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PODCAST

DEV

Software and web development from the side that has to ship it and then live with it. Architecture decisions with a cost attached, scoping, technical debt, hiring and vendor selection, and the AI tooling question every engineering team is now answering whether they planned to or not. Each episode takes one decision — rewrite or refactor, framework choice, build versus buy, how to scope a fixed-bid project honestly — and works through the tradeoffs, including the ones that only show up in year two

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All Episodes

7:14

Stop Writing API Docs by Hand — Let AI Do the First...

DEV ·

2026/07/23

Every developer knows the feeling: the code ships clean, the tests pass, and then the documentation tab sits open, untouched, for days. API docs have a way of drifting out of sync with the codebase almost immediately after they're written — and the real cost isn't inconvenience, it's the compounding miscommunication that erodes team trust over time. This episode of Development explores how large language models are changing that dynamic by handling the grunt work of the first draft, drawing

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7:38

Training a Diffusion Model from Scratch: A...

DEV ·

2026/07/22

Most developers who work with generative AI stop at the API layer — and that's fine, until curiosity kicks in. This episode of Development pulls back the curtain on what it genuinely takes to train a diffusion model from the ground up, drawing on the step-by-step guide to training a diffusion model for custom image generation published at DEV. Whether the goal is a specialized creative tool, a proprietary image pipeline, or simply a deeper understanding of how these systems work, this

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7:45

Neural Network Quantization: Shrinking Models Without...

DEV ·

2026/07/21

Deploying a well-trained machine learning model to a resource-constrained environment — a smartphone, an IoT sensor, or a cost-sensitive cloud setup — often reveals a painful gap between a model's theoretical requirements and what real hardware can deliver. This episode of Development tackles that gap head-on, exploring how neural network quantization makes production AI more practical across the board. The discussion is drawn from this in-depth article on reducing model size without losing

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7:03

AI-Powered Linting: Smarter Static Code Analysis With...

DEV ·

2026/07/20

Traditional linters are reliable workhorses, but they can only flag what someone thought to write a rule for. This episode of Development explores what happens when you pair static code analysis with machine learning — moving beyond deterministic rule enforcement toward a tool that can recognize subtle, historically problematic patterns the way a seasoned developer does. The discussion is grounded in this deep-dive on building an AI-powered linter with ML models, and goes further into the

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7:34

Edge AI Explained: Running Smarter Models on Tiny...

DEV ·

2026/07/19

Running intelligence directly on constrained hardware — smartwatches, industrial sensors, smart cameras — is no longer a niche research problem. It's a core skill for modern developers. This episode of Development digs into the practical side of edge AI, drawing on the in-depth guide to integrating AI in edge computing and IoT to explain what it actually takes to deploy capable models on devices with severe memory, power, and connectivity limits.Here's what the episode covers:Why edge AI

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8:28

Best Web Development Stacks to Use in 2026

DEV ·

2026/07/18

Stack decisions are among the most consequential choices a developer or technical founder makes — and they're often made too quickly, too early, or for the wrong reasons. This episode of Development uses the best web development stacks guide for 2026 as its foundation, offering a structured tour of today's most relevant technologies and a practical framework for choosing between them before a single line of code is written.The episode moves through the three layers of the modern web stack —

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7:57

Why Your GPU Is Loafing: Optimizing Deep Learning...

DEV ·

2026/07/17

Provisioning a powerful GPU only to watch utilization flatline is one of the most common — and costly — frustrations in deep learning. This episode of Development digs into the systemic reasons why large-scale training runs underperform, drawing on this in-depth guide to optimizing GPU utilization for large-scale deep learning models. Rather than hunting for a single silver-bullet fix, the episode frames GPU performance as an interconnected system where small inefficiencies compound — and

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7:08

Zero-Copy Data Pipelines: What Apache Arrow Actually...

DEV ·

2026/07/16

Data wrangling is the silent tax on every machine learning project — endless format conversions, redundant buffer copies, and library-to-library shuffling that eats hours without producing a single model improvement. This episode of Development takes a practical look at Apache Arrow and the zero-copy pipeline architecture it enables, drawing on this deep-dive article on zero-copy data pipelines for ML workloads to separate genuine capability from inflated hype.The episode walks through how

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7:19

Why Custom CUDA Kernels Could Be Your Deep Learning...

DEV ·

2026/07/15

GPU hardware is only as useful as the code running on it. For deep learning teams chasing faster training loops and tighter inference times, the bottleneck isn't always the model or the data pipeline — sometimes it's the abstraction layer between your workload and the silicon. This episode of Development explores building custom CUDA kernels for deep learning performance, making the case that going low-level isn't just for systems programmers — it's a practical tool for anyone serious about

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6:24

What Your Food Truck Website Is Missing — And Why It...

