Research and rank the top five skills worth learning in any domain using current trends, job demand, future potential, and practical learning paths.

The Climate Risk AI Engine is a machine learning framework designed to predict regional climate vulnerability and optimize land-use strategies. It processes environmental telemetry, models risk indices, and provides data-driven insights to support climate resilience, sustainable agriculture, and environmental governance.

Machine learning for exoplanet candidate classification and ranking using NASA TESS data

🔨 [NeurIPS 2026] Official implementation of “TabFORGE: A Tabular Foundation Model for Generative Modelling”

Offline-first Android app that detects coffee, cacao and banana diseases from a phone photo.

Open-source AI, ranked: apps, agents, models and the tools around them, each with strengths, weaknesses and a score from real GitHub signals. Re-checked twice a day.

Multi-agent LLM data analyst in Python. Ask questions about any CSV/Excel in plain English: planner, analyst and reviewer agents run pandas/SQL in a secure sandbox, use forecasting, anomaly detection, statistical tests and clustering, catch hallucinated numbers and generate HTML reports. Works with Claude, OpenAI, Ollama or offline.

SpecLess is an open source Python framework for photometry based machine learning classification of galaxies across multiple astronomical surveys.

AI-assisted bank-response classification by Ruturajsinh Zala (Root2Raj): chronological evaluation, probability calibration, distribution shift and model-risk evidence.

A curated list of AI agent frameworks, coding agents, browser agents, tools, platforms and research.

Institutional Quantitative Framework Predicting Tail Volatility Risk (>=350 Pips) And Asymmetric Directional Skew In Gold (XAU/USD) From US CPI Macro Surprise Deviations.

Top ML Model Monitoring Drift Detection (Opensource) Star if you like it!

Transformer-free closed-corpus correlator. Ask, cite, or refuse from a frozen pack. Not a chatbot LLM.

IFCD107 · Especialista en Inteligencia Artificial · Material del alumnado (Servicio Canario de Empleo)

Live dashboard of California's power grid that forecasts tomorrow's demand, solar and wind every morning and grades itself in public against CAISO's own forecast. Python, PostgreSQL, scikit-learn, Plotly Dash.

On Data Scaling in Tabular ML: More Data, Better Generalization?

Fast, offline-first image annotation for boxes, polygons and classes. Free, MIT, LabelMe-compatible; exports YOLO, COCO, Labelme and CSV.

Solar filament segmentation using U-Net architectures in TensorFlow/Keras, benchmarking custom CNN baseline vs. EfficientNetB0 transfer learning and fine-tuning.

GenClass - The version of Jev that runs on your browser

Mistral Timestral: Tiny 209M AI Predicts Stocks Locally on CPU! Zero-shot probabilistic forecasting foundation model.

Agentic AI portfolio and data-science mission control: Groq-powered assistant (Nova), CSV data lab, RAG vault, in-browser CNN, live GitHub stats. Built by NIKHIL CHARY SRIRAMOJU......

Daily AI news, fact-checked and traced back to its roots. A source-checked history of AI since 1943, a glossary with lineage, and the labs and people behind it. Open data, built nightly on free Gemini and Groq tiers.

Modul praktis belajar data mining dan machine learning bertahap dari EDA hingga pipeline end-to-end menggunakan Python dan scikit-learn.

Institutional NLP Framework Predicting Extreme Tail Volatility (>400 Pips) In Gold (XAU/USD) From FOMC Press Conference Transcripts Using N-Gram TF-IDF And Cosine Similarity.

One Python client for NVIDIA's structured-data foundation models: ask a predictive question of one table or of a whole relational schema, and get an answer without training anything.

Liquid Neural Network language model with LTC and CfC dynamics

Curiosity → Perseverance rover wheel skin: Ansys FEA, analytical failure analysis, and a PCA–Gaussian-process surrogate model with a MATLAB GUI for instant stress/strain prediction.

Bike-share reliability: live Citi Bike stations, evaluated demand forecasts, commute recommendations and historical replay.

Statistically rigorous, causal evaluation for LLM apps on top of DeepEval: confidence intervals, causal interventions (RAG grounding, perturbations, agent attribution), and judge validity (bias audits, calibration, PPI).

Monte Carlo audit of anomaly thresholds under calibration-set contamination