Fermac AI Systems

Improving a model with a well-designed dataset is still manual, ad hoc and hard to reproduce. As a contract software engineer at Turing.com, I see how much of that loop depends on one-off scripts. MARKOV is the harness I wished existed: one pipeline from dataset to trained weights to scored benchmarks, using Meta's models at every stage. Why it matters? The weights are the demo; the loop is the product. Any team with tasks and a way to check answers can reuse this pipeline to mint their own specialist agents ops runbooks, research triage, code repair for the price of a training run, not a pretraining program. Why this is Frontier work? We didn't prompt a model and hope. We gave a machine a genuinely new capability acting reliably inside real software environments and proved it with independent benchmarks, not vibes. That's what happens when Meta's models meet the real world: the loop is open, reproducible, and waiting for the next domain. Live Demo link - https://huggingface.co/spaces/pranavupadhyaya52/muse_markov-benchtest