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Carney's so-called "AI" policy is pixie dust. Tinker Bell would be proud. #Canada #machinelearningtools #sentencecompletionmachine #bullshite
Yes - there is danger all around.
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The AI/ML software stack is a layered toolkit that covers the full AI/ML workflow ๐Ÿ˜Ž๐Ÿ‘‡ Find high-res pdf ebooks with all my #technology related infographics at study-notes.org/technology-iโ€ฆ #machinelearning #machinelearningtools #ai #deeplearning #softwarestack
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Most RAG setups look perfectly fine at first glanceโ€ฆ until you dig in and realize half the retrieved chunks arenโ€™t even relevant ๐Ÿ˜… Thatโ€™s the silent killer of RAG performance, you donโ€™t just have bad outputs, you have bad inputs feeding the model from the start. Thatโ€™s exactly where RAGAS becomes a game-changer. It doesnโ€™t just tell you โ€œyour RAG isnโ€™t working.โ€ It scores every part of your pipeline โ€” retrieval relevance, hallucination likelihood, answer coverage, reasoning alignment, and more. All the invisible failure modes that you cannot catch by just reading responses manually. In the bootcamp, we use RAGAS as a diagnostic tool: we test โ†’ measure โ†’ fix each step โ†’ test again. No more guessing. No more hoping the retrieval was good. Just measurable improvements that make your agents 10ร— more accurate and reliable. If you want to build RAG systems based on engineering, not guesswork, Register now for our upcoming Agentic AI Bootcamp happening in January & February -> hubs.la/Q03XkD7T0 ๐Ÿš€ #RAG #RAGAS #LLMEngineering #AIAgents #AgenticAI #RetrievalAugmentedGeneration #AIEngineering #LLMRetrieval #AIOptimization #MachineLearningTools #AIForDevelopers
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๐Ÿš€ Hey devs! Just launched MultiMindSDK โ€“ an all-in-one open-source AI SDK Tired of juggling multiple tools just to fine-tune or deploy an LLM? ๐ŸŽฏ MultiMindSDK lets you: โœ… Fine-tune transformer & non-transformer models โœ… Orchestrate multi-agent & RAG pipelines โœ… Convert models (PyTorch โ†” ONNX, etc.) โœ… Use in Python or JavaScript (new NPM SDK!) โœ… Soon: No-code builder for non-devs! Itโ€™s built with devs in mind โ€“ minimal config, plug-and-play architecture, and designed to scale. ๐Ÿ”— GitHub: github.com/multimindlab/multโ€ฆ ๐Ÿ“ฆ pip: pip install multimind-sdk ๐Ÿ“ฆ npm: npm install multimind-sdk ๐ŸŒ Website: multimind.dev ๐Ÿง  Reddit: reddit.com/r/OpenGenAI Would love feedback, use cases, or contributions from the community. ๐Ÿ’ฌ Drop your questions or ideas โ€” Iโ€™m here to chat and improve it with you! #MultiMindSDK #OpenSourceAI #AIInfrastructure #AIEngineering #LLMops #FineTuningModels #TransformersAndBeyond #GenAIStack #AIDeveloperTools #AIWorkflow #MachineLearningTools #FullStackAI #AIPlatform #CodeWithAI #MLDevTools #BuildWithAI #LangChainAlternative #HackTheModel #NoCodeAI #EdgeToCloudAI #multimindsdk
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๐Ÿง  ๐‚ ๐ข๐ง ๐€๐ˆ: ๐๐จ๐ญ ๐‰๐ฎ๐ฌ๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐Ÿš€ Python may be the poster child for AIโ€”but C is the ๐ฆ๐ฎ๐ฌ๐œ๐ฅ๐ž ๐ฎ๐ง๐๐ž๐ซ ๐ญ๐ก๐ž ๐ก๐จ๐จ๐. This infographic unpacks where ๐‚ ๐ก๐จ๐ฅ๐๐ฌ ๐ข๐ญ๐ฌ ๐ ๐ซ๐จ๐ฎ๐ง๐ (๐š๐ง๐ ๐ž๐ฏ๐ž๐ง ๐จ๐ฎ๐ญ๐ฉ๐š๐œ๐ž๐ฌ) ๐๐ฒ๐ญ๐ก๐จ๐ง in AI development, and when to leverage each like a pro: โšก ๐’๐ฉ๐ž๐ž๐ C delivers blazing ๐ž๐ฑ๐ž๐œ๐ฎ๐ญ๐ข๐จ๐ง ๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ๐š๐ง๐œ๐ž, ideal for real-time inference, custom ML engines, or GPU-intensive tasks. Python? Smooth and flexibleโ€”but ๐ฌ๐ฅ๐จ๐ฐ๐ž๐ซ ๐ฎ๐ง๐๐ž๐ซ ๐ญ๐ก๐ž ๐ก๐จ๐จ๐, often relying on C -based libraries. ๐Ÿ“ฆ ๐ƒ๐ž๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐ž๐ง๐ญ C compiles into ๐ฌ๐ž๐ฅ๐Ÿ-๐œ๐จ๐ง๐ญ๐š๐ข๐ง๐ž๐ ๐›๐ข๐ง๐š๐ซ๐ข๐ž๐ฌ, making it ideal for ๐ž๐๐ ๐ž ๐๐ž๐ฏ๐ข๐œ๐ž๐ฌ, ๐ฅ๐จ๐ฐ-๐ฅ๐š๐ญ๐ž๐ง๐œ๐ฒ ๐ฌ๐ฒ๐ฌ๐ญ๐ž๐ฆs, and platforms where Python environments are bulky. Python excels in ๐ฉ๐ซ๐จ๐ญ๐จ๐ญ๐ฒ๐ฉ๐ข๐ง๐  ๐š๐ง๐ ๐€๐๐ˆ-๐Ÿ๐ข๐ซ๐ฌ๐ญ ๐๐ž๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐ž๐ง๐ญ๐ฌ, but may require Dockerization or virtual environments. ๐Ÿ–ฅ๏ธ ๐‡๐š๐ซ๐๐ฐ๐š๐ซ๐ž ๐‚๐จ๐ง๐ญ๐ซ๐จ๐ฅ C = full access to ๐ฆ๐ž๐ฆ๐จ๐ซ๐ฒ ๐ฆ๐š๐ง๐š๐ ๐ž๐ฆ๐ž๐ง๐ญ, ๐ฉ๐š๐ซ๐š๐ฅ๐ฅ๐ž๐ฅ ