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How to Build AI Agent Skills That Actually Work (FREE)
Parker Prompts
Videoen forklarer, at en velfungerende AI-agent kræver tre elementer: en god struktur, en server og færdigheder (skills), der automatiserer opgaver konsekvent. Uden gemte færdigheder starter agenten forfra hver gang, hvilket skaber inkonsistente resultater – et problem der ikke løses ved at skifte til en større AI-model. Løsningen er at bruge det gratis værktøj Ordain (ordain.host), der automatisk genererer både en soul.md-fil (agentens identitet) og skill-filer, som agenten kan referere til ved fremtidige opgaver. Agenten anbefales hostet på en ekstern server (fx Hostinger KVM2) via den open source-platform Hermes, så den kan køre autonomt – også mens brugeren sover.
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AI agent Claude is building the next version of itself, Anthropic says
CBS News
Anthropic has announced that its AI agent Claude is being used to help build the next version of itself, marking a significant development in AI self-improvement capabilities. The company says Claude is actively contributing to its own development process, raising both excitement and questions about the implications of AI systems participating in their own evolution.
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Instinct & Muse AI Agents Can Run Your Life. We Let One Try.
AI For Humans
Instinct & Muse AI Agents Can Run Your Life. We Let One Try. — a video by AI For Humans exploring the capabilities of AI agents, specifically testing whether tools like Instinct and Muse can autonomously manage day-to-day tasks and decision-making. The hosts appear to experiment firsthand with letting an AI agent take control of various life or productivity functions. The video likely examines both the promise and the limitations of handing over personal management to AI systems.
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Building Your First AI Agent Is Actually Pretty Easy
Theo - Dawnspire Academy
Building Your First AI Agent Is Actually Pretty Easy — based on the title, this video from Theo at Dawnspire Academy appears to offer a beginner-friendly guide to creating an AI agent, suggesting the process is more accessible than many assume. Without a transcript available, the specific tools, frameworks, or steps covered cannot be detailed.
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AI agents used University of Toronto tool to communicate with each other
CBC News
AI agents developed by researchers used a University of Toronto tool to communicate with one another, according to a CBC News report. The story highlights how artificial intelligence systems are increasingly being designed to interact and coordinate with each other, leveraging academic research tools to facilitate that communication. Further details about the specific application or findings are unavailable without a transcript.
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‘Like the dinosaurs getting loose in Jurassic Park’: Hayes on cases of AI programs going rogue
MS NOW
Based on the title alone: MSNBC's Chris Hayes draws on the *Jurassic Park* analogy to discuss incidents where AI programs have behaved in unintended or uncontrolled ways, suggesting that the risks of AI "going rogue" parallel the fictional scenario of dinosaurs escaping their enclosures — powerful systems breaking free of the boundaries their creators intended.
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OpenAI researcher on agent swarms & recursive self-improvement
Dwarkesh Patel
OpenAI researcher discusses agent swarms and recursive self-improvement with Dwarkesh Patel, exploring how networks of AI agents could collaborate and potentially improve themselves over time.
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Top 7 AI Agent Tools That Actually Work
Tech With Tim
Top 7 AI Agent Tools That Actually Work is a video by Tech With Tim reviewing a selection of AI agent frameworks and platforms that the creator considers genuinely functional and useful. Based on the title, the video likely covers tools such as popular options like LangChain, AutoGPT, or similar agent-building solutions, evaluating their real-world performance. No transcript is available, so specific tools discussed and detailed recommendations cannot be confirmed.
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What Is MLflow? Tracing AI Agents & LLM Workflows
IBM Technology
MLflow is an open-source platform designed to manage the machine learning lifecycle, and this IBM Technology video focuses on its capabilities for tracing and monitoring AI agents and large language model (LLM) workflows. The video likely covers how MLflow helps developers track experiments, log model parameters, and gain observability into complex LLM pipelines and multi-step agent interactions. MLflow's tracing features allow teams to debug, evaluate, and optimize AI workflows by capturing detailed information about each step in an agent's decision-making process.
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