HeyJaredCode Conversations
Code Conversations, is a podcast for software developers, engineers, and tech enthusiasts of all levels.
- Indexed episodes, last 90 days
- 13
- Latest publication
- Sep 28, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 9, 2026
Latest episodes
AI Model vs Agentic Harness (opens the original)
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AI models alone aren’t what makes systems powerful. Martin Keen explains the difference between AI models and agentic harness components like tools, memory, and loops. Learn how generative AI agents work and what drives real system performance. Ref: https://www.youtube.com/watch?v=ZELPNFXJ4_o
Harnesses in AI: A Deep Dive (opens the original)
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The agent hit a login page, panicked, reported success anyway, and the upvote never happened. Tejas Kumar's diagnosis: not a prompt problem. A harness problem.The demo builds a browser agent on GPT-3.5 Turbo (consciously choosing a VERY old model to show how good harness eng can improve it a lot) against Hacker News and layers in a harness without touching the prompt once. Guardrails cap iterations and compact context. A verify step reads the tool call history to catch the agent lying about what
How Harness Engineering Creates AI Agents (opens the original)
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Most developers have heard of prompt engineering. Many know about context engineering and RAG. But the real frontier in AI today is harness engineering — the structured environment that transforms an ordinary LLM into a powerful agentic system. In this episode of the Code to Care series, I walk through the complete evolution of how developers have learned to work with large language models (LLMs): from the early days of prompt engineering, through the rise of context engineering and RAG (Retriev
Harness Engineering Fixes AI Digital Amnesia (opens the original)
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Agent harnessing and harness engineering is a growing topic - and yet the term requires more clarification on what it is and why agentic systems evolved the way it did to where we are today.
Llama.cpp vs vLLM (opens the original)
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Choosing a local LLM engine can make or break performance. Cedric Clyburn breaks down Llama.cpp versus vLLM for real‑world local inference. Learn which tool fits personal hardware, production scale, and AI agent workloads. Ref: https://www.youtube.com/watch?v=0ujh7hfutq0
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