Fragmented - AI Developer Podcast

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

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Youtube

Website

Co-hosts:

Kaushik Gopal

Iury Souza

[!fyi] We transitioned from Android development to AI starting with
Ep. #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 with
Ep. #300. Listen to that episode for the full story behind

our new direction.

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.

Contact us

Newsletter

Youtube

Website

Co-hosts:

Kaushik Gopal

Iury Souza

[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind

our new direction.

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.

Contact us

Newsletter

Youtube

Website

Co-hosts:

Kaushik Gopal

Iury Souza

[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind

our new direction.

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.
Contact us
Newsletter
Youtube
Website
Co-hosts:
Kaushik Gopal
Iury Souza
[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.