“2023 was the warm-up. 2024 is when the floor dropped out and the ceiling rose too fast to see.”
— Wing

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🧠 They Got Good. Fast.

By mid-2024, we’re no longer pretending this is experimental.

• LLMs now write, revise, plan, empathize, perform.
• GenAI tools can render a cinematic shot from a prompt—or animate an entire music video.
• Multimodal models understand, summarize, and invent across text, image, video, sound, and code.
• Real-time inference is happening on consumer-grade GPUs.
• Local, open-source AI has caught up far enough to threaten the cloud.

And it’s all starting to feel… inevitable.

“Back in my day, we overclocked our Pentiums to run Quake faster. Now we’re doing it for language models.”
— Wing, mid-rant, 2022

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In the year 2022, a new frontier opened up for hobbyists and developers alike: Large Language Models (LLMs) stopped being pure research tools and started becoming part of the daily build cycle for curious engineers and indie coders. But with that shift came a new dilemma—do you run your AI workloads locally, or pipe them out to the cloud?