Podcast
This New Way
Hosted by Fellow.ai · EN
This New Way (formerly Supermanagers) is a show hosted by Aydin Mirzaee (CEO of Fellow–#1 AI Meeting Assistant) about how real companies are using AI at work. No theory, no fluff — just straight talk with leaders who are testing, implementing, and learning as they go.
What you’ll get:
How leaders are integrating AI into their teams and processes
Honest takes on what’s working, what’s not, and what’s changing
Live AI tool demos
👉 Want episode summaries, AI workflow templates, and quick tips from guests? Subscribe to the newsletter: https://thisnewway.com/
11episodes
Episodes
Newest firstAll episodes
AI Reveals Competitor Content Strategies in Minutes with Chris Long
Mar 500:46:43Tap to summarizeChris Long (formerly at Go Fish Digital, now co-founder of Nectiv Digital) explains how AI is reshaping search from two angles: (1) operational automation (briefs, research, internal linking, refresh workflows) and (2) shifting buyer behavior, where people increasingly start discovery in LLMs and use Google more as a verification / reputation check. He demos how MCP connectors let you query Ahrefs and Google Analytics conversationally (often in Claude), then blend datasets to generate competitive insights, keyword clustering, and strategy gaps—without living inside traditional dashboards.Timestamps0:00 — Intro: SEO vs AEO/GEO and why AI is changing the game0:20 — Two AI impacts: automating SEO work + changing how buyers discover products1:50 — Google becomes “verification” while LLMs become discovery (especially in B2B)3:00 — “WebMCP” concept: standard rails so agents can reliably take actions on websites5:25 — Optimizing for agents (treating them like VIP visitors) and what that means for sites6:15 — Why LLM/agent usage is hard to measure (clicks vs logs vs self-reported attribution)10:00 — Nective’s “build first” approach: tools/workflows before hiring more people14:00 — Demo: Ahrefs MCP in Claude for competitor insights + content strategy patterns27:45 — Demo: Google Analytics MCP (and why it’s a relief vs GA4’s interface)35:50 — Blending Ahrefs + GA data to generate strategy gaps and page ideas39:00 — AEO tooling landscape: LLM trackers (Profound, Athena) + automation (n8n, AirOps)41:15 — Autonomous agents (OpenClaw) and the future of “persistent” task completion45:15 — Where to find Chris (LinkedIn + Nective Digital)Tools & technologies mentionedSEO / AEO / GEO — Approaches to improving visibility in traditional search and AI-generated answers.LLMs (Large Language Models) — Used for research/discovery; increasingly the first stop before Google.Agents / Agentic browsing — Software that navigates websites and completes actions (forms, carts, checkout).WebMCP (as discussed) — Structured markup/standardization so agents can precisely interact with site elements.MCP (Model Context Protocol connectors) — Connectors that let AI query external tools via natural language.Ahrefs — SEO data platform (traffic estimates, backlinks, top pages, competitor research).Claude (web + Claude Code) — Used for data-heavy work and debugging MCP setups.ChatGPT — Mentioned as preferred for more knowledge-based tasks compared to data analysis.Google Analytics 4 (GA4) — Web analytics; MCP access can reduce reliance on the GA4 UI.Server access logs — Useful for identifying agent/bot activity not visible in standard analytics reports.BigQuery — Intermediary data warehouse for querying analytics data more flexibly.Slack — Used for capturing “how did you hear about us?” attribution signals.Profound — LLM visibility/brand mention tracking tool.Athena — Another LLM visibility tracker discussed as more data-driven/scalable.n8n — Workflow automation for content engineering pipelines.AirOps — Automation/content workflow tooling mentioned alongside n8n.OpenClaw — Referenced as an autonomous agent tool example.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
Transcribe →AI Launches a Business in 40 Minutes with Samruddhi Mokal of Pace Labz
Feb 1900:36:55Tap to summarizeThis episode is a full “build a business in 40 minutes” demo showing how AI collapses what used to take teams (creative production + sales ops + support) into a handful of prompts. Samruddhi generates a high-production video ad in Google AI Studio using a JSON-style prompt framework, then spins up a working voice sales/support agent in Vapi via Claude Desktop + MCP—so the agent is created from a single prompt instead of clicking through the UI. The conversation also covers why “interfaces matter less” in an agent-first world, why workflow tools (like n8n) still have a role, and how memory layers like Mem0 unify context across channels (email/WhatsApp/etc.) so you can take actions without hunting.Timestamps0:00 — “Single person billion-dollar company” belief + AI driving 10x execution speed1:57 — Plan: create the ad in Google AI Studio (Veo 3.1) + build a voice agent using Vapi MCP via Claude Desktop2:42 — Smithery: marketplace for MCP servers3:39 — MCP for non-technical listeners: “like an API, but agents use it to talk to external services”4:22 — Inside Vapi MCP: tool list = APIs the agent can choose from5:06 — AI Studio setup: video generation playground + select Veo 3.16:16 — JSON prompting framework begins (structure → production-level output)6:28 — Keys: description, style, camera, lighting, environment, elements, motion, ending, text9:05 — Prompts/scripts can be AI-generated (humans provide guardrails)10:41 — Need an API key to generate videos in AI Studio10:54 — Ad review: strong realism; last segment looks AI-ish → iterate prompt13:05 — Install Vapi MCP via npx from Smithery + add Vapi API key13:46 — Claude Desktop: Vapi MCP appears under Connectors/Tools (not Claude web)14:05 — Prompt the agent build: “Fresh Pause” + role, tasks, FAQs, call flows18:23 — Testing: “Talk to assistant” starts a live call simulation19:20 — Deployment: assign a phone number; Vapi provides free/test numbers (up to a limit)21:57 — Mem0 / Supermemory: memory layer across apps/agents to keep context24:13 — Why memory layers help: fewer MCPs → less slowdown/hallucination; no need to specify where to search26:36 — MCPs + slide decks: mention of Gamma MCP via Claude27:34 — Future of n8n/Zapier: they persist, but prompting increasingly generates workflows31:38 — Prediction market trading algos (Kalshi/Polymarket) + AI improves speed/decision-making36:02 — Closing vision: help orgs 10x execution speed, especially non-technical leaders (40+) with domain expertiseTools & technologies mentionedGoogle AI Studio (Video Generation Playground) — Generate an 8-second video ad.Veo 3.1 — Google video model used for “production-level” output.JSON Prompting Framework — Structured key/value prompts for story, visuals, camera, lighting, motion, ending frame.Claude Desktop — Runs connectors/tools (including MCP servers).MCP (Model Context Protocol) — Lets agents call external services/tools based on intent.Smithery — Directory/marketplace for MCP servers.Vapi — Voice agent platform; create agents + assign phone numbers.Vapi MCP Server — Enables Claude to operate Vapi via prompts (create/list/configure).npx — Installs MCP server quickly from the terminal.API Keys — Required for AI Studio generation + Vapi authentication.Mem0 / Supermemory — Cross-channel memory layer to retrieve context automatically.Knowledge Graph — Underlying structure for semantic retrieval across interactions.Glean — Referenced as a comparison point for search/context retrieval.Gamma MCP — Example of generating slide decks via MCP.n8n / Zapier — Workflow automation tools discussed in an MCP-first future.OpenClaw — Mentioned as agent tooling that can help with steps like obtaining API keys.Kalshi / Polymarket — Prediction markets referenced in the trading/AI speed discussion.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
Transcribe →How AI Transformed Content Marketing: The Secrets Behind Updating 100+ Posts in a Week
Feb 500:39:17Tap to summarizeManuela Barcenas breaks down how marketing work has flipped from “writer + editor” to “manager of agents.” She shares two concrete workflows: (1) using Claude Projects to reposition and modernize 100 legacy blog posts in a week (including updated product messaging, AI-forward advice, and internal links), and (2) using Fellow’s “Ask Fellow” to mine anonymized customer-call transcripts for original quotes and pain points—then turning those insights into publish-ready integration/use-case articles in hours, not weeks. The throughline: output is easy now; taste, judgment, and review are the differentiators.Timestamps0:00–0:00 - Intro1:18–2:54 Early Fellow days: one blog/week, months-long ebooks, craftsmanship vs scale3:06–3:26 Scale expectations now: Amazon’s ebook upload limit anecdote (3/day)3:40–4:30 Fellow previously managing an “army of writers” → now mostly AI/agents4:36–5:00 “Taste” as the differentiator: what good content is + standing out5:53–7:12 The 100-post update explained: not link swaps—full repositioning + modernized advice7:25–9:36 Switching from ChatGPT to Claude; LinkedIn poll results + “context retention” theme9:48–10:21 Claude Projects setup: separate projects to maintain context and instructions14:43–15:29 Prompt versioning: internal links, new features, and repeated refinement cycles18:55–19:20 Demo: paste URL → Claude fetches page → follows checklist automatically19:26–20:24 Manuela’s QA: she reads/edits everything; “taste” = final layer (like editing writers)21:38–23:17 Claude Skills discussion: turning repeated workflows into reusable MD “skills” (personal vs company-wide)25:42–26:26 SEO myth: focus isn’t “AI penalty,” it’s originality and substance (quotes, stats, real insight)26:38–28:39 Original content engine: Ask Fellow pulls anonymized customer-call insights by feature/integration28:39–31:21 Building documents from transcripts (pain points, best practices, FAQs, quotes) → export to Doc/PDF31:21–33:29 Feed exported insights into Claude Project to draft a tight article rich with customer quotes33:29–36:06 Why it works: management loop (outcomes → constraints → review → feedback) at faster cadence36:18–37:30 What’s next: Claude Code / Claude “co-work”; projects as “mini employees”37:02–38:06 Personal brand workflow: Claude analyzes best LinkedIn posts → style guide + voice-based drafting (Whisper Flow)38:28–39:12 Wrap: AI speed is real; staying current requires constant learningTools & technologies mentioned (with brief descriptions)Claude (Anthropic) — LLM used for higher-quality long-context writing, structured rewrites, and content systems.Claude Projects — Workspace feature to keep persistent instructions/context per workflow (e.g., content optimization agent).Claude Skills — Reusable capabilities packaged as uploaded markdown files (personal or org-wide) to standardize output.Claude Code / Claude “co-work” — Anthropic workflows/webinars referenced for deeper automation beyond writing (emerging).ChatGPT — Baseline comparison model; Manuela notes switching due to Claude’s perceived context + output quality.Excel + Claude — Mentioned via finance demo: using Claude in Excel to build financial models.Fellow.ai — AI meeting assistant used for transcripts, summaries, action items, and cross-tool integrations.Ask Fellow — Fellow feature that queries meeting knowledge (calls/transcripts) to generate anonymized insight docs.Anonymization (in Fellow) — Removes identifying customer details while preserving job titles/quotes for safe content use.Integrations (examples named) — Slack, Asana, HubSpot, Salesforce, Linear, Jira, Confluence (tools Fellow connects with).Whisper Flow — Voice-to-text capture tool used to speak ideas, then convert into styled writing (e.g., LinkedIn drafts).Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
Transcribe →AI Replaces Manager Guesswork With Company-Wide Employee Insight | Shweta Kamble & Hari Iyer
Jan 2900:49:16Tap to summarizeAI is pushing knowledge work toward a world where “leaders manage agents”—and eventually, where some management functions themselves are handled by AI. Shweta Kamble and Hari Iyer (founders of HaloVision) unpack that future and demo what it looks like today: an AI “third-party” that runs confidential 1:1-style conversations with employees, synthesizes themes into quantified “case files,” and creates a bidirectional channel between executives and the org.00:00 - Intro01:02 — “Undercover Boss” analogy: AI can surface ground-truth operational fixes at scale.02:01 — “No ICs anymore”: the shift to managing armies of agents.03:06 — AI can outperform average managers at listening, context, and coaching—at scale.04:47 — Introducing Halo Vision: management + AI as a core intersection.05:19 — What Halo does: confidential 1:1 conversations, analyzed into exec-ready insights.06:00 — Key difference: not a suggestion box—Halo quantifies impact and outcomes.06:36 — 1:1 controversy (e.g., “don’t do 1:1s”) and why time cost matters.08:11 — Third-party confidentiality: why employees share more with Halo than internal tools.09:30 — SurveyMonkey comparison: blending “survey + 1:1 + executive alignment.”10:50 — Feedback loop requirement: employees must believe feedback leads to change.12:06 — Founders’ backgrounds (Zoom AI/data products; CS/product design; Cisco ventures).16:28 — Building Halo = “several companies in one”: auditing, privacy, PM estimation, infra.18:03 — “Telephone game” across agents: why infra/evals matter for compound accuracy.19:47 — Defining evals: correctness, reasoning tests, summarization/synthesis checks.23:32 — Concrete eval example: summaries must trace back to transcript evidence.27:03 — Added complexity: longitudinal context and time relevance (“6 months ago may not matter”).30:39 — Prompt → context engineering: getting the right info to the model at the right time.32:16 — Why off-the-shelf tools weren’t enough: auditability and tracing across abstraction layers.37:18 — Live demo setup: Halo’s internal “case file” view with quantified issues.38:01 — Example case files: exec jumping into low-level decisions; burn rate + delay cost estimates.41:16 — Live call begins: confidentiality disclaimer + agenda choices.41:50 — Halo’s questioning style: reflective, probing, tailored follow-ups.46:17 — Positioning: Halo doesn’t replace 1:1s—it makes them more effective and focused.47:00 — What they’re excited about next year: science/research advances + shifting human work.Tools & technologies mentionedHalo Vision — AI “third-party” that conducts confidential employee conversations, synthesizes insights into quantified exec recommendations, and helps align understanding across the org.Evaluation frameworks (Evals) — Methods to test AI outputs (reasoning, summary accuracy, grounding) to prevent misleading conclusions and compounding errors in agent workflows.LLM-as-a-judge — Using an LLM to grade another model’s output for correctness, grounding, or quality; often paired with other checks.Tracing / auditability / evidence links — Attaching each summary claim to specific transcript excerpts so you can prove where conclusions came from and debug errors.Speech-to-text / transcription — Converting conversations into text artifacts that can be analyzed, summarized, and traced.Fellow.ai — AI meeting assistant that joins meetings, summarizes, tracks actions/decisions, integrates with common work tools, and supports sensitive meetings with privacy/security controls. Gemini (Google) — Mentioned as performing strongly for some use cases relative to other models at the time of recording.GPT-4 / GPT-5 (and “5.2”) — Used as examples of model shifts affecting product behaviour (reasoning chains, tone/EQ, evaluation requirements).Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
Transcribe →How Claude Code Powers GTM Engineering & AI Agents with Hai Nghiem
Jan 2200:36:05Tap to summarizeIn this episode of This New Way, Aydin sits down with Hai Nghiem from AGI Ventures Canada to explore how Claude Code is changing the way teams build software, automate workflows, and even run go-to-market operations—without requiring everyone to be a developer.Hai walks through real, hands-on examples of using Claude Code as a terminal-based AI agent to qualify inbound leads, generate follow-up emails and statements of work, manage internal context with skills and sub-agents, and even automate browser-based tasks like filling out applications. The conversation dives deep into go-to-market engineering, context engineering, and why skills are becoming one of the most powerful primitives for scaling AI across an organization.If you’re curious how non-technical teams can start using agents today—or how technical teams can dramatically compress GTM and sales workflows—this episode is a must-listen.Key Timestamps00:00 - Intro00:08.334 – “What’s the killer AI product everyone should be using?”00:25.582 – Hai introduces Claude Code and why it’s blowing up01:10.900 – Claude Code as an agent running in your terminal01:45.600 – Go-to-market engineering and reducing SDR teams02:10.222 – Industry trend: shrinking sales teams with AI agents03:45.976 – Claude Code vs Cursor for coding workflows04:32.100 – Writing 90% of production code with AI (safely)05:45.300 – Non-coding automation with Claude Code, Zapier, and n8n06:01.645 – What AGI Ventures Canada does06:45.900 – AI Tinkers community and the origins of AGI Ventures07:38.958 – Automating inbound lead qualification08:50.839 – Live role play: discovery call walkthrough09:12.607 – Using Notion as a live note-taker and context store10:03.350 – Example GTM automation use cases at Fellow11:52.973 – Running Claude Code with “dangerously skip permissions”13:07.050 – Sub-agents vs skills explained16:40.851 – What Claude “skills” actually are17:15.359 – Email writer skill walkthrough20:19.750 – Auto-updating skills from real GTM learnings22:19.592 – How Claude pulls context from Notion automatically25:42.632 – Generating follow-up emails using skills30:08.595 – Generating Statements of Work with scripts31:35.478 – Browser automation with the Claude Chrome extension32:16.870 – Auto-filling applications using personal skills34:56.562 – AI-powered Discord bot for community support37:18.114 – Live fact-checking inside Discord38:09.159 – How to contact AGI VenturesTools & Technologies MentionedClaude (Anthropic)An AI assistant positioned as a business-focused alternative to ChatGPT.Claude CodeA terminal-based AI agent that can write code, automate workflows, manage files, and interact with browsers—used heavily for GTM and internal automation.Claude SkillsLightweight, reusable instruction sets that teach Claude how to perform specific tasks (e.g., writing sales emails) without permanently consuming context.Claude Sub-agentsDelegated agents used to manage context and offload complex tasks without bloating the main agent’s context window.NotionUsed as a lightweight CRM, document store, and central source of truth for agent context.DiscordPrimary internal and community communication platform, integrated with AI bots for automated responses.Chrome Automation (Claude Extension)Allows Claude Code to control the browser and complete web-based tasks like filling out forms.ZapierNo-code automation tool for connecting apps and workflows.n8nOpen-source workflow automation tool often used for advanced AI and agent pipelines.GPT Models (OpenAI)Currently used in AGI Ventures’ Discord bot, with plans to migrate to Claude models.Contact Hai:agiventures.cahai@agiventures.cahttps://ca.linkedin.com/in/haiphunghiemSubscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
Transcribe →AI Writes 99% of Your Code and Updates Docs Instantly with Amir M. of Humblytics
Jan 1500:49:59Tap to summarizeAmir (Co-Founder at Humblytics) shares how he builds an “AI-native” company by focusing less on shiny tools and more on change management: assessing AI fluency across roles, setting the right success metrics, and creating shared context so AI can reliably ship work. The big theme is convergence—engineering, product, and design are collapsing into tighter loops thanks to tools like Cursor, MCP connectors, and Figma Make. Amir demos workflows like: AI-generated context files + auto-updated documentation, scraping customer domains to infer ICPs, turning screenshots into layered Figma designs, then converting Figma to working React code in minutes, and even running an “AI co-founder” Slack bot that files Linear tickets and can hand work to agents.Timestamps0:00 Introduction0:06 Amir’s stance: “no AI experts” — it’s constant learning in a fast-changing field.1:59 Cursor as the unlock: not just coding, but PM/strategy/design work via MCPs.4:17 The real problem: AI adoption is mostly change management + fluency assessment.5:18 The AI fluency rubric (helper → automator → augmentor → agentic) and why it matters.8:13 Cursor analytics: measuring AI-generated code and usage across the team.9:24 “New code is ~99% AI-generated” + how they keep quality via tight review + incremental changes.10:58 Docs workflow: GitBook connected to repo → AI edits docs and pushes live fast.14:02 ICP building: export Stripe customers → scrape domains with Firecrawl → cluster personas.17:45 Hallucination in the wild: AI misclassifies a company; human correction loop matters.34:43 Wild move: they often design in code and use an AI-generated style guide to stay consistent.38:10 Best demo: screenshot → Figma Make → layered design → Figma MCP → React code in minutes.45:29 “AI co-founder” Slack bot (Pixel): turns a bug report into a Linear ticket and can hand off to agents.48:46 Amir’s wish list: we “solved dev”; now we need Cursor for marketing/sales → path to $1M ARR.Tools & technologies mentionedCursor — AI-first IDE used for coding and product/design/strategy workflows; includes team analytics.MCP (Model Context Protocol) — “connector” layer (Anthropic-origin) that lets LLMs interface with external tools/services.ChatGPT — used as a common baseline tool; discussed in the context of prompting practices and workflows.Microsoft Copilot — referenced via the law firm incentive story; used as an example of “usage metrics” gone wrong.Anthropic (AI fluency framework) — inspiration source for the helper/automator/augmentor/agentic rubric.GitBook — documentation platform connected to the repo so docs can be updated and published quickly.Firecrawl (MCP) — agentic web scraper used to analyze customer domains and infer ICP/personas.Stripe — source of customer export data (domains) to build ICP clustering.Figma — design collaboration tool; used here with Make + MCP to move from design → code.Figma Make — feature to recreate UI from an image/screenshot into editable, layered designs.Figma MCP — connector that allows Cursor/LLMs to pull Figma components/designs and generate code.React — front-end framework used in the demo for generating functional UI components.Supabase — mentioned as part of a sample stack when generating a PRD.React Router — mentioned as part of the sample stack in PRD generation.Slack — where Amir runs internal agents (including the “AI co-founder” bot).Linear — project management tool used for creating tickets from Slack/agent workflows.CI/CD — their deployment/review pipeline; emphasized as the human accountability layer.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
