Skip to content
SystemOne.dev
Blog

Why We Built SystemOne.dev, and an Open System One Model

SystemOne maintainers
systemonedev

Something has been quietly true for a while now: a large share of what teams use large language models for isn’t generation. It’s classification, routing, scoring and gating: decisions dressed up as text.

We noticed it the way most people do, by writing the same defensive code for the fifth time:

res = llm.chat(messages=[...])                    # "ONLY RETURN VALID JSON..."
try:
    parsed = json.loads(strip_fences(res.text))
except json.JSONDecodeError:
    parsed = retry_with_stricter_prompt(text)
if parsed.get("category") not in ALLOWED:
    parsed = {"category": "unknown", "confidence": 0}

None of that is business logic. All of it exists because we asked a text generator a multiple-choice question and got back a paragraph.

The gap

Section titled “The gap”

System One models fix that problem directly: they answer typed questions about your program’s state with a probability for every option, in tens of milliseconds, without generating text. The architecture is real and it works.

Two things were missing.

The engineering literature. Search for how to pick a threshold and you find “0.95 is a good starting point”. Search for what calibration drift looks like in production and you find research papers. Every team is working it out alone, and making the same four mistakes.

Open models. The best-known System One model was a hosted service. You couldn’t run it on your own data, inspect how it was trained, or change it.

What we’re launching

Section titled “What we’re launching”
  • SystemOne.dev: the patterns, written down once, with code that runs.
  • Kenning: an open System One model under Apache-2.0. It’s 435M parameters, runs on a ~2 GB GPU footprint or a CPU, and its training data and licences are documented.
  • SystemOne Builder: an open toolkit to serve, train, distil and benchmark your own System One models on one GPU, from a dashboard.
  • systemone-client: one Python client for Kenning, Cloudflare’s Clef and TypeSafe’s Jev. Switch engines by changing a URL.

Two commitments

Section titled “Two commitments”

We’ll stay honest about limits. Kenning v0.4 is faster and much smaller than the big engines, and behind them on subtle phishing. We publish those numbers, not just the flattering ones. “No hallucination” gets a page that spends as much space on what doesn’t disappear as on what does. Calibration is something you verify on your own data, so every page tells you how.

We’ll stay open and engine-neutral. The code, the weights, the benchmark suites and this site are all open. Every engine that speaks the format is welcome here, and pages about other engines are welcome contributions.

There’s a version of this site that’s more exciting to read and less useful to build on. We’re not going to write that one.

Start here

Section titled “Start here”

If you’re already running a decision model in production, we especially want to hear from you. Real numbers on real workloads are the scarcest thing in this whole space.

Tags: