AI Coding Assistants
360 episodes · Page 8 of 15
#2323: Why Nitrogen Changes Everything
Discover the physics of Guinness’s nitrogen foam, the engineering behind the widget, and why it feels so different from other beers.
#2287: Is AI Code Generation the Future of Low-Code?
Exploring the rise of AI code generation and its potential to reshape the low-code movement.
#2255: Typst vs. LaTeX: The AI-Ready Document Engine
Can Typst succeed LaTeX as the go-to tool for programmatic typesetting, especially for AI agents? We compare the two and explore what makes a docum...
#2254: How to Test an AI Pipeline Change
When you tweak one part of a complex AI agent system, how do you know if it actually improved anything? The answer lies in engineering checkpoints.
#2253: Why AI Agents Get Three Steps, Not Infinity
Why do AI agents get exactly three rounds of tool use? It's a critical guardrail against infinite loops and runaway costs, not a limit on intellige...
#2249: Building Custom Benchmarks for Agentic Systems
Public benchmarks fail for agentic systems. Learn how to build evaluation frameworks that actually predict production behavior.
#2243: What Enterprise AI Pricing Actually Negotiates
Enterprise customers rarely get the deep discounts they expect from AI APIs. What they actually negotiate for—and why the ramp-up requirement exist...
#2242: AI as Your Ideation Blind Spot Spotter
How to use AI not to answer questions you already know to ask, but to surface possibilities your expertise has made invisible to you.
#2219: Spec-Driven Life: How AI Planning Beats Project Paralysis
What makes AI agents reliably productive? A structured spec that externalizes memory and chunks work into manageable pieces. Can the same framework...
#2207: Specs First, Code Second: Inside Agentic AI's New Era
As AI coding agents evolve from autocomplete to autonomous cloud workers, the bottleneck has shifted—now it's about how clearly you specify what ne...
#2205: When AI Coding Agents Forget: Five Approaches to Context Rot
As coding agents handle longer sessions, they accumulate noise and lose crucial information. Five competing frameworks are solving this differently...
#2193: Running Claude in Your Apartment (The Physics Says No)
Building a local AI inference server to rival Claude Code sounds great until you do the math on heat, noise, and neighbor relations.
#2191: Making Multi-Agent AI Actually Work
Research from Google DeepMind, Stanford, and Anthropic reveals most multi-agent systems waste tokens and amplify errors. Single agents with better ...
#2184: The Economics of Running AI Agents
Production AI agents can cost $500K/month before optimization. Learn model routing, prompt caching, and token budgeting to cut costs 40-85% without...
#2182: Can You Actually Review an AI Agent's Plan?
Most AI agents have plans the way you have a plan while half-asleep—something's happening, but you can't see it. We map the five major planning pat...
#2180: The Sandboxing Tradeoff in Agent Design
AI agents need broad permissions to be useful—but every permission expands the attack surface. We map the real threat landscape and the isolation t...
#2178: How to Actually Evaluate AI Agents
Frontier models score 80% on one agent benchmark and 45% on another. The difference isn't the model—it's contamination, scaffolding, and how the te...
#2174: Role-Playing as Orchestration
How a role-playing protocol from NeurIPS 2023 became one of AI's most underrated agent frameworks—and what happens when you scale it to a million a...
#2170: Pricing Agentic AI When Nothing's Predictable
How do you charge fixed prices for systems that operate in fundamental uncertainty? Consultants are discovering frameworks that work—but they requi...
#2169: How Enterprises Are Rethinking Agent Frameworks
Twelve major agentic AI frameworks exist—yet many serious developers avoid them entirely. What patterns emerge in real enterprise adoption?
#2168: What Serious Agentic AI Developers Actually Need to Know
Python, TypeScript, LangGraph, and the frameworks reshaping how agents work. A technical map of the skills and concepts that separate prototypes fr...
#2166: Code vs. Canvas: How Developers Pick Their Tools
LangGraph or Flowise? The honest answer isn't obvious. Developers gain speed and integrations with visual builders—but lose version control, testin...
#2165: Strip Your Agent to Bash
The frameworks matter less than you think. What separates a working agent from a failing one is the harness—the orchestration, memory, and tool des...
#2160: Claude's Latency Profile and SLA Guarantees
Claude is measurably slower than competitors—and Anthropic's SLA promises are even thinner than the latency numbers suggest. What enterprises actua...