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BONUS: From 3,000 Scripts to 3 Tools – Building AI-Last Software With Conversational AI Pioneer Peter Swimm

In this special BONUS episode, Peter Swimm—conversational AI veteran, creator of BotKit (the open-source chatbot framework that powered Slack and Teams bots), and former Principal Product Manager at Microsoft Copilot Studio—shares what 25+ years in tech taught him about working with AI. From his brutal experiment of running an entire business on voice-based AI for a week, to why he treats AI more like R2-D2 than C-3PO, Peter offers a grounded, practical perspective on where AI fits in software development teams.

From BotKit to Copilot Studio: A Front-Row Seat to the AI Evolution

“We had the number one bot in the Slack app store, because there were only 8 bots, and ours used regex. To show you how far we’ve come.”

Peter’s journey into conversational AI started with a newspaper ad and a creative writing background. When Slack launched its API, Peter and BotKit co-creator Ben Brown immediately saw that building bots wasn’t just a technical challenge—it was a social and creative one, like writing scripts for plays that interface with people in their daily lives. That insight powered BotKit into becoming the backbone of Slack and Teams bots, and eventually led to Microsoft acquiring the company. Peter spent years inside Microsoft shaping Copilot Studio, working on connectors that bridge the gap between APIs and real-world work. But the experience also gave him a healthy dose of perspective: he can show you slide decks from 2016 that promise the same things today’s AI pitches promise, always saying “within 5 years.” That pattern recognition shapes his practical, no-hype approach.

The 3,000 Scripts Experiment: Why AI-Last Beats AI-First

“At the end of the day, if I’ve been prompting all day, I should have a computer program that works offline, that works without a subscription. Otherwise, I didn’t really make anything.” Continue reading BONUS: From 3,000 Scripts to 3 Tools – Building AI-Last Software With Conversational AI Pioneer Peter Swimm

AI-Assisted Coding: Why a Distinguished Engineer Stopped Reading Code — Lights-Out Codebases and the End of the IC

Philip Su has spent two decades at the highest levels of software engineering — Microsoft, Meta (where he reached Distinguished Engineer, IC9), OpenAI, and now building his own product solo with AI. In this episode, he makes a provocative case: the individual contributor role as we know it is over, code reviews are becoming a liability, and the best engineers are already managing AI agents instead of writing code themselves.

From Dirty Fuel to Clean Fuel — Philip’s Unusual Career Arc

“When I look at those employment opportunities, I feel like maybe one theme is moving from what people are calling dirty fuel into clean fuel — meaning, what sorts of things motivate you? Dirty fuel would be something like, my parents never complimented me and so now I’m going to drive and prove them wrong. Clean fuel would be, I’ve always loved computer science and I’m here to do it.”

Philip’s path through tech is anything but linear. He became a dev manager at Microsoft before 30, then spent the next decade comfortable but stagnating — until he interviewed at Google, Amazon, and Meta and received offers three levels below his Microsoft position. That fear drove him to Facebook in 2010, when it had just 500 employees and people were still saying Friendster would come back. He scaled the London engineering office from a dozen engineers to 500+, then stepped away entirely.

Depression led him to take a warehouse floor job at Amazon during Peak 2021 — 11-hour shifts, moving six tons of packages a day with his arms. He documented the experience in his Peak Salvation podcast. The lessons were sharp: incentives shape behavior (warehouse workers got raises based on tenure, not performance, which incentivized doing the minimum), changing machines that are already operating is brutally hard, and misplaced pride is dangerous. Workers on the floor laughed when robots failed. Philip’s takeaway was the opposite: “Do you think the corporate robot designers just gave up and went home? Or do you think V2 is coming and V3 is coming until finally it takes over your job?” He later joined OpenAI as an individual contributor before leaving to build Superphonic, an AI-powered podcast player.

No More Code Reviews: The Lights-Out Codebase

“I grew up in the 80s and 90s. Today, even Stockfish, free on your phone, can beat the world’s best chess masters. What if I proposed to you today that you should take that free software, and when you’re playing some other player, have a human chess master review every move, just to make sure the AI hasn’t made a mistake? I would think that was patently ridiculous.” Continue reading AI-Assisted Coding: Why a Distinguished Engineer Stopped Reading Code — Lights-Out Codebases and the End of the IC

AI Assisted Coding: The Five Levels of AI-Augmented Coding—From Prompter to Orchestrator

What separates vibe coding from truly effective AI-assisted development? In this episode, Adam Bilišič shares his framework for mastering AI-augmented coding, walking through five distinct levels that take developers from basic prompting to building autonomous multi-agent systems.

Vibe Coding vs AI-Augmented Coding: A Critical Distinction

“The person who is actually creating the app doesn’t have to have in-depth overview or understanding of how the app works in the background. They’re essentially a manual tester of their own application, but they don’t know how the data structure is, what are the best practices, or the security aspects.”

Adam draws a clear line between vibe coding and AI-augmented coding. Vibe coding allows non-developers to create functional applications without understanding the underlying architecture—useful for product owners to create visual prototypes or help clients visualize their ideas.

AI-augmented coding, however, is what professional software engineers need to master: using AI tools while maintaining full understanding of the system’s architecture, security implications, and best practices. The key difference is that augmented coding lets you delegate repetitive work while retaining deep knowledge of what’s happening under the hood.

From Building Features to Building Systems

“When you start building systems, instead of thinking ‘how can I solve this feature,’ you are thinking ‘how can I create either a skill, command, sub-agent, or other things which these tools offer, to then do this thing consistently again and again without repetition.'” Continue reading AI Assisted Coding: The Five Levels of AI-Augmented Coding—From Prompter to Orchestrator

AI Assisted Coding: Building Reliable Software with Unreliable AI Tools

In this special episode, Lada Kesseler shares her journey from AI skeptic to pioneer in AI-assisted development. She explores the spectrum from careful, test-driven development to quick AI-driven experimentation, revealing practical patterns, anti-patterns, and the critical role of judgment in modern software engineering.

From Skeptic to Pioneer: Lada’s AI Coding Journey

“I got a new skill for free!”

Lada’s transformation began when she discovered Anthropic’s Claude Projects. Despite being skeptical about AI tools throughout 2023, she found herself learning Angular frontend development with AI—a technology she had no prior experience with. This breakthrough moment revealed something profound: AI could serve as an extension of her existing development skills, enabling her to acquire new capabilities without the traditional learning curve. The journey evolved through WindSurf and Claude Code, each tool expanding her understanding of what’s possible when developers collaborate with AI.

Understanding Vibecoding vs. AI-Assisted Development

“AI assisted coding requires judgment, and it’s never been as important to exercise judgment as now.”

Lada introduces the concept of “vibecoding” as one extreme on a new dimension in software development—the spectrum from careful, test-driven development to quick, AI-driven experimentation. The key insight isn’t that one approach is superior, but that developers must exercise judgment about which approach fits their context. She warns against careless AI coding for production systems: “You just talk to a computer, you say, do this, do that. You don’t really care about code… For some systems, that’s fine. When the problem arises is when you put the stuff to production and you really care about your customers. Please, please don’t do that.” This wisdom highlights that with great power comes great responsibility—AI accelerates both good and bad practices.

The Answer Injection Anti-Pattern When Working With AI

“You’re limiting yourself without knowing, you’re limiting yourself just by how you formulate your questions. And it’s so hard to detect.” Continue reading AI Assisted Coding: Building Reliable Software with Unreliable AI Tools

AI Assisted Coding: Augmented AI Development – Software Engineering First, AI Second

In this special episode, Dawid Dahl introduces Augmented AI Development (AAID)—a disciplined approach where professional developers augment their capabilities with AI while maintaining full architectural control. He explains why starting with software engineering fundamentals and adding AI where appropriate is the opposite of most frameworks, and why this approach produces production-grade software rather than technical debt.

The AAID Philosophy: Don’t Abandon Your Brain

“Two of the fundamental developer principles for AAID are: first, don’t abandon your brain. And the second is incremental steps.”

Dawid’s Augmented AI Development framework stands in stark contrast to “vibecoding“—which he defines strictly as not caring about code at all, only results on screen. AAID is explicitly designed for professional developers who maintain full understanding and control of their systems. The framework is positioned on the furthest end of the spectrum from vibe coding, requiring developers to know their craft deeply. The two core principles—don’t abandon your brain, work incrementally—reflect a philosophy that AI is a powerful collaborator, not a replacement for thinking. This approach recognizes that while 96% of Dawid’s code is now written by AI, he remains the architect, constantly steering and verifying every step.

In this segment we refer to Marcus Hammarberg’s work and his book The Bungsu Story.

Software Engineering First, AI Second: A Hill to Die On

“You should start with software engineering wisdom, and then only add AI where it’s actually appropriate. I think this is super, super important, and the entire foundation of this framework. This is a hill I will personally die on.”

What makes AAID fundamentally different from other AI-assisted development frameworks is its starting point. Most frameworks start with AI capabilities and try to add structure and best practices afterward. Dawid argues this is completely backwards. AAID begins with 50-60 years of proven software engineering wisdom—test-driven development, behavior-driven development, continuous delivery—and only then adds AI where it enhances the process. This isn’t a minor philosophical difference; it’s the foundation of producing maintainable, production-grade software. Dawid admits he’s sometimes “manipulating developers to start using good, normal software engineering practices, but in this shiny AI box that feels very exciting and new.” If the AI wrapper helps developers finally adopt TDD and BDD, he’s fine with that.

Why TDD is Non-Negotiable with AI

“Every time I prompt an AI and it writes code for me, there is often at least one or two or three mistakes that will cause catastrophic mistakes down the line and make the software impossible to change.” Continue reading AI Assisted Coding: Augmented AI Development – Software Engineering First, AI Second