A Local Coding Assistant for Private Business Code

A local coding assistant can explain a project, prepare changes and help verify code on business equipment. For an entrepreneur, define the work it supports: application maintenance, shop integration or an internal module. The model is one component of development alongside the repository, tests, review and release approval.

Define the task and project boundaries

Start with small operations: explaining a function, locating a business rule or proposing a bounded correction. Use a working copy and a separate trial branch. Retain a reference state so every change can be compared and reverted.

The acceptance list describes required behaviour and checks. For an illustrative rule accepting quantities from 1 to 20, tests cover the lower boundary, upper boundary, zero and a value above the limit. These are test-design examples rather than results obtained in a customer application.

Connect the editor to a local engine

Continue documents connecting an editor to Ollama. Configuration identifies the local provider and exact model, with the engine running on the selected computer. First verify a direct model request, then the same function from the editor. This separates integration errors from engine installation or weight loading.

Record extension version, model configuration and request destinations. Applications can use separate components for conversation, completion, embeddings and other functions. Check every path used. A local conversation configuration does not automatically establish where every other editor feature processes its data.

Select hardware around the required code and context

A pilot with 64 GB RAM, an SSD and a 16 GB GPU can assess a compact instruct model on small changes. For a larger candidate, such as quantised Qwen3.8-27B, assess a configuration with more available memory, including a 128 GB unified-memory system. Check engine and artefact compatibility before selecting equipment.

Model context includes the requirement, files, messages and tool results. Select relevant code rather than loading the complete repository into every request. Measure explanation, proposal and verification times on representative tasks. Generation speed is one component of time to an accepted change.

Control accessible files and secrets

Prepare a working copy containing required files and excluding credentials, data exports and customer information unnecessary for the trial. Enforce access through permissions and tool configuration rather than relying solely on a model instruction. Check configuration files, logs and artefacts that can contain sensitive values as well.

For code search, indexing and embeddings must follow approved processing paths. Test an exclusion rule with a demonstration file and check whether it can enter context. Continue's offline documentation covers local models and disabling telemetry; separately check the editor, extensions and connected tools.

Request small changes and inspect the difference

A useful request specifies current behaviour, required behaviour and relevant files. Ask for an explanation of the change location before requesting a patch. Inspect the difference for logic, style and dependencies. New modules or packages require justification through the project requirement.

Apply the proposal in a separate workspace and run appropriate checks: lint, existing tests and cases covering the correction. A test reproducing the implementation's mistaken logic does not establish the requirement. Keep known-answer examples and boundary cases so validation checks program behaviour.

Separate terminal access from code generation

A model able to propose commands should not automatically inherit every computer permission. Tools run with project-appropriate rights and a defined working directory. Commands modifying data, starting services or communicating externally require approval rules. Keep production credentials outside the trial environment.

For isolated operation, prepare build and test dependencies too. A local model can function while a package manager attempts downloads. Inventory the editor, tools, packages and services used during verification. This supports testing which parts of development work without external connectivity.

Acceptance follows approved changes

Build a set of small jobs representative of the business technologies. For each, retain the requirement, supplied context, proposal, tests and reviewer observations. Measure working time to an accepted change and corrections required. Compare models using identical requirements and access conditions.

A practical trial in a project copy

Select a small function and describe the requirement independently of the implementation being checked. Prepare inputs and expected outputs, then ask the assistant to locate the function and explain dependencies. The correction should remain a bounded, reviewable difference without automatically rewriting unrelated files.

After applying it, run the agreed tests and separately inspect configuration changes, generated files and installed packages. The reviewer retains the original request to check that the modification solves the intended problem. Repeat after returning to the reference state. The aim is a controllable, reproducible process, recording human interventions and tools used in the pilot. Compare the retained outputs and explain any relevant difference before adopting the assistant for routine maintenance work.

WebHat uses a customised local AI system and provides consultancy on selecting models and integrating them into development. A custom AI application can include project-specific functions and permissions. The guide to AI-assisted programming with verified code complements review and testing, while the local configuration documents processing location and repository access.

Questions about local coding assistants

Can the assistant work with a private repository?

Yes, through an authorised environment and permitted files. Configuration must control context, tools, indexing and logs that may contain code.

Must every request contain the whole project?

Selecting relevant functions and files reduces unnecessary context. Local retrieval can help locate them, with permissions applied before reading.

Does local generation permit direct production releases?

Releases follow project procedures: review, tests, approvals and rollback. The model's execution location does not change those criteria.

What should be checked after changing the model?

The same reference jobs, permissions and editor integration. Retain the previous version and compare accepted changes and time required.