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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

7:59

The Context Problem: Why Your AI Agent Keeps Getting...

DEV ·

2026/08/11

Demos lie. An AI agent can look flawless in testing and then consistently produce confident, plausible, wrong answers the moment it touches real work. This episode of Development digs into the single most common — and most expensive — failure mode teams encounter when deploying AI agents inside their businesses: the context problem. It's not about the model. It's about what the agent actually knows when it has to make a decision.The episode covers what context really means for an AI agent

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7:10

The 30-Day Response, Budgeted Backwards

DEV ·

2026/08/10

Most proposal teams treat the federal government's 30-day minimum response window as a schedule — spending time from the front until it runs out. This episode of Development examines what changes when you reverse that logic entirely, drawing on this worked example of budgeting a 30-day RFP response backwards. The math is simple; the implications for how teams allocate drafting, review, and decision time are anything but.The episode walks through two versions of the same 30-day window — one

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8:42

The Complete List of Programming Languages 2025: What...

DEV ·

2026/08/09

Not all programming languages are created equal — and in 2025, the gap between choosing the right one and the wrong one can mean the difference between a system that scales and one that fails. This episode of Development cuts through the noise to examine the languages actually shaping modern software, drawing on the complete 2025 programming language guide to deliver a clear-eyed look at where each language fits, what it costs, and what it's worth.Here's what the episode covers:C and C++ —

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8:25

The SaaS Audit: How to Find the Tools Worth Replacing...

DEV ·

2026/08/08

Most small and mid-sized businesses carry a SaaS stack that feels both essential and absurd at the same time. Some of those subscriptions are load-bearing walls; others are doing something a weekend script could handle. The hard part isn't knowing that the bloat exists — it's knowing where to cut first, and in what order, without breaking anything that actually matters. This episode of Development walks through a structured audit methodology designed for operators who want answers, not

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7:22

Bid/No-Bid Is Arithmetic Before It Is Judgment

DEV ·

2026/08/07

Most bid/no-bid conversations jump straight to judgment — incumbency risk, relationships, competitive fit — without ever running the numbers that should come first. This episode of Development makes a case that's simple but easy to overlook: arithmetic is the gate, and judgment is only useful once a pursuit clears it. The discussion is grounded in the bid/no-bid arithmetic article from RFP.co, which lays out the underlying model in full.Here's what the episode covers:The expected-value

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8:01

Why AI Won't Replace Manual QA Anytime Soon

DEV ·

2026/08/07

As AI tools take on more of the software development lifecycle, it's tempting to assume quality assurance is next on the automation chopping block. But a closer look at what QA actually demands — curiosity, empathy, judgment, and collaboration — reveals a much more complicated picture. This episode of Development digs into the case for why manual QA remains essential, even as AI-powered testing tools grow more capable by the month.The episode walks through four core reasons why human

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8:41

Web Development Trends Shaping 2026 and Beyond

DEV ·

2026/08/06

The gap between websites that feel polished and purposeful and those that feel dated is growing fast — and the difference often comes down to a handful of deliberate choices. This episode of Development works through the top 20 web development trends for 2026 and beyond, separating the durable shifts from the passing fads and offering a grounded look at what's actually defining the modern web.Here's what the episode covers:High-contrast design: Why today's dark-mode aesthetic is more

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8:24

Python in 2025: Why the World's Favorite Language...

DEV ·

2026/08/05

Python has quietly become the connective tissue of modern software. It powers platforms used by hundreds of millions of people, sits at the heart of the AI revolution, and remains the go-to language for data scientists and financial analysts worldwide. This episode of Development digs into the key Python development trends shaping 2026 — examining not just how popular the language is, but why that popularity keeps compounding.Here's what the episode covers:Python's real-world footprint:

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8:29

Why Your AI Is Slower Than a 1998 Modem — And How to...

DEV ·

2026/08/04

A model that aces every benchmark but makes users wait five seconds for a response isn't ready for production — it's a liability. This episode of Development tackles one of the most common and costly gaps in modern AI deployment: the difference between a transformer model that works and one that works fast enough. Drawing from this in-depth guide on optimizing transformer models for low-latency inference, the episode walks through why inference is so expensive by design, and what

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7:52

Fine-Tuning LLaMA 3 With LoRA: Making AI Work for...

DEV ·

2026/08/03

General-purpose language models are impressive until you need them to be specific. This episode of Development examines how teams can close the gap between what LLaMA 3 knows broadly and what a real-world application demands, using a technique that has quietly made fine-tuning accessible to organizations well outside Big Tech. The discussion is grounded in this practical guide to fine-tuning LLaMA 3 on a custom dataset with LoRA — a step-by-step resource for developers ready to move from

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7:33

FAISS and HNSW: The Duo Making Vector Search Actually...

