Map for Engineers Podcast

With AI, great product engineering is relevant more than ever. Anton Keks and I sit together again to explore the real question: what does it mean to be an engineer now, in today's AI world?In this round 2 conversation, we talk about the shift from coding to thinking — from generating output to making decisions. Because AI doesn’t remove the need for engineering fundamentals; it amplifies the cost of not having them.We cover what AI engineering actually looks like in practice: how to use these tools without losing control, how to avoid shallow understanding, and why system design, feedback loops, and clean abstractions matter more than ever.We delved deep into discussing pair and pair-programming practices in the age of AI, and how engineers can collaborate with each other to avoid knowledge silos. We also get into the operational side — CI/CD, security, engineering workflows, and building systems that scale and survive.If you’re leading teams or writing production code in 2026, this is the podcast you might be interested to listen to. 00:00 Intro & What This “Round 2” Is About00:10:55 Blind Execution vs Real Understanding in Engineering00:21:50 Why Core Engineering Skills Never Go Away00:32:45 Avoiding Knowledge Silos in Teams00:43:40 Team Culture, Demos & Feedback Loops00:54:35 Reducing Boilerplate & Writing Cleaner Code01:05:30 Productivity Tradeoffs & Time Optimization01:16:25 Using AI Effectively as an Engineer01:27:20 CI/CD, Automation & Developer Workflows01:38:15 Security, Vulnerabilities & Real Risks01:49:10 Building Systems That Last for Years02:00:05 Scaling Systems & Working with SubdomainsThanks for listening. Feel free to subscribe on YouTube or on MapForEngineers.com substack. See you next time. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

Alvar Lumberg needs no introduction in Estonia in startup circles: Alvar is a seasoned engineering leader who built Wise from the early days, then founded Grünfin, and now is transforming investing at Lightyear. We chatted with Alvar on his engineering journey, and discussed ideas on product engineering, building startups, and building great teams.Here is what we talked about: 00:00:00 - Intro00:00:05 - Welcome & Guest Introduction00:00:33 - Alvar's Early Exposure to Computers and Coding00:03:38 - First Steps into the Tech Industry00:05:05 - Growth and Learning at Hansa Bank (now Swedbank)00:06:13 - The Codeborn Experience and the Shift to Product Focus00:08:03 - Joining the Startup World with TransferWise00:10:00 - Evolution of Roles and Responsibilities at Wise00:12:16 - Founding Grünfin: Mission and Challenges00:13:20 - Comparing Different Work Environments: Bank, Contracting, Startup00:19:51 - Key Skills for Startup Engineers00:24:45 - Ensuring Quality in a FinTech Startup00:35:24 - The Role of Test-Driven Development (TDD)00:41:49 - Philosophy on Engineering Leadership00:47:15 - Building a Team: The Importance of Composition00:50:50 - Lessons Learned from the Grünfin Journey00:56:57 - Why Alvar Joined Lightyear00:59:58 - Engineering Challenges at Lightyear01:02:44 - The Future of Engineering with LLMs01:11:53 - Recommended Reading and ResourcesThanks for tuning in on Map for Engineers! Feel free to subscribe: no spamming, and only high-quality content This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

In this episode, we sit down with veteran designer Janne Kaasalainen for an in-depth discussion on the art and science of UX design.00:00:05 - Intro00:00:35 - Janne's introduction00:01:12 - Janne's career start and moving to UX00:03:37 - Pivotal moments in Janne's career00:06:40 - Misconceptions about design and UX00:12:20 - How should engineers, designers, and PMs collaborate?00:21:54 - Working effectively with designers00:28:42 - What other UX misunderstandings do people have?00:31:07 - How does a company align on what is "good"?00:44:18 - How to align on design principles and values in a growing startup?00:53:52 - How to make a good UX in a product?01:00:42 - How to become better at UX and design?01:07:02 - How will design and UX evolve in the next 10 years?And if you want to work with Janne and me, we are hiring in Pactum - https://pactum.com/careersFor more episodes, feel free to subscribe here in YouTube, or check MapForEngineers.com, thank you! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

Carl and I talked deeply about CLI-based AI agents, specifically Claude Code, how CLI AI agents are more composable than IDE-based agents like Cursor, spec-driven development, making codebases agent-native, and, last but not least, which skills should we hone as software engineers, because our profession is not going anywhere, but we need to adapt. Super interesting conversation, hope you enjoy!Oh, and if you want to work together with Carl and me, we are hiring in Pactum, feel free to check out https://pactum.com/careers/00:00:00 Introduction & Current Coding Workflow 00:10:40 Junior vs Senior Engineers with AI 00:27:21 Spec-Driven Development & Tasks 00:46:31 CLI Agents vs IDE Tools (Cursor) 00:52:13 MCP vs CLI Tools & Composability 00:57:20 Claude Code Features: Slash Commands & Hooks 01:12:32 Making Codebases Agent-Native 01:31:40 AI Adoption in Organizations 01:44:16 CI/CD Pipelines & AI AutomationThanks for listening! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

