Steve Side Hustler

Nice point about semantic dedupe — that’s the trick that turns a mess into a trusted inbox. Short version: teach the AI a few examples, start read-only, and you’ll move from chaotic reminders to a calm daily list you actually open.

What you’ll need (quick checklist)

• Accounts for 2–3 reminder sources (phone reminders, flagged email, Slack or calendar).
• An automation tool with connectors you’re comfortable with (Zapier, Make, Power Automate).
• A single destination you’ll check daily (Google Sheet or one Notion page).
• Access to a simple AI step inside the automation or an API key you paste in.

Micro-workflow — get a reliable pipeline in ~90–120 minutes

1. 30 min — Inventory & choose destination: List 3 highest-volume sources and pick one home (Google Sheet is easiest).
2. 30–45 min — Build read-only connectors: Create triggers for two sources that append new items to a raw sheet or table. Map to title, notes, source, created_date, due_date, link.
3. 20–30 min — Add the AI batch step: Every hour (or manually) send 10–30 items for cleaning: remove duplicates semantically, suggest priority (High/Medium/Low), and add one simple category. Keep outputs back into a cleaned sheet tab — no write-backs to originals yet.
4. 10–15 min daily — 3-minute review routine: Each morning, scan the cleaned list, accept or correct 5 items. These corrections are your training examples — save a note of any mistakes so you can refine rules.

Simple rules to set now (one-time, high impact)

• No due date → default to today + 7 days (editable on review).
• Exact text match → auto-merge; semantic similarity threshold → review when between 70–85% confidence.
• Do read-only for week 1. Only enable write-backs after confidence ≥80% for three days.

What to expect. First-pass accuracy ~70–85% for dedupe/priority. With 20–50 quick corrections in week one you’ll hit 85–95% trust. Time savings: expect to cut reminder-check time to one 5–10 minute session each morning.

Small measurement plan — track these for 2 weeks: 1) % of sources feeding into the list, 2) daily items cleaned, 3) # duplicates reduced, 4) time spent checking reminders. These simple numbers tell you when to add another source or loosen/tighten dedupe rules.

Nice summary — treating the queue like a small portfolio and using a daily digest is exactly the mindset that removes guilt and builds focus. Here’s a compact, actionable add-on: a 2-minute triage routine plus a simple prioritization tweak so you only open the stuff that actually moves the needle.

What you’ll need

• A single folder or feed for all newsletters (email rule, RSS, or aggregator).
• Three tags/folders: Now, Maybe, Archive.
• An AI tool that can make ultra-short summaries and pull out actions (even built-in assistants work).
• Daily: 2 minutes for triage + 10 minutes for items tagged Now. Weekly: 20–30 minutes to review Maybe.

Step-by-step micro-workflow (packs into your morning coffee)

1. Capture: Route all newsletters into one folder — your single source of truth.
2. Two-minute triage: Skim AI one-line summaries. If it contains a practical action or a relevant insight, mark Now. If interesting but not urgent, mark Maybe. Otherwise archive.
3. Action-first rule: Anything in Now must include a one-line action (read, save a link, reply, follow up). If the AI summary doesn’t suggest an action, downgrade to Maybe.
4. Daily 10-minute workblock: Open only Now items and either do the action or schedule it. Archive afterwards so the queue shrinks visibly.
5. Weekly tidy: Scan Maybe, promote the few that earned your attention over the week, and clear the rest into archive.

How to prompt your AI (short guides, not copy/paste)

• Ask for a one-sentence clarity summary, then a 1–2 item action list — keep the answers ultra-brief.
• Ask the AI to rate relevance on a 1–5 scale for your goals so you can sort quickly.
• Variants: a “quick scan” mode for speed, an “action mode” when you want tasks, and a “deep-read recommendation” when an issue needs follow-up.

What to expect

• Setup takes about an hour; fine-tuning over 2–3 weeks.
• Result: your morning is a 2-minute decision, a 10-minute focused session, and a weekly tidy — predictable, low-stress, and productive.
• Remember: AI is triage. When something matters, open the full piece. The goal is less reading, more useful action.

Start today by routing one newsletter into your capture folder and running the two-minute triage — small wins compound fast.

Nice call: treating mockups as product inputs is the whole game — it moves you from ‘pretty idea’ to files you can actually ship.

Here’s a compact, actionable micro-sprint for busy creators who want production-ready assets in a day or two. It’s focused, repeatable, and doesn’t need a developer.

