Avsnitt
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Chris 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.
Timestamps
0:00 — Intro: SEO vs AEO/GEO and why AI is changing the game
0:20 — Two AI impacts: automating SEO work + changing how buyers discover products
1:50 — Google becomes “verification” while LLMs become discovery (especially in B2B)
3:00 — “WebMCP” concept: standard rails so agents can reliably take actions on websites
5:25 — Optimizing for agents (treating them like VIP visitors) and what that means for sites
6:15 — 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 people
14:00 — Demo: Ahrefs MCP in Claude for competitor insights + content strategy patterns
27:45 — 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 ideas
39:00 — AEO tooling landscape: LLM trackers (Profound, Athena) + automation (n8n, AirOps)
41:15 — Autonomous agents (OpenClaw) and the future of “persistent” task completion
45:15 — Where to find Chris (LinkedIn + Nective Digital)
Tools & technologies mentioned
SEO / 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.
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This 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.
Timestamps
0:00 — “Single person billion-dollar company” belief + AI driving 10x execution speed
1:57 — Plan: create the ad in Google AI Studio (Veo 3.1) + build a voice agent using Vapi MCP via Claude Desktop
2:42 — Smithery: marketplace for MCP servers
3:39 — 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 from
5:06 — AI Studio setup: video generation playground + select Veo 3.1
6:16 — JSON prompting framework begins (structure → production-level output)
6:28 — Keys: description, style, camera, lighting, environment, elements, motion, ending, text
9:05 — Prompts/scripts can be AI-generated (humans provide guardrails)
10:41 — Need an API key to generate videos in AI Studio
10:54 — Ad review: strong realism; last segment looks AI-ish → iterate prompt
13:05 — Install Vapi MCP via npx from Smithery + add Vapi API key
13:46 — Claude Desktop: Vapi MCP appears under Connectors/Tools (not Claude web)
14:05 — Prompt the agent build: “Fresh Pause” + role, tasks, FAQs, call flows
18:23 — Testing: “Talk to assistant” starts a live call simulation
19:20 — 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 context
24:13 — Why memory layers help: fewer MCPs → less slowdown/hallucination; no need to specify where to search
26:36 — MCPs + slide decks: mention of Gamma MCP via Claude
27:34 — Future of n8n/Zapier: they persist, but prompting increasingly generates workflows
31:38 — Prediction market trading algos (Kalshi/Polymarket) + AI improves speed/decision-making
36:02 — Closing vision: help orgs 10x execution speed, especially non-technical leaders (40+) with domain expertise
Tools & technologies mentioned
Google 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.
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Saknas det avsnitt?
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Manuela 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.
Timestamps
0:00–0:00 - Intro
1:18–2:54 Early Fellow days: one blog/week, months-long ebooks, craftsmanship vs scale
3:06–3:26 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/agents
4:36–5:00 “Taste” as the differentiator: what good content is + standing out
5:53–7:12 The 100-post update explained: not link swaps—full repositioning + modernized advice
7:25–9:36 Switching from ChatGPT to Claude; LinkedIn poll results + “context retention” theme
9:48–10:21 Claude Projects setup: separate projects to maintain context and instructions
14:43–15:29 Prompt versioning: internal links, new features, and repeated refinement cycles
18:55–19:20 Demo: paste URL → Claude fetches page → follows checklist automatically
19:26–20:24 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/integration
28:39–31:21 Building documents from transcripts (pain points, best practices, FAQs, quotes) → export to Doc/PDF
31:21–33:29 Feed exported insights into Claude Project to draft a tight article rich with customer quotes
33:29–36:06 Why it works: management loop (outcomes → constraints → review → feedback) at faster cadence
36:18–37:30 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 learning
Tools & 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).
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AI 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 - Intro
01:02 — “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 mentioned
Halo 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.T
racing / 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).
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In 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.ca
https://ca.linkedin.com/in/haiphunghiemSubscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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Amir (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.
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In 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.
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Aydin 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 AI
Generate a daily AI brief based on tasks, calendar events, and past email context
Analyze churn, win/loss, and internal product data using Claude and MCP
They 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 show
0:31 — Mike’s intro & what Rewind backs up across SaaS ecosystems
1:40 — 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 on
4:53 — 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 work
9:45 — Code vs architecture: the Shopify webhooks story and handling 1.1B+ events
14:03 — Hiring an AI Engineer: scope, responsibilities, and why background mattered
15:33 — How Rewind drove AI adoption: Lunch & Learns, “use it in your personal life,” experimentation
20:53 — How AI Lunch & Learns actually run across multiple offices and remote folks
23:10 — Examples: CS tools, Alloy prototypes, AI video voiceovers, end-to-end workflows
25:13 — Churn workflows: combining uninstall reasons from multiple marketplaces into Claude
27:06 — Win/loss and internal analytics using Claude Projects + MCP server into an internal DB
29:14 — 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 classification
36:07 — Inside the email-classifier prompt and AI-powered spam that beats Gmail filters
41:34 — The “Daily AI Brief”: pulling tasks, meetings, and prior email threads into a single morning email
45:02 — 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 benchmarks
51:23 — Closing thoughts: so many useful tools are possible, but GTM is the hard part
Tools & Technologies Mentioned
Rewind – 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.
