13 minutesAttacking LLM - Prompt Injection
Model prompt injection as untrusted-data mixing and design boundaries around tool use.
AdvancedAI Safety & Data Privacy
Claude has taken control of my computer...
Fireship. The clearest short explanation on YouTube of the screenshot–action–screenshot loop, including the honest failure modes (Claude wandering off to look at Yellowstone, token burn, latency per step). Fireship is light on production detail by design — read the article for that — but it leaves you with the right intuition for why these systems are expensive and brittle before you commit one to your stack.
AI Expert note
Keep this as intuition-building, not production guidance. The video intentionally compresses the topic; production systems need sandboxing, secrets isolation, audit logs, rate limits and rollback paths.
What you should get from this
Understand why screenshot-based computer use is powerful, slow, expensive and brittle compared with API-native automation.
Watch or know first
Know the difference between an LLM calling an API tool and an LLM controlling a browser or desktop.
Last reviewed: May 18, 2026
Watch next
Continue through the same learning path with the next curated companion videos.
13 minutes
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You can estimate when prompt caching pays off by weighing cache-write surcharges against read savings for your real workloads.
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Understand why serving engines, batching and KV-cache memory dominate self-hosted inference economics.
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