As organizations seek to harness their proprietary data while maintaining security and compliance, Amazon Bedrock provides a comprehensive framework for building tailored AI applications. Using Amazon Bedrock Knowledge Bases and Amazon Bedrock Data Automation, organizations can create AI solutions that truly understand their unique business context, terminology, and requirements. Combined with Amazon Bedrock Guardrails, these capabilities enhance the accuracy and relevance of AI-generated responses, while e ...

Product Updates - Gemini Live API GA is now powered by Google's cost-effective thinking model Gemini 2.5 Flash [1] - An experimental version of the Live API powered by Google's native audio offering is available for trial, enabling seamless, emotive, steerable, multilingual dialogue [1] Key Capabilities - The Gemini Live API combined with Pipecat unlocks capabilities for developers, focusing on session management, turn detection, tool use (including async function calls), proactivity, multilinguality, and integration with telephony and other infrastructure [1] - Pipecat extends realtime multimodal capabilities to client-side applications such as customer support agents, gaming agents, and tutoring agents [1] Industry Impact - Pipecat is a widely used, open-source, vendor-neutral voice agent framework supported by NVIDIA, Google, and AWS, and used by hundreds of startups [1] Personnel - Kwindla Kramer (Kwin) from Daily is the originator of Pipecat [1] - Shrestha Basu Mallick is Group Product Manager and product lead for Gemini API at Google DeepMind [1]

[Music] We're here to talk about real time conversational video uh with Pipecat. That's me, and with Tavis, that's Brian. We'll introduce ourselves a little bit more, but in the interest of keeping it moving, let's talk about what we're here for.If anybody Have any of you ever seen one of these robot concierge things. Do they work. No, they don't.They're terrible, right. Um, it's actually possible nowadays to build this kind of thing, but actually good. Um, it's a little bit tricky, but that's what we're he ...

Vector Database Benchmarking Challenges - The vector database market is filled with misleading benchmarks, where every database claims to be both faster and slower than its competitors [1] - Meaningful vector search benchmarks are uniquely tricky to build [1] - It is crucial to tailor benchmarks to specific use cases to get useful results [1] - Benchmarks should be tweaked and verified independently to avoid blindly trusting marketing claims [1] Recommendations for Benchmarking - Avoid trusting glossy charts and marketing materials when evaluating vector databases [1] - Build meaningful benchmarks tailored to specific use cases to get accurate performance assessments [1] - Independently verify and tweak benchmarks to ensure they reflect real-world performance [1] About the Speaker - Philipp Krenn leads Developer Relations at Elastic, the company behind Elasticsearch, Kibana, Beats, and Logstash [1]

[Music] So I'm here to talk about taming rogue AI agents but essentially want to talk about uh evaluation driven development observability driven but really why we need observability. So, who uses AI? Is that Jim's stupid most stupid question of the day? Probably. Who trusts AI? Right. If you'd like to meet me after, I've got some snake oil you might be interested in buying. Yeah, we do not trust AI in the slightest. Now, different question. Who reads books? That's reading books. If you want some recommenda ...

Security is the biggest blocker for agent orchestration adoption in regulated industries for SWE agents. Gitpod's agent orchestration went from an originally self-hosted kubernetes architecture to the current 'bring your own cloud' model that enables deployment our SWE agent orchestration platform in secure environments. The architecture allows customers to securely connect their foundational models and agent memory solutions and comes with features like auto-suspend and resume for agent fleets. In this tal ...

Industry Trends - Software development has two loops: an inner loop focused on development and an outer loop focused on review [1] - AI adoption is increasing among developers, with nearly every developer surveyed using AI tools [2] - 46% of code on GitHub is being written by AI, indicating a significant shift in code generation [3] - The inner loop is changing due to AI, making developers more productive and producing higher volumes of code [3][4] - The outer loop is becoming a bottleneck as developers have to review, test, merge, and deploy higher volumes of code [5] Graphite's Solution (Diamond) - Graphite aims to create a new outer loop to address the challenges posed by increased code volume [6] - Graphite's AI code review platform, Diamond, focuses on high signal, low noise, deep understanding of codebase and change history [13] - Diamond summarizes, prioritizes, and reviews each change, integrating with CI and testing infrastructure [13] - Diamond aims to reduce code review cycles, enforce quality and consistency, and keep code private and secure [13] - AI-generated feedback from Diamond's comments are accepted at a 52% rate, higher than human comments (45-50%) [15][16]

[Music] Hello everybody. Uh my name is Ima. I'm a program man uh product manager at Microsoft.It's a pleasure to talk to you today about Foundry local which enables developers to easily build up crossplatform applications powered by local AI. So let's get started. Uh the first question is if the cloud AI is so powerful why do we need local AI.So here are four key reasons based on our conversations and observations with our customers. So first of all, how does cloud AI work in environments with low network b ...

"Vibe coding" often falters in complex enterprise environments. Drawing from real implementations, this talk demonstrates systematic approaches to customizing AI assistants for challenging codebases. We'll explore specialized techniques for navigating complex architectures, evidence-based strategies for undocumented legacy systems, methodologies for maintaining context across polyglot environments, and frameworks for standardizing AI usage while preserving developer autonomy. Through case studies from finan ...

Vibe Coding Concepts - Introduces "Vibe Coding" as a fast, creative, and iterative approach to coding, particularly useful for rapid prototyping and learning [3][4][9] - Defines three types of vibe coding: YOLO (fast, instant gratification), Structured (maintainable, balanced), and Spec-Driven (scalable, reliable) [4][6][7] - YOLO vibe coding is suitable for rapid prototyping, proof of concept, and personal projects, not for production [4][8][9] - Structured vibe coding adds guard rails for maintainability and is suitable for enterprise-level projects [5][6] - Spec-driven vibe coding scales vibe coding to large codebases with reliability [7] VS Code Features for Vibe Coding - Highlights the use of VS Code Insiders for accessing the latest features, released twice daily [1][2] - Emphasizes the use of agent mode in VS Code, along with auto-approve settings, to streamline the coding process [9][10][11] - Introduces a new workspace flow in VS Code for easier vibe coding [13][16] - Mentions the built-in voice dictation feature in VS Code for interacting with AI [11][16] - Suggests using auto-save and undo/revert options in VS Code for live updates and error correction [17][18] AI and Iteration - Encourages embracing AI to build intuition and baseline its capabilities [21] - Recommends using frameworks like React and Vite for grounding and iteration [21] - Highlights the importance of iteration, starting from scratch, and working on specific items [22] - Stresses the importance of review, committing code often, and pausing the agent to inspect [32][33] Structured Vibe Coding Details - Templates with consistent tech stacks and instructions can guide the copilot flow [23] - Custom tools and MCPs (presumably, more context providers) can provide more reliable and consistent results than YOLO mode [23][31] - Workspace instructions, prompts, and MCPs can be made dynamic for specific parts of the codebase [30] - VS Code's access to problems and tasks allows it to fix code as mistakes are made [32]