[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-a8a77e4504265570-openai-s-shift-to-agentic-workflows-for-non-engine-summary":3,"summaries-facets-categories":96,"summary-related-a8a77e4504265570-openai-s-shift-to-agentic-workflows-for-non-engine-summary":7802},{"id":4,"title":5,"ai":6,"body":13,"categories":46,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":51,"navigation":77,"path":78,"published_at":79,"question":48,"scraped_at":80,"seo":81,"sitemap":82,"source_id":83,"source_name":84,"source_type":85,"source_url":86,"stem":87,"tags":88,"thumbnail_url":48,"tldr":93,"tweet":48,"unknown_tags":94,"__hash__":95},"summaries\u002Fsummaries\u002Fa8a77e4504265570-openai-s-shift-to-agentic-workflows-for-non-engine-summary.md","OpenAI's Shift to Agentic Workflows for Non-Engineers",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",9055,804,4260,0.00346975,{"type":14,"value":15,"toc":39},"minimark",[16,21,25,29,32,36],[17,18,20],"h2",{"id":19},"the-evolution-of-ai-harnesses","The Evolution of AI Harnesses",[22,23,24],"p",{},"OpenAI is transitioning from simple chat interfaces to \"agentic\" workflows via ChatGPT Work. The core engineering challenge lies in the \"harness\"—the software layer that connects an LLM to external tools (email, Slack, Notion, Figma). While software engineers have long used CLI-based agents for coding, OpenAI is now building graphical, intuitive interfaces to bring this capability to non-technical professionals. The design philosophy currently balances \"magic box\" simplicity with necessary UI elements (buttons\u002Fplugins) to aid discoverability, acknowledging that users are not yet ready for fully autonomous, invisible AI agents.",[17,26,28],{"id":27},"the-challenge-of-non-technical-workflows","The Challenge of Non-Technical Workflows",[22,30,31],{},"Unlike coding, where success is binary (the code works or it doesn't), knowledge work—such as business strategy, sales, or communications—is nuanced and harder to evaluate. OpenAI is using its internal \"GDPVal\" benchmark, which tests models across 44 occupations, to refine these agents. A significant hurdle is the \"messy world\" of legacy software and non-digitized decision-making processes. Because these workflows are less structured than code, the agents require more user-in-the-loop interaction to avoid errors. OpenAI engineers emphasize that the goal of harness design is to provide the model with just enough context to act, betting that as models improve, the need for complex, hard-coded \"if-then\" logic will diminish.",[17,33,35],{"id":34},"competitive-dynamics-and-economic-sustainability","Competitive Dynamics and Economic Sustainability",[22,37,38],{},"OpenAI faces stiff competition from Anthropic’s Claude, which gained early traction by prioritizing a back-and-forth, collaborative interaction style over OpenAI’s initial \"AGI-pilled\" approach of letting the model attempt full autonomy. While OpenAI is catching up in adoption, the industry remains divided on whether model-specific harnesses or model-agnostic tools (like those used by Harvey or Clay) will win. Furthermore, the economic model remains a challenge: agentic tasks are token-intensive, often costing significantly more than the $20\u002Fmonth subscription fee. OpenAI is banking on rapid improvements in model efficiency and price reductions to make these high-utility agents commercially viable at scale.",{"title":40,"searchDepth":41,"depth":41,"links":42},"",2,[43,44,45],{"id":19,"depth":41,"text":20},{"id":27,"depth":41,"text":28},{"id":34,"depth":41,"text":35},[47],"AI & LLMs",null,"md",false,{"content_references":52,"triage":71},[53,58,64,67,69],{"type":54,"title":55,"author":56,"context":57},"book","Rocket Billionaires: Elon Musk, Jeff Bezos and the New Space Race","Tim Fernholz","mentioned",{"type":59,"title":60,"author":61,"url":62,"context":63},"other","The Bitter Lesson","Rich Sutton","http:\u002F\u002Fwww.incompleteideas.net\u002FIncIdeas\u002FBitterLesson.html","cited",{"type":65,"title":66,"context":57},"tool","Claude