We don't stream pixels. We stream understanding.
Cut the noise.
Stream the signal.
As humans we naturally focus on what matters in the moment. Our brains actively discard what we already know, so our attention lands only on what's new.
AI has no such filter. Every step, it reads everything: signal and noise, from scratch. JD Codec gives AI that same selective focus. Strip away the known. Surface what matters.
Same task. Same model. Same tools.
One finishes before
the other starts reading.
Task: Filter products by status 4/6
Same model · Same task · Same tools
99% fewer tokens. $2.23 saved per task.
Setup
Install. Connect.
Start saving.
Install
Pick your flavour. One command.
npx @jdcodec/cli
pip install jdcodec
Connect
Drop-in layer between your agent and the browser. No changes to your agent, model, or tools.
jdcodec start
Save
86% fewer input tokens. Higher task success rates. Faster execution. Your agent sees only what changed, and performs better because of it.
Benchmarked
Head-to-head with
standard browser automation.
Same model (Claude Sonnet 4). Same tasks. Same environment. The only difference: standard snapshots vs. JD Codec.
| Task | Baseline | JD Codec | Baseline tokens | JD Codec tokens | Reduction |
|---|---|---|---|---|---|
| Navigate to Products | PASS | PASS | 108.8k | 10.2k | -91% |
| Navigate to Customers | PASS | PASS | 30.6k | 8.6k | -72% |
| Navigate to Orders | PASS | PASS | 54.3k | 9.9k | -82% |
| Filter products by status | PASS | PASS | 748.9k | 5.2k | -99% |
| Change customer group | FAIL | FAIL | 203.2k | 50.8k | -75% |
| Toggle product status | FAIL | PASS | 543.5k | 145.3k | -73% |
| Total (6 tasks) | 4/6 | 5/6 | 1,689k | 230k | -86.4% |
Methodology: 6 real-world tasks on production web app. Input tokens via tiktoken. Wall-clock includes all latency.
Your agents have been
operating blind.
Alpha access is limited to high-velocity teams. Join the waitlist to cut agent costs by 86% and convert failures into completions.