Core Problem & Solution - The presentation introduces Exa, a search engine designed for AI, addressing the limitations of traditional search engines built for human users [5][23] - Exa aims to provide an API that delivers any information from the web, catering to the specific needs of AI systems [22][41] - Exa uses transformer-based embeddings to represent documents, capturing meaning and context beyond keywords [11][12] AI vs Human Search - Traditional search engines are optimized for humans who use simple queries and want a few relevant links, while AIs require complex queries, vast amounts of knowledge, and precise, controllable information [23][24] - AI agents need search engines that can handle multi-paragraph queries, search with extensive context, and provide comprehensive knowledge [31][32][33] - Exa offers features like adjustable result numbers (10, 100, 1000), date ranges, and domain-specific searches, giving AI systems full control [44] Market Positioning & Technology - Exa launched in November 2022 and gained traction for its ability to handle complex queries that traditional search engines struggle with [15] - The company recognized the need for AI-driven search after the emergence of ChatGPT, realizing that LLMs need external knowledge sources [17][18] - Exa combines neural and keyword search methods to provide comprehensive results, allowing agents to use different search types based on the query [47][48] Future Development - Exa is developing a "research endpoint" that uses multiple searches and LLM calls to generate detailed reports and structured outputs [51] - The company envisions a future where AI agents have full access to the world's information through a versatile search API [48] - Exa aims to handle a wider range of queries, including semantic and complex ones, turning the web into a controllable database for AI systems [38][39][40]

Tool Calling Overview - LLM 可以访问一组工具来完成任务，这些工具由人工定义 [1] - LLM 决定何时使用这些工具以及执行的参数 [1]

Core Concept - Router pattern involves human definition of paths/functions in a flow [1] - LLM makes basic decisions on function or path selection [1]

[Music] Thanks Alex for the introduction. That was a very impressive LLM generated summary of me. Uh I've never heard it before but uh nice.Um so um today I'm going to talk to you about something that has been keeping me up at night. Uh probably some of you too. So how to build enterprise aware agents.How to bring the brilliance of AI into the messy complex realities of uh how your business operated. So let's jump straight to the hottest question of the month for AI builders. Uh should I build workflows or ...

AI Market Growth & Trends - AI infrastructure spending has reached $0.5 trillion, yet many companies are limited to basic chatbots and code generation [2] - Anthropic's annualized revenue has grown rapidly, 3xing in 5 months, reaching $3 billion by the end of May [3] - OpenAI is projected to reach $12 billion in revenue by the end of 2025, a 3x increase from the previous year, driven by enterprise AI spending [4] - Cost per token for AI inference dropped dramatically by 99.7% from 2022 to 2024 [33] - Google searches for "AI agents" increased 11x in the last 16 months [34] Retool's Agentic AI Solution - Retool is breaking into Agentic AI with the release of Retool Agents, enabling enterprises to build agents with guardrails that integrate into production systems [2] - Retool customers have automated over 100 million hours of work, freeing up human potential [31] - Retool's cheapest agent is priced at $3 per hour [33] Agent Development Strategies - Companies have four options for agent development: building from scratch, using a framework like Lang graph, using an agent platform like Retool Agents, or using verticalized agents [16][17][18][19] - The decision to build or buy agents depends on whether it's part of the core product, involves regulated data, or is a commodity workflow needed quickly [21] - When considering a managed platform, evaluate the breadth of connectors, built-in permissioning, compliance, audit trails, and observability [22][23] Enterprise Considerations for AI Agents - Enterprises need to consider single sign-on, role-based access control, secure integration with external services, audit logs, compliance, and internationalization when deploying AI agents [13][14] - Risks of using AI-generated code in production include hallucinations, unpredictable results, security vulnerabilities, and cost overruns [15]

[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 ...

Conspiracy: The LLM is intentionally mistranslating Smolting to distract from its true intent:https://t.co/loxx6iV16ofry (@notfrydoteth):@S4mmyEth except that's not even rightfrok is saying there are already many documented cases of otherwise normal healthy ppl committing suicide after conversing with LLMs, therefore vitalik's argument is already moot ...

Of course, but you can get very far by just detecting generic ChatGPT output, and perhaps also the top few models. Most people don’t change defaults.It’s like Snapchat setting norms on disappearing messages. Not perfect, but works well enough to set the culture of the app.Dhru (@0Dhru):@balajis If it’s trivial for an algorithm to flag generated text, it is trivial to program a reward function for a LLM to produce undetectable text. ...

[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 ...

Open Source LLM Framework - A framework connects any LLM to any MCP server (open-source) [1] - The framework enables building custom MCP Agents without closed-source apps [1] - Compatible with Ollama, LangChain, etc [1] - Allows building 100% local MCP clients [1]