Code",{"type":65,"title":68,"context":57},"Composio",{"type":65,"title":70,"context":57},"Databricks",{"relevance":72,"novelty":73,"quality":73,"actionability":74,"composite":75,"reasoning":76},5,4,3,4.15,"Category: AI & LLMs. The article discusses OpenAI's shift to agentic workflows, which directly relates to AI automation and product strategy, addressing the audience's interest in practical applications of AI in non-technical workflows. It provides insights into the challenges and design philosophies behind these new tools, although it lacks specific actionable steps for implementation.",true,"\u002Fsummaries\u002Fa8a77e4504265570-openai-s-shift-to-agentic-workflows-for-non-engine-summary","2026-08-24 15:00:00","2026-08-25 03:09:45",{"title":5,"description":40},{"loc":78},"a8a77e4504265570","TechCrunch — AI","article","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F08\u002F24\u002Fopenai-is-building-an-ai-agent-for-everything-will-everyone-use-them\u002F","summaries\u002Fa8a77e4504265570-openai-s-shift-to-agentic-workflows-for-non-engine-summary",[89,90,91,92],"llm","automation","product-strategy","ai-agents","OpenAI is expanding beyond coding tools with 'ChatGPT Work,' an agentic platform designed to automate complex, multi-step tasks across common business software, aiming to move AI from simple Q&A to autonomous project 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Excessive context often introduces noise and makes models susceptible to SEO-heavy, low-quality data.",[7831,7839,7840,7843],{},[7834,7841,7842],{},"Curate Inputs:"," Replace broad internet access with high-quality, curated documentation. This forces the model to rely on verified information rather than the \"hallucination-prone\" noise of the open web.",[7831,7845,7846,7849,7850,7854],{},[7834,7847,7848],{},"Embrace Constraints:"," Just as early game developers achieved massive results with limited memory (e.g., ",[7851,7852,7853],"em",{},"Crash Bandicoot","), AI engineers should use constraints to drive creativity. If you cannot perform a task manually, you should not be automating it.",[17,7856,7858],{"id":7857},"practical-engineering-principles","Practical Engineering Principles",[7828,7860,7861,7867,7873,7879],{},[7831,7862,7863,7866],{},[7834,7864,7865],{},"Treat LLMs as Databases:"," View models as flexible databases capable of semantic math rather than sentient entities. This mindset shift helps developers focus on data representation and transformation.",[7831,7868,7869,7872],{},[7834,7870,7871],{},"Use Multiple Representations:"," Don't rely on a single format. Use Markdown for human-readable hierarchy, graph relationships for connections, and folder structures for fast retrieval. The structure of data is a property of the observer, not the object itself.",[7831,7874,7875,7878],{},[7834,7876,7877],{},"Shorten Feedback Loops:"," Avoid building complex agentic workflows that are difficult to debug. If a simple tool like HTML can replace a complex agentic application (as McLean found with his CV), choose the simpler path.",[7831,7880,7881,7884],{},[7834,7882,7883],{},"Build Your Own Harness:"," Experiment by building your own memory and compaction layers rather than relying solely on black-box frameworks. This builds a deeper understanding of the underlying data and improves prompt control.",{"title":40,"searchDepth":41,"depth":41,"links":7886},[7887,7888,7889],{"id":7815,"depth":41,"text":7816},{"id":7822,"depth":41,"text":7823},{"id":7857,"depth":41,"text":7858},[47],{"content_references":7892,"triage":7898},[7893,7897],{"type":7894,"title":7895,"author":7896,"context":63},"paper","Attention Is All You Need","Vaswani et al.",{"type":59,"title":60,"author":61,"context":63},{"relevance":72,"novelty":73,"quality":73,"actionability":73,"composite":7899,"reasoning":7900},4.35,"Category: AI & LLMs. The article provides a deep dive into the concept of bounded autonomy in AI agents, addressing specific pain points such as the pitfalls of excessive context and complexity, which are relevant to developers