Code generation tool

How this question breaks down

1. 1ContextCore20 minPick the code the model should see, from the current file, open tabs and imported symbols. Score each candidate snippet and pack the best into a 2,048-token prompt, all on the developer's laptop in about 15 milliseconds.
2. 2ModelCore20 minTrain a code model to fill the gap between the code before and after the cursor. Cut each training file at random characters, move the middle to the end, and add the same kind of repository context it will see live. Pick the largest model that still answers inside the latency budget, and teach it when to stop.
3. 3LatencyCore24 minGet a suggestion on screen before the developer types the next character. Debounce keystrokes, cancel or reuse stale requests, and keep each session's prompt prefix cached on one GPU. Size the fleet for prefill, because prompt tokens outnumber output tokens about 90 to 1.
4. 4EvaluationCore19 minGrade code suggestions twice. Offline, run them against tests on tasks the model cannot have memorised and score pass@k; online, count the code developers keep, and pair every rate with a volume so a model cannot win by suggesting less.

Work through the parts in order and you’ve covered the full design. Each part is one topic.