This New Way

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
[email protected]
https://ca.linkedin.com/in/haiphunghiemSubscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

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.

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.

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.

Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

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.