Infinite conversation and coding in a single session — a companion agent that remembers everything from yesterday.
JLC no-prefix route active
MEM project state persisted across session
ZERO handoff · switching · onboarding ready
OMM mistake converted into project warning
RUN turn 9,842 · compact 0 · clear 0 · no ceiling
An LLM remembers nothing between turns. Existing agents hide this by replaying the entire conversation every turn — fighting their own nature, until the context fills and collapses.
JARVIS CODE is built for statelessness. Working with the grain, not against it — it never slows as it grows, and never forgets.
Every time you re-explain yesterday, you lose your most precious resource: time.
Every time it forgets, you're the one filling the gap.
The AI built to get smarter only gets dumber as the chat grows.
JARVIS CODE starts today with yesterday intact.
Your time goes to creating — not re-explaining.
/compacted — context compressed and lost/clear throws memory awayAttention is O(n²) in length.
The longer the conversation, the more cost and load balloon — until /compact·/clear throw memory away. The Transformer's curse.
It forgets — so you never have to.
Instead of piling memory into the context, the JLC codec carries it outside.
With no giant prefix to drag, the cost curve bends from quadratic to linear.
unlocks what was trapped inside the context window.
Memory lives in the codec, not the model.
Swap models, or drop to a smaller one — your project context stays.
The curve goes from O(n²) to O(n).
Endless long-running work finishes without a cost blowup.
You don't have to fit everything in one window.
Your work isn't capped by the model's window size.
Plain reasoning for everyday chat.
For coding, push reasoning to the limit and pull out the model's very best.
Backed by JLC's massive token savings — dive deep, with no fear of burning context.
Why it survives long work — and how.
It doesn't drag a giant conversation prefix.
Cost per turn is linear, not O(n²).
A bloated KV cache devours memory until computation stalls — and history gets compacted or cleared.
Carry memory outside, the cache stays lean, and the model keeps reasoning without ever hitting the memory wall.
Context persists across shutdown, restart, and model swaps.
No compact, no clear — infinite conversation and coding in one session.
No turn ceiling by design, proven over 10,000 public turns.
/compact·/clear, we don'tInformation is spread across state and memory — no context ceiling.
Validated by a public 10,000-turn run and artifacts.
Published on Zenodo with a citable DOI.
Three things the engine delivers — zero.
Pick up the work context seamlessly.
Across sessions, machines, models, interruptions.
Jump between projects instantly.
No path setup, re-registration, or re-explaining.
Understand a new project right away.
Start on an unfamiliar codebase instantly.
Day 3 of refactoring 200 files. You close the laptop Friday night, open it Monday morning. JARVIS CODE resumes right where it stopped — remembering even why you abandoned approach X.
OMM records its own mistakes.
Without you pointing them out, the same mistake never repeats — and the more you work on a project, the smarter it gets on its own.
Compounding you can't fake.
You may already use another agent. It's perfect at first, too.
The moment the chat grows and the context fills, it leaves you behind.
JARVIS CODE begins exactly there.