When I started experimenting with AI integrations, I wanted to create a chat assistant on my website, something that could talk like GPT-4, reason like Claude, and even joke like Grok. But OpenAI, Anthropic, Google, and xAI all require API keys. That
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My interest in running AI models locally started as a side project with part curiosity and part irritation with cloud limits. There’s something satisfying about running everything on your own box. No API quotas, no censorship, no signups. That’s what pulled me
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Like it or not, AI is here to stay. For those who are concerned about data privacy, there are several local AI options available. Tools like Ollama and LM Studio makes things easier. Now those options are for the desktop user and
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The rise of AI-powered coding tools has reshaped developer workflows worldwide. Interactive development environments are becoming more intelligent, adapting to how programmers work. Microsoft is actively evolving VS Code into an AI-first IDE by integrating powerful language models and automation. Meanwhile, Amazon
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It took me way longer than I’d like to admit to wrap my head around MCP servers. At first glance, they sound like just another protocol in the never-ending parade of tech buzzwords decorated alongside AI. But trust me, once you understand
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Large Language Models (LLMs) are powerful, but they have one major limitation: they rely solely on the knowledge they were trained on. This means they lack real-time, domain-specific updates unless retrained, an expensive and impractical process. This is where Retrieval-Augmented Generation (RAG)
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Once upon a time, coding meant sitting down, writing structured logic, and debugging for hours. Fast-forward to today, and we have Vibe Coding, a trend where people let AI generate entire chunks of code based on simple prompts. No syntax, no debugging,
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Ollama has been a game-changer for running large language models (LLMs) locally, and I’ve covered quite a few tutorials on setting it up on different devices, including my Raspberry Pi. But as I kept experimenting, I realized there was still another fantastic
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Ollama is one of the easiest ways for running large language models (LLMs) locally on your own machine. It’s like Docker. You download publicly available models from Hugging Face using its command line interface. Connect Ollama with a graphical interface and you
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Ever since I realized that AI was shaping the future, I’ve been fascinated by its endless possibilities. I’m someone who enjoys testing large language models (LLMs) on my devices, and the open-source approach to data has always been my preference. Why? Because
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