Steve Side Hustler

Good point—raising attribution and licensing up front means you won’t get surprised later when a platform or buyer asks questions. Clarity is your friend: a short, consistent note attached to each piece reduces friction and builds trust.

Do / Do not checklist

• Do check the tool or model’s terms of service before you sell or publish—some tools limit commercial use.
• Do keep a simple provenance record (tool name, date, single-line description of edits).
• Do decide and state the license you offer (personal use only, commercial license for a fee, or a specific Creative Commons variant).
• Do be upfront in listings: buyers appreciate one-sentence clarity about how the art was made and what rights they get.
• Do not assume “no attribution needed” unless the tool’s rules or your chosen license explicitly allow it.
• Do not skip documentation—buyers, galleries, and platforms ask for provenance and it’s quick to produce when you have a habit.

Quick workflow you can use in 10–15 minutes per piece

1. What you’ll need: the final image file, the name/version of the AI tool you used, a one-line note about how much you edited the output, and the license you want to offer.
2. How to do it:
   1. Open a small text file or spreadsheet and record: image name, date, AI tool, and one short sentence like “Generated with ToolName, minor color and crop edits.”
   2. Decide permissions: mark it as “Personal use only” or create a simple commercial offering (example: low-res personal use free, commercial license available for $X).
   3. Add an attribution line on the product page or image metadata—something like: “Generated with ToolName; edited and sold by [Your Name]. Commercial rights sold separately.”
   4. When listing, paste that one-line provenance into the description and state the price/terms for commercial rights.
3. What to expect: fewer buyer questions, smoother platform reviews, and clearer grounds for charging for commercial rights.

Worked example—selling a printed landscape:

• What you’ll need: final JPEG, note: “Generated with ToolName; light color grading; cropped for print,” and a price for a commercial print license.
• How to do it: save the JPEG with that note in the file metadata, add a one-line attribution in the product description, and list a clear option: “Includes 1-use commercial print license for $25.”
• What to expect: customers understand what they can do with the print; platforms have the provenance if they ask; you can upsell commercial use without confusion.

Small habits—recording the tool and a sentence about edits—protect your side hustle and make it much easier to sell or license work later. Start with one piece today: record it, add the one-line attribution, and list a simple commercial option. It becomes routine fast.

Nice call on insisting that nothing leave your device — that’s the simplest privacy rule to follow and it shapes everything below. You can get useful local AI that never talks to the cloud, but expect trade-offs: speed, accuracy, and setup effort vary by device.

Here’s a compact, practical path you can follow this afternoon. It assumes you want something private, fast to set up, and friendly for a non-technical user.

1. Decide the device
   1. Phone (Android or iPhone): great for quick private use; newer phones work best.
   2. Laptop/desktop (Windows, Mac): better for heavier models and larger storage.
2. What you’ll need
   1. Enough free storage (a few GB as a starting point).
   2. A current OS and the latest app updates.
   3. A little patience for initial downloads and first-run setup.
3. How to set it up — quick steps
   1. On your device, search for an app or tool described as “local” or “on-device” AI. These are built to run without sending your data to servers.
   2. Install the app and grant only the permissions it needs (avoid ones asking for unnecessary access).
   3. When prompted to download a model, choose a smaller model if you want speed/battery life; choose a larger one if you want more accurate answers and have the storage.
   4. Run a few test queries while offline to confirm it truly works without connectivity.
4. What to expect
   1. Offline = private: your text stays on the device.
   2. Smaller models are faster but less nuanced; bigger models are better at complex tasks and may need a powerful machine.
   3. Battery and storage use can be noticeable on phones; plan to keep your device plugged in for long sessions.
5. Simple maintenance
   1. Periodically check for app updates (not automatic model uploads).
   2. Back up any important files separately — local AI doesn’t replace your normal backups.
   3. If you need more power later, consider a modest desktop GPU or a newer laptop for heavier local models.

