Steve Side Hustler

Small win first: pick one product PNG, a neutral AI background that matches the light direction, and a single soft contact shadow — you can get a believable hero image in under 15 minutes. Do that once to prove the process, then scale with a simple template.

What you’ll need

• A clean product PNG (transparent background) shot with consistent camera height and light direction.
• A basic editor with layers and blur (Photopea, Photoshop, or similar).
• An AI image tool that can generate or edit backgrounds (focus on inpainting or text-to-image features).
• A folder and a single composite template with placeholder, contact-shadow layer, and color-adjustment layer.

Step-by-step (do this now)

1. Open your PNG in the editor and note the light direction (left/right) and shadow hardness — write it down so you communicate it to the AI.
2. Ask the AI for a background that matches those notes: specify camera height (table-level for tabletop items), light direction, surface material, and shallow depth of field. Don’t paste a full prompt here — keep it brief and explicit about lighting and perspective.
3. Place your PNG over the generated background. Align the product base with the foreground plane and scale using a known reference if you have one (plate, coaster, hand in a test shot).
4. Add a contact shadow: draw a soft filled ellipse under the product, set blend mode to Multiply, opacity ~20%, then apply a Gaussian blur (tune 30–120px to match image resolution). Shift it slightly opposite the light source to ground the object.
5. Color-match with tiny adjustments: ±5% exposure or small temperature shifts and a 1–2px edge blur if edges look too crisp. Keep changes subtle — realism is in small moves.
6. Export two files: a web-optimized JPG/PNG and a high-res master. Name them clearly so you can batch later.

What to expect

• First pass: a clear visual upgrade that looks professional if lighting and shadow match.
• Tweaks: plan for 2–3 quick iterations (shadow softness, exposure, scale) to hit full realism.
• Speed-up: once a template works, you can process batches in a repeated, 5–10 minute-per-image routine.

3-step action plan for the week

1. Today: make one polished image using the steps above (prove it works).
2. Next two days: process 10 products with the same template; record time per image and note the best background style.
3. End of week: A/B test the top images on one listing and keep the winner as your template for the next batch.

Short version: pick a 1-week pilot that turns noisy feedback into three prioritized actions. You don’t need to be an engineer — use a spreadsheet, a cheap embedding service, and a small human review loop to get meaningful themes fast.

What you’ll need

1. Data: export 30 days of VOC (surveys, tickets, reviews) into a CSV. Expect duplicates and filler.
2. Tools: a spreadsheet or simple DB, an embeddings service or low-code AI tool, and a clustering option (many low‑code platforms include this).
3. People: one data owner for the pipeline and 2 SMEs (product/support) for quick validation.

How to do it — 7 micro-steps (what to do, how long, what to expect)

1. Export & sample (1–2 hrs): pull 500–1,000 items. Expect ~20–30% noise.
2. Clean (2–3 hrs): trim, remove PII, dedupe. Output: id, text, channel, date.
3. Embed (30–90 mins): send texts to an off‑the‑shelf embedding endpoint or use a low-code app. Expect processing time per 1k items to vary, but plan for an hour.
4. Cluster (30–60 mins): run HDBSCAN/DBSCAN for unknown counts or k‑means if you want fixed groups. Look for clusters sized 5–200 items; adjust min size to avoid micro-themes.
5. Summarize & enrich (30 mins): for each top cluster, ask your AI tool to produce a short theme name, 1-line summary, sentiment, and suggested owner. Give the model 10–50 example items per cluster — keep instructions simple and review results.
6. Validate (2–3 hrs): have SMEs review a 5–10% sample across clusters, correct labels, and flag noisy clusters. Use their corrections to tweak clustering thresholds.
7. Prioritize & act (1–3 days): pick the top 3 clusters by volume × negative sentiment × impact owner, create tickets or experiments, and assign owners.

What to track and expect in week 1

• Coverage: % of items assigned to a theme — aim for 70%+.
• Cluster precision: % human‑validated correct — target 80% on sampled clusters.
• Time-to-action: measure how long from insight to ticket — aim under 7 days for at least one quick fix.

