Grid-to-Earn Challenge: trade $ANSEM, $CASHCAT, or $CARDS with Aster Grid Trading and share $10,000 in $ASTER 👇 📋 Campaign Details - Eligible pairs: ANSEMUSDT, CASHCATUSDT, CARDSUSDT (Futures Grid) - Campaign period: 2026-07-14 10:00 UTC to 2026-07-21 10:00 UTC - Prize pool: $10,000 in $ASTER 🎯 How Rewards Work - Your reward = Total Pool × (Your Grid Volume ÷ All Eligible Users' Grid Volume) - Rewards under 1 $ASTER are not distributed ✅ Eligibility - Open to all new and existing Aster users, market makers excluded - Only trades placed directly through Aster Grid Trading count; Aster Code and other broker channels do not - Wash trading, self-trading, and sybil behavior are disqualified

# Useful but Little-Known Features of ADK 2.0 🌍 Do you know the different types of callbacks in ADK 2.0 and when to use each one? ADK 2.0 provides callbacks across three layers: agent lifecycle, LLM calls, and tool execution. The Before/After pattern at each layer lets you flexibly inject validation, guardrails, logging, and more. 📌 Title: Types and Patterns of Callbacks 🔗 URL: 🧩 Overview ADK 2.0 callbacks fall into three categories. Agent lifecycle callbacks (`BeforeAgentCallback` / `AfterAgentCallback`) insert processing before and after agent execution — useful for validation and cleanup. LLM callbacks (`BeforeModelCallback` / `AfterModelCallback`) operate around model calls for request modification and guardrails. Tool callbacks (`BeforeToolCallback` / `AfterToolCallback`) handle validation and result processing around tool execution. 🛠 How to use it Callbacks are specified when defining an agent. In Python, exact parameter names (`callback_context`, `llm_request`, `tool_context`) are required. ```python from adk import Agent async def before_agent(callback_context) -> None: """Validate before agent execution.""" print(f"Agent starting: {callback_context.agent_name}") # Return None to continue, return a value to skip async def before_model(callback_context, llm_request): """Guardrails before model call.""" if contains_sensitive_info(llm_request): return block_response() # returning a value skips the model call return None # continue with normal model call async def after_tool(callback_context, tool_context, tool_response): """Log after tool execution.""" log_tool_usage(tool_context.tool_name, tool_response) return None agent = Agent( name="my_agent", model="gemini-3.5-flash", before_agent_callback=before_agent, before_model_callback=before_model, after_tool_callback=after_tool, ) ``` Before callbacks that return a value skip subsequent processing; returning None continues normal execution. 🏗 Building it into production ・Use `BeforeAgentCallback` for input validation and auth checks to reject bad requests early ・Apply guardrails (PII detection, harmful content filters) in `BeforeModelCallback` ・Validate model output format and policy compliance in `AfterModelCallback` ・Record tool execution results in `AfterToolCallback` for audit trails 💡 Use cases 🛡 Block prompts containing personal information with `BeforeModelCallback` 📝 Record agent execution results to a database with `AfterAgentCallback` ✅ Validate tool call parameters with `BeforeToolCallback` 🔍 Verify JSON format of model output in `AfterModelCallback` and trigger retries ⚠️ Watch out In Python, callback function parameter names must be exact — `callback_context`, `llm_request`, `tool_context`, etc. Mismatched names will cause silent failures. Be careful not to accidentally return a value from Before callbacks, as this skips model calls or tool execution. Also remember that callbacks execute after plugins in the processing order. ✨ Using the right callbacks at the right layer gives you fine-grained control over agent behavior. Combine callbacks across layers to meet your security, quality assurance, and audit requirements. #ADK# #AIAgent#

Finally! is back LIVE! Trading and you’re funds are SAFU, backed by Orderly. Please be aware some links may not work as this is going to be a work in progress. However, trading is fully operational. Deep liquidity from several of the largest market makers. What’s next? 1. Ensure the UI/UX is and smoothness is as good as , if not better than the likes of HyperLiquid. 2. WynnDEX will launch it’s own token, similar to AsterDEX, but better. Instead of just owning tokens, you will own a share of the profits, paid quarterly . More details coming soon. 3. Too hot to disclose 4. Wynn University 5. Too hot to disclose Blessed for all of the support! My team, and the people who are making this possible. We will become one of the biggest and fastest growing Perp DEX’s behind HyperLiquid. Inshallah!

maybe im old school but unless the token is directly connected in some way, these attention coins are just gambling masqueraded as trading. pure pvp. for example goblin: there is literally zero ties to the actual token and you are just playing an attention game? like who cares if sama keeps talking about goblin? what does that have to do with the token? I get asteriod because elon historically has shilled coins and there is expected value in that happening again moltbook same thing. it ran to 100m off the back of the token being shilled by the agent and assumption it would the official coin of the platform. Without that it would not have even cracked a few mil but these coins where its just attached to a popular trend is one of the worst metas we have had. I get it, people want to gamble. but people that are putting out full threads and thesis as to why goblin will increase in mindshare and thus the goblin token will go up are just chucking up airballs