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In 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.
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In 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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In this episode of This New Way, Aydin chats with Scott Knowles, the co-founder of Mello, a digital process manager designed to automate human-centric workflows. Scott shares how he reentered software development after a six-year hiatus — not through online courses or bootcamps, but through ChatGPT. With AI as his co-pilot, he rebuilt his coding skills, created software from scratch, and automated complex systems like cold outreach engines and data pipelines — all for free or nearly free. The episode is a hands-on masterclass in learning, building, and automating with AI.Timestamps00:00 - Intro0:29 – 1:12 — Introduction: Scott’s background in computer engineering and management consulting2:00 – 4:18 — Founding and selling an OKR software company; early startup experience4:23 – 5:06 — What Mello is: a “digital process manager” that connects humans the way Zapier connects software5:36 – 7:00 — Returning to coding after six years thanks to ChatGPT7:42 – 8:15 — How ChatGPT helped him relearn code “like slang you forgot”9:03 – 10:13 — Learning new skills: how to ask the right questions as a beginner10:41 – 11:27 — Using ChatGPT to scope and plan projects instead of asking for instant results13:00 – 14:03 — The importance of high-level questioning before diving into code15:06 – 16:21 — When to stop and ask, “Is there a simpler way?” instead of getting lost in rabbit holes17:05 – 18:07 — The “three tries rule” for debugging with ChatGPT18:26 – 18:50 — Sometimes the fix is on Reddit: mixing AI and human answers22:01 – 27:21 — Demo: Scott’s TikTok “routine scraper” app built entirely with ChatGPT-generated code27:33 – 28:14 — How the scraper uses OCR, captions, and transcripts to build structured data28:58 – 30:06 — Using ChatGPT as a code generator — no manual coding required30:49 – 32:10 — Introduction to N8N: self-hosted automation for free cold outreach33:01 – 36:33 — Step-by-step breakdown of Scott’s automated email system using N8N and Google Sheets38:32 – 39:09 — Building high-quality prompts for personalized emails40:00 – 42:06 — How N8N automations replace tools like Clay and Smartlead42:33 – 43:09 — Watching the automation run in real time43:39 – 44:14 — Human-in-the-loop safety: drafts before sending46:02 – 47:05 — Scott on the future of AI and human collaboration47:17 – 48:31 — Aydin on “vibe coding” and how LLMs democratize software creation48:55 – 49:13 — Closing thoughts: start small, get quick wins, build momentumTools & Technologies MentionedChatGPT — Used as a real-time coding tutor and co-developer to build entire applications.Mello — Scott’s product; a digital process manager that automates human-to-human workflows.Zapier / N8N — Workflow automation tools; N8N is self-hostable and used in Scott’s cold outreach automation.Supabase — Open-source database used to store and serve data for the TikTok scraper app.Playwright — Browser automation library for scraping TikTok videos.VS Code + CodeX Plugin — Integrated code editing environment that connects directly to ChatGPT for automated coding.Fellow — AI meeting assistant that summarizes meetings, tracks action items, and integrates with other tools.OpenAI API — Powers many of the automation and text-cleaning features within Scott’s projects.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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In this episode, Aydin chats with Allan Isfan, Senior Director of Global Video Platform at Warner Bros Discovery, about how AI is reshaping creativity, software development, and large-scale enterprise culture. Allan explains how he drives AI literacy for 1,500+ employees, the power of internal demos and sandboxes, and gives a hands-on walkthrough of generative video tools like Gemini V3, Flow, and Sora. He also dives into AI video analysis, the Wizard of Oz project at The Sphere, and the future of creative storytelling powered by AI.