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<video:title>Prompt Caching Compared: Opus 5.5 vs GPT-6.1 Sol vs Gemini 4 Argon</video:title>
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<video:description>Claude Opus 5.5, GPT-6.1 Sol, and Gemini 4 Argon all advertise 95% off cached input tokens — yet multi-turn coding agents still rack up 2x to 5x API bills. Here is what prompt caching actually stores in KV memory, the exact read and write multipliers across Anthropic, OpenAI, and Google, the four billing traps that silently wipe your hits, and the 10-turn math on a 100K-token repo prefix.</video:description>
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<video:title>Running Local AI on Your GPU: The MXFP4 and MoE Breakdown</video:title>
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<video:description>A 117B parameter model can now run on a single 80 GB GPU, generating tokens at the speed of a tiny 5B model. Here is the math behind why active parameters dictate token speed while total parameters dictate VRAM, how gpt-oss-120b and gpt-oss-20b fit on real hardware with MXFP4, and how to avoid out-of-memory crashes from the KV cache.</video:description>
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<video:title>Can You Trust What AI Tells You? Explained in 8 Minutes</video:title>
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<video:description>A confident, well-formatted answer is not evidence. It is just good prose, and good prose is what these models are trained to produce. This is where models are solid and where they are thin, the two ways they fail you, and five habits that make you check the right answers in under a minute.</video:description>
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<loc>https://buildverse.fun/videos/claude-code-auto-mode-explained-in-7-minutes</loc>
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<video:title>Claude Code Auto Mode Explained in 7 Minutes</video:title>
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<video:description>Claude Code asks you to approve every command, and Anthropic says people approve 93% of those prompts anyway. Auto mode puts a second model in the loop: it reviews each action, blocks what reaches past your intent, and hands the hard calls back to you. Here is how the classifier decides what runs, what stops, and how to set the trust boundary for your own repos, buckets and internal services.</video:description>
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<video:title>What Happens When You Talk to AI (Clearly Explained)</video:title>
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<video:description>Your answer was not found. It was finished, one token at a time, by a model that had never seen your question before. Here is the whole path from keystroke to reply, with every claim checked against Anthropic's docs. We walk through tokens, next-token prediction, the context window as the model's only working memory, the system prompt, and why the same question twice gives two different answers.</video:description>
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<video:description>A fresh Claude Code session spends about 40,000 tokens before your first message. Half of it is the system prompt and built-in tools, and the rest is skills, plugins, agents and MCP names you installed. This is the measured breakdown from a real cleanup, the five fixes, and the 10,000 tokens I got back without breaking anything.</video:description>
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<video:description>An agent is a model, a system prompt, a tool and a loop; memory, MCP and permissions decide whether it survives production. We build one chocolate-shop support agent for order 4821, then show where a plain workflow is the cheaper, safer answer.</video:description>
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<video:title>Local AI Explained in 12 Minutes (Beginner's Guide)</video:title>
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<video:description>Local AI means the weights are a file on your disk, and the math runs on your CPU or GPU. Here is the memory formula, what fits in 8, 16, 32, and 64 GB, and one command to run a first model.</video:description>
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<video:title>Reverse Engineer Anything: What the Viral Repo Actually Does</video:title>
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<video:description>Reverse Engineer Anything connects coding agents to analysis tools. We open the repo to explain its MCP server, investigation workflow, and the evidence behind the DX-Ball example. We also separate the Adobe claim from what REA demonstrates, explain the limits, and outline a first experiment on a tiny program you wrote yourself.</video:description>
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<video:title>Best Local Coding AI for Your GPU: 8 GB to 128 GB</video:title>
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<video:description>Pick a local coding model for 8, 16, 24, 32, 64 or 128 GB of memory, with exact download files and published sizes. We explain quantization, leave room for context, separate original model benchmarks from quant results, and show how to check what actually loaded.</video:description>
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<video:title>Gemini 4 Argon vs Opus 5.5 vs GPT-6 Astra: Which One for Coding?</video:title>
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<video:description>Gemini 4 Argon, Claude Opus 5.5, and GPT-6 Astra for coding: compare vendor-reported benchmarks, context limits, API prices, speed claims, and coding tool availability. Use a duplicate-order checkout bug to see how to evaluate fixes, review evidence, and choose a setup for your repository. This is a published-evidence comparison, not a shared hands-on test.</video:description>
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<video:title>Beam AI Review: Efficient Reasoning, One Big Catch</video:title>
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<video:description>Beam is Reflection AI's American open-weight model, built for coding and reasoning with fewer generated tokens. Here is what its published results show, where it falls behind, and who should try it.</video:description>
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<video:description>Claude Code isn't a chatbot. It's an agent: it reads your project, edits files, runs commands and checks its own work. Here's how it works, how to set it up, and the 3 mistakes that waste the most time.</video:description>
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