{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"NC Tweener Talks","title":"[Redacted] an NC Tweener Times Podcast: The AI Workflow Graveyard: CRMs, Agents, and... Tamagotchis?","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/63b1ef1d\"></iframe>","width":"100%","height":180,"duration":2282,"description":"In episode 2 of Redacted, David and Taylor get into the messy middle of building with AI inside a real business.After compressing Offline from a 34-person team to a much smaller operating crew, AI stopped being a fun experiment and became a necessity. This episode is about what that actually looks like: rebuilding lead-gen workflows, trying to make HubSpot reflect reality, keeping AI agents alive like Tamagotchis, and testing whether Claude Code can help generate a real shareholder update from scattered company data.What They CoverWhy David and Taylor are sharing their AI experiments publiclyHow Offline compressed from 34 full-time employees to a much smaller team while still serving hundreds of restaurants and thousands of subscribersWhy CRM cleanup is way harder than it soundsThe difference between n8n workflows and locally built AI agent systemsTaylor’s attempt to build a multi-agent flow for HubSpot cleanupThe “AI existential crisis” that happens when a system kind of works, but not enoughDavid’s shareholder update experiment using Claude CodeHow AI pulled context from financials, GitHub commits, payroll, board notes, and prior updatesWhy the best AI workflows are often context problems, not prompt problemsThe takeaway: AI can do a lot more than send one email, but only if you teach it where the business actually lives.Timestamps00:00 — Welcome back to Redacted 00:36 — “Why should people even listen to us?” 02:07 — How Offline compressed from 34 employees to a tiny team 03:58 — The original AI lead-gen and CRM automation experiments 06:27 — Translating complicated human workflows into AI systems 07:00 — AI-powered inbound lead classification and HubSpot automation 08:09 — Using RSS feeds and AI to discover restaurant leads 08:52 — Where CRM automation becomes extremely difficult 10:16 — Why AI workflows become “Tamagotchis” 11:10 — Taylor’s multi-agent HubSpot cleanup system 12:18 — Why clean CRM data matters more than people think 13:08 — The tradeoff...","thumbnail_url":"https://img.transistorcdn.com/UEzoK7N1siD9YRaSVBWfZ8B3suSS2aonEIZ5NwBH1Gs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS80NjBh/YWVhZTA4ZTUyY2Fl/MDdhNzQwZWFhNDI4/MDc3Zi5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}