MiniMax Releases MiniMax M3 with MSA Architecture Supporting 1M-Token Context, Native Multimodality, and Agentic Coding
MiniMax officially released MiniMax M3 on June 1, 2026. The model introduces MSA (MiniMax Sparse Attention), a new sparse attention architecture that gives M3 a 1M-token context window. M3 also supports image and video input and desktop computer operation natively. The API is live now. MiniMax M3 is available today via MiniMax Code, the MiniMax […]
RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem
six months to fine-tuning their RAG pipeline. They ran five Optuna sweeps. They added a custom reranker. They fine-tuned an embedding model on their own data. Production accuracy never moved. Pilots kept complaining about the same wrong answers. Six months in, the bug was in the parser. The team was lost, not stuck. RAG is […]
How to Combine Claude Code and Codex for Maximum Coding Power
and Codex are incredibly powerful coding agents by themselves. I’ve extensively tried both models, and in my opinion, they’re both very good and comparable, at least comparing the model Claude Opus 4.8 and Codex with GPT-5.5. However, they definitely have different strengths and weaknesses. In some scenarios, I find myself using Claude, and in other […]
Meet Memory OS: A 6-Layer Open-Source Memory Stack Built on Top of Hermes Agent
Hermes Agent already remembers across sessions. The open-source agent from Nous Research ships with curated memory files and full-text session search. But a new community project argues that built-in memory is too shallow for serious work. A new library named ‘Memory OS‘ has been released under an MIT license by a developer (ClaudioDrews). It stacks […]
Ensuring Data Integrity with Cryptographic Hashing and the Ethereum Blockchain
science workflows, teams often need access to a shared dataset that stays perfectly synchronized and cannot be modified, e.g., in distributed machine learning environments where multiple teams rely on the exact same feature set. In this article, I’ll walk through a simple, fee-free method for cryptographically hashing a dataset of any size and storing its […]
It’s the Lessons We Learned Along the Way. Or, Is It?
ChatGPT a typical month-long internship problem in the data space. The problem in some sense got “solved” but I’m not sure it means what I thought it would. For data and AI practitioners, this is now a very practical question. Many teams use interns or research spikes to explore ideas: is AI good enough now? […]
Escaping the Valley of Choice in BI
Introduction been killed because they operate within the Valley of Choice in BI. The Valley of Choice describes the perfect ratio between the complexity of a problem and the effort we are willing to put in to solve it. For example, a business critical question about how revenue is formed, and why different versions of […]
The Roadmap for Mastering LLMOps in 2026
# llm_with_tracing.py # Purpose: A production-ready LLM call wrapper with full observability. # Every call is traced in Langfuse: input, output, tokens, cost, latency. # # Prerequisites: # pip install langfuse anthropic python-dotenv # # Setup: # 1. Create a free account at https://cloud.langfuse.com # 2. Get your keys from Settings > API Keys # […]
The future of automated trading with the best forex robot reviews
Automation is becoming a bigger part of how financial markets are approached, and forex trading is one area where this is becoming easier to notice. As the tech world improves, more traders are looking for ways to stay involved in the market without the need to sit in front of charts for hours at a […]
Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch
The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention […]