Map for Engineers Podcast

Vitalii Lakusta

Creating a map of knowledge and tools for software engineers. log.mapforengineers.com

Episodes

  1. 15 AUG

    On Product Engineering from Alvar, early Wiser | Ep. 9 with Alvar Lumberg

    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 - Intro 00:00:05 - Welcome & Guest Introduction 00:00:33 - Alvar's Early Exposure to Computers and Coding 00:03:38 - First Steps into the Tech Industry 00:05:05 - Growth and Learning at Hansa Bank (now Swedbank) 00:06:13 - The Codeborn Experience and the Shift to Product Focus 00:08:03 - Joining the Startup World with TransferWise 00:10:00 - Evolution of Roles and Responsibilities at Wise 00:12:16 - Founding Grünfin: Mission and Challenges 00:13:20 - Comparing Different Work Environments: Bank, Contracting, Startup 00:19:51 - Key Skills for Startup Engineers 00:24:45 - Ensuring Quality in a FinTech Startup 00:35:24 - The Role of Test-Driven Development (TDD) 00:41:49 - Philosophy on Engineering Leadership 00:47:15 - Building a Team: The Importance of Composition 00:50:50 - Lessons Learned from the Grünfin Journey 00:56:57 - Why Alvar Joined Lightyear 00:59:58 - Engineering Challenges at Lightyear 01:02:44 - The Future of Engineering with LLMs 01:11:53 - Recommended Reading and Resources Thanks 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

    1h 16m
  2. 20/10/2024

    Lauri Koobas: Data Engineering - from early startup to scaling | Ep. 4

    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/@mapforengineers Annotated chapters in timeline: 00:00:00 Sneak peek of episode 00:01:21 Episode overview 00:02:44 Introduction, Lauri's background 00:20:48 Starship robots: huge amount of data there 00:23:37 Data lake, data warehouse, data lakehouse 00:26:44 Devil is in the details: timestamps, texts, character sets... 00:49:44 Moving data from prod to data warehouse 00:53:09 Analytics tools: PostHog, Amplitude, Redash, Databricks 01:00:15 Analytics tools vs real-time monitoring like Prometheus/Grafana 01:04:15 Usability matters: each tool for its job 01:06:38 Startup grows: needs in data analytics 01:11:09 Multiple data sources: when data warehouse really begins 01:19:55 Data and (de-)coupling: software engineers should not be blocked by analytics 01:22:51 Data ETL 01:24:59 Changes in data model: multi-phase migrations 01:29:38 Change data capture, incremental imports 01:34:21 Should analytics have new data in real time? Maybe not? 01:39:02 Importing data into DWH through business events 01:43:37 When DWH subscribes to business events, data model can evolve freely 01:47:16 Quick recap what we discussed so far 01:52:25 GDPR and Data Compliance: start early 01:56:05 PII data: know exactly where you store it, control it well 02:03:37 Lauri's books recommendations on data engineering - Kimball 02:07:18 Lauri's podcast on data engineering, in Estonian 02:08:28 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

    2h 9m
  3. 04/10/2024

    Carl Rannaberg: Latest AI Tools in Software Development - Cursor, Continue.dev, v0, Claude | Ep. 3

    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.com Annotated chapters in timeline on topics that Carl and I covered: 00:00:00 - Start 00:04:05 - Small talk, getting into the groove 00:08:18 - Carl's background: ex-Pipedrive, now engineer in Pactum 00:19:44 - Early tools: simple autocomplete and simple prompting without context 00:27:11 - AI tools with context: Cursor IDE, Continue.dev 00:43:35 - Cursor IDE Composer - Prompt+Apply to Code Instantly 00:47:29 - Ollama - following docker philosophy 00:55:15 - V0.dev - LLM to create frontend components 00:59:27 - Cursor IDE + v0.dev combination as a workflow 01:02:23 - Claude 3.5 Sonnet 01:03:10 - OpenAI o1 01:04:56 - LLMs vs SQL Queries - still to be solved 01:08:17 - LLM in TDD and Testing Workflows 01:16:04 - Focus on engineering fundamentals - LLM does not replace your engineering fundamental knowledge 01:20:13 - Book recommendations 01:29:09 - Hosting models yourself - expensive 01:33:27 - Fine-tuning models 01:35:33 - RAG 01:49:50 - Chain of thought 01:51:46 - vyce.app - GenAI helping with compliance questions 01:54:25 - Summary of tools we covered so far 01:58:43 - GenAI vs engineering careers 02:05:39 - 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

    2h 7m
  4. 25/09/2024

    Ürgo Ringo: Product Engineering | Ep. 1

    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 company Work 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 silos It’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 ends Ürgo 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 important Coming 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 Engineering GenAI 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

    1h 26m

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Creating a map of knowledge and tools for software engineers. log.mapforengineers.com