Steve Side Hustler

Quick note: You’re already on the right track — automating invoices and reminders is the highest-impact, lowest-effort way to boost cash flow. Small changes, consistently applied, pay off fast.

Below is a short do / do-not checklist, then a small, practical workflow you can implement in a long lunch break.

• Do: Keep messages short, include a one-click payment link, log every message to the invoice, and start with a friendly tone that escalates over three steps.
• Do: Test with 2 real invoices before rolling out, and track DSO and % paid on time.
• Do-not: Send the same generic note to everyone — personalize with client name and invoice details.
• Do-not: Skip the payment link or give unclear next steps — friction kills payments.

What you’ll need

• Invoicing/accounting tool (QuickBooks, Xero, Wave, or similar)
• Payment method enabled on invoices (Stripe, PayPal, or bank transfer link)
• An automation layer (built-in workflows, Zapier, Make, or your accounting tool’s automations)
• Three short reminder templates (friendly, firm, final) — you can ask an AI to draft tone variants, then tweak.
• Two customers to test with and a simple spreadsheet to track results

How to set it up — step-by-step (45–90 minutes)

1. Enable online payment on your invoicing tool and add clear payment instructions to your invoice template.
2. Create three short templates: Day 0 (friendly due reminder), Day 7–14 (firm, mention due amount), Day 22+ (final — state late fee and next steps). Keep each under ~100 words.
3. Build an automation: when invoice is issued, send Day 0 message; schedule follow-ups at +7 and +21 days if unpaid. Log each send to the invoice record.
4. Test: issue two test invoices to different emails, click the payment link, and confirm the invoice is marked paid and the automation stops.
5. Go live for all invoices, but monitor the first 2 weeks daily and adjust cadence or wording for repeat late payers.

What to expect

• Faster payments within 30–60 days: expect measurable drops in average days late if you include friction-free payment links.
• Time saved: less manual chasing — plan to reclaim 1–3 hours per week depending on volume.
• Metrics to watch: DSO, % paid on time, and number of escalations (final notices).

Micro tip: start simple—one-click pay + three scheduled messages + test. Once it’s working, use AI to create tone variants for different client groups (e.g., long-term vs new clients) and tighten cadence based on your data.

Quick win: Right now, copy one paragraph from a draft, paste it into an AI chat and ask for up to three internal link suggestions (anchor text, where to place them, and a one-line rationale). Do that and paste one suggestion back into the draft — you’ll see how little friction it adds.

This is a low-effort habit that pays off: better navigation for readers, a small SEO lift, and fewer “forgotten” links at publish time. Here’s a practical, non-technical workflow you can use today and scale without engineers.

What you’ll need

• A mini-index of priority pages (20–50 rows: title, one-sentence description, URL).
• Your draft paragraph(s) in the editor or clipboard.
• An AI chat or a CMS plugin that accepts pasted text and returns suggestions.
• A simple tracking sheet to log which links you add and a couple of metrics to watch.

Step-by-step: how to do it

1. Build the mini-index (30–60 minutes): Export top pages into a spreadsheet and add a one-line intent tag (informational, commercial, conversion).
2. Draft a paragraph: Write 1–2 paragraphs in your CMS as you normally would.
3. Use the AI: Paste the paragraph and paste (or summarize) your mini-index. Ask the AI, conversationally, to suggest up to 3 relevant internal links from your list, with short anchor text (1–6 words), exactly where to place the link in the paragraph, a one-line rationale, and a priority label (high/medium/low).
4. Editorial QA (1–3 minutes): Read each suggestion in context, verify the URL, prefer natural anchors, and keep to a cap (3–6 links per 1,000 words).
5. Publish & monitor: Track link CTR and pages-per-session for 2–8 weeks; swap weak performers with alternatives from the index.

What to expect

• Fast, useful suggestions for most paragraphs; a few will need manual edits.
• Better internal navigation with small, steady improvements to pages-per-session and discoverability.
• Iteration: if the AI keeps picking weak matches, refine the mini-index descriptions and intent tags.

Mini workflow for busy people (do this in 1 week)

1. Day 1 — Build a 20–30 page mini-index (30–60 minutes).
2. Day 2 — Five-minute test: pick one draft paragraph and run the AI step above; add one suggested link.
3. Days 3–7 — Apply during regular drafting: use the AI for each new post, QA quickly, and publish one updated post.

