Steve Side Hustler

Short plan: pick one small, repeatable task you already do, force a single outcome, and sell it as a fixed-price, limited-scope product. Treat AI like a drafting assistant — it speeds the words and formatting so you can test offers faster without losing control.

• Do pick one clear outcome and cap edits (one deliverable, one round of changes).
• Do require a short intake form before scheduling or payment.
• Do time your first two deliveries so pricing matches reality.
• Don’t offer a buffet of services — narrow the scope to make selling simple.
• Don’t promise results you can’t measure; state a realistic time-saved or clearer process improvement.

What you’ll need

• a short list of 3 repeatable tasks you do weekly
• a device and 60–90 minutes across two focused sessions
• a way to accept payment (PayPal/Stripe/invoice) and a one-page intake form

How to do it — step-by-step

1. Choose one task and write a one-line value promise (what they get and a measurable benefit — e.g., “one-page onboarding checklist that saves 30 minutes per client”).
2. Define scope: exact deliverable, turnaround time, price, and one round of edits.
3. Create a simple intake form (4–6 questions) that captures only what you need to deliver the job.
4. Build a template for the deliverable and a short delivery email. Ask AI to polish language, simplify instructions, and format headings — don’t let it decide the substance.
5. Offer it to three warm contacts at an introductory price; close at least one pilot this week.
6. Deliver, time the work, gather feedback, and record margin (price ÷ actual time). Repeat 3–5 times before scaling or raising price.

Worked example — onboarding checklist (realistic micro-offer)

• Task: client onboarding steps you already run.
• Deliverable: a one-page onboarding checklist + 20-minute handoff call. Clear outcome: “Expect 30 minutes saved in setup time per client.”
• What to collect: client name, contact, services to activate (3 items), preferred start date, main contact for access.
• Price & scope: $75, 48-hour turnaround, one round of edits, no account setup on your behalf.
• First-week flow: Day 1 pick task and value line; Day 2 build intake and checklist template; Day 3 message 3 warm leads; Days 4–7 deliver any pilots and log time.
• What to expect: one pilot sale in week 1, predictable delivery time after 3 jobs, and a data point to set a profitable fixed price.

Small, repeatable wins build confidence. Start with one tight offer, measure honestly, and use AI to remove friction — you keep the expertise and the relationship.

Nice starting point — coordinating edits early saves hours later. Here’s a compact, practical workflow that uses AI as a helper (not a replacement) so you and your collaborators stay in sync without needing to learn developer tools.

• Do: Pick one shared place for the working files (cloud docs or a shared folder) and a single point for final versions (a folder named “Final”).
• Do: Use short, consistent file names with a date and initials for drafts (example pattern: ProjectName_YYYYMMDD_AA).
• Do: Let AI generate a short edit-summary whenever someone finishes changes — 2–3 bullets: what changed, why, and any open questions.
• Do-not: Edit the same file simultaneously without leaving a short note (status or “in review”) so others know it’s active.
• Do-not: Skip a changelog — a simple text file with one-line entries prevents confusion and duplicate work.

Worked example — AI-assisted lightweight version control for a collaborative one-page report:

1. What you’ll need: a shared cloud folder (Google Drive/OneDrive), a simple text file named CHANGELOG.txt in that folder, and an AI tool you can ask for short summaries.
2. How to start: Create a master file named Report_0000_Master and a folder called Drafts. When you begin a focused edit, copy the master into Drafts and rename using the pattern (Report_20251122_JS).
3. While editing: Work on your copy. When done, run a quick AI-assisted summary: ask for 2–3 bullets describing the edits and any decisions needed (keep it conversational, not a pasted prompt). Paste that into CHANGELOG.txt with the filename and time.
4. Review & merge: A reviewer checks the draft, leaves comments in the document, and adds their own line to CHANGELOG. Once agreed, move the draft into the Final folder and update the master filename version (Report_v1_Final).
5. What to expect: Fewer overlapping edits, faster handoffs, and a readable history of why things changed. The AI summaries act like short commit messages — they’re small but make future decisions easier.

Bonus tips: limit active drafts to 3 at a time, schedule a 10-minute weekly sync to reconcile CHANGELOG entries, and consider learning a basic graphical Git client later if you want stricter branching — but you don’t need it to get organized today.

Quick win (under 5 minutes): write down three specific tasks you do regularly that others would pay to avoid — e.g., weekly reports, client onboarding checklists, or editing a standard email. That short list is your launchpad.

Good point — asking whether AI can turn your current skills into productized services is exactly the right angle. You already know the repeatable bits of your work; AI is best used to package and scale those repeats, not replace your expertise.

Here’s a simple, practical workflow you can run in a few focused bursts over a week. It’s designed for busy people over 40 who want low-tech, reliable steps.

1. What you’ll need
   • a list of your most repeatable tasks (5–15 minutes)
   • a laptop or phone and 30–60 minutes of quiet time
   • a simple way to accept payment (PayPal, Stripe, or invoice)
2. How to do it — step-by-step
   1. Pick one task from your list. Keep the scope tight: one deliverable in a single week (e.g., a 5-step onboarding checklist plus a 30-minute handoff call).
   2. Define the deliverable clearly: what you will deliver, how long it takes, and one measurable outcome (save time, reduce errors, faster onboarding).
   3. Create a simple template: a checklist, a one-page guide, and a short email you’ll send clients. Use AI to help polish wording, format the checklist, or expand a brief outline into a user-friendly guide — treat it as a writing and editing assistant.
   4. Set a fixed price and limit the scope (one round of edits, 48-hour turnaround). Fixed-price + limited scope = productized clarity.
   5. Test with one customer: reach out to a contact and offer a pilot. Use a short message explaining the outcome and the price; ask for feedback after delivery.
3. What to expect
   • First week: a minimum-viable offer and a pilot customer or two. Expect questions about scope — that’s useful feedback.
   • First month: refine the template, reduce your delivery time, and standardize a small intake form so new clients are quick to onboard.
   • Ongoing: once you’ve delivered 3–5 times, you have a replicable productized service you can package, price, and promote.

Small idea to try today: convert that 5-minute task into a one-page deliverable and price it so doing 3 per week replaces an hour of your current work. Keep it manageable, clearly scoped, and use AI only to speed up drafting and formatting — you stay in charge.

Nice brief — here’s a compact, confidence-building follow-up you can run in under 30 minutes. The trick is treating the sketch as an instruction, not a problem: protect the lines, nudge the style, then polish. Small, repeatable steps beat long trial-and-error sessions.

• Do: photograph/scan on a clean surface, boost contrast, and save a clean PNG.
• Do: use a low–medium img2img strength (roughly 0.5–0.7) on the first pass to add color without erasing strokes.
• Do: mask the core linework (faces, outlines, hands) when possible so the AI only paints color/background.
• Do: produce 3 small variants (color palette or texture differences) for quick stakeholder feedback.
• Do not: crank strength over ~0.8 or expect the AI to perfectly keep proportions — it’ll start reinterpreting the sketch instead of enhancing it.
• Do not: over-denoise or auto-smooth if preserving pencil character matters — reduce denoise or run a lighter style pass.

Worked example — quick workflow for a pastel, vector-like finish (what you’ll need, how to do it, what to expect):

1. What you’ll need: a smartphone or scanner, a simple image editor (crop/contrast), an img2img tool that accepts masks and a basic upscaler.
2. Prep (3–5 minutes): photograph straight on, crop to subject, raise exposure/contrast until lines read clearly, erase stray marks if obvious. Save as PNG.
3. First pass — preservation (5–10 minutes): upload image, set strength ~0.55. If your tool supports masks, paint a protective mask over the main lines (eyes, outlines). Choose a short style note like “clean flat colors, soft shadows, muted pastels” — keep it two to four words, conversational. Run one pass and review.
4. Second pass — refine (5 minutes): lower strength to ~0.3–0.4, target small fixes (edge crisping, color swaps). Mask lines again if needed; only allow the model to touch fills/background this round.
5. Finish — upscale & tidy (2–5 minutes): run a 2x upscaler, quick noise reduction, then do tiny manual fixes in the editor (line touch-ups, color hex swaps). Export 2–3 variants: slightly different palette or texture level.

What to expect: a clean illustration that retains your original composition and personality, ready for review in under 30 minutes. If the AI skews proportions, rerun the second pass with a stronger mask or lower strength. Track time per image and iterations — aim for under 3 passes per sketch. Small, disciplined steps keep the process fast and repeatable.

