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 SF. Meeting with any and all hustlers, gangsters, thugs, hoes, thots, investors, investees, traders, speculatoors, zoomers, doomers and boomers. DM open Also for the duration of the flight, for medical reasons, my shoes will be off. If you dislike strong odors, gtfo

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