Podcasting
**Fragmented – AI Developer Podcast**

Fragmented is an AI developer podcast for engineers who want to go beyond vibe coding and ship real software.

We cover AI-assisted development the way working engineers actually use it: prompting strategies, code review, testing, debugging, workflows, and building production-grade software with AI tools. No hype. No “I shipped a SaaS in a weekend” stories. Just tactics that work.

Hosted by Kaushik Gopal and Iury Souza — software engineers using AI daily to build and ship real products.

From vibe coding to software engineering — one episode at a time. Our goal: help you use AI to become a better engineer, not be replaced by one.

Latest Episodes:

311 - Self learning harnesses are here Sep 06, 2026

Show notes

Claude Code and Codex have been the mainstay agent harnesses, with OpenCode and Pi as the popular open-source alternatives. But two new entries are pushing what a harness even is: DeepSeek's Cordis kernel, where everything — down to the agent loop itself — is a plugin you can hot-swap at runtime, and Prime Agent, an RLM-based harness that treats sub-agents and context as function calls in a live REPL. We break down what actually makes each one different.

Full shownotes: https://fragmentedpodcast.com/episodes/311

310 - Mitchell Hashimoto on Ghostty & His Agentic Coding Workflow Apr 14, 2026

Show notes

Mitchell Hashimoto co-founded HashiCorp, built some of the most impressive DevOps tools like Vagrant and Terraform, sold the company to IBM — and then built a terminal. Ghostty is now where a huge chunk of agentic coding actually happens. Mitchell was an AI skeptic. We walk through his six-step adoption framework and the workflows he uses day to day — warm-start research, Hail Mary prompts across twenty GitHub issues, and knowing when to let the agent slam dunk it.

Full shownotes at fragmentedpodcast.com.

Show Notes

HashiCorp

Ghostty

Ghostty - Mitchell's fast, native terminal built for platform integration across Mac and Linux
Terminal shell
SSH - secure shell
PTY - pseudoterminals
Terminal Multiplexers
- tmux - most popular open source one
XTGETTCAP by xterm
libghostty - the cross-platform terminal emulation library that powers Ghostty's core
xterm-js - powers terminal for apps like VSCode and the cloud
Jedi Term - Intellij's embedded terminal
Ghostty is now a non-profit
cmux - native macOS terminal
multiplexer built on libghostty — a fork Mitchell champions
Free Software Definition -
the 4 essential freedoms
1. The freedom to run the program as you wish, for any purpose.
2. The freedom to study how the program works, and change it to make it do
  what you wish.
3. The freedom to redistribute copies so you can help others.
4. The freedom to distribute copies of your modified versions to others.
Mitchell's tweet on unsolicited PRs and transfer of ownership

The AI Adoption Journey

My AI Adoption Journey -
Mitchell's blog post outlining his five-step framework
Step 1: Drop the Chatbot
- Episode 301 - AI Coding ladder - Different stages of AI
  adoption
Step 2: Reproduce Your Own Work
Step 3: End-of-Day Agents
- OpenAI Deep Research -
  kick off research tasks for a "warm start" the next morning
- Spine AI research - deep research tool for
  longer, hour-long analysis tasks
Step 4: Outsource the Slam Dunks
- Claude status hooks - warcraft peons
- Conductor
Step 5: Engineer the Harness
- Episode 307 - Harness Engineering - Fragmented's deep dive
  on harness engineering, heavily inspired by Mitchell's post
Step 6: Always have an Agent running
Peter Steinberger
Codex plugin for Claude Code

Get in touch

We'd love to hear from you. Email is the best way to reach us or you can check our contact page for other ways.

We want to hear all the feedback: what's working, what's not, topics you'd like to hear more on.

Co-hosts:

309 - Background Agents Apr 01, 2026

Show notes

Andrej Karpathy says the goal is to maximize how long an agent runs without your intervention. But there's a false summit most teams hit first: individual speed goes up while system speed stalls, your laptop roars under four parallel Gradle builds, and review queues back up. Kaushik and Iury trace the full arc — from local multitasking to cloud-hosted async work to fully autonomous agents that fire on repo events and put PRs in your inbox.

