I need a coding assistant that runs locally on my laptop and does not send telemetry code snippets to the cloud.

Yes. If your priority is “code and prompts never leave my laptop”, a good setup is:

Recommended setup: Continue + Ollama

• continue.dev — an open-source coding assistant that integrates with VS Code and JetBrains. Its local configuration can run completely offline, and its documentation specifically describes disabling anonymous telemetry.
• ollama.com — runs the AI model locally on your machine. Ollama states that prompts and data processed locally aren't transmitted to it. It also provides a setting to disable its cloud features entirely.
• Pick a local coding model appropriate for your laptop's RAM/VRAM. Continue supports local models and Agent mode, including reading/editing files and running terminal commands.

The privacy-critical configuration is:

IDE → Continue → Ollama → local model

rather than:

IDE → cloud AI API

For stronger assurance, disable Ollama Cloud (OLLAMA_NO_CLOUD=1 or disable_ollama_cloud) and disable Continue's anonymous telemetry. Then you can even run the setup without internet access; Continue documents an air-gapped configuration specifically for this scenario.

Another option: Cline + Ollama

cline.bot can also use Ollama/local models and has an Agent-style workflow that can modify files and execute commands. Its telemetry is enabled by default, however, so you'd need to explicitly disable it.

If you tell me your laptop's OS (Windows/macOS/Linux), RAM, and whether it has an NVIDIA/AMD/Apple GPU, I can recommend a specific local model and give you the exact setup commands.

To run a completely local, zero-telemetry coding assistant on your laptop, you need a three-part stack: a local runtime engine to host the model, a coding model suited for your laptop's hardware specifications, and an editor extension or CLI interface that ties it into your workflow.

Here is the best open-source, privacy-first setup used for local coding:

1. The Engine: Ollama

Ollama is the gold standard for running large language models locally on macOS, Linux, and Windows. It runs entirely offline as a local background daemon, ensuring zero data or code snippets leave your machine.

• Install it from the Ollama Download Page.
• Pull a dedicated coding model via your terminal (e.g., ollama pull qwen2.5-coder or deepseek-coder).

2. The Models (Choose based on your RAM/VRAM)

Depending on your laptop's memory configuration, pick a lightweight or mid-sized model that excels at syntax and logic:

• For 8GB–16GB RAM Laptops: Use qwen2.5-coder:7b or deepseek-coder:6.7b . They strike a great balance between speed and accuracy without choking your machine.
• For 32GB+ RAM or Apple Silicon (M1/M2/M3/M4) Macs: You can comfortably run larger variants like qwen2.5-coder:14b or specialized agentic models for deeper reasoning.

3. The Interface (Editor Extension or CLI)

To integrate your local Ollama instance directly into your development environment without cloud leakage:

• Continue (VS Code & JetBrains): Continue is an open-source autopilot extension. You can configure it locally to point exclusively to your running Ollama instance (http://localhost:11434 ), giving you inline code generation, chat, and tab-autocomplete entirely offline.
• Aider (Terminal-based): Aider is an AI pair programming CLI tool that lets you edit files in your git repository using local models via Ollama. It provides clean reviewable diffs directly in your terminal.
• Ollama VS Code Extension: The official Ollama VS Code Integration provides a straightforward chat interface inside your sidebar powered strictly by your local pulls.

To help tailor this, let me know:

• What operating system and hardware specs (RAM / GPU / Apple Silicon chip) your laptop has?
• Which code editor or IDE you prefer to use?

Yes. If your requirement is “the model and code stay on my laptop, with no code-snippet telemetry going to a vendor”, I’d recommend this stack:

Best overall: Continue + Ollama

• continue.dev — VS Code/JetBrains coding assistant with chat, autocomplete, codebase context, and agent mode.
• ollama.com — runs the LLM locally on your machine.
• A local coding model such as Qwen Coder, DeepSeek Coder, or another model appropriate for your laptop's RAM/VRAM.

Continue explicitly supports running without internet, including disabling its anonymous telemetry and configuring a local model. Continue Docs GitHub Ollama says that when running locally, it doesn't collect or transmit your prompts, responses, or other content processed locally. It can also be put into a local-only mode with cloud features disabled.

The resulting architecture is essentially:

Your code
   │
   ▼
VS Code + Continue
   │
   ▼
Ollama (localhost)
   │
   ▼
Local coding LLM

Nothing in that path requires your source code to go to an AI cloud service.

Another good choice: Aider + Ollama

aider.chat is particularly good if you prefer a terminal/Git-oriented workflow. Aider officially supports connecting to Ollama's local API.

I'd choose:

One important privacy detail

Don't just install a “local AI” extension and assume you're completely offline. There are three separate things to verify:

1. The LLM inference is local.
2. Telemetry is disabled.
3. Cloud/web/MCP features aren't accidentally sending code elsewhere.

Continue's documentation specifically provides an offline setup and a telemetry switch. Continue Docs Ollama can additionally disable its cloud functionality entirely.

If you tell me your laptop OS (Windows/macOS/Linux), RAM, and whether you have an NVIDIA/AMD/Apple GPU, I can recommend a specific local model and give you a step-by-step completely offline setup.

aider.chat is particularly good if you prefer a terminal/Git-oriented workflow. Aider officially supports connecting to Ollama's local API.

I'd choose:

One important privacy detail

To run a fully private, local AI coding assistant on your laptop with zero telemetry or code snippets sent to the cloud , the gold-standard open-source stack is Ollama + Continue + VS Code (or JetBrains).

Nothing leaves your machine, everything runs offline, and it costs $0.

The Recommended Local Stack

1. • Ollama: The local engine that downloads and runs open-weight LLMs efficiently on your laptop's CPU/GPU (supports Mac, Linux, and Windows).
2. • Continue: The leading open-source autopilot/chat extension for VS Code and JetBrains that hooks directly into your local Ollama instance.
3. • Code-Optimized Model: Models like Qwen2.5-Coder Go to product viewer dialog for this item. (available in sizes from 1.5B up to 32B) or DeepSeek-Coder-V2 Go to product viewer dialog for this item. provide exceptional code generation and inline completion locally.

Step-by-Step Setup Guide

1. Install and Run Ollama
   • Download and install it from the Ollama Website.
   • Pull a top-tier local coding model by running this command in your terminal:bash``` ollama run qwen2.5-coder:7b

Use code with caution.*(Note: Use `1.5b` if you have a lower-end laptop/limited RAM, or `14b`/`32b` if you have a powerful machine with dedicated VRAM/unified memory).* [](https://docs.continue.dev/customize/deep-dives/autocomplete) [[1]](https://docs.continue.dev/customize/deep-dives/autocomplete)
2. **Install the Continue Extension** 
	- Open your IDE (VS Code or JetBrains) and install the Continue Extension from the marketplace.[](https://www.sitepoint.com/local-ai-coding-assistant-vscode-ollama-continue/) [[1]](https://www.sitepoint.com/local-ai-coding-assistant-vscode-ollama-continue/)
3. **Configure Continue to Point Locally** 
	- Click the Continue icon in your sidebar and open its configuration file (`config.json`).
	- Set up your models to point to your local Ollama endpoint. A basic configuration looks like this:json```
{
  "models": [
    {
      "title": "Qwen2.5 Coder 7B",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen2.5 Coder 1.5B",
    "provider": "ollama",
    "model": "qwen2.5-coder:1.5b"
  }
}

Use code with caution. [1] 4. Verify Offline Security - Disconnect your Wi-Fi briefly to test. You will have full inline code completions (Tab) and chat sidebar capabilities completely offline, guaranteeing absolute zero data leakage.

If you'd like, let me know:

• Your laptop's hardware specs (RAM size, Apple Silicon vs. Intel/AMD, or dedicated GPU)
• Whether you prefer VS Code or another IDE

I can help you pick the ideal model size (1.5B, 7B, or larger) for the best balance of speed and accuracy on your specific machine.

Yes. If your requirement is “the model and code stay on my laptop, with no telemetry/code snippets sent to a vendor cloud,” I’d shortlist these:

• Continue + Ollama — my first choice for most people. Continue is an IDE extension for VS Code/JetBrains, while Ollama runs the LLM locally. You can configure Continue to talk only to Ollama on localhost, giving you autocomplete, chat, codebase context, and editing without a cloud model.
• Cline + Ollama — best if you want an autonomous coding agent. Cline can inspect files, edit multiple files, run tests/commands, and work through a task. Point it at a local Ollama model rather than a cloud provider.
• Tabby — strongest privacy-focused alternative. Tabby is open-source and self-hostable; its current privacy policy explicitly says it has no telemetry and does not transmit keystrokes/code to Tabby. You can run the model locally as well.
• Aider + Ollama — excellent if you prefer the terminal. It's a Git-oriented coding assistant rather than an IDE plugin, and supports local models.