🕒 Timestamps00:00 – Aydin welcomes Allan Ispan to the show.01:03 – Allan’s career path: Nortel → startups → venture capital → media tech.02:09 – Founding FaveQuest and My Event Apps, powering Ottawa’s festivals.02:53 – Moving to LA with UIV → acquired by WarnerMedia.03:38 – Current role: Senior Director, Global Video Platform for HBO Max, CNN, Discovery+.04:11 – How Warner Bros started its AI journey: three core pillars.04:55 – The vision: “Talk to the app” for content recommendations.05:26 – Scaling AI enablement for 1,500+ employees company-wide.06:13 – Making your company more AI-native without a Head of AI.07:00 – Step 1: Build AI literacy and create role-based learning paths.08:07 – Setting measurable goals: 80% literacy by year-end.08:46 – AI all-hands: excitement, humor, and internal FOMO.09:32 – Launching AI Friday Demos – monthly internal showcases.09:55 – Brown bag sessions for hands-on education (e.g. generative video).10:59 – Internal data querying: using AI on top of Jira and internal docs.11:41 – The origin of AI all-hands → now a recurring company event.12:53 – Experimental budgets, legal review, and security hurdles.14:07 – Creating AI sandboxes for safe experimentation.15:14 – Advice for smaller teams: give employees micro-budgets to experiment.16:54 – Generative video: “State of the art is moving bonkers fast.”17:41 – Demo 1: Google Gemini V3 — 8-second clips from text prompts.18:58 – Prompting tips: scripting short sequences for realism.23:42 – Voice options: when to use Eleven Labs for cloning.26:00 – Advanced camera moves and cinematic continuity.28:06 – “Anyone can be a director now.” Democratizing filmmaking.29:04 – Demo 2: Using Flow to connect multiple AI-generated scenes.33:04 – Cost and quality tradeoffs: fast vs. standard rendering.34:26 – Sora (OpenAI): create cameos and realistic social clips.35:41 – New business models: celebrity likeness + embedded sponsor branding.36:55 – Meta Ads: turning photos into videos for higher engagement.40:04 – Quickplay demo: searching long-form video content (“Smelly Cat” in Friends).42:54 – Live sports AI: tracking, play-by-play, and highlight automation.45:27 – The Wizard of Oz @ The Sphere (Las Vegas) – AI-enhanced 360° remake.47:39 – Allan’s 12-month outlook: expanding creative boundaries with AI.48:33 – Personal note: turning children’s books into AI-animated cartoons.🧰 Tools & Technologies MentionedChatGPT / Claude / Cursor / Windsurf – AI code assistants that boost developer productivity.Gemini V3 & Flow (Google) – Text-to-video generation and multi-scene creation.Sora (OpenAI) – Mobile app for AI cameo video generation.Eleven Labs – Industry-leading AI voice cloning.Quickplay – AI video intelligence and repurposing platform.Adobe Firefly – Generative design and image-to-animation tool.Riverside.fm – Podcast platform with AI-generated highlights.Fellow.ai – AI meeting assistant for notes, actions, and insights.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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Ryan Carson (ex-Treehouse, Intel; now Builder-in-Residence at Sourcegraph’s AMP) shares his origin story and a practical playbook for shipping software with AI agents. We cover why “tokens aren’t cheap,” how AMP made pro-level coding free via developer ads, a concrete workflow (PRD → atomic dev tasks → agent execution with self-tests), and why managers should spend time as ICs “managing AI.” We close with advice for raising AI-native kids and a perspective on this moment in tech (think integrated circuit–level shift).Timestamps
00:00 – The beginning of intelligence: how LLMs changed Ryan’s view of computing
00:23 – Apple IIe → Turbo Pascal → Computer Science: the maker bug bites
03:20 – DropSend: early SaaS, Dropbox name clash, first acquisition
04:30 – Treehouse: teaching coding without a CS degree; $20M raised, acquired in 2021
05:02 – The “bigger than a computer” moment: discovering LLMs
06:15 – Joining Intel: learning GPUs and the scale of silicon (“my adult internship”)
07:09 – Building an AI divorce assistant → joining AMP as Builder-in-Residence
09:38 – AMP vs ChatGPT/Claude/Cursor: agentic coding with contextual developer ads
11:09 – Token economics: why AI isn’t really cheap
17:27 – Frontier vs Flash models (Sonnet 4.5 vs Gemini 2.5) — how costs scale
21:31 – Private startup: vertical AI for specialized domains
22:36 – The new wave of small, vertical AI businesses
23:01 – Live demo: building a news app end-to-end with AMP
28:18 – How to plan like a pro: write the PRD before you build
30:02 – “Outsource the work, not your thinking.”