Common hiccups & fixes

• Too many tiny clusters — raise minimum cluster size or merge similar ones manually.
• High noise in clusters — tighten preprocessing or drop items below a word-count threshold.
• No follow-through — assign clear owners for each theme and add a quick success metric (e.g., CSAT lift, bug reopen rate).

Small, repeatable cycles beat perfect models. Run the pilot, lock in the review loop, and you’ll have a reliable feed of prioritized customer actions in days—not months.

You can turn vague resume bullets into measurable achievements without rewriting your whole CV. Small concrete numbers and timeframes make hiring managers sit up; the tool’s job is to help you surface and phrase those numbers clearly. Below is a quick checklist to follow, a simple 3-step workflow, and a worked example you can emulate.

• Do: pull one bullet at a time and include any real or reasonable estimated numbers (percent, dollars, headcount, time saved).
• Do: mention the scope (team size, region, project length) so achievements have context.
• Do: aim for impact-focused language (reduced, increased, delivered, saved).
• Don’t: invent precise numbers—use estimates and mark them as such if unsure.
• Don’t: try to rewrite your entire resume in one go; iterate bullet by bullet.

1. What you’ll need: one resume bullet you want to improve, the job title or responsibility area, any numbers or timeframes you remember (even rough ones), and a short note on tools or process used.
2. How to do it: pick a single bullet; write down the context (why it mattered), the activity, and any outcomes you recall. Ask for a rewrite that adds a metric, timeframe, and result—then review and tweak to keep it honest.
3. What to expect: a clear measurable sentence you can paste into your resume, plus 1–2 alternative phrasings (one concise, one descriptive). You’ll usually need one quick pass to correct tone and one to confirm numbers.

Worked example (follow the pattern, don’t copy exact words):

• Original: Managed social media for a small business.
• Rewritten (measurable): Managed social media content and scheduling for a 3-person retailer, growing follower engagement by about 40% and increasing online sales attributed to social campaigns by an estimated 15% over 12 months.

How to refine that quickly: if you don’t have exact percentages, use ranges (about 10–20%) or time buckets (within 6–12 months). Keep one version that’s concise for ATS and one sentence with a short context line you can use in interviews. Repeat this for 3–4 bullets most relevant to the job you’re applying for—those will have the biggest impact.

Nice call. The 60‑second AI scan is a great way to standardise vetting — it saves time and gives you a repeatable trigger to either negotiate or walk. I like your emphasis on tracking scores and keeping vetting under 10 minutes.

Here’s a micro‑workflow you can use immediately — built for busy people who want practical steps they can follow in 5–15 minutes per gig.

What you’ll need

• Full gig text (title, deliverables, payment terms)
• Client profile snippet (reviews or history if available)
• Your project floor or minimum hourly rate
• An AI chat tool or a quick checklist card

1. 60‑second scan: Paste the gig summary into the AI or run your checklist. Get back a 0–10 risk score, top 3 red flags, and quick ROI sense. If score ≥6 tag it “high risk.”
2. Two‑minute counter: If risk is high or ROI looks low, send two short items to the client (pick one): request escrow/30% upfront, or propose a small paid sample (one page or two hours). Keep wording short and practical — you’re setting boundaries, not arguing.
3. Five‑minute decide: Use your minimums: if proposed terms meet your upfront percent and timeline, accept; if not, archive or walk. Don’t negotiate extensively on gigs that fall below your floor.
4. Record the outcome: One line per gig in a simple spreadsheet: title, risk score, action (accept/negotiate/walk), time spent, result. This builds your signal over time.
5. Weekly 15‑minute review: Tally average risk, wins after negotiation, and any patterns (clients asking free samples, short deadlines). Adjust your one‑line contract and negotiation defaults accordingly.

What to expect

• Less time wasted: most low‑ROI gigs are filtered in under 5 minutes.
• Higher effective hourly rate: you’ll stop doing unpaid or underpaid work.
• Faster decisions: clear rules remove indecision and small‑talk negotiations.

Small habit: pick one rule today (e.g., require 30% upfront) and apply it to the next 5 gigs. You’ll feel the momentum within a week.

Nice — you’re one step from turning chores into a 30‑second ritual. Keep it simple: use your task list as the only input, ask the AI to summarize each row into one short line, and do a quick 2–3 minute check each morning. Small routine, big time saved.