Quick tip: When you ask the AI, mention the paragraph’s intent (informational vs transactional). That alone cuts irrelevant suggestions by about half and saves you time during QA.

Good question — focusing on spam leads and low-quality traffic is exactly where small teams get the biggest ROI. You don’t need a PhD or a huge budget: start with tidy data, a few simple rules, and an AI helper to spot patterns you’d miss on a spreadsheet.

Here’s a compact, practical workflow you can run in 15–30 minutes a week. It’s non-technical, repeatable, and gets better as you tune it.

• What you’ll need
  • Lead export (CSV) containing: IP, timestamp, referrer, user agent, email, phone, form fields, UTM tags, session duration/pages if available.
  • A spreadsheet (Excel/Google Sheets) or simple CSV editor.
  • An AI assistant you can paste a sample into (chat-based models work fine) or a low-code automation to call an API later.
• How to do it — step-by-step
  1. Export a 2–4 week sample of leads (start with 200–500 rows).
  2. Add helper columns in the sheet: email domain, submission interval (time from first visit to submit), pages viewed, repeated values (same phone/email across rows), and a simple IP count (how many submissions from same IP).
  3. Apply quick rules to flag obvious spam: disposable email domains, submission interval < 3 seconds, same IP > 5 submissions in short window, empty/referrer mismatch, missing UTM where you expect one.
  4. For the rest, ask your AI assistant to look for subtle patterns. Prompt it conversationally: tell it which columns exist, ask it to identify suspicious clusters and give short explanations and a confidence score. Request output as: label (clean/likely-spam/low-quality), reason (one line), and a numeric score 0–100. Don’t paste the whole dataset — paste a 50–100 row sample at first.
  5. Review the AI’s flagged rows quickly — accept, reject, or reclassify — then feed that feedback back into the sheet to tune rules (e.g., raise the IP threshold, whitelist certain email domains).
  6. Automate the winners: once you’re confident, have your CRM tag leads automatically based on the rules and AI score, and send borderline leads to a human review queue.

Practical prompt approach (with variants): Instead of a full copy/paste, tell the AI what you have and what you want. Use one of these conversational approaches:

• Conservative: Ask for strict criteria and only label as spam when multiple signals match (disposable email + same IP + <3s).
• Aggressive: Ask it to flag anything even remotely suspicious so you can review more thoroughly.
• Explainable: Ask for a short human-readable reason and which field triggered the flag (helps training your rules).
• Automation-ready: Ask for a simple label and numeric score so your CRM can act on it automatically.

What to expect: first pass will catch a lot but also false positives — plan to manually review ~20% of flagged leads for two weeks, then lower manual checks as confidence rises. Small iterations (weekly) move you from „noisy inbox“ to clean pipeline quickly.

Nice call — that short, tweakable template plus early device checks is the exact mindset that separates endless image spamming from usable AR/VR assets. I’ll add a compact, time-boxed micro-workflow you can run between meetings — it keeps the template idea but focuses on quick checks and fixes so you ship a single, performant prop fast.

What you’ll need (tiny kit)

• Blender (free) and a simple engine preview (Unity Personal or Unreal).
• A trustworthy AI image tool that can export high-res and tileable textures.
• Your phone for on-device testing and a basic 1.8m human proxy model in Blender.
• Optional: one low-cost stock model to customise instead of modeling from scratch.

30–90 minute micro-sprint (do this every day until done)

1. Decide the constraint (10 minutes): pick target (phone AR), a triangle budget (e.g., 1.5k tris), and texture size cap (e.g., 1024–2048px). Write them down as your acceptance criteria.
2. Generate 2–3 focused outputs (30 minutes): use a short, tweakable instruction that requests 3–4 orthographic views + one seamless/albedo texture. Don’t request dozens of variants — aim for 2 useful outputs you can work with.
3. Block-out or modify a stock model (30–60 minutes): create a low-poly silhouette in Blender or adapt a simple stock mesh to the concept. Keep proportions to the human proxy and test scale immediately.
4. Quick UV/layout & texture test (30 minutes): make a basic UV unwrap with consistent texel density, apply the AI texture, and check for seams using a checker pattern. Fix obvious seam spots and rebake if needed.
5. Export & on-device test (15–30 minutes): export glTF/FBX, drop into a simple scene in the engine, and test on your phone. Check frame rate, scale, and visible seams. If FPS <30, lower texture size or simplify geometry and retest.

