Agentic Collective Engineering
At its core, ACE surpasses vibe coding by providing scalable, production-ready systems with zero tech debt.
Ryan Watts
Orchestrating Intelligence: Distributed Computing and Autonomous Agents
The convergence of distributed computing architectures, modern workflow orchestration, and autonomous AI agents is creating a new paradigm for building intelligent, scalable systems that can think, coordinate, and execute across vast computational landscapes.
Ghosts in the Machine: AI, Memory, and the Architecture of Recall
Memory shapes intelligence—human and artificial alike. In brains, it’s adaptive and fallible; in AI, it’s precise yet indiscriminate. As we design systems that remember and forget, we’re deciding not just how machines store data, but how future intelligence will think.
Vibe Coding: The Thrill, the Trap, and the Path to Mastery
Vibe coding lets AI build fast, but speed without understanding is a trap. Treat AI’s output as a draft, not gospel. The real win is using it to accelerate ideas you own—because code you don’t understand will own you when it breaks.
Streamline Agentic AI Engineering with MCP OpenAPI Proxy and MCPO
MCP OpenAPI Proxy is a Python-based MCP server that dynamically converts APIs, defined by OpenAPI specs, into MCP-compatible tools that AI agents can invoke. MCPO complements this by exposing MCP tools as RESTful HTTP APIs, instantly making them accessible to services outside of the MCP ecosystem.
Data Engineering is a Cost-Saving Catalyst in Agentic AI
Data engineering powers Agentic AI by delivering clean, structured, and purpose-ready data. Efficient pipelines and pre-aggregation reduce compute costs, speed up decision-making, and scale autonomy—turning backend optimization into a key driver of intelligent, cost-effective systems.
Agentic AI Matched with MindsDB is a Powerhouse
The next wave of artificial intelligence is agentic – AI systems capable of proactively gathering information, reasoning, and acting to achieve goals. Unlike a simple question-answering bot, an agentic AI can plan and execute tasks autonomously, often by interacting with various data sources and tools. Building such systems is challenging because
Anthropic’s Model Context Protocol (MCP) for Tool-Enabled Agentic AI
MCP, or Model Context Protocol, is emerging as a prominent standard for engineering agentic AI tools and functionalities. Understanding how to leverage the capabilities of this standard to develop AI-centric products will equip engineering teams for rapid and efficient product development.
MCP vs OpenAI’s OpenAPI Tools: A High-Level Comparison
Overview
Model Context Protocol (MCP): MCP is an open standard introduced by Anthropic in late 2024 for connecting AI models with external tools and data sources . It aims to replace the many fragmented, one-off integrations in AI applications with a single unified protocol . In Anthropic’s words, MCP provides a
Writing Effective Instructions for Marvin 3.0 AI Agents
Marvin 3.0 is a Python framework for building agentic AI workflows, where tasks are delegated to Large Language Model (LLM) agents . A core idea in Marvin is that each unit of work (a Task) should have a clear objective described by instructions, and one or more Agents specialized to