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Ryan J. Salva

Episode #252

VP of Product

GitHub

🎯Product Strategy🔍User Research⚡Execution

📝Full Transcript

10,298 words
Ryan J. Salva (00:00:00): We had actually created a snapshot of GitHub's public code for what we call the Arctic Code Vault, right? Essentially, this is up in like way in the Northlands of Finland, there's a seed vault. We were like, you know what? Seed vaults are really there to preserve the diversity of the world's flora in seeds in case of some crazy either natural or manmade disaster. But another really important asset to the world is our code, our open source. This represents actually a lot of the collective, well, certainly software, if not intelligence of kind of the modern world, right? Ryan J. Salva (00:00:44): We had put this snapshot of public repositories on this silver film that would be preserved for thousands of years in this Arctic Code Vault. Well, we took that same data snapshot and we brought it to our friends over at OpenAI to see like, okay, what can we do with these large language models built on public code? Well, it turns out we can do some pretty cool things. Lenny (00:01:13): Ryan Salva is VP of product at GitHub, where, amongst other projects, he incubated and launched GitHub Copilot, which in my opinion is one of the most magical products that you'll come across. If you haven't heard of it, it uses OpenAI's machine learning engine to autocomplete code for engineers in real time as they're coding. I think it's one of the biggest advances in product development and productivity that we've seen in a while. I'm always really curious how a big product like this starts, gets buy in, build momentum, and then launches, especially at a big company like Microsoft and especially a product like Copilot that has surprising ethics challenges, scaling challenges, business model questions. Lenny (00:01:55): Also, this came out of a small R&D team that GitHub has, and it's so interesting to hear what Ryan has learned about incubating big bets within a large company, and then taking them from prototype to Microsoft scale. Ryan is also just super interes...

💡 Key Takeaways

  • 1Innovation often requires separating R&D (GitHub Next) from Engineering/Product to protect creativity from operational constraints.
  • 2The 'Transfer Protocol' from R&D to Production requires researchers to embed with the product team until they can be replaced by a full-time hire.
  • 3For AI coding tools, the latency sweet spot is around 200ms to maintain developer flow.
  • 4Treating AI as a 'Persona' (e.g., a Pair Programmer) helps define ethical boundaries and user experience expectations.
  • 5A healthy product portfolio allocation is ~60% incremental progress, 25-30% operations, and 5-10% bold bets.

📚Methodologies (3)

🎯 Product Strategy

A structured approach to graduating projects from an innovation lab (GitHub Next) to the core product organization (EPD). It focuses on rigorous personnel management and roadmap ownership transfer to ensure continuity and operational excellence.

Core Principles

  • 1.Ring-fence the R&D team: Protect researchers from uptime/security constraints initially to foster creativity.
  • 2.Embed Researchers: Move key researchers into the product squad temporarily during the transition.
  • 3.Replacement-in-Seat Rule: Researchers cannot return to the lab until a permanent engineer/PM has learned the domain and replaced them.
  • +1 more...

"The criteria for moving researchers back into their R&D team... can't be based on a calendar. It needs to be based on a replacement in seat."

#r&d-to-production#transfer#protocol
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The AI Persona Framework

by Ryan J. Salva

🔍 User Research

By assigning a human metaphor (Persona) to the AI, product teams can establish intuitive guidelines for behavior, ethics, and user expectations. This helps in decision-making regarding content filtering and interaction design.

Core Principles

  • 1.Define the Metaphor: Identify the human equivalent role (e.g., 'Pair Programmer').
  • 2.Apply Human Standards: If a human colleague did X (e.g., shouted slurs), would it be acceptable? If not, the AI shouldn't do it either.
  • 3.Graduated Filters: Start with crude blocks (blocklists) and evolve to nuanced AI sentiment models.
  • +1 more...

"If Copilot is your AI pair programmer and they're whispering crazy stuff into your ear... you're probably not going to be able to focus on your work."

#persona#research#users
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⚡ Execution

A resource management heuristic for product leaders to ensure immediate business needs are met while securing the company's long-term future through high-risk bets.

Core Principles

  • 1.60% Incremental Progress: Iterative improvements on in-market products (Horizon 1).
  • 2.25-30% Operations: Maintenance, reliability, security, and 'keeping the lights on'.
  • 3.5-10% Moonshots: Bold, audacious bets with high uncertainty (Horizon 2 & 3).

"I certainly try to make sure that we're always reserving some capacity for bold, audacious experimental research projects."

#60-30-10#portfolio#allocation
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