Source-linked terminal coding assistant console

For

Software teams and developers who work in the terminal and want one owned assistant for coding, testing and shipping

Solves

Developers rent several terminal AI coding tools, and each one covers only part of the job, so changes, tests, commits and reviews stay split across subscriptions and lose their source links.

Delivers

Reviewer-approved code changes linked to commits and pull requests

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce tool sprawl while keeping every code change traceable to its source.

1. Run an AI coding assistant in the terminal.
2. Accept plain-English task descriptions.
3. Apply AI-generated edits and refactors to source files.
4. Create git commits with meaningful messages.
5. Analyze the repository and open pull requests.
6. Run test suites and builds.
7. Execute shell commands and read their output.
8. Support multiple LLMs, including local options.
9. Keep the source open for inspection and modification.
10. Run parallel agent sessions.
11. Search the codebase with natural-language queries.
12. Fetch current documentation from websites.
13. Detect and apply consistent code formatting.
14. Save, restore and manage chat sessions.
15. Show an embedded AI side panel that reads terminal output and suggests commands.
16. Arrange terminals in a flexible multi-pane grid.
17. Accept dragged files and screenshots as attachments.
18. Accept voice input for assistant commands.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before merge.
21. Export a versioned reviewer-approved code changes linked to commits and pull requests with source references and unresolved questions.

Everything these tools do, in one app

• Terminal-based AI coding Provides an AI assistant directly in the terminal for coding tasks.Found in Cosine CLI, Aider, Coderrr and 7 more
• Natural language commands Allows developers to describe desired changes or tasks in plain English.Found in Aider, Agent Mode in Warp AI, CodeCompanion.AI and 1 more
• Code editing and refactoring Applies AI-generated edits directly to source files, including refactoring.Found in Cosine CLI, Aider, Coderrr and 1 more
• Automated git commits Automatically commits changes with meaningful commit messages.Found in Aider
• Repository analysis and PR creation Analyzes the repository and creates pull requests for end-to-end change delivery.Found in Cosine CLI
• Run tests and builds Executes test suites and builds as part of the coding workflow.Found in Cosine CLI
• Execute shell commands Runs shell commands and reads their output.Found in Cosine CLI, CodeCompanion.AI
• Multiple LLM support Works with various large language models, including local options.Found in Aider, Coderrr, opencode and 1 more
• Open source The tool's source code is available for inspection and modification.Found in Coderrr, opencode, Codebuff and 2 more
• Parallel agent sessions Runs multiple AI agents concurrently to speed up tasks.Found in opencode, 1Code
• Codebase search Searches the entire codebase using natural language queries.Found in CodeCompanion.AI
• Web documentation integration Fetches up-to-date documentation from websites.Found in CodeCompanion.AI
• Automatic code formatting Detects and applies consistent code formatting styles.Found in Codebuff
• Session management Saves, restores, and manages chat sessions for continuity.Found in Open Interpreter
• Embedded AI side panel Provides an AI assistant in a side panel that can read terminal output and suggest commands.Found in Clide
• Multi-pane terminal layout Organizes terminals in a flexible grid layout.Found in Clide
• Drag-and-drop attachments Allows dragging files and screenshots into the chat.Found in Clide
• Voice input Enables voice commands for interacting with the assistant.Found in Clide

What goes in, what comes out

The workflow

1. In
   Start with

   Repository files, terminal output, test results and documentation

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository files

4. 3

   Terminal output

5. 4

   Test results and documentation

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved code changes linked to commits and pull requests

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository language and build setup; final merge and release decisions remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Repository and session setup, Editable change preview, Review and delivery. Use a session list for repositories, a large central diff canvas, and a right-hand panel for terminal output, sources and comments. Let users compare agent sessions side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant file and line. Make the task-specific outcome reviewer-approved code changes linked to commits and pull requests visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, repository access, session versions, reviewer comments, approval states, usage allowances, token limits, command history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Developer-owned repositories, authorized issue trackers and permitted documentation sources. Cloud code storage, git hosting and CI destinations. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: run an AI coding assistant in the terminal; accept plain-English task descriptions. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   10 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will software teams and developers who work in the terminal and want one owned assistant for coding, testing and shipping use it to solve "developers rent several terminal AI coding tools, and each one covers only part of the job, so changes, tests, commits and reviews stay split across subscriptions and lose their source links"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted changes per developer hour and rework after review.
4. Measure, then decide. Track accepted changes per developer hour and rework after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One repository language and build setup; final merge and release decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: run an AI coding assistant in the terminal; accept plain-English task descriptions. Support the remaining modules with operator review: apply AI-generated edits and refactors to source files; run test suites and builds; create git commits with meaningful messages; analyze the repository and open pull requests. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved code changes linked to commits and pull requests. Retain the explicit scope boundary: One repository language and build setup; final merge and release decisions remain with the engineering team.

What the build depends on. Repository upload and preview, asynchronous agent jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository language and build setup; final merge and release decisions remain with the engineering team.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined repository package. Offer a monthly production allowance after repeat demand. Quote complex multi-repository or regulated-environment work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved code changes linked to commits and pull requests. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce tool sprawl while keeping every code change traceable to its source. Demonstrate a concrete reviewer-approved code changes linked to commits and pull requests using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams and developers who work in the terminal professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved code changes linked to commits and pull requests from a small authorized input set, with a transparent calculation of accepted changes per developer hour and rework after review and no promised savings.

The first 30 days

1. Week 1: interview five software teams and developers who work in the terminal and inspect a recent example of rented terminal AI coding tools covering only part of the job.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted changes per developer hour and rework after review, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted changes per developer hour and rework after review. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted changes per developer hour and rework after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved code changes linked to commits and pull requests. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved coding patterns, repository constraints and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for software teams and developers who work in the terminal. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Cosine CLI, Aider, Coderrr, Agent Mode in Warp AI, opencode, CodeCompanion.AI, Codebuff, Open Interpreter, Clide and 1Code. Compare this product with the buyer's present method on accepted changes per developer hour and rework after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, repository processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved code changes linked to commits and pull requests. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, license accuracy and usage permissions. Engineering owners approve substantive changes and release scope. One repository language and build setup; final merge and release decisions remain with the engineering team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.