𝘈𝘯 𝘦𝘹𝘤𝘪𝘵𝘦𝘥 𝘈𝘐 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳 𝘢𝘥𝘷𝘰𝘤𝘢𝘵𝘦 @jbaru.ch, and 𝘢 𝘤𝘺𝘯𝘪𝘤𝘢𝘭 𝘴𝘦𝘯𝘪𝘰𝘳 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘮𝘢𝘯𝘢𝘨𝘦𝘳, Leonid Igolnik, take the stage to debate whether AI-driven development is finally ready for prime time or just another way to get things wrong.

Maybe we’ve been 👀 at this the wrong way. AI might be trustworthy but only if we rethink how we guide it. What if there were a way to ensure it understands intent before it writes a single line of code? A way to catch mistakes before they happen instead of fixing them afterward?

That leaves us manually checking everything. 🧐 The safest bet is to assume it’s wrong and review every line yourself, which doesn’t exactly scream “𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘷𝘪𝘵𝘺 𝘣𝘰𝘰𝘴𝘵.”

So what’s the alternative? 🤔

Having AI test its own work doesn’t help. If we can’t trust it to write code, why would we trust it to write tests after the fact? That’s not verification; it’s an echo chamber.

If your organization values ✅ correctness, ⚡ velocity, and 🔍 clarity in the age of AI, this is a conversation you need to be part of.

(the link is below. you know what to do)
ai.bythebay.io/register

“Human-in-the-loop” is no longer a sufficient standard.
This is about human in charge — because long-term maintainability, trust, and resilience depend on it.

🧬 The use of Zig for ultra-efficient inference, including breakthroughs from ZL.
🔐 Type-safe systems, advanced patterns, and the enduring value of correctness.
🔁 The implications for CI/CD, observability, compliance — and developer accountability — in context-driven IDEs.

This track addresses what it means to engineer software with rigor in an AI-native world.

We’ll examine:

🚀 The growing role of Rust-backed Python tooling like UV and Pydantic, reshaping performance and reliability.
👇

Modern development is increasingly shaped by tools like Microsoft Copilot, Cursor, Codeium, and platforms like Replit that promise full-stack code generation. But these gains come with trade-offs. When the environment becomes opaque or fails unpredictably, developer autonomy is at risk.

🎯 This is why we’re having the Thoughtful & AI-Native Coding track at AI By the Bay.

We’re not chasing trends. We’re focused on the foundational question:
🧠Can you still understand, debug, and take responsibility for the systems you build — even with AI in the loop?

What happens when your test tab disappears right before launch?
That’s not hypothetical. In a recent demo from a major platform, the developer experience collapsed when key interface elements failed to appear.
❌ No tests.
❌ No control.
❌ No clarity

We’re lucky to have him and even luckier to call him a regular. ❤️

We invite everyone to join us in welcoming Julien Le Dem! We're thrilled to have him back in our program for the second time in a row. 👏

Mary is a proven quality marker of outstanding conferences—if she’s involved, you know it’s an event worth attending! ❤️

🌺 Leah McGuire, ML engineer at Faros AI and also a keynote speaker at our latest edition, explored operationalizing ML models and building trust in AI systems, challenging us to rethink our approach to LLMs.

…and many more incredible women shaping the future of AI! ❤️

🌺 Holden Karau, an open-source and distributed computing champion, explored Apache Spark optimizations and scalable data engineering.
🎉 Special congratulations on Holden’s new role as co-founder of Fight Health Insurance, leveraging AI & LLMs to drive positive impact!

🌺 Shreya Rajpal, founder of Guardrails AI, brought deep insights into AI safety and reliability, showing how to build LLM applications that are not just powerful but also trustworthy.