Chain of Thought

• Using AI to Modernize Your Legacy Applications | MongoDB’s Rachelle Palmer

  Imagine cutting your legacy code modernization timeline from years to months. It’s no longer science fiction and this week’s guest is here to tell us how. Rachelle Palmer, Director of Product Management at MongoDB, joins hosts Conor Bronsdon and Atindriyo Sanyal, for a discussion on the groundbreaking ways AI is modernizing legacy applications. At MongoDB, Rachelle's forward-deployed AI engineering team is tackling the challenge of transforming complex, outdated codebases, freeing developers from technical debt. She details how LLMs are automating tasks like improving documentation, test generation, and even business logic conversion, dramatically reducing modernization timelines from years to months. What once demanded teams of dozens can now be achieved with a small, highly efficient team.Chapters:00:00 Introduction and Host Welcome00:58 Challenges in Modernizing Legacy Applications02:52 Real-World Examples of Code Modernization04:00 The Role of LLMs in Code Modernization08:01 Measuring Success in AI-Powered Modernization12:28 The Future of AI in Engineering16:17 Evaluating Modernization Success21:12 Returning to Your Startup Roots29:07 Forward Deployed AI Engineers35:36 Importance of Academic Research in AI42:10 Conclusion and FarewellFollow the hostsFollow⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠ Vikram⁠⁠⁠⁠Follow⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)⁠Rachelle PalmerMongoDBApplication Modernization FactoryCheck out Galileo⁠⁠⁠⁠⁠Try Galileo⁠⁠

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• Can Your AI Strategy Be Future-Proof? | Galileo’s Vikram Chatterji

  This week, we're sharing a special episode courtesy of 'Dev Interrupted.' Our co-host, Galileo CEO Vikram Chatterji, recently joined theDev Interrupted team for an engaging discussion on AI strategy. We were so impressed by the conversation that we wanted to share it with our audience, and they were kind enough to let us. We hope you enjoy it!From Dev Interrupted:"Vikram Chatterji joins Dev Interrupted’s Andrew Zigler to discuss how engineering leaders can future-proof their AI strategy and navigate an emerging dilemma: the pressure to adopt AI to stay competitive, while justifying AI spending and avoiding risky investments.To accomplish this, Vikram emphasizes the importance of establishing clear evaluation frameworks, prioritizing AI use cases based on business needs and understanding your company's unique cultural context when deploying AI."Chapters:00:00 Introduction and Special Announcement01:14 Welcome to Dev Interrupted01:42 Challenges in AI Adoption03:16 Balancing Business Needs and AI06:15 Crawl, Walk, Run Approach10:52 Building Trust and Prototyping13:07 AI Agents as Smart Routers13:50 Galileo's Role in AI Development16:25 Evaluating AI Systems25:36 Skills for Engineering Leaders27:35 Conclusion Follow the hostsFollow⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠Follow⁠⁠⁠ Vikram⁠⁠⁠Follow⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠Follow Dev InterruptedPodcastSubstackLinkedInFollow Dev Interrupted HostsAndrewBenCheck out Galileo⁠⁠⁠⁠Try Galileo⁠⁠

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• The Making of Gemini 2.0: DeepMind's Approach to AI Development and Deployment | Logan Kilpatrick

  Google’s strength in AI has often seemed to get lost in the midst of OpenAI announcements or DeepSeek fervor - yet Gemini 2.0 is more than good for many tasks; it’s the model to beat - and we have the research to back it up. This week, Logan Kilpatrick, senior product manager at Google DeepMind, joins us to discuss Gemini’s creation story, its emergence as the premiere model in the AI race, and why the launch of Gemini 2.0 is great news for developers.During the conversation Conor and Logan explore the exciting world of multimodal AI, Gemini's strengths in agentic use cases, and its unique approach to function calling, compositional function calling, and the seamless integration of tools like search and code execution.They also chat about Logan’s vision for a future where AI interacts with the world more naturally, offering a view of the potential of vision-first AI agents, and why Google's hardware advantage is enabling Gemini's impressive performance and long context capabilities. Follow along with the discussion using Galileo’s AI Agent Leaderboard:https://huggingface.co/spaces/galileo-ai/agent-leaderboardChapters:00:00 DeepMind's Role in Gemini's Development03:49 Gemini 2.0 Updates and Developer Highlights06:08 Agentic Use Cases and Function Calling11:29 Multimodal Capabilities16:15 Putting AI in Production21:06 Gemini's Differentiation and Hardware31:22 Future Vision for Gemini and G Suite Integration35:23 Gemini for Developers39:02 Conclusion and FarewellFollow the hostsFollow⁠⁠⁠Atin⁠⁠⁠Follow⁠⁠⁠Conor⁠⁠Follow⁠Vikram⁠Follow⁠⁠⁠Yash⁠⁠⁠Follow LoganTwitter:@OfficialLoganKLinkedIn:https://www.linkedin.com/in/logankilpatrick/Show NotesTry Gemini for yourself:gemini.google.comGemini for Developers:aistudio.google.comCheck out Galileo⁠⁠Try Galileo⁠⁠

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• DeepSeek Fallout, Export Controls & Agentic Evals

  This week, hosts Conor Bronsdon and Atindriyo Sanyal discuss the fallout from DeepSeek's groundbreaking R1 model, its impact on the open-source AI landscape, and how its release will impact model development moving forward. They also discuss what effect (if any) export controls have had on AI innovation and whether we’re witnessing the rise of “Agents as a Service”.  To tackle the increasing complexity of agentic systems, Conor and Atin highlight the need for robust evaluation frameworks, discussing the challenges of measuring agent performance, and how the recent launch of Galileo's agentic evaluations are empowering developers to build safer and more effective AI agents. Chapters: 00:00 Introduction 02:09 DeepSeek's Impact and Innovations 03:43 Open Source AI and Industry Implications 13:44 Export Controls and Global AI Competition 18:55 Software as a Service 19:29 Agentic Evaluations 25:14 Metrics for Success 31:34 Conclusion and Farewell Follow the hosts Follow ⁠⁠Atin⁠⁠ Follow ⁠⁠Conor⁠ Follow Vikram Follow ⁠⁠Yash⁠⁠ Check out Galileo Try Galileo Show Notes On DeepSeek and Export Controls Introducing Agentic Evaluations

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  32:41
• AI, Open Source & Developer Safety | Block’s Rizel Scarlett

  As DeepSeek so aptly demonstrated, AI doesn’t need to be closed source to be successful. This week, Rizel Scarlett, a Staff Developer Advocate at Block, joins Conor Bronsdon to discuss the intersections between AI, open source, and developer advocacy. Rizel shares her journey into the world of AI, her passion for empowering developers, and her work on Block's new AI initiative, Goose, an on-machine developer agent designed to automate engineering tasks and enhance productivity. Conor and Rizel also explore how AI can enable psychological safety, especially for junior developers. Building on this theme of community, they also dive into topics such as responsible AI development, ethical considerations in AI, and the impact of community involvement when building open source developer tools. Chapters: 00:00 Rizel's Role at Block 02:41 Introducing Goose: Block's AI Agent 06:30 Psychological Safety and AI for Developers 11:24 AI Tools and Team Dynamics 17:28 Open Source AI and Community Involvement 25:29 Future of AI in Developer Communities 27:47 Responsible and Ethical Use of AI 31:34 Conclusion Follow Conor Bronsdon: https://www.linkedin.com/in/conorbronsdon/ Rizel Scarlett LinkedIn: https://www.linkedin.com/in/rizel-bobb-semple/ Website: https://blackgirlbytes.dev/ Show Notes Learn more about Goose: https://block.github.io/goose/

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