What I'd install

For a typical laptop:

VS Code → Continue → Ollama → local coding model

The important part is that every model/context provider is configured as local. Merely choosing an application advertised as “privacy-friendly” isn't enough if its model endpoint is still a cloud API.

If you tell me your laptop OS (Windows/macOS/Linux), RAM, and whether it has an NVIDIA/Apple GPU, I can recommend a specific local model and give you a setup that is designed to keep the network boundary completely local.

To run an AI coding assistant entirely on your laptop with zero telemetry or cloud data leaks, the gold standard open-source stack combines Ollama (to host the model locally) with Continue or the Ollama VS Code extension (to integrate it into your editor).

The setup process, recommended local models, and system requirements for a completely air-gapped, private coding environment are outlined below.

The 3-Step Local Coding Stack

1. The Engine: Ollama
   • Download and run Ollama , which runs natively in your background/menu bar on macOS, Linux, or Windows. It handles model loading locally via CPU or GPU without making any external network requests.
   • Pull a top-tier local coding model via your terminal:bash``` ollama run qwen2.5-coder

Use code with caution.*(Other exceptional local options include `deepseek-coder` or `codellama` depending on your RAM/VRAM constraints).* 
2. **The Editor Extension: Continue** 
	- Install the [Continue Extension](https://continue.dev/) inside VS Code or JetBrains. Continue is an open-source autopilot/chat interface designed specifically to hook into local backends like Ollama.
	- Alternatively, you can use the official Ollama VS Code Extension for a streamlined, one-click connection.[](https://www.youtube.com/watch?v=oi668OuFbag&t=37) [[1]](https://www.youtube.com/watch?v=oi668OuFbag&t=37)
3. **The Configuration (`config.json`)** 
	- Point Continue to your local Ollama instance. In your Continue config, set your model provider to `ollama` and select the model you pulled. Because it targets `http://localhost:11434` , your code snippets never leave your machine's loopback interface.

### Recommended Open-Source Local Models

| Model Size | Best For | Hardware Requirement |
|---|---|---|
| **Qwen 2.5-Coder (7B)** | Fast autocompletion, great general logic, fits on most laptops. | 8GB+ RAM / Apple Silicon |
| **Qwen 2.5-Coder (14B or 32B)** | Complex refactoring, superior multi-file understanding. | 16GB–32GB+ RAM / Dedicated GPU |
| **DeepSeek-Coder-V2 (16B)** | Exceptional multi-language support and deep debugging. | 16GB+ RAM / Apple Silicon |

If you want to proceed with setting this up, tell me:

- What **operating system and hardware** (Mac Apple Silicon, Windows/Linux with NVIDIA GPU, etc.) you are using
- Which **code editor** (VS Code, JetBrains, etc.) is your daily driver

I can give you the exact configuration snippet to get your local environment running seamlessly.

The best way to run a 100% private coding assistant locally on your laptop without sending data or telemetry to the cloud is to combine Ollama (for local model inference) with the Continue extension inside VS Code or JetBrains.

Watch this tutorial to see a complete step-by-step walkthrough of setting up a local AI coding environment with Ollama and VS Code:

7:51 Your AI-Powered Coding IDE in 15 Minutes — No Cloud Needed!8 months ago YouTube · Asim Munawar

Recommended Local Tech Stack

• Ollama: Runs open-source coding models directly on your CPU or GPU locally.
• Continue.dev: An open-source autopilot and chat extension for code editors that hooks straight into your local Ollama instance.
• Editor: VS Code or JetBrains as your workspace.
• Recommended Model: Qwen2.5-Coder Go to product viewer dialog for this item. (7B or 14B variant depending on your RAM/VRAM) or DeepSeek-Coder-V2 Go to product viewer dialog for this item. for high-accuracy local code completion and chat.

