most LLM crews are delusional about scale.
1. long context? dead long before a GB, the KV cache balloons and latency kills you.
2. standard RAG? at the terabyte level, it becomes "retrieval roulette." Vector collision creates pure noise.
we built lium for this specific wall.
we reason over terabytes of technical data without the context bloat or the retrieval hallucinations.
founders circle open. Only for teams facing actual scale. hmu!
most teams in #astrophysics, #earth observation, #oil & gas, and #geothermal are sitting on terabytes of sensor/seismic/satellite data that llms completely choke on.
context windows die, rag hallucinates, and petabytes stay useless.
we built https://t.co/fZI3Tb5bzu to turn that raw physical data into natural language you can actually query and reason with.
to prove it: the first 50 serious teams get $100 in lium tokens just to come try it.
no bs.
if you're dealing with real terabyte-scale science or energy data, dm me
@_CallMeMacy whhatchu think ? we've been workin hard on something super UNIQUE "what if we could make GTM like a game?" :P lmk if we should submit https://t.co/HFJobUtEXI