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detectSource(e){return/youtube\.com|youtu\.be/.test(e)?"youtube":/reddit\.com/.test(e)?"reddit":/github\.com/.test(e)?"github":/twitter\.com|x\.com/.test(e)?"twitter":/substack\.com|beehiiv\.com/.test(e)?"newsletter":/\.pdf$/.test(e)?"pdf":/notion\.so/.test(e)?"notion":/podcast|rss|mp3/.test(e)?"podcast":"article"}Object.assign(window.VIEWS,{diyApp:DiyAppView}),window.VIEW_OPTIONS.push({value:"diyApp",label:".diy app",icon:"handyman"}),(function(){const t=window.KIND_REGISTRY||{};t.domDiy&&Object.assign(t.domDiy,{defaultView:"diyApp",defaultDetail:"none",description:".diy upgraded \xB7 11 sources \xB7 5 modes \xB7 10 formats \xB7 source player inline.",sources:[{type:"youtube",label:"YouTube",free:!0},{type:"article",label:"Articles",free:!0},{type:"podcast",label:"Podcasts",free:!0},{type:"reddit",label:"Reddit",free:!1},{type:"github",label:"GitHub repos",free:!1},{type:"newsletter",label:"Newsletters",free:!1},{type:"rss",label:"RSS 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A working implementation of prompt caching with 90% cost reduction.

## Setup

1. claude mcp add anthropic-prompt-cache
2. Set ANTHROPIC_API_KEY env var
3. Run: python code/cache_call.py

## Files

- prompts/system.md       \u2014 the cacheable system prompt
- prompts/cache_template.md \u2014 the dynamic user prompt template
- code/cache_call.py       \u2014 the cached completion handler
- tests/cache_test.py      \u2014 verification suite

## Cost savings

Measured: 92.4 % token-cost reduction on conversations >5 turns.
Source: Anthropic announcement (2026-05-24, 43-min talk).`},{id:"briefing",label:"Briefing",body:`# Briefing \xB7 Anthropic prompt caching deep-dive

**Problem:** Long-context agents re-pay full prompt tokens on every turn. At Claude Sonnet 4.5 rates, a 50K-token system prompt costs ~\u20AC0.15 per turn.

**Approach:** Anthropic ships a server-side prompt cache. Mark the cacheable prefix; first call writes, subsequent calls reuse for ~5 min. Cost on cache hits drops to ~10 % of full call.

**Key insights:**
1. Cache breakpoints must be explicit \u2014 no auto-detection
2. Cache TTL is 5 min by default; extendable to 1 hour on enterprise
3. Cache survives across users \u2014 verify isolation requirements

**TL;DR:** Drop-in optimization for agents with stable system prompts + variable user inputs. 90 % cost reduction at production scale.`},{id:"podcast_notes",label:"Show Notes",notes:[{t:"00:00",text:"Intro \u2014 what changes when system prompts are cached"},{t:"04:18",text:"Live demo \u2014 cache hit on second turn, latency drop 220\u219248 ms"},{t:"12:40",text:"Cost math \u2014 \u20AC0.15 \u2192 \u20AC0.014 per turn at 50K system prompt"},{t:"24:50",text:"Edge case \u2014 cache key collision across users (and how to avoid it)"},{t:"38:12",text:"Production checklist \u2014 what to verify before rolling out"}]},{id:"social_content",label:"Social",variants:[{platform:"LinkedIn",chars:1248,limit:3e3,text:`Anthropic just shipped prompt caching \u2014 and the numbers are wild.

92.4 % token-cost reduction on multi-turn agent conversations. We tested it on AaaS Decisions runs over the weekend; the drop in our LLM bill was instant.

Three things worth knowing:

1. Cache breakpoints are explicit. You mark the cacheable prefix yourself \u2014 no magic auto-detection.
2. TTL is 5 min by default. For sticky agents (think: support chat, ops dashboards), extend to 1 hour.
3. Cache survives across users by default. Make sure your prompt structure doesn't leak per-user context above the breakpoint.

Link to the IDE package in comments \u2014 drop-in implementation including the verification suite.`},{platform:"X",chars:268,limit:280,text:`Anthropic prompt caching = 92 % cost cut on multi-turn agents.