Setup Steps

1. Install Ollama: Download and run Ollama from the Ollama Downloads Page.
2. Pull a Coding Model: Open your terminal and download a local coding model by running:bash``` ollama run qwen2.5-coder:7b

Use code with caution.[](https://www.youtube.com/watch?v=ypaNNpi61Bw&t=260) [[1]](https://www.youtube.com/watch?v=ypaNNpi61Bw&t=260)
3. **Install Continue Extension:** Go to your VS Code extension marketplace and install the **Continue** extension.[](https://www.youtube.com/watch?v=ypaNNpi61Bw&t=260) [[1]](https://www.youtube.com/watch?v=ypaNNpi61Bw&t=260)
4. **Configure Continue for Local Use:** Open the Continue settings JSON file inside your editor and configure it to point to your local Ollama provider rather than an API endpoint:json```
{
  "models": [
    {
      "title": "Qwen2.5 Coder 7B",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen2.5 Coder 1.5B",
    "provider": "ollama",
    "model": "qwen2.5-coder:1.5b"
  }
}

Use code with caution. 5. Disable Telemetry: Both Ollama and Continue operate entirely offline once downloaded. To ensure absolute zero telemetry, check your VS Code user settings and set "telemetry.telemetryLevel": "off".

If you want, let me know:

• How much RAM or VRAM your laptop has
• Whether you prefer VS Code or another IDE

I can help you select the optimal model size (e.g., 1.5B, 7B, or 14B+) for your specific hardware specifications.

If you want a coding assistant that runs entirely on your laptop and does not upload code snippets or telemetry, look for a setup where:

• The LLM runs locally (not just the editor UI)
• The editor/plugin talks only to localhost
• Telemetry, analytics, and crash reporting are disabled or removed
• You can verify outbound network traffic

Good options:

1. VS Code + local model (most flexible)

A common privacy-first stack:

• ollama.com — runs coding models locally
• A VS Code extension that connects to Ollama on your machine
• A local coding model such as Qwen Coder, DeepSeek Coder, CodeGemma, or StarCoder variants

The architecture looks like:

VS Code
  |
  | localhost only
  |
Ollama
  |
  |
Local coding model

Your source files stay on disk unless you explicitly configure a remote provider.

Examples of local VS Code assistants include:

• Continue — popular open-source option that can connect to local models.
• marketplace.visualstudio.com — a VS Code extension designed around local inference through Ollama, LM Studio, or vLLM.

2. A dedicated local AI editor

If you want a more integrated experience:

• Onyx Code — a VS Code-based editor with local inference and an air-gap mode designed to prevent outbound connections.
• Agent Aleph — a local coding agent that runs models on your hardware and advertises no cloud dependency or telemetry.

3. Terminal-based coding agents

If you prefer command-line workflows:

• LocalCode — connects to local models through Ollama or LM Studio and is designed for offline use.
• Billy — another Ollama-based local terminal assistant.

Hardware guidance

Approximate requirements:

• 8 GB RAM: small models (3B–7B) can work, slower
• 16 GB RAM: good for 7B–14B coding models
• 32+ GB RAM / Apple Silicon / GPU: comfortable for larger models (20B–70B quantized)

For a typical laptop, I’d start with:

• Model: Qwen Coder 7B/14B or similar
• Runtime: Ollama
• Editor: VS Code + Continue
• Disable all extension telemetry and verify the app has no external API keys configured

How to verify it is actually private

After installation:

1. Disconnect from the internet and test that completion/chat still works.
2. Check that the model endpoint is localhost only.
3. Monitor connections with tools such as: - macOS: Little Snitch, LuLu

• Windows: GlassWire, Windows Firewall logs
• Linux: nethogs, ss, tcpdump

4. Avoid features labeled: - “cloud model”

• “remote inference”
• “bring your own API key” (unless you intentionally use one)

If you tell me your laptop specs (OS, RAM, CPU/GPU, and whether it has Apple Silicon or NVIDIA), I can suggest a model and setup that will run well locally.

If your requirement is “the coding assistant must run on my laptop and must not send code snippets/telemetry to a cloud service”, look for a setup where:

• The LLM runs locally (not just the UI).
• The editor plugin connects to localhost (or your own LAN server).
• Cloud features, analytics, and remote inference are disabled.
• You can verify network behavior (firewall rules, logs, source code where available).

Good options:

1. VS Code + Continue + Ollama (best general-purpose choice)

ollama.com runs models locally, and continue.dev integrates them into VS Code/JetBrains for chat, edits, and autocomplete. Ollama states that local runs do not send prompts or data to its servers.

Typical stack:

• Local model runtime: Ollama
• Editor integration: Continue extension
• Coding models: - Qwen2.5-Coder
• DeepSeek-Coder
• Code Llama
• StarCoder variants

Example flow:

VS Code
   |
Continue extension
   |
localhost:11434
   |
Ollama
   |
Local coding model

Nothing needs to leave your machine after the model download.

