Brick 1
Framework-as-Diagnostic Overlay
Greg's 7-tactic framework overlaid on Yuri's current infra: | Tactic | Greg's framework says | We have | Verdict | |---|---|---|---| | 1. MCP server as sales team | Publish to Smithery/MCPT/OpenTools for $0 CAC discovery | Zero — neither BizBuilder/KPDD nor Solacian have an MCP server | **REAL GAP** — but applicability depends on whether the product answers a queryable question; Solacian (Maze-dissolving AI) plausibly does, KPDD (PMF discovery) plausibly does | | 2. Programmatic SEO at 10K-page scale | Next.js + Firecrawl + AI content for "best X for Y" patterns | Zero programmatic pages; KPDD/Solacian don't have SEO surface | **REAL GAP** for both; needs a keyword pattern decision before any code | | 3. Free tool as top of funnel | Grader/analyzer/calculator, instant value, share-driven loop | KPDD itself is arguably a discovery tool but is gated by signup, not free-tool-grade | **PARTIAL GAP** — KPDD could expose a free analyzer slice; Solacian could expose a free "Maze diagnostic" | | 4. AEO (Answer Engine Optimization) | Top-20 questions + FAQ schema + monitoring via Otterly/Profound | Zero structured FAQ surface on either product site | **REAL GAP** for bo…
Source: src/lib/bricks/sources/greg-isenberg-bootstrap-distribution.md
Brick 2
Strategy 2: Programmatic SEO (10,000 Pages)
The thesis: create 10,000 SEO pages in 48 hours via keyword patterns + structured data + AI-generated unique content. Math model: - 10,000 pages × 30 visits/month each = 300,000 monthly visitors - 2% conversion = 6,000 conversions/month - $10 each = **$60,000/month from pages built once** - Caveat: 30 visits/month doesn't happen overnight; SEO compounds over time Critical: content must not feel like AI. Lots of optimization needed. Start with a few pages, scale once quality is right. "Press one button" myth is rejected explicitly. Playbook (start this week): 1. Pick a keyword pattern: `[product type] for [niche]` or `[service] in [city]`. Examples: "CRM for dentists", "roofing in Miami" 2. Build dataset via Firecrawl (scrapes + clean structured data) or existing databases 3. Create page template in your framework: Next.js, Webflow, WordPress — pick what you use 4. Use AI to generate unique paragraphs per page (not variable swaps; high-quality content) 5. Human-in-the-loop editing on a sample 6. Publish 100 pages as MVP 7. Monitor indexation 8. Scale once indexed Applies to: services, SaaS, apps, boring businesses, agencies. Programmatic SEO is still under-tapped.
Source: src/lib/bricks/sources/greg-isenberg-bootstrap-distribution.md
Brick 3
Flow 1: SEO Content Machine
Research keywords -> scrape page-1 -> write with perspective -> publish via CMS API -> track GSC -> optimize -> batch all keywords.
Source: src/lib/bricks/sources/gtm-engineering-flows-combined.md
Brick 4
1.5 Competitor Creative Mining + AI Tagging (Apify pipeline)
Source: [[competitor-creative-mining-apify-pipeline.md]] **When to use**: entering a niche, no creative ideas, need to know what's working in competitor ads RIGHT NOW. Pre-step before §1.3 (code generation) — gives you the parameter library to remix from. **Process**: 1. Apify actor `curious_coder/facebook-ads-library-scraper` ($0.20-0.75 / 1K ads) pulls competitor ads from Meta Ads Library by search URL or competitor page list 2. Edit node computes `days_running = (end_date - start_date) / 86400` — proxy for performance (winners run 30+ days, losers killed in <7) 3. Download creatives to Google Drive or Supabase 4. GPT-4o vision analyzes each image, returns structured JSON: `ai_hook`, `ai_offer`, `ai_cta`, `ai_psychology`, `ai_utp`, `ai_jtbd`, `main_object`, `main_color`, `ai_description` 5. Code node validates JSON schema (one column = one property) 6. Merge node joins AI tags with metadata 7. Google Sheets append: one row per ad with image, params, days running, reach, est. spend 8. **Spend hack**: `est_spend = reach × avg_CPM_in_geo`; rank by `est_spend × days_running` to find competitor hero creatives 9. Output sheet feeds downstream creative-generation a…
Source: src/lib/bricks/sources/perf-marketing-playbook.md
Brick 5
Strategy 4: Answer Engine Optimization (AEO)
The thesis: be the source AI cites. Old SEO (30,000-word blog posts, backlink building, keyword stuffing) is declining; zero-click searches growing. AEO in 2026 = SEO in 2010. First movers will own niches for years. Goal: get cited by ChatGPT and Perplexity via structured direct answers, FAQ format, schema markup, comparison tables that AI can parse. Evidence: Peter Levels' AI referrals jumped from 4% to 20% in one month. Expected to keep increasing across e-commerce, SaaS, apps. Playbook (start this week): 1. Google the top 20 questions your customer asks 2. Write definitive structured answers for each — NOT 3,000-word fluff. Clear, direct, citation-worthy 3. Add schema markup and FAQ blocks 4. Publish on a domain with authority (or build authority through other strategies) 5. Monitor citations via Otterly, Profound, or manual testing Why Peter Levels wins faster: domain authority compounded over time. No better moment to start building authority than now.
Source: src/lib/bricks/sources/greg-isenberg-bootstrap-distribution.md
Brick 6
B076 — Apple Search Ads on App Store keywords
Apple Search Ads on App Store keywords. Lowest CPI for mobile. **Tool:** T075 Apple Search Ads API.
Source: _reference/bricks/README.md
Brick 7
PART V: BIZBUILDER MATCHING ALGORITHM SCHEMA
For BizBuilder to surface relevant case studies, index by these dimensions: ``` { "company": "string", "market_type": "marketplace | saas | social | consumer | b2b | content | fintech | health", "product_type": "platform | tool | app | service | hardware", "gtm_motion": "1-12 (from taxonomy)", "stage": "pre-launch | first-100 | first-1000 | scaling", "channel": "forums | community | PR | influencer | paid | organic | viral | street-team | build-in-public", "constraint_type": "no-money | no-network | no-product | no-market | geographic | regulatory", "trust_requirement": "low | medium | high", "network_effect": "none | weak | strong | embedded", "meta_pattern": [1-9 from meta-cognitive patterns] } ``` **Matching logic**: When a founder describes their startup, extract these dimensions. Find case studies that match on 3+ dimensions. Surface the NON-OBVIOUS insight first (not the tactic), because tactics are context-dependent but insights transfer.
Source: src/lib/bricks/sources/first1000-pmf-patterns-library.md