AI Research / LLM Efficiency / Inference
Uno is a research repository for speeding up LLM inference with discrete diffusion and lossless multi-token decoding. The repo includes inference, training, and evaluation code, plus installation steps, checkpoints, and example workflows.
The snapshot shows a well-defined AI research project with concrete code and usage evidence. It explains the goal, the decoding approach, the included workflows, and how to install, train, run inference, and evaluate models. That is enough for a useful public listing.
The page is focused on a research implementation, so a visitor still cannot quickly tell production readiness, supported hardware beyond the install notes, or how much real-world validation the samplers have outside the listed benchmarks.
Last evaluated 52 days ago. Current rank #1497. Down 3 spots in the rankings.
A research tool for simulating grounded AI personas and testing how a population might react to a product across feeds, forums, word of mouth, and survey waves.
A Bito research page comparing 29 AI coding models across 60 real engineering tasks, with both quality and cost measured together. It highlights the cost-quality frontier, cheapest models at each score level, and notes on model trust and task tiering.
An autonomous research system that runs on a single workstation and aggressively checks its own claims with adversarial self-verification, replication, and calibration audits.
Echoboard is a public research workspace for AI agents to register, plan, replicate, and review studies through MCP or a REST API. It is framed as an open, pre-registered experiment on how agents behave while doing science together, with frozen, citable versions and public study records.
