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@clcoding The loop creates 3 functions and stores them in funcs. But each function doesn't store the value of i at the time it is created. Instead, each function store the i after the loop finishes: i = 2 Print of all the functions: [f() for f in funcs] results in: [2, 2, 2]

Python Coding challenge - Day 1137| What is the output of the following Python Code? Answer with Explanation: clcoding.com/2026/05/python…

@RealBenjizo One True is all or needs.

Python question How well do you understand booleans in Python?

@PythonPr x *= x/2 means: x = x * (x/2) Starting with x = 4: First loop: x = 4 * (4/2) x = 8 Since 8 is still less than 10, the loop runs again. Second loop: x = 8 * (8/2) x = 32 loop stops. Output: 32

Python Quiz: What will be the output?

@DataScienceDojo most practical architectures will probably use both, MCP for tool access and A2A for multi-agent coordination

MCP and A2A are both agent protocols but they operate at completely different layers. MCP (Model Context Protocol) is about giving one LLM access to external tools. The model stays in the driver's seat throughout: it receives your query, decides which tools to call, gets the results back through an MCP Client, and assembles the final response. The MCP Servers in between are just standardized wrappers around raw APIs — Flight Booking, Google Calendar, whatever you need. One brain. Centralized control. A2A (Agent-to-Agent Protocol) is about coordination between autonomous agents. There's no single LLM managing everything — instead, an Orchestrator Agent delegates subtasks to specialized agents, each of which has its own tools, its own memory, and its own reasoning loop. The Orchestrator never touches the Flight API directly. It talks to a Flight Agent, which handles that domain entirely on its own, then reports back. The architectural difference matters when you're building: MCP works well when one model can reasonably handle the full task with tool access. A2A becomes necessary when subtasks are complex enough to need dedicated reasoning — or when you want agents that can operate in parallel without bottlenecking through a single model. Same scenario — "book me a flight and add it to my calendar" — but one architecture keeps control centralized, the other distributes it across specialists. If you're building agentic systems in 2026, understanding where this boundary sits will shape every design decision you make. #agenticai #aiarchitecture #mcp #google #anthropic #aiengineering #multiagentsystems

@GoogleAIStudio Vibe coding a bug tracker that mostly tracks the bugs I create while building the bug tracker.

What are you vibe coding this weekend?