After Notion's developer platform launch, Workers and Dashboards make a credible BI stack for most small teams. Why it's now my default recommendation, and where the warehouse-plus-Tableau setup still has the edge.
How reusable AI skills across Claude, Notion, and Linear have replaced prompt engineering as the highest-leverage way to make AI tools actually useful.
We built a custom CRM in Notion — powered by 30+ Zapier automations, AI agents, and deep integrations — that tracks contacts, emails, meetings, deals, invoices, and leads automatically. Here's the full architecture.
How NTUC cut hours of manual work by automating social media comment exports, AI analysis, and reporting
AI and automation are reshaping work, often in ways that prioritise cost-cutting over people. This post details how we at work.flowers are grappling with these issues and striving to balance the incredible potential for productivity gains with the real-world impact on jobs and people.
After Notion's developer platform launch, Workers and Dashboards make a credible BI stack for most small teams. Why it's now my default recommendation, and where the warehouse-plus-Tableau setup still has the edge.
Five small, purpose-built apps I vibe-coded to replace SaaS tools, fix the missing 10% of off-the-shelf platforms, and stop the recurring bills.
Five layers — files, plugins, sub-agents, memory, and scheduled tasks — that turn Claude Cowork from a chatbot into the coworker running ops before you've had coffee.
We built a contact enrichment pipeline for our Notion CRM using Apollo.io, HarvestAPI via Apify, and Zapier — for roughly $0–5/month. Here's the full architecture, the cost breakdown, and why CRM vendors charging $2–3K/year for the same capability is a broken model.
Lessons from building multi-turn AI agents across Zapier Agents, Notion Custom Agents, and Relevance AI — covering context window management, system prompt bloat, sub-agent trade-offs, and context rot.
How reusable AI skills across Claude, Notion, and Linear have replaced prompt engineering as the highest-leverage way to make AI tools actually useful.