DEV ·

2026/07/14

Great food alone doesn't keep customers coming back — they have to be able to find you first, and then stay connected between visits. This episode of Development explores how food truck owners can transform a bare-bones website into a 24/7 business-building tool, drawing on 11 essential elements for a food truck website to help operators close the gap between good food and loyal regulars.The conversation covers a wide range of practical, actionable improvements — from the basics that most

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8:31

Writing Efficient Memory Allocators for PyTorch...

DEV ·

2026/07/13

Building a custom PyTorch extension is hard enough — but for engineers targeting specialized hardware or unconventional data pipelines, the default memory management layer can quietly become the biggest performance bottleneck of all. This episode of Development draws on this in-depth guide to writing efficient memory allocators for PyTorch extensions to walk through everything from the fundamentals of PyTorch's memory model to practical pooling strategies, debugging techniques, and the

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9:19

AI-Assisted Data Labeling: How Active Learning Loops...

DEV ·

2026/07/12

For most machine learning teams, the real bottleneck isn't compute power or model architecture — it's labeled data quality. This episode of Development digs into how active learning loops are reshaping the data annotation process, drawing on this in-depth article on AI-assisted data labeling to make the technique feel practical and immediately applicable, not just academically interesting.Rather than front-loading an entire labeling budget on a massive, undifferentiated dataset, active

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9:06

Checkpoint Versioning for Continual Learning Pipelines

DEV ·

2026/07/11

Managing checkpoints in a continual learning pipeline is one of those engineering problems that feels like housekeeping — until it isn't. When a production model misbehaves at 2 a.m. and your checkpoint directory is a graveyard of files named "final_really_this_time.pt," the cost of poor versioning becomes very real, very fast. This episode walks through the key ideas from this deep-dive on managing checkpoint versioning for continual learning pipelines, translating seven concrete practices

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8:43

ONNX + TensorRT: The Smart Path to Faster AI Inference

DEV ·

2026/07/10

Getting a deep learning model to perform well in training is one challenge — getting it to run efficiently in production is a different beast entirely. This episode of Development tackles that gap head-on, exploring the powerful combination of ONNX and TensorRT as a practical path to faster, leaner inference. The discussion is grounded in this in-depth guide to runtime optimization of ONNX models with TensorRT, and covers everything from the fundamentals to the real-world trade-offs

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8:50

Multi-GPU Training With Model Parallelism in DeepSpeed

DEV ·

2026/07/09

Modern AI models have grown far beyond what a single GPU can hold in memory — and that's not a problem you can optimize your way out of on one device. This episode of Development tackles the architecture, tooling, and practical considerations behind multi-GPU training, using Microsoft's DeepSpeed framework as the focal point. It's grounded in this in-depth guide to multi-GPU training with model parallelism, which is worth having open alongside your own training setup.The episode walks

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8:30

Why Your LLM Service Needs an Async Prompt Queue

DEV ·

2026/07/08

Shipping an LLM-powered product is one thing — keeping it responsive when traffic spikes is another challenge entirely. This episode of Development digs into a foundational infrastructure decision that separates hobby demos from production-grade AI services, drawing on this practical deep dive into async LLM serving architecture published on DEV. If your service handles user-submitted prompts synchronously today, this episode explains exactly why that will eventually break and what to build

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8:15

Compressing Transformer Models With Weight Clustering

DEV ·

2026/07/07

Large Transformer models like BERT and GPT have redefined what's possible in natural language processing — but their enormous parameter counts create serious deployment headaches. Memory constraints, sluggish inference, and ballooning cloud costs can make shipping a production-ready model feel like an engineering wall. This episode of Development digs into weight clustering, a compression technique that doesn't always get the attention it deserves but can meaningfully reduce model size

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8:10

Building a Static AI Code Assistant with Tree-Sitter...

DEV ·

2026/07/06

Inheriting a messy, multi-language codebase is one of those challenges that used to mean hours of manual archaeology. This episode of Development explores a more intelligent approach: a static AI code assistant powered by abstract syntax trees (ASTs) and Tree-sitter. The discussion is grounded in this practical deep-dive on building a static AI code assistant, and it covers everything from the foundational concepts to real-world deployment in a CI/CD pipeline.Here's what the episode walks

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8:31

Synthetic Data and GANs: The Edge ML Playbook You...

DEV ·

2026/07/05

Edge ML deployments have a nasty habit of exposing a fundamental tension: the models that would benefit most from rich training data are often running on devices that can't collect it — blocked by privacy regulations, hardware limits, or unreliable connectivity. This episode of Development tackles that problem head-on, walking through a structured engineering approach to building a GAN-powered synthetic data generator designed specifically for constrained environments. The discussion draws

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8:21

How to Hire a JavaScript Developer: Skills Checklist...

DEV ·

2026/07/04

Finding a JavaScript developer who can actually ship clean, maintainable code is harder than it looks. This episode of Development draws on this practical hiring guide for JavaScript roles to walk hiring managers and technical leads through a structured, no-fluff approach — from vetting core language fundamentals all the way through final-round interview tactics.Here's what the episode covers:Non-negotiable JS fundamentals — Why a candidate's grasp of closures, scope, hoisting, and the

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