NC Tweener Talks
NC Tweener Talks
NC Tweener Fund
[Redacted] an NC Tweener Times Podcast: The AI Workflow Graveyard: CRMs, Agents, and... Tamagotchis?
38 minutes Posted May 20, 2026 at 6:00 am.
— 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 between API costs and AI workflow complexity 14:11 — The “unlock” of passing reasoning between LLMs 15:11 — Turning AI reasoning into actual HubSpot actions 16:41 — The AI existential crisis: “This will never work” 17:50 — Wanting AI systems that can simply ask questions when stuck 18:12 — PTSD from n8n and broken workflows 19:14 — Teaching AI systems to learn from mistakes 20:16 — The tradeoffs between local AI systems and n8n 21:59 — “Every CRM is chronically out of date” 23:57 — Why clean data is foundational for AI outbound sales 25:11 — Bottom-up vs top-down AI automation strategies 26:40 — The challenge of defining “objective reality” in business data 27:14 — David’s AI-generated shareholder update workflow 28:08 — Building “super skills” with Claude Code 29:18 — Mapping every data source needed for shareholder updates 31:00 — AI reading financials, GitHub commits, payroll, and board notes 32:14 — “I could’ve just written the shareholder update myself” 33:08 — How the shareholder update skill is structured 34:03 — The first AI-generated shareholder update draft 35:00 — AI recognizing profitability and company milestones automatically 35:40 — AI analyzing GitHub commits and engineering work 36:35 — Why this kind of context-heavy AI work matters 37:16 — Final thoughts and what’s next for RedactedWhere to Find David:LinkedIn: https://www.linkedin.com/in/davidshaner/Where to Find Taylor:LinkedIn: https://www.linkedin.com/in/taylorcotner/More about Offline: https://www.linkedin.com/company/offline-media-inc-/--- This episode of Redacted is hosted by David Shaner and Taylor Cotner, and presented and produced by NC Tweener Fund.We couldn’t share posts like this without our amazing sponsors: Platinum: NC IDEA: https://ncidea.orgGold Sponsors: Balentine: https://www.balentine.com/triangle-entrepreneurs - EisnerAmpner: https://www.eisneramper.com - Robinson Bradshaw: https://www.robinsonbradshaw.com  Silver Sponsors: - Automated Consulting Group: https://automated.co - Bank of America: https://business.bofa.com/en-us/content/technology-industry-group.html
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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 between API costs and AI workflow complexity 14:11 — The “unlock” of passing reasoning between LLMs 15:11 — Turning AI reasoning into actual HubSpot actions 16:41 — The AI existential crisis: “This will never work” 17:50 — Wanting AI systems that can simply ask questions when stuck 18:12 — PTSD from n8n and broken workflows 19:14 — Teaching AI systems to learn from mistakes 20:16 — The tradeoffs between local AI systems and n8n 21:59 — “Every CRM is chronically out of date” 23:57 — Why clean data is foundational for AI outbound sales 25:11 — Bottom-up vs top-down AI automation strategies 26:40 — The challenge of defining “objective reality” in business data 27:14 — David’s AI-generated shareholder update workflow 28:08 — Building “super skills” with Claude Code 29:18 — Mapping every data source needed for shareholder updates 31:00 — AI reading financials, GitHub commits, payroll, and board notes 32:14 — “I could’ve just written the shareholder update myself” 33:08 — How the shareholder update skill is structured 34:03 — The first AI-generated shareholder update draft 35:00 — AI recognizing profitability and company milestones automatically 35:40 — AI analyzing GitHub commits and engineering work 36:35 — Why this kind of context-heavy AI work matters 37:16 — Final thoughts and what’s next for RedactedWhere to Find David:LinkedIn: https://www.linkedin.com/in/davidshaner/Where to Find Taylor:LinkedIn: https://www.linkedin.com/in/taylorcotner/More about Offline: https://www.linkedin.com/company/offline-media-inc-/--- This episode of Redacted is hosted by David Shaner and Taylor Cotner, and presented and produced by NC Tweener Fund.We couldn’t share posts like this without our amazing sponsors: Platinum: NC IDEA: https://ncidea.orgGold Sponsors: - Balentine: https://www.balentine.com/triangle-entrepreneurs - EisnerAmpner: https://www.eisneramper.com - Robinson Bradshaw: https://www.robinsonbradshaw.com  Silver Sponsors: - Automated Consulting Group: https://automated.co - Bank of America: https://business.bofa.com/en-us/content/technology-industry-group.html 
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