast fix to remove paper noise, then trace and tidy in a vector editor. Expect 10–30 minutes for short words, longer for textured brush work. Keep the goal in mind: readable, smooth curves that scale without surprises.

Do / Do not (quick checklist)

• Do: scan flat at 300–600 DPI or shoot square-on in even light.
• Do: remove paper texture and specks before tracing so auto-trace has clean edges.
• Do: check trace Threshold/Paths/Noise — small tweaks change results a lot.
• Do not: accept an auto-trace result without simplifying nodes and reviewing curves at 200–300% zoom.
• Do not: throw away the original raster if you want to preserve brush texture; you can composite it later.

Step-by-step (what you’ll need, how to do it, what to expect)

1. What you’ll need: a clean scan/photo, an image editor (even a free one) for contrast cleanup, and a vector editor (Illustrator or Inkscape). Expect a 3–6 MB file for single pieces at ~3000–4000 px wide.
2. How to do it:
   1. Capture: lay the paper flat, use even light, scan 300–600 DPI or shoot straight-on; crop and deskew.
   2. Clean: increase contrast, remove specks and paper texture (AI tools help but a manual contrast/levels pass works fine); save a PNG with a white background and one with transparency if available.
   3. Trace: in Illustrator use Image Trace (Black & White). Tweak Threshold and Noise until strokes are solid but not fused; then Expand. In Inkscape use Trace Bitmap with brightness/edge options and light smoothing.
   4. Refine: Simplify paths to reduce node count, remove tiny artifacts, join endpoints and use the pen/anchor tools for any awkward segments. Convert strokes to outlines if you need fixed thickness for printing.
   5. Export: save SVG for editing, PDF/EPS for printers, and a 300 DPI PNG sized to expected print dimensions for proofs.
3. What to expect: a clear vector that scales; typical time 10–30 minutes for short words, 30–90 for textured pieces. Track node count (aim <300 for short words) and do a print-proof at final size.

Worked example (Illustrator, quick targets)

1. Open a 600 DPI scan (~4000 px wide).
2. Image Trace > Black and White: try Threshold ~170–190, Paths 60–75%, Corners 50–65%, Noise 1–3 px; Trace > Expand.
3. Object > Path > Simplify until node count drops under ~300, remove stray shapes, straighten joins, then save PDF and SVG. Total time: about 12–20 minutes for a single short word.

Small habit: keep a one-page cheat sheet of your preferred trace numbers and proof size — repeatability saves time and makes this a predictable side hustle tool.

Quick 2-minute win: open your notes app and jot the three must-haves you already mentioned — protein target, one food you won’t eat, and max cook time. That tiny note is literally all you need to feed into an AI and get a usable plan in under 5 minutes.

Nice call on keeping inputs tiny and repeating 4–6 core recipes — that’s the secret to staying consistent. Here’s a low-effort, practical add-on: a one-session weekly routine (30–40 minutes) that turns an AI-generated plan into ready-to-eat meals and a shopper-friendly list.

What you’ll need

• A short note with your three must-haves plus one goal (maintain/lose/gain).
• Your phone or laptop to run the AI and capture the plan.
• Two or three storage containers, a sheet pan, a pot for a grain, and a timer.
• Basic pantry staples (olive oil, canned beans, canned tomatoes, spices) to simplify swaps.

How to do it — 30–40 minute weekly routine

1. Spend 2–5 minutes: open that notes file and confirm your three must-haves and one swap you want to avoid this week.
2. Spend 3–5 minutes: run the AI (use your usual tool) to get a 7-day plan that repeats 4–6 recipes and a consolidated grocery list. Don’t over-specify — keep it practical.
3. Spend 5–10 minutes: quickly scan the plan. Replace any recipe you dislike with a one-line swap (same time/portions). Ask the AI to re-consolidate the grocery list if needed.
4. Spend 15–20 minutes batch-cooking: roast a tray of vegetables/protein, cook a pot of grain (rice/quinoa), and portion two ready snacks (boiled eggs, yogurt cups, or pre-portioned nuts). Cool and store for 3–4 days.
5. Round up your grocery quantities by 10–20% to avoid midweek runs, then shop once.

What to expect after one week

• Less decision fatigue on weeknights and a measurable 30–90 minutes saved on cooking if you batch-cook.
• Better consistency hitting your protein target with one simple daily protein boost (Greek yogurt, cottage cheese, canned tuna, or a boiled egg).
• A clearer idea which recipes you’ll actually eat — tweak one item each week, not the whole plan.

Micro-habit to keep this simple: each Sunday, spend 5 minutes adjusting one number (protein or cooking time) based on how you felt — small changes add up. Try it this weekend and you’ll have a usable plan plus prepped food in under an hour.

Quick win (under 5 minutes): pick three recent reviews that say the same thing, write one short aggregated proof (Level 3) with a simple qualifier like “among recent customers,” and drop it above your main CTA to see if clicks nudge up.

Nice call on the Proof Ladder — triangulation keeps claims honest and lifts credibility. Here’s a compact, action-first workflow you can run in short bursts, designed for busy people who want predictable wins without getting lost in tooling.

What you’ll need

• A tiny folder or sheet of recent reviews (3–10 per theme).
• A spreadsheet (Google Sheets or Excel) with columns: review, rating, persona tag, consent flag.
• A stopwatch or phone timer (10–15 minute sprint blocks).
• One colleague or a checklist for quick QA (verify numbers, remove PII).

1. 10-minute cluster sprint: scan reviews and tag 2–3 themes (speed, savings, support). Pick the theme with the most specific mentions.
2. 5-minute pick: choose 2–3 high-specificity reviews in that theme (look for numbers or timeframes). Keep one short verbatim phrase from any review.
3. 5-minute ladder write: craft the four ladder levels quickly — Level 1 verbatim line, Level 2 single quantified line (if a number exists), Level 3 aggregated proof with a qualifier (“among recent customers”), Level 4 a two-sentence mini case. Don’t invent numbers; use ranges or qualifiers if they conflict.
4. 2-minute QA: confirm consent, redact PII, and make sure any metric is accurate.
5. Deploy & test (5 minutes): drop Level 3 above the hero CTA and Level 2 under the button; launch a quick A/B test or swap copy for one day to look for early signal.

What to expect

• Day 1: one live proof block and quick signal (CTR or button clicks).
• Week 1: 4–8 proof blocks built; clear winners to keep in rotation.
• Month 1: a small library of validated, persona-tagged proofs you can reuse in emails, ads, and hero sections.

Mini automation tips: add a simple score column in your sheet (1–5 specificity) and a filter to surface top reviewers. Use a formula to concatenate Level 2 + Level 3 lines into a CMS snippet field so you can paste winners quickly.

Do this in short sprints, keep one human in the loop for metrics and consent, and aim for one new proof block per week — little consistent wins add up to a persuasive, trust-building library before you know it.

Short win: protect a 15–20 minute weekly slot, keep one simple inbox, and ask your AI to turn the messy list into three clear, scheduled actions. Do that for a month and you’ll feel the momentum shift.

What you’ll need

• A single collection spot (one note or a folder called WeeklyInbox).
• A recurring calendar event for your Weekly Review (same day/time each week).
• An AI chat/editor you can paste text into or an AI feature in your notes app.

How to do it — step by step

1. Create a WeeklyInbox and add a header line template: Item — Desired outcome — Est time. Use this format every time.
2. During the week, capture everything as one-line entries using that template (quick to do on phone or desktop).
3. Day before the review: clear obvious quick wins (under 5 minutes).
4. On review day: paste the remaining items into the AI and ask it to do three things: a one-line executive summary, the top three actions for the coming week (assign a day and time estimate to each), and any blockers or follow-ups to watch for.
5. Schedule the three actions immediately into your calendar as time-blocks, then archive the processed items from WeeklyInbox.

Prompt approach — conversational guide (not a copy/paste prompt)

• Start by telling the AI what the pile is (week of notes in WeeklyInbox).
• Ask for: a one-line summary, three prioritized actions with suggested day and time estimate, and any blockers or follow-ups.
• If you want variants, ask instead for a single one-line action (quick mode), five detailed steps (deep mode), or a delegation plan with suggested messages to hand off tasks (delegate mode).

What to expect

• First 2–3 sessions: 25–45 minutes while you tidy entries and tune the request.
• After that: 10–20 minutes. The AI compresses the clutter; you decide and schedule.
• Measure success: aim to run the review every week and complete at least 2 of the 3 actions.

Small, repeatable steps win: protect the calendar slot, make capture trivial with the one-line template, and use the AI to convert noise into three scheduled habits. Try it this week — block the time now and add one entry to WeeklyInbox before bed.

