ML Engineer & Technical Writer
Daniel Evershaw is a machine learning engineer and technical writer with over eight years of experience building AI systems at scale. Previously at major tech companies working on NLP pipelines and recommendation systems, Daniel now focuses on making AI concepts accessible to practitioners through clear, practical writing. When not writing about transformers and embeddings, Daniel contributes to open-source ML tooling and mentors aspiring data scientists.
The Musk v. Altman trial ended with a jury dismissal, but the real verdict is that AI governance remains in the hands of flawed founders, not accountable stewards.
Anthropic acquires Stainless, the SDK automation startup used by OpenAI and Google. Analysis of the strategic move and its impact on the AI developer ecosystem.
SandboxAQ integrates its drug discovery models with Claude, making advanced AI accessible to researchers without a PhD in computing.
Build your own ChatGPT clone with LangChain, OpenAI, and Streamlit in five steps. Create a conversational AI with memory and real-time streaming.
As AI drives electricity demand, Lake Tahoe faces soaring energy prices. This article explores the broader implications for tech hubs and the clean energy transition.
OpenAI merges ChatGPT and Codex into a unified agentic platform, with Greg Brockman leading product as the company bets big on AI agents.
The Musk v. Altman trial closes with a central question: can we trust AI leaders? As SpaceX eyes a massive IPO, the founder-driven machine spins on.
OpenAI launches Codex on mobile, letting developers write and edit code directly from smartphones. A major shift in AI-assisted development workflows.
Richard Socher's new startup aims to build AI that improves itself indefinitely. We analyze the implications for the industry and what it means for AGI.
OpenAI may sue Apple over a failed ChatGPT deal, citing unmet subscriber and prominence promises. Tensions escalate between AI and tech giants.
A clear explanation of how LLMs work under the hood, from tokenization to generation, without the jargon or hype.
A practical comparison of the leading AI coding tools in 2026, covering capabilities, pricing, and real-world performance.
A practical guide to RAG systems that ground LLM responses in real data, reducing hallucinations and keeping answers current.
Why prompt engineering remains essential in 2026, how it has evolved beyond simple tricks, and the systematic approaches that work.
A breakdown of the actual costs involved in deploying LLMs at scale, from inference compute to hidden operational expenses.
A detailed comparison of open-source and closed AI models across performance, cost, privacy, and customization for production use.
Why AI agents went from demo curiosities to production tools in 2026, and the architectural patterns that made them reliable.
The practical reality of AI in customer support: what works, what fails, and how teams are implementing it without alienating customers.
A clear overview of the legal landscape around AI training data, ongoing lawsuits, and what practitioners need to know.
A decision framework for choosing between fine-tuning, RAG, and prompt engineering based on your specific use case and constraints.
An explanation of multimodal AI systems that process text, images, audio, and video, with practical applications and limitations.
A step-by-step guide to building a useful AI application in a weekend, from idea selection to deployment.
A practical explanation of vector databases: what they are, when you need one, and how to choose between the options available.
How to evaluate AI models for your specific use case when public benchmarks do not tell the full story.
A grounded analysis of how AI will reshape work over the next year, based on current adoption patterns rather than speculation.