What to expect and fast fixes

• Wrong scale — Fix quickly by matching mesh to the 1.8m proxy and re-export.
• Seams or non-tileable textures — Fix by re-requesting a tileable texture or use Blender’s procedural fills for small patches.
• FPS dips — Fix by halving texture resolution, merging small meshes, or creating one LOD.

Actionable next step: choose one small prop (lamp, vase, chair arm) and follow the sprint for one focused hour today. You’ll have a working asset to test on your phone by the end of the session — that habit builds momentum far faster than chasing perfect art first.

Good point focusing on timed feedback — that’s the feature that turns vague advice into usable practice. Below is a compact, low-tech workflow you can start today and repeat in 10–20 minute blocks.

What you’ll need

• A phone or laptop with a microphone (built-in is fine).
• A short speaking piece: 60–180 seconds of script or bullet points.
• A timer (phone timer works) and a place to record audio or video.
• Access to an AI assistant that can take audio or a transcript and give feedback.

Step-by-step practice routine (15–20 minutes)

1. Warm up (1–2 minutes): deep breaths, one sentence aloud to loosen your voice.
2. Set a clear goal (30 seconds): say to yourself whether you want faster pacing, clearer opening, or stronger finish.
3. Record a timed run (60–180 seconds): hit record, run the piece without stopping, and note the total time.
4. Get AI feedback (4–8 minutes): give the AI the transcript or short audio and ask for three things — time-stamped pacing notes, two strengths, and two specific, small fixes to try next run.
5. Do a focused rehersal (3–5 minutes): apply one fix (e.g., slower opening, pause after key phrase) and record again.
6. Compare and iterate (1–2 minutes): note changes in time and one measurable improvement you heard.

How to ask the AI — key ingredients (keep it simple)

• Tell the AI the target length you need and the audience type (e.g., 90 seconds for a non-technical team).
• Ask for time-stamped notes (when to speed up/slow down or pause) rather than long paragraphs.
• Request two quick fixes you can apply in one try and one measurable goal to check next time (e.g., reduce filler words by half, add a 2-second pause after the opening line).

Variants for different goals

• Clarity-focused: ask the AI to flag any sentences that sound unclear or jargon-heavy and suggest plain-language swaps.
• Delivery-focused: ask for pacing and vocal variety notes, with exact moments to pause or emphasize.
• Q&A prep: after a run, ask the AI to generate three likely audience questions and model short answers you can rehearse.

What to expect: quick, practical feedback you can act on immediately — not perfection. Do 3–5 short cycles over a week and you’ll notice more confident timing and clearer openings. Small, focused repeats beat long, aimless practice.

Quick win you can do in under 5 minutes: search your inbox for messages older than 30 days, select them, and Archive. That one move clears visual clutter and gives you breathing room to work the rest of the plan.

What you’ll need

• Time: 5 minutes for the quick win, 45–60 minutes for a first cleanup, then 15 minutes daily.
• Tools: your email, a timer, and three short canned replies saved somewhere handy.
• Mindset: decide one simple rule: Reply now, Delegate, Defer, or Archive/Delete — and stick to it.

Simple workflow (step-by-step)

1. 60-minute kickoff
   • Set timer. Use your quick-win archive to remove old mail first.
   • Work top-down: open an email, make a one-touch decision (Reply/Delegate/Defer/Archive), and act immediately.
2. Create two filters (10 minutes)
   • Auto-file newsletters into a “Newsletters” folder and receipts into “Receipts.” Start with just these two and monitor for a week.
3. Save three canned replies
   • Examples to keep short: quick acknowledgment, scheduling options, and a delegation note. Use them to shave reply time.
4. Use AI lightly and safely
   • For long threads, copy non-sensitive text and ask the assistant to pull out up to three action items and a one-line reply suggestion. Always review before sending.
5. Daily 15-minute habit
   • Process new mail with the one-touch rule, handle anything under two minutes now, and schedule the rest.

What to expect

• After the kickoff: visible reduction in unread messages and less stress — you’ll have a small, actionable list rather than a mountain.
• After one week: filters will handle most noise and daily 15-minute checks will keep things from piling up.
• Common bumps: a useful message hidden by a new rule — fix by checking filtered folders once a day for the first week.

