@richyliftz: Ending is so worth it #CapCut

richylifts

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Monday 06 April 2026 23:26:26 GMT

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🚬 :

Trying to impress enyone bro🖤

2026-04-21 18:33:17

46

Stacey Major 💀🌪️⚡️ :

Lowkey like the other acc bc 😏

2026-04-16 09:08:46

177

hubixd :

Ts shi so peak🙌🏻🙌🏻🙌🏻

2026-04-06 23:28:29

5

🤍jemma🤍 :

we're you from

2026-04-26 14:40:26

0

mil🤍 :

im earlyyyy😜😜

2026-04-06 23:33:50

4

melissa_imso fan page(^-^) :

melissa right now seeing this

2026-08-15 20:04:20

1

onikmd :

2026-04-25 19:26:20

2

Unknown :

Gym routine / shoulder workout

2026-04-14 21:52:01

1

￴ ꨄmina❦✨️💍🎧 :

2026-06-10 07:00:21

0

E.thaqi🖤❤️ :

Oh shit ❤️

2026-06-23 11:38:44

0

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🧠 I've sat through 100+ system design interviews. The candidates who pass don't memorize architectures — they carry ONE template in their head and adapt it live. So I drew the whole thing. Screenshot this. 📸 Here's how a request actually flows through a modern, AI-native system 👇 ━━━━━━━━━━━━━━━ 1️⃣ ENTRY & EDGE 🌍 Every request starts here. 👤 Client → 🌐 DNS + CDN → ⚖️ Load Balancer → 🚪 API Gateway The gateway is your bouncer: auth, rate limits, routing. If this layer is weak, nothing behind it matters. 🔒 ━━━━━━━━━━━━━━━ 2️⃣ APPLICATION CORE ⚙️ The classic workhorse. 📦 Stateless services (so you can scale horizontally) → ⚡ Cache → 🗄️ SQL / NoSQL Slow work? Don't block the user. Push it to an 📨 Event Bus / Queue → 🛠️ Workers → 🔔 Search + Notifications. Big files go to 🪣 Object Storage, not your DB. 👉 The whole game here is: decouple slow work so the request path stays fast. ━━━━━━━━━━━━━━━ 3️⃣ AI-NATIVE INFERENCE 🤖 (the part most diagrams skip) This is where 2025 systems live. 🧠 AI Gateway / Orchestrator → 🛡️ Safety + Policy → 📝 Context Builder → 🔀 Model Router → 🖥️ Inference Server → 🌊 Token Stream The Context Builder is fed by 3 things: 📚 RAG / Vector DB → 🧩 Memory → 🔧 Tools + APIs The Model Router picks based on ⚖️ quality vs cost vs latency (GPT-class for hard stuff, small + fast for the rest). The Inference Server is where the real money burns: 📦 batching, 🔑 KV cache, 🎮 GPU utilization. The mental model that makes it all click 👉 Prompt → Context → Model → Tokens. ✨ ━━━━━━━━━━━━━━━ 4️⃣ DATA & OPERATIONS 📊 Nothing improves if you can't see it. 🌊 Event Stream → 🏞️ Lake / Warehouse → 🔁 Batch + Stream Jobs → 🧬 Features + Embeddings (which feed right back into your RAG 🔄). Wrapped around everything: 👁️ Observability (logs · metrics · traces) 📈 AI Evals (quality · safety · drift) 🔐 Security (IAM · secrets · encryption) 🛡️ Reliability (retries · backpressure · failover) ━━━━━━━━━━━━━━━ ✅ THE DESIGN CHECKLIST (say these out loud in the interview) ☑️ Requirements ☑️ Scale + QPS ☑️ Latency + SLO ☑️ Data model ☑️ Failure modes ☑️ Cost + trade-offs ━━━━━━━━━━━━━━━ 🎨 Reading the diagram: ▪️ Solid line = synchronous (request waits) ⏱️ ▫️ Dashed line = asynchronous (fire and forget) 📤 🟡 Gold path = the AI flow Here's the real unlock 🔓 — the skill isn't drawing this. It's being able to look at a system (or AI-generated code) and ask: where's the bottleneck? what breaks under 10x load? which shard gets hot? 🕵️ That judgment is what separates engineers who use AI from those who architect with it. 🚀 Save it. Share it with someone prepping for interviews. 🔖 Which layer should I break down next — 🤖 AI-Native Inference or ⚙️ the Application Core? Tell me below 👇 . . . #HackProduct #systemdesign #softwareengineering #systemdesigninterview #AIengineering

🧠 I've sat through 100+ system design interviews. The candidates who pass don't memorize architectures — they carry ONE template in their head and adapt it live. So I drew the whole thing. Screenshot this. 📸 Here's how a request actually flows through a modern, AI-native system 👇 ━━━━━━━━━━━━━━━ 1️⃣ ENTRY & EDGE 🌍 Every request starts here. 👤 Client → 🌐 DNS + CDN → ⚖️ Load Balancer → 🚪 API Gateway The gateway is your bouncer: auth, rate limits, routing. If this layer is weak, nothing behind it matters. 🔒 ━━━━━━━━━━━━━━━ 2️⃣ APPLICATION CORE ⚙️ The classic workhorse. 📦 Stateless services (so you can scale horizontally) → ⚡ Cache → 🗄️ SQL / NoSQL Slow work? Don't block the user. Push it to an 📨 Event Bus / Queue → 🛠️ Workers → 🔔 Search + Notifications. Big files go to 🪣 Object Storage, not your DB. 👉 The whole game here is: decouple slow work so the request path stays fast. ━━━━━━━━━━━━━━━ 3️⃣ AI-NATIVE INFERENCE 🤖 (the part most diagrams skip) This is where 2025 systems live. 🧠 AI Gateway / Orchestrator → 🛡️ Safety + Policy → 📝 Context Builder → 🔀 Model Router → 🖥️ Inference Server → 🌊 Token Stream The Context Builder is fed by 3 things: 📚 RAG / Vector DB → 🧩 Memory → 🔧 Tools + APIs The Model Router picks based on ⚖️ quality vs cost vs latency (GPT-class for hard stuff, small + fast for the rest). The Inference Server is where the real money burns: 📦 batching, 🔑 KV cache, 🎮 GPU utilization. The mental model that makes it all click 👉 Prompt → Context → Model → Tokens. ✨ ━━━━━━━━━━━━━━━ 4️⃣ DATA & OPERATIONS 📊 Nothing improves if you can't see it. 🌊 Event Stream → 🏞️ Lake / Warehouse → 🔁 Batch + Stream Jobs → 🧬 Features + Embeddings (which feed right back into your RAG 🔄). Wrapped around everything: 👁️ Observability (logs · metrics · traces) 📈 AI Evals (quality · safety · drift) 🔐 Security (IAM · secrets · encryption) 🛡️ Reliability (retries · backpressure · failover) ━━━━━━━━━━━━━━━ ✅ THE DESIGN CHECKLIST (say these out loud in the interview) ☑️ Requirements ☑️ Scale + QPS ☑️ Latency + SLO ☑️ Data model ☑️ Failure modes ☑️ Cost + trade-offs ━━━━━━━━━━━━━━━ 🎨 Reading the diagram: ▪️ Solid line = synchronous (request waits) ⏱️ ▫️ Dashed line = asynchronous (fire and forget) 📤 🟡 Gold path = the AI flow Here's the real unlock 🔓 — the skill isn't drawing this. It's being able to look at a system (or AI-generated code) and ask: where's the bottleneck? what breaks under 10x load? which shard gets hot? 🕵️ That judgment is what separates engineers who use AI from those who architect with it. 🚀 Save it. Share it with someone prepping for interviews. 🔖 Which layer should I break down next — 🤖 AI-Native Inference or ⚙️ the Application Core? Tell me below 👇 . . . #HackProduct #systemdesign #softwareengineering #systemdesigninterview #AIengineering

A nói e nge #tonymom

A nói e nge #tonymom

الرد على @user92281369800297أحبهه و ما اعرف مثل اليمثلون #foryoupage #fyp #viral #foryou #الشعب_الصيني_ماله_حل😂😂

الرد على @user92281369800297أحبهه و ما اعرف مثل اليمثلون #foryoupage #fyp #viral #foryou #الشعب_الصيني_ماله_حل😂😂

I hope this can inspire you if you’re about to have a baby to know that you can still do normal things you used to do even with a newborn💗 ##Newborn##Mom##Motherhood##NightRoutine##USA

I hope this can inspire you if you’re about to have a baby to know that you can still do normal things you used to do even with a newborn💗 ##Newborn##Mom##Motherhood##NightRoutine##USA

‎#amore ‎#italia ‎#palermo ‎#italian ‎#cartomancie

‎#amore ‎#italia ‎#palermo ‎#italian ‎#cartomancie

تقيل و تقيل - مجاهدة الراشدين - انور 9 - زنق سوداني قديم كاملة كاملة #الترند_الاول #اخبار_الفنانين_السودانين

تقيل و تقيل - مجاهدة الراشدين - انور 9 - زنق سوداني قديم كاملة كاملة #الترند_الاول #اخبار_الفنانين_السودانين

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