GPT-5 now rolled out to 20% of paid users and doing >2B TPM on the API! so far so good...excellent work by the eng and infra teams! ...

Agent Experience Definition and Importance - Agent experience is defined as how easily agents can access, understand, and operate within digital environments to achieve user-defined goals [5] - The industry believes agent experience is the only experience that matters because agents will be the largest user base [33] - The industry suggests that if a tool requires human intervention, it hasn't fully addressed agent needs [33] The Shift in Development Tools - 37% of the latest YC batch are building agents as their products, indicating a shift from co-pilots and legacy SAS companies [1] - The industry argues that tools built for humans are for the past, and the focus should be on building tools for agents [3] - The industry emphasizes the need to build tools that enable agents to operate autonomously [12][13] Key Components of Agent Experience - Seamless authentication is crucial; agents should be able to authenticate without exposing passwords [6][7] - Agent-readable documentation is essential, with standards like appending ".md" to URLs and using llm's.txt [8][9] - API-first design is critical, providing agents with machine-native interfaces to access functionality efficiently [10] Daytona's Approach to Agent Native Runtime - Daytona aims to provide agents with a computing environment similar to a laptop for humans [19] - Daytona's initial focus was on speed, achieving a spin-up time of 27 milliseconds for agent tools [21] - Daytona preloads environments with headless tools like file explorers, Git clients, and LSP to help agents do things faster [22] Daytona's Features for Autonomous Agents - Daytona offers a declarative image builder, allowing agents to create and launch new sandboxes with custom dependencies [27] - Daytona provides Daytona volumes, enabling agents to efficiently share large datasets across multiple machines [29] - Daytona supports parallel execution, allowing agents to fork machines and explore multiple options simultaneously [31]

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]

Overview - The video provides a brief introduction to the new ChipScoPy API [1]

RT xAI (@xai)We’re thrilled by the overwhelming demand for Grok 4 through the xAI API.To support our API customers, we’ve increased the default rate limits for Grok 4. Happy building! ...

Core Problem & Solution - Traditional agent development suffers from slow iteration cycles due to code modifications for each use case, hindering business teams' experimentation [1] - LangGraph Assistants solve this by separating agent architecture from configuration, enabling code reuse across different use cases and faster experimentation [2] Key Features & Benefits - **Customization:** Allows customization of prompts, models, and tools without altering the underlying code, enabling rapid experimentation [3] - **Deployment:** Facilitates quick deployment of agent variations, allowing developers to push configuration changes without code deployments and business teams to launch assistants rapidly [4] - **Control:** Offers programmatic control for developers to automate assistant lifecycles, manage configurations at scale, and integrate with CI/CD pipelines [5] - **Configuration:** Configuration allows specifying customizable details such as prompts, models, and tools, enabling the same graph to have different capabilities based on runtime configuration [7] - **Versioning:** Provides robust version control and rollbacks, allowing for A/B testing and safe experimentation with configuration changes [44][45][46] LangGraph Studio - LangGraph Studio is a visual agent IDE that allows users to visualize and test agents [14][15] - It enables instant experimentation with different agent configurations, whether debugging locally or pulling production deployments [22] - It simplifies the configuration of complex multi-agent systems by allowing individual nodes to be configured separately [31][32][33][34][35][36] LangGraph Platform - LangGraph Platform is Langchain's enterprise solution for developing, deploying, and managing AI agents [38] - It allows users to create production-ready versions of assistants and access them via API [40][41][42] - It provides a complete REST API specification for creating, managing, and updating assistants programmatically [42][54] SDK & API - LangGraph provides an SDK and API for programmatically creating, using, and managing assistants [47][54] - The SDK allows integration with existing applications and systems, enabling management of the complete lifecycle of agents and assistants from code [54]

AI Readiness & API Transformation - Every website must be "Agent-ready" in the coming era [1] - APIs need to be transformed into reliable, AI-ready tools [2] - Postman's 90-day AI readiness playbook details how to turn APIs into reliable, AI-ready tools [2] Key Components for AI-Ready APIs - Predictable structures are essential for AI agents [3] - Machine-readable metadata is crucial for AI understanding [3] - Standardized behavior is necessary for seamless AI interaction [3] Postman Playbook Highlights - Automatic documentation can be achieved by standardizing API format, Postman's Spec Hub automatically generates and validates API docs for both humans and AI agents without any manual work [2] - Validated specs can be turned into hosted, function-style endpoints, letting AI agents invoke APIs like native commands [3] Impact of AI Agents - Agents will make purchases, not humans [3] - Agents will find the best options, not humans [3] - Agents will fill out job applications, not humans [3]

Core Argument - The presentation argues that event-driven architecture (EDA), while seemingly loosely coupled at runtime, is tightly coupled at design time, leading to complexities and challenges in AI agent development [21][22] - It proposes a shift in focus from events to durable execution as the core of AI agent architecture, which simplifies development and handles failures more effectively [26][27] Problems with Event-Driven Architecture - EDA sacrifices clear APIs, as events lack the documentation and structure of traditional APIs [15] - Business logic becomes fragmented and scattered across multiple services, making debugging and understanding the system more difficult [16] - Services become ad hoc state machines, leading to potential race conditions and difficult-to-debug issues [18][19] - EDA can lead to reluctance to iterate on architecture due to fear of breaking existing functionality [25] Durable Execution as a Solution - Durable execution is presented as a crash-proof execution environment that automatically preserves application state, virtualizes execution, and is not limited by time or hardware [27][28][29][30][31][32][33][34] - It allows developers to focus on business logic rather than managing events and queues [38] - Temporal provides durable execution as an open-source, MIT-licensed product with SDKs for multiple programming languages [38][39] - Durable execution abstracts away the complexities of events into the software layer [40][43] Temporal's Offering - Temporal's durable execution system offers automatic retries for failures, such as LLM downtime or rate limits [36] - It supports polyglot programming, allowing functions written in different languages to be called seamlessly [39] - Temporal is available for demonstration and further discussion at the company's booth and Slack channel [44][45]

Group 1 - The article discusses the advancements in AI, LLM (Large Language Models), and APIs, emphasizing their significance in the current technological landscape [1] - It highlights the role of Mumbai as a hub for affordable tech labor, which is crucial for the growth of the tech industry in India [2] - The article mentions Gujarat as a region known for its business acumen, indicating its potential in contributing to the tech sector [3]

Steps:- Initiate your OpenAI client.- In the response API, specify your MCP server as the tools parameter.- Specify your query in the input parameter.MindsDB GitHub repo: https://t.co/PaFtyjuULv(Don't forget to star 🌟) ...