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# ![@ksaksham39 Avatar](https://lunarcrush.com/gi/w:26/cr:twitter::1426851749046874122.png) @ksaksham39 Saksham

Saksham posts on X about llm, $500day, open ai, $50day the most. They currently have XXX followers and XX posts still getting attention that total XXX engagements in the last XX hours.

### Engagements: XXX [#](/creator/twitter::1426851749046874122/interactions)
![Engagements Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::1426851749046874122/c:line/m:interactions.svg)

- X Month XXXXX +11,680%
- X Months XXXXXX +1,932%
- X Year XXXXXX +13%

### Mentions: X [#](/creator/twitter::1426851749046874122/posts_active)
![Mentions Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::1426851749046874122/c:line/m:posts_active.svg)


### Followers: XXX [#](/creator/twitter::1426851749046874122/followers)
![Followers Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::1426851749046874122/c:line/m:followers.svg)

- X Week XXX +77%
- X Month XXX +85%
- X Months XXX +563%
- X Year XXX +563%

### CreatorRank: XXXXXXXXX [#](/creator/twitter::1426851749046874122/influencer_rank)
![CreatorRank Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::1426851749046874122/c:line/m:influencer_rank.svg)

### Social Influence [#](/creator/twitter::1426851749046874122/influence)
---

**Social category influence**
[technology brands](/list/technology-brands) 

**Social topic influence**
[llm](/topic/llm) #131, [$500day](/topic/$500day), [open ai](/topic/open-ai), [$50day](/topic/$50day)
### Top Social Posts [#](/creator/twitter::1426851749046874122/posts)
---
Top posts by engagements in the last XX hours

"Day 2: The Caching Strategy That Cuts Your LLM Bill by XX% Learning production LLM engineering in XX days. Day X covers the one optimization that separates expensive demos from profitable products. Your demo costs $50/day in OpenAI calls. You launch and suddenly it's $500/day. The difference You're processing the same prompts over and over. Smart caching isn't just storing responses. It's understanding that most LLM requests have patterns. Two types of caching every production app needs: - Exact match caching for identical prompts (instant retrieval) - Semantic caching for similar meaning"  
![@ksaksham39 Avatar](https://lunarcrush.com/gi/w:16/cr:twitter::1426851749046874122.png) [@ksaksham39](/creator/x/ksaksham39) on [X](/post/tweet/1947664651438690790) 2025-07-22 14:26:53 UTC XXX followers, XXX engagements


"Day 1: ObservabilityWhy Most LLM Apps Fail in Production Im learning production LLM engineering in XX days. Follow to learn alongside meone topic no jargon all practical. Lets start with why demos die after launch: observability. Most teams only check if their app is up. Thats not enough for LLMs. Production failures happen for reasons you cant spot with simple uptime checks. What real observability means for LLMs: You want to see not just if your app is livebut if your model is giving useful safe and accurate answers. Its working is different from answers make sense and cost isnt exploding"  
![@ksaksham39 Avatar](https://lunarcrush.com/gi/w:16/cr:twitter::1426851749046874122.png) [@ksaksham39](/creator/x/ksaksham39) on [X](/post/tweet/1947305536614973745) 2025-07-21 14:39:53 UTC XXX followers, XXX engagements

[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]

@ksaksham39 Saksham

Saksham posts on X about llm, $500day, open ai, $50day the most. They currently have XXX followers and XX posts still getting attention that total XXX engagements in the last XX hours.

Engagements: XXX #

X Month XXXXX +11,680%
X Months XXXXXX +1,932%
X Year XXXXXX +13%

Mentions: X #

Followers: XXX #

X Week XXX +77%
X Month XXX +85%
X Months XXX +563%
X Year XXX +563%

CreatorRank: XXXXXXXXX #

Social Influence #

Social category influence technology brands

Social topic influence llm #131, $500day, open ai, $50day

Top Social Posts #

Top posts by engagements in the last XX hours

"Day 2: The Caching Strategy That Cuts Your LLM Bill by XX% Learning production LLM engineering in XX days. Day X covers the one optimization that separates expensive demos from profitable products. Your demo costs $50/day in OpenAI calls. You launch and suddenly it's $500/day. The difference You're processing the same prompts over and over. Smart caching isn't just storing responses. It's understanding that most LLM requests have patterns. Two types of caching every production app needs: - Exact match caching for identical prompts (instant retrieval) - Semantic caching for similar meaning"
@ksaksham39 on X 2025-07-22 14:26:53 UTC XXX followers, XXX engagements

"Day 1: ObservabilityWhy Most LLM Apps Fail in Production Im learning production LLM engineering in XX days. Follow to learn alongside meone topic no jargon all practical. Lets start with why demos die after launch: observability. Most teams only check if their app is up. Thats not enough for LLMs. Production failures happen for reasons you cant spot with simple uptime checks. What real observability means for LLMs: You want to see not just if your app is livebut if your model is giving useful safe and accurate answers. Its working is different from answers make sense and cost isnt exploding"
@ksaksham39 on X 2025-07-21 14:39:53 UTC XXX followers, XXX engagements