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Fragmented - AI Developer Podcast
Fragmented - AI Developer Podcast
Author: Kaushik Gopal, Iury Souza
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© 2026 The Fragmented Podcast
Description
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.
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.
271 Episodes
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What does it take to keep an agent working after its process dies? Pi creator Mario Zechner and Earendil co-founder Armin Ronacher join KG to explain Pi Durable: explicit tasks, crash recovery, and the primitives for multiplayer agents. We also dig into why Pi added MCP and codemode, and why a harness that can rewrite itself still needs a clear goal.Full shownotes at fragmentedpodcast.com.Show NotesPi 1.0, Earendil and Pi DurableMario Zechner - Pi’s creator and our guest.Armin Ronacher - Earendil co-founder and our guest.Pi 1.0 - The stable release and the design choices behind it.Pi Durable - Mario’s introduction to the experimental harness for long-running agents.Codemode and MCP: why Pi added both“You Said No MCP!” - Why codemode brought MCP and non-LLM models into Pi.Blender MCP - The Blender integration Armin uses as an MCP example.Durable workflows and crash recoveryPi Durable documentation - Tasks, explicit phases, replay-safe tools, ownership, and storage.Temporal - The workflow engine compared with Pi Durable’s explicit task model.Absurd Workflows: Durable Execution With Just Postgres - Armin’s earlier workflow design, and the trade-offs discussed here.Subagents, multiplayer and networked pluginsPi Durable: abort and subagents - How task ownership connects parent work and child conversations.Chord - Networked objects, versioned contracts, and hot reload across clients.Self-improving harnesses and their limitsEpisode 311 - Self learning harnesses are here - The previous discussion that prompted KG’s questions.Prime Agent - The harness discussed while questioning goals, evaluation, and unconstrained rewrites.Terminal-Bench - The benchmark used to explain narrow optimization and benchmark gaming.Earendil and advice for developersEarendil - The team’s broader goal: AI tools that work beyond software engineering.Bryan Cantrill’s The Observation Deck - The writing Armin recommends to software engineers.Reflecting on Life - Armin’s broader advice on work, commitment, and personal growth.Get in touchPlease check our contact page for the best ways to reach out to us.We want to hear all the feedback: what's working, what's not, topics you'd liketo hear more on.Contact usNewsletterYoutubeWebsiteCo-hosts:Kaushik GopalIury Souza
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
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
Vagrant
Terraform
IBM acquires 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
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
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.
Contact us
Newsletter
Youtube
Website
Co-hosts:
Kaushik Gopal
Iury Souza
[!fyi] We transitioned from Android development to AI starting withEp. #300. Listen to that episode for the full story behind
our new direction.
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.
Contact us
Newsletter
Youtube
Website
Co-hosts:
Kaushik Gopal
Iury Souza
[!fyi] We transitioned from Android development to AI starting withEp. #300. Listen to that episode for the full story behind
our new direction.
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.
Contact us
Newsletter
Youtube
Website
Co-hosts:
Kaushik Gopal
Iury Souza
[!fyi] We transitioned from Android development to AI starting withEp. #300. Listen to that episode for the full story behind
our new direction.





