GPT-5 arrived with an enormous amount of expectation attached to it. By the time it launched, OpenAI had already set a precedent with GPT-4, GPT-4o, and the reasoning models that followed. Each release raised the bar, not just technically, but emotionally. As users, we have come to expect clear leaps forward, and GPT-5 seemed to miss the high benchmark mark for the majority of users. From a purely technical standpoint, GPT-5 is undeniably powerful. It improves on reasoning, long-context handling, and multimodal capability. Yet the initial reaction across the AI community was mixed, and in some cases openly critical. That contrast between capability and perception is what makes GPT-5 such an interesting release to reflect on.

Like most people, I spend a lot of time reading, researching, and collecting things from across the web. I am always aware of how easily useful content can disappear or be forgotten over time. Blog posts, documentation, bash scripts, YouTube videos, services I want to explore later, and images which sparked a bout of nostalgia. Over time, I learned that browser bookmarks alone were not enough. I needed a smart, searchable digital scrapbook and that is how I discovered Raindrop.

If there’s one AI tool I reach for every day without fail, it’s Perplexity. It has become my go-to assistant for staying up to date with the latest news and exploring new topics. I wanted to share how I use it, why I keep coming back to it, and a few features I think are genuinely worth exploring. My Two Main Use Cases: News and Learning I use Perplexity primarily in two ways:

In my previous post about Tailscale, I covered what Tailscale is and why it’s become a staple in my homelab toolkit. Today, I want to share a real-world example of just how handy it can be—specifically, how it helped me retrieve personal documents from my homelab while sat on a train, armed with nothing more than my iPhone and a 5G connection. The Situation: Needing Remote Access on the Go We’ve all been there: you need a file, but it’s tucked away on your home network, and you’re nowhere near your desk. For me, this happened while I was on a train, miles away from home. The documents I needed were only accessible from my local network—or so I thought.

I’ve spent some time exploring GPT-4.1, OpenAI’s follow-up to GPT-4 and 4o, and it feels like a more focused refinement rather than a broad shift in how the model interacts. Where GPT-4o leaned heavily into multimodality and new interaction styles, GPT-4.1 feels more about tightening the core experience, particularly around reasoning, instruction-following, and coding tasks. GPT-4.1 was initially released on 14 April 2025 through the OpenAI API and the OpenAI Developer Playground. At that stage, it felt clearly aimed at developers and technical users who wanted early access. A month later, on 14 May 2025, OpenAI made GPT-4.1 available in ChatGPT for Plus and Pro subscribers, bringing those refinements into day-to-day conversational use.