Autonomous AI research team: write a plan.md, spawn agents, train, verify, and deliver a clean ML repo.

C++/Python microstructure research engine for event-driven limit order book prediction and reproducible quantitative experiments.

Open ML systems platform for training, profiling, evaluating, and serving AI models.

Controlled mini-benchmark for context visibility, shortcut regimes, and composition in tiny causal transformers.

GitHub profile README for Nicholas Kashani Motlagh

A jekyll site for the Feast and Fast exhibition at the Fitz

Reproducible evaluation suite for LLM behavior research: epistemic pathology, delegated introspection, and temporal consciousness diagnostics

Adnane Arharbi

A follow along of “Build a Large Language Model (From Scratch)” by Sebastian Raschka, which is explained with an insightful YouTube series “Building LLM from Scratch” by Dr. Raj Dandekar (MIT PhD).

Research-engineering lab for LLM post-training, behavior evaluation, regression detection, and reliability reporting.

Lightweight LLM behavior evaluation harness with prompt/output fixtures, metrics, error taxonomies, regression checks and generated reports.

Portfolio landing page for compact AI evaluation artifacts.

Automates hermetic environments (macOS/HPC) to eliminate drift. Provisions offline RAG (Gemma 2), compiles LaTeX manuscripts, and indexes local knowledge. Unifies infrastructure, writing, and inference into a single, audit-ready artifact.

ChronosLOB: Research platform for limit order book representation learning, calibration and execution-aware validation.

Research-grade MLE repo for structured CNN pruning under explicit accuracy guard rails, with deployment-aware evaluation across parameter reduction, MACs, latency, and pruning overhead.

Profile README and portfolio landing page for LLM post-training, model evaluation and reliable production ML.