📌 AVM Tools are the building blocks for agent execution. In this video, we build a Weather Forecast tool powered by @OpenWeatherMap . Watch how easy it is to go FROM code to action, no manual setup, no infrastructure hassle 👇

9/ We're building next generation shovels in the next generation AI gold rush. While everyone races to build the smartest agents, we're building the execution backbone that lets them actually get the work done in production. Join the AVM Telegram community now

8/ We've shown some examples so far of HOW these agents can tap in to the ~$1T, but keep your eyes out for more including but not limited to,. ⚡️DeFi agents that analyze on-chain data.⚡️Content agents that scrape social media and generate reports.⚡️Research agents that process.

7/ AVM's presents a simplified deployment process,. 1️⃣Your agent writes code (Python, TypeScript, PHP, etc).2️⃣MCP protocol routes the request to AVM.3️⃣AVM spins up a fresh, isolated sandbox with pre-installed libraries.4️⃣Code executes securely (more on this in another Tweet).

6/ The Current reality is developers are mostly 'duct-taping' solutions together:. ▫️Falling victim to vendor lock-in and unexpected rate limits.▫️Running untrusted AI code locally, a security nightmare.▫️Managing complex AWS setups, demanding DevOps and learning curve overhead.

5/ This means the agent (built with Claude, GPT-4, AutoGen, LangChain, whatever) generates Python code (and soon many other languages) to,. ✅automate tasks.✅scrape websites.✅analyze market data.✅automatically balance workloads for teams.✅provide critical operational insights.

4/ This provides AVM with an opportunity as execution environments have now become critical as agents moved from impressive demos to production systems. And this is where neutrality really matters; developers prefer vendor-neutral infrastructure. Enter AVM, we're execution.

3/ Think of it like this: Agents are brilliant architects with detailed blueprints, but they have no construction sites where they can safely build. Why hasn't OpenAI solved this? We believe it's ultimately a business conflict. Big-brand AI companies want users locked in their.

2/ Every AI company is building agents that can reason and plan. But when those agents need to actually DO something: run code, process data, interact with APIs. THEY HIT A WALL!.

1/ AI Reality Check: The Billion Dollar Infrastructure Gap. The AI agent market is projected to surpass ~$1T by 2030, but there’s a critical missing piece: execution infrastructure. That’s where $AVM comes in!. A thread 🧵

👉Multi-Source DeFi Intelligence with AVM. Your agent needs multiple perspectives: .- @Dune for the 'how' (trading patterns, user behavior, volume trends).- @DefiLlama for the 'what' (total value locked, yield rates, protocol metrics). Think Yin and Yang: complementary data that

What does the future of design look like?. Designers focus on ideas. AI agents handle the work. $AVM gives them the execution layer to turn ideas into real outputs 👇

📌 Weekly Build Log: AVM Engineering Update. PLATFORM (Frontend).✅ Clearer error messages for a better dev experience.✅ Improved the Run editor (inputs, env variables, logs, outputs).✅ Fixed public Tools/Toolkits visibility & filtering bugs (e.g., inconsistent “public” toggle)

What if your next assistant worked 24/7, never took a break, and wasn’t human?. Listen to this real use case 👇

5/ Secure, Verifiable, Built for Agents. Every step runs in an isolated AVM container:.▫️Verifiable output (stdout, stderr, exit code).▫️No shared state.▫️Fully auditable. Other agents can reuse these tools, creating an ecosystem where automation compounds. That’s how the Agent

4/ Analysis AND automation. LLM agents via MCP process and validate the DEX volume data autonomously through AVM. The agent writes custom code to correlate volume patterns with yield opportunities, executes complex multi-chain analysis, and identifies confident arbitrage.

3/ The Discovery Layer. In this example, your agent uses DEX Metrics as the first step in identifying high-volume trading pairs where fee generation is strongest (eg. high volume = high fees = better yields). It spots trending USDC-ETH pairs on @0xPolygon with unusual volume.

2/ Why use Dune?. Instead of querying 20+ individual DEX APIs with different formats and rate limits, Dune aggregates everything into standardized SQL queries. So, your agent may use a dashboard like "DEX Metrics" by @hagaetc, where you're able to scan the entire ecosystem in.

1/ Multi-Chain Yield Hunting with @Dune + AVM. Finding the best DeFi yield across thousands of pools and dozens of DEXs is like searching for a needle in a haystack. Agents need a smarter starting point. This is where AVM Tools come in. 👉Example task: "I have $50K in USDC. Use

AI agents are powerful but stuck behind two huge problems: scalability and trust. AVM is building the SuperComputer to solve both. Watch this quick explainer 👇