What you’ll need

• Daily task export or filtered view (CSV, Excel, Trello/Asana list). Columns: title, owner, status, % complete, notes.
• An AI chat box or simple automation you can paste into or call.
• 2–3 minutes a.m. for a spot-check until you trust the output.

How to do it — quick workflow (do this now)

1. Export today’s tasks and filter to In Progress / Done / Blocked. Keep it tidy: aim for ≤10 rows per person.
2. Normalize columns so every row has owner, status and percent (even “0%” or blank if unknown).
3. Ask the AI (in plain language) to turn each row into a 1–2 sentence standup line that includes: owner, what was completed (or percent), the next step, and any blocker. Tell it explicitly not to invent progress and to stay concise (about 20–30 words per task).
4. Combine the task lines by owner into 1–3 short lines each and paste into Slack or your standup tool.
5. Spot‑check 3 items: confirm owner, percent, and that no new progress was invented. Fix any bad rows and re-run only those.

What to expect & quick tips

• Output: short, scannable lines per person — fewer clarifying questions in the meeting.
• Daily time: ~10 minutes setup, then ~2 minutes each morning (generate + spot‑check).
• Enforce one owner per task and require a next step in the task notes to avoid vague outputs.
• Limit rows per person so updates stay under 3 lines; group small, related tasks into one row when useful.

One‑week micro plan (busy-person version)

1. Day 1: Export 5 sample rows and try the AI summarization; tweak the instruction wording until concise.
2. Day 2: Expand to full team for one standup; do thorough spot‑checks and log errors.
3. Day 3: Create a one‑click export or template so copying is painless.
4. Day 4–5: Run daily; collect two pieces of feedback from the team (clarity, tone).
5. Day 6–7: Measure time saved and blocker resolution; lock the routine if accuracy >95%.

Small idea you can try this week: add a column called “next step (one short phrase)” to every task — it forces clearer outputs and makes the AI summaries instantly useful.

Nice call — making the task list your single source of truth and using the status/progress/blocker structure is the core win. That foundation makes automation reliable instead of flaky.

Here’s a compact, busy-person plan to get auto-standups working in a morning and running in minutes each day. I’ll give you micro-steps, what to expect, and three prompt-style variants (described, not pasted) so you can pick the voice that fits your team.

What you’ll need

• A daily export or filtered view of tasks (CSV, Excel, or your tool’s list) with columns: title, owner, status, percent complete, notes.
• An AI chat tool you can paste into or a simple automation that can call an AI service.
• 2–3 minutes of review time after generation for a quick accuracy check.

How to do it — step-by-step (busy-person version)

1. Set aside 10 minutes: create a one-row template with the five columns above and practice exporting 5 tasks into it.
2. Filter: keep only In Progress / Done / Blocked for today — aim for ≤10 rows per person so outputs stay short.
3. Give the AI clear rules (describe them conversationally): include owner, one-line result/metric if present, next step, and blocker if any; do not invent progress; prefer active voice; limit to ~25–30 words per task.
4. Generate: paste the normalized rows and ask for one 1–2 sentence line per task, then combine per owner into a short message.
5. Quick check (1–3 minutes): confirm owner matches, percent consistent, and no invented progress. Fix any rows and re-run only if needed.
6. Post to Slack/email or paste into your standup tool. Expect ~2 minutes daily after the first run.

Prompt-style variants (pick one)

• Concise developer mode: strict 1 sentence per task, outcome + next step, no more than 20–25 words — ideal for engineers who want minimal noise.
• Team-friendly mode: 1–2 sentences, include a small metric or result if available and a clear blocker line — friendly tone, still tight.
• Executive summary: group by owner into 2–3 lines each: top achievement, top risk/blocker, next milestone — useful when leaders scan quickly.

What to expect & quick metrics

• Output: 1–3 short lines per person, fewer clarifying questions in standups.
• Daily time: ~10 minutes setup, ~2 minutes per morning to generate + spot-check.
• Track: percent of updates with clear next step (>90%), and daily spot-check accuracy (>95%) — adjust rules if AI drifts.

Two-minute daily checklist: export filtered tasks, run generation, spot-check 3 items, paste to channel. If a blocker appears, ping the owner immediately. Small routine, big time saved.

