The Kelly Criterion is a position-sizing tool based on probability and odds that helps you maximize long-term compounding growth when you have an edge, while avoiding the ruin that comes from over-betting.
Joseph Moore's "How to Get Rich in American History" uses 300 years of data to show that "can't get ahead" is an illusion. But what's worth truly worrying about isn't the pessimism itself—it's how pessimism corrodes your capacity to act.
Malus.sh uses AI clean-room techniques to clone open source software and strip away license obligations. This article examines its technical mechanisms, legal controversies, and the structural threat it poses to the open source ecosystem.
A comparison of the latest 2026 releases: the core differences between OpenClaw and Hermes Agent in architecture, security, skill systems, and memory mechanisms, and how to choose the AI agent framework that fits your scenario.
Warp open-sourced its terminal under the AGPL license and adopted an Agent-first collaboration model. An analysis of its open-source motives, free vs. paid differences, impact on users, and future direction.
The Claude desktop client puts Chat, Cowork, and Code side by side. This article compares the core differences, underlying architecture, and ideal use cases of Cowork vs. Code to help users pick the right tool.
Pi is a terminal AI coding agent with a core of just 418 lines, created by Mario Zechner, the author of libGDX. This article dissects its architecture, extension mechanism, and position in the community.
An introduction to web-based presentation tools like reveal.js, Slidev, Marp, and Spectacle, plus AI generation options such as Presenton and PPTAgent, with selection advice.
By rewriting low-level CUDA kernels and optimizing memory, Unsloth speeds up LLM fine-tuning by 2-5x and cuts GPU memory usage by 80%, making it possible for individual developers to train large models.
A breakdown of the core differences between standard Q4 and Unsloth Dynamic Q4 (UD-Q4), exploring the benchmark status of 4-bit quantization in local LLM deployment and how the technique is evolving.
