Programming Throwdown

Running AI in loop hands-free.

In this episode, Patrick and Jason cover Agentic Coding!

Patrick and Jason discuss what it means to become a manager and how the role differs from individual engineering work. They cover hiring, coaching, performance management, team goals, and when moving into management is or is not the right choice.

Patrick and Jason break down workflow orchestrators and why they matter for batch jobs, long-running tasks, and resumable distributed systems. They compare tools such as Airflow, Dagster, Temporal, Ray, and Kubeflow while explaining the infrastructure patterns behind them.

Patrick and Jason explain asynchronous programming and how it differs from traditional multithreading and multiprocessing. They cover coroutines, blocking versus non-blocking operations, promises, callbacks, async/await, and the tradeoffs behind each approach.

Patrick and Jason are joined by Mark Cunningham to discuss how software engineers can find strong job opportunities and perform well throughout the interview process. They cover sourcing strategies, reverse interviews, negotiation, hiring-manager expectations, and common mistakes candidates should avoid.

Patrick and Jason discuss how AI-assisted coding tools can speed up development, answer questions about a codebase, and reduce boilerplate work. They compare common workflows and tools such as Copilot, Cursor, and command-line assistants while talking through where these systems help most.

Patrick and Jason cover memory management from both the operating-system and language-runtime perspectives. They discuss heap management, virtual memory, garbage collection, ownership models, and practical techniques for diagnosing and reducing excessive memory use.

Patrick and Jason introduce reinforcement learning and place it alongside supervised and unsupervised learning. They cover Q-learning, SARSA, policy gradients, actor-critic methods, PPO, imitation learning, and why training and evaluating RL systems is so challenging.

Patrick and Jason discuss project planning and management for software teams. They cover why planning matters, how frameworks like SMART goals, Gantt charts, Scrum, Agile, and Kanban fit together, and how to deal with uncertainty, dependencies, and scheduling risk.

Patrick and Jason revisit working from home and the realities of remote engineering work. They cover communication, scheduling, home-office setup, motivation, distractions, and why remote work is not equally effective for every team or every person.

Patrick and Jason explain vector databases by starting with embeddings, similarity metrics, and approximate nearest-neighbor search. They discuss how these systems store and query high-dimensional vectors and where tools like pgvector, Weaviate, Pinecone, and Milvus fit.

James Morse: Software Engineer at Cisco System Administrator to DevOps Difference between DevOps and MLOps Getting Started with DevOps Luke Marsden: CEO of Helix ML How to start a business at 15 years old BTRFS vs ZFS MLOps: the intersection of software, DevOps and AI Fine-tuning AI on the Cloud Some advice for folks interested in ML Ops Yuval Fernbach: CTO MLOps & JFrog Starting Qwak Going from a jupyter notebook to production ML Supply Chain Getting started in Machine Learning Stephen Chin: VP of DevRel at Neo4J Developer Relations: The Job What is a Large Language Model? Knowledge graphs and the Linkage Model How to Use Graph databases in Enterprise How to get into ML Ops.

Patrick and Jason discuss how to write a strong technical resume that gets attention without becoming bloated or misleading. They cover what to include, what to avoid, how automated screening changes resume writing, and how career choices shape the resume you build over time.

Patrick and Jason explain DevOps and how it relates to site reliability, build systems, testing infrastructure, and release processes. They cover infrastructure as code, CI/CD, deployment strategies, operational metrics, and the kinds of failures good DevOps practices are meant to prevent.

Patrick and Jason discuss unit testing, regression testing, and system testing, with a focus on when mocking actually helps. They explain mocks versus fakes, testing tradeoffs, and the practical role of testing libraries across several major languages.

Patrick and Jason explain transformers and large language models from the ground up. They cover attention, encoders and decoders, self-supervised learning, RLHF, and the key architectural ideas that made modern LLMs possible.

Patrick and Jason walk through the differences between compilers and interpreters, starting from machine code and assembly and moving up to high-level languages. They cover bytecode, JIT compilation, intermediate representations, and the tradeoffs between portability and performance.

Predictions: Jason VR for Work Lowering AI training cost/ improved efficiency RISC-V takeoff Patrick Ai claim of AGI Ai peer reviewer Ai Video Generator More space vehicles reaching orbit Early career, finding role at FAANG, liaising vs shipping code. Upcoming in tech What are essential programmer knowledge items?