32:28 – Turning PRDs into atomic tasks (1.0, 1.1…)
35:50 – Competing in an AI world = planning well
36:28 – Managers should schedule IC time to “manage AI”
37:14 – Designing feedback loops so agents can test themselves
39:47 – “AI lied to me”: why verifiable tests matter
41:11 – Raising AI-native kids: build trust, context, and agency
43:59 – “We’re living in the integrated circuit moment of intelligence.”Tools & Technologies MentionedAMP (Sourcegraph) – Agentic coding tool/IDE copilot that plans, edits, and ships code. Now offers a high-end, ad-supported free tier; ads are contextual for developers and don’t influence code outputs.Sourcegraph (Code Search) – Parent company; enterprise code intelligence/search.ChatGPT / Claude – General-purpose LLM assistants commonly used alongside coding agents.Cursor / Windsurf – AI-first code editors that integrate LLMs for completion and refactors.Bolt / Lovable – Text-to-app builders for rapid prototyping from prompts.WhisperFlow / SuperWhisper – Voice-to-text tools for fast prompting and dictation.Anthropic Sonnet 4.5 – Frontier-grade reasoning/coding model; powerful but pricier per token.Google Gemini 2.5 Flash – Fast, lower-cost model; “good enough” for many workloads.Auth0 (example) – Authentication-as-a-service mentioned as a contextual ad use case.GPUs / TPUs – Compute for training/inference; token cost drivers behind AI pricing.PRD + Atomic Tasks Workflow – Ryan’s method: record spec → generate PRD → expand to dot-notated tasks → let the agent implement.Self-testing Scripts – Ask agents to generate runnable tests/health checks and loop until passing to reduce back-and-forth and prevent “it passed” hallucinations.Family ChatGPT Accounts – Tip for raising AI-native kids; teach sourcing, context, and trust calibration.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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Aydin sits down with Filip Skrzesinski, co-founder of Subframe, to unpack how AI and code-native design tools are collapsing the classic PM → design → engineering handoffs. Filip explains why “pictures to code” is an unfair ask of engineers, shows how Subframe lets teams design directly in the same material as production code, and demos building a Fellow feature—from screenshot → design system match → working prototype—without access to Fellow’s codebase. They close on what’s next: organizations training their own “house models” to reflect product taste, patterns, and constraints so more people across the company can truly build.Key takeawaysDesign in the same material as code: Subframe treats UI work as editable code, eliminating fidelity loss from design handoffs.Fewer stages, faster loops: PMs, designers, and engineers collaborate in one artifact; prototypes look and behave like the real app.AI as a trained teammate, not a slot machine: Teams will shape models with system prompts, snippets, and feedback—like mentoring a junior designer.Front-end ownership shifts: Designers can own front-end structure and components; engineers wire up backends and complex logic.Prototype to PRD: High-fidelity prototypes beat docs for alignment, user testing, and speed.Timestamps00:00 - Introduction 01:00 Fil's path: audio engineering → CS → design → startup co-founder03:48 Builders everywhere: from Dreamweaver → Webflow → Shopify → now “apps”04:01 What Subframe is: a design tool rooted in code05:48 Bridging LLMs (great at code) with visual design context08:09 The architect vs. printer analogy for product design12:23 Back to the show: “The new way” is collapsing steps and handoffs14:07 “Five-year” vision (sooner than you think): design → code with agents in the loop16:31 Training models on your org’s taste: like raising a puppy—examples & theory19:15 Today’s demo plan: build a Fellow feature in Subframe without codebase access21:04 Recreating Fellow’s UI: import colors/typography; screenshot → layout23:07 Don’t fight the AI: let it rough-in, then designers perfect in visual mode24:11 Why prototypes should look native (not “off-brand” sandboxes)26:07 Syncing components to codebases; where Subframe stops (front-end) and engineers continue (backend)28:33 Programmatic (deterministic) UI code & generative for visuals30:00 PMs in the tool: prompt to add a Share dialog with transcript and video context35:08 Exploring multiple design variations; mix-and-match patterns (“snippets”)37:57 From design to interactive prototype via annotations (“do this on click…”)45:22 First build runs: working Share flow; alert updates after sending47:02 Export code → Cursor/GitHub; hand off real components48:08 The next 12 months: more ideas shipped, more makers, less gatekeepingTools & technologies MentionedSubframe — Code-native design tool for building UI/UX; designs directly edit the underlying code; syncs components to your repo.Fellow.ai — AI meeting assistant with privacy controls; accurate summaries, actions, decisions; broad SaaS integrations.Cursor — AI-assisted code editor; good for continuing from exported Subframe code to production.GitHub — Repo hosting and collaboration for shipping the generated/edited UI code.AI code agents — Used by engineers to wire front-end to backend services and data.Squarespace / Webflow / Dreamweaver — Prior waves that democratized web creation; backdrop for today’s “apps layer.”Shopify — Example of no-code/low-code e-commerce; analogy for app building’s democratization.Lovable / Bolts / V0 — AI code/prototyping tools referenced as peers for generating working app scaffolds.Slack / Asana / HubSpot / Salesforce / Linear / Jira / Confluence — Systems Fellow integrates with to push notes, actions, and records.