How to use Claude hacks to 10x AI speed

TL;DR

The most useful fact is to rethink AI spend and invest in skills and knowledge bases. Start by turning repeatable workflows into reusable skills, then build an internal knowledge base for your AI to access quickly. Avoid overbuilding and plan before you build to maximize speed and output.

Transcript

In this video, I'm going to break down the best and worst advice for using AI to build 10 times faster. Because the truth is, there have never been more people who are sharing horrible AI advice that is completely backwards, and it is destroying how people use AI in their day-to-day. Now, if you don't know me, my name is Austin, and I'm not just a ... Read More

Key Insights

• D tier advice to switch to free open source models is risky because it may save money but increases time or lowers output.
• Turn repeatable workflows into skills to own the intelligence and reduce platform lock-in.
• Provide AI with contextual information up to a point where it remains actionable and not overwhelming.
• Build an LLM knowledge base to organize and map data so AI can access relevant information efficiently.
• If you can build it, evaluate whether it will be a 10x move or a 2x move before starting.
• Plan before you build to avoid unnecessary redevelopment and feature bloat.
• Use multiple agents to distribute tasks and prevent context switching bottlenecks.
• Experiment deliberately to learn what strategies actually improve output and speed.

Questions & Answers

Q: How can I turn repetitive workflows into reusable AI skills?

Turning repetitive workflows into skill files involves identifying a task, prompting the AI to create a skill from the conversation, running the skill to test it, and correcting its output if needed. This process locks your approach into a portable format that can be reused across tools like Claude or Codeex, avoiding platform lock-in and enabling faster replication of effective methods.

Q: Why is owning the intelligence better than renting it when using AI?

Owning the intelligence means creating skills that travel with you beyond a single tool or conversation, which reduces dependency on any single AI provider. This approach protects you from price changes and service lock-ins, enabling you to reuse proven methods across different platforms and workflows, ultimately increasing reliability and speed.

Q: What is an LLM knowledge base and why is it valuable?

An LLM knowledge base is a structured collection of contextual files and raw source material that AI can query efficiently. By separating raw information from distilled key learnings in a wiki-like folder, you give AI a reliable map to access high quality content quickly, improving accuracy and reducing context search time during tasks.

Q: How does context management affect AI performance?

Context management affects performance by providing high quality, relevant information while avoiding outdated or conflicting data. Too much irrelevant context can slow and confuse the AI, while well curated context helps it make better decisions and generate more useful outputs, increasing speed and accuracy.

Q: What is the difference between a 10x move and a 2x move in AI projects?

A 10x move creates a substantial leap in capability or efficiency, often by restructuring processes or building foundational knowledge bases, whereas a 2x move makes incremental improvements. Deciding between these helps avoid wasted effort and focus on strategies that deliver exponential gains in speed or output.

Q: Why should you avoid universal automation without strategy?

Automating everything without a clear strategy can create brittle, hard to maintain systems that fail under edge cases. A thoughtful approach ensures automation targets high value tasks, maintains quality, and reduces the risk of replacing meaningful work with fragile processes that require extensive maintenance.

Q: How can multiple agents improve AI workflows?

Using multiple agents helps distribute tasks and reduces context switching, enabling parallel processing of different aspects of a project. This leads to faster turnaround and better specialization, as different agents can focus on distinct parts of the workflow while sharing a common knowledge base.

Q: What should you consider before rebuilding a software tool for your business?

Before rebuilding, ask whether the change is a 10x or a 2x improvement and whether the new tool actually solves an unmet need. Rebuilds should be justified by significant efficiency gains, otherwise it is often better to adapt existing solutions and avoid sunk costs in maintenance and feature parity.

Summary & Key Takeaways

• Building faster with AI starts by converting repeatable tasks into skills and owning the intelligence, not renting it.

• An internal LLM knowledge base acts as an industrial fridge for context, allowing AI to access high quality information quickly without clutter.

• Plan before you build and avoid building unnecessary tools; focus on 10x moves that amplify impact, not just feature parity.