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I tried a few free AI tools to prototype small local models. Some outputs were useful, others sounded like a motivational coach I didn’t ask for.Has anyone else tested these? Did it actually help, or just add extra comedy to your workflow?
Salesforce is evolving rapidly, and Agentforce skills are becoming essential for every admin. With AI-driven automation and smarter workflows, Salesforce Admins who master Agentforce can streamline processes, improve user experience, and boost business productivity.Through Salesforce agentforce training, professionals can learn how to leverage Agentforce effectively, making them more valuable in today’s competitive job market. These skills not only enhance problem-solving but also prepare admins for the future of AI-powered CRM. Investing in Salesforce admin training ensures that admins stay ahead, drive innovation, and meet the growing demands of modern organizations. Lets share your views to make it helpful
Image generated using Gemini CanvaThe Hidden Challenge of Connecting AI to Your ServicesPicture this: You’ve built a fantastic API that powers your application. It’s well-documented, follows REST principles, and serves thousands of requests daily. Now, with AI agents becoming mainstream, you’re excited to make it available to LLMs through the Model Context Protocol (MCP). There’s even a tool that can auto-convert into an MCP server. What could go wrong?As it turns out, quite a lot.The rush to make existing APIs available to AI agents reveals a fundamental mismatch: APIs designed for traditional software weren’t built with AI’s unique constraints and capabilities in mind. Let’s explore why simply converting your API into an MCP tool might be setting your AI integration up for failure.Understanding the Players: Agents, Tools, and MCPBefore diving into the problems, let’s establish our terminology. In the AI ecosystem:Agents are LLMs that can take actions beyond just generating text Tool
Image credit — GPT-5: What’s New in OpenAI’s Latest ChatGPT Model? | Built InThe buzzword in the AI for this week is GPT — 5, while there are mixed reviews about the model itself, the conversation is shifting from sheer performance benchmarks to critical questions of reliability and trust. For anyone who uses large language models (LLMs) regularly, their flaws — confident hallucinations, sycophantic agreement, and unhelpful refusals — are all too familiar.OpenAI’s flagship model isn’t just about getting bigger; it’s about getting smarter, safer, and more honest. Based on recent explainers, GPT-5 is being engineered with five targeted improvements designed to address these persistent issues. Here’s a practical, theory-grounded tour of what those changes are and why they represent a crucial step toward AI we can actually trust.Understanding the Core FlawsBefore diving into the solutions, it’s important to understand the problems. Here’s a quick breakdown of the key issues GPT-5 is desig
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