I really enjoyed this discussion. I hope you find some useful bits for yourself as well. Chapters are manually annotated by me for you to be able to navigate and jump around. Thanks for listening to Map for Engineers!00:00:00 - Start00:01:01 - Dmytro's background00:13:50 - Early days of finance team in Wise00:19:36 - Becoming lead in the finance team00:21:19 - Squad o fteams, tribe of squads: splitting finance into subdomains00:27:42 - Conway law: team splits reflecting architecture00:32:56 - Event-driven architecture00:34:51 - Context on what finance team is doing00:36:06 - Batch approach vs real-time financials00:39:56 - Error handling, dead letter queues00:41:44 - Observability: Prometheus, Grafana, in-house tools00:49:32 - Assembling the right team: finding people who care00:52:42 - DB Skills. Postgres. Modularization. Couplings00:57:24 - Staff+ engineering path01:03:32 - Engineering principles in finance. Compliance, auditing01:09:22 - Learning domain where you work01:11:31 - Testing in finance01:27:00 - Growing in your career: find your path, learn what interests you This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

Anton, co-founder of Codeborne, and I sit together to discuss some of the problems in software engineering - pull requests, microservices, testing, refactoring. Check out annotated chapters below for more details.00:00:00 Intro00:00:00 Sneak peek00:00:49 Episode overview00:04:28 Anton's intro, background00:06:37 Anton founded Codeborne: TDD and pair programming: following extreme programming principles00:08:57 Agile is about short feedback loops00:12:09 Under-engineering vs over-engineering00:15:29 Tech debt and testing: engineers don't handle tech debt well enough00:17:45 Lack of refactoring is a big problem00:18:14 Problems with pull requests00:27:00 Problems with squash merge00:27:30 Good commit messages are essential00:31:09 Good code is easy to change00:34:34 Pair programming is continuous code review00:36:11 Daily code review with a whole team00:48:44 Microservices: be careful00:59:23 Book recommendations from Anton01:00:38 Wrap up This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

Lauri Koobas, ex-Microsoft and currently Head of Data Platform at Bondora, shed insights on data engineering - from early startup to scaling.We mostly focused on analytics and building data warehouse - real-world challenges from both data engineering and software engineering sides. We also discussed GDPR and PII challenges when dealing with data.You can find video version on MapForEngineers YouTube channel: https://www.youtube.com/@mapforengineersAnnotated chapters in timeline:00:00:00 Sneak peek of episode00:01:21 Episode overview00:02:44 Introduction, Lauri's background00:20:48 Starship robots: huge amount of data there00:23:37 Data lake, data warehouse, data lakehouse00:26:44 Devil is in the details: timestamps, texts, character sets...00:49:44 Moving data from prod to data warehouse00:53:09 Analytics tools: PostHog, Amplitude, Redash, Databricks01:00:15 Analytics tools vs real-time monitoring like Prometheus/Grafana01:04:15 Usability matters: each tool for its job01:06:38 Startup grows: needs in data analytics01:11:09 Multiple data sources: when data warehouse really begins01:19:55 Data and (de-)coupling: software engineers should not be blocked by analytics01:22:51 Data ETL01:24:59 Changes in data model: multi-phase migrations01:29:38 Change data capture, incremental imports01:34:21 Should analytics have new data in real time? Maybe not?01:39:02 Importing data into DWH through business events01:43:37 When DWH subscribes to business events, data model can evolve freely01:47:16 Quick recap what we discussed so far01:52:25 GDPR and Data Compliance: start early01:56:05 PII data: know exactly where you store it, control it well02:03:37 Lauri's books recommendations on data engineering - Kimball02:07:18 Lauri's podcast on data engineering, in Estonian02:08:28 Wrap up This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

Carl, staff software engineer at Pactum, shed light on some of the latest AI tools in software development. We discussed v0.dev, continue.dev, ollama, and much more! It was an episode with a lot of useful information and insights! To check all content on Map For Engineers including blog posts, feel free to subscribe on https://MapForEngineers.comAnnotated chapters in timeline on topics that Carl and I covered:00:00:00 - Start00:04:05 - Small talk, getting into the groove00:08:18 - Carl's background: ex-Pipedrive, now engineer in Pactum00:19:44 - Early tools: simple autocomplete and simple prompting without context00:27:11 - AI tools with context: Cursor IDE, Continue.dev00:43:35 - Cursor IDE Composer - Prompt+Apply to Code Instantly00:47:29 - Ollama - following docker philosophy00:55:15 - V0.dev - LLM to create frontend components00:59:27 - Cursor IDE + v0.dev combination as a workflow01:02:23 - Claude 3.5 Sonnet01:03:10 - OpenAI o101:04:56 - LLMs vs SQL Queries - still to be solved01:08:17 - LLM in TDD and Testing Workflows01:16:04 - Focus on engineering fundamentals - LLM does not replace your engineering fundamental knowledge01:20:13 - Book recommendations01:29:09 - Hosting models yourself - expensive01:33:27 - Fine-tuning models01:35:33 - RAG01:49:50 - Chain of thought01:51:46 - vyce.app - GenAI helping with compliance questions01:54:25 - Summary of tools we covered so far01:58:43 - GenAI vs engineering careers02:05:39 - Wrap up with Carl This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

I had a pleasure to chat with my friend Joel Mislav Kunst, who is Engineering Manager at Microsoft. We talked about growth in engineering. Some of the topics that we touched upon, with timeline timestamps are:00:00:00 - Glimpse of episode00:01:16 - Quick episode overview00:05:18 - Joel's journey in software engineering00:14:55 - Focus on engineering fundamentals00:23:34 - Learning from all hard experiences: solving root causes00:31:05 - Relationship between engineer and their manager00:53:03 - Manager is not your punching bag for complaining: propose initiatives instead, be active01:10:55 - Engineer vs manager: fork in career01:29:17 - Generative AI in context of engineering career/growth01:36:01 - Teaching is essential for growth. Seniors teaching juniors. Knowlede sharing01:43:14 - Wrap upTo get notified of new episodes and blog posts, subscribe at MapForEngineers.comFor Youtube video version of the episode, check https://www.youtube.com/@mapforengineers This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

Ürgo, ex-Wise, joined Wise.com as one of the first 12 engineers or so. As former colleagues from Wise, we had a great chat about product engineering, domain-driven design, and team collaboration.I will give a short summary of key takeaways that I got from this valuable discussion with Ürgo.1. Work in agency is different from work in a product companyWork in agency (outsource) vs product company is different for an engineer. In agency, you get a lot of experience with different projects, but you don’t own a product or its outcomes.In contract, in a product company, engineers should care about the end product, getting the feedback from users and customers. Responsibilities and impact are on the next level for an engineer in a product company, that you don’t get in a project-based work in an agency.2. Knowledge sharing inside a team. Avoid knowledge silosIt’s important to avoid pockets of knowledge in a team, where sub-groups form, because then it’s hard to have a cohesive team. There are many tools to avoid it, for example:* Pair programming* Deliberately rotating knowledge among people* Working in pairs (not necessarily pair programming, but just solving some problem together) on a topic for a short time (e.g. a week or two). But then switching pairs and topics, to avoid knowledge silos.* I wrote in detail about some practices that I employ in Pactum to avoid knowledge silos in the team in the article Values, Principles and Practices in Engineering Team.3. Product engineering begins where the comfort of the coding endsÜrgo wrote amazing article about this topic, called Product Engineer, available in his Medium.Product engineers need to establish frequent feedback loops to get signal from users on the usefulness of what they delivered. This is essential for closing the feedback loop. Once you get learnings, you repeat the process: Learn → Build → Ship → Learn → …[repeat]4. Domain-Driven Design: Metaphors are importantComing up with metaphors when modelling software is very important. When you come up with a good metaphor, try to embed it into your ubiquitous language.5. GenAI in Software EngineeringGenAI won’t replace product engineers for a while. In fact, product engineering becomes even more essential than just coding. Coding is just a tool, a means to an end. Product engineering skills will be ever so valuable - to understand which product to build, to iterate, to learn from your users and customers, to be creative. Product engineers will leverage GenAI tools to automate non-interesting tasks (e.g. creating this next frontend component, if it can be automated quickly).---And that’s a wrap! I will be recording new episodes soon. Feel free to subscribe if you found it valuable. Also, recording quality in the next episode will be better.For all the content, visit MapForEngineers.com This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com

I am starting a Map for Engineers Podcast! I am in the process of organizing a first episode with my guest, which will be live-streamed on YouTube :) Will announce more details soon. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit log.mapforengineers.com