What happens when you throw out traditional team structures and rebuild them around missions instead of functions? Jan Hegewald, VP of Engineering at Taxdoo and former engineering leader at Zalando and SumUp, shares how he dissolved static component teams in favor of flexible, outcome-focused "mission teams" that reformed every quarter around specific goals. The experiment worked brilliantly until it didn't.

Jan brings nearly two decades of experience building high-volume systems across e-commerce, FinTech, and point-of-sale infrastructure. At Zalando, his teams processed hundreds of thousands of product offers during Black Friday. At SumUp, he built the point-of-sale system now visible in stores across Europe, Australia, and Brazil. His career offers a rare perspective on what actually transfers between startup culture and enterprise scale.

In this episode, we explore:

How mission-based teams increase ownership but create tricky problems around code quality, knowledge retention, and maintenance work, plus the practical solutions that worked (and the seniority level required to pull it off)

Why treating QA as an enabling function rather than a gatekeeper changes the game for release velocity, even in heavily regulated FinTech environments

The hidden operational costs of AI that teams overlook: model drift, retraining cycles, and why "garbage in, garbage out" still determines success more than any algorithm

What engineering leaders should actually hire for now, why the "I only do backend" engineer is becoming obsolete and what holistic skill set replaces narrow specialization

Jan also pushes back on AI hype with examples from failed experiments (merchant price recommendations that solved nothing) versus measurable wins (size recommendations that cut returns). His advice for teams pressured to "do AI": treat it like normal product development, iterate fast using modern tools, and stop experiments that don't deliver customer value. Plus: why the old career advice to "stay at one company forever" is now the riskiest move you can make.

Connect with Jan:LinkedIn: https://www.linkedin.com/in/jan-hegewald/Link to the tshirt: https://www.amazon.de/dp/B0FBKJJX7S

Connect with me:LinkedIn: https://www.linkedin.com/in/christianbarra/

Check out our awesome sponsor, dearmachines.com, QA AI Agents for Continuous Testing: https://www.dearmachines.com

⁠In this episode, I sit down with Roman, CTO of Breakfast, a social tech startup fostering offline human connections in Lisbon.⁠

Roman brings over a decade of tech experience, having navigated the trenches from Rails engineer to engineering manager at companies like Loca (later acquired by Capgemini) and Grammarly, before taking the helm as CTO at an early-stage startup.

What We Cover

The Consulting to Product Journey: We explore Roman's seven-year tenure at Loca, where he grew a PagerDuty account from 3 to 25 engineers, and his transition to Grammarly's 700-person organization. Roman candidly shares the mindset shift required when moving from keeping external customers happy to truly understanding internal users and being proactive about product decisions.

Platform Team Transformation: Roman details how he transformed a critical feature team at Grammarly, one handling APIs for all applications, into a proper platform team. We discuss tackling dependencies, managing a Slack channel with 400 people asking questions, and the importance of clarifying team charter and identity.

Startup CTO Challenges: We dive into the realities of leading a 4-person engineering team at Breakfast, juggling everything from hands-on backend development to architecture decisions and team mentoring. Roman shares insights on hiring for "the flame," prioritizing curiosity and drive over pure technical skills, and managing the lack of defensive layers that protect against burnout in larger companies.

Leadership and Authenticity: Throughout our conversation, Roman emphasizes emotional intelligence, candor, and self-awareness as foundations of good leadership. He shares his daily meditation practice using the Waking Up app and how it helps him stay present and connected to objective reality.

Connect with Roman:

LinkedIn: https://www.linkedin.com/in/roman-skvirskyi/

Connect with me:

LinkedIn: https://www.linkedin.com/in/christianbarra/

Check out our awesome sponsor, dearmachines.com, QA AI Agents for Continuous Testing: https://www.dearmachines.com

Iwan Gulenk, founder of a Swiss tech recruitment agency, maps the new reality of tech hiring

What’s inside:

1) Market reset and hiring rigor: After years of growth peaking in 2021, 2024 hammered agencies and slowed decisions. Inbound has exploded (think LinkedIn Easy Apply), time-to-fill is unpredictable, and exact-match hiring dominates. Iwan’s “life partner” analogy underscores the variability and risk focus.

2) Remote/hybrid onboarding realities: Juniors and mid-levels suffer without close guidance. Practical fixes: three short daily check-ins (morning/afternoon/evening) and paying for tooling that prevents no-shows and miscommunication—small frictions compound into failed ramp-ups.

3) Nearshoring’s new normal in Switzerland: SMEs now lose business without a nearshore bench—one 20-person firm did. Still, German/Swiss German needs and client culture shape who actually works. Meanwhile, cloud transformations continue, but cloud bills are biting; one client saw costs triple, creating demand for cost optimization.

4) Senior candidate playbook: Treat your resume as a sales document. The top third should signal location, phone, language, role seniority, tech stack, and outcomes. Don’t omit job titles. Read the ad—his team hid “Swiss cheese” to test attention, and many missed it. Freelancers moving to FTE must credibly explain long-term intent.

Why it matters: Whether you’re hiring or job-hunting, the easy-money era is over. With innovation labs being cut and 2025 budgets still tight, advantage goes to teams that design thoughtful setups (including nearshore) and candidates who present crisply and execute. AI helps as tooling, but the human nuance still closes the deal.

Connect with Iwan:

- LinkedIn: https://www.linkedin.com/in/iwan-gulenko/

Connect with me:

- LinkedIn: https://www.linkedin.com/in/christianbarra/

Check out our awesome sponsor, dearmachines.com, QA AI Agents for Continuous Testing: ⁠https://www.dearmachines.com⁠

Giuseppe Birardi is CTO of Orma Lab, an Italian consultancy focused on R&D and industrial AI.

A former researcher turned PM and developer, he also co-organizes the Python Bar community.

What we cover:

- The geometry of meaning: Embeddings turn words into vectors with direction and magnitude, allowing context to “pull” meanings (money-bank vs. river-bank). Dimensionality reduction compresses co-occurrence statistics into 500–700D spaces where vectors become transformative, not static points.

- Inside transformers: Multi-head attention re-weights tokens to resolve ambiguity across a sequence, then MLP layers with ReLU activations “fold” the space—think approximating a circle with linear cuts after repeated folds—so nonlinear problems become linearly separable.

- Prompt engineering as activation: How phrasing can turn on skills learned during training (e.g., TL;DR for summarization, “step by step” for task decomposition). Why chain-of-thought often simulates reasoning and can still hallucinate.

- Probing the latent space: From mechanistic interpretability to feature-level observability in open models (e.g., GemmaScope) and why steering features is promising but not yet turnkey for production. Concrete example: apparent “decryption” often reflects seen patterns (like Caesar shifts) rather than true cryptanalysis.

Why it matters: If you’re building RAG or agentic applications, these mental models help you design better prompts, set up experiments, choose SOTA models first, and then optimize cost/latency. Giuseppe also shares how Italian firms—via soft-finance-backed R&D—are moving real AI products into production across domains.

If you want to learn more about the geometry of the latent space: https://www.lesswrong.com/posts/nfGZtKzz8WzxF3MAs/on-the-geometrical-nature-of-insight

Connect with Giuseppe:

- LinkedIn: https://www.linkedin.com/in/giuseppe-birardi-18a7b011/

Connect with me:

- LinkedIn: https://www.linkedin.com/in/christianbarra/

Check out our awesome sponsor, dearmachines.com, QA AI Agents for Continuous Testing: ⁠https://www.dearmachines.com⁠

Csaba Mézes, former delivery manager at Nordcloud with 25 years in the industry, shares his hard-earned insights on software delivery, team management, and why most projects fail. Here's what you'll learn from this episode:

1/ 🎯 Scope flexibility is your secret weapon. Unlike physical construction where you might hit bedrock requiring special tools, software development has no immovable obstacles. Everything is replaceable, which means keeping scope open throughout the project isn't just smart - it's essential for survival.

2/ 🤝 The team player test that most people fail. When life events affect your work, transparency matters more than perfection. Csaba shares the story of an excellent developer who tanked team morale by accepting sprint commitments he couldn't deliver on, then disappearing without communication.

3/ 📊 Why the "Sue Goes to Work" exercise beats every prioritization framework. Create user stories for morning routines, then simulate oversleeping. What's truly essential when time is cut in half? This simple workshop reveals your real MVP and teaches the difference between must-have and nice-to-have features.

4/ 📝 Shared documents ≠ shared understanding. Just because everyone has access to the requirements doesn't mean they understand them the same way. Creating space for questions and discussion prevents costly misinterpretations that derail projects months later.

5/ 💰 Time-based pricing is fundamentally broken. Stop thinking in hourly rates and start thinking in value delivered. Csaba's renovation contractor story shows how focusing on outcomes (square meters of tiles, cubic meters of debris removed) creates better relationships and clearer expectations than tracking hours.

Connect with Csaba Mézes:

• LinkedIn: https://www.linkedin.com/in/mezescs/

Connect with the hosts:

• Christian: https://www.linkedin.com/in/christianbarra/

Check out our awesome sponsor, dearmachines.com, QA AI Agents for Continuous Testing: ⁠https://www.dearmachines.com⁠

Duarte O.Carmo, machine learning consultant based in Copenhagen, shares his honest take on AI tools, local models, and the reality behind the hype. Here's what you'll learn from this episode:

1/ 🎯 Don't throw LLMs at every problem. Duarte explains why binary classification problems don't need ChatGPT when logistic regression works better. Learn his framework for choosing the right tool - whether it's traditional ML or LLMs - based on what you're actually trying to solve.

2/ 🔧 Real AI coding tools comparison. From Neo Vim to Cursor, local models to Claude 3.5 Sonnet - get the unfiltered truth about what actually works. Duarte runs models on his MacBook Pro M3 and shares when local beats cloud, plus why agent mode isn't always better.

3/ 📚 Documentation still beats AI for learning. Despite trying MCP servers and LLMs.txt files, Duarte found nothing replaces 35 minutes reading good docs. Discover why libraries like Polars trip up LLMs and how to learn new tech effectively in the AI age.

4/ 🧠 The mentality shift in AI-assisted development. It's not about writing code anymore - it's about solving problems one level up. Learn how to focus on architectural decisions and business logic while letting AI handle implementation details.

5/ ⚖️ When AI hype meets production reality. Duarte shares real consulting experiences where companies want to use LLMs for everything. Get practical advice on evaluating AI solutions, building proper datasets, and why looking at your data still matters most.

Connect with Duarte O.Carmo:

• LinkedIn: https://www.linkedin.com/in/duarteocarmo/
• Website: https://duarteocarmo.com/

Connect with me:

• LinkedIn: https://www.linkedin.com/in/christianbarra/

Check out our awesome sponsor, dearmachines.com, QA AI Agents for Continuous Testing: ⁠https://www.dearmachines.com⁠

Ellen König, Head of Engineering at Alcemy, shares her practical insights on engineering leadership, remote team management, and building sustainable tech companies. Here's what you'll learn from this episode:

1/ 📚 Most tech leadership books are actually marketing in disguise. Ellen reveals why books from consulting companies give you just enough information to understand concepts but not enough to actually apply them—and what works instead for developing real leadership skills.

2/ 🏠 Remote work isn't going anywhere, but it requires intentional management. Learn Ellen's approach to leading a 10-person engineering team distributed across Germany, including quarterly in-person weeks and why engineers actually thrive with remote focus time.

3/ 💰 The zero interest rate era is over, and engineering leadership has fundamentally changed. Discover how to scale your product without scaling your team, why "throwing people at problems" no longer works, and what this means for startup growth strategies.

4/ 🔧 Performance management in Germany requires patience and process. Ellen shares her 6-month approach to handling underperformance, why transparent feedback prevents most issues, and how to navigate strict European labor laws while maintaining team quality.

5/ 🤖 AI tools are changing development workflows, not replacing developers. Learn how Ellen's team uses Copilot effectively, why take-home coding assignments are getting harder to evaluate, and what the current brutal job market means for career transitions.

Connect with Ellen:

• LinkedIn: https://www.linkedin.com/in/ellenkoenig/

Connect with me:

• LinkedIn: https://www.linkedin.com/in/christianbarra/

Check out our awesome sponsor, dearmachines.com, QA AI Agents for Continuous Testing: https://www.dearmachines.com

Anastasiia, former Head of Engineering at Native Instruments, shares her practical insights on engineering leadership, team building, and quality-driven development. Here's what you'll learn from this episode:

1/ 🔍 Discover what truly matters when hiring engineers. Anastasiia reveals why soft skills often outweigh technical expertise and how to identify candidates who will strengthen your team culture, not just your codebase.

2/ 🔄 Changing team culture is delicate work. Learn Anastasiia's proven approach to implementing meaningful change without causing mass exodus, including the "one big change" strategy that minimizes team disruption while maximizing impact.

3/ 🚀 Quality vs. speed is the eternal engineering dilemma. Hear how Anastasiia managed this balance when developing DJ software where crashes aren't just annoying—they're career-ending for users performing live on stage.

4/ 💼 Junior developers face unique challenges in today's market. Anastasiia shares actionable strategies for standing out when every company seems to want "senior-only" talent, including the power of meetups and personal connections.

5/ 🤖 AI is transforming the hiring process. Learn how engineering managers are adapting their interview techniques as tools like ChatGPT change how candidates approach technical assessments, and what this means for your next job hunt.

This episode is perfect for engineering leaders, aspiring managers, and developers looking to understand the human side of building great tech teams.

Don't forget to subscribe to the podcast for more insights from industry leaders reshaping B2B SaaS and marketing!

Connect with Anastasiia⁠:

- LinkedIn: https://www.linkedin.com/in/atymoshchuk/

- PyBerlin meetup: https://www.meetup.com/PyBerlin/

Connect with me:

- LinkedIn: https://www.linkedin.com/in/christianbarra/

Check out our awesome sponsor, dearmachines.com, QA AI Agents for Continuous Testing: ⁠https://www.dearmachines.com⁠