Building Claude Code: Origin, Story, Product Iterations, & What's Next // Siddharth Bidasaria // #342

Building Claude Code: Origin, Story, Product Iterations, & What's Next // Siddharth Bidasaria // #342

Author: Demetrios October 21, 2025 Duration: 50:28

Building Claude Code: Origin, Story, Product Iterations, & What's Next // MLOps Podcast #342 with Siddharth Bidasaria, Member of Technical Staff at Anthropic.


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// Abstract

Demetrios Brinkmann talks with Siddharth Bidasaria about Anthropic’s Claude code — how it was built, key features like file tools and Spotify control, and the team’s lean, user-focused approach. They explore testing, subagents, and the future of agentic coding, plus how users are pushing its limits.


// BioSoftware engineer. Founding team of Claude Code. Ex-Robinhood and Rubrik.


// Related Links

Bio: https://sidb.io/

Sid's Blog: https://sidb.io/posts/

I Let An AI Play Pokémon! - Claude plays Pokémon Creator: https://youtu.be/nRHeGJwVP18

How Data Platforms Affect ML & AI // Jake Watson // MLOps Podcast #207: https://youtu.be/xWApMuyct_4

The Agent Landscape - Lessons Learned Putting Agents Into Production: https://youtu.be/lRGldru7ohU


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Connect with Marco on LinkedIn: /siddharthbidasaria/


Timestamps:

[00:00] MCP servers usage creativity

[00:34] Claude's code origin story

[05:17] R&D freedom and tools

[09:08] Model potential discovery

[12:06] Model adaptation strategies

[19:13] Steerability vs pattern alignment

[22:09] Features to delete

[24:12] Moore's law in LLMs

[32:42] Power user surprises

[35:56] Sub-agent evolution insights

[39:54] Agent communication governance

[45:26] At-scale agent coordination

[49:56] Wrap up


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

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