Steve Side Hustler

Nice callout: the two-step move (LLM → mind‑map importer) is exactly the fast lane. That’s the quickest way to turn noise into structure without rebuilding the map by hand.

Here’s a tiny, practical workflow I use when I’m short on time — it gets a messy page to an import-ready outline in about 3–8 minutes, then 2–5 minutes of tidy in the map app.

1. What you’ll need
   • A phone or laptop with photos/text of your notes.
   • Access to an LLM (ChatGPT or similar).
   • A mind‑map app that accepts indented lists or OPML imports.
   • 2–10 minutes of uninterrupted time.
2. Quick workflow — step by step
   1. Capture (1–2 min): snap photos of handwritten notes or paste meeting text into one document. Don’t clean yet—speed matters.
   2. Triage (1–3 min): scan the combined text and mark each line with a tiny tag (A for action, D for decision, I for info). Do this straight in the text file — one pass only. This makes the LLM’s job clearer and keeps important items visible.
   3. Structure (1–3 min): ask the LLM to convert the triaged text into a hierarchical indented list or OPML for import, and to preserve your A/D/I tags. Keep the instruction short and focused; you don’t need to rewrite the whole prompt every time.
   4. Import & tidy (2–5 min): paste/import the output into your mind‑map tool. Collapse low-priority branches, promote actions to a top-level “Next Steps” node, and color-code A (red), D (blue), I (grey) so your eyes go to what matters.

What to expect: first run will need tiny layout fixes (move a node, merge two similar items). Total time usually under 15 minutes for an hour-long meeting’s notes. You’ll come away with clearly highlighted actions and a visual map you can share in the next check-in.

Quick tips for busy people

• If the map has too many nodes, ask the LLM to group by theme and keep up to 3 sub‑nodes per theme.
• Prefer indented text for quick trials; switch to OPML only when you automate imports regularly.
• Keep one short SOP file with your triage tags and the single-line instruction you use most — reuse it each time.

Try this on your next set of messy notes: 3 minutes to triage, 3 minutes to structure, 3 minutes to tidy — and you’ll have a shareable map that actually gets work done.

Nice — the reliability anchor (quote each finding) is gold. I’ll build on that with a tight, time-boxed micro-workflow you can run between meetings. Small steps, big trust gains.

• Do: feed the AI Abstract + Results + Discussion (or upload the PDF) and tell it the exact deliverable format (one-sentence executive summary, 3 bullets with confidence and one supporting quote).
• Do: run a verification pass asking the AI to list every number it used and point to the sentence/figure.
• Don’t: rely only on the abstract or accept numbers without a quick cross-check against the PDF.
• Don’t: ask for free-form summaries when you need decisions — demand structured outputs (magnitude, direction, confidence, quote).

What you’ll need:

• Paper PDF or copied sections: Abstract, Results (tables/figure captions), Discussion.
• An AI chat or document tool that accepts pasted text or uploads.
• A stopwatch or phone timer (15 minutes max).
• A one-line audience label (e.g., “non-technical manager”) so the AI knows tone and length.

15-minute micro-workflow (do this now):

1. Set timer to 15 minutes. Open the paper and copy Abstract + Results (include table captions) into one paste.
2. Ask the AI for a fixed structure: 1-sentence executive summary; 3 plain-language findings — each with effect size/magnitude, a confidence tag (High/Med/Low) and a single supporting sentence quoted from the paper. Keep bullets short.
3. Run a 3-minute verification: ask the AI to list every numeric value it used and show the exact sentence/figure for each. Check mismatches against the PDF and correct any numbers in the brief.
4. Polish 2 minutes: trim language to match your audience, and add one concrete next step (what to do, one resource needed, and the metric to watch).

What to expect: a one-line executive summary, three concise findings with effect size and a confidence tag, each tied to a quoted evidence sentence, plus one clear next action — ready for a meeting handout.

Worked example (fictional, trimmed for clarity):

• Executive summary: A 6-month home-exercise pilot modestly increased walking speed in older adults versus usual care.
• Finding 1 — Mobility (Medium): walking speed +0.12 m/s; supports: quoted sentence from Results; reason: moderate n and CI width.
• Finding 2 — Falls (Low): fewer reported falls but wide confidence intervals; supports: quoted sentence from Discussion.
• Finding 3 — Adherence (High): 82% session completion; supports: quoted sentence from Results.
• Next action: Run a 3-month pilot at one site measuring walking speed; resource: physiotherapist 4 hrs/week; metric: mean walking speed change.

Run this twice in a week and you’ll turn long papers into meeting-ready decisions — fast, defensible, and repeatable.

Quick win (under 5 minutes): pick one customer problem phrase, Google it, open the top 5 results and the top 5 Reddit threads, and copy any single-sentence complaints or “I wish…” lines into a note. That single action gives you raw, real-world language you can use tomorrow.

What you’ll need

• A simple spreadsheet (Google Sheets or Excel)
• A browser and a notes app
• Access to public Google results and Reddit search

Step-by-step: quick collection

1. Pick 3 target keywords people would type when frustrated (e.g., “slow tax app,” “router keeps disconnecting,” “client invoicing nightmare”).
2. For each keyword: open top 10 Google results. Copy 1–2 lines that read like a complaint or question into the sheet: headline / PAA item / meta line. Note source and date.
3. Search Reddit for the same keyword, filter by Top/Month. Copy post titles and the top comment sentence. Do not copy usernames or private messages—paraphrase anything that might identify someone.
4. Use columns like: id, keyword, source, date, url, excerpt (paraphrased), upvotes/comments, sensitive_flag.

Step-by-step: simple analysis (non-technical)

1. Read the excerpts and highlight repeating words or phrases (e.g., “takes too long,” “hidden fees,” “confusing setup”).
2. Group excerpts into 6–12 themes on the sheet (drag rows into theme buckets or add a theme column).
3. Count how many excerpts fall into each theme to get a frequency signal. Mark themes with High/Medium/Low frequency.
4. Paraphrase any excerpts flagged sensitive. Never publish verbatim PII.

What to expect

• A short ranked list of the top 6–12 pain themes with 2–3 example lines each.
• Concrete messaging ideas: one short headline that speaks to the #1 pain, one FAQ entry, and one micro-test to run.
• A clean, ethical dataset you can share with teammates without exposing identities.

Next micro-step to get results fast: pick the top theme and write three headline variants that address it directly. Run a tiny A/B test (email subject line or landing headline) for one week and measure CTR. That single loop—collect, cluster, test—turns messy chatter into clear decisions without needing fancy tools.

Nice framework — small correction: don’t paste raw support emails into an AI tool without removing names, emails, order numbers or any PII. The 5‑minute “extract” is realistic for a quick draft, but plan 20–60 minutes for anonymizing and human review before publishing.

Here’s a compact, no‑code playbook you can run in an afternoon. It’s tuned for busy folks over 40 who want practical steps and fast wins.

1. What you’ll need:
   1. A sample set of support content (10–50 items) saved in one doc — scrubbed for personal info.
   2. An account in one KB tool (Notion for quick start or HelpDocs/HelpScout for built‑in KB features).
   3. Access to an AI assistant to speed drafting (use it to summarize, not to publish without review).
2. How to do it — 6 simple steps:
   1. Collect (30 minutes): Export recent tickets, support emails, and recurring questions into a single file and remove PII.
   2. Summarize (5–15 minutes): Use the AI to convert the raw text into short Q&A drafts — ask for 8–12 concise items and internal tags. Don’t copy the exact prompt; keep it conversational and tell the AI to flag anything uncertain.
   3. Edit & organize (20–40 minutes): Create 1–2 short articles per topic in Notion or your KB tool. Start each article with a 30‑second summary, then 2–3 steps or bullets and 1 screenshot if useful.
   4. Publish (10 minutes): Make pages public or enable your KB widget. Add a visible help link in your site footer or product header.
   5. Test (30 minutes): Ask 8–10 colleagues or customers to find answers and time them. Record where they struggled and what was missing.
   6. Iterate weekly (15–30 minutes): Fix the top 3 confusion points, add links/screenshots, and re‑run the AI only to generate draft edits for review.
3. What to expect:
   • Immediate: clearer answers you can publish same day; expect manual review to be the slow part.
   • 2–4 weeks: visible reduction in repeat tickets for covered topics if you place the KB where customers look.
   • Ongoing: aim for 15–30 minutes a week to keep content current.

Quick tips for low effort, high impact:

• Prioritize the top 10 tickets that repeat most often — solve those first.
• Keep every article scannable: headline, 1‑sentence answer, 3 bullets, link to deeper steps.
• Track one simple metric first: ticket count for KB topics week over week.

Follow that rhythm and you’ll have a practical KB that reduces workload without needing developers — the AI helps you draft, you keep the judgment.

Quick win (under 5 minutes): open a blank sheet and create a single row called “Benchmark Snapshot.” Add three cells: this-week activation %, this-week ARPU (cohort-averaged), and this-week churn %. Add a fourth cell titled “50th target.” Put your current numbers in the first three cells and type a realistic 50th‑percentile target in the fourth (even a guess is fine). That small snapshot gives you immediate clarity on one KPI to move this week.

Nice call on the PII warning — anonymize and use weekly aggregates. Here’s a tidy, time-smart workflow you can run in a single workweek with one extra hour of focus each day.

1. What you’ll need
   • 3–6 months of weekly aggregates (no names/IDs): DAU, new signups, activation rate, MRR, ARPU by cohort, churn, and one performance metric (error rate or median latency).
   • A short peer list (3–5 companies in your ARPU band) and a spreadsheet.
   • An AI helper that accepts file uploads or secure input; one owner for data prep and one for experiments.
2. How to do it — quick, focused steps
   1. Day 1 (30–60 min): Create weekly aggregates and tag each row with a cohort (SMB monthly, SMB annual, mid-market, etc.). Strip any PII.
   2. Day 2 (30 min): Build the scorecard: columns = KPI | You | 25th | 50th | 75th | Gap-to-50th. Fill “You” from your aggregates for each cohort.
   3. Day 3 (15–30 min): Gather industry percentiles from public summaries or ask the AI to read only aggregated industry tables (no raw data). Enter 25/50/75 estimates into the scorecard.
   4. Day 4 (20–40 min): Prioritize two experiments — one quick win (onboarding tweak, microcopy change) and one medium bet (pricing tier or retention email flow). For each, write owner, 4–8 week timeline, and a single clear acceptance criterion (e.g., +8pp activation or +$6 ARPU).
   5. Day 5 (15–30 min): Set a Monday check-in: snapshot the key metric, log progress, decide to scale or iterate after four weeks.

What to expect

• A compact scorecard that shows where you sit vs. peers and the single biggest gap to close.
• Two measurable experiments you can start in 7–10 days with clear stop/scale rules.
• Weekly evidence you can present — no spreadsheets full of raw logs, just defensible aggregates and a repeatable cadence.

Micro-tip: pick one KPI to defend for 90 days. Treat everything else as learning. Small, focused bets beat big vague plans every time.

Nice callout on keeping raw data untouched and using manifests — that’s the backbone of traceability. I’ll add a very small, repeatable ritual you can do in under 10 minutes each time you publish or update a dataset, so busy teams actually follow the rules.

What you’ll need (simple & non-technical)

• A file location (cloud object store or shared drive) with folder permissions you control.
• A one-page manifest template (plain text) with a few fields you fill out.
• A checksum tool (built into your OS or a basic utility) and a short checklist you keep near your files.

Quick 8–10 minute versioning ritual (do this every release)

1. Collect: save originals into /raw/YYYY-MM-DD/ and note the file list.
2. Checksum: compute checksums for each file and paste them into the manifest (one line per file).
3. Tag & copy: create a release folder named YYYY-MM-DD_vX.Y and copy only the files that belong in this release.
4. Manifest: open the template and fill these minimal fields:
   1. Version tag and date.
   2. Short description of contents and the source (one sentence).
   3. Checksums for files and the parent release ID (if derived).
   4. One-line transformation summary and who approved it.
5. Validate & record: run a quick schema check (spot-check a row or two) and add any warnings to the manifest; save the manifest into the release folder.
6. Share & lock: update access controls for the release folder and note in an audit log who published it and when (a shared spreadsheet works fine).

What to expect

1. Initial friction for the first few releases, then 5–10 minutes each time once it’s routine.
2. Clear ability to reproduce experiments and a safe rollback path (pick an older version folder).
3. Faster reviews — reviewers only need the manifest + checksums to understand changes.

Micro-tip: pick a single person to own the ritual for a week or two; responsibility + a short checklist beats perfect tooling every time. Start simple, then automate the boring parts later.

Nice call — the slide budget and two-pass approach are exactly the parts that save time and keep your delivery calm. Here’s a compact, actionable micro-workflow you can run in a single, focused session when you’re short on time.

What you’ll need

• Lesson notes reduced to 4–7 ideas (bullets or a short paragraph).
• Any chat-style AI tool you can type into.
• Slide editor (PowerPoint or Google Slides).
• Optional: simple image library or image search for one photo/icon per slide.

20-minute power session (fast first draft)

1. Set the slide budget (1 min): Pick total talk time and divide by 60–75 seconds per slide. e.g., 6 slides for a 7-minute talk.
2. Prep notes (4 min): Trim to 4–7 key ideas, one idea = one slide. Write each idea as a single short sentence.
3. Ask the AI for a skeleton (5 min): Request slide titles, 3 short bullets, and one-line speaker note per slide. Keep tone plain and practical for adults 40+ (don’t paste long prompts — keep it conversational).
4. Quick edit (5 min): Replace jargon, add one local example to a speaker note, and flag any questionable fact for a fast check.
5. Import fast (5 min): Paste Title + bullets into blank slides (Title & Content layout). Add one image per slide using the AI’s image keywords or a quick search.

Polish session (optional — +20–30 min)

1. Set fonts: Title 36–44pt, Bullets 24–28pt; use high contrast.
2. Run a second AI pass to tighten language and add a practical example to each speaker note.
3. Rehearse with a timer; trim by removing the weakest bullet first if you overrun.

What to expect

• 20 minutes → usable first draft (titles, bullets, speaker notes).
• 40–60 minutes → delivery-ready slides with visuals and rehearsal.
• Cleaner slides, clearer delivery, and a reusable template for next lessons.

Common friction & quick fixes

1. Bullets too long — shorten to 6–8 words and start with a verb.
2. Generic images — add context words (location, object, mood) to the keywords.
3. Time overrun — cut one bullet per slide rather than a whole slide.

Micro-habit: run the 20-minute power session once this week on a single lesson. Keep the skeleton as a template — speed comes from repetition, not perfect first drafts.

Nice point — your three-layer pipeline is exactly the right backbone. Small addition: treat the pipeline like a quick checklist each time someone asks for a query — that keeps busy teams from skipping validation when they’re hurried.

• Do: Always share only schema + anonymized samples, require parameterized outputs, and run a linter/parser before any execution.
• Do: Use a read-only sandbox for initial runs, capture EXPLAIN plans, and use least-privilege credentials for production.
• Do-not: Never send production credentials or raw PII to the model or accept inline values without review.
• Do-not: Trust a single-pass acceptance for complex joins/aggregates — expect a short iterate-and-verify loop.

What you’ll need (5-minute checklist)

• Simple schema file (tables, columns, types, FK notes).
• 5–20 anonymized sample rows (one per table) to clarify data shape.
• SQL dialect noted (Postgres/MySQL/etc.) and a short template rule list (parameterize, no destructive commands).
• Access to a model interface, a SQL linter/parser, and a read-only sandbox or replica.

How to do it — quick micro-workflow for busy people (each step = 5–20 minutes)

1. Export schema and paste the small sample rows into a single reference doc. Time: 10 min.
2. Have a short rule-list you always attach: dialect, parameter style, forbidden keywords, explicit columns. Time: 5 min to set up once.
3. Send the plain-English request with the schema and rule-list to the model. Treat the first output as draft. Time: 5–10 min per request.
4. Run the returned SQL through your parser/linter: check for syntax, banned words, explicit column lists, and parameter placeholders. Time: 2–5 min (automated).
5. If linter passes, run in read-only sandbox and capture runtime + EXPLAIN. If EXPLAIN shows costly ops, tweak schema hints or add an index suggestion back into the draft. Time: 5–15 min.
6. When satisfied, convert to prepared statement with least-privilege creds and log the execution for audit. Time: 5–10 min.

What to expect: Expect 60–85% first-pass accuracy; with the linter + sandbox loop you’ll reach >90% safe executions within a few tries. Most fixes are small: missing join condition, wrong column name, or inline values.

Worked example (tiny, practical): Plain-English: “Top 10 employees in Marketing hired after 2020-01-01, by salary desc.” Result you should accept only if it’s parameterized and lists columns — for Postgres an acceptable SQL looks like: SELECT id, name, department_id, salary, hired_date FROM employees WHERE department_id = $1 AND hired_date > $2 ORDER BY salary DESC LIMIT $3; Expect to run it in sandbox, check EXPLAIN for index use, then promote with bind parameters and least-privilege creds.