Streamlining Code Reviews with Integrated Diff Tools

Nicolay here,

Most AI coding tools obsess over automating everything. This conversation focuses on the right

balance between human skill and AI assistance - where manual context beats web search every time.

Today I have the chance to talk to Ben Holmes, a software engineer at Warp, where they're building the

AI-first terminal.

Manual context engineering trumps automated web search for getting accurate results from

coding assistants.

Key Insight Expansion

The breakthrough insight is brutally practical: manual context construction consistently outperforms

automated web search when working with AI coding assistants. Instead of letting your AI tool search

for documentation, find the right pages yourself and feed them directly into the model's context

window.

Ben demonstrated this with OpenAI's Realtime API documentation - after an hour of

back-and-forth

with web search, he manually found the correct API signatures and saved them as a reference file.

When building new

features, he attached this curated documentation directly, resulting in immediate

success rather than repeated failures from outdated or incorrect search results.

This approach works because you can verify documentation accuracy before feeding it to the AI, while

web search often returns the first result regardless of quality or recency.

In the podcast, we also touch on:

Why React Native might become irrelevant as AI translation between native languages improves

Model-specific strengths: Gemini excels at debugging while Claude dominates f

unction calling

The skill of working without AI assistance - "raw dogging" code for deep learning

Warp's architecture using different models for planning (O1/O3) vs. coding (Claude/Gemini)

💡 Core Concepts

Manual Context Engineering: Curating documentation, diagrams, and reference materials directly

rather than relying on automated web search.

Model-Specific Workflows: Matching AI models to their strengths - O1 for planning, Claude for

f

unction calling, Gemini for debugging.

Raw Dog Programming: Coding without AI assistance to build f

undamental skills in codebase

navigation and problem-solving.

Agent Mode Architecture: Multi-model system where Claude orchestrates task distribution to

specialized agents through f

unction calls.

📶 Connect with Ben:

Twitter/X, YouTube, Discord (Warp Community), Website

📶 Connect with Nicolay:

LinkedIn, X/Twitter, Bluesky, Website, nicolay.gerold@gmail.com

⏱ Important Moments

React Native's Potential O

bsolescence: [08:42] AI translation between native languages could

eliminate cross-platform frameworks

Manual vs Automated Context: [51:42] Why manually curating documentation beats AI web

search

Raw Dog Programming Benefits: [12:00] Value of coding without AI assistance during Ben's first

week at Warp

Model-Specific Strengths: [26:00] Gemini's superior debugging vs Claude's speculative code

fixes

OpenAI Desktop App Advantage: [13:44] O

utperforms Cursor for reading long files

Warp's Multi-Model Architecture: [31:00] How Warp uses O1/O3 for planning, Claude for

orchestration

Function Calling Accuracy: [28:30] Claude outperforms other models at chaining f

unction calls

AI as Improv Partner: [56:06] Current AI says "yes and" to everything rather than pushing back

🛠 Tools & Tech Mentioned

Warp Terminal, OpenAI Desktop App, Cursor, Cline, Go by Example, OpenAI Realtime API, MCP

📚 Recommended Resources

Warp Discord Community, Ben's YouTube Channel, Go Programming Documentation

🔮 What's Next

Next week, we continue exploring production AI implementations with more insights into getting

generative AI systems deployed effectively.

💬 Join The Conversation

Follow How AI Is Built on YouTube, Bluesky, or Spotify. Discord coming soon!

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