ClawUp @ClawUpAI just got a major redesign. True-black theme Neon-orange accents 🦞 Wireframe lobster login Redesigned Overview, Billing, Agents, Teams, Tools, Identity & Quests. Improved i18n (EN/中文/Español), clearer errors, and refreshed docs. Same platform. Sharper experience. Apply Now

# 🚀 Sharing Our Team's Recent RGB Protocol Learnings With the stellar documentation from @aaronzhang and the dev environment built by the bitlightlabs team, we successfully validated the RGB v0.11 token transfer demo and gained a grasp of the protocol’s transfer mechanics. Docs reference: ## 🧩 Current Problem To date, no developer has rolled out a fully functional integration environment for RGB v0.12. What’s more, devs struggle to get a clear picture of the key feature upgrades from v0.11 to v0.12. ## 💻 Our Improvements Building on the rgb-sandbox repo and existing demo code (incompatible with v0.12) from the @RGBAssociation organization, we revamped the legacy ` script to enable seamless execution of complex RGB v0.12 demo scenarios. We also created a comprehensive walkthrough report for the demo, breaking down core logic to ease the learning curve for new developers. ## 🎁 Open Source Contributions ✅ Code PR submitted to the official RGB-WG repo: [RGB-WG/rgb-sandbox#1#]( ✅ RGB v0.12 demo walkthrough report: [rgbstashlabs/rgb-sandbox/demo-analysis-en.md]( 💡 💡 New developers can dive into the demo mechanics by reading the report, or simply run the ` script locally.

# Practical and Useful Patterns for OpenAI Agent SDK 🌍 Same agent, different experience for every user. Dynamic instructions let you build prompts on the fly based on runtime context. Pass a function to instructions that receives RunContextWrapper and Agent, and dynamically generate system prompts at runtime. 📌 Title: Agents -- Dynamic instructions 🔗 URL: 🧩 Overview The `instructions` parameter of an Agent accepts not just strings but also functions. The function receives a `RunContextWrapper` and an `Agent`, returning a string. This lets you inject information only available at runtime -- logged-in user details, current time, user plan, locale, and more. Async functions are also supported, enabling database lookups before prompt construction. 🛠 How to Use Define a function `dynamic_instructions(context: RunContextWrapper[UserContext], agent: Agent) -> str` that accesses user details from `context.context` and builds a prompt string with the user's name, plan, and current time. Pass this function as `instructions=dynamic_instructions` to the `Agent` for runtime-dynamic system prompts. 🏗 Practical Usage Patterns **Runtime Injection of User Name, Plan, and Datetime** Reflect logged-in user information in prompts for personalized responses. Define a `@dataclass` called `UserContext` with `name: str`, `plan: str`, and `timezone: str`. In the `personalized_instructions(context: RunContextWrapper[UserContext], agent: Agent) -> str` function, access `context.context` for user details and ` for the current time, then branch the prompt based on `user.plan` (`"pro"`, `"enterprise"`, or free). Set `instructions=personalized_instructions` on the `Agent` and pass `context=UserContext(name="Alice", plan="pro", timezone="US/Eastern")` to ` at execution time. **Multi-language Switching by Locale** Automatically switch instruction language based on the user's locale setting. Store locale-specific system prompts in an `INSTRUCTIONS_BY_LOCALE` dictionary keyed by `"ja"`, `"en"`, and `"zh"`. In the `locale_instructions(context: RunContextWrapper[UserContext], agent: Agent) -> str` function, read `context.context.locale` and look up the matching prompt with `INSTRUCTIONS_BY_LOCALE.get(locale, INSTRUCTIONS_BY_LOCALE["en"])`, falling back to English. **Async Function to Fetch Data from DB into Prompt** Retrieve the user's recent purchase history from a database to provide context-aware support. Define `async def instructions_with_history(context: RunContextWrapper[UserContext], agent: Agent) -> str` that calls `await db.fetch_recent_orders( limit=5)` to fetch recent purchase history from the database. Format the orders into a summary string and embed it in the prompt for context-aware support. Set `instructions=instructions_with_history` on the `Agent` to use this async function. 💡 Use Cases 👤 Inject logged-in user's name, plan, and datetime at runtime for personalized responses 💎 Branch behavior by plan -- advanced feature guidance for Pro, upgrade suggestions for Free 🌐 Auto-switch instruction language based on user locale for seamless multi-language support 📦 Fetch recent purchase history from DB via async function for context-aware customer support ⚠️ Caveats - Heavy DB queries in async instruction functions delay agent response start. Consider caching or query optimization. - Exceptions inside instruction functions cause the entire agent to fail. Implement proper error handling and fallbacks. - Watch out for dynamically generated prompts growing too long -- this increases token consumption. ✨ Dynamic instructions let you deliver diverse user experiences from a single agent definition. Leverage runtime information to build truly personalized agents! #OpenAIAgentSDK# #AIAgent#

En route to the venue | Leaving space within the haze.
Fog is an emotion still sealed, waiting to unfold. #BIFAN# #BucheonInternationalFantasticFilmFestival# #제30회부천국제판타스틱영화제# 📸 诗扬/Shiyang

En route to his @Kia NBA MVP… Shai Gilgeous-Alexander became the first player to score 20+ points in every game of the regular season (min. 50 games) since Wilt Chamberlain in 1963-64 🤯