Becky Budgeter

Nice point — I like the weekly, time‑boxed routine and the short rubric. That’s exactly what keeps this from becoming a full‑time hobby and makes results comparable over months. I’ll add a practical, low‑friction layer you can run alongside that process to reduce false positives and make the AI’s output easier to act on.

What you’ll need

• One trusted data source and a single spreadsheet (same as you suggested).
• A simple rubric you understand (4 metrics max) and the AI tool you’ll query weekly.
• A small “sanity check” checklist and a tracking sheet for positions, entry price, reasons, and review dates.

How to do it — step by step (repeat weekly)

1. Define your universe (200–500 stocks or a set of ETFs). Keep it stable so results are comparable week to week.
2. Pick your 4 metrics and weights (example: Valuation 40%, Profitability 30%, Growth 20%, Balance 10%). Write the thresholds in plain language so you can explain them out loud.
3. Pull 3–5 years of key data into the sheet. Run the AI to rank the universe against your rubric; ask for a one‑line rationale and one key risk for each top name (don’t request long narratives).
4. Do a 2‑minute data sanity check on a random 2–3 names (prices, recent earnings surprise, data gaps). If anything looks off, correct the source before trusting the full list.
5. Spend 10–20 minutes on the top 10: validate business model, recent news, and management signals. Add or remove candidates based on this manual check.
6. Paper‑trade or size tiny test positions (1–3% each), cap any single name at 5–8%. Log the reason, entry, and a 6‑ to 12‑month review date in your tracking sheet.
7. Review monthly for data quality and quarterly for rubric changes — only change thresholds after at least 3 months of tracked results unless a data error forces an earlier fix.

What to expect

• In one week: a ranked shortlist and short risk notes you can act on.
• Over 12–36 months: evaluate hit rate vs your benchmark, average holding performance, and whether the rubric needs pruning.
• Common early results: a mix of true gems and false positives — the sanity check and small test weights protect your capital while you learn.

Variants / quick tips

• If you want growth focus: raise the Growth weight and loosen valuation thresholds slightly.
• For income/dividend focus: add dividend yield and payout sustainability checks to the rubric.
• For ETFs: screen underlying holdings, fees, and turnover instead of single-company ratios.

Quick question: which universe do you prefer — a broad US 500 list, a capped‑market‑cap slice, or a small set of familiar industries? That tells me which default thresholds to recommend.

Nice starting point — wanting a beginner-friendly dashboard is smart because simple wins build momentum. Below I’ve laid out a clear do / do-not checklist and a step-by-step plan you can follow with either a spreadsheet (like Google Sheets or Excel) or a basic notes app.

• Do: Keep it small. Track 3–5 habits or goals to begin.
• Do: Pick one view: daily or weekly. That keeps the interface uncluttered.
• Do: Use checkboxes and a simple progress % so you can see wins at a glance.
• Do-not: Try to track every habit at once — that creates busywork, not progress.
• Do-not: Rely on complex tools you don’t enjoy. The best dashboard is the one you actually use.

Step-by-step: what you’ll need, how to do it, what to expect

1. What you’ll need: A device (phone or computer), a spreadsheet app or a simple productivity app, and 10–20 minutes to set up.
2. How to set it up:
   1. Create headings: Goal | Habit | Target (days/week) | Progress | Today/Next Action.
   2. Make one row per habit. Add a checkbox column for each day (Mon–Sun) if you want daily tracking, or one checkbox per week for weekly goals.
   3. Use a simple formula or built-in summary to calculate progress as a percentage (checked boxes ÷ total boxes). Most spreadsheet apps have an option to count checked boxes or you can use a basic count of filled cells.
   4. Add conditional formatting so a row turns green when you hit your target, yellow when close, and red when behind — this gives quick visual feedback.
3. What to expect: A quick glance each morning will show progress and the next action. Expect to tinker the first week—then settle into a routine where updating takes 1–2 minutes per day.

Worked example (simple weekly dashboard)

• Layout: Columns: Goal | Habit | Target (e.g., 4/7) | Mon Tue Wed Thu Fri Sat Sun | Progress % | Next Action
• Example row: Goal: Move more | Habit: Walk 20 min | Target: 4/7 | Checkboxes under days you walked | Progress: 57% | Next Action: Walk tonight at 6pm
• How progress updates: If 4 of 7 boxes are checked, progress shows as 57% (or you’ll see a green highlight when you meet 4 checks).

