E-commerce Evolution with AI - E-commerce has evolved from physical stores to online platforms, and AI is now digitizing participants and their interactions, moving from static websites to merchant and consumer agents [1][2][5] - The goal remains transaction completion, but the focus shifts to dynamic, real-time, and generative interfaces for both human and agentic consumers [6][7] Challenges and Solutions in the Agentic Commerce - The industry faces challenges in enabling software agents to complete transactions, with solutions including delegated authentication via partners like Visa [13][14][15] - Moving from inferred buyer intent (keyword searches, click data) to explicitly captured intent through conversation data is crucial [16] - Merchants are exploring how to convert fuzzy intent into specific product SKUs, noting higher conversion rates, dollar values, and lifetime values from AI channels [17][18] - Ensuring product availability across numerous stores requires moving beyond existing product feed infrastructure and web scraping towards a unified API for product data [20][21][22] - Representing buyer and seller preferences needs to evolve from siloed data to rich context across all aspects of their lives, with market design challenges addressed by third-party institutions [23][24][26] The Future of Retail and Brand Strategy - Fortune 500 companies are adapting to technological shifts, with examples like Samsung evolving from a fish merchant to a technology leader [29][30] - Brands are creating APIs and MCP servers for chat clients, abstracting complex product systems into consistent APIs [31][32] - Companies are connecting product data with brand and design systems to experiment with generative interfaces and conversational commerce [33][34] - Enabling payment flows for bot traffic is essential, as AI chat users demonstrate higher intent and conversion rates [35][36] - The industry believes stores will evolve back to their original form: a conversation, with brands owning surfaces in various applications [36][40]

[Music] Hi everyone, I'm Yogi. I work at Faxet, a financial data and software company. And today I'll be sharing some of my experience while building agent.In last few years we have seen tremendous growth in AI and especially in last couple of years we are on exponential curve of intelligence growth and yet it feels like when we are develop AI applications driving a monster truck through a crowded mall with a tiny joysticks. So AI applications have not seen its charge GPD moment yet. There are many reasons ...

Core Business & Services - Kalin builds custom solutions for clients, ranging from Fortune 500 companies to startups, focusing on app development and database migrations [1][2] - The company leverages generative AI to automate business functions, such as intelligent document processing for logistics management, achieving faster and better results than human annotators [20][21] - Kalin offers services ranging from chatbot and co-pilot development to AI agent creation, tailoring solutions to specific client needs [16] Technology & Architecture - The company utilizes multimodal search and semantic understanding of videos, employing models like Nova Pro and Titan v2 for indexing and searching video content [6][7] - Kalin uses various databases including Postgress, PG vector, and OpenSearch for vector search implementations [13] - The company builds AI systems on AWS, utilizing services like Bedrock and SageMaker, and custom silicon like Tranium and Inferentia for price performance improvements of approximately 60% over Nvidia GPUs [27] AI Development & Strategy - Prompt engineering has proven highly effective, sometimes negating the need for fine-tuning models [40] - Context management is crucial for differentiating applications, leveraging user data and history to make strategic inferences [33][34] - UX design is important for mitigating the slowness of inference, with techniques like caching and UI spinners improving user experience [36][37]

[Music] [Applause] So, uh, nice to meet you all. Thank you for having me. Um you know probably two two and a half years ago like many other companies out there you know we sort of started on this journey of of building assistants and sort of the north star that we had when we were building these assistants were that they were helpful you know and obviously we wanted them to be as accurate they could and to reference citations when they could and these kinds of things but at the end of the day we wanted it t ...

[Music] Hi everyone. So, I'm Beth Glenfield. I actually flew in from Ireland.So, uh I will try and slow down my accent for everybody. I have heard that I do get a bit American sometimes. So, uh, yeah, may not be the Irish accent you're expecting.So, I'm going to talk to you today about how I believe AI is breaking how we hire technically. So, I know everyone's very busy, but just a few questions to think about. Is that okay.Is, are you using AI today in your recruitment process both as an interviewee and an ...

Hey All - gave a talk on building stateful environments for vertical agents at AI tinkerers and ppl really liked it, happy to do again. Here's the repo - general code that endows environments like Pokemon Red, Minecraft, Swe-Bench, and others with the same interface for development and agent training. github.com/synth-laboratories/Environments Recorded at the AI Engineer World's Fair in San Francisco. Stay up to date on our upcoming events and content by joining our newsletter here: https://www.ai.engineer/ ...

[Music] So, my name is Sukash Ganditski and I've been programming since I was a little kid and I want to tell you about my newest project um books reimagined. So, how to use AI to create new experiences for things you already know. So, how it all started, I was reading a book about uh Donald Trump re-election and since, as you can hear, I'm not from the United States.Um, there was a bit a few too many characters to me. I didn't follow everyone. Uh, so I decided to vip code my way through the understanding.I ...

[Music] Thank you all so much for coming to this talk. Um, thank you for being at this conference. Generally, my name is Tomas.I'm one of the co-founders of Graphite and I'm here to talk to you around AI power entomology. If you don't know, entomology is the study of bugs. It's something that we do.We is very near and dear to our heart and part of what our product does. So, Graphite, for those of you that don't know, builds a product called Diamond. Diamond is an AI powered code reviewer.You go ahead, you u ...

[Music] Hey everyone. Um hope you are having a great conference. Um so I'm going to talk about uh how to run events at scale and thinking beyond accuracy or similarity.Uh so in the last uh presentation we we learned about like how to art u architect the AI applications um and then whys are important. In this presentation I am going to talk about like the importance of ewells as well as what type of ewells we have to choose when we are crafting an application. This is a bit about me.Um so I work as a lead en ...

GPU Profiling & Performance Optimization - The industry emphasizes improving performance and saving costs by optimizing software, potentially reducing server usage by 10% [4] - Sampled profiling is used to balance data volume and continuous monitoring, with examples of sampling 100 times per second resulting in less than 1% CPU overhead and 4MB memory overhead [5] - The industry highlights the importance of production environment profiling to observe real-world application performance with low overhead [8] - The company's solution leverages Linux EVPF, enabling profiling without application instrumentation [9] Technology & Metrics - The company's GPU profiling solution uses Nvidia NVML to extract metrics, including overall node utilization (blue line), individual process utilization (orange line), memory utilization, and clock speed [11][12] - Key metrics include power utilization (with power limit as a dashed line), temperature (important to avoid throttling at 80 degrees Celsius), and PCIe throughput (negative for receiving, positive for sending, e g 10 MB/s) [13][14] - The solution correlates GPU metrics with CPU profiles collected using EVPF to analyze CPU activity during periods of less than full GPU utilization [14] GPU Time Profiling - The company introduces GPU time profiling to measure time spent on individual CUDA functions, determining start and end times of kernels via the Linux kernel [18] - The solution displays CPU stacks with leaf nodes representing functions taking time on the GPU, with colors indicating different binaries (e g blue for Python) [19][20] Deployment & Integration - The company's solution can be deployed using a binary on Linux, Docker, or as a DaemonSet on Kubernetes, requiring a manifest YAML and token [21] - Turbo Puffer is interested in integrating the company's GPU profiling to improve the performance of their vector engine [22]