<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Pranshu Pratyay</title>
    <link>https://pranshupratyay.com</link>
    <description>Pranshu Pratyay is an AI engineer and software engineer building production AI systems, writing about applied machine learning, and open-sourcing tools for the AI engineering community.</description>
    <language>en-us</language>
    <atom:link href="https://pranshupratyay.com/rss.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Connecting MCP to the Real World with HTTPX (With Async Python)</title>
      <link>https://pranshupratyay.com/blog/connecting-mcp-to-the-real-world-with-httpx</link>
      <guid>https://pranshupratyay.com/blog/connecting-mcp-to-the-real-world-with-httpx</guid>
      <pubDate>Sun, 02 Aug 2026 00:00:00 GMT</pubDate>
      <description>Hardcoding fake data in an MCP server is fine for a demo. Making real, asynchronous REST API calls to external services is where the magic actually happens. Here&apos;s how to pair FastMCP with HTTPX.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>Self-Attention Explained: The Mechanism Behind Every Transformer</title>
      <link>https://pranshupratyay.com/blog/self-attention-explained</link>
      <guid>https://pranshupratyay.com/blog/self-attention-explained</guid>
      <pubDate>Sun, 02 Aug 2026 00:00:00 GMT</pubDate>
      <description>How self-attention lets a model decide which words matter to each other — the query/key/value mechanics, scaled dot-product attention, multi-head attention, and causal masking, with a from-scratch PyTorch implementation.</description>
      <category>AI Engineering</category>
    </item>
    <item>
      <title>5 Agent Mistakes I Made So You Don&apos;t Have To</title>
      <link>https://pranshupratyay.com/blog/5-agent-mistakes-i-made-so-you-dont-have-to</link>
      <guid>https://pranshupratyay.com/blog/5-agent-mistakes-i-made-so-you-dont-have-to</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>Every one of these cost me real debugging time. Each has a one-line fix — here they are, with the code.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>Claude Code vs. Copilot vs. Cursor: What &apos;Agentic&apos; Actually Means Here</title>
      <link>https://pranshupratyay.com/blog/claude-code-vs-copilot-vs-cursor-what-agentic-means</link>
      <guid>https://pranshupratyay.com/blog/claude-code-vs-copilot-vs-cursor-what-agentic-means</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>All three get called &apos;AI coding assistants,&apos; but they&apos;re built on genuinely different philosophies. Here&apos;s the real difference, in plain terms, with an actual terminal transcript.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>I Connected Claude to My Terminal and Broke My Laptop (Lessons on Agent Guardrails)</title>
      <link>https://pranshupratyay.com/blog/claude-terminal-agent-guardrails-lessons</link>
      <guid>https://pranshupratyay.com/blog/claude-terminal-agent-guardrails-lessons</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>A weekend project, a bash tool with no restrictions, and the exact three mistakes that let it happen — plus the guardrails that would have stopped every one of them.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>Embeddings, Explained: From a Word Corpus to Vectors That Mean Something</title>
      <link>https://pranshupratyay.com/blog/embeddings-explained-corpus-to-vectors</link>
      <guid>https://pranshupratyay.com/blog/embeddings-explained-corpus-to-vectors</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>Start from a plain sentence, tokenize it with NLTK, and build up through every major embedding type — one-hot, TF-IDF, Word2Vec, GloVe, and modern contextual embeddings — with working code for each.</description>
      <category>AI Engineering</category>
    </item>
    <item>
      <title>How Much Does an Agentic Claude Workflow Actually Cost? (Real Numbers)</title>
      <link>https://pranshupratyay.com/blog/how-much-does-an-agentic-claude-workflow-cost</link>
      <guid>https://pranshupratyay.com/blog/how-much-does-an-agentic-claude-workflow-cost</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>Almost nobody publishes real token math for multi-step agents. Here&apos;s a worked example — a 6-turn agent loop, priced out — plus a small calculator function you can reuse.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>I Gave Claude Hands: My First Tool-Use Agent in 20 Lines</title>
      <link>https://pranshupratyay.com/blog/i-gave-claude-hands-first-tool-use-agent</link>
      <guid>https://pranshupratyay.com/blog/i-gave-claude-hands-first-tool-use-agent</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>Agents sound complicated until you see the actual request-response loop underneath. Here&apos;s the whole thing, stripped down to one tool and 20 lines of Python.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>Multi-Agent Systems Explained: When One Claude Isn&apos;t Enough</title>
      <link>https://pranshupratyay.com/blog/multi-agent-systems-explained</link>
      <guid>https://pranshupratyay.com/blog/multi-agent-systems-explained</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>One agent with a huge system prompt and thirty tools gets confused fast. Splitting it into focused agents that hand off work to each other usually works better — here&apos;s the pattern, with code.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>PyTorch, Explained: Tensors to Training Loops</title>
