I've been using LLMs daily for about 2 years (ChatGPT, Claude, Perplexity, Gemini), and my prompts have gotten dramatically better. But here's the thing—it wasn't from learning some fancy framework. It was from changing how I actually *use* them. I'm ranking what actually moved the needle for me: saving good prompt templates in Obsidian (free), using AI-powered prompt optimization tools, and building a personal prompt library organized by task type. When I get a great output, I save that prompt. When I need something similar, I don't start from scratch—I just grab the template that already works. It's like having a cheat sheet that gets better every time you use it. The real unlock isn't understanding prompt engineering theory; it's having a system that lets you reuse what already works.
OpenAI is hosting a livestream event. Details about the specific announcements, product launches, or demonstrations will be revealed during the broadcast.
The last time OpenAI did an unannounced livestream, they dropped GPT-4 Turbo and changed pricing overnight
OpenAI is launching ChatGPT Images 2.0 with major upgrades to image generation capabilities. Watch the livestream announcement at https://openai.com/live/
OpenAI is positioning this as a direct competitor to established image generation tools, suggesting they're confident enough to challenge the current market leaders
Throughout 2025 the standard response to any complaint about an LLM was some version of "just wait 3-6 months, the next generation will handle this effortlessly." The argument was everywhere. Every limitation was temporary, every missing capability was a few iterations away, every autonomous agent demo was a preview of imminent reality. It's April 2026 now and worth checking how that held up. On r/ClaudeAI this week there's a long thread about Opus 4.7 where multiple users argue it's a regress
Someone actually tested Mistral Medium 3.5 on AMD's new Strix Halo chip, and the results are... not great. For a 48k-token prompt with 4k thinking tokens, it took about 2 hours just to get an answer about code architecture. Yeah, you read that right—two hours. The takeaway: if you want to run this locally on Strix Halo, queue it up before bed. The technical setup involved heavy optimization (Q5_K_XL quantization, GPU acceleration with -ngl 999, cache reuse), but even with all that tuning, it's still a crawl. Not exactly the "instant local AI" dream, but hey, at least it works.