AI
Agentic AI Fundamentals: Architectures, Frameworks, and Applications
This course provides an overview of the primary moving pieces in agentic systems. Note that the video is industry-agnostic, so don’t expect to see specific products or vendors mentioned to help implement agentic AI.
Has This Report EXPOSED THE TRUTH About AI Assisted Software Development?
- The 2025 DORA report on AI usage states that 95% depend on AI, 80% find it improves their productivity, and 30% didn’t trust the output.
- Successful adoption of AI takes more than just the tools: clear and communicated AI stance, healthy data ecosystems, AI-accessible internal data, strong version control practices, working in small batches, user-centric focus, quality internal platforms
- Programming fundamentals
- Precise spec of what we want (natural languages are not as precise as programming languages)
- Verification that we got what we want
- Ability to make progress in small steps
- AI is not reliable on its own because it’s not deterministic and it makes things up (unsupported claims)
- Some companies limit junior devs’ access to AI (unaware of the risk they’re taking if they don’t verify AI output)
- Verify that it’s doing what you want it to after every small change.
Data
Introduction to Azure Data Pipeline Patterns and Use Cases
This course (current as of July 2025) describes what Microsoft Azure provides in terms of processing data. It explains batch vs streaming, ETL vs ELT, and data lakes vs data warehouses. Azure-specific functionality includes Synapse, Databricks, and SQL Data Warehouse.
Data Cleaning and Processing for Data Scientists
This course provides an overview of data cleaning and transformation processes. The presenter used Google Colab and various Python libraries; he also mentioned cloud-based tools.
ETL Processes and Tools for Data Engineers
This course also covers the basics of data processing and pipelines. One of the modules demonstrates Apache NiFi as a tool you can run locally.
Software Development
POSITIVE ACTIONS Technical Leaders can take to address bad code
- Code quality is part of your job
- Do a little bit over time
- Skills and leadership are needed
- For skills, GenAI is too unpredictable. Know your IDEs and practice on katas.
- Leaders set tone, defining “this is how we do things here.” To be effective, refactoring is a team effort (not just one dev). Make sure you’re leveling up juniors.