Local coding assistants rebound
A how‑to piece described setting up a locally run coding‑assistant inside an editor as a response to subscription costs and data‑control concerns, arguing local stacks can reduce cloud reliance. (makeuseof.com) The article frames local setups as practical alternatives for developers who want on‑device tooling without sending all code to external services. (makeuseof.com)
Developers are rebuilding coding assistants on their own machines, using editor plug-ins and local model runners instead of paying for another cloud subscription. (makeuseof.com) The basic setup is simple: a model runner such as Ollama downloads and serves a language model on a laptop or desktop, and an editor extension such as Continue connects that model to chat, autocomplete, and code edits inside Visual Studio Code. Continue says the stack works on Windows, macOS, and Linux, with at least 8 gigabytes of memory and 10 gigabytes of free storage, and Ollama documents a direct Visual Studio Code integration as well. (docs.continue.dev) (docs.ollama.com) MakeUseOf’s April 13, 2026 walkthrough used three parts — Ollama, Continue.dev, and Alibaba’s Qwen2.5-Coder — and said the result ran offline inside Visual Studio Code after setup. The piece highlighted inline autocomplete, a docked chat panel, code explanation, refactoring, and test generation without sending code to a remote service. (makeuseof.com) A local model is the artificial intelligence engine stored on your computer, like keeping a reference book on your desk instead of calling a service across the internet. Ollama said in 2024 that Continue could run entirely on a laptop or be pointed at a remote server, and it recommended different models depending on whether a user wanted chat, autocomplete, or both. (ollama.com) The appeal is cost control and data control. MakeUseOf framed the shift as a response to subscription fatigue and concern that professional or sensitive code is otherwise sent to servers a developer does not manage. (makeuseof.com) The trade-off is hardware. Continue’s current Ollama guide says 8 gigabytes of memory is the minimum and 16 gigabytes or more is recommended, while larger coding models can demand far more video memory than a thin laptop has available. (docs.continue.dev) (ollama.com) Model choice now shapes the experience more than the editor does. MakeUseOf pointed to Qwen2.5-Coder, saying the 7 billion parameter version could run on 8 gigabytes of video memory, while Continue’s documentation lists smaller 1.5 billion parameter variants for autocomplete and larger models for chat or editing. (makeuseof.com) (docs.continue.dev) The rebound in local tools is also colliding with a market that is still heavily cloud-based. OpenAI’s Codex command-line tool runs locally on a user’s machine, but it signs users into ChatGPT or an application programming interface account and offers cloud tasks alongside local code changes. (developers.openai.com) That leaves developers with a clearer split than a year ago: fully local stacks for privacy and fixed costs, or hybrid tools that run on-device but stay tied to hosted models and accounts. The new how-to guides are less about experimentation now and more about making local assistance feel routine inside the editor people already use. (makeuseof.com) (docs.continue.dev)