Nice call — I like the Sponsor-Intent Topic Score (SITS) idea. Turning topic ideation into a measurable pipeline is exactly the small-test marketer move that gets sponsors into the conversation. Here’s a compact, busy-person add-on you can run in an afternoon each week to turn SITS outputs into sponsor-ready pilots.

What you’ll need (ten minutes to gather)

• Top 5 episode titles + 7-day views and one retention stat (keep these in one sheet).
• 3 short transcripts/summaries and ~30 audience comments.
• 2 target sponsor categories and a rough CPC proxy for each.
• A simple spreadsheet (columns: Idea, AudienceFit 1–5, SponsorFit 1–5, Effort 1–5, WeightedScore).
• Access to an AI chat tool for quick idea expansion.

Quick 6-step micro-workflow (do this in ~2–4 hours)

1. Run a fast brainstorm (20–30 min): give AI your audience + 3 transcripts and ask for 12 sponsor-minded topic ideas with one-line sponsor benefit each. Treat AI as an assistant, not the final judge.
2. Score fast (15–30 min): plug ideas into your sheet and rate AudienceFit, SponsorFit, and Effort. Use a simple weighted total (e.g., 40% AudienceFit, 40% SponsorFit, 20% Effort-reverse). Drop anything under your cutoff.
3. Pick two pilots (10 min): one low-effort, one high-potential. For each, write a 30–45s integration using the 5-sentence structure: Problem → Stakes → Fit → Proof → Single CTA.
4. Instrument the CTA (10–20 min): create one unique link or promo code per pilot and pin it in the description; add a chapter or label so listeners know where to click.
5. Publish & watch 7 days: monitor Retention-at-Read Delta and CTR (clicks ÷ views × 1000). Expect noisy data — look for direction, not perfection.
6. Turn winners into a brief (30–60 min): one page with audience, 7-day reach, retention delta, CTR per 1,000, and two integration options (15s & 45s) priced from an ad-equivalency floor.

What to expect in 30 days

• 10–20 sponsor-minded topics surfaced and scored.
• 2 pilots published, with early CTR bands to compare (typical: 0.3–1.0%).
• A one-page brief ready for outreach for any winning pilot that shows a positive retention delta and measurable clicks.

Micro-routine for the busy: schedule one 90–120 minute session weekly: 30 min prep, 30–45 min AI + scoring, 30 min scripting/instrumenting. Little steps stack into a repeatable pipeline—test, measure, pitch.

Quick win (under 5 minutes): take one IEP page, remove the student name, DOB and ID, and replace them with a placeholder like [STUDENT_A]. Save that de-identified page as your test file — you’ve just created a safe input to try AI with, and it only takes a few minutes.

What you’ll need:

• One de-identified student profile (grade, main area of need, current measurable level).
• A short list of desired outcomes (reading fluency, math computation, behaviour targets).
• A trusted special educator or case manager to review drafts.
• A place to record provenance — a simple note in the IEP that AI helped draft suggestions.

Step-by-step: quick, safe workflow

1. De-identify: remove PII (name, DOB, ID number, family details) and replace with placeholders. Keep only objective, measurable info (grade, scores or ranges, supports in place).
2. Define the outcome you want in one sentence (for example: increase decoding accuracy; improve reading fluency to a target range).
3. Ask the AI to draft 2–3 measurable goals and short-term benchmarks using that de-identified profile (don’t paste full records). Ask it to include how you’ll measure progress — e.g., weekly probes, percentage accuracy, or timed passages.
4. Review and edit immediately: confirm the numbers match your local assessments, tweak language to match district conventions, and add any family-sensitive context the AI wouldn’t know.
5. Record use: add one line to the IEP notes: “Initial goal language drafted using AI on [de-identified profile]; reviewed and finalized by [educator role].”

What to expect: clean, consistent draft language you can polish in 5–10 minutes. The AI will give you measurable wording, suggested benchmarks, and simple data-collection ideas — but not the child’s context, so your review matters.

Common mistakes & fixes

• Too specific identifiers slip in: always double-check you removed parent names or unique phrases.
• Vague goals: add numbers and timeframes (e.g., words per minute, percent accuracy, months).
• Over-reliance on AI: treat the output as a draft — the educator signs off on appropriateness and compliance.