building AI-powered products. It offers practical engineering principles that can be directly applied, such as treating LLMs as databases and minimizing context, making it actionable for the target audience.","\u002Fsummaries\u002F6918cb675b9d44a5-bounded-autonomy-engineering-ai-agents-with-constr-summary","2026-05-25 13:00:06","2026-05-25 15:11:57",{"title":7805,"description":40},{"loc":7901},"6918cb675b9d44a5","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=t4359sKBu4w","summaries\u002F6918cb675b9d44a5-bounded-autonomy-engineering-ai-agents-with-constr-summary",[89,91,90,92],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002Ft4359sKBu4w\u002Fhqdefault.jpg","To build effective AI agents, developers should embrace constraints, minimize context, and prioritize simplicity over complexity. Treat LLMs as flexible databases and avoid over-automating tasks you cannot perform yourself.","This talk is a practical, non-technical argument for simplifying AI agent workflows by embracing constraints. The speaker advocates for stripping away excessive context and internet access in favor of curated documentation, arguing that developers often over-engineer solutions when simpler, more focused approaches are more effective.",[92],"D97QG6OtVgEC3_GsLg2todTtIYsg-fz6QlWFvj3rTa8",{"id":7918,"title":7919,"ai":7920,"body":7925,"categories":7995,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":7996,"navigation":77,"path":8004,"published_at":8005,"question":48,"scraped_at":8005,"seo":8006,"sitemap":8007,"source_id":8008,"source_name":8009,"source_type":85,"source_url":8001,"stem":8010,"tags":8011,"thumbnail_url":48,"tldr":8012,"tweet":48,"unknown_tags":8013,"__hash__":8014},"summaries\u002Fsummaries\u002Fa2e69b3ac342f3a6-decomposing-ai-workflows-into-reusable-skills-summary.md","Decomposing AI Workflows into Reusable Skills",{"provider":7,"model":8,"input_tokens":7921,"output_tokens":7922,"processing_time_ms":7923,"cost_usd":7924},4073,581,3472,0.00188975,{"type":14,"value":7926,"toc":7991},[7927,7931,7934,7960,7964,7967,7988],[17,7928,7930],{"id":7929},"the-four-pillar-decomposition-framework","The Four-Pillar Decomposition Framework",[22,7932,7933],{},"The 'Workflow-to-Skill' (W2S) approach addresses the common challenge of monolithic AI agent architectures by breaking down complex tasks into a structured, modular format. By separating the logic of a skill into four distinct dimensions, developers can create more reusable and maintainable AI components:",[7828,7935,7936,7942,7948,7954],{},[7831,7937,7938,7941],{},[7834,7939,7940],{},"Routing:"," Defines the decision-making logic that determines when and how a specific skill should be invoked based on input context.",[7831,7943,7944,7947],{},[7834,7945,7946],{},"Workflow:"," Maps the sequence of operations or sub-tasks required to execute the skill, effectively acting as the 'control flow' for the agent's action.",[7831,7949,7950,7953],{},[7834,7951,7952],{},"Semantics:"," Encapsulates the domain-specific knowledge and definitions required to interpret inputs and generate meaningful outputs, ensuring the agent 'understands' the context of the task.",[7831,7955,7956,7959],{},[7834,7957,7958],{},"Attachments:"," Manages external dependencies, such as API keys, database connections, or specific file assets, that are required for the skill to interact with the real world.",[17,7961,7963],{"id":7962},"improving-agent-modularity-and-scalability","Improving Agent Modularity and Scalability",[22,7965,7966],{},"By decoupling these four elements, the W2S framework allows builders to treat AI capabilities as discrete 'skills' rather than hard-coded logic. This modularity provides several practical advantages for production systems:",[7968,7969,7970,7976,7982],"ol",{},[7831,7971,7972,7975],{},[7834,7973,7974],{},"Reusability:"," Once a 'Workflow' or 'Semantics' block is defined for a specific domain, it can be reused across different agent implementations without modification.",[7831,7977,7978,7981],{},[7834,7979,7980],{},"Debugging:"," Isolating