Show Notes

Andrej Karpathy on agents and token throughput - NoPriors podcast — maximize agent runtime, not token burn
Cursor Agent Mode - Multiagent interface - introduced the multi-agent board as a new paradigm for local parallel agents
Google Antigravity - Agent Manager interface
Claude Code Agent Teams - spawn
sub-agents from a main orchestrator, with tmux pane integration
Git worktrees - /reddit

Remote Background Agents in the cloud

Google Jules - hosted GitHub-connected agent,
proposes a plan, edits code, runs tests, opens a PR
Cursor Cloud Agents - remote agents
that clone your repo in the cloud and work in parallel
OpenAI Codex - cloud software
engineering agent for parallel tasks
Claude Code on the web - cloud-hosted Claude Code
sessions decoupled from your local machine

Building trust

Episode 307 - Harness Engineering - the earlier episode on
shaping agent environments — and why this ceiling exists

Get in touch

We'd love to hear from you. Email is the best way to reach us or you can check our contact page for other ways.

We want to hear all the feedback: what's working, what's not, topics you'd like to hear more on.

Co-hosts:

308 - How Image Diffusion Models Work - the 20 minute explainer Mar 24, 2026

Show notes

You already know how LLMs work from our popular 20-minute explainer. Now we take it to images. What does Michelangelo have to do with stable diffusion? More than you'd think. Walk away knowing how image generation actually works — and what it has in common with the text models you already understand.

Full shownotes at fragmentedpodcast.com.

Show Notes

Episode 303 - How LLMs work in 20 minutes - text generation
VAE -
Variational Autoencoder
RGB Color model - wikipedia
Word2Vec technique - wikipedia
- Efficient Estimation of Word Representation -
  original Word2Vec paper by Mikolov et al.
High-Resolution Image Synthesis with Latent Diffusion Models -
Rombach et al. (2022) — the paper behind Stable Diffusion
Image Training data
- LAION-5B - 5 billion image-text pairs
  scraped from the web, used to train many image generation models
- WebLI - Google's internal image-text
  dataset
Michelangelo

Get in touch

We'd love to hear from you. Email is the
best way to reach us or you can check our contact page for other
ways.

We want to hear all the feedback: what's working, what's not, topics you'd like
to hear more on.

Co-hosts:

307 - Harness Engineering - the hard part of AI coding Mar 17, 2026

Show notes

The hard part of AI coding isn't generating code — it's controlling quality, safety, and drift. Kaushik and Iury break down harness engineering: the five pillars for shaping an agent's environment and what it looks like when teams build custom harnesses from scratch.

Full shownotes at fragmentedpodcast.com.

Show Notes

Why it matters

Harness Engineering -
OpenAI's post on building their Codex codebase (~1M lines of code, 1,500 PRs
merged, zero manually written)

Shaping the harness

The Feed's Lost and Found -
Iury's newsletter consolidating harness engineering themes

Agent legibility
Closed feedback loops
Persistent memory
Entropy control
Blast radius controls

Building the harness

Minions: Stripe's one-shot, end-to-end coding agents -
Stripe forked Goose to build custom agents for their codebase
Goose - open-source coding agent from Block
Superpowers by Jesse Vincent - skills
that enforce a proper software engineering process
Open Code - open-source coding agent you can fork and
customize

Other resources

Agent Harness Glossary -
Latent Patterns
Towards self-driving codebases -
Cursor
Agentic Workflows -
GitHub Next
Future of Software Development -
ThoughtWorks

Get in touch

We'd love to hear from you. Email is the
best way to reach us or you can check our contact page for other
ways.

We want to hear all the feedback: what's working, what's not, topics you'd like
to hear more on.

Co-hosts:

306 - Keeping your agent instructions in sync and effective Mar 10, 2026

Show notes

AGENTS.md is becoming the common language for AI coding tools, but keeping repo
rules, personal rules, and tool-specific files in sync is still messy. In this
episode, Kaushik and Iury break down the sync problem, compare their own setups,
and unpack what the latest AGENTS.md research actually says.

Full shownotes at fragmentedpodcast.com.

Show Notes

The sync problem

AGENTS.md - Official spec
Custom instructions with AGENTS.md -
Open AI
Keep your AGENTS.md in sync - Kaushik's post
Rulesync - What Iury uses
Tweet by Ryan Carson and Claude frustrations

Get in touch

We'd love to hear from you. Email is the
best way to reach us or you can check our contact page for other
ways.

We want to hear all the feedback: what's working, what's not, topics you'd like
to hear more on.

Co-hosts:

305 - Subagents explained - What they are, when (not) to spawn them Feb 17, 2026

Show notes

Subagents are becoming a core primitive for serious AI-assisted development. In this episode, Kaushik and Iury disambiguate "agent" terminology, unpack plan mode vs subagents, and explain how parallel, scoped workers improve research quality without polluting the main thread.

Full shownotes at fragmentedpodcast.com.

