<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Muhammad Farooq · Writing</title><description>Essays on retrieval systems, large language models, agents, and the engineering around them.</description><link>https://engineerprompt.ai/</link><item><title>How agent harnesses manage context: cap, slice, search, store</title><link>https://engineerprompt.ai/writing/agent-context-management/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/agent-context-management/</guid><description>What occupies an agent&apos;s context window and the four moves harnesses use when content does not fit: cap it, slice it, search it, or store it elsewhere.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Compaction is the hardest problem in agent engineering</title><link>https://engineerprompt.ai/writing/compaction/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/compaction/</guid><description>Why agent harnesses summarize old history, what a careless summary destroys, the failure modes that follow, and the patterns that make compaction safe.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>DeepSeek visual primitives: teaching models to reason with a cursor</title><link>https://engineerprompt.ai/writing/deepseek-visual-primitives/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/deepseek-visual-primitives/</guid><description>Notes on DeepSeek&apos;s briefly public paper Thinking with Visual Primitives: boxes, points, and paths placed inside the reasoning trace, and its honest limits.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>DiffusionGemma: what Google&apos;s open text diffusion model actually changes</title><link>https://engineerprompt.ai/writing/diffusion-gemma/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/diffusion-gemma/</guid><description>Notes on DiffusionGemma, Google&apos;s first open-weight text diffusion model: how block diffusion refines a 256-token canvas in parallel, the official speed and benchmark numbers, and what it takes to run locally.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>DwarfStar 4: how a 284B model runs on a MacBook</title><link>https://engineerprompt.ai/writing/dwarfstar-4/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/dwarfstar-4/</guid><description>A 284B parameter model needs 568 GB stored normally. DwarfStar runs it on 128 GB machines at usable speeds. The quantization recipe, SSD streaming, and the numbers.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>How to evaluate an agent harness</title><link>https://engineerprompt.ai/writing/evaluating-harnesses/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/evaluating-harnesses/</guid><description>Harness configurations cluster at 74-76% resolve rate while cost varies fourteen times. A five-step method for judging harnesses on accuracy and cost.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Loop engineering: what it is, when to use it, and when to stay away</title><link>https://engineerprompt.ai/writing/loop-engineering/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/loop-engineering/</guid><description>Loop engineering means designing systems that prompt your agents instead of prompting them yourself. What a loop is, what a serious one needs, and the caveats that matter.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>RAG beyond similarity search: how a modern retrieval pipeline works</title><link>https://engineerprompt.ai/writing/rag-beyond-similarity-search/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/rag-beyond-similarity-search/</guid><description>Traditional RAG embeds chunks and hopes similarity search finds the right ones. What replaced it: hybrid retrieval, reranking, enrichment, verification, with localGPT as a working example.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Sub-agents: when one context window is not enough</title><link>https://engineerprompt.ai/writing/sub-agents/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/sub-agents/</guid><description>Why single-context agents hit a wall, how harnesses isolate work in child agents with the spawn, restrict, collect pattern, and when delegation backfires.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Tools vs skills vs MCP: how agents acquire capabilities</title><link>https://engineerprompt.ai/writing/tools-skills-mcp/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/tools-skills-mcp/</guid><description>Tools are primitives. Skills are knowledge. MCP is neither: a protocol that connects external tool servers to any harness. How the three fit together.</description><pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Harness engineering: why agent performance now lives outside the model</title><link>https://engineerprompt.ai/writing/harness-engineering/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/harness-engineering/</guid><description>Same model, same benchmark, six times the performance difference. Two March 2026 papers show the code around the model now matters more than the model. Here is what they found.</description><pubDate>Wed, 10 Jun 2026 00:00:00 GMT</pubDate></item><item><title>What is an agent harness? The nine components of a great one</title><link>https://engineerprompt.ai/writing/what-is-an-agent-harness/</link><guid isPermaLink="true">https://engineerprompt.ai/writing/what-is-an-agent-harness/</guid><description>A harness is the fixed architecture that turns a model into an agent. What it is, how it differs from a framework, and the nine components every modern harness needs.</description><pubDate>Wed, 10 Jun 2026 00:00:00 GMT</pubDate></item></channel></rss>