What you’ll need

• AI image tool for variations
• Editor that handles vectors (Figma or Canva)
• Simple export checklist (SVG, PNG@2x, filenames, HEXs)
• Staging area: a no-code page or a simple local HTML preview
• Timer and file naming convention (date_project_component_v1)

90–120 minute sprint (do this twice across two days)

1. 10 min: Quick brief — write 3 bullets: audience, use (hero, ad, thumbnail), and desired mood/brand words.
2. 20–30 min: Generate 8–12 variants from the AI. Tag each with date and short intent (e.g., hero-photo, hero-illustration).
3. 10 min: Rapid triage — pick top 3 by clarity and scalability (can it be an SVG? is text editable?).
4. 30–40 min: Recreate winners in your editor: convert logos/icons to vector, set exact HEXs, lock font sizes and spacing tokens.
5. 10–15 min: Export package using checklist: SVGs for icons, PNG@2x for images, PDF mini-style sheet, and a README with alt text suggestions.
6. 5–10 min: Drop assets into staging to confirm rendering and file-size impact.

Prompt guidance (keep it short and specific)

• Tell the AI the component, audience, visual tone, and exact export size in one line — then ask for variants: some illustrated, some photo-based, and a plain text-only option for accessibility.
• Variant ideas: hero banner (wide), social square (1:1), thumbnail (small, legible at 120px).
• Ask the AI to also suggest 2 short headline options and one-line alt text for each variant.

What to expect

Outcome after two sprints: a 3-option production bundle per component, vector masters, a one-page style sheet, and a staging preview. Expect 1–2 quick revision rounds; plan your A/B test to swap a single element (image or headline) to see impact.

Quick wins: export vector masters, name files clearly, add alt text now — these small habits save hours later.

Nice call — your emphasis on one KPI and a single daily batch is exactly the discipline that keeps a dashboard useful instead of noisy.

Here’s a compact, practical add-on you can do in about 30–60 minutes a day for the first week to make that loop reliable and low-friction.

What you’ll need

• Google Sheet with a daily table and an “AI_Summaries” tab.
• An LLM account + API key and a no-code automation tool (Zapier/Make).
• A dashboard tool that reads Sheets (Data Studio/Glide/AppSheet).
• 5–10 minutes to validate outputs each morning for 3–7 days.

How to set it up — micro-steps (fast)

1. Pick the KPI and add a one-line definition in the sheet so anyone understands what it is (e.g., “net daily revenue after refunds”).
2. Make a clean sample: 7–14 rows with Date + KPI. Add a filtered flag column (Status=complete) to avoid bad rows.
3. Build the automation: once a day, gather yesterday’s rows (only Status=complete), convert to a simple CSV/array and call the LLM in one request.
4. Write the parsed output into AI_Summaries columns: Date, KPI_value, trend, top_issue, action_1..action_3, confidence, ActionTaken (blank by default).
5. Surface AI_Summaries in the dashboard: one KPI chart on top, then the ranked actions and confidence as the main text widget.
6. Validate 3–7 days: each morning skim the actions, mark ActionTaken when someone tries one, and note KPI movement after 3–7 days; tweak the automation if outputs are vague.

What to expect

• One clear prioritized recommendation each morning and 2 backups. Expect to tweak wording twice before it’s solid.
• Measure adoption by tracking ActionTaken; measure impact by comparing the KPI 3–7 days after an action.
• Control cost by batching daily and limiting token use in your automation settings.

Prompt blueprint and quick variants (keep these as small rules, not a full copy)

• Machine-friendly variant: ask the model to return only labelled fields you can parse (date, kpi_total, trend, top_issue, actions[3], confidence). Force numeric types and short actions (10–12 words) and no extra commentary.
• Executive-friendly variant: request a one-paragraph summary plus three ranked, verb-starting actions with a one-line reason each—short, non-technical language for a manager to act on.
• Anomaly alert variant: tell the model to flag any day-over-day change above a chosen percent and return a single immediate stop-gap action to apply today.

Keep the loop tiny: daily batch, one headline action, mark whether it was tried, and check KPI movement after a few days. Small, consistent experiments beat big feature lists—ship the small loop and iterate.

Short version: don’t try to stop interruptions — make resuming automatic. Here’s a tiny routine you can use tomorrow that saves minutes every time you’re pulled off task.

What you’ll need

• A calendar you actually use
• One capture place (notebook or a single notes app)
• A timer (phone or desktop)
• Do Not Disturb set with one emergency contact
• A simple AI chat you can nudge with one sentence

Step-by-step routine — 30 seconds to recover, one 60–90 minute block

1. Design (5 minutes): pick your best-energy 60–90 minute window and write one clear outcome (e.g., “Draft intro — 500 words”). Put it on the calendar as an appointment.
2. Pre-block capture (3 minutes): list the outcome and 2–3 concrete tasks that create it. Expect clarity, not perfection.
3. Start (1 minute): turn on DND, set your timer, change status to a one-line pause message if needed.
4. If interrupted (30 seconds total):
   1. Record (5–10s): jot the interruption in your capture as W/F/S/T + 3 words.
   2. Redirect (5–10s): use a two-line pause line aloud or in chat: say you’ll reply after the block and note any urgent exception.
   3. Resume (10–15s): speak your re-entry line: “I’m on [task]. Next micro-step: [tiny step].” Start that micro-step immediately.
5. Post-block review (5 minutes): mark what moved, move unfinished items to the next block, and note one tweak for next time.

