Production focused Self-harnessed LM runtime (RLM) that allows the LM to call its sub-lm with DSPy signatures. Define your inputs, outputs, and tools — the model handles its own control flow. Get fully interpretable trajectories and performance that scales directly with model improvements. Without context rot.
Tracking just started for Trampoline-AI/predict-rlm. Cross-source signals (mentions, momentum, deltas) will populate on the next collector tick — usually within an hour.
Coverage1 / 13 sources fired (7d)
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// WHY · ORGANIC
Trampoline-AI/predict-rlm sits at 337 GitHub stars with steady Python traction — organic growth keeps it on the trending list.
* Reddit bar shows a per-repo velocity proxy (raw score / 100); the score formula uses the corpus-normalized version so a single repo's bar may not match its contribution to the corpus-wide ranking.
// PROJECT SURFACE MAP · ENTITY LINKSSURFACES ONLY