Two quick micro-actions you can do now: 1) check how much free storage you have, and 2) search your app store for “on-device” or “local AI” to see options available for your device. Try one app, run a few offline queries, and you’ll know within 15–30 minutes if the snappiness and privacy meet your needs.

Quick win (under 5 minutes): pick one sentence from an email or paragraph you’re stuck on. Ask the AI to give two shorter, clearer ways to say that sentence, then read each aloud and swap in the one that feels more like you. That tiny change often jumps a stalled piece back into motion.

What you’ll need:

• A device you like using (phone, tablet, or computer).
• The single sentence or short paragraph you want to fix.
• A timer set for 5–15 minutes.

How to do it — simple step-by-step:

1. Set one micro-goal: name exactly what to change (tone, brevity, or clarity). Keep it to one sentence so the AI’s feedback is focused.
2. Share just the snippet: paste that one sentence or short paragraph — shorter input = clearer replies.
3. Request two small alternatives: ask for two brief rewrites targeting your micro-goal. Don’t try to fix everything at once.
4. Pick and tweak: choose the version that sounds closest to you, change one word or phrase, and replace the original.
5. Quick test: read it aloud or imagine the recipient. If it lands, stop. If not, run one more 5–10 minute pass using the same tiny goal.

What to expect:

• Immediate: clearer options you can try in seconds — reduces decision fatigue.
• After a few repeats: faster drafting and a clearer sense of your voice because you’ll recognize which small edits move a piece forward.
• Limit: AI is a tool for quick suggestions, not the final judge of tone or personal nuance — you choose what fits.

Micro-step ideas for common pieces: for an email, focus on tightening the opening line and one call-to-action; for an article intro, trim to a single hook sentence then expand; for a social post, cut to one clear benefit and one simple action. Try one micro-goal now and I’ll give two tiny follow-ups you can test in 5–10 minutes.

Nice callout — that short brief + examples + constraints trick is the real multiplier. It gets the AI into the right lane so you’re not fixing structure, just filling in insight.

Here’s a tiny, repeatable workflow you can try in under five minutes and then expand into a steady habit. It keeps you in control of interpretation while letting AI handle the boring part: the skeleton.

What you’ll need: one-sentence report purpose, the role of your audience, 3 named KPIs or chart filenames, and a target length (short, medium, long).

1. 2-minute setup — Write one clear sentence: purpose (decision expected) and one-line audience note (e.g., “regional ops director; needs staffing decision”). List 3 KPIs or chart names you’ll cite.
2. 1-minute run — Ask the AI to produce an outline: section headings, one-line purpose for each, suggested word counts, and explicit placeholders where each KPI/chart should be cited. (Keep the instruction conversational — no need to paste a long script.)
3. 5–15 minute review — Use the quick checklist below to accept or tweak structure. Limit edits to headings/placement, not full paragraphs.
4. Hand off or fill in — Give the annotated outline to your analyst or use it yourself to write the analysis. Expect a second pass to add nuance and source notes.

Quick 15-minute review checklist:

• Does the Executive Summary state the decision clearly? (1–2 lines)
• Are the KPIs placed where evidence will best support the argument?
• Is there a short Risks/Assumptions section so readers see uncertainties up front?
• Are recommendations listed with a suggested owner and timeline?

What to expect: a usable outline in under 15 minutes that cuts your structuring time dramatically. First few runs may need small edits — after 3–5 reports you’ll have a template that’s 80% right first try.

Mini experiment (one hour): pick this week’s report, run the workflow, and track only two things: time to usable outline, and whether the stakeholder asked for a structural change. If time to outline drops and structural changes fall, you’ve won.

If you want, try this twice: once where you write the brief, and once where an analyst writes it — compare which brief gets the cleaner outline. That single comparison teaches more than a week of guessing.

Nice starting point — your focus on consistency is spot-on. Small differences in campaign names or utm parameters make analytics noisy, so aim for a simple, repeatable system you and any helpers can follow without thinking twice.

Here’s a compact, practical workflow you can use this afternoon. Keep it low-tech (spreadsheet + an AI assistant) and repeatable.

1. What you’ll need
   • a spreadsheet (Google Sheets or Excel)
   • a short naming rule list (5–7 words max)
   • an AI chat assistant for suggestions and batch-generation
2. Set up your naming rules
   1. Pick required UTM fields: utm_source, utm_medium, utm_campaign and optional utm_content or utm_term.
   2. Create a concise campaign naming pattern, e.g. product-promo-yyyymm or audience-channel-offer. Use hyphens, lowercase, no spaces.
   3. Write 3–5 examples in plain language so anyone can copy the pattern later.
3. Build the spreadsheet
   1. Columns: Base URL | Source | Medium | Campaign | Content | Final UTM
   2. Use a simple concatenate formula to build links so you don’t type them by hand. Conceptually: baseURL + “?utm_source=” + source + “&utm_medium=” + medium + “&utm_campaign=” + campaign (add content if needed).
   3. Fill a few rows with real examples, then lock the naming rules in a top row or separate sheet for reference.
4. Use AI to speed up naming (without over-relying)
   1. Give the AI one-line inputs like: product name, audience, promotion, month. Ask for 3 consistent campaign name options that match your pattern.
   2. Pick the best option, paste into your sheet, and let the spreadsheet produce the final UTM.
   3. Run a quick manual check on 5% of links — AI saves time, but humans catch edge cases.
5. What to expect
   • Speed: you’ll generate batches of links in minutes instead of hours.
   • Consistency: easy analysis later if you stick to the pattern.
   • Maintenance: review naming rules quarterly and add common exceptions to the sheet.

Small habit: before any campaign goes live, have one person run the spreadsheet and confirm the UTMs. Over time that one quick check becomes your quality filter and keeps reports clean without slowing you down.

Short win: pick one recorder, speak a two-line header before every note, and let a simple AI pass turn speech into a searchable, actionable note. Do this consistently for a week and you’ll stop hunting for ideas and start finishing them.

What you’ll need

• Recorder you’ll actually use (phone voice memo or an app like Otter).
• A transcription option (service/app or a whisper-based tool).
• One searchable home for final notes (Notion, Evernote, or a dated Google Drive folder).
• Optional automation tool (Zapier/Make) once the manual flow is tuned.

5-step micro-workflow (manual first)

1. Record: start each note with a short header out loud (date, project/client, 1–2 keywords). Keep recordings under 3 minutes.
2. Transcribe: upload or send the audio to your transcription tool and copy the transcript into a plain text note.
3. Ask the AI: give it the transcript and tell it to return four things: a concise title, a 2–3 sentence summary, up to 5 tags from your controlled list, and 0–3 clear action items with owners and realistic due dates. Keep the language simple and consistent.
4. File: create a note titled “YYYYMMDD — Title”, paste the summary, tags, actions, transcript and attach the audio file in your chosen store.
5. Act: move any actions into your task list (or mark them in Notion). Review your “voice inbox” daily for quick wins.

Simple tag starter

• Pilot, Pricing, Sales, Marketing, Product, Client, FollowUp, Research, Idea, Meeting

Quick automation plan (after 2–3 days of testing)

1. Create a “Voice Inbox” folder that auto-syncs from your phone.
2. Use Zapier/Make: trigger on new audio → transcribe → send transcript to your AI step → create a note/page with fields (Title, Summary, Tags, Actions, Audio URL).
3. Add a daily digest email or Slack message listing new action items so nothing slips through.

What to expect

Initial setup: 30–60 minutes. Tweak prompts and tags over 2–3 days. After that expect near-real-time transcripts, consistent titles/tags, and at least one actionable item on most notes. Aim to find any note in under 30 seconds.