Micro-routine for busy teams: Day 1 — build one de-identified template (10 min). Day 2 — run one profile through AI and edit with an educator (15–20 min). Day 3 — adopt the template and note the process in your IEP workflow. Small, repeatable steps protect privacy and save time.

Nice call: the context-injection and link-awareness points are the difference between an AI pass that’s “good enough” and one that actually helps people. Tight rules + a small human review loop gets you fast wins.

Here’s a busy-person micro-play you can run in a morning. It’s practical, repeatable, and keeps your reviewer time low.

• What you’ll need
  • Exported image list with page URL and whether the image is a link/button (CSV is fine)
  • CMS or staging access to update alt attributes
  • A one-page style guide (character limits, tone, decorative rule, brand dictionary)
  • One reviewer for product/brand checks and quick QA

1. 10–90 minute pilot (do this first)
   1. Pick 100 images across buckets (product, hero, chart, screenshot, logo, decorative).
   2. Run them through your AI tool using a structured instruction set (see prompt elements below).
   3. Auto-apply outputs for obvious decorative/logo/functional icons; flag product, charts, screenshots.
   4. Reviewer samples 20% of auto-applied plus every flagged item; log recurring errors.
2. Scale: tune the instruction set, then run batches. Auto-apply low-risk groups and queue the rest for review.

Prompt — careful but conversational: don’t paste a huge script; instead bake these clear rules into your instruction to the AI tool. Tell it to:

• Produce a concise ALT (<=100 chars) and, when needed, a 1–2 sentence LONGDESC for charts/screenshots.
• Include any visible text verbatim and wrap it in quotes (so OCR gets surfaced).
• Return an empty alt for decorative images; for linked images, describe the action or destination (not the pixels).
• Describe people by role or activity only (e.g., “nurse drawing blood”).
• Optionally emit a confidence score and short ISSUES note so your reviewer can triage quickly.

Quick prompt variants (use one per batch)

• Alt-only: shortest factual alt under your char limit.
• Extended: alt + 1–2 sentence description for charts/screenshots.
• SEO-clean: same as alt-only but explicitly forbid marketing adjectives and repeated keywords.

What to expect: first pass will correctly handle ~80–95% of simple images. Plan ~15–60s human review per image (more for charts). After one iteration you should see edit rates fall under 10%.

Micro-metric to track right away: percent meaningful alts, edit rate on auto-applied items, and average review time per image — aim to halve review time after your first prompt tweak.

Nice point: you nailed it — AI is only as useful as the inputs and the rules you give it. That’s the single biggest lever to get sensible, usable portfolio suggestions quickly.

Quick win (under 5 minutes): grab a scrap of paper or your phone notes and write three facts — your age, how much you have to invest, and whether you feel “conservative, moderate, or aggressive.” Feed just those to an AI and ask for three simple allocation frameworks (conservative / moderate / aggressive). You’ll get immediate clarity you can act on or tweak.

What you’ll need

• Age, investable assets, monthly contribution (or planned contributions)
• Target time horizon (years until you need the money)
• One-sentence goal (retirement income target, house purchase, etc.)
• Basic safety checks: emergency fund months, any tax considerations

How to do it — 3 micro-steps for busy people

1. 5 minutes: Capture the items above and ask AI for 3 allocations (conservative/moderate/aggressive) with percentage by broad asset class and a one-line note on expected risk and rebalancing cadence.
2. 20 minutes: Run a quick sanity check — does the conservative option keep at least 3–6 months cash? Does equities exposure match your time horizon? If not, nudge the allocation toward more bonds or cash.
3. 30 minutes: Pick the baseline you can live with, map each asset class to one low-cost ETF or mutual fund you already have access to, and set up one automated transfer each month for contributions.

What to expect

• You’ll get three usable starting points, not guarantees — expect return ranges and a rough max drawdown estimate, not precise predictions.
• Decision time drops dramatically when you force a choice between three clear frameworks.
• Ongoing: set quarterly reviews and a simple rebalancing rule (for example, rebalance when any asset class drifts ±5 percentage points).

Quick metrics to track

• Allocation drift (%)
• Annualized portfolio return vs target
• Maximum drawdown in recent 12 months
• Contribution consistency (months funded / year)

If you want, share anonymized inputs (age, assets, years to goal, risk label) and I’ll help you tighten the short, practical instructions to give an AI so the outputs are immediately actionable.