Mark cacheable prefix \u2192 cache hits drop calls to ~10 % of full cost.
TTL 5 min default, 1 hour on enterprise.

IDE package + tests below \u2193`},{platform:"Mastodon",chars:412,limit:500,text:`Anthropic prompt caching ships \u2014 92 % cost cut on multi-turn agents.

Mark the cacheable prefix explicitly; cache hits cost ~10 % of a fresh call. TTL 5 min default.

We tested it on AaaS Decisions runs over the weekend, full drop in LLM bill immediately.

Full IDE package with tests linked.`}]},{id:"mcp_server",label:"MCP Server",mcp:{tools:["cache_breakpoint","cache_status","cache_metrics"],resources:["cache_keys","cache_metrics_24h"],prompts:["cache_optimization_review","cache_breakpoint_suggest"]}}],source:{type:"youtube",duration:"43:21",title:"Prompt caching for 90% cost reduction \xB7 Anthropic dev talk",channel:"Anthropic \xB7 28 K subscribers",transcript:[{t:"00:00",text:"Welcome \u2014 today we're walking through prompt caching, a server-side optimization we just shipped."},{t:"00:24",text:"The headline: 90 % token-cost reduction for agents with stable system prompts and variable user inputs."},{t:"00:48",text:"Let's start with the cost math, then walk through the implementation, then the gotchas."},{t:"01:12",text:"A 50K-token system prompt costs \u20AC0.15 per turn at Sonnet 4.5 rates. Cached: \u20AC0.014."},{t:"01:38",text:"That math holds at any prompt size \u2014 it's about turn-count, not absolute cost."}]}},{id:"pkg-vector-rerank",name:"Vector reranking \xB7 production patterns",creator:"gauri",source_type:"article",mode:"Briefing",generated:"24.05.2026",installs:"1,820",proOnly:!0,formats:[],source:{type:"article",readMin:12,title:"Why you reranker matters more than your embedder",domain:"gauri.dev/blog",lead:"When your retrieval relevance plateaus at ~0.78, the next gain comes from reranking \u2014 not from a better embedder. Here's how to wire one in without breaking your latency budget."}},{id:"pkg-finetune-econ",name:"Fine-tuning economics in 2026",creator:"sarah",source_type:"podcast",mode:"Briefing",generated:"20.05.2026",installs:"2,840",proOnly:!1,formats:[],source:{type:"podcast",duration:"54:18",title:"The fine-tuning calculator \xB7 Practical AI #318",channel:"changelog.com/practicalai"}},{id:"pkg-mcp-intro",name:"MCP servers \xB7 the missing intro",creator:"alex",source_type:"youtube",mode:"Implementation",generated:"14.05.2026",installs:"6,820",proOnly:!1,formats:[],source:{type:"youtube",duration:"18:42",title:"MCP in 18 minutes",channel:"AaaS \xB7 12 K subscribers"}},{id:"pkg-langgraph",name:"LangGraph 0.2 parallel branches",creator:"patrick",source_type:"github",mode:"Extract",generated:"12.05.2026",installs:"942",proOnly:!0,formats:[],source:{type:"article",readMin:6,title:"LangGraph 0.2 release notes",domain:"github.com/langchain-ai/langgraph",lead:"Native fan-out for decision-branch patterns."}},{id:"pkg-eval-frameworks",name:"Eval frameworks \xB7 2026 landscape",creator:"demir",source_type:"newsletter",mode:"Compare",generated:"08.05.2026",installs:"1,142",proOnly:!0,formats:[],source:{type:"article",readMin:8,title:"Comparing 12 eval frameworks side-by-side",domain:"demir.substack.com",lead:"MT-Bench, MMLU, HumanEval, GAIA, and 8 others \u2014 when to use which."}}],runs:[{id:"rn-018",stage:"generate",source:"youtube.com/watch?v=xyz123 \xB7 43m",cost:"0,028"},{id:"rn-017",stage:"transcribe",source:"changelog.com/practicalai/318",cost:"0,012"},{id:"rn-016",stage:"render",source:"github.com/langchain-ai/langgraph",cost:"0,008"}]})})();