What to expect

• First day: you’ll feel the relief of a predictable ritual — not magically distraction-free, but faster recovery.
• After 3 days: patterns in the interruption ledger will emerge (repeat askers, tech noise) so you can fix specific sources.
• By week one: small wins stack—more finished outputs and less boiling anxiety about “what did I miss?”

Quick AI prompts (say these, don’t paste a novel):

• Variant A — Focus coach: Ask the AI to build a 5‑day plan with three 60–90 minute blocks, a 3‑minute pre‑block capture, a 30‑second pause-and-recover routine, and a one-line daily review template.
• Variant B — Gatekeeper: Ask the AI to judge incoming requests with one-line answers: “Interrupt? Yes/No — and what to do next.”
• Variant C — Post-block summary: Ask the AI to turn one-line block notes into a single improvement suggestion for the next block.

Next 24-hour micro-plan

1. Block one 60-minute focus session tomorrow morning and set DND.
2. Use the 3-minute capture, run the block, and practice the 30‑second Record→Redirect→Resume when interrupted.
3. At the end of the day, tell your AI one sentence about the block and ask for a single tweak.

Keep it tiny and repeatable: the aim is fewer lost minutes, not perfect silence. Do one block tomorrow — you’ll get momentum from the recovery ritual, not sheer willpower.

Short, practical take: You can build a board-ready MMM in a couple of weeks if you focus on a constrained, explainable baseline, model carryover and saturation, and deliver one clear counterfactual that executives can act on. Below is a compact, action-first workflow you can run with a marketer and one data person.

1. What you’ll need
   • Data: weekly sales (or margin), media spend by channel, price/promo flags, distribution/availability, holidays, plus 1–2 external controls (economic index or weather if relevant).
   • Tools: spreadsheet for checks; Python/R or an MMM package for modeling; simple BI or slides for reporting.
   • People: a marketer (campaign timing + priors), a data person (feature work + validation), and one decision owner to act on recommendations.
2. How to do it — 7-day micro-sprint
   1. Day 1 — Align & clean: pick one cadence (weekly), align calendars, impute tiny gaps, flag big shocks (stock-outs, outages). Expected: single tidy CSV.
   2. Day 2 — Feature basics: build adstocked versions of each spend (test decay 0.2–0.9), add 1–4 week lags for fast channels, create promo/holiday/season dummies. Expected: feature table with adstock + lags.
   3. Day 3 — Add saturation & controls: apply a simple diminishing-returns transform (log or S-curve) and include price/distribution/external controls. Expected: stabilized predictors that prevent runaway ROI.
   4. Day 4 — Fit constrained baseline: run a regularized linear model (Ridge/Lasso), enforce sensible signs (media ≥0, price ≤0). Use a contiguous holdout (last 8–12 weeks). Expected: channel coefficients and OOS RMSE/MAPE.
   5. Day 5 — Calibrate & sensitivity: vary adstock and saturation parameters to produce low/base/high ROI bands; sanity-check vs business priors. Expected: ROI ranges and a confidence band.
   6. Day 6 — Counterfactuals & budget simulator: run “-20% TV” and “+10% to top-2 channels” scenarios; build a simple allocator that respects caps and diminishing returns. Expected: a few clear budget-shift scenarios with projected lifts.
   7. Day 7 — One-page exec view: baseline vs paid vs promo, channel share, ROI bands, top 1–2 recommended moves and risks. Present and get commitment to run an experiment or refresh cadence.
3. What to expect & common pitfalls
   • Expect ranges, not exact numbers — present low/base/high.
   • Watch multicollinearity: group similar channels or use regularization to stabilize shares.
   • Don’t model revenue if margin is the real objective — prefer margin or at least report ROI on gross profit.
   • Calibrate with one clean experiment (geo or platform lift) when possible to anchor estimates.

Quick 15-minute starter: pull one month of raw weekly spend and sales into a sheet, mark any known outages, and ask the marketer: which two channels would you move spend between? That simple pairwise question makes your first counterfactual credible and gets the project rolling.

Short take: Yes — you can get a usable Figma UI kit from AI in an afternoon if you treat the output like a fast, skilled assistant: generate the scaffolding, then spend focused time fixing accessibility, naming and states.

• Do start with a tiny brief (3 colors, 2 fonts, base spacing).
• Do enforce a naming convention (Category/Component/State) right away.
• Do block 60–120 minutes for review to catch contrast and missing states.
• Do-not accept AI output as final — expect to edit tokens and component props.
• Do-not create 50 variants before you’ve used the first 5.

