It's a perfect time to pivot to local LLMs and learn real skills
Don't shortcut anything. Get you a GPU (32GB VRAM recommend, multiple cards work too), Mac Mini, or Ryzen AI 395+ PC (what I went with). Budgets vary, but there are options even if you dont want to spend thousands. (Which you probably should if you're heavily using AI)
If you go the GPU or Ryzen AI route, install Linux server. Learn how it works. Set up your workflow exactly how you want to use it. Compile llama.cpp (not ollama, they are different and Ollama is easier but way worse). If it's too daunting you can even use Gemini CLI to set it up for you the first time. You will pick up on useful skills just working through everything with it.
You'll learn how to set up your firewall, understand how LLMs work, understand how Linux works, understand how to do simple stuff like SSH. You can secure knowledge sources like wikipedia or stack overflow through kiwix and use it as a RAG (searchable database for your LLM) to use it when the Internet is not available or when AIs start getting blocked. Your LLM quality will be unphased regardless of what happens, you only need electricity. AI is significantly more impressive when you can pull the Ethernet plug and still have all that intelligence running only just for you. And you will never have a limit again.
You will gain personal skills and guarantee you will have a workflow that works for you even if changes in cloud providers or web searching happens. You can custom build applications for yourself to make the LLM more effective for what you want it to do, or even integrate the LLM into it.
You can still use a low tier cloud model for stuff that you can't do locally yet, but local tends to follow about 8 months behind frontier. Open-Weight models come out almost every week. And when they do you can drop it into your setup and see immediate benefit. Gemini will help you start, that's how I got into it at first.
For most users at this moment, you will want to run Qwen 3.6 27B (Dense model which is slower but smarter, better for GPUs) or Qwen 3.6 35A3B (MoE model which is faster and better for the Ryzen AI 395+ PCs) at Q6 quants or higher (if not coding, q4 is fine). They are you best bang for your buck and will do most, if not all you are currently doing with Gemini. Opencode is one of the open source alternatives to Claude code, Codex, and Gemini CLI. Local LLMs can run agentically just like cloud models.
Big tech will rug pull you, it will only get worse from here and you can go ahead and learn what you need while Gemini is still good enough to guide you through the process.
I started this process a little over a month ago and everything described I already have set up and working. It's been extremely fun and worthwhile.