running local models changed how I think about data privacy for client work

<!-- SC_OFF --><div class="md"><p>Been noticing that once you use LLMs for real dev work, the cost conversation gets messy fast. It is not just raw API spend. It is retries, long

<!-- SC_OFF --><div class="md"><p>Maybe this is just where my brain has gone lately, but I’m finding myself less impressed by raw model benchmarks and more interested in

<!-- SC_OFF --><div class="md"><p>Most of my LLM cost was on the wrong tier of work.</p> <p>Classification, extraction, JSON formatting, summarization I'm going to review anyway.

<!-- SC_OFF --><div class="md"><p>Hello everyone, I am currently building a RAG pipeline. Since it involves highly sensitive medical data, I have deployed the models locally to

<!-- SC_OFF --><div class="md"><p>Hi,</p> <p>I’d like to practice answering scientific questions about a confidential project, and I'm considering using an LLM. As this is about a

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s7xv6u/got_a_9b_abliterated_claudedistilled_model/"> <img alt="Got a 9B Abliterated Claude-Distilled model

<h1> Local Models vs Cloud Models — The AI Advantage Is Moving From Access to Infrastructure </h1> <h2> Core thesis </h2> <p>AI is no longer new.</p> <p>The question isn't

<!-- SC_OFF --><div class="md"><p>I've been thinking about why local-first AI agent architectures are getting serious enterprise traction in 2026, beyond the obvious &quot;keep