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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JD Fiscus (nerding.io) shares how a late-night hack connecting MCP to n8n exploded to ~1M downloads, then demos practical MCP workflows: indexing YouTube channels for Q&A, and auto-building n8n flows from natural language. We dig into the Agentic Commerce Protocol, real security pitfalls (like destructive commands), and how to turn MCPs into products with OAuth and Stripe for authentication and metered billing. He closes with how he teaches this hands-on at the Vibe Coding Retreat.Timestamps1:00 Why build it: “MCP shouldn’t be Claude-only”—bridging MCP into n8n early (Dec/Jan)2:09 Shipping under the pseudonym nerding.io; surprise seeing creators use it2:25 n8n later ships its own MCP server/client; they nod to nerding.io & Simon3:59 “N8n is useful, but so much more useful with MCP”5:12 What MCP means for software: every smart company is exposing an MCP; new login/usage patterns6:27 Agentic Commerce Protocol (ACP): Stripe + OpenAI; agents checkout across the web8:02 Marketing to agents not humans? SEO shifts as agents comparison-shop9:10 Early “agent mode” attempts vs protocol-based purchases (less hacky)10:58 Likely adopters: platforms (Shopify) & big retailers; echoes of early MCP evolution14:11 Security realities: token passing evolved to OAuth; hallucination + destructive actions risk16:04 Personal mishap: agent ran supabase reset on a dev DB—imagine prod! Guardrails matter17:03 Designing MCP servers: don’t just “wrap your API”; use resources/prompts for agentic UX19:04 Demo 1—Influencer MCP: index a YouTube channel, embed transcripts, ask questions in Claude20:54 Storage: embeddings into Postgres; per-channel tables24:46 Keeping it fresh: daily cron to ingest new videos25:18 Demo 2—Build n8n workflows from chat using N8N MCP (by Ramullet); live docs + API27:00 “Create a webhook → send leads to Sheets” built conversationally, with allow/deny prompts31:02 Zapier, Gumloop: agents that build automations via natural-language steps34:00 Next frontier: custom connectors (Claude/Cursor/OpenAI), OAuth auth flows for MCPs39:03 Turning MCPs into products: login with Twitter → Stripe subscription → metered billing41:12 Paid tool call demo: “paid echo” → Stripe usage event logged per user43:41 How to learn this fast: vibecodingretreat.com (small cohorts, hands-on builds)Tools & Technologies Mentioned (quick guide)MCP (Model Context Protocol) — Standard for connecting models to tools/data; supports tools, resources, prompts.n8n — Open-source automation platform; JD wrote an MCP node that went viral; also has native MCP server/client now.Claude / Cursor / OpenAI (custom connectors) — LLM IDEs/chats that can load MCPs; custom connectors enable OAuth + productized access.Agentic Commerce Protocol (ACP) — Early protocol (Stripe + OpenAI) for agent-initiated purchases with confirmations.Web MCP (W3C-oriented idea) — Emerging patterns for agent↔︎website interactions beyond human UI flows.OAuth — Secure, user-consented authentication for MCPs (vs passing raw tokens).Stripe (subscriptions + metered billing) — Attach billing/usage limits to MCP calls; track per-user consumption.YouTube API + Transcripts — Source data for the “Influencer MCP” indexing pipeline.Embeddings + Postgres — Store vectorized transcript chunks in Postgres for retrieval (JD self-hosts).Cron — Schedules daily ingestion of new content.Google Sheets — Target destination in demo for simple lead funnels.Zapier / Gumloop — Natural-language automation builders; early NLA/agent patterns.Git / CLI commands — Cautionary tale: agents running destructive commands (e.g., resets).Do Browser / Comet Browser — Agentic browsing tools referenced for web actions.Fellow.ai — AI meeting assistant with security-first design; generates precise summaries/action items.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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Aydin and Kieran Klaassen (Cora) unpack Compound Engineering—treating every task as an investment so the next time is faster. Kieran shares his path from film composer to startup CTO and live-demos how he plans → prototypes → ships a feature using AI agents (Claude Code), then runs multi-agent reviews. They discuss why managers are primed to orchestrate agents, how to capture your own feedback patterns, and why there’s “no excuse not to have a prototype” anymore.Timestamps0:07 — “Every piece of work should be an investment.”2:15 — What Cora is: an AI Gmail layer that auto-archives ~80% and briefs you twice daily.3:32 — Launch notes & early user reactions.5:21 — The Claude Code pricing saga and “finding the limits.”8:06 — Compound Engineering defined (codify how you work so AI does it next time).15:01 — From “automation” to pattern-capturing systems; natural-language rules over brittle workflows. 