DEV ·

2026/08/02

Vector search sits at the heart of modern AI applications, but the gap between a working prototype and a production-ready system can be enormous. This episode of Development digs into the engineering reality of similarity search at scale, drawing on this deep-dive on implementing HNSW with FAISS for scalable AI to explain why the tools most teams start with eventually betray them — and what to use instead.The episode traces the full arc of the vector search problem: from naive approaches

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8:04

When One AI Agent Just Isn't Enough: Multi-Agent...

DEV ·

2026/08/01

Scaling an AI workflow beyond a single agent sounds like a natural next step — until you're wrangling shared memory pools, circular communication loops, and agents that flatly disagree with each other. This episode of Development digs into the real engineering work behind multi-agent collaboration, drawing on this in-depth guide to implementing multi-agent systems with AutoGPT to unpack what it actually takes to make autonomous agents cooperate at scale.AutoGPT is more than a GPT wrapper —

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7:42

Building an AI Code Refactoring Tool With GPT 5.6 Sol

DEV ·

2026/07/31

Legacy codebases don't clean themselves — but what if an AI could do the heavy lifting? This episode of Development digs into the architecture, tradeoffs, and hard-won lessons behind building a custom AI-powered code refactoring tool using GPT 5.6 Sol. Drawing from this in-depth guide to building an AI code refactoring tool, the episode goes well beyond the hype to examine what a real, production-minded implementation actually requires.The conversation covers the full lifecycle of designing

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8:29

Stop Whack-a-Mole: Using Reinforcement Learning to...

DEV ·

2026/07/30

Kubernetes autoscaling works — until it doesn't. For teams managing microservices under unpredictable, spiky traffic, the gap between a metric crossing a threshold and new pods actually serving users is exactly where incidents are born. This episode of Development examines whether reinforcement learning can close that gap for good, drawing on this deep-dive article on optimizing microservices scaling with RL as its foundation.The conversation covers both the promise and the genuine

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8:09

Building a Custom AI Code Refactoring Tool With...

DEV ·

2026/07/29

Technical debt doesn't clean itself — but what if a well-engineered AI tool could do most of the heavy lifting? This episode of Development examines the practical architecture behind a custom GPT-4-Turbo refactoring tool, drawing on this deep-dive article on building a custom AI code refactoring tool. It's a candid look at what it genuinely takes to turn a powerful language model into something trustworthy enough to run against a real codebase.The episode walks through four interconnected

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7:51

When Your AI Forgets the World Changed: Data Drift...

DEV ·

2026/07/28

A machine learning model can be technically flawless the day it ships and still become a liability six months later — not because anyone broke it, but because the world it was trained on no longer exists. This episode of Development tackles one of the most underappreciated threats to production AI systems: data drift. Drawing on this in-depth guide to implementing online monitoring pipelines for AI systems, the episode walks through why drift happens, how to recognize its different forms,

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7:58

Real-Time ML Inference: Wrangling Kafka and...

DEV ·

2026/07/28

Real-time machine learning inference has moved from a competitive advantage to a baseline expectation. This episode of Development tackles the architectural and operational realities of building a streaming inference pipeline — one that can ingest live events, score them against an ML model, and return predictions in milliseconds. The discussion is grounded in the deep-dive on streaming ML inference with Kafka and TensorFlow Serving and covers everything from initial design decisions to the

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7:44

Why Deploying LLMs on Serverless Is a Beautiful Disaster

DEV ·

2026/07/26

Serverless computing promises infinite scale, zero infrastructure management, and pay-per-use economics — so the idea of running a large language model on top of it is understandably tempting. But the gap between that pitch and production reality turns out to be enormous. This episode of Development unpacks the very real architectural friction explored in this deep-dive on deploying large language models in serverless environments, walking through the core challenges and the hybrid

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7:45

The Future of Coding Is No Coding at All — And Why...

DEV ·

2026/07/26

The rise of no-code and low-code platforms isn't a passing trend — it's a structural change in how software gets built. This episode of Development draws on the article exploring why coders need to adapt to a no-code world to examine what this shift actually means for working developers: not the end of the profession, but a fundamental redefinition of where developer value lives.Hosts walk through the economic logic behind the no-code movement, the tools reshaping production workflows, and

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7:33

Custom Tokenization Pipelines: The NLP Skill You...

DEV ·

2026/07/24

Tokenization is the step most NLP developers treat as an afterthought — until their model starts mangling stock tickers, shredding hashtags, and treating multi-word entities like random word salad. This episode of Development makes the case that tokenization is a foundational design decision, not a checkbox, drawing on this in-depth guide to building custom tokenization pipelines for NLP models. If your model's behavior has ever felt inexplicably broken despite clean-looking data, the

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