Show Notes

Resources & Documentation

Official Documentation

Agents, Modes, Subagents: official harness docs

Research Papers & Articles

Introducing Claude Opus 4.5
Deep-Research Agents Paper
Post: GPT-5 System Card by Alex
Xu
Self-Driving Codebases Blog -
multi-agent systems making 1,000 commits/hour

Get in touch

We'd love to hear from you. Email is the
best way to reach us or you can check our contact page for other
ways.

We want to hear all the feedback: what's working, what's not, topics you'd like
to hear more on.

Co-hosts:

304 - Agent Skills - when to use them and why they matter Feb 09, 2026

Show notes

Agent Skills look simple, but they are one of the most powerful building blocks
in modern AI coding workflows. In this episode, Kaushik and Iury break down when
to use skills, how progressive disclosure works, and how skills compare with
commands, instructions, and MCPs.

Full shownotes at fragmentedpodcast.com.

Show Notes

Main References

Progressive Disclosure -
how skills are loaded into context
Agent Skills Open Specification
AAIF (Agentic AI Foundation) -
Linux Foundation initiative for AI interoperability
Needle in a Haystack Problem - original
"Lost in the Middle" paper
Agent-Invokable vs User-Invokable -
merging skills and commands in Claude Code

Creating Skills

Skill Creator -
Anthropic's skill for creating new agent skills
Claude Code frontmatter reference
- see model: * & context: fork

Using other Skills

Anthropic Skills GitHub Repository -
official collection of Claude skills and examples
Clawdhub - Clawdbot's skill hub. All versions are
archived here
SKILLS.sh - Vercel's skills hub

Warnings before installing random skills

Prompt Injection Attacks -
OWASP guide to LLM prompt injection vulnerabilities
OpenClaw
OpenClaw Security Analysis -
analysis of prompt injection risks in open agent frameworks
Malware found in a top-downloaded Clawhub skill -
incident report thread

Additional resources

Few-Shot Prompting -
improving outputs with examples
.agents/skills - proposal
to standardize the skills folder path
Vercel: AGENTS.md vs Skills -
comparison of agent instruction methods

Get in touch

We'd love to hear from you. Email is the
best way to reach us or you can check our contact page for other
ways.

We want to hear all the feedback: what's working, what's not, topics you'd like
to hear more on.

Co-hosts:

303 - How LLMs Work - the 20 minute explainer Feb 02, 2026

Show notes

Ever get asked "how do LLMs work?" at a party and freeze? We walk through the full pipeline: tokenization, embeddings, inference — so you understand it well enough to explain it. Walk away with a mental model that you can use for your next dinner party.

_Full shownotes at fragmentedpodcast.com.

Show Notes

Words -> Tokens:

OpenAI Tokenizer visualizer -
Visualize how text becomes tokens

Tokens -> Embeddings:

RGB Color model - wikipedia
Word2Vec technique - wikipedia
- Efficient Estimation of Word Representation -
  original Word2Vec paper by Mikolov et al.

Embeddings -> Inference:

Get in touch

We'd love to hear from you. Email is the
best way to reach us or you can check our contact page for other
ways.

We want to hear all the feedback: what's working, what's not, topics you'd like
to hear more on. We want to make the show better for you so let us know!

Co-hosts:

302 - MCPs Explained - what they are and when to use them Jan 26, 2026

Show notes

MCPs are everywhere, but are they worth the token cost? We break down what Model Context Protocol actually is, how it differs from just using CLIs, the tradeoffs you should know about, and when MCPs actually make sense for your workflow.

Full shownotes at fragmentedpodcast.com/episodes/302.

Show Notes

MCP - Model Context Protocol
Remote MCP server example - Glean
AAIF -
Agentic AI Foundation setup by Linux foundation
Github MCP
Github gh CLI
Playwright MCP
Context7 MCP
Anthropic's announcement on
Advanced Tool Use

Tips

Iury: use ast-grep to structurally
search code faster
KG: use agent-browser by Vercel to give browsing
power to your agent

Get in touch

We'd love to hear from you. Email is the
best way to reach us or you can check our contact page for other
ways.

We want to hear all the feedback: what's working, what's not, topics you'd like
to hear more on. We want to make the show better for you so let us know!

Co-hosts:

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Fragmented – AI Developer Podcast

Show Notes

Ghostty

The AI Adoption Journey

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Remote Background Agents in the cloud

Building trust

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Show Notes

Why it matters

Shaping the harness

Building the harness

Other resources

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Show Notes

The sync problem

Other links

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Resources & Documentation

Official Documentation

Research Papers & Articles

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Show Notes

Main References

Creating Skills

Using other Skills

Warnings before installing random skills

Additional resources

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Show Notes

Words -> Tokens:

Tokens -> Embeddings:

Embeddings -> Inference:

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