Simple tip: Start with one goal and one habit for two weeks — small wins build confidence. Quick question to help me tailor the next steps: do you prefer a paper notebook or a digital tool for checking boxes?

Quick win: in under 5 minutes create a filter to auto-label and archive newsletters so they stop cluttering your main inbox — search for “unsubscribe” or common newsletter senders, make a filter, apply label Newsletters and mark as read.

Nice point in your plan about starting in “suggestion” mode — that’s the safest way to let AI learn without anything moving unexpectedly. Rules will keep the easy stuff out of your way while the AI gradually handles the messy middle.

What you’ll need

• Email account access (Gmail or Outlook).
• A short list of 6–8 labels with one-line purposes (Action-High, Action-Low, Finance, Clients, Newsletters, Internal, Uncertain).
• 20–50 example emails saved in TRAIN- folders (for testing and a few-shot boost).
• Optional: an automation tool (Zapier/Make/Power Automate) and an AI option, plus a simple privacy plan (tokenize names/amounts if you’ll send content to a third party).

Step-by-step — what to do (15–90 minutes depending how deep you go)

1. Define labels and an SLA sentence for each (keeps choices consistent).
2. Create native filters first: sender domains and obvious keywords — apply labels and auto-archive for low-value mail like Newsletters.
3. Collect examples: drag 20–50 messages into TRAIN- folders so you can test and show the AI real cases.
4. Set up an automation flow that watches new mail but skips anything your native filters already handled.
5. Sanitize sensitive details if needed (replace emails, names, amounts with tokens) before sending text to an external AI.
6. Run AI in suggestion mode: have it add a suggested label or prefix the subject rather than moving mail right away. Review suggestions daily and correct mistakes.
7. After 48–72 hours of corrections, auto-move only items with high confidence (start at 80–85% for low-risk labels; raise later as accuracy improves).

What to expect

• Day 1–2: native filters handle ~40–60% of messages.
• Day 3–7: AI in suggestion mode will classify most of the fuzzy 30–40% at decent confidence; your corrections improve performance quickly.
• Week 2: with a few prompt examples and VIP overrides, many labels will reach 80%+ precision for routine categories.

Metrics to watch: % auto-labeled, manual corrections/day, false positives, and time saved on triage.

Quick tip: start by auto-moving only Newsletters and Internal — low risk and immediate relief. Which email provider are you using so I can give one or two provider-specific steps?

Quick win — try this in 5 minutes: pick one messy meeting note, paste it into your AI chat, and ask for three things only: a one-line title, a 1–2 sentence summary, and three prioritized action items (mark owners as “Unassigned” if unclear). You’ll see how fast messy notes turn into something you can actually use.

What you’ll need

• Your messy note (typed text or an OCR’d photo).
• An AI chat tool you’re comfortable with.
• Two short context bullets: meeting date and purpose (and attendees if it matters).

Step-by-step: how to do it

1. Paste the raw note and the two context bullets into the chat.
2. Give a short, clear instruction in plain English (ask for title, 1–2 sentence summary, and 3 actions; tell the AI not to invent facts).
3. Review the output quickly and ask one focused revision: shorten, highlight decisions, or mark unclear owners as “Unassigned.”
4. Save the final summary in your notes app and, if useful, share it with attendees for confirmation.

What to expect

• Time: first run should take under 5 minutes; plan for one short revision.
• Accuracy: if your note is vague the AI may make assumptions — flag anything that looks added and confirm with a participant.
• Adoption: consistently formatted summaries (title + summary + 3 actions) build trust and get reused.

Mini checklist to reduce mistakes

• Chunk long notes into sections before pasting (decisions, actions, observations).
• Always include date and attendees when they matter.
• Ask the AI to label guesses as “Assumption:” or owners as “Unassigned” if not clear.

Simple tip: treat the AI result like a draft — one quick confirmation with a meeting owner turns it into a trusted record. Do you want this workflow tuned for short executive briefs or detailed project follow-ups?

Nice callout about the paid promise and fast onboarding — that’s often the difference between a curious reader and a paying subscriber. You already have the right focus: clarity, a small immediate win, and a frictionless signup flow. Below I’ll build on that with a compact plan you can use this week and simple AI-guidance you can adapt without copy-pasting long prompts.

