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เป้าหมายสร้าง Passive Income ด้วย หุ้นปันผล และ Options ตั้งใจเกษียณไม่เกินอายุ 55

Joined September 2009
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"เลิกหมกมุ่นกับการเรียนเขียน Prompt ได้แล้ว" ❌ ภาพวิวัฒนาการ AI ในปี 2026 มันฟ้องชัดเจนเลยว่า โลกก้าวข้ามยุค ChatGPT/Claude Prompts ไปไกลมากแล้ว ตอนนี้มันคือยุคของ Agentic AI, MCP และ Loop Automation สิ่งที่เราต้องโฟกัสไม่ใช่การสั่งคำพูดสวยๆ แต่คือการเข้าใจ "กระบวนการทำงาน (Workflow)" เพื่อสั่งให้เอเจนท์หลายๆ ตัวทำงานร่วมกันได้ต่างหาก ในบรรดาคีย์เวิร์ดฝั่งปี 2026 (เช่น RAG 2.0, Agentic AI, MCP) มีตัวไหนที่เพื่อนๆ กำลังศึกษาหรือเอามาใช้ในงานจริงแล้วบ้าง? มาแชร์กัน🙋 #AI #AIprompt #การทำงานยุคใหม่
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ปกติเทรด Option ใช้กลยุทธ์ CSP กับ CC ตอนนี้กำลังศึกษากลยุทธ์ #PutRatioSpread อยู่ น่าลองนะครับ แต่ก็ต้องดูความเสี่ยงด้วย เดี๋ยวมีโอกาสเปิดสัญญาเมื่อไหร่จะมาเล่าให้ฟังครับ ขอบคุณภาพจาก optionstrat
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10 AI Trading GitHub Repos You Should Have on Your Radar AI is moving into trading — from LLM-powered analysts to reinforcement learning and automated quant research. If you're interested in AI + finance, these 10 open-source repos are worth exploring. 1. TradingAgents A multi-agent LLM framework that simulates a trading firm with specialized analysts, traders and risk-management agents. 🔗 github.com/TauricResearch/Tr… 2. FinRL An open-source financial reinforcement-learning framework for training AI agents on trading and portfolio-allocation tasks. 🔗 github.com/AI4Finance-Founda… 3. FinRL-X An AI-native quantitative trading infrastructure combining ML, reinforcement learning, LLM-ready workflows and risk controls. 🔗 github.com/AI4Finance-Founda… 4. Qlib Microsoft's open-source AI-oriented quantitative investment platform for research, modeling and strategy development. 🔗 github.com/microsoft/qlib 5. FinGPT An open-source financial LLM project focused on financial sentiment analysis, prediction and other finance-related AI tasks. 🔗 github.com/AI4Finance-Founda… 6. NautilusTrader A high-performance algorithmic trading platform with backtesting, live trading and infrastructure for AI trading agents. 🔗 github.com/nautechsystems/na… 7. OpenBB An open-source investment research platform that brings financial data and analysis tools into one ecosystem. 🔗 github.com/OpenBB-finance/Op… 8. FinMem A research project exploring memory-augmented LLM agents for financial decision-making and trading. 🔗 github.com/pipiku915/FinMem-… 9. FinAgent A multimodal financial AI agent that combines financial information with visual market/chart analysis. 🔗 github.com/pipiku915/FinAgen… 10. VectorBT A fast Python framework for quantitative research and vectorized backtesting of trading strategies. 🔗 github.com/polakowo/vectorbt All open source. AI + Finance. Research + Trading. Save this list — the future of quantitative trading is getting increasingly AI-native.
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New PDF just dropped 1. Claude Trading Folder 2. Trading Prompts Completely free Who wants it?
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Trading tools for AI agents via MCP github.com/atilaahmettaner/t…
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Resources for quantitative finance roles github.com/cybergeekgyan/Qua…
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n8n templates for AI agents github.com/gyoridavid/ai_age…
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Converts social media sentiment into trading signals github.com/danilobatson/ai-t…
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Multi-agent framework for financial trading github.com/TauricResearch/Tr…
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Build your own personal assistant with @openclaw and Ollama using your models! ollama launch clawdbot Thank you for building something amazing @steipete!
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How CI/CD pipelines work (explained in 2 mins or less): A CI/CD pipeline is an automated workflow that facilitates continuous integration (CI) and continuous delivery or deployment (CD) by managing code building, testing, and release processes. It integrates the various stages of the software development lifecycle (SDLC) into a seamless, repeatable process. These stages include source code management, automated testing, artifact creation, and deployment orchestration. Continuous ‘delivery’ and ‘deployment’ are sometimes used synonymously. But there is a clear and important distinction between the two. Delivery is about ensuring the software can be released at any time. It requires manual intervention to deploy to production. Deployment, on the other hand, does the release through automated workflows. Learn more here: lucode.co/ci-cd-explained-li… -- 👋 PS: Want our Architecture Patterns Playbook for free? Join our system design newsletter with 25,000+ software engineers: lucode.co/luc-newsletter-lm1… -- 🔖 Save this for later, repost it to help others learn system design. 🙋🏻‍♀️ Follow Nikki Siapno, turn on notifications 🔔
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$AMD AMD Q3 FY25: • Revenue +36% Y/Y to $9.2B ($0.5B beat). • Operating margin 14% (+3pp Y/Y). • Non-GAAP EPS $1.20 ($0.03 beat). • Q4 revenue +25% Y/Y to $9.6B ($0.4B beat).
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$PLTR Palantir Q3 FY25: • Net dollar retention 134% (+16pp Y/Y). • Revenue +63% Y/Y to $1.18B ($90M beat). • Non-GAAP EPS $0.21 ($0.04 beat). FY25 guidance: • Revenue +53% Y/Y to $4.4B ($252M raise). • Adjusted margin 49% (3pp raise).
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Many DevOps Engineers don’t fully understand how DevOps and GitOps CI/CD pipelines differ. In a DevOps CI/CD pipeline, everything runs in one flow from code commit, tests, build, image creation, to direct deployment into the Kubernetes cluster. It is fast and centralized but tightly couples deployment with the CI process, leaving little room for separation or control. In GitOps pipeline, CI handles tests and builds, but deployment happens through Git commits. A controller syncs changes from the repo to the cluster. Here, I’ve made this to help you better understand. 56K+ read my DevOps and Cloud newsletter: techopsexamples.com/subscrib… What do we cover: DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps 🔁 Consider a Repost if this is helpful
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$SOFI SoFi Q3 FY25: • Members +35% to 12.6M. • Revenue +38% Y/Y to $962M. • Adj. revenue $950M ($55M beat). • Adj. EBITDA +49% Y/Y to $277M. • Non-GAAP EPS $0.11 ($0.03 beat). • FY25 revenue growth 36% ($165M raise).
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A Go-based self-hosted WAF(Web Application Firewall) / reverse proxy to protect your web apps from attacks and exploits. #golang github.com/chaitin/SafeLine
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Docker explained in 2 minutes! Most developers use Docker daily without understanding what happens under the hood. Here's everything you need to know. Docker has 3 main components: 1️⃣ Docker Client: Where you type commands that talk to the Docker daemon via API. 2️⃣ Docker Host: The daemon runs here, handling all the heavy lifting (building images, running containers, and managing resources) 3️⃣ Docker Registry: Stores Docker images. Docker Hub is public, but companies run private registries. Here's what happens when you run "docker run": • Docker pulls the image from the registry (if not available locally) • Docker creates a new container from that image • Docker allocates a read-write filesystem to the container • Docker creates a network interface to connect the container • Docker starts the container That's it. The client, host, and registry can live on different machines. This is why Docker scales so well. Understanding this architecture makes debugging container issues much easier. You'll know exactly where to look when something breaks. ____ If you found it insightful, reshare with your network. Find me → @akshay_pachaar ✔️ For more insights and tutorials on ML and AI Engineering!
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Many Cloud Engineers think load balancers are just for traffic distribution. There are plenty of use cases in modern cloud architectures proving otherwise. Here, I’ve made this to help you better understand. 55K+ read my DevOps and Cloud newsletter: techopsexamples.com/subscrib… What do we cover: DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps 🔁 Consider a Repost if this is helpful
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Sometimes it's helpful to visualize your network traffic in real-time. So here, @chairahalkar helps you build a network traffic dashboard with Python and Streamlit. You'll learn how to capture raw network packets from the NIC, process the data, and create visualizations that update in real time. freecodecamp.org/news/build-…
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THE TOP 15 GROWTH PERFORMERS OF 2025 1. $OKLO +669% 2. $IREN +518% 3. $MP +418% 4. $QBTS +356% 5. $OPEN +348% 6. $NVTS +311% 7. $NBIS +310% 8. $CIFR +304% 9. $ASTS +296% 10. $HOOD +249% 11. $PL +224% 12. $EOSE +209% 13. $RGTI +204% 14. $ONDS +197% 15. $PGY +185%
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