@kyla.dodds: @Mom boat day 🤍

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Monday 30 March 2026 23:59:46 GMT

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Mom :

Mommy

2026-03-31 00:01:14

1

Ed S :

gorgeous girls 🥰🥰

2026-04-10 17:49:44

1

Chicken nugget man :

Stunning 🔥🔥💕

2026-03-31 00:11:28

1

Emmanuel L. Duran :

😍😍😍

2026-03-31 00:04:17

1

Mamba :

❤️❤️❤️

2026-03-31 17:53:01

1

christopherrobso7 :

❤️

2026-04-01 09:53:06

1

joc1111 :

💙💙💙😊

2026-04-10 19:45:36

1

Vinny :

😍😍

2026-03-31 16:56:09

0

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Other Videos

deixa eu entrar no seu coração| #veigh #trap #romantico #musicasparastatus #fyp

deixa eu entrar no seu coração| #veigh #trap #romantico #musicasparastatus #fyp

🌳 The LLM Cost Tree: Optimize Outcomes, Not Tokens Most teams try to reduce AI costs by negotiating cheaper tokens. That helps—but it rarely fixes the real problem. Your actual cost is closer to: Cost per success = tokens × model price × retries × tool loops A “cheap” model becomes expensive when it needs three retries. A smaller prompt becomes irrelevant if an agent loops 20 times. A powerful model is wasteful when the task only needs classification or extraction. The smarter approach is to optimize the entire execution path. ⚙️ 🧠 Spend less per call Use smaller models for predictable tasks, route by complexity, and escalate only when confidence is low. 📚 Send fewer tokens Trim irrelevant history, summarize long conversations, and retrieve only the evidence required for the current task. ✍️ Generate less Set output ceilings, request structured responses, and use deterministic tools when reasoning adds no value. ⚡ Avoid repeated work Cache exact responses, reusable prompt prefixes, and semantically equivalent requests. 🛡️ Control execution Batch asynchronous workloads, cap agent turns and tool calls, enforce timeouts, and track the cost of successful outcomes. The important engineering principle: The cheapest token does not guarantee the cheapest completed task. Measure what actually reaches production: ✅ Task success rate ✅ End-to-end latency ✅ Tokens consumed ✅ Tool calls and retries ✅ Human-review time ✅ Cost per successful outcome Because production AI optimization isn’t about making every request cheap. It’s about spending intelligence only where intelligence creates value. 🌱 Save this tree for your next AI architecture or cost-review meeting. 📌 #HackProduct #AIEngineering #LLM #GenerativeAI #AgenticAI

🌳 The LLM Cost Tree: Optimize Outcomes, Not Tokens Most teams try to reduce AI costs by negotiating cheaper tokens. That helps—but it rarely fixes the real problem. Your actual cost is closer to: Cost per success = tokens × model price × retries × tool loops A “cheap” model becomes expensive when it needs three retries. A smaller prompt becomes irrelevant if an agent loops 20 times. A powerful model is wasteful when the task only needs classification or extraction. The smarter approach is to optimize the entire execution path. ⚙️ 🧠 Spend less per call Use smaller models for predictable tasks, route by complexity, and escalate only when confidence is low. 📚 Send fewer tokens Trim irrelevant history, summarize long conversations, and retrieve only the evidence required for the current task. ✍️ Generate less Set output ceilings, request structured responses, and use deterministic tools when reasoning adds no value. ⚡ Avoid repeated work Cache exact responses, reusable prompt prefixes, and semantically equivalent requests. 🛡️ Control execution Batch asynchronous workloads, cap agent turns and tool calls, enforce timeouts, and track the cost of successful outcomes. The important engineering principle: The cheapest token does not guarantee the cheapest completed task. Measure what actually reaches production: ✅ Task success rate ✅ End-to-end latency ✅ Tokens consumed ✅ Tool calls and retries ✅ Human-review time ✅ Cost per successful outcome Because production AI optimization isn’t about making every request cheap. It’s about spending intelligence only where intelligence creates value. 🌱 Save this tree for your next AI architecture or cost-review meeting. 📌 #HackProduct #AIEngineering #LLM #GenerativeAI #AgenticAI

#CapCut #saraskeditz

#CapCut #saraskeditz

I thought this was gonna be a fun unboxing… until my youngest son lost the battle to ride first 😭😂 We definitely should’ve bought two. #Kids #FamilyFun #GoKart #tiktokshop #tokyodrift

I thought this was gonna be a fun unboxing… until my youngest son lost the battle to ride first 😭😂 We definitely should’ve bought two. #Kids #FamilyFun #GoKart #tiktokshop #tokyodrift

Professeur Barhama 🧠 limoye fathie tanke youye Métti ✅

Professeur Barhama 🧠 limoye fathie tanke youye Métti ✅

zane tipografia . . . #foryou #ninjago #edit #zane

zane tipografia . . . #foryou #ninjago #edit #zane

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