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AI research and data
Research systems, evaluation methods and data workflows for evidence-led AI decisions.
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Machine learning algorithms: how to choose the right family for a real problem
Regression, trees, clustering and neural networks solve different kinds of problems. This practical map starts with the decision, the data and the cost of being wrong—not a list of fashionable algorithms.
Ravi Kapoor · 13 min readTypes of machine learning models: a practical guide from regression to transformers
Model types are easier to understand when organized by what they learn, what data they consume and how their output will be used. Here is a practical taxonomy for choosing and evaluating them.
Ravi Kapoor · 12 min readPerplexity AI review 2026: excellent research navigation, but verification is still your job
Perplexity combines live web search, multiple model options, cited answers and persistent research projects. We evaluate where that workflow saves time—and where source quality and synthesis still need human review.
Elena Morris · 14 min readGPT-5.6 arrives across ChatGPT, Codex and the OpenAI API
OpenAI's July flagship release brings one model family to consumer chat, coding agents and the API, followed by substantial price cuts for its Luna and Terra variants.
Ravi Kapoor · 7 min readChatGPT Work turns OpenAI's chatbot into a long-running work agent
ChatGPT Work can act across apps and files, break goals into steps and stay with a project for hours, pushing ChatGPT further from answer engine to execution layer.
Linh Nguyen · 7 min readX is letting AI draft Community Notes—but humans keep the vote
The AI Note Writer API offers a revealing model for human-AI research systems: machines can find sources and propose context, while people with diverse viewpoints decide what becomes visible.
Priya Raman · 7 min read