failures becomes significantly easier; a developer can determine if an issue stems from the routing logic, the workflow sequence, or the semantic interpretation of the data.",[7831,7983,7984,7987],{},[7834,7985,7986],{},"Dynamic Composition:"," Agents can dynamically assemble skills at runtime, selecting the appropriate routing and workflow paths based on the user's intent, rather than relying on rigid, pre-defined chains.",[22,7989,7990],{},"This approach shifts the paradigm from building 'all-in-one' agents to building a library of specialized skills that can be orchestrated to handle complex, multi-step business processes.",{"title":40,"searchDepth":41,"depth":41,"links":7992},[7993,7994],{"id":7929,"depth":41,"text":7930},{"id":7962,"depth":41,"text":7963},[47],{"content_references":7997,"triage":8002},[7998],{"type":7894,"title":7999,"author":8000,"url":8001,"context":63},"Workflow-to-Skill: Skill Creation via Routing-Workflow-Semantics-Attachments Decomposition","arXiv","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.06893",{"relevance":72,"novelty":73,"quality":73,"actionability":73,"composite":7899,"reasoning":8003},"Category: AI Automation. The article presents a structured framework for decomposing AI workflows into reusable skills, addressing a key pain point for developers in creating modular AI agents. It offers practical advantages such as reusability and easier debugging, which are directly applicable to building AI-powered products.","\u002Fsummaries\u002Fa2e69b3ac342f3a6-decomposing-ai-workflows-into-reusable-skills-summary","2026-06-08 12:56:53",{"title":7919,"description":40},{"loc":8004},"a2e69b3ac342f3a6","arXiv cs.AI","summaries\u002Fa2e69b3ac342f3a6-decomposing-ai-workflows-into-reusable-skills-summary",[89,90,92],"The 'Workflow-to-Skill' framework improves AI agent modularity by decomposing complex processes into four distinct components: Routing, Workflow, Semantics, and Attachments.",[92],"V8SKbx2bTbTlIPpKlB35j-kcY7_UI_GghVKHsne5GyY",{"id":8016,"title":8017,"ai":8018,"body":8023,"categories":8051,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":8052,"navigation":77,"path":8062,"published_at":8063,"question":48,"scraped_at":8063,"seo":8064,"sitemap":8065,"source_id":8066,"source_name":8067,"source_type":85,"source_url":8068,"stem":8069,"tags":8070,"thumbnail_url":48,"tldr":8072,"tweet":48,"unknown_tags":8073,"__hash__":8074},"summaries\u002Fsummaries\u002Ff2ff7f7605612041-how-invideo-uses-gpt-6-astra-for-agentic-video-edi-summary.md","How Invideo Uses GPT-6 Astra for Agentic Video Editing",{"provider":7,"model":8,"input_tokens":8019,"output_tokens":8020,"processing_time_ms":8021,"cost_usd":8022},6060,541,2579,0.0023265,{"type":14,"value":8024,"toc":8046},[8025,8029,8032,8036,8039,8043],[17,8026,8028],{"id":8027},"enhancing-agentic-reasoning-and-planning","Enhancing Agentic Reasoning and Planning",[22,8030,8031],{},"Invideo utilizes GPT-6 Astra as an agentic engine to manage the technical and creative demands of video editing. The model excels at translating high-level editor instructions into precise, frame-level sequences. A key performance gain noted by CEO Sanket Shah is the model's efficiency in reasoning; Astra completes complex tasks using fewer reasoning steps and output tokens compared to previous models, allowing it to maintain the editor's original intent over longer, more complex workflows.",[17,8033,8035],{"id":8034},"improving-color-grading-and-correction","Improving Color Grading and Correction",[22,8037,8038],{},"Color grading often requires navigating overlapping techniques like isolation, LUT application, and regeneration. Previously, Invideo faced high failure rates when automating these tasks, particularly when complex requirements—such as changing a background while preserving specific skin tones—were involved. By integrating GPT-6 Astra, the company achieved a 3x increase in the success rate for color-grading and correction tasks, demonstrating the model's superior ability to handle multi-step visual