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"AI gold rush draws crypto firms away from Bitcoin **Companies that once filled warehouses with computers to earn Bitcoin - a process known as mining - are now using that computing power for AI.** Bitcoin mining companies invested large sums on giant banks of powerful computers as the price of Bitcoin [rocketed in recent years](https://www.bbc.co.uk/news/articles/c5ywp0l0xrxo). But those mining companies have faced falling rewards since the value of the cryptocurrency has slumped from its peak in October 2025. Now many are pivoting to AI and signing deals with companies such as Anthropic - who are spending billions of dollars on the infrastructure needed to keep up in the AI race. BBC News article: [https://www.bbc.co.uk/news/articles/c07ler8x9kpo](https://www.bbc.co.uk/news/articles/c07ler8x9kpo)"

"[Workflow] Optimize Claude Context Clearing: Focus on Task Changes, Not Percentage, to Reduce Costs # Optimize Claude Context Clearing: Focus on Task Changes, Not Percentage, to Reduce Costs **Workflow value:** 85/100 **Status:** active · **Freshness:** 70/100 · **Confidence:** 0.90 · **Level:** intermediate **Categories:** Quality Control, Token Saving, Context & Memory, Debugging **Original source:** [r/ClaudeCode post/comment](https://www.reddit.com/r/ClaudeCode/comments/1vy73ay/at_what_do_you_clear_context_and_open_a_new_chat/p5yjuf6/) ## What problem this solves Misconception about when to clear context in large language models to optimize cost and performance, by understanding the role of cached tokens. ## Summary This workflow advises against clearing Claude's context based on a fixed percentage of the context window. Instead, it recommends clearing context only when the task or topic fundamentally changes. This strategy aligns with Claude's caching mechanism, where cached tokens are re-read at full price only when they are no longer relevant. Users are encouraged to validate this by examining `input_tokens` and `cache_read_input_tokens` in their session transcripts, and a specific open-source tool is provided for visualization. ## Why it is useful This workflow corrects a common misconception about context management in large LLMs, providing a more accurate and cost-effective strategy based on Claude's underlying caching mechanism. It empowers users to understand and optimize their token usage, which is critical for long-running or complex tasks, potentially leading to significant cost savings and improved performance. ## Workflow 1. Stop clearing Claude context based on a fixed percentage of the context window. 2. Instead, clear context only when the current task or topic fundamentally changes. 3. Examine your Claude session transcripts, specifically the `input_tokens` and `cache_read_input_tokens` fields for each response. 4. Observe how th..."