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#Databreak Reel by @dataimpulse - Why AI models fail even with perfect architecture 📊 Everyone talks about model architecture - more parameters, better benchmarks, new frameworks. Bu
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@dataimpulse
Why AI models fail even with perfect architecture 📊 Everyone talks about model architecture - more parameters, better benchmarks, new frameworks. But many AI projects struggle for a much simpler reason: poor data collection. In this video, we highlight several mistakes teams keep repeating when preparing data for AI training. Things like relying on massive datasets instead of high-quality signals, ignoring rare events, overusing synthetic data, or accidentally introducing target leakage, which makes models look accurate during testing but fail in production. We also touch on how unstable data pipelines, duplicates, outdated records, or missing documentation can quietly undermine even the best model architecture. Good AI systems don’t come only from better models - they come from carefully collected and maintained data. 🎬 If you’d like to see the full breakdown and practical tips, watch the video👇🏼 #DataImpulse #Proxy #AI #MachineLearning #DataEngineering #WebScraping
#Databreak Reel by @aickstudio - Stop labeling data. Let AI learn from you. 🤯 This video shows a new way to train AI through real conversations, failed steps, and quick fixes instea
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@aickstudio
Stop labeling data. Let AI learn from you. 🤯 This video shows a new way to train AI through real conversations, failed steps, and quick fixes instead of huge datasets and slow batch jobs. That means smarter agents, faster feedback, and more privacy on your own hardware. Would you trust an AI that learns as you work with it? Comment “BLUEPRINT” and I’ll share more like this. #ai #machinelearning #opensourceai
#Databreak Reel by @yvesmulkers - The most in-demand skill isn't AI. Data Modeling surged 400% in one day. The fundamentals are winning. . . . #DataIntelligence #DataModeling #AITren
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@yvesmulkers
The most in-demand skill isn't AI. Data Modeling surged 400% in one day. The fundamentals are winning. . . . #DataIntelligence #DataModeling #AITrends
#Databreak Reel by @codingmermaid.ai (verified account) - I'm the 100x data scientist than I used to be Manus is a fully autonomous AI agent I discovered when I needed to research for a new feature we were
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@codingmermaid.ai
I'm the 100x data scientist than I used to be Manus is a fully autonomous AI agent I discovered when I needed to research for a new feature we were building. And honestly, I wish it was something I had when I was a freshman because learning, researching and building is very simplified. Manus employs a multi-agent architecture with at least three coordinated agents: a Planner Agent (breaks down tasks into sub-tasks and formulates strategy), an Execution Agent (carries out actions), and additional coordination modules. It also has Manus computer view so you can view what's happening as well as asynchronous execution and VM Sandbox! I love using this and if you're serious about building something that sticks in the market you should try it too! Comment "MANUS" to get my link and try it for free!
#Databreak Reel by @datawithsai (verified account) - @openai 's internal AI agent just replaced 200 lines of SQL with 1 question in plain English. And 10 table joins, 5 CTEs, hours of debugging, done in
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@datawithsai
@openai 's internal AI agent just replaced 200 lines of SQL with 1 question in plain English. And 10 table joins, 5 CTEs, hours of debugging, done in minutes. That's not a small upgrade but a complete workflow shift. Most analysts spend 80% of their time writing code just to get to the insight. The new reality is that the insight comes first, and the code is just background noise. That's a completely different mindset, and most people in data haven't caught up yet. The analysts who will thrive aren't the ones who write the most complex SQL. They're the ones who ask the best questions, understand what the data should tell them, and know how to use AI to get there in minutes instead of hours. Domain knowledge + AI fluency = the new unfair advantage in data. Drop a 🙋 if you're in data and actively learning how AI is changing your workflow. Curious how many of us are already on this. [dataanalyst, datascience, openai, aiagents, futureofwork, sql, python, analytics, datacareer, artificialintelligence, machinelearning, techjobs, careergrowth, dataengineering, llm] #dataanalyst #datascience #openai #aiagents
#Databreak Reel by @datascience.swat - Layers of observability in AI systems, explained visually 🔍 If you're deploying LLM-powered apps to real users, you need visibility into what's happ
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@datascience.swat
Layers of observability in AI systems, explained visually 🔍 If you’re deploying LLM-powered apps to real users, you need visibility into what’s happening at every step of your pipeline. Think of your AI system as a sequence of stages. Take RAG as an example. A user asks a question, it moves through multiple components, and eventually a response is generated. Each step takes time, each step can fail, and each step comes with its own cost. If you’re only paying attention to the final input and output, you’re missing what’s really happening under the hood. Credits: dailydoseofds_ Follow @datascience.swat for more daily videos like this Shared under fair use for commentary and inspiration. No copyright infringement intended. If you are the copyright holder and would prefer this removed, please DM me. I will take it down respectfully. ©️ All rights remain with the original creator (s)
#Databreak Reel by @kiran_mondal_official - Can AI agents really write SQL queries-and even handle database logic? Yes… but not perfectly. AI can generate clean queries, optimize performance, an
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@kiran_mondal_official
Can AI agents really write SQL queries—and even handle database logic? Yes… but not perfectly. AI can generate clean queries, optimize performance, and automate repetitive tasks. But understanding real business logic, data relationships, and edge cases still needs human thinking. The future isn’t AI vs developers—it’s AI + developers working smarter, faster, and more efficiently.
#Databreak Reel by @code2aicareer - Building AI Data Assistants with LangChain 🤖 SQL Agent using LangChain + LLMs that allows users to query databases using natural language. Instead
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@code2aicareer
Building AI Data Assistants with LangChain 🤖 SQL Agent using LangChain + LLMs that allows users to query databases using natural language. Instead of writing SQL queries, users can simply ask: “Total quantity and revenue of product Tablet” The agent automatically: understands the question generates SQL runs the query returns the answer. This is a powerful step toward AI-driven data analysis and intelligent assistants. #ai I #langchain #dataengineering #llm #openai Databases SQL AIEngineer
#Databreak Reel by @jumpstart2tech - In this training, I'll break down: ✅ What an AI Data Technician actually does (no fluff) ✅ How companies use data to power AI systems ✅ The exact too
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@jumpstart2tech
In this training, I’ll break down: ✅ What an AI Data Technician actually does (no fluff) ✅ How companies use data to power AI systems ✅ The exact tools to learn first (Excel, SQL, Python, Labeling tools) ✅ How to build real data projects that make you job-ready AI doesn’t work without clean data. These roles are becoming one of the fastest entry points into AI and tech. If you’re serious about building real, high-leverage skills… Comment “TRAINING” to get started.
#Databreak Reel by @themadhurmehta (verified account) - If I had to master Data + AI in 2026, I'd bookmark these 10 resources immediately. No fluff. Just leverage. 1️⃣ Real-World Datasets → Kaggle- https:
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@themadhurmehta
If I had to master Data + AI in 2026, I’d bookmark these 10 resources immediately. No fluff. Just leverage. 1️⃣ Real-World Datasets → Kaggle- https://lnkd.in/gKgtRgi7 → Google Dataset Search- https://lnkd.in/gExwmFT8 2️⃣ Practice SQL Like It’s a Sport → StrataScratch- https://lnkd.in/gzPcWHWQ → LeetCode- https://leetcode.com 3️⃣ AI Fundamentals (Actually Understand What’s Happening) → DeepLearning AI- https://lnkd.in/gMUn9X3f → fast.ai- https://www.fast.ai 4️⃣ Build With LLMs (Not Just Talk About Them) → OpenAI API Docs- https://lnkd.in/gBeKS9z9 → Hugging Face- https://huggingface.co 5️⃣ Data Visualization Mastery → Tableau Public- https://public.tableau.com → Power BI- https://lnkd.in/gGEDKHjF 6️⃣ Cloud + Data Engineering Basics → Amazon Web Services Free Tier- https://lnkd.in/gnT9vxvv → Google Cloud Skills Boost- https://lnkd.in/gKUyTqUq 7️⃣ AI Research (Stay Ahead of the Curve) → arXiv- https://arxiv.org → Papers with Code- https://paperswithcode.com 8️⃣ Build Public Proof → GitHub- https://github.com → Substack- https://substack.com 9️⃣ Learn Business Thinking with Data → Harvard Business Review- https://hbr.org 🔟 AI Workflow Automation → Zapier- https://zapier.com → Make- https://www.make.com In 2026, the winners won’t be “AI enthusiasts.” They’ll be AI implementers. Most people consume AI content. Very few ship AI tools. 📩Save this. ♻️Share it with someone trying to break into AI. . . . Data, AI, Machine, Learning, Datasets, Tools, APIs, Courses, Resources, Projects, Skills, Analytics, Visualization, SQL, Cloud, Automation, HuggingFace, OpenAI, Career, Growth, Productivity, Future, Tech, Learning, Innovation
#Databreak Reel by @data_engineer_academy - Databases go extinct Are traditional databases going extinct in the age of AI and modern data stacks? Explore how databases are evolving-and why they
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@data_engineer_academy
Databases go extinct Are traditional databases going extinct in the age of AI and modern data stacks? Explore how databases are evolving—and why they’re still a critical part of data engineering. 📊🚀 #Databases #DataEngineering #AI #ModernDataStack #TraditionalDatabases #DatabaseEvolution #DataStacks #AIandData #FutureOfData #TechTrends

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