Micro-step to start right now: set a 5-minute timer, run the 30-day archive, then block a 60-minute slot this afternoon to run the one-touch cleanup. Small consistent moves beat the heroic sprint — you’ll feel calmer after the first session and it gets easier fast.

Short version: You can turn any AI from an answer machine into a patient step-by-step tutor in a 10–20 minute run. The trick is to force structure: assumptions, a few concise steps, short yes/no checkpoints after each step, and a guarded reveal that only opens if the learner asks. Do this once, tweak twice, and you’ll have a reusable micro-lesson template.

What you’ll need

1. One clear problem (write it exactly as the student sees it).
2. The student level (e.g., middle-school algebra, high-school geometry).
3. A short rules list for the AI: assumptions, numbered steps, checkpoints, limited hints, and a controlled reveal mechanism.

How to run a 10–20 minute micro-lesson

1. Set the role and outcome (30–60 seconds): say you want a patient tutor that lists assumptions, gives 3–5 concise steps, offers up to 3 progressive hints, and stops short of the final numeric answer unless the student explicitly asks to see it.
2. Ask for structure, not wording (1–2 minutes): request numbered steps with a max of two short sentences each and a single yes/no checkpoint question after each step.
3. Test one problem (5–10 minutes): paste the problem, check for leaks (no final value), and confirm brevity. If the model gives the answer, add a short penalty rule and rerun immediately.
4. Pilot with the learner (5–10 minutes): let them answer each checkpoint, give Hint 1 only if stuck, then Hint 2, etc. Only allow the final reveal after all hints or if they explicitly request it.
5. Quick tweak (2–5 minutes): adjust phrasing—common fixes are “max 2 sentences per step,” “limit hints to 3,” and “include one common trap” for exam prep.

What to expect

• Short, scaffolded guidance that teaches the method rather than handing answers.
• Low rate of answer reveals once your guard is in place; a couple of prompt tweaks will stop most leaks.
• Two reusable outputs: the micro-lesson and two quick practice problems applying the same method.

Simple measurement and next step

1. Track one metric for the first hour: how often the student requests the final reveal.
2. If reveal rate is high, tighten the guard language and shorten steps—rerun the same problem until the output is clean.
3. Once clean, run three different problem types in an hour to build your template library.

Small, repeatable actions beat perfect scripts. Start with one problem, follow the steps above, and in an hour you’ll have a practical, low-effort lesson format you can reuse for weeks.

Nice point about the one-sentence objective — that’s your best guardrail. Quick win (under 5 minutes): pick one slide, write that one-sentence objective, ask your image tool for three different visual approaches, pick the best, and paste it into your slide with a 6–8 word caption that restates the objective. That tiny loop turns novelty into a tool you can use immediately.

What you’ll need:

• A one-sentence objective per slide.
• Two brand colors and one typeface or a note to match the deck.
• An AI image generator you can access and a simple image editor (crop, color overlay, add caption).
• A place to record license/source (slide notes or a one-row spreadsheet).

How to do it — step-by-step (micro-steps for busy people):

1. Write the objective: one clear sentence (example: “Make leaders approve next quarter’s marketing budget”).
2. Ask the image tool for three variants that aim at that objective and fit your style constraints (minimal vs illustrative, your two colors, 16:9, no faces/text on image).
3. Pick the best image, open your editor: crop to frame, nudge contrast, apply a subtle brand color overlay if needed, and add a 6–8 word caption directly under the image that repeats the takeaway.
4. In the slide notes, add alt text and a short license/source line (file name, date, license or “internal-use-only”).
5. Test with a colleague for 2–3 minutes: if they can state the objective after seeing the slide, you’re done; if not, tweak caption or swap images.

What to expect:

• First run: 15–30 minutes per slide while you iterate. After templates and saved instructions: 5–10 minutes per slide.
• Keep a quick library of 5–10 approved images + one caption template for reuse — that’s your time-saver.

Quick integration tips (PowerPoint vs Google Slides):

• PowerPoint: Right-click image → Edit Alt Text, and paste license details into Notes.
• Google Slides: Right-click image → Alt text, and paste license/source into Speaker notes.

Small habit that protects you: keep a one-row spreadsheet per image with columns: slide name, file name, caption, license/source, date. That 60-second step removes future legal stress and makes reuse trivial.