Overview Running large language models locally is becoming increasingly practical, even for developers without access to massive GPU clusters. In this post, I walk through how to: Run Qwen3.6 35B (A3B, Q4_K_M quantized) locally using Ollama Integrate the model into VS Code Use it as a local coding assistant This setup is especially useful for: Air-gapped environments

Clicking the copy button of plantuml plugins of vscode, one can get png from plantuml files. however png files look blurry when even slightly resized. Is there any better way to avoid that ? SVG is a good option. Plantuml provides the option to export as svg. It might be troublesome to get a svg file by several steps using it. It is also slow. Is there any other better options?

iPhone Enable developer mode: Setting -→ PPrivacy & Security -→ Developer Mode IDE Android Studio It is quite straight forward in Android Studio. Install flutter plugin and restart Android studio VSCode select the Run and Debug icon in the Activity Bar Select "Dart & Flutter" in "RUN AND DEBUG" dropdown list Edit flutter iphone setting Change launch.

Dapr development is quite difficult to setup correctly. You can get the idea from (this issue) as the issue is still open now since it was created. I followed the instruction to install dapr vs code extension in dapr. My environment is WSL based. I tried several times to try to debug the dapr applications and failed. I noticed in the following screenshot that there was a warning icon before kubernetes.

python-guestbook at https://github.com/GoogleCloudPlatform/cloud-code-samples is used to practice debug python containers in kubernetes. Google cloud python code vscode plugin 1.7.0 failed to run python on kubernetes or there is no obvious way to do that as depicted in the below pic. Steps to make it: run skaffold debug Import ptvsd in the python file to debug, add breakout() before the line to debug