Quick win (under 5 minutes): copy one paragraph or the headline of the research-y article, paste it into your AI tool, and ask for a one‑line plain-English summary plus three possible red flags. That little check will immediately tell you whether the piece sounds solid or sketchy — no deep digging required.

What you’ll need:

• The text you want to check (a short paragraph, title, or the article’s claimed findings).
• About 10–20 minutes for a quick triage, or 30–60 if you want to verify sources.
• A web browser for one brief manual spot-check (to confirm a cited study or a journal name).

How to do it — a compact workflow for busy people:

1. Paste the paragraph into the AI and ask for a one-line summary and three red flags. Keep the request simple and conversational.
2. Ask the AI to classify the type of evidence cited (single study, systematic review, opinion piece, press release) and say briefly how strong that type usually is for the topic.
3. Have the AI list any named studies, authors, or journals mentioned. If it gives titles, treat them as leads to verify, not facts yet.
4. Do a 5-minute manual spot-check: search one study title or the journal name. Confirm date, whether it’s peer-reviewed, and whether the authors have obvious conflicts of interest (industry funding, etc.).
5. Give the claim a quick confidence score (1–5) and pick one next action: accept, seek another source, or ask an expert. Keep a short note on why you chose that score.

What to expect:

• Fast, usable summaries and likely problems from the AI — great for triage.
• Occasional mistakes: AI can omit sources or invent details, so always confirm study titles or numbers independently before acting on them.
• When the AI and your manual spot-check agree, you’ll get a trustworthy read quickly; when they disagree, treat the claim as unsettled and prioritize further verification.

A simple habit to adopt: for every research-like claim you see, spend five minutes on the workflow above, add a one-line confidence note, and move on. Over time you’ll build a muscle for spotting weak science without getting bogged down — practical, low-effort protection for your time and trust.

Quick win (under 5 minutes): take the most recent patent alert in your inbox, copy the title+abstract into your chosen summarizer and ask for a 2‑sentence summary and a simple label (Relevant / Maybe / Ignore). You’ll immediately feel the time saved — that one move shows how much busywork an LLM can remove while you keep the decision-making.

Nice tip in your post about keeping a one-line human review — I’ll build on that with tiny operational tweaks so a busy person over 40 can run this reliably in 15–25 minutes a week.

What you’ll need (5–60 minutes to set up):

• An account on a patent database that sends alerts (email or RSS).
• A simple automation tool that can move email items into a spreadsheet (many are point‑and‑click).
• An LLM-based summarizer (a web service or small subscription).
• A spreadsheet with these columns: title, link, abstract, 2-sentence summary, label, confidence, reviewer, notes.

Step-by-step micro-workflow (1–2 hours to set, 15–25 min/week to run):

1. In the patent site, create a narrow search (3–6 keywords + 1 classification code). Save it and enable alerts.
2. Make an email rule that tags patent alert messages and forwards them to your automation tool; route extracted title+abstract into the spreadsheet automatically.
3. Configure the summarizer to work on the abstract only (cheaper and less noisy). Ask it to return a 2‑sentence summary, 3 keywords, a short novelty estimate, a suggested label, and a one-line reason for the label. Don’t paste full PDFs into the automation.
4. In the sheet, add conditional formatting: color rows where confidence=high and label=Relevant so they rise to the top during review.
5. Weekly triage (15–25 minutes): open the sheet, scan high-confidence/Relevant rows first, confirm or change the label, and move true hits to a separate working tab for deeper review.
6. Monthly tune (20–40 minutes): sample 10% of items labeled Ignore to catch false negatives, and add or remove one keyword or a classification code based on what you find.

What to expect: the first week takes the longest (1–2 hours) to get alerts and automation right. After that you should hit 15–25 minutes/week. Expect a fair share of false positives initially — use the confirm/adjust step to train your search and the triage habit.

Tiny productivity tips:

• Use two priority labels instead of one (Urgent / Review / Ignore) so you only deep-dive on Urgent items.
• Create three quick checklist questions for your weekly review: “Does it mention my core tech? Is the applicant a competitor or new player? Is novelty flagged high?” — answer each in one word.
• Set a calendar block of 20 minutes for the weekly review and treat it like a meeting — consistent small steps beat occasional marathon scans.