AI Research / LLM Training / Write-up
A detailed technical write-up about training a 3.8B-parameter language model from scratch, including setup, throughput improvements, ablations, benchmarks, and cost/performance results.
The page clearly describes an AI-related project with substantial technical detail: a 3.8B LLM trained from scratch, the hardware used, training time, cost, model architecture, results, and experiments. It offers enough evidence for a useful public listing and is clearly aimed at AI builders and researchers.
The snapshot is a long article rather than a packaged tool or reusable project, and it does not show a repo link, code release, checkpoints, or a way for visitors to try the model directly.
Last evaluated 46 days ago. Current rank #2183. Up 6 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.