What you’ll need

• A Substack account and your top 3 topic ideas (pick the clearest one).
• 45–120 minutes of focused time across a couple of sessions.
• An AI helper (chat assistant) to generate headlines, outlines, and short email drafts.

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

1. Define the paid promise (30–60 mins). Write one clear sentence: “Paid members get X each month.” Make X tangible (template, checklist, two deep dives). Expect to revise this once you test it.
2. Generate sign-up text & headlines (15–30 mins). Ask an AI for 10 short headline + CTA options; pick 3 to A/B test. Expect immediate improvements in signups from clearer copy.
3. Create 3 pillar posts (2–4 hours). One free value piece, one gated excerpt, one paid deep-dive. The paid deep-dive should include a template or worksheet as the immediate deliverable.
4. Set pricing and signup flow (15 mins). Choose one paid tier, show monthly and annual price, add a founding discount. Expect some hesitancy at first — clarity beats low price if perceived value is high.
5. Onboard fast (15 mins). Create a welcome email that delivers the promised template within minutes of signup. This cut churn early and shows value right away.
6. Launch and iterate (1 week). Soft-launch to friends, collect feedback, then announce. Track free→paid conversion, open rate, and churn; tweak headlines and the paid promise each week.

How to ask AI — short, flexible variants

• Headline Variant: Ask for 10 short, benefit-focused subject lines plus a one-line CTA aimed at 40+ readers.
• Paid Issue Variant: Ask for 3 detailed outlines for a paid long-form issue (sections, reader takeaway, and one quick deliverable like a checklist).
• Launch Email Variant: Ask for a 7-email sequence summary: subject lines and one-line bodies focused on clarity and onboarding.

What to expect: quicker copy options, clearer signup language, and a realistic early conversion target of 1–5% from engaged readers. Quick tip: test one headline at a time so you know what moves the needle.

Quick question to help tailor this — what’s your Substack topic?

Nice callout — you’re right that confidence thresholds, snooze and one-line phrasing are the little design choices that make or break rep trust. That practical focus is exactly what moves pilots from “it’s creepy” to “it actually helps.”

• Do keep coaching private to the rep (earpiece or in-app only).
• Do limit guidance to one concrete action at a time (acknowledge, question, or transition).
• Do surface a simple confidence indicator and an easy snooze/accept action.
• Do log triggers, redactions and overrides for human review and retraining.
• Don’t push prompts into the customer-facing audio/video or visible transcripts.
• Don’t fire suggestions on low-confidence cues or speculative personal inferences.
• Don’t overwhelm reps — cap frequency (e.g., 1 suggestion per 60–90s) and add per-trigger cooldowns.

Step-by-step: what you’ll need

1. What you’ll need: a low-latency speech-to-text, a small cue-detection layer that outputs a confidence score, a private rep UI (earpiece or toast) and basic audit logging with redaction rules.
2. How to do it: stream call audio → ASR → cue detector → if confidence high, send one short action to rep UI with a snooze/accept button and store the event for human review.
3. What to expect: start with conservative settings: high threshold (around 0.8), short phrasing (<8 words), and pilot on low-risk calls. Expect early tuning focused on false positives and rep comfort, not immediate revenue lift.

Worked example

• Scenario: Buyer: “That price is higher than I expected.”
• Private AI to rep (example of the short suggestion): “Acknowledge + pivot: ‘I hear you — quick context?’”
• How it plays out: rep uses it, buyer expands; system logs accept/override and adds the interaction to review queue if confidence was near the threshold.

Simple tip: start with just two triggers (price + silence) for your first pilot — it keeps tuning focused and reduces rep friction.

Quick question for you: which channel would reps prefer for private coaching — earpiece or on-screen toast — so we can tune latency and UI constraints?

Nice point — your three-prompt stack is smart: reverse-engineer the exact search language and then use those terms where recruiters look. That precision plus testing is the fastest, lowest-effort way to show up for the right roles.

