Magic.dev

Frontier Code Models & RL

Combine frontier-scale pretraining with domain-specific reinforcement learning to build models that plan, write, review, and test code across large codebases. Instead of pattern-matching snippets, the system reasons over repositories, docs, and tickets to propose changes, draft PRs, and iterate with feedback. The goal is production-grade assistance that improves with use while keeping failure modes observable and recoverable inside normal dev workflows.

Ultra-long Context (toward 100M tokens)

Ultra-long context lets models consider architecture, dependencies, and history at once. Research toward 100M-token windows enables reading monorepos, design docs, and logs without lossy truncation. With more of the system in view, suggestions align with real constraints—interfaces, tests, and performance budgets—reducing brittle edits and back-and-forth. Engineers keep control by reviewing diffs with citations back to source files and commits. Large context reduces prompt juggling.

Safety, Alignment & Guardrails

Safety and alignment research guide training and deployment. Guardrails constrain actions by repo, environment, and cost; evaluations stress-test reasoning under ambiguity; and logs capture inputs, outputs, and rationales for audit. This makes it feasible to pilot in sensitive stacks, measure error profiles, and expand scope confidently—treating the model like a junior engineer with supervision, not an auto-merger. Fallback and rollback paths keep production stable as capabilities grow.

Cloud-scale Training & Inference

Cloud-scale training and inference underpin the models. Partnerships and supercomputer builds enable high-throughput training runs and inference-time compute for harder tasks, including retrieval and tool use. This supports long-context reasoning, stronger code synthesis, and rapid iteration on post-training and RL. For teams, the payoff is steadier accuracy on real repositories and the ability to evaluate new checkpoints without wholesale toolchain rewrites or brittle adapters.

Ecosystem & Roadmap

Each update focuses on context windows, data pipelines, and product surfaces that connect models to day-to-day engineering. Expect improvements in repository understanding, planning, and verification rather than one-off demo tricks. Documentation, examples, and benchmarks aim to make evaluations reproducible. The focus is practical: integrate with editors, CI, and review flows so teams see value safely—building toward an AI engineer that complements humans on real work.