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.
Watch next
Continue through the same learning path with the next curated companion videos.
17 minutesDefending LLM - Prompt Injection
Review prompt-injection defenses such as taint analysis, output-shape restrictions, user isolation, deterministic settings and redundant checks for critical paths.
Watch next
56 minutesBuild Hour: Prompt Caching
Use prompt caching only when stable prefixes, latency and cost behavior match the workload.
Watch next
32 minutesFast LLM Serving with vLLM and PagedAttention
Understand why serving engines, batching and KV-cache memory dominate self-hosted inference economics.
Watch nextRelated videos
18 minutesAWS re:Invent 2025 - Implementing Human-in-the-Loop Controls for Multi-Agent AI Systems (CNS428)
17 minutes12-Factor Agents: Patterns of reliable LLM applications — Dex Horthy, HumanLayer
6 minutesAI Voice Agents: How They Actually Work & Why They Sound So Human
8 minutesAnthropic's Claude Computer Use Is A Game Changer | YC Decoded
Take it further
Hand-picked external courses that go deeper on this topic.
Coursera · Vanderbilt UniversityChatGPT: Excel at Personal Automation with GPTs, AI & Zapier
The clearest path from "I use ChatGPT in a tab" to "my AI handles my inbox while I sleep." Three-course specialization built around Zapier — no Python required. By the end you'll have agents that summarise emails, update spreadsheets, and trigger workflows when conditions are met.
Hugging FaceAI Agents Course
The clearest open-source treatment of agentic systems available. Anchored in the three frameworks engineers actually evaluate (smolagents, LlamaIndex, LangGraph) rather than one vendor's stack. Concludes with a benchmark assignment and public leaderboard — accountability your team can verify.