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Solver is an autonomous coding agent that transforms natural language prompts into complete software solutions within Git repositories. It handles tasks like bug fixes, feature implementation, and testing without human supervision, using proprietary models and repository-based reasoning. Built by the creators of Siri and Viv, Solver enables teams to scale development output while focusing on more strategic work.
This Isn’t Just Autocomplete—It’s Autonomous Coding
Solver operates as a Level 4 autonomous coding agent. It is designed to take complete ownership of software tasks, executing them with minimal human supervision. While most current AI coding assistants function at Level 1 or 2—offering suggestions or completing lines of code based on patterns—Solver independently processes full tasks. These include writing new features, generating tests, fixing bugs, and applying security patches.
This capability is made possible through Solver’s elastic engineering API, which allows it to adapt to the scope and complexity of any assignment. It integrates directly with Git repositories, enabling contextual awareness of the codebase and its dependencies. Solver uses a proprietary model family known as TOTAL-HUNK, trained on both internal and external software datasets. Its repository-based reasoning gives it the ability to interact with the structure of actual projects rather than operating in isolated code snippets.
Solver handles intricate tasks such as replicating real-world production crashes by analyzing logs and observability data, improving test coverage using code analysis tools, and reading external documentation to close knowledge gaps when required. It functions without pause, refining its output as it works.
Talk to Your IDE Like You Talk to a Teammate
Solver interprets natural language input as engineering intent. A developer, product manager, or any team member can describe a task in plain terms, and Solver responds by creating the relevant implementation. This may include writing logic, generating test cases, and preparing code in a pull request or a standalone branch.
Users describe this interaction as “vibe coding”—a hands-free approach where communication mimics the style of assigning tasks to a human colleague. Tasks suitable for this model include:
- Implementing API endpoints
- Creating authentication layers
- Fixing known bugs using logs
- Building initial project structures
- Automating common coding patterns
Solver’s capacity to execute end-to-end workflows makes it distinct from tools that offer only inline suggestions or completions. It doesn’t assist developers line-by-line; it replaces the need for manual labor in well-scoped scenarios, allowing teams to focus on more strategic goals.
How Solver Works While You Walk Away
Once a task is assigned, Solver begins working independently. It executes multiple steps in the background, such as identifying relevant parts of the codebase, referencing internal and external sources, and generating implementations ready for review. The result is delivered as a pull request or a new branch.
Solver’s autonomous behavior includes reading documentation when unfamiliar concepts are encountered, iterating over drafts until stability is achieved, and cross-referencing changes to ensure integration with the existing system. The platform continues refining its output even in the absence of active prompts.
The self-driven design makes Solver suitable for engineering teams overwhelmed with routine development work. It allows contributors to delegate repeatable or complex jobs without supervising each stage of implementation.
Built for the Stack You Already Use
Solver is compatible with widely used programming languages. It performs particularly well in environments using Python, JavaScript, TypeScript, Go, and Rust. Support is also extended to various other languages through its repository-based reasoning engine.
The platform functions as a cloud-based API that connects with developers’ existing IDEs. No new systems or learning paths are needed, allowing teams to integrate it into their workflows immediately.
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The Minds Behind the Machine
Solver was developed by a team with a history in building and scaling widely adopted AI products.
- Mark Gabel: Co-founder with a Ph.D. in Computer Science from UC Davis
- Daniel Lord: Co-founder with a B.S. in Computer Science from Cal Poly, San Luis Obispo
- Dag Kittlaus: Chairman, known for co-founding Siri (acquired by Apple) and Viv (acquired by Samsung)
The leadership team previously built voice assistant platforms used by over 2 billion users per month, achieving a combined 95% accuracy rate and handling more than 35 billion monthly queries. Their experience in high-scale AI deployment shaped the development philosophy behind Solver.
Is Solver Right for Your Team?
Solver is designed for developers who want to accelerate workflows by outsourcing technical implementation. It is especially effective for:
- Cleaning up error-prone code
- Bootstrapping new projects
- Automating repetitive patterns
- Creating MVPs and internal tools
- Enhancing test coverage and reliability
The tool is already being used not just by engineers but also by product managers who benefit from simplified interfaces to software development. Early community engagement, such as the Product Hunt rollout, has influenced the roadmap for non-technical user support.
Solver enables teams to focus on higher-level problems by handling detailed development work. Its strength lies in autonomous task completion across varied technical scopes, anchored directly in Git environments.
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