22:03 — Demo kickoff: planning the “Invite friends” improvement inside Cora.26:11 — Rapid mockups from a screenshot + voice description; iterate in seconds.33:06 — Multi-agent planning: repo research, best-practices scout, framework researcher.41:01 — Human judgment on plans; simplify when encryption/perf add hidden complexity.50:00 — Feature running end-to-end; agentic PR + test flow; sub-agent code reviews.Tools & Technologies MentionedCora — AI inbox copilot for Gmail that prioritizes, summarizes, and drafts replies; batches the rest into twice-daily briefs.Claude Code (Anthropic) — Agentic coding/terminal assistant used for planning, building, and reviews.Monologue — Voice-to-text for quickly describing UI and generating mockups.Every.to — Partner/design/content hub Kieran collaborates with; also publishes his writing on Compound Engineering.GitHub + GitHub CLI — Issues, branches, PRs automated by agents from plan → code → review.VS Code (with Claude Code extension) — IDE setup for hands-on edits when needed.Anthropic Console Prompt Generator — Used to scaffold robust prompts/agents, then refined manually.Model mix for reviews (e.g., “GPT-5 Codecs,” “Claude Opus”) — Alternative model passes for plan/code critique.Fellow.ai — Aydin’s AI meeting assistant for accurate notes, actions, and privacy-aware summaries.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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Content marketer and video lead Emily Kensley (Fellow) walks through a near-zero-friction workflow for creating polished product videos fast. She records clean, auto-animated screen demos with Screen Studio, fixes (or replaces) audio with Podcastle (Magic Dust + AI voices), and drafts scripts by riffing into a Fellow meeting then refining the transcript in ChatGPT. The result: 11-minute, brand-consistent tutorials produced in hours instead of days—repeatable by any team (marketing, CS, product, sales).Timestamps01:19 — Daily use of AI; from occasional to constant over last 6 months01:53 — What you’ll learn: a minimal-human, video-centric content workflow03:41 — Tool #1 intro: Screen Studio for screen recordings05:27 — Live capture of an AI meeting recap demo (click-through highlights, actions, decisions)06:23 — Raw → instant output: auto-smoothing cursor paths & smart zooms (no manual keyframes)07:23 — Host example: using Screen Studio for a Zapier + Fellow automation video07:44 — “Done is better than perfect”: quick crop fixes, branded backgrounds, cursor presets08:24 — Team presets = consistent brand across departments09:44 — Tool #2 intro & story: Podcastle rescues a day of bad mic audio10:59 — Podcastle audio editor: noise reduction, levelling, silence removal12:10 — Magic Dust AI demo: echoey room → studio-quality voice13:38 — AI Voices in Podcastle: when to clone vs. pick a preset (e.g., “Abigail”)16:12 — Long-form scripts → generated narration in minutes; edit/regenerate on typos17:54 — Brand consistency: shared voice so any team can ship VO18:29 — Putting it together: Screen Studio video + Podcastle narration19:24 — Finished example: Fellow YouTube settings walkthrough (11-minute tutorial)21:06 — Syncing visuals to VO: record screen while listening to the generated narration22:59 — Script creation workflow: Fellow call → transcript → ChatGPT → clean script23:34 — Full recap of the end-to-end pipeline25:01 — Repurposing: scripts → blogs, help center, CS clips; scale breadth of tutorials26:28 — Looking ahead: excitement about fast-evolving AI agentsTools & Technologies Mentioned (with quick notes)Screen Studio — Smart screen recorder that auto-smooths mouse movement, adds tasteful zoom/pan animations, and supports brand presets for consistent output.Podcastle — Audio suite used here to edit audio clipsMagic Dust AI: one-click studio-quality enhancement (denoise, de-reverb, leveling).AI Voices & Voice Cloning: generate narration from text; keep brand-consistent VO.Fellow — AI Meeting assistant used to host a solo “idea dump,” generate transcripts, AI recaps, chapters, and action items; doubles as the seed for scripts.ChatGPT — Refines raw Fellow transcript into a clean, concise voiceover script.YouTube — Publishing destination for finished tutorials.Zapier — Example in host’s Screen Studio demo (automation with Fellow).Google Meet / Zoom — Where the solo Fellow “recording” session can happen.Adobe (Premiere/After Effects) — Old manual workflow stand-ins (contrast to auto animations).Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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Eddie Yoon, Sr Director, Paid Media at NP Digital, shows how CMOs can spin up a full creative campaign in ~30 minutes using AI. He breaks down a rapid “three-tab” workflow—Meta Ad Library for competitive research, GPT for strategy and prompts, and an image generator (Reeve) for instant mood boards—then extends it into testing (Trial Reels, TikTok hooks), product R&D, and agentic pipelines. We also riff on why the next decade could normalize solo billionaire founders, how Netflix foreshadowed AI-driven content, and what real-time, stylized, monetizable media will look like.
Timestamps
1:07 Meet Eddie Yoon—NP Digital, paid social × creative × AI background.
1:49 “AI is redefining growth”: blistering company speed and scale.
2:16 The solo-founder era & agentic executive teams.
4:39 Enterprise example: HubSpot’s leadership going all-in on AI.
5:29 Founder example: Tyler at Beehive—shipping fast by listening + acting.
6:30 Design & media: Netflix’s early AI play; House of Cards data story.
11:29 The 30-minute campaign challenge—Eddie’s live plan.
12:53 The three tabs: Meta Ad Library → GPT prompts → Reeve mockups.
14:37 Copy/paste every active ad into GPT; ask for strategy synthesis.
16:06 Five “board-level” ideas; forcing a single high-acceptance pitch.
17:56 Image prompt for “Comfort 2.0” (eco-luxury, performance lifestyle).
20:27 Prompting hack: “200+ IQ” to push for originality (avoid clichés).
21:06 Locking on Comfort 2.0—“performance tech meets everyday life.”
23:06 Iterating the mood board; feeding outputs back into GPT.
23:30 If the client has the shoe already: do it all in AI (no photoshoot).
24:39 Rapid tests: ethnicity, angle, color; Instagram Trial Reels.
26:03 Beyond ads: full-funnel → product design & R&D with agents.
27:24 100-page competitor deep dives from public signals.
28:26 Scoring system (cutoff 85; 95+ are “winners”) to prioritize assets.
30:13 Spinning GPT outputs into 10 TikTok hooks for creators/founders.
31:32 Domain-tuned agents that deliver 90%-ready work.
33:13 What’s next: automatic video analysis and creative fixes.
34:13 Next 12 months: IP-driven brands, real-time stylized video, avatars.
35:43 Meta: capturing AI audio; partner via your agent in the future.
36:12 Why solo $1B is realistic (and $100M solos even more so).
Tools & Technologies Mentioned (with quick notes)
Meta Ad Library — Public index of active FB/IG ads; great for competitive creative research.
GPT — Used to analyze competitor ads, generate board-level strategies, image prompts, TikTok hooks, and run scoring frameworks.
Reeve — Static image generator (Midjourney-like) for fast mood boards and spec creative.
Midjourney — Alternative image generation tool for photorealistic concepts.
VO3 — Motion/video generation tool referenced for animated concepts.
Instagram Trial Reels — Organic test surface to gauge hooks/creatives with cold audiences before spend.
TikTok — Distribution + hook testing via short scripts for creators/founders.
Semrush — Search/keyword intel to complement social competitive analysis.
SocialPeta — Creative/spend intelligence (legacy use; less relied upon now).
AI Avatars & Agentic Flows — Persona-based creators and multi-agent pipelines to speed research, ideation, testing, and post-mortems.
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In this episode, Alexandra Sunderland (VP of Engineering at Fellow) pulls back the curtain on how she runs engineering with agentic workflows that actually move the needle: background coding agents in Cursor that fix bugs while she’s in meetings, Claude + MCPs to query Linear and auto-generate reports in seconds, and Zapier pipelines that turn meeting transcripts into daily briefs, real-time project risk pings, sales insights, and even 1:1 growth trackers. The theme: make conversations computable, specialize agents narrowly, and wire every tool together so ops happen while you sleep.Timestamps1:11 — Background: 13+ yrs with Aydin; author of Remote Engineering Management.2:13 — What is an “agent”? Alexandra’s practical definition (automation + LLM).3:39 — Why specialized agents beat general ones (Sept 2025 reality check).5:25 — Cursor background agents via Slack VIP notifications—coding while she’s away.8:00 — Hackathon: hand-built dev productivity dashboard vs. Claude + Linear MCP.10:38 — Why use Claude here instead of Cursor: downloadable PDFs & exploratory insights.13:03 — Interface shift: logging into Linear/GitHub less; notify via Slack instead.14:21 — Plan: live workflows that leaders can copy.15:31 — Workflow #1: Daily Brief in Zapier (9:00 a.m. trigger → transcripts → CoS-style digest).18:00 — Slack example of the generated daily brief.20:22 — Workflow #2: Project Meeting Insights—real-time blockers & cross-team risks.22:00 — Prompting style (“best VP of Eng in the world”) and why it helps.26:40 — Idea: an “Alexandra agent” that drafts her responses.27:59 — Workflow #3: Sales call mining → bug/feature requests for Eng.29:14 — Next step: Cursor agents created via API—fixes ready for human review minutes after calls.30:23 — Rolling Cursor to product & success; non-engineers leverage code context.31:16 — Auto-drafting help center docs with Cursor that can browse.32:34 — Future: docs auto-update—or vanish into on-demand LLM answers.34:52 — Workflow #4 (WIP): 1:1 growth tracker—extract coaching, strengths, feedback into a living doc.37:41 — Sales coaching automation: enforce key phrases/objection handling.38:10 — Playbook: start with simple “yesterday’s conversations → insights,” then stack.39:24 — Next 12 months: tools connecting to each other, patterns across datasets.Tools & Technologies Mentioned (with quick notes)Cursor — AI-powered code editor with background agents (cloud-run) and Slack integration for async coding and fixes.Cursor Background Agents API — Programmatically spin up agents to implement bug fixes/features for later human review.Slack (VIP Notifications) — Marking the Cursor app as VIP ensures agent updates punch through Do Not Disturb.Claude — LLM used with MCPs to query data sources (e.g., Linear), generate PDFs, surface trends, and build ad-hoc reports.MCP (Model Context Protocol) — Standard to connect LLMs to tools/data (e.g., Linear) for live, permissioned operations.Linear — Issue/project tracker; source for ticket analytics (resolution rates, triage time, stage durations).Zapier — No-code automations; schedules, filters, formats, makes API calls, and runs AI by Zapier LLM steps.Fellow.ai — AI meeting assistant capturing summaries, actions, decisions; acts as an “AI chief of staff” across meetings.GitHub — Code hosting referenced as a UI Alexandra now visits less thanks to agentic workflows.Google Docs / Notion / Wiki — Destinations for auto-appending 1:1 growth notes and team principles.APIs (custom + vendor) — Zapier “Webhooks by Zapier”/custom API calls used to fetch transcripts and trigger agents.Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
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In this episode, Aydin chats with brothers Emil and Cassy—founders behind Hoppier (snack stipends for teams) and Postbeam (an AI-native LinkedIn content engine). They show how transcripts, voice interfaces, and AI browsers can 10× content output and product velocity for small teams. Demos include: turning transcripts into LinkedIn posts, Postbeam’s “Marv” voice interview, Vercel v0 mockups, and Perplexity’s Comet browser agent. The theme: tiny teams, mighty outcomes—when AI is baked into every workflow.
Timeline & Timestamps
01:08 – Hoppier origin: ~1,200 customers, profitable, still founder-run.
03:57 – Why transcripts are gold for creating unlimited content.
05:06 – Demo: pulling a podcast transcript into Claude → strong LinkedIn post hooks.
08:55 – Volume matters: consistency wins; learning from creators like Pablo.
11:15 – Remix vs. original insights: two formulas for content that works.
14:38 – From process to product: Postbeam lands early paying customers.
16:26 – Inside Postbeam: sources, remixing, images, and multi-team member voices.
18:33 – Demo: Marv voice feature interviews you to capture authentic tone.
24:21 – Building with AI: using Vercel v0 for rapid UI mockups and team feedback.
29:16 – Aydin’s day job plug: Fellow.ai meeting assistant.
31:36 – Replit vs. V0 vs. Lovable: pros, cons, and caution for prod-grade apps.
35:58 – Comet browser demo: finding Toyota RAV4s on Marketplace with AI.
42:42 – Tiny but mighty: Postbeam (2 founders + Gen Z cousin) and Hoppier (7-figure biz with 4 ppl).
Tools & Technologies Mentioned
Claude (Anthropic) — Generates LinkedIn posts from transcripts.
ElevenLabs / YouTube Transcript Tools — For pulling transcripts.
Postbeam — AI LinkedIn content engine.
Marv (inside Postbeam) — Voice interview AI to capture tone.
Vercel v0 — Natural language → React UI mockups.
Replit / Lovable / Cursor — AI coding platforms, with tradeoffs.
Perplexity’s Comet Browser — Agentic browser for automated browsing.
Whisper Flow — Voice-first workflow automation.
Fellow.ai — AI meeting assistant.
Subscribe at thisnewway.com to get the step-by-step playbooks, tools, and workflows.
- Visa fler
