<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Data Science Origin</title><description>One path through modern AI — twenty articles, beginner to frontier.</description><link>https://datascienceorigin.com/</link><item><title>World models and physical AI: what comes after chat</title><link>https://datascienceorigin.com/articles/world-models-and-physical-ai/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/world-models-and-physical-ai/</guid><description>From predicting text to predicting the physical world — what world models are and why robotics needs them.</description><pubDate>Mon, 18 May 2026 00:00:00 GMT</pubDate></item><item><title>Running frontier models locally: what it takes in 2026</title><link>https://datascienceorigin.com/articles/running-frontier-models-locally/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/running-frontier-models-locally/</guid><description>The hardware and quantization tradeoffs for running capable models on your own machine.</description><pubDate>Mon, 11 May 2026 00:00:00 GMT</pubDate></item><item><title>Small models beating giants: the efficiency frontier</title><link>https://datascienceorigin.com/articles/small-models-beating-giants/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/small-models-beating-giants/</guid><description>How distillation and better data let small models match frontier models on narrow tasks.</description><pubDate>Mon, 04 May 2026 00:00:00 GMT</pubDate></item><item><title>The inference cost collapse: why AI got 1000x cheaper</title><link>https://datascienceorigin.com/articles/the-inference-cost-collapse/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/the-inference-cost-collapse/</guid><description>The hardware, algorithmic, and market forces that drove inference prices down, tracked with dated sources.</description><pubDate>Mon, 27 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Mixture-of-experts and hybrid architectures, explained</title><link>https://datascienceorigin.com/articles/mixture-of-experts-and-hybrid-architectures/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/mixture-of-experts-and-hybrid-architectures/</guid><description>Why sparse activation lets models grow without growing inference cost at the same rate.</description><pubDate>Mon, 20 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Fine-tuning vs. prompting: when each one wins</title><link>https://datascienceorigin.com/articles/fine-tuning-vs-prompting/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/fine-tuning-vs-prompting/</guid><description>A practical decision framework for when to fine-tune, when to prompt, and when to do both.</description><pubDate>Mon, 13 Apr 2026 00:00:00 GMT</pubDate></item><item><title>RLVR: reinforcement learning from verifiable rewards</title><link>https://datascienceorigin.com/articles/rlvr-reinforcement-learning-from-verifiable-rewards/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/rlvr-reinforcement-learning-from-verifiable-rewards/</guid><description>How training against checkable answers, not human preference, produced today’s reasoning models.</description><pubDate>Mon, 06 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Agent security: prompt injection and the new attack surface</title><link>https://datascienceorigin.com/articles/agent-security/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/agent-security/</guid><description>How agents get hijacked, why tool use makes it worse, and the defenses that actually hold up.</description><pubDate>Mon, 30 Mar 2026 00:00:00 GMT</pubDate></item><item><title>Harness engineering: the discipline behind reliable agents</title><link>https://datascienceorigin.com/articles/harness-engineering/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/harness-engineering/</guid><description>The tools, permissions, and guardrails that turn a capable model into a trustworthy agent.</description><pubDate>Mon, 23 Mar 2026 00:00:00 GMT</pubDate></item><item><title>Evals as governors: how to keep agents on track</title><link>https://datascienceorigin.com/articles/evals-as-governors/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/evals-as-governors/</guid><description>Treating evaluation as a live control system, not a one-time test suite, for agents that act in the world.</description><pubDate>Mon, 16 Mar 2026 00:00:00 GMT</pubDate></item><item><title>Multi-agent orchestration: patterns that scale</title><link>https://datascienceorigin.com/articles/multi-agent-orchestration/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/multi-agent-orchestration/</guid><description>When to split one agent into many, and the coordination patterns that keep multi-agent systems from thrashing.</description><pubDate>Mon, 09 Mar 2026 00:00:00 GMT</pubDate></item><item><title>The agent loop: how autonomous agents actually work</title><link>https://datascienceorigin.com/articles/the-agent-loop/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/the-agent-loop/</guid><description>Model, tool, result, repeat — the loop underneath every agent framework, stripped of marketing.</description><pubDate>Mon, 02 Mar 2026 00:00:00 GMT</pubDate></item><item><title>Embeddings and vector search, from first principles</title><link>https://datascienceorigin.com/articles/embeddings-and-vector-search/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/embeddings-and-vector-search/</guid><description>How meaning becomes a vector, how nearest-neighbor search works at scale, and where it quietly fails.</description><pubDate>Mon, 23 Feb 2026 00:00:00 GMT</pubDate></item><item><title>Agent memory: working, episodic, and long-term state</title><link>https://datascienceorigin.com/articles/agent-memory/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/agent-memory/</guid><description>The three tiers of memory production agents actually use, and when each one is the wrong choice.</description><pubDate>Mon, 16 Feb 2026 00:00:00 GMT</pubDate></item><item><title>RAG done right: retrieval that doesn’t hurt accuracy</title><link>https://datascienceorigin.com/articles/rag-done-right/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/rag-done-right/</guid><description>Why naive retrieval crowds out the signal, and the precision-first patterns that keep it from backfiring.</description><pubDate>Mon, 09 Feb 2026 00:00:00 GMT</pubDate></item><item><title>Model Context Protocol, explained by building one</title><link>https://datascienceorigin.com/articles/model-context-protocol-explained-by-building-one/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/model-context-protocol-explained-by-building-one/</guid><description>How tools connect to models — write your first MCP server and see the handshake end to end.</description><pubDate>Mon, 02 Feb 2026 00:00:00 GMT</pubDate></item><item><title>Context engineering: the discipline that replaced prompt engineering</title><link>https://datascienceorigin.com/articles/context-engineering/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/context-engineering/</guid><description>The four-layer stack production agents assemble on every call.</description><pubDate>Mon, 26 Jan 2026 00:00:00 GMT</pubDate></item><item><title>Reasoning models: how &quot;thinking&quot; tokens actually work</title><link>https://datascienceorigin.com/articles/reasoning-models-how-thinking-tokens-work/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/reasoning-models-how-thinking-tokens-work/</guid><description>What a model is doing when it emits a chain of thought, and why that changed what these systems can solve.</description><pubDate>Mon, 19 Jan 2026 00:00:00 GMT</pubDate></item><item><title>Attention and transformers, explained</title><link>https://datascienceorigin.com/articles/attention-and-transformers-explained/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/attention-and-transformers-explained/</guid><description>The mechanism that let models look at an entire sequence at once, and why it replaced everything that came before it.</description><pubDate>Mon, 12 Jan 2026 00:00:00 GMT</pubDate></item><item><title>What actually happens inside an LLM</title><link>https://datascienceorigin.com/articles/what-actually-happens-inside-an-llm/</link><guid isPermaLink="true">https://datascienceorigin.com/articles/what-actually-happens-inside-an-llm/</guid><description>Tokens, embeddings, and next-token prediction — how a language model turns text into math, explained without the hype.</description><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate></item></channel></rss>