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@carlosjr_cortes: #fypシ゚ #viralvideo

Cortes_ tropa_do_só☝🏻
Cortes_ tropa_do_só☝🏻
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Region: AO
Friday 04 September 2026 22:59:06 GMT
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Comments

gata11576
Mabuyhú ❤️❤️🥰 :
Essa moça me deu muita raiva
2026-09-04 23:46:34
49
123456789jovanymalau
jøvany 🥉🏅🏆 :
Está moça não pode falar com o Helton
2026-09-05 08:00:59
11
djamila_607
𝒟𝒿𝒶𝓂𝒾𝓁𝒶💕 :
são 0,10 cêntimos um bidon*
2026-09-07 13:30:30
4
nizethlucianapach
nizethlucianapach :
A live já acabou?
2026-09-04 23:25:48
5
szn_millena
ミレーナ :
Marceloo...😭😭🤌
2026-09-11 23:21:38
0
elizamiguel36
Elizandro L :
essa não é a Cláudia no se quê 😅
2026-09-05 00:56:05
8
kilsonse2
@cleverson :
pesadora phá😅😅
2026-10-04 21:51:43
1
jpire01
J’pire ^_^ :
você é emocionada ohh jovem 😅
2026-09-04 23:48:39
2
luciochess
LBA :
É normal apanhar bidon
2026-09-05 07:45:19
0
ost2780
Vampiro 🫆 :
😂😂😂
2026-09-05 09:52:02
0
kevinbernardo55
KB :
😂😂😂
2026-09-20 06:17:36
0
To see more videos from user @carlosjr_cortes, please go to the Tikwm homepage.

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Most AI engineering teams aren’t slow because the models are bad. They’re slow because the workflow is. I keep seeing the same mistakes over and over: ❌ Stuffing everything into a global memory file and burning tokens on every session. ❌ Installing random community skills because they have GitHub stars. ❌ Letting AI optimize for development cost instead of long-term architecture. ❌ Reviewing every generated line of code manually instead of building automated validation pipelines. The best AI engineers don’t just write better prompts. They build better systems. That means: • Small, focused context. • Trusted tools. • Clear engineering principles. • Automated reviews. • Humans making decisions—not reviewing every diff. The biggest productivity gains won’t come from a smarter model. They’ll come from better orchestration. That’s the shift from AI-assisted coding to AI-native engineering. Follow @hackproduct for more practical mental models on AI Engineering, Agentic Workflows, System Design, and building production-ready AI systems. #AIEngineering #AgenticAI #ClaudeCode #SoftwareEngineering #SystemDesign
Most AI engineering teams aren’t slow because the models are bad. They’re slow because the workflow is. I keep seeing the same mistakes over and over: ❌ Stuffing everything into a global memory file and burning tokens on every session. ❌ Installing random community skills because they have GitHub stars. ❌ Letting AI optimize for development cost instead of long-term architecture. ❌ Reviewing every generated line of code manually instead of building automated validation pipelines. The best AI engineers don’t just write better prompts. They build better systems. That means: • Small, focused context. • Trusted tools. • Clear engineering principles. • Automated reviews. • Humans making decisions—not reviewing every diff. The biggest productivity gains won’t come from a smarter model. They’ll come from better orchestration. That’s the shift from AI-assisted coding to AI-native engineering. Follow @hackproduct for more practical mental models on AI Engineering, Agentic Workflows, System Design, and building production-ready AI systems. #AIEngineering #AgenticAI #ClaudeCode #SoftwareEngineering #SystemDesign

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