Hey there, friend using a screen reader. I've carefully labeled every button and image on this site. Hope you have a smooth experience. If anything is inconvenient, my email is in the footer — just let me know and I'll fix it. — Store Owner
Loading…
Figure 1: ThinkingBox runs an agent against isolated MCP tool sessions, then grades the terminal backend state and side effects it leaves behind. From our ThinkingBox paper .
Hugging Face
Eligibility
What you will practice
The source does not list specific skills yet. Read the original page before deciding.
Source and verification
Keep exploring
The Shift to Tabular Foundation Models Tabular data is the backbone of enterprise machine learning. Customer records, transactions, sensor logs, claims, and orders all live in tables, and predicting churn, default, demand, or price from them is among the most common machine learning tasks in industry. For two decades,…
As models become faster and workloads scale, that balance begins to shift. Training on massive datasets, serving many concurrent requests, or repeatedly processing long inputs can put enough pressure on the tokenizer that it starves the model of data.
Running AI models on your laptop has become much easier, and llama.cpp has been a big part of that. Its inference engine powers local AI tools such as Ollama, LM Studio, and Jan. Alongside projects like MLX , it has helped make local inference a practical option for everyday use.
Our latest paper, ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents (read it on Hugging Face , or on arXiv in the meantime), targets that gap. The failure mode we care about is one we call cross-source conflation: a claim that is true somewhere in the evidence, but attributed to the wrong…