processing.",[17,8040,8042],{"id":8041},"rapid-custom-effect-generation","Rapid Custom Effect Generation",[22,8044,8045],{},"Beyond standard editing, Astra enables the creation of custom effects based on natural language descriptions and visual references. The agent generates the necessary code for these effects, places them on the timeline, and exposes controls for human editors to refine the output. This capability significantly accelerates production, with Invideo editors successfully creating 50 custom effects in a single day. This workflow preserves the human editor's creative control while offloading the tedious, frame-by-frame execution to the AI.",{"title":40,"searchDepth":41,"depth":41,"links":8047},[8048,8049,8050],{"id":8027,"depth":41,"text":8028},{"id":8034,"depth":41,"text":8035},{"id":8041,"depth":41,"text":8042},[47],{"content_references":8053,"triage":8060},[8054,8057],{"type":65,"title":8055,"url":8056,"context":57},"Invideo","https:\u002F\u002Finvideo.io\u002F",{"type":65,"title":8058,"url":8059,"context":57},"GPT-6 Astra","https:\u002F\u002Fopenai.com\u002Findex\u002Fgpt-6-astra\u002F",{"relevance":72,"novelty":73,"quality":73,"actionability":73,"composite":7899,"reasoning":8061},"Category: AI & LLMs. The article provides a detailed case study on how Invideo uses GPT-6 Astra for video editing, addressing practical applications of AI in automation and creative workflows. It offers specific performance metrics, such as a 3x improvement in color grading success rates, and outlines actionable insights into integrating AI for enhanced productivity.","\u002Fsummaries\u002Ff2ff7f7605612041-how-invideo-uses-gpt-6-astra-for-agentic-video-edi-summary","2026-09-24 03:21:00",{"title":8017,"description":40},{"loc":8062},"f2ff7f7605612041","OpenAI News","https:\u002F\u002Fopenai.com\u002Findex\u002Finvideo-builds-with-gpt-6-astra","summaries\u002Ff2ff7f7605612041-how-invideo-uses-gpt-6-astra-for-agentic-video-edi-summary",[90,89,92,8071],"video-editing","Invideo leverages GPT-6 Astra to automate complex video editing tasks, achieving a 3x improvement in color-grading success rates and enabling the rapid creation of custom, editable effects.",[92,8071],"aqXL0l0FJdH3x_W3hRJaCkPFhfa4WcrTWuXxWmjcaIo",{"id":8076,"title":8077,"ai":8078,"body":8083,"categories":8158,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":8159,"navigation":77,"path":8170,"published_at":8171,"question":48,"scraped_at":8172,"seo":8173,"sitemap":8174,"source_id":8175,"source_name":8176,"source_type":7908,"source_url":8177,"stem":8178,"tags":8179,"thumbnail_url":8181,"tldr":8182,"tweet":8183,"unknown_tags":8184,"__hash__":8185},"summaries\u002Fsummaries\u002F8ca0bc8ca37c1fad-building-reliable-ai-systems-with-graph-engineerin-summary.md","Building Reliable AI Systems with Graph Engineering",{"provider":7,"model":8,"input_tokens":8079,"output_tokens":8080,"processing_time_ms":8081,"cost_usd":8082},5287,710,3759,0.00238675,{"type":14,"value":8084,"toc":8152},[8085,8089,8092,8096,8099,8117,8121,8128,8132,8135,8149],[17,8086,8088],{"id":8087},"the-case-against-monolithic-prompts","The Case Against Monolithic Prompts",[22,8090,8091],{},"Monolithic prompts—where a single LLM call attempts to fetch data, analyze context, and generate strategy—are prone to hallucinations. Because the model lacks access to real-time APIs or external data, it often fabricates information. Moving to a graph-based architecture allows developers to treat the workflow as a series of connected nodes, where deterministic functions handle data retrieval and LLMs handle reasoning.",[17,8093,8095],{"id":8094},"core-graph-design-patterns","Core Graph Design Patterns",[22,8097,8098],{},"Graph Engineering relies on specific wiring patterns to manage data flow and execution:",[7828,8100,8101,8111],{},[7831,8102,8103,8106,8107,8110],{},[7834,8104,8105],{},"Fan-Out & Join:"," Use fan-out to execute independent tasks (like fetching weather, course data, and fitness stats) in parallel. The ",[7834,8108,8109],{},"Join Node"," acts as a synthesizer, waiting for all