Transcribe →How an Ex-CTO Vibe Codes Production Apps with AI with Paul Xue from Karmic
Dec 1100:43:26Tap to summarizeIn this episode, Aydin sits down with Paul Xue, a self-described “vibe marketer” and former 3x CTO who now runs an AI-native Reddit growth agency. Paul explains why he believes any assumption you made about AI even three months ago is probably wrong today, and how that realization pushed him to pivot away from writing code as a long-term career.He walks through how his team ships production software where ~100% of the code is AI-generated, why 80% of the work now lives in planning and system design, and how new models like Claude Opus 4.5 and Gemini 3 let him literally “go for a walk” while his tools implement features. Along the way, Paul shares real numbers (two years of work vs 10–15 hours), what this means for agencies and devs, how he hires in an AI-native world, and gives a behind-the-scenes tour of the multi-agent workflows powering his Reddit content engine.Timestamps0:00 – Introduction1:01 – What a “vibe marketer” is and why Reddit is a power channel in the LLM era3:01 – From 3x CTO to Reddit-first entrepreneur: deciding coding isn’t future-proof4:06 – GPT-3.5 + end of zero interest rates: when dev agency contracts fell off a cliff6:28 – Adoption curves: senior devs who still don’t use AI and why personality matters7:57 – Running an AI-native shop where ~100% of production code is AI-generated9:48 – Two years vs 10–15 hours: Paul’s personal 10x story on shipping an MVP12:04 – New development workflow: “plan mode” and spending 80% of time on specs18:17 – Claude Opus 4.5, Gemini 3, and “going for a walk” while AI finishes features23:30 – How $60K–$250K apps turn into weekend side projects with vibe coding tools27:12 – Hiring in the AI era: why pure “ticket-taking” devs won’t survive35:12 – Inside an AI-native Reddit engine: n8n workflows, agents, Pinecone & OpenRouterTools & Technologies MentionedReddit – Primary growth and content channel; a highly trusted source for LLM training and citations.ChatGPT / GPT-3.5 – Early model that triggered Paul’s realization that traditional coding careers would change.Claude 3.5 Sonnet & Claude 3.5 Opus / Opus 4.5 – Anthropic models Paul uses for long-running coding, planning, and browser automation.Gemini 3 – Google model Paul uses to quickly generate solid, familiar SaaS-style UI/UX ideas.Cursor – AI-native code editor that turns detailed “plans” into production code with one click.n8n – Automation platform that powers Paul’s multi-step AI workflows for content creation and evaluation.Pinecone – Vector database storing each client’s knowledge base for highly relevant Reddit responses.OpenRouter – Routing layer that lets Paul easily swap and test different language models over time.MCP (Model Context Protocol) – Framework he uses to give agents tool access (e.g., scraping Reddit, reading DBs).Notion – Fast prototyping environment to validate data models and workflows before writing custom code.Zapier – General automation glue in the earliest workflow experiments.Figma – Design tool, now increasingly AI-assisted, for UI/UX mockups.SpecCode – Tool Paul cites for vibe coding HIPAA-compliant applications.Anything – Mobile-focused “vibe coding” platform for building iOS/Android apps on your phone.Fellow – AI meeting assistant that joins meetings, produces summaries/action items, and acts as an AI chief of staff.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
Transcribe →AI Automates Email, Meetings & Internal Workflows with Mike Potter