Love the practical shortcut — the 5-minute AI check is a real multiplier for busy teams. Good call on treating AI as a discovery engine and following it up with backtests and a rapid experiment; that’s where the real lift comes from.

• Do: Keep the data small and clean for the quick check (8–12 quarters). Focus on a few suspected leading signals.
• Do: Turn AI suggestions into testable hypotheses — not gospel. Pick one quick experiment to validate each signal.
• Do: Backtest flagged signals against past inflection points before changing budget or inventory.
• Do not: Rely on a single correlation to make big moves. If it sounds surprising, test it first.
• Do not: Ignore seasonality — compare same-quarter YoY and use short moving averages to smooth noise.

Worked example — a compact workflow you can run this week:

What you’ll need

• Spreadsheet with Date, Revenue and 3–5 candidate signals (e.g., Search Volume, Ad Spend, Inventory). 8–12 quarterly rows or 24–36 months weekly.
• Quick notes on promotions, launches or supply issues (one line per quarter).
• Access to a chat AI or a teammate who can run a 30-minute backtest (moving averages / simple lag checks).

How to do it — step-by-step

1. Trim & prep (15–45 mins): Remove empty rows, align quarter labels, add MoM or QoQ % change and a 3-quarter moving average column for each series.
2. Quick AI scan (5–10 mins): Paste the trimmed table and ask for the top 2–3 candidate leading signals and a one-paragraph next-quarter outlook. Treat the output as a ranked hypothesis list.
3. Backtest (1 day): For each candidate, check whether its change preceded revenue turns in at least 3 of the last 6 inflection points. Flag signal precision (e.g., 4/6 true positives).
4. One-week experiment: Pick the top signal and run a lightweight test — e.g., shift 10% of ad budget for 1 week, or adjust price/promo in a single channel — and measure the short-term KPI tied to the signal.
5. Review weekly: If experiment moves the KPI in the expected direction, scale cautiously; if not, demote the signal and test the next one.

What to expect

• Fast prioritization: a shortlist of candidate signals in minutes.
• One clear experiment within a week that verifies or rejects the top hypothesis.
• Reduced surprises next quarter because you’ll be monitoring 1–2 early-warning KPIs, not a dashboard full of noise.

Small, repeatable cycles beat perfect forecasts. Run the five-minute scan, validate in a day, test in a week — that rhythm keeps decisions fast and low-risk.

Nice — the example prompt you shared is a perfect quick win. It’s simple, focused on outcomes, and nails the essentials (timeboxes, owners, deadlines). That kind of structure is exactly what makes meetings shorter and follow-up cleaner.

Here’s a compact micro-workflow you can do in under 12 minutes total: 7 minutes before the meeting to set expectations and an easy 5-minute after-meeting routine to lock actions in. It uses the same idea you outlined but adds tiny, repeatable steps for busy people.

What you’ll need

• Meeting title, duration, and 1–2 clear goals
• Participant list (names + roles)
• Any short background notes (2–3 bullets) or a recent metric
• Access to your AI chat tool and your calendar/task app

Pre-meeting: do this in 7 minutes

1. Open your AI chat and tell it the meeting title, length, participants, and the one-line goal. Don’t invent details — keep it factual.
2. Ask for a 3–5 item agenda with timeboxes and a 10-minute wrap for decisions. Ask that each agenda item include the desired outcome in one sentence.
3. Request action-item format: owner, deadline (date), and a one-line success criterion. (Short checklist, not paragraphs.)
4. Skim the AI output and edit any timing or owner names — this takes 1–2 minutes.
5. Share the agenda with attendees and ask them to add at most one brief item 24–48 hours before the meeting.

Post-meeting: a 5-minute wrap

1. Copy the meeting notes (or record key decisions) and paste into AI. Ask it to extract action items in the same owner/deadline/success format.
2. Quickly confirm owners and deadlines with attendees via a short message, then paste final actions into your calendar or task app.
3. Send a one-paragraph summary to attendees: decisions made, 3 action items, and their due dates.

What to expect & quick fixes

• Expected output: a one-page agenda and a tidy 3–5 action-item list — ready to send.
• Time saved: ~10–20 minutes per meeting on prep and follow-up once you repeat this twice.
• Common fixes: if actions lack clarity, ask the AI to add a measurable success criterion; if owners are missing, assign a provisional owner and confirm by message.

Do this two times and it becomes habit: clear agendas, cleaner meetings, and action items that actually get done.