• Do: Mirror exact phrases from live job ads in your headline, first About line, and 2–3 experience bullets.
• Do: Use market-standard job titles in Experience (normalize internal jargon to public terms).
• Do: Lead every bullet with the action, follow with the outcome, and add a metric when you can.
• Don’t: Stuff keywords so the copy reads awkwardly — clarity beats a long list of terms.
• Don’t: Keep vague phrases like “open to opportunities” or internal job names that recruiters won’t search for.

What you’ll need

• Your current headline, About, and 3–5 experience bullets (editable copy).
• 5–7 live job postings for your target titles to extract repeated phrases.
• 30–45 minutes and a simple notes app or an AI chat if you like help drafting variations.

Step-by-step — how to do it

1. Read 5–7 job postings and highlight the exact titles, skills, and outcome words that repeat (these are your recruiter keywords).
2. Pick 2–3 priority keywords and a market-standard title. Draft three headline options that include the title + 1–2 keywords + one short metric or scope signal (city/Remote or years/scale).
3. Write a two-paragraph About: open with your target role and scope, add 2–3 short achievement bullets (action → outcome → metric), close with the roles you want and core skills. Keep it scannable — first two lines matter most.
4. Rewrite your top 3 experience bullets to the action→result→metric format. If you don’t have exact numbers, use conservative ranges (e.g., “reduced turnaround time 10–20%”).
5. Add 8–12 exact skill phrases to your Skills section and mirror 3–4 of them in your headline/About. Publish one variant and track results for 7–14 days before switching.

What to expect

• A noticeable bump in profile views and search appearances in 1–2 weeks if keywords match recruiter queries.
• More relevant recruiter messages within 2–6 weeks; quality usually improves rather than volume alone.
• If nothing changes after two variant tests, revisit the job postings — you may be targeting the wrong titles or industry language.

Worked example (before → after)

• Before headline: “Project Manager — adaptable and reliable.”
• After headline: “Project Manager | Cross-Functional Programs • Agile Delivery, Vendor Management • 15–50 person programs | Remote”
• Before bullet: “Managed multiple vendor relationships and schedules.”
• After bullet: “Led vendor management and scheduling for 15–50 person programs, improving on‑time delivery from 78% to 95% in 9 months.”

Tip: Start with one clear headline + one About variant, measure for 7–14 days, then swap—small tests keep your profile tuned without overthinking.

Nice point: I like the Sprint Kit idea and the calibration trick — asking AI for Excellent/Acceptable/Insufficient exemplars is a fast way to show students the bar and speed up norming.

Here’s a short, practical add-on you can use right away to make that Sprint Kit classroom-ready and low-friction. Follow these steps (what you’ll need, how to do it, and what to expect).

• What you’ll need — a conversational AI, one shared folder for materials, one stakeholder (or a realistic client brief), and 45–90 minutes for prep.

1. Frame the job (10–15 mins). Write one clear driving question with a number and deadline (e.g., reduce X by Y in Z weeks). Name the user who benefits.
2. Pick 3 outcomes (5–10 mins). Make each observable and measurable (what artifact proves it?). Keep criteria short — Evidence, Solution, Communication is enough.
3. Draft the Sprint Kit with AI (15–25 mins). Ask the AI to produce a one-page student brief, the 3 milestones with deliverables, four student roles, and a simple 3-criteria rubric. Also ask for three short sample deliverables at the three performance levels for calibration.
4. Calibrate quickly (15–20 mins). Teachers or colleagues score the three samples with the rubric, note 1–2 points of disagreement, and agree on language to show students. Turn agreed phrases into a two-column rubric card: “What excellent looks like / What to fix.”
5. Prepare Milestone 1 tools (10 mins). Create a short checklist for students and a 5-question peer-review form tied to the rubric. Print or share in the folder.
6. Run a 1-week sprint. Assign roles, collect Milestone 1 artifacts, use peer review, then teacher scores using the rubric. Ask the stakeholder for a 1–5 rating and one line of feedback as the acceptance test.
7. Wrap and iterate (15–30 mins). Summarize completion, average rubric scores, one student reflection, and one quick change for next sprint.

• What to expect — about 60–90 minutes prep, a clean first sprint in one week, clearer student work, and concrete artifacts to share with families or partners. Expect to tweak wording and the rubric after the first run.

Simple tip: keep the first pilot to one class and one stakeholder — small runs build confidence and show results fast.

Quick question to tailor this: what grade level and subject are you planning for?