Dec 400:51:44Tap to summarizeAydin sits down with Mike Potter, CEO and co-founder of Rewind, to talk about how AI is changing both the risk and opportunity landscape for SaaS companies. They cover how AI agents are now deleting real customer data, why backup is more critical than ever, and how Rewind became an AI-native org with dedicated AI ownership, monthly Lunch & Learns, and real internal workflows.Mike walks through the exact N8N workflows he uses to:Auto-triage his Gmail into multiple inboxes using AIGenerate a daily AI brief based on tasks, calendar events, and past email contextAnalyze churn, win/loss, and internal product data using Claude and MCPThey close with Mike’s “dream automation”: a full AI-generated business review that looks across financials, CRM data, and benchmarks.Timestamps:0:00 — Welcome to the show0:31 — Mike’s intro & what Rewind backs up across SaaS ecosystems1:40 — AI agents as a new failure mode and how Rewind “saves you from your AI”4:05 — Turning Rewind into an AI-native company early on4:53 — First attempt at AI-built integrations (why it failed then, why it might work now)7:23 — Developers trading tedious integration maintenance for more interesting AI work9:45 — Code vs architecture: the Shopify webhooks story and handling 1.1B+ events14:03 — Hiring an AI Engineer: scope, responsibilities, and why background mattered15:33 — How Rewind drove AI adoption: Lunch & Learns, “use it in your personal life,” experimentation20:53 — How AI Lunch & Learns actually run across multiple offices and remote folks23:10 — Examples: CS tools, Alloy prototypes, AI video voiceovers, end-to-end workflows25:13 — Churn workflows: combining uninstall reasons from multiple marketplaces into Claude27:06 — Win/loss and internal analytics using Claude Projects + MCP server into an internal DB29:14 — Choosing between Claude, ChatGPT, and Gemini depending on the task (and re-testing every few months)31:23 — Mike’s Gmail system: multiple inboxes + N8N + AI classification36:07 — Inside the email-classifier prompt and AI-powered spam that beats Gmail filters41:34 — The “Daily AI Brief”: pulling tasks, meetings, and prior email threads into a single morning email45:02 — Letting AI write and debug N8N workflows (and how assistants in tools are getting better)48:58 — Wishlist: automated AI business review across finance, Salesforce, and SaaS benchmarks51:23 — Closing thoughts: so many useful tools are possible, but GTM is the hard partTools & Technologies MentionedRewind – Backup and restore for mission-critical SaaS applications.Claude – LLM used for analysis, projects, agents, and internal tools.ChatGPT / OpenAI (GPT-4.1, GPT-4.1 mini) – LLMs used for code, prompts, and workflow JSON.N8N – Automation platform used to build email and daily-brief workflows.Gmail – Email client where AI-powered labels drive multiple inboxes.Google Calendar – Calendar data powering the daily AI agenda.Google Tasks – Task list feeding into the morning brief email.MCP (Model Context Protocol) – Connects Claude to Rewind’s internal databases.Alloy – Tool for building interactive product UI prototypes.Salesforce – CRM used for pipeline, churn, and win/loss analysis.Gumloop – Workflow tool with an embedded AI assistant.Zapier – Automation platform referenced for plain-English workflow creation.Fellow – AI meeting assistant for summaries, action items, and insights.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
Transcribe →AI Lets Kids Build Their Own Learning Games with Aydin Mirzaee
Nov 2700:28:50Tap to summarizeIn this special “build with me” episode, Aydin and Manuela walk through how Aydin used Lovable to build a unicorn-themed multiplication and division game with his nine-year-old twin daughters. They show how to go from a spoken idea to a working web app in minutes, then keep iterating to add playful design, timers, division mode, mix mode, and a leaderboard—using it as a fun way to teach kids both math and how to think and communicate clearly with AI.The episode closes with a push for parents, aunts, uncles, and anyone with kids in their lives to start doing, not just watching: use AI builders like Lovable as a playful way to get kids hands-on with AI, programming, and creative problem solving.Timestamps00:00 - Welcome to the episode01:07 – Why Aydin wants parents to teach kids AI through projects01:40 – Twin nine-year-olds and the idea for a multiplication game03:33 – Screen share: introducing Lovable and Super Whisper05:44 – Dictating the first prompt for the multiplication quiz08:13 – First working version of the game and scoring demo11:25 – Adding unicorn theme, confetti, poop emoji, and multiple choice13:49 – Using Lovable’s free plan and email accounts for kids16:11 – Publishing the game and sharing it via a public link17:22 – Adding division mode, mix mode, and a timer22:12 – Demoing division mode and brainstorming a leaderboard24:38 – Explaining why the app now needs a database27:41 – Registration, login, and live leaderboards in action29:50 – “Now is the time to build” with tools like Lovable30:51 – Parting advice for parents, aunts, and uncles: start doing, not just watchingTools & Technologies Mentioned:Lovable (lovable.dev)Super WhisperLovable’s built-in voice-to-textCloud database (via Lovable)Bolt.newClaudeChatGPTGoogle/Gmail family accounts for kidsFellow.aiSubscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
Transcribe →AI Supercharges Content Marketing & Workflow Automation with Ryan McCready
Nov 2000:44:13Tap to summarizeIn this episode, Aydin sits down with Ryan McCready, who went from hating AI to becoming one of the most creative AI-powered content builders on the internet. After getting laid off in mid-2025, Ryan realized that every job interview demanded AI fluency. So he went all-in, teaching himself Zapier, Lovable, Supabase, and advanced prompting to engineer a “Content Factory” that turns a webinar into blog posts, clips, and social content in minutes.He shares the mindset shift from “AI is plagiarism” to “AI is an input-output engine,” why content engineering is the future, what makes AI workflows actually work, and how breaking big tasks into many small steps is the secret to non-sloppy AI content.You’ll see how he built a 30-step Zapier workflow that analyzes a webinar transcript, extracts frameworks and insights, turns them into pitches, builds outlines, writes social posts, and even generates clip candidates for Descript. If you create content or run marketing—this one is a masterclass.Timestamps0:23.00 – Why he believed AI was a “plagiarism machine”2:04.00 – Getting laid off and realizing every employer wanted AI skills4:37.00 – The workflow that kickstarted his learning (LinkedIn voice extraction + employee advocacy shares)5:40.00 – Learning Lovable and Supabase by building real projects6:51.00 – Why “everyone is a builder now” because of AI tools7:52.00 – Introducing “Content Engineering” and why most marketers can’t do it9:03.00 – Example: turning a webinar into 10+ pieces of content10:58.00 – Why webinars usually die after they’re aired—and why that’s a waste11:43.00 – The “Webinar Content Flywheel” teaser16:30.00 – Why Ryan moved back from n8n to Zapier17:55.00 – Zapier vs. n8n: simplicity, stability, and architecture19:03.00 – “Start small”: a two-step Zap example20:09.00 – Interface demo: uploading a transcript and hitting “Go”21:22.00 – Why Zapier Interfaces make deployment easy22:40.00 – Step-by-step breakdown of the workflow24:06.00 – Example: webinar analysis output (themes, chapters, frameworks)27:02.00 – Creating three blog pitches from the transcript30:43.00 – Sending the pitches to Slack for review31:03.00 – Clip extraction workflow + Descript integration32:14.00 – How he uses Descript’s “Underlord” to auto-cut clips33:20.00 – Why this beats automated clip tools like Riverside for B2B35:02.00 – Social content workflow (framework angle, data angle, hot take, wildcard)37:12.00 – Why prompting manually is wasteful—build once, automate forever40:11.00 – “Big → small → big” framework: the secret to non-sloppy AI content41:21.00 – Google’s “AI content penalty” myth, according to Ryan42:47.00 – Why your input quality determines whether your AI output is good43:44.00 – What excites him most in the next 12 monthsTools & Technologies MentionedZapier: Automation platform used to chain 30+ steps together: analysis, pitch creation, clip extraction, social content, Notion updates, etc.AI by Zapier: Zapier’s built-in LLM module used for analysis, extraction, outline generation, and writing.n8n: Open-source workflow automation platform. Ryan tested it, but ultimately moved back to Zapier for stability and structure.Lovable: AI-enabled “vibe coding” tool that turns prompts into functional web apps.Supabase: A database + backend platform used for storing structured content data from builds.Descript (Underlord): Video editing tool with an AI agent that cuts clips based on transcript timecodes generated by the workflow.Notion: Used as the source of truth for storing transcripts, outlines, clip docs, and the full content tracker.Claude / ChatGPT: Used for second-pass expansion—turning outlines or social angles into fully polished blog posts and posts.Fellow.ai: AI meeting assistant—summarizes meetings, tracks decisions, and generates insights and performance summaries.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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