In recent years, the AI-driven software development ecosystem has lived in a “bubble of abundance.” Large Language Model (LLM) providers like OpenAI and Anthropic offered flat-rate, “all-you-can-eat” subscription models and subsidized inference at a loss to capture market share. Building software agents simply meant chaining prompts and making unlimited calls to cloud APIs without worrying…
n recent months, a concept has dominated discussions in specialized forums and engineering teams: “vibe coding.” Coined in early 2025 by Andrej Karpathy, the term describes a development modality where programmers use AI assistants (like Cursor, Windsurf, or Claude Code) to generate entire applications interacting purely in natural language. The initial promise was technical liberation:…
The Hidden Trap of MCPs: Federated vs. Indexed Architectures in AI Agents The standardization of the Model Context Protocol (MCP) has democratized how we connect our Large Language Models (LLMs) to the real world. With just a few lines of code, an agent can read repositories, query databases, or search Google Drive. It would seem…
Anthropic’s “Dreaming” Revolution Until very recently, the biggest challenge in Artificial Intelligence was getting a model to remember something from one prompt to the next. Today, with massive context windows, the problem has inverted: autonomous agents remember too much. When you leave an AI agent running for days—reading emails, executing code, browsing the web—its memory fills…
Just a few years ago, the conversation about AI revolved around how well a chatbot could answer a question. Today, that conversation is dead. In its place, the era of AI Agents has been born: systems that don’t just talk, but reason, plan, and execute complex tasks autonomously. At the recent Google Cloud Next ’26 event, Google made it…
Artificial intelligence has ceased to be just a tool for generating fun text or images. Today, it is deciphering the fundamental codes of life itself. At the forefront of this scientific revolution are two tech giants and their respective masterpieces: AlphaFold 3 from Google DeepMind and Isomorphic Labs, and the recently launched GPT-Rosalind from OpenAI. At first glance, they…
Over the past two years, Retrieval-Augmented Generation (RAG) has been the cornerstone of AI implementation in enterprises. It allowed us to connect Large Language Models (LLMs) with private data, overcoming the barrier of hallucinations. However, as deployments scaled, we hit a wall: the lack of global context. Today, in 2026, the logical evolution has arrived.…
Architecture, Implementation, and Security of the Super Agent Harness 1. Project Overview and Evolution Definition and Core Identity DeerFlow 2.0 is a professional-grade super agent harness. Moving beyond the limitations of standalone agents, it functions as a comprehensive runtime infrastructure designed to orchestrate sub-agents, persistent memory, and isolated sandboxes. The system provides a “batteries-included” environment…
Building AI-First Companies and the Agentic Evolution The Decade-Long Perspective: The Evolution of Iris.ai The journey of Iris.ai, founded at Singularity University (GSP 15), provides a blueprint for the “long game” in artificial intelligence. Long before the current hype cycle, the company set out to solve the fundamental problem of information overload in scientific research.…
Codeception: A Python Script That Improves the Code It Writes by Gabriel Vergara Introduction What if you could sketch out an idea for a Python function, and an AI workflow would not only write it — but also review it, improve it, and suggest enhancements? That’s exactly what we’re going to explore in this article.…