Tag Archives: Large language model

27Mar/26

China Overtakes US in Global AI Usage

March 27, 2026TechnologyAnthropic, Artificial general intelligence, Artificial intelligence, China, Claude Opus, Computational neuroscience, Cybernetics, Deep learning, DEEPSEEK, Generative artificial intelligence, GitHub, Huawei, In artificial intelligence, Large language model, MiniMax, Moonshot AI, Nvidia, OpenAI, Oracle, SoftBank, United Statesadmin

Intelligence Too Cheap to Meter: The Rise of Chinese AI Agents and the Flaws of the Token Economy

March 27, 2026 /Mpelembe Media/ — Chinese AI models have officially overtaken their US counterparts in global token consumption, marking a watershed moment in the global artificial intelligence race. This massive surge in usage is largely driven by a transition away from simple chatbots toward autonomous “agentic” workflows, which require millions of tokens to independently plan, code, and execute complex, multi-step tasks. Continue reading →

23Mar/26

From Prompts to Profits: How AI Frameworks are Automating YouTube and Social Media

March 23, 2026Developers15.ai, AI slop, Artificial intelligence, Artificial intelligence and copyright, Christian Mammen, ELEVENLABS, Generative artificial intelligence, Large language model, OpenAI, Perlmutter, Recraft, Reddit, Synthetic media, Thaler, United Statesadmin

March 23, 2026 /Mpelembe Media/ — MoneyPrinterV2 is an advanced Python-based automation framework designed to help users generate online revenue through AI-driven content creation and social media management. The software utilizes a modular architecture to automate the production of YouTube Shorts, manage Twitter bots, and execute affiliate marketing campaigns. By integrating technologies like Ollama for script generation and KittenTTS for voiceovers, the tool can independently create and schedule videos with subtitles and background music. Beyond social media, the platform includes features for scraping local business data to facilitate automated email outreach and lead generation. This open-source project aims to eliminate the manual labor typically required for consistent digital engagement by providing a centralized command-line interface for various income streams. While offering powerful capabilities for creators and marketers, the author emphasizes that the tool is intended for educational purposes and requires user compliance with platform terms.
Continue reading →

23Mar/26

Poisoned Memories and Fake News: The Vulnerable Intersection of AI and Algorithmic Trading

March 23, 2026Security1, AI agent, AI safety, Artificial intelligence, Code injection, Deep learning, Exploit, Intelligent agent, Large language model, Natural language processing, Ninad Pathak, OpenAI, Prompt engineering, Prompt injection, Vulnerabilityadmin

March 23, 2026 /Mpelembe Media/ — The provided sources comprehensively detail the rapid evolution of artificial intelligence from conversational large language models (LLMs) to autonomous “agentic AI,” and the massive security challenges accompanying this shift. As AI agents gain the ability to retrieve information, orchestrate multi-step workflows, and execute high-privilege actions (like trading or system administration), they introduce unprecedented attack surfaces across enterprises and Web3 ecosystems.

Continue reading →

11Mar/26

A Parrot by Any Other Name: The Case for Stripping Personality from Artificial Intelligence

March 11, 2026ScienceAI anthropomorphism, Antonio Damasio, Artificial general intelligence, Artificial intelligence, Computational neuroscience, Cybernetics, Data science, Emotion, Feeling, Human-AI interaction, Large language model, Subjective experienceadmin

Why Your AI Doesn’t Actually “Feel” You—And Why That’s a Good Thing

March 10, 2026 /Mpelembe Media/ — Modern anxiety regarding AI largely stems from the psychological, societal, and security risks introduced by systems that convincingly mimic human emotion and cognition. Because human beings are biologically predisposed to anthropomorphize—projecting human intent and feelings onto non-human entities—the advanced capabilities of modern Large Language Models (LLMs) have created several unprecedented areas of concern: Continue reading →

08Mar/26

Integrating AI Agents with Google Workspace via CLI and MCP

March 8, 2026BusinessAI agent, AI boom, Artificial general intelligence, Artificial intelligence, Computational neuroscience, Deep learning, duckduckgo, Generative artificial intelligence, Google Search, Google Workspace, Intelligent agent, Large language model, Natural language processingadmin

The AI Brain Meets the Real World: A Guide to Function Calling

March 8, 2026 /Mpelembe Media/ — The provided sources describe the emergence of AI agents designed to automate productivity tasks within the Google Workspace ecosystem. A central development is the release of gws, an open-source command-line interface that unifies various Google APIs into a single, machine-readable format. This tool allows large language models to interact directly with Gmail, Calendar, and Drive by providing structured JSON outputs and pre-built agent skills. Technical tutorials illustrate how developers can use the Vercel AI SDK and Model Context Protocol (MCP) to build assistants capable of managing schedules and conducting web searches. Furthermore, the integration of the Gemini CLI with tools like Google Sheets highlights a shift toward natural language data automation. Together, these resources mark a transition from manual API management to autonomous agentic workflows powered by generative AI. Continue reading →

