Magentic-One by Microsoft, is an open-source, multi-agent system designed to solve complex tasks using artificial intelligence. Magentic-One utilises a team of specialised agents, each possessing unique skills like web browsing, file handling, and code execution, all coordinated by an Orchestrator agent. This modular design allows for flexibility and extensibility, enabling the system to adapt to...
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Magentic-One von Microsoft – GUI-Automatisierung Ante Portas
Magentic-One von Microsoft ist ein quelloffenes Multi-Agenten-System, das komplexe Aufgaben mit Hilfe künstlicher Intelligenz lösen kann. Magentic-One nutzt ein Team spezialisierter Agenten, von denen jeder über Fähigkeiten wie Webbrowsing, Dateiverarbeitung und Codeausführung verfügt, die alle von einem Orchestrator-Agenten koordiniert werden. Dieser modulare Aufbau ermöglicht Flexibilität und Erweiterbarkeit, so dass das System an verschiedene Szenarien angepasst...
The llms.txt Standard and the Rise of Human-AI Infrastructure
The World Wide Web stands at the threshold of a profound transformation. A new proposal called llms.txt signals the emergence of something remarkable: a web that serves not just human readers, but artificial intelligences as first-class citizens. This isn't merely another technical standard—it's the beginning of a fundamental shift in how we think about digital...
Der llms.txt-Standard und das Aufkommen der Mensch-KI-Infrastruktur
Das World Wide Web steht an der Schwelle eines tiefgreifenden Wandels. Ein neues Proposal namens llms.txt signalisiert das Entstehen von etwas Bemerkenswertem: ein Web, das nicht nur menschlichen Lesern, sondern auch künstlichen Intelligenzen als Bürgern erster Klasse dient. Dies ist nicht nur ein weiterer technischer Standard - es ist der Beginn eines grundlegenden Wandels in...
Test-Time Training: A Breakthrough in AI Problem-Solving
In a groundbreaking new paper from MIT researchers, artificial intelligence has taken a significant step forward in its ability to solve novel, complex problems. The research demonstrates that with a technique called "test-time training" (TTT), AI systems can dramatically improve their reasoning abilities—matching human-level performance on some challenging tasks. Let's dive into what this means...
Anthropic releases automatic Prompt Improver – Is Prompt Engineering over?
Anthropic has released new features in its developer console to improve the quality of prompts used with its language model, Claude. The prompt improver automates the refinement of existing prompts using techniques such as chain-of-thought reasoning and example standardization. The console also allows users to manage multi-shot examples in a structured format and provides a...
Test-Time Compute: The Next Frontier in AI Scaling
Major AI labs, including OpenAI, are shifting their focus away from building ever-larger language models (LLMs). Instead, they are exploring "test-time compute", where models receive extra processing time during execution to produce better results. This change stems from the limitations of traditional pre-training methods, which have reached a plateau in performance and are becoming too...
Test-Time Compute: Die nächste Stufe der KI-Skalierung
Große KI-Labors, darunter OpenAI, verlagern ihren Schwerpunkt weg von der Erstellung immer größerer Sprachmodelle (LLMs). Stattdessen erforschen sie “ Test-Time Compute“, bei dem die Modelle während der Ausführung zusätzliche Verarbeitungszeit erhalten, um bessere Ergebnisse zu erzielen. Diese Änderung ergibt sich aus den Grenzen der herkömmlichen Pre-Training-Methoden, deren Leistung ein Plateau erreicht hat und die zu...
Raw Story v. OpenAI: A Landmark Decision Shaping AI Copyright Law
In a significant ruling that could help define the boundaries of AI training and copyright law, Judge Colleen McMahon of the Southern District of New York has dismissed Raw Story Media and AlterNet Media's copyright infringement lawsuit against OpenAI. This November 2024 decision provides crucial insights into how courts may approach the intersection of AI...
The Future of Programming According to GitHub: When Everyone Becomes a Developer
In GitHub's San Francisco headquarters, Chief Product Officer Mario Rodriguez paints a picture of the future that's both audacious and contentious: a world where a billion people create software, many without writing a single line of code. It's a vision that challenges our fundamental understanding of what it means to be a developer – and...
