Moonshot AI's Kimi K3 model scales to 2.8T total parameters with 104B activated per token, using hybrid attention, Attention Residuals, and Stable LatentMoE to support 1M-token agentic trajectories. T…
Jason Strimpel's 'Python for Algorithmic Trading Cookbook' provides 68 recipes for building a complete algorithmic trading pipeline, from sourcing market data with the OpenBB Platform to live executio…
A new paper introduces the Harness Handbook, a behavior-centric representation for AI agent harness codebases, and Behavior-Guided Progressive Disclosure (BGPD), a workflow to help coding agents locat…
A new book, 'Time Series with PyTorch' by Graeme Davidson and Lei Ma, offers a practical guide for time-series forecasting using PyTorch, covering evaluation, architectures, and tasks beyond point for…
Google Developer Expert Andriy Lukyanyuk tested Google Antigravity 2.0 with Gemini 3.1 Pro by entering a Kaggle Playground Series competition. The agentic IDE built a pipeline with LightGBM, CatBoost,…
A researcher used Anthropic's Claude Code agent to implement puzzle solvers for two Kaggle competitions, IHES Picture Cube and Megaminx, reformulating them as Cayley graph searches. The agent wrote tr…
Niels Cautaerts and Hossein Ghorbanfekr's book 'GPU-Accelerated Computing with Python 3 and CUDA' teaches Python developers to write GPU-accelerated code using Numba-CUDA, CuPy, RAPIDS, and JAX. The b…
MiniMax introduced a sparse attention mechanism that selects per-group blocks to enable cost-effective inference on million-token sequences. The technique reduces computational overhead while maintain…
MiniMax released M3, the first open-weights model combining frontier-level coding ability, a 1M-token context window, and native multimodality, which a developer tested on real tasks including a code …
Mike X Cohen's new book "50 ML Projects to Understand LLMs" uses GPT-2 as a scientific specimen, teaching readers to investigate the model through 50 hands-on projects focused on code, statistics, and…
Researchers have developed Gamma-World, a multi-agent video world model that can simultaneously generate consistent future observations for multiple independently acting agents sharing the same enviro…
Testing the MiniMax M2.7 model via API on three real-world workflows—refactoring a PyTorch project, drafting knowledge-base notes, and scaffolding a Kaggle competition entry—using Claude Code as the a…
DeepSeek-V4 shifts the focus from simply offering a million-token context window to making that window practically usable through efficient attention mechanisms. Its architecture employs hybrid compre…
FIPO (Future-Impact-based Policy Optimization) is a reinforcement learning method that improves LLM reasoning by assigning token-level credit based on each token's future impact on the policy, rather …
This summary is based on the provided article. The second edition of *Unlocking Data with Generative AI and RAG* by Keith Bourne expands its coverage to include agentic RAG, graph retrieval, semantic …
This book review summarizes "A Practical Guide to Reinforcement Learning from Human Feedback" by Sandip Kulkarni, which covers the full RLHF pipeline from classical reinforcement learning to modern me…
The author redesigned their personal website in two waves, first adding infrastructure improvements like full-text search, dark mode support, and structured data, then overhauling content and layout u…
HACRL (Heterogeneous Agent Collaborative Reinforcement Learning), a method where multiple AI models of different sizes and capabilities share successful training trajectories to learn from each other,…
DroPE, a method that removes positional embeddings from pretrained large language models (LLMs) after training to enable zero-shot generalization to sequences far longer than their original training c…
The Kimi k2.5 is an open-source multimodal AI model with 1 trillion parameters that uses joint pre-training and reinforcement learning to integrate text and vision natively, rather than adding vision …