branches to complete before aggregating the results into a single data structure. This eliminates the need for custom merger code or additional aggregator agents.",[7831,8112,8113,8116],{},[7834,8114,8115],{},"Router Patterns:"," Choosing between an LLM-based router and a deterministic router is critical for cost and reliability. Use a deterministic router (if\u002Felse logic) when the decision criteria are closed and the signal is clear. Reserve LLM routers for open-ended requests where the input is unstructured and requires semantic classification.",[17,8118,8120],{"id":8119},"optimizing-for-cost-and-predictability","Optimizing for Cost and Predictability",[22,8122,8123,8124,8127],{},"Effective graph design follows the principle: ",[7851,8125,8126],{},"Predictable work goes in functions, reasoning goes in the model."," By offloading data fetching to standard Python functions, you reduce LLM token costs significantly. In the marathon example provided, the entire workflow—including three parallel data fetches and a final strategy generation—requires only a single LLM call, as the other nodes execute via deterministic code.",[17,8129,8131],{"id":8130},"when-to-use-graph-workflows","When to Use Graph Workflows",[22,8133,8134],{},"Graphs are not always necessary, but they are essential when you need structure and reliability.",[7828,8136,8137,8143],{},[7831,8138,8139,8142],{},[7834,8140,8141],{},"Static Workflows:"," If you can map the process before the input arrives, use a static graph.",[7831,8144,8145,8148],{},[7834,8146,8147],{},"Dynamic Workflows:"," If the structure depends on the input (e.g., deep research tasks where the number of steps is unknown), use dynamic graph generation to define the shape at runtime.",[22,8150,8151],{},"ADK 2.0 provides the framework to implement these patterns, allowing developers to build production-ready systems that are testable, reliable, and cost-efficient.",{"title":40,"searchDepth":41,"depth":41,"links":8153},[8154,8155,8156,8157],{"id":8087,"depth":41,"text":8088},{"id":8094,"depth":41,"text":8095},{"id":8119,"depth":41,"text":8120},{"id":8130,"depth":41,"text":8131},[47],{"content_references":8160,"triage":8168},[8161,8165],{"type":65,"title":8162,"url":8163,"context":8164},"Google Agent Development Kit (ADK)","https:\u002F\u002Fadk.dev","recommended",{"type":59,"title":8166,"url":8167,"context":8164},"ADK 2.0 Codelab","https:\u002F\u002Fcodelabs.developers.google.com\u002Fadk2\u002Finstructions#0",{"relevance":72,"novelty":73,"quality":73,"actionability":73,"composite":7899,"reasoning":8169},"Category: AI & LLMs. The article provides a deep dive into graph-based architectures for AI systems, addressing the pain point of hallucinations in LLMs and offering practical design patterns for implementation. It includes specific techniques like fan-out and join nodes, making it actionable for developers looking to optimize their AI workflows.","\u002Fsummaries\u002F8ca0bc8ca37c1fad-building-reliable-ai-systems-with-graph-engineerin-summary","2026-09-10 19:12:02","2026-09-11 03:12:18",{"title":8077,"description":40},{"loc":8170},"8ca0bc8ca37c1fad","Google Cloud Tech","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Mzr7byMFy_4","summaries\u002F8ca0bc8ca37c1fad-building-reliable-ai-systems-with-graph-engineerin-summary",[89,90,92,8180],"software-engineering","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FMzr7byMFy_4\u002Fhqdefault.jpg","Move from monolithic prompts to graph-based architectures to eliminate hallucinations, reduce LLM costs, and improve reliability by separating deterministic logic from reasoning.","This is a practical introduction to building multi-agent systems using [Google's Agent Development Kit (ADK)](https:\u002F\u002Fcodelabs.developers.google.com\u002Fadk2\u002Finstructions#0). The video demonstrates how to move away from monolithic prompts by using graph-based patterns like fan-out, join nodes, and routers to create more reliable, cost-effective workflows.",[92,8180],"271XyM6kV_Dq3oqVcExfsRTOvjiqClI4ip12PcbYt7w"]