<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Fantástico Mundo de Jon</title><description>A lab notebook on AI running on my own hardware: quantization, throughput, VRAM and the real cost of running LLMs locally.</description><link>https://blog.jonathanschenker.com.br</link><language>en</language><item><title>Coloring Pages with Local AI: From Prompt to a File That Prints Well</title><link>https://blog.jonathanschenker.com.br/en/posts/coloring-drawings-with-local-ai-from-prompt-to-print-ready-file</link><guid isPermaLink="true">https://blog.jonathanschenker.com.br/en/posts/coloring-drawings-with-local-ai-from-prompt-to-print-ready-file</guid><description>Generating the image is the easy part, and that&apos;s where most tutorials end. What determines whether the child can paint is what comes next: binarizing, vectorizing, and printing without jagged edges.</description><pubDate>Tue, 30 Jun 2026 00:00:00 GMT</pubDate><category>image</category><category>Flux</category><category>local AI</category><category>printing</category><category>MLX</category></item><item><title>Qwen3.6 27B: How Much VRAM It Really Needs</title><link>https://blog.jonathanschenker.com.br/en/posts/qwen3-6-27b-how-much-vram-it-really-needs</link><guid isPermaLink="true">https://blog.jonathanschenker.com.br/en/posts/qwen3-6-27b-how-much-vram-it-really-needs</guid><description>A 27B model that uses half the KV cache of a Llama 3 8B. The hybrid architecture breaks the usual calculation — and decides, for less than 1 GB, which cards are left out.</description><pubDate>Thu, 25 Jun 2026 00:00:00 GMT</pubDate><category>Local LLM</category><category>VRAM</category><category>Qwen</category><category>Hybrid Attention</category><category>GPU</category></item><item><title>How much VRAM an LLM really uses</title><link>https://blog.jonathanschenker.com.br/en/posts/how-much-vram-an-llm-really-uses</link><guid isPermaLink="true">https://blog.jonathanschenker.com.br/en/posts/how-much-vram-an-llm-really-uses</guid><description>The back-of-the-envelope math — parameters times bits — is off by several gigabytes, because it ignores the KV cache. Here is the full calculation, with the formula, the numbers and how to verify it on your own GPU.</description><pubDate>Wed, 10 Jun 2026 00:00:00 GMT</pubDate><category>local LLM</category><category>VRAM</category><category>quantization</category><category>GPU</category></item><item><title>I measured whether a spec file makes the model more accurate. It doesn&apos;t.</title><link>https://blog.jonathanschenker.com.br/en/posts/spec-file-doesnt-make-model-accurate</link><guid isPermaLink="true">https://blog.jonathanschenker.com.br/en/posts/spec-file-doesnt-make-model-accurate</guid><description>In July I argued that structured spec changes the outcome. I measured in October: 360 generations, 3 local models, 3 ways of asking for the same thing. What pays off is the written information, not the file format.</description><pubDate>Tue, 14 Oct 2025 00:00:00 GMT</pubDate><category>spec-driven</category><category>code agents</category><category>Aider</category><category>local LLM</category><category>benchmark</category></item><item><title>The PRD comes first: the spec that prevents the agent from writing incorrect code</title><link>https://blog.jonathanschenker.com.br/en/posts/prd-comes-first-spec-prevents-agent-errors</link><guid isPermaLink="true">https://blog.jonathanschenker.com.br/en/posts/prd-comes-first-spec-prevents-agent-errors</guid><description>The model doesn&apos;t make mistakes due to ignorance: it makes mistakes because it filled the gap the prompt left, and filled it in a plausible way. What changes in the code when the contract comes before the request.</description><pubDate>Tue, 15 Jul 2025 00:00:00 GMT</pubDate><category>code agents</category><category>spec-driven</category><category>Claude Code</category><category>PRD</category></item></channel></rss>