@tvcantho: Săn ‘thủy quái’ rừng tràm ở U Minh Thượng

Cần Thơ - Báo & PTTH
Cần Thơ - Báo & PTTH
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Saturday 09 May 2026 13:50:58 GMT
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hoai.duy.phan
Duy 68 :
ở bìa rừng đây ạ 🥰
2026-05-09 14:05:31
1
tho.nguyen0520
Thơ Nguyễn 68 Kiên Giang :
ca nhiều lắm
2026-05-09 14:07:10
0
user8151869628415
phạm oanh :
cho hỏi tâm ngàn hay 8 chục ngàn
2026-05-09 13:58:46
0
hiaqn8
Ahihi :
2026-05-09 13:56:51
0
tho.nguyen0520
Thơ Nguyễn 68 Kiên Giang :
quê em đó
2026-05-09 14:06:50
0
pug_2011
Ueirt :
cá lóc = thủy quái 🤣🤣🤣
2026-05-09 13:59:53
0
nguynngc214
thanhlam :
😂😂😂
2026-05-10 12:27:43
0
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Your Airflow DAG failed at 2:00 AM. Does an engineer need to wake up first? 🚨 This is Graph Engineering in production: Airflow alert → incident router → specialist agents → shared evidence → safe retry → verified recovery ✅ Instead of one AI agent guessing everything, a crew investigates in parallel: 🟢 Log Agent checks errors and stack traces 🔵 Data Agent validates upstream dependencies 🟡 Infra Agent checks memory, CPU and infrastructure 🟣 Change Agent reviews recent deployments Their findings are stored in a shared incident state containing evidence, hypotheses and retry history. Then comes the critical decision: Is the failure transient—and is the task safe to retry? ✅ Yes → rerun only the failed task 🛑 No → pause and escalate to the human on-call But a successful retry is not enough. The system verifies that the task passed, downstream dependencies are healthy and the DAG is green before closing the incident. That is the difference between blind automation and trustworthy agentic operations: ❌ Retry everything ❌ Let one agent improvise ❌ Close after a command succeeds ✅ Route by failure type ✅ Preserve shared incident state ✅ Use bounded retries ✅ Escalate uncertainty ✅ Close only after verification The future of on-call isn’t “AI replaces engineers.” It’s AI handling repetitive diagnosis and safe recovery while humans control ambiguous, high-risk decisions. 🧠 Would you trust this system with your next Airflow failure? 👇 Follow @hackproduct for practical AI engineering, data engineering and system design. #Airflow #DataEngineering #AIEngineering #AIAgents #AgenticAI
Your Airflow DAG failed at 2:00 AM. Does an engineer need to wake up first? 🚨 This is Graph Engineering in production: Airflow alert → incident router → specialist agents → shared evidence → safe retry → verified recovery ✅ Instead of one AI agent guessing everything, a crew investigates in parallel: 🟢 Log Agent checks errors and stack traces 🔵 Data Agent validates upstream dependencies 🟡 Infra Agent checks memory, CPU and infrastructure 🟣 Change Agent reviews recent deployments Their findings are stored in a shared incident state containing evidence, hypotheses and retry history. Then comes the critical decision: Is the failure transient—and is the task safe to retry? ✅ Yes → rerun only the failed task 🛑 No → pause and escalate to the human on-call But a successful retry is not enough. The system verifies that the task passed, downstream dependencies are healthy and the DAG is green before closing the incident. That is the difference between blind automation and trustworthy agentic operations: ❌ Retry everything ❌ Let one agent improvise ❌ Close after a command succeeds ✅ Route by failure type ✅ Preserve shared incident state ✅ Use bounded retries ✅ Escalate uncertainty ✅ Close only after verification The future of on-call isn’t “AI replaces engineers.” It’s AI handling repetitive diagnosis and safe recovery while humans control ambiguous, high-risk decisions. 🧠 Would you trust this system with your next Airflow failure? 👇 Follow @hackproduct for practical AI engineering, data engineering and system design. #Airflow #DataEngineering #AIEngineering #AIAgents #AgenticAI

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