- 59 minutes 43 seconds310 - Mitchell Hashimoto on Ghostty & His Agentic Coding Workflow
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
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- The freedom to run the program as you wish, for any purpose.
- The freedom to study how the program works, and change it to make it do
what you wish. - The freedom to redistribute copies so you can help others.
- 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
- Episode 301 - AI Coding ladder - Different stages of AI
- 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
- OpenAI Deep Research -
- Step 4: Outsource the Slam Dunks
- Step 5: Engineer the Harness
- Episode 307 - Harness Engineering - Fragmented's deep dive
on harness engineering, heavily inspired by Mitchell's post
- Episode 307 - Harness Engineering - Fragmented's deep dive
- 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:
[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.14 April 2026, 12:00 am - 25 minutes 37 seconds309 - Background Agents
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:
[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.1 April 2026, 12:00 am - 24 minutes 44 seconds308 - How Image Diffusion Models Work - the 20 minute explainer
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.
- Efficient Estimation of Word Representation -
- High-Resolution Image Synthesis with Latent Diffusion Models -
Rombach et al. (2022) — the paper behind Stable Diffusion - Image Training data
- 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:
[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.24 March 2026, 12:00 am - 29 minutes 55 seconds307 - Harness Engineering - the hard part of AI coding
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:
[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.17 March 2026, 7:22 am - Harness Engineering -
- 23 minutes 22 seconds306 - Keeping your agent instructions in sync and effective
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
Other links
- Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?
- Harness engineering - Check the section about using AGENTS.md as a table of contents
- OpenCode
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:
[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.10 March 2026, 7:07 am - 26 minutes 38 seconds305 - Subagents explained - What they are, when (not) to spawn them
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
- Claude Code Subagents
- Gemini CLI Subagents
- Opencode Subagents
- Cursor Subagents
- Antigravity Agent Modes
- AOE Scouting
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:
[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.17 February 2026, 4:08 am - 26 minutes 47 seconds304 - Agent Skills - when to use them and why they matter
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
[!warning] Don't install from hubs blindly.
Inspect the repo code before adding anything to your agent.
- Prompt Injection Attacks -
OWASP guide to LLM prompt injection vulnerabilities - OpenClaw <- MoltBot <- Clawdbot
- 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:
[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.9 February 2026, 5:00 am - Progressive Disclosure -
- 25 minutes 45 seconds303 - How LLMs Work - the 20 minute explainer
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.
- Efficient Estimation of Word Representation -
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:
[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.2 February 2026, 8:00 am - OpenAI Tokenizer visualizer -
- 19 minutes 9 seconds302 - MCPs Explained - what they are and when to use them
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:
We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.26 January 2026, 5:00 am - 24 minutes 38 seconds301 - The AI coding ladder
Most folks reference "AI coding" like it's one thing. It's really not. In this foundational episode Kaushik & Iury walk through (at least) four paradigms — from super autocomplete to agent orchestration — each with different workflows, expectations, and mental models.
What do most developers follow today? Where is the frontier? What's coming in the future?
Listen to the episode and find out!
Full shownotes at fragmentedpodcast.com.
Show Notes
Gen 1: Super autocomplete
Gen 2: Chat Oriented Programming
Gen 3: Agent
- Nvidia's definition of an Agent
- ReAct Prompting
- Chain of Thought was a prompting hack
- DeepSeek
- TUI tools (or Harnesses):
- IDE style tools
- Headless Tools:
Gen 4: Agent Orchestration
Tips
- Iury: Transfer between agents using your own
compact command - KG: Ask the agent to clarify your prompt
Confirm if my requirements are clear. If you have follow up questions, ask me
first and clarify before executing anything.Contact us
Co-hosts:
19 January 2026, 9:00 am - 8 minutes 44 seconds300 - From Vibe coding to Software engineering
Fragmented is changing. New direction, new cohost. Kaushik explains the pivot
from Android to AI development and introduces Iury Souza.From vibe coding to software engineering — one episode at a time.
Full shownotes at fragmentedpodcast.com.
Contact us
Co-hosts:
12 January 2026, 9:00 am - More Episodes? Get the App