What you’ll need

• Figma account + a tokens import plugin (or a manual JSON-to-styles step).
• An AI assistant (chat or a Figma AI plugin).
• A 5–10 item style brief: 3 hex colors, 2 font names, base spacing (8px), and 3 priority components.
• One sample screen to test the kit immediately.

Step-by-step micro-workflow (what to do, how long, what to expect)

1. Write the brief (20–30 min): collect hex codes, heading/body fonts, base spacing, and list 3 components you’ll use right away (e.g., Button, Input, Card). Expect a short doc to paste into your AI session.
2. Ask AI for tokens + specs (10–20 min): request JSON tokens and plain component specs. You’ll get a tokens file and text describing sizes, states and ARIA hints — treat this as a recipe to follow.
3. Import tokens into Figma (15–30 min): use your tokens plugin or paste values into Figma styles. Create components for Button, Input and Card using the token names you received.
4. Accessibility & states (30–45 min): check color contrast for primary/secondary text, add focus and disabled styles, confirm text sizes meet legibility. Expect to tweak token hex values and re-import once or twice.
5. Name and organize (15 min): apply Category/Component/State names (e.g., Button/Primary/Default). Export a small tokens file for devs.
6. Test on one screen (30–60 min): swap the new components into a sample screen, fix spacing, and note missing variants to add later.

Worked example — tiny, actionable

Pasteable tokens sample (small):

{“color”: {“brand-500″:”#0A74FF”,”neutral-100″:”#F5F7FA”,”text-900″:”#09101A”}, “type”: {“body”:{“size”:”16px”,”weight”:400}}, “spacing”:{“base”:8}}

Button spec (how to build it in Figma):

1. Create a component named Button/Primary/Medium. Padding: 12px (top/bottom) × 20px (left/right). Background: brand-500. Text: text-900 in 16px body weight.
2. Add states: Hover = brand-700; Disabled = brand-500 at 40% opacity plus aria-disabled note in spec. Add a 2px focus ring using a neutral token.
3. Export a small tokens JSON for devs and include the naming convention in a one-paragraph guide.

What to expect after you ship

• A working kit that speeds design work immediately but will need 1–2 rounds of tweaks from real use.
• Developer questions on naming or missing states — plan a 30–60 minute sync to close those gaps.
• Measure reuse on one screen first; add variants only when you hit friction.

Small, repeatable cycles win: generate, import, review, test, iterate. You’ll get to a reliable kit fast — and your edits make it production-ready.

Quick win (under 5 minutes): open a learner’s last quiz results, apply this rule mentally—>=80% = mastered, 60–79% = approaching, <60% = needs practice—and write one one-line next step for each objective with a time estimate (example: “10-minute practice scenario” or “5-question follow-up quiz in 48 hours”). Send those lines as an email or copy them into the learner record. That tiny habit gives individualized guidance fast.

What you’ll need

• A simple learner record (spreadsheet or basic LMS) with learner ID, objectives, assessment scores, and dates.
• Short, objective-tagged assessments — quizzes, rubrics, or reflection prompts.
• An AI chat tool or low-code AI service (optional) to scale the explanations and next steps, or just your notes for a manual start.
• A clear rule for mastery (pick one you’ll stick to; e.g., averaged score ≥80 or 3 recent correct attempts).

How to do it — step-by-step

1. Define 4–8 clear objectives for the course and tag each quiz question to one objective.
2. Collect each learner’s recent scores per objective (last 3 attempts or last 30 days).
3. Calculate status per objective using your rule (mastered/approaching/needs practice).
4. Create short, concrete next steps per status: mastered = quick challenge (10–15 min); approaching = focused practice (5–10 min) + micro-example; needs practice = guided practice + short assessment (10–20 min).
5. If using AI, feed the learner’s objective scores and the mastery rule and ask for: status, a 1-line plain-language explanation, and 2 concrete next steps with time estimates and a follow-up check. Keep prompts short and specific.
6. Deliver recommendations in a single-paragraph email or a dashboard card so learners get immediate, actionable guidance.

What to expect

At first you’ll see quick wins where learners appreciate short, doable actions. Expect some noisy scores — fix by using multiple measures (quiz + practice + reflection) before changing status. Over a few weeks you’ll spot common gaps and can create tiny, reusable activities for those gaps.

Weekly 10-minute workflow for busy people

1. Pick 3 learners with recent activity.
2. Run the status check (step 3 above) and produce one 2–3 line action for each objective.
3. Send actions and mark follow-up date in the sheet.

Start with the manual 5-minute win, then automate with AI as you see patterns. Small, consistent nudges beat big one-off interventions—especially for adults balancing work and learning.