The code assistant that re-trains itself overnight.
Karl runs on your machine. It learns from the code you write, fine-tunes a candidate on a schedule, and swaps in the new model the moment it beats the old one. No cloud round-trip. No telemetry. No per-seat licence.
Why now
2026 proved AI can really code
A year ago, “train your own coding model” was a science project. This spring, frontier models showed the category is real — and that a smaller model tuned on your codebase can punch far above its size on the code your team actually writes.
Per-seat pricing just jumped
The cloud assistants raised their per-seat prices, and whole engineering orgs are openly looking for an alternative they own outright. Karl is that alternative: a one-time licence, runs on hardware you already have, no per-seat meter.
Nobody owns “your own model”
The few who tried self-hosted fine-tuning pivoted to cloud agents and walked away from it — just before the price shift made owning your model matter. Karl walks in through the door they left open, with the one thing they never shipped: proof the fine-tune improved on your code.
How it works
Your code stays on your machine
A single Windows binary plus a VS Code extension. The model runs against a local Ollama backend. No outbound traffic, no analytics. Works offline once the model is pulled.
Gets sharper on your repo
Karl mines fill-in-the-middle training pairs from your commits and from the suggestions you accept. A LoRA fine-tune runs on a schedule. The candidate only goes live if it beats the incumbent on a held-out benchmark. On a team, one GPU box can train and the rest pull the promoted model automatically.
Captures how your team reasons
Karl indexes the full path your team takes to solve a problem — not just the final commit. That powers duplicate-work detection across teams, “already decided” guardrails, and search that finds the person who once worked through the same reasoning.
By the numbers
Base model qwen2.5-coder:1.5b-base
scored 0.406. Karl’s candidate scored
0.438 after one cycle. First-line exact-match jumped from
0.352 to 0.408 (+5.6 pp). Promotion rule: the candidate
has to beat the paired baseline on the same seed and the
same holes by at least a full point, otherwise it’s
discarded.
Every cycle writes a JSON evidence artefact to disk and renders it in Karl’s built-in proof viewer. The free trial ships a real recorded artefact from one of these cycles — open the proof viewer and inspect exactly how the promotion decision was made. The full version runs the cycle against your own repo and writes a fresh artefact every night.
Quick start
Three commands. Five minutes.
# 1. Download and unzip Expand-Archive .\karl-demo-0.1.0-win-x64.zip -DestinationPath .\karl # 2. Run the demo (auto-installs Ollama if needed) cd karl .\run-demo.bat # 3. Install the VS Code extension code --install-extension .\karl-vscode.vsix
Open any project in VS Code — ghost-text completions
appear as you type. The learning dashboard lives at
http://localhost:5117/learn.
Get in touch
The demo is a taste. The full version is the product.
The download is a 14-day evaluation: 200 inline completions, 30 chat turns, 5 agentic plan-and-patch sessions, fine-tuning switched off. Enough to feel how Karl behaves on real code.
The full version lifts the gates, runs the overnight fine-tuner against your repo, ships the trajectory and convergence layer, and includes the admin dashboard plus on-prem install. Pilots, design partners, and quieter acquisition conversations all welcome.