Hi everyone, Is anyone automating their daily tasks, such as workspace setup, code commits, or running scripts/software? I occasionally need to set up my workspace, and while the process is mostly the same, it requires some minor tweaks each time. Automating this would save me 1-2 hours every time I do it.

I am no legal counsel but this came up with one of my consulting clients a while back and i have been trying to make actionable sense of it all . So , i thought i'd share. Anyone creating or selling AI solutions in their operations EU zone should at least be in then know with the upcoming act regulations. Here’s the perplexity low down . The EU AI Act and AI Automations: What SMEs Need to Know As we approach the implementation of the EU AI Act in 2024-2025, SMEs using AI automation capabilities need to understand their compliance obligations and opportunities. Risk Assessment for Automations When building AI automations , your solutions will typically fall into these categories: Low-Risk Applications - Basic workflow automations - Data processing pipelines - Simple chatbot integrations - Email automation and classification Potential High-Risk Areas - HR-related automations for recruitment - Customer credit scoring - Healthcare data processing - Public service applications Compliance Requirements Documentation Needs - Technical documentation in simplified SME format - Risk assessment documentation - Performance metrics and monitoring capabilities - Data governance procedures Practical Steps - Implement transparency measures for AI interactions - Ensure human oversight capabilities - Maintain logs of AI system decisions - Regular monitoring of system performance Cost Considerations The implementation costs for automations under the AI Act are expected to be manageable for SMEs: - Conformity assessment: €3,500-7,500 per high-risk system - Documentation and monitoring: Approximately 17% overhead - Reduced fees for SMEs through proportionate pricing Recommended Actions 1. Use the AI Act Compliance Checker to assess your specific obligations: https://artificialintelligenceact.eu/assessment/eu-ai-act-compliance-checker/ 2. Document your Make.com automation architecture

"Leading AI developers and startups are increasingly deploying agentic AI systems that can plan and execute complex tasks with limited human involvement. However, there is currently no structured framework for documenting the technical components, intended uses, and safety features of agentic systems. To fill this gap, we introduce the AI Agent Index, the first public database to document information about currently deployed agentic AI systems. For each system that meets the criteria for inclusion in the index, we document the system's components (e.g., base model, reasoning implementation, tool use), application domains (e.g., computer use, software engineering), and risk management practices (e.g., evaluation results, guardrails), based on publicly available information and correspondence with developers. We find that while developers generally provide ample information regarding the capabilities and applications of agentic systems, they currently provide limited information regarding safety and risk management practices. The AI Agent Index is available online at the link below. " https://arxiv.org/abs/2502.01635 https://aiagentindex.mit.edu/

Have you explored DeepLearning.ai founded by Andrew Ng? If not, do it now. Andrew is a legend by himself in the field of AI. Deep Learning offers free courses of top-notch quality designed in collaboration with industry experts, covering cutting-edge topics like neural networks, NLP, computer vision, and reinforcement learning. The courses are short but power-packed and completely hands-on. For example, I have enrolled in the following FREE courses: - How Transformer LLMs Work - Serverless LLM Apps Amazon Bedrock - Serverless Agentic Workflows with Amazon Bedrock - LangChain for LLM Application Development - ChatGPT Prompt Engineering for Developers - Building Systems with the ChatGPT API - Finetuning Large Language Models - Pair Programming with a Large Language Model - Build LLM Apps with LangChain.js - Efficiently Serving LLMs - Function-calling and data extraction with LLMs - Pretraining LLMs - LLMs as Operating Systems: Agent Memory - Building an AI-Powered Game - Evaluating AI Agents

Everyone’s talking about AI agents. But the truth? Most demos you see online are just that—demos. Even big players like Apple and Amazon struggle to make their AI features work in the real world due to issues like hallucinations and unreliable outputs. In this week’s video, I break down the differences between simple workflows and true AI agents and share practical strategies for building reliable AI systems, including: - How to use workflow patterns like prompt chaining and routing to solve real problems effectively - Why agent frameworks might not be the solution you think they are - The #1 thing you need to scale AI systems successfully (hint: it’s not a new tool) Learn how to move beyond the hype and build AI systems that actually work.