A free ESG stock screener that publishes its losses and methodology
Publishes losing trades openly, unlike most hype-driven stock pickers.
Blog post about content strategy, not an actual tool or product to evaluate.
Content marketers, SEO professionals
The Architecture: Trend Discovery → AI Drafting (fine-tuned) → Human Editing Gate (45 min) → Automated Scheduling → Performance Loop
Results from 7 days: • 47 articles published across 3 blogs • 12 hit Google first page within 3 weeks • Average human time: 45 min/article (vs 4-6 hours before) • AI costs: $380 total • Cost per article dropped from $157 to ~$9
Key insight: Thin AI content died in 2024. Comprehensive, data-rich, human-edited pieces are winning. Google's March 2026 update rewards "experience + authenticity" — AI helps with drafting, but the strategic angles must be human.
The bottleneck shifted from writing to idea generation.
What questions do you have about building an AI-native content pipeline?
Publishes losing trades openly, unlike most hype-driven stock pickers.
ChatGPT traffic via auto-publishing, but Surfer AI, MarketMuse already do this.
Rigorous 38-day Gemini drift study with citation-mapped predictions and confidence scores.
The pitch is simple and attractive: the product promises end-to-end SEO work — keyword discovery, competitor research, daily article writing and auto-publishing to WP/Shopify/Webflow — which is convenient if you want volume. The landing lists useful primitives (SEO/GEO score, content calendar, multilingual output), but nothing here reveals a defensible edge over established tools; the critical issues are quality control, avoidance of thin/duplicative content, and transparency around ranking methodology.
Curated list of AutoResearch wins, but it's just a README with links, not a tool.
Replaces manual Playwright scripting, but Claude-generated tests and GitHub Copilot already cover this.