You don't just buy enterprise software. You pay for the privilege of it being explained then sold to you.

Why is the internet constantly trying to make you angry? In this episode, we explore how being spectacularly wrong became one of the most profitable business models online. Starting with a personal Stack Overflow flamewar over a MySQL question (spoiler: I was wrong), we dive into the psychology of why we can't resist correcting people, and how platforms weaponize this compulsion for engagement.

From Yahoo Answers memes to mobile game ads showing frustratingly wrong gameplay, we examine how strategic wrongness triggers our primal need to correct and prove our knowledge. Every angry comment, every correction, every outrage-driven share tells algorithms that the content is "engaging"—regardless of whether it's true or false, helpful or harmful.

Drawing on The Simpsons' classic "Just Don't Look" solution and CGP Grey's warnings from a decade ago, we explore why simply ignoring rage bait isn't enough anymore when algorithms ensure controversial content finds us. The episode offers practical frameworks for conscious digital literacy: recognizing when content is designed to provoke rather than inform, and choosing what we feed with our attention.

Because the wrongness economy only works if we keep feeding it. And every click is a vote for the kind of internet we're building.

"Being wrong triggers something primal in us. The compulsion to correct, to engage, to prove our superior knowledge. It's the digital equivalent of someone saying 'actually' at a dinner party, except now that dinner party has millions of attendees."

"The algorithm doesn't care whether that engagement is positive or negative. Engagement is engagement."

"We have more power than we realize. The wrongness economy only works because we feed it our attention. Every click is a vote, every share is a signal, every comment is a contribution to the kind of digital world we're creating."

We all laughed at Juicero's absurd business. A $400 Wi-fi enabled juicer that only accept $8 proprietary juice packets. But turns out, we all subscribed to their business model when it comes to streaming services.

Join me as I discuss the resemblances, and how we went from the convenience of DVDs to the fragmented world of inconvenient streaming.

Ibrahim Diallo delivers a brutal truth bomb for startups and small teams: your "future-proof" architecture is killing your present.

• The 15-minute CI/CD pipeline for a service nobody used

• Why microservices turn into a debugging pilgrimage without Google's resources

• The "Formula 1 for grocery shopping" analogy for over-engineering

• How chasing hypothetical billions of users makes you slower today

• Why your greatest asset is agility, not premature optimization

A liberating case for building what you need—not what you think you'll need in a fantasy future.

Key Quote: "Don't architect for a hypothetical future of billions. Architect for the reality of your talented small team."

"This is the direct cost of building for a scale you will never reach. It’s the architectural version of buying a Formula 1 car to do your grocery shopping."

"We operate under a dangerous assumption that our companies are inevitably on a path to become the next Google or Meta. So we build like they do... It won't. It just present-proofs us."

"Suddenly, 'pinpointing the error' is no longer a simple task. It’s a pilgrimage. You journey through logging systems, trace IDs, and distributed dashboards, hoping for an epiphany."

"Don’t architect for a hypothetical future of billions of users. Architect for the reality of your talented small team."

"When you're small, your greatest asset is agility. Excessive process stifles this inherent flexibility."

Ibrahim Diallo uncovers the secret law of software: every user only cares about a tiny slice of your application, and it's a different slice for everyone.

- The childhood discovery that Excel's only purpose was copying tables into Word

- How **Microsoft's "bloat" is someone else's vital feature

- Why ignoring the 1% created a $10M opportunity for a search engine startup

- The Figma & Notion playbook for beating giants by serving a neglected niche

- How VS Code and Slack mastered the art of letting users build their own perfect tool

A liberating guide to building software that users love, by finally accepting that they will ignore 80% of it.

"In my mind, the only reason Excel existed was to copy and paste tables into Word."

"Most users will only ever use about 20% of your application's features, but each user uses a *different* 20%."

"It's not just that users don't use the other 80%, they may actively resent it for getting in the way of their 20%."

"The goal isn't to build a product that does everything for everyone. That's how you end up with bloated software that frustrates more users than it delights."

"The goal is to build a product that does the right thing for *each* user, even if that means accepting that they'll ignore most of what you've built."

What do you do when everyone in the room knows more than you do about their specialty? In this candid exploration of technical leadership, we challenge the myth that leading experts means being the biggest expert yourself. Drawing from real experiences managing teams of backend developers, frontend specialists, and product managers who each possess deeper domain knowledge, we examine what leadership actually looks like when you can't out-expert the experts.

From translating the same technical decision three different ways for different audiences, to knowing when heated debates are productive versus personal, we explore the often-overlooked social skills that make technical leadership effective. Learn why "I don't know, but let's figure it out" is a superpower, why your job isn't having all the answers but asking the right questions, and why the worst leadership move is saying "because I said so."

This isn't about dumbing down technical work or managing from ignorance—it's about understanding that expert teams don't need another expert voice. They need someone who can provide context, remove obstacles, translate between perspectives, and create space for expertise to flourish.

