So, following a blog post[1] from @webology and some guides about #VSCode plugins online, I set up #ollama with a number of models and connected that to the Continue plugin.
My goal: see if local-laptop #llm code assistants are viable.
My results: staggeringly underwhelming, mostly in terms of speed. I tried gemma3, qwen2.5, and deepseek-r1; none of them performed fast enough to be a true help for coding.
[1] https://micro.webology.dev/2024/07/24/ollama-llama-red-pajama/
micro.webology.dev · 🦙 Ollama Llama 3.1 Red Pajama For a few weeks, I told friends I was excited to see if the new Llama 3.1 release was as good as it was being hyped.
Yesterday, Llama 3.1 was released, and I was impressed that the Ollama project published a release to Homebrew and had the models ready to use.
➜ brew install ollama ➜ ollama serve # (optionally) I run Ollama as a background service ➜ brew services start ollama # This takes a while (defaults to the llama3.
During our scans we found ~70K applications exposing their VSCode SFTP config.
No 