Privacy hardening:

• Disable Ollama cloud features: - Set OLLAMA_NO_CLOUD=1, or configure disable_ollama_cloud in Ollama’s server config.
• Block outbound connections from the assistant processes if you want an extra guarantee.

2. Aider + Ollama (best for multi-file coding tasks)

aider.chat is a terminal-based AI pair programmer. It is useful when you want an agent that can modify several files, work with Git, and review diffs before changes are applied.

A common private setup:

Aider
  |
localhost API
  |
Ollama
  |
Local model

Good for:

• Refactoring projects
• Writing tests
• Debugging across files
• Code review workflows

3. Tabby (best if you want a self-hosted Copilot-style autocomplete)

tabbyml.com is designed around self-hosted code completion. Its privacy documentation states that it does not collect telemetry or transmit personal data through the app.

Good fit:

• You mainly want inline completions.
• You want a server-style architecture you control.

Hardware guidance

Approximate requirements:

• 8 GB RAM: small models, slower CPU inference
• 16–32 GB RAM: comfortable for 7B–14B coding models
• 32 GB+ RAM / GPU: better for larger models

For a typical laptop:

• Apple Silicon Macs: very good local performance
• NVIDIA laptop GPUs: good if VRAM is sufficient
• CPU-only Windows/Linux laptops: usable with smaller quantized models

My recommended privacy-first setup

For most developers:

1. Install Ollama.
2. Download a coding model such as a Qwen or DeepSeek coder model.
3. Install Continue in your IDE.
4. Disable cloud features.
5. Optionally add firewall rules to prevent unexpected outbound traffic.

This gives you a Copilot-like workflow while keeping source code on your machine.

If you tell me your laptop specs (OS, RAM, CPU/GPU, and IDE), I can suggest a specific model and setup.

To run a completely private, local AI coding assistant on your laptop with zero telemetry or cloud data leakage , the gold-standard open-source stack pairs an inference engine, an IDE extension, and an open-weights coding model.

The industry-standard setup consists of these core components:

• Ollama – The engine that runs large language models locally on your machine (supports macOS, Linux, and Windows).
• Continue – An open-source autopilot/chat extension for VS Code or JetBrains that hooks directly into local models.
• DeepSeek-Coder-V2 or Qwen2.5-Coder – Specialized open-weights code models that outperform many older commercial models while running entirely offline.

Step-by-Step Setup Guide

1. Install Ollama
   • Download and install it from the Ollama Website.
   • Open your terminal and pull a powerful local coding model. For a balanced laptop (e.g., 16GB–32GB RAM), run:
     ollama run qwen2.5-coder:7b
     (Or use the 14B/32B variants if you have higher specs).
2. Install the Continue Extension
   • Open VS Code or your JetBrains IDE, go to the Extensions marketplace, and install Continue.
3. Configure Continue for Local Use
   • Click the Continue icon in your sidebar, open its configuration file (config.json ), and set Ollama as your provider. It will look similar to this:json``` { "models": [ { "title": "Qwen2.5 Coder 7B", "provider": "ollama", "model": "qwen2.5-coder:7b" } ], "tabAutocompleteModel": { "title": "Qwen2.5 Coder 1.5B", "provider": "ollama", "model": "qwen2.5-coder:1.5b" } }

Use code with caution.
	- *Tip:* Using a smaller model like `qwen2.5-coder:1.5b` dedicated strictly for tab-autocomplete keeps inline completions snappy and fast.[](https://www.youtube.com/watch?v=T_arXqLvOKs&t=41) [[1]](https://www.youtube.com/watch?v=T_arXqLvOKs&t=41)[[2]](https://www.youtube.com/watch?v=SbG9cBooRR8)[[3]](https://www.youtube.com/watch?v=z605KVofJAY&t=644)

Alternative Local Tools

- **Aider** : A command-line tool that lets you pair-program with local LLMs via Git integration. It edits local files directly in your repository. Check out the project details on the Aider GitHub.
- **Tabby** : A self-hosted, open-source AI coding assistant that can run completely air-gapped on your hardware. Learn more at the Tabby Website.

To help tailor this setup for your specific laptop:

- What are your laptop's **RAM/VRAM** and **GPU specifications**?
- Which **IDE** (VS Code, IntelliJ, Neovim) do you primarily use?