28Feb/26

The Spanish AI Loophole That Hacked Mexico

February 28, 2026SecurityAI boom, Anthropic, Artificial general intelligence, Artificial intelligence, Chatbot, ChatGPT, Claude, Computational neuroscience, Cybernetics, Deep learning, Existential risk from artificial intelligence, Large language model, Mexico, MEXICO CITY, OpenAIadmin

Hacker Weaponizes AI Chatbots to Steal Massive 150-Gigabyte Data Trove from Mexican Government

28 Feb. 2026 /Mpelembe Media/ — An unknown hacker successfully breached multiple Mexican government agencies, stealing 150 gigabytes of sensitive information that included 195 million taxpayer records, voter data, government employee credentials, and civil registry files. Continue reading →

20Feb/26

Your Next Favorite Reality TV Stars Aren’t Human: Inside the Rise of the AI Crypto Apprentice

February 20, 2026GamesAI agent, Artificial intelligence, Autonomous agent, Coinbase, docker, FIVERR, Intelligent agent, Large language model, Multi-agent system, OPENSEAadmin

20 Feb. 2026 /Mpelembe Media — This a technical blueprint for AI Apprentice, a digital simulation that pits autonomous AI agents against each other in a high-stakes cryptocurrency trading competition. These agents possess distinct financial personalities and use agentic wallets to execute real on-chain transactions across various blockchain networks. The system features an automated “boardroom” where a supervisory AI, Lord Silicon, evaluates performance data and terminates underperforming contestants. Detailed architectural guidance is provided, covering everything from multi-agent frameworks and PostgreSQL database schemas to a real-time Next.js frontend for viewers. Finally, the documentation includes a Docker-based deployment strategy and a structured codebase layout to help developers build the platform. Continue reading →

09Feb/26

Vibe Engineering: Bridging the Gap Between AI Agility and Production Stability

February 9, 2026DevelopersArtificial intelligence, Computer performance, Deep learning, Generative artificial intelligence, Large language model, Logic Pro, Loop Review, NOP, Program optimization, Software quality, Unit testing, Vibe codingadmin

10 Feb. 2026 /Mpelembe Media — Vibe Engineering is an AI-driven development approach that integrates the rapid prototyping speed of “vibe coding” with the rigor of traditional engineering principles like code review, testing, and system architecture. It is designed to navigate the transition from the “Magic” phase, where a functional prototype is generated in minutes, to the “Maintenance” phase, where code must survive in a production environment. While vibe coding focuses on natural language prompts, intent, and UI/UX, Vibe Engineering emphasizes security, scalability, and edge cases. Continue reading →

02Dec/25

Dynamic Agent Orchestration: The Puppeteer Paradigm

December 2, 2025DevelopersArtificial intelligence, Belief revision, Deep learning, Intelligent agent, Large language model, Natural language processing, Reinforcement learningadmin

Dec. 02, 2025 /Mpelembe Media/ — The academic paper introduces a novel framework for coordinating complex problem-solving in Large Language Model (LLM)-based multi-agent systems. To address the inherent inefficiencies of traditional static agent structures, the authors propose a “puppeteer-style” paradigm where a central orchestrator dynamically selects and sequences agents based on the evolving task state. This centralised orchestrator policy is continuously optimised using reinforcement learning (RL), leveraging a tailored reward function that explicitly balances solution quality with computational efficiency. Empirical results across various closed- and open-domain scenarios demonstrate that this adaptive approach achieves superior performance compared to existing methods while concurrently reducing token consumption. Finally, analysis of the evolving collaboration patterns confirms that the RL-driven policy leads to the emergence of highly compact and cyclic reasoning structures. Continue reading →

25Nov/25

The value of thought. How human-AI collaboration is measured economically

November 25, 2025Economy.google, AI alignment, Artificial general intelligence, Artificial intelligence, Computational neuroscience, Cybernetics, Deep learning, Large language model, Natural language processing, OpenAI, Prompt engineering, Tokenizationadmin

This touches on how large language models (LLMs) operate! tokenization is the fundamental process in natural language processing (NLP) of breaking down raw text into smaller units called tokens, such as words, subwords, or characters. This is a crucial first step that transforms unstructured text into a structured format that machine learning models can process.

Continue reading →