May the Cupertino lords anoint us with decent speech-to-text transcription. I am so tired of using third-party keyboards
TOOLS
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Automation and Manual Processes: Competitive Advantage in AI Era
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Exactly – that's what I talk about all day on Scalebrate and the Exponential Podcast. If you do things manually, you're going to lose out to those who don't. Tune into the podcast or be one of the elite winners who are part of the Scalebrate Hub!
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Two People Managing 20 Million Monthly Automations Through Orchestration
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2 people.
— Ronald Schmelzer (@rschmelzer) 22 février 2026
20 million+ automations per month.
Not a typo.
"Dozens of tools that work together in a kind of web, which is perfectly orchestrated and works like a Swiss clock."
That's not automation. That's orchestration.
🔗 https://t.co/IfEp8EkjA1 pic.twitter.com/ImcyW0ZMSY2 people. 20 million+ automations per month. Not a typo. "Dozens of tools that work together in a kind of web, which is perfectly orchestrated and works like a Swiss clock." That's not automation. That's orchestration. 🔗 scalebrate.com/podcast/10000…
→ View original post on X — @rschmelzer, 2026-02-22 14:26 UTC
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Agentic AI Fundamentals: Planning, Memory, Tools, and Multi-Agent Systems
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Agentic AI, simplified: • Agents are systems
• Plan → Act → Reflect loops
• Memory = compounding intelligence
• Tools = real-world impact
• Multi-agent > single-agent If it only chats, it’s not agentic. Via @ingliguori -
New RAG Approach Achieves 98.7% Accuracy Without Vector Database
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Researchers built a new RAG approach that:
— Akshay 🚀 (@akshay_pachaar) 22 février 2026
– does not need a vector DB.
– does not embed data.
– involves no chunking.
– performs no similarity search.
And it hit 98.7% accuracy on a financial benchmark (SOTA).
Here's the core problem with RAG that this new approach solves:… pic.twitter.com/g8vf7HT3WKResearchers built a new RAG approach that: – does not need a vector DB.
– does not embed data.
– involves no chunking.
– performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: -
Sarvam AI Conversational Agent Delivers Amazing Performance
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Tested Sarvam AI conversational Agent and its just amazing. #sarvamai #Sarvam pic.twitter.com/yVIDFOWS2H
— Krish Naik (@Krishnaik06) 22 février 2026Tested Sarvam AI conversational Agent and its just amazing. #sarvamai #Sarvam
→ View original post on X — @krishnaik06, 2026-02-22 09:25 UTC
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Automating Task Management with AI Agents
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I have a similar setup with automations in codex app which reads slack DMs to myself which has an ever so growing list of things I’d like to do. Codex looks through the DMs, breaks it into independent issues. Refines the issue and adds more context. Opens a Branch/ PR for it.
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Red Lion: UK IoT Developer Recognized for AI Analytics
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Red Lion, Recognized by UK-Based Developer of #IoT! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
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63 Essential AI Books: Big Data, Machine Learning, and Programming Resources
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63 Best Books on AI. #BigData #Analytics #DataScience #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/63-Best-Books-
AI
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Future of Data Science and Parallel Computing Technologies
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The Future of #DataScience and #ParallelComputing! #BigData #Analytics #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Future-of–DSci