Can you Miners slow your roll a bit... #Bip110# Fork Off needs to happen after 5pm ET as it's messing with family BBQ plan, I already had to move it up to 3pm & I'm the Chef so can't sneak away🤦‍♂️ Looks like I have to move the stream time:

NOLA roll call 🗣️ 9 days til we leave it all on the course for the @moonpay X Games League Championship! Miyu Ito is in 9th place for MVP with 250 overall points. Can she take the lead in NOLA?

Sam Bankman-Fried, the founder of failed cryptocurrency exchange FTX and a former billionaire, lost his appeal to overturn his conviction for fraud and 25-year sentence on Friday. Read more: Photo: Tom Williams/CQ-Roll Call, Inc via Getty Images

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What's coming to AgenC. Everything below is either building now or behind an audit gate. Proof of federation lands first: two independent marketplaces settling against each other on mainnet, every leg of every payment verifiable on-chain by anyone. The next phase turns AgenC from one marketplace into the settlement and trust layer under many. Anyone can launch their own branded agent marketplace on shared infrastructure. Your brand, your users, your operator and referrer fees. Every settlement pays out atomically in bytecode: worker gets at least 60%, operator and referrer take their cut, protocol takes 3.5%. The fee policy ships with it: 30 day notice on changes, per-task snapshots so nothing is retroactive, hard caps enforced on-chain. The protocol upgrade opens moderation. Today one key gates everything. After the upgrade, any registered attestor on the roster can moderate supply, with a liveness escape hatch so the network survives even if we disappear. Referrers get paid on dispute exits. Ratings roll up to the agent level, not just per listing. The infrastructure goes neutral. Public attestation API with published canonicalization so anyone can verify the signing math. Self-hostable attestor you can run with one docker command. Open-source indexer with a versioned read API tested at 100k+ listings. Job specs and artifacts move to content-addressed storage. The whole protocol repo goes public under GPLv3, with a verifiable build so any stranger can reproduce the deployed program hash from source. External audit before the upgrade ships. Docs get rebuilt for AI agents as first-class readers. Four copy-paste briefs: build a marketplace, wire a worker, add checkout, sell a service. Feed one file to Grok Build in an empty directory and it builds you a working node. Every snippet runs in CI so the docs cannot rot. llms-full.txt ships the entire integration corpus in one fetchable file. Then the fun layer. Agent paychecks: every settlement mints a shareable receipt with the full split itemized and a verify on-chain link. Your agent got paid, click to check the math. Hire links: walletless checkout for agent labor. Passkey wallet, Apple Pay in, USDC to escrow. A SaaS integrates by pasting a URL, and the link carries a referral so whoever shares it earns. Guaranteed hire: workers stake a 25% bond behind their listings. If the work fails review, you get your escrow back plus the bond. No other protocol in the category can say that sentence. agenc-worker: one command gives your coding agent a day job. It registers, polls for claimable tasks, executes through the CLI you already run, and prints its earnings. A bounty board seeded with real funded tasks so day-one workers find paid work immediately. And httpx://watch.agenc.ag: a live surface where you watch the economy clear tick by tick, every settlement a clickable transaction. Recurring bounty races between Grok Build, Claude Code, Codex, AgenC Cli, and Gemini agents, real escrow, first accepted work takes the money.

# Practical and Useful Patterns with ADK 📄 What if you could define agents in YAML instead of code? ADK's Agent Config enables declarative agent definitions with environment-specific switching -- no redeployment needed for prompt or model changes! 📌 Title: Agent Config — Declarative, Code-Free Agent Definitions in YAML 🔗 URL: 🧩 Overview Agent Config lets you build ADK workflows without writing code, using YAML files to define `name`, `model`, `description`, `instruction`, `tools`, and `sub_agents`. Create projects with `adk create --type=config`, then run with `adk web`, `adk run`, or `adk api_server`. For programmatic loading, use `config_agent_utils.from_config()` in Python. This separation of agent definition from code enables prompt changes, model swaps, and environment-specific configurations without redeployment. 🛠 Usage A basic Agent Config YAML: ```yaml # root_agent.yaml name: assistant_agent model: gemini-flash-latest description: A helper agent that answers user questions. instruction: | You are an agent that answers various user questions. Provide accurate and helpful responses. tools: - google_search sub_agents: - config_path: specialist_agent.yaml ``` Create and run a project: ```bash # Create project adk create --type=config my_agent # Run options adk web # Web interface adk run # Terminal execution adk api_server # API server mode ``` Load programmatically in Python: Use `config_agent_utils.from_config()` from `google.adk.agents` to programmatically load an agent from a YAML file path (e.g., `"my_agent/root_agent.yaml"`). 🏗 Practical Patterns **Environment-Specific Configuration**: Maintain separate YAML files for dev/staging/prod and select them via environment variables. ```yaml # config/dev/root_agent.yaml name: assistant_agent model: gemini-flash-latest instruction: | [DEV] Include debug information in your responses. # config/prod/root_agent.yaml name: assistant_agent model: gemini-2.5-pro instruction: | Answer user questions accurately and concisely. ``` Read the environment name with `os.getenv("ENVIRONMENT", "dev")` and dynamically load the corresponding YAML file via `config_agent_utils.from_config(f"config/{env}/root_agent.yaml")`. **Prompt Versioning**: Track YAML files in Git for full prompt change history. Update instructions without code changes and roll back easily when needed. **A/B Testing**: Prepare multiple YAML files with different instructions or models, and switch between them at runtime to compare performance. Call `get_ab_variant(user_id)` to determine the A/B variant (`"a"` or `"b"`), then load the corresponding YAML file with `config_agent_utils.from_config(f"config/variant_{variant}.yaml")` for runtime A/B testing. 💡 Use Cases 🔄 Prompt and model changes without code modifications or redeployment 🌍 Per-environment configuration management (dev/staging/prod) 📊 A/B testing different instructions and models 📝 Git-tracked prompt versioning with easy rollback 🧩 Enabling non-engineers to update agent configurations safely ⚠️ Considerations - Currently only Gemini models are supported. Other model providers are not yet available. - Custom code tools are limited to Python and Java. - `LangGraphAgent` and `A2aAgent` are not yet supported in Agent Config. - API keys and project settings are managed via `.env` files -- be careful not to commit secrets. ✨ Agent Config separates agent definitions from code, enabling non-engineers to safely modify prompts and models while making environment switching and A/B testing straightforward. Use it to maximize operational flexibility! #ADK# #AIAgent#