0x฿88r retweeted
"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

ALT An infographic showing the evolution of AI from 2016 to 2026, visualized through growing human silhouettes: 1. 2016 (Toddler): Early AI era with foundational models like AlphaGo, ImageNet, CNN, RNN, Yolo, GAN, ResNet, Transformer, and Bert. 2. 2022 (Child): Generative AI era featuring ChatGPT, Claude, and "Prompts" text. 3. 2026 (Adult): Advanced AI ecosystem with a long list of complex technologies, including Orchestration, Context Understanding, Loop Automation, Knowledge Graphs, MCP & Protocols, Agentic AI, Multi-Agent Collaboration, RAG 2.0, Memory Architecture, and Vector Databases.
ปกติเทรด Option ใช้กลยุทธ์ CSP กับ CC ตอนนี้กำลังศึกษากลยุทธ์ #PutRatioSpread อยู่ น่าลองนะครับ แต่ก็ต้องดูความเสี่ยงด้วย เดี๋ยวมีโอกาสเปิดสัญญาเมื่อไหร่จะมาเล่าให้ฟังครับ
ขอบคุณภาพจาก optionstrat
25
0x฿88r retweeted
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.
0x฿88r retweeted
New PDF just dropped
1. Claude Trading Folder
2. Trading Prompts
Completely free
Who wants it?
0x฿88r retweeted
Trading tools for AI agents via MCP
github.com/atilaahmettaner/t…
0x฿88r retweeted
Resources for quantitative finance roles
github.com/cybergeekgyan/Qua…
0x฿88r retweeted
n8n templates for AI agents
github.com/gyoridavid/ai_age…
0x฿88r retweeted
Converts social media sentiment into trading signals
github.com/danilobatson/ai-t…
0x฿88r retweeted
Multi-agent framework for financial trading
github.com/TauricResearch/Tr…
0x฿88r retweeted
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…
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🔖 Save this for later, repost it to help others learn system design.
🙋🏻♀️ Follow Nikki Siapno, turn on notifications 🔔
0x฿88r retweeted
$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).
0x฿88r retweeted
$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).
0x฿88r retweeted
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
0x฿88r retweeted
$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).
0x฿88r retweeted
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
0x฿88r retweeted
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!
0x฿88r retweeted
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
0x฿88r retweeted
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-…



