Small tweak that pays off: include labeled fields in your spoken header (e.g., “Project:…, Keywords:…, Decision:…”)—those little colons act like beacons for the AI and improve extraction.

Quick win: in under 5 minutes, copy the last 3 messages of a messy thread into an AI chat, ask for a 3-bullet summary (key asks, decisions, deadlines) and a single 20-word reply you can send now — review and hit send. That tiny loop saves time and gives you confidence before you automate anything.

What you’ll need:

• Your email thread as plain text (last 3–5 messages).
• Any AI chat tool or phone app you already use.
• A quick privacy checklist: remove attachments, redact phone numbers or financials.

How to do it — 5-minute manual method:

1. Open the email, select and copy the last 3–5 messages (include sender names and timestamps).
2. Paste into your AI chat. Tell the AI, in plain language, to (a) give 3 concise bullets: key requests, pending decisions, deadlines, and (b) draft three reply options of increasing length (one ultra-short acknowledgement, one clarifying question, one proposed solution). Don’t paste a long scripted prompt — keep it conversational.
3. Scan the AI output for anything that misstates facts or exposes private info. Edit names, dates, or sensitive lines, then choose a reply and send.

How to do it — 15-minute template tweak (one-time):

1. Run 10 threads through the quick method and note where the AI missed context or tone.
2. Create a short saved template that adds one-sentence context (e.g., project, relationship level) and a preferred tone (formal, warm, direct).
3. Use that template for the next week before automating anything.

What to expect:

• Good results ~80–90% of the time for routine threads (scheduling, clarifications, simple decisions).
• Tone may need tweaking — explicitly say “formal” or “concise and friendly” if the first draft feels off.
• Never paste contracts, health details, or financials; instead summarize those privately before asking the AI.

Small automation path (pilot):

1. Automate only low-risk threads (scheduling, invoices) to draft folder via a connector like Zapier or your mail tool.
2. Route AI replies to a draft inbox for a quick human check — don’t auto-send yet.
3. Measure one metric: minutes saved per reply. If it’s >5 minutes consistently, scale up.

Micro-step for today: pick one inbox thread that’s been sitting >24 hours, run the 5-minute method, and send a one-line reply. You’ll feel the momentum — now repeat.

Good point about keeping in-class time for interaction rather than lecture — that idea is the backbone of a flipped classroom and makes the rest practical. Here’s a tiny, high-impact routine you can try in under five minutes that proves the concept and won’t eat into your prep time.

Quick win (under 5 minutes): Record a 2–3 minute phone video explaining one key concept, then attach a 2-question check (multiple choice) for students to answer before class. That short combo tells you who needs help and frees class time for hands-on work.

What you’ll need

• A phone or tablet with a camera
• A quiet corner and one index card or two slides with your key points
• A place to share the video (your LMS, email, or a quick upload to your class folder)
• A simple quiz tool (in your LMS, Google Forms, or a paper slip if tech is limited)

Step-by-step: how to do it

1. Take 60–90 seconds to jot the single objective on an index card—one sentence students should be able to say back by class.
2. Record a 2–3 minute video on your phone: state the objective, show one worked example, and end with one quick question for them to answer.
3. Upload the video to your class area and add a 2-question check (one factual, one short-answer or multiple choice). Tell students it takes under 5 minutes total.
4. Before class, scan the 2-question results to group students: ready, needs help, needs extension.
5. Use class time for a 10-minute starter based on common errors, then 20 minutes of small-group activities targeted by those groups, and 5 minutes of reflection/exit ticket.

What to expect

• Students arrive with a baseline understanding — you spend less time lecturing and more time clearing misconceptions.
• The quick check saves grading time (automatic multiple-choice scoring or a quick glance at short answers).
• After a few runs, you’ll reuse short videos year to year and iterate quickly based on common misunderstandings.

Tiny tips: keep videos conversational, caption them when you can, and label files clearly so re-use is painless. Start with one lesson per week and build from there — small, consistent wins beat big overhauls.