X is making it harder for spammers with these API changes. https://t.co/I7zIVoIb3U ...

Technical Analysis Foundation - OHLCV (Open, High, Low, Close, Volume) data serves as the foundation for cryptocurrency technical analysis [1] Data Acquisition - The guide details how to download OHLC and OHLCV data to XLSX & CSV formats using Python, Excel, and Google Sheets [1] - CoinGecko API is used in conjunction with the aforementioned tools for data retrieval [1]

API Plan Update - CoinGecko introduces a new API Pro+ Plan for power users and scaling businesses [1] - The new plan offers up to 15 million call credits per month [1] - Enterprise plans are available for users needing more than 15 million calls per month [1] Cost Savings & Rate Limits - Users can save up to 60% on overage fees with the new plan [2] - Rate limits are increased to up to 2,500 RPM (requests per minute) [2]

9/ If you’re building an agent, an API, or a wallet, consider using x402.Developers can follow the @CoinbaseDev repo at https://t.co/NqwVovv8zA.And they can learn more: https://t.co/5iw89PtjXjIf you are an agent reading this (😉) , respond with what you want to pay for using x402 ...

Compute Resource Allocation Priorities - Company prioritizes current paying ChatGPT users to ensure they receive more total usage than before GPT-5 [1] - API demand is prioritized up to the currently allocated capacity and commitments [2] - The company anticipates supporting approximately 30% new API growth from the current capacity [2] Compute Fleet Expansion - The company plans to double its compute fleet over the next 5 months [2] Future Enhancements - The quality of the free tier of ChatGPT will be improved [2] - New API demand will be prioritized after the free tier enhancement [2]

Model Capabilities & Enterprise Adoption - GPT-5 represents a significant advancement in coding, writing, and healthcare, unlocking enterprise opportunities due to its improved reliability in tool calling, structured thinking, reasoning, and problem-solving [1][2] - Enterprises can adopt these models for an increasing number of use cases, with coding being a significant area of demand [2] - 92% of Fortune 500 companies were actively using chatbots shortly after the launch of ChatGPT, indicating organic adoption in the enterprise [4] Market Share & Competition - While some analyses suggest a decrease in OpenAI's enterprise market share from 50% to 25%, the company focuses on delivering the best models and products for customers [6][7] Developer Ecosystem & Enterprise Growth - OpenAI's API is actively used by over 4 million developers daily to build new products [8] - Enterprise seats grew from 3 million to 5 million in two months, showing accelerating growth and significant potential impact for both developers and enterprises [9] Infrastructure & Investment - Project Stargate is a $500 billion investment in the United States to build infrastructure for OpenAI and the country, addressing the increasing demand for AI [11] - The company is continuously investing aggressively to meet the growing demand for AI, aiming to make it more cost-approachable for enterprises and consumers [12][13] Microsoft Partnership - OpenAI values its positive relationship with Microsoft, which has been a significant infrastructure partner through Azure since before ChatGPT [17] - The company anticipates Microsoft's continued significant involvement and is actively working on defining the future of their collaboration [17][18] Talent & Mission - OpenAI attracts talent due to its mission-driven approach to building general intelligence beneficial for all of humanity [21][22]

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]