      <link>https://pranshupratyay.com/blog/pytorch-explained-tensors-to-training-loops</link>
      <guid>https://pranshupratyay.com/blog/pytorch-explained-tensors-to-training-loops</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>A practical, from-scratch walkthrough of PyTorch — tensors, autograd, building a model, training it, and where to go next — with links to the official docs and tutorials.</description>
      <category>AI Engineering</category>
    </item>
    <item>
      <title>RAG + Agents: The Difference Nobody Explains Well</title>
      <link>https://pranshupratyay.com/blog/rag-vs-agentic-rag-the-real-difference</link>
      <guid>https://pranshupratyay.com/blog/rag-vs-agentic-rag-the-real-difference</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>RAG and agentic RAG get used interchangeably constantly, and they&apos;re not the same thing. The difference is one word: whether the model can decide to look again.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>Reweighting, Rescaling, Smoothing, and Bagging: The Data Techniques Behind Every Model</title>
      <link>https://pranshupratyay.com/blog/reweighting-rescaling-smoothing-bagging-explained</link>
      <guid>https://pranshupratyay.com/blog/reweighting-rescaling-smoothing-bagging-explained</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>Five terms that show up in almost every ML pipeline, explained simply and with code: word/sample reweighting, time scaling, data rescaling, smoothing, and bagging.</description>
      <category>AI Engineering</category>
    </item>
    <item>
      <title>The Agent Loop: Why Every Claude Agent Is Just a While Loop</title>
      <link>https://pranshupratyay.com/blog/the-agent-loop-why-every-claude-agent-is-a-while-loop</link>
      <guid>https://pranshupratyay.com/blog/the-agent-loop-why-every-claude-agent-is-a-while-loop</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>Strip away the hype and every agentic system — Claude Code, MCP-connected agents, multi-step research bots — is running the same five-line loop underneath. Here it is, in full.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>What Is MCP, Actually? (Explained Like You&apos;re Five, With Code)</title>
      <link>https://pranshupratyay.com/blog/what-is-mcp-actually-explained-simply</link>
      <guid>https://pranshupratyay.com/blog/what-is-mcp-actually-explained-simply</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>MCP explainers are usually either a marketing slide or a 40-page spec. Here&apos;s the plain version — one analogy, one 15-line server — and why it matters more than it sounds like it should.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>Word Embeddings in Detail: How Machines Turn Language Into Geometry</title>
      <link>https://pranshupratyay.com/blog/word-embeddings-in-detail-from-one-hot-to-transformer-vectors</link>
      <guid>https://pranshupratyay.com/blog/word-embeddings-in-detail-from-one-hot-to-transformer-vectors</guid>
      <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
      <description>A deep dive into word embeddings: from one-hot encoding limitations and TF-IDF matrices to Word2Vec skip-gram mechanisms, GloVe factorization, and contextual Transformer embeddings — with Python code for each.</description>
      <category>AI Engineering</category>
    </item>
    <item>
      <title>Building a Claude Agent That Remembers You (Without a Vector Database)</title>
      <link>https://pranshupratyay.com/blog/claude-agent-memory-without-vector-database</link>
      <guid>https://pranshupratyay.com/blog/claude-agent-memory-without-vector-database</guid>
      <pubDate>Fri, 31 Jul 2026 00:00:00 GMT</pubDate>
      <description>Memory is the first thing everyone asks about agents, and the first answer is always &apos;embeddings and a vector DB.&apos; Here&apos;s the version that fits in a JSON file, and why you might not need more than that.</description>
      <category>Agentic AI</category>
    </item>
    <item>
      <title>Designing Production RAG Systems That Don&apos;t Fall Over</title>
      <link>https://pranshupratyay.com/blog/designing-production-rag-systems</link>
      <guid>https://pranshupratyay.com/blog/designing-production-rag-systems</guid>
      <pubDate>Fri, 12 Jun 2026 00:00:00 GMT</pubDate>
      <description>Lessons from moving retrieval-augmented generation from a demo to something that survives real traffic and real data.</description>
      <category>AI Engineering</category>
    </item>
    <item>
      <title>A Practical Framework for Evaluating LLM Agents</title>
      <link>https://pranshupratyay.com/blog/evaluating-llm-agents</link>
      <guid>https://pranshupratyay.com/blog/evaluating-llm-agents</guid>
      <pubDate>Thu, 28 May 2026 00:00:00 GMT</pubDate>
      <description>Why traditional unit tests fall short for agentic systems, and what a useful evaluation harness actually looks like.</description>
      <category>AI Engineering</category>
    </item>
    <item>
      <title>The Model Context Protocol, Explained for Builders</title>
      <link>https://pranshupratyay.com/blog/mcp-explained</link>
      <guid>https://pranshupratyay.com/blog/mcp-explained</guid>
      <pubDate>Sat, 09 May 2026 00:00:00 GMT</pubDate>
      <description>A grounded look at what MCP actually solves, and where it fits in a real AI product&apos;s architecture.</description>
      <category>AI Engineering</category>
    </item>
  </channel>
</rss>