Unlike rule-based systems that only detect known fault signatures, AI models recognize earlier warnings in subtle deviations from normal operation.
MACHINE LEARNING
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GenAI and RAG Automate Industrial Diagnostics and Troubleshooting
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GenAI and RAG instantly search manuals, logs, and forums to provide probable cause and recommended action.
— Lucian Fogoros (@fogoros) 31 mars 2026
No more digging through 6,000-page turbine manuals. AI agents layer over ML tools to give context to anomalies.
From alert to answer in seconds. pic.twitter.com/eYiljr3nPuGenAI and RAG instantly search manuals, logs, and forums to provide probable cause and recommended action.
No more digging through 6,000-page turbine manuals. AI agents layer over ML tools to give context to anomalies.
From alert to answer in seconds. -
Complete AI Stack: Tools and Frameworks for Modern Development
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gemini for research copilot for coding claude for deep searches and agentic code openai for quick searches cerebras for faster ai inferences tavily for search api firecrawl for web scraping langchain for ai app development langsmith for agentic ai observation fastAPI for backend server docker for containerising the app streamlit for frontend interface
→ View original post on X — @avikumart_, 2026-03-31 17:40 UTC
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Liquid AI Releases LFM2.5-350M Compact Agentic Model
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Very proud of our tiny powerhouse. Amazing performance in data extraction and tool use at such a small scale. Enjoy! 🚀 Liquid AI (@liquidai) Today, we release LFM2.5-350M. Agentic loops at 350M parameters. A 350M model trained for reliable data extraction and tool use, where models at this scale typically struggle. <500MB when quantized, built for environments where compute, memory, and latency are constrained. 🧵 — https://nitter.net/liquidai/status/2039029358224871605#m
→ View original post on X — @maximelabonne, 2026-03-31 17:29 UTC
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LFM2.5-350M: Agentic Model for Data Extraction and Tool Use
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Today, we release LFM2.5-350M. Agentic loops at 350M parameters. A 350M model trained for reliable data extraction and tool use, where models at this scale typically struggle. <500MB when quantized, built for environments where compute, memory, and latency are constrained. 🧵
→ View original post on X — @thom_wolf, 2026-03-31 17:17 UTC
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Perplexity Launches Secure Intelligence Institute for Security Research
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Today, we're launching the Secure Intelligence Institute. SII partners with top cryptography, security, and ML teams to advance security research and industry collaboration. It is led by Dr. Ninghui Li at Purdue. perplexity.ai/secure-intelli…
→ View original post on X — @aravsrinivas, 2026-03-31 17:16 UTC
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Agent Improvement Loop: Tracing as Foundation for Enhancement
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New conceptual guide: The agent improvement loop starts with a trace Tracing is the foundational primitive for improving agents. A trace gives you the full behavioral record of what an agent actually did. From there, teams can enrich traces with evals and human feedback,
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Designing and Running Successful AI Initiatives Simply
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Keep It Simple: How To Design And Run A Successful #AI Initiative
by @kedkorte @Forbes Learn more: https://
bit.ly/4rVzmbt #ArtificialIntelligence #MachineLearning #ML -
Detecting and Preventing Distillation Attacks in AI Models
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Detecting and preventing distillation attacks \ Anthropic bit.ly/3ORbiJ7 #AI #MachineLearning #DeepLearning #LLMs #DataScience
→ View original post on X — @miketamir, 2026-03-31 16:47 UTC
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Convolutional Networks History and Attribution in AI
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I have read him (a) that’s not true (eg see long history of convnets that precedes him, and (b) he brings receipts. seeya