Essential listening for anyone who's ever felt impostor syndrome in a leadership role, or wondered what their value is when they're not the most technically proficient person on the team.

"Technical leadership in expert environments isn't about being the smartest person in the room. It's about being the most effective translator, connector, and context-provider."

"Your technical credibility gets you a seat at the table, but your social skills determine whether anything productive happens once you're there."

"You're not the conductor trying to play every instrument yourself. You're the one helping the orchestra understand what song they're playing together."

Google's new Chrome browser is "AI everything." Meta's smart glasses promise to revolutionize how we cook and browse. But here's the problem: they want to automate the things we actually enjoy doing while creating solutions for problems that don't exist.

In this episode, I explore why 95% of AI initiatives fail to deliver value, and why that's actually a good thing. Drawing inspiration from Catch-22's observation that "while none of the work we do here is very important, it is important that we do a great deal of it," I argue that the current AI bubble serves as a crucial filter—helping us separate genuine innovation from opportunistic noise.

The Pleasure Theft Problem: Why AI tools that browse the web for us miss the point entirely

• "When I search for something, I'm not just seeking an answer; I'm seeking the journey to that answer."

The Wrapper Economy: How most AI startups are building unnecessary layers over existing tools

• "They're solutions in search of problems, built by teams who've mistaken the impressive capabilities of foundation models for proof that any application built on top of them will be equally impressive."

The Successful 5%: What the companies actually getting value from AI are doing differently

• "They solve clear, measurable problems... work behind the scenes, augmenting rather than replacing human judgment... and integrate seamlessly into existing workflows."

The Great Filter: Why all this "waste" is actually necessary

• "Every failed AI gadget teaches us something about the difference between technological capability and actual utility."

"The real breakthroughs won't come from the companies promising to revolutionize everything. They'll come from the teams quietly solving specific problems with appropriate tools, building on solid foundations rather than chasing the latest trend."

The AI bubble will deflate, but the signal will emerge from the noise. It always does.

From charging "a fair price" on Craigslist to discovering the brutal reality of what clients actually pay for development work.

After completing one JavaScript class, I confidently advertised my programming services online. Then came the email that changed everything: "I'm a humble man with little means, but I will pay. Please tell me, how much do you charge for a website?"

I had no idea.

This is the story of accidentally discovering that a "shitty website" costs $25,000, watching a nonprofit budget $35,000 for a "small and simple" site, and finding out through corporate JSON responses that recruiting companies were charging $78/hour while paying me $40.

Key moments:

• "If a shitty website could cost twenty-five thousand, how much was 'a very little and simple' website?"
• "None of our developers here gets paid 160k a year" - discovering the true cost of contractors
• "There are no prices for a small or complex website"

The uncomfortable truth about freelance pricing, told through years of trial, error, and accidentally stumbling into the real numbers behind professional development work.

Perfect for freelancers, consultants, and anyone who's ever struggled with the question: "How much should I charge?"

We're making the same mistake with AI that game developers made a decade ago with open world design - trying to do everything instead of doing one thing exceptionally well.

Drawing from real experience building customer service automation, this episode explores why "conversational AI" often fails spectacularly (looking at you, Taco Bell's 18,000 cups of water), while focused, form-like AI systems succeed.

The solution? Treat AI applications like well-designed HTML forms with clear validation, defined success criteria, and linear paths to completion. Your users want solutions, not conversations.

Key takeaways:

• Why conversational AI invites scope creep and failure
• The difference between "open world" and "linear" AI design
• Real examples of what works (and what doesn't) in drive-thru automation
• How to build AI that matches interaction model to mission

Perfect for product managers, developers, and anyone building AI-powered applications.

Think the bottleneck in programming is typing speed? Think again. In this eye-opening episode, we explore why the real challenge in software development isn't writing code—it's reading and understanding it. Drawing from years of contracting experience jumping between unfamiliar codebases, we dive deep into the concept of "mental models" and why building them is where all the real cost lives.

From tracing through a seemingly simple getUserPreferences() function to understanding why programmers always want to rewrite "legacy" code, we examine the cognitive load of comprehension that no amount of AI code generation can eliminate. We explore how this insight changes everything about how we should think about productivity, AI tools, and the future of programming itself.

Why didn't that lawyer read the ChatGPT-generated legal cases before citing them? Why do Stack Overflow questions get downvoted for lacking context? Why is debugging harder than coding? The answer to all of these connects back to the same fundamental principle: understanding is harder than generating.

Essential listening for any developer who's ever wondered why that "simple" bug fix took three days, or why AI code generation doesn't feel as productive as promised.

"Writing code is forward motion—you're laying down fresh pavement. Reading code means retracing someone else's steps, jumping between files, chasing function calls, and deciphering intentions that aren't written down."

"The real bottleneck in software development isn't writing, it's understanding. We've solved the 'typing speed' problem. We can generate more code than we could ever hope to read."

"The future of programming might not be about generating more code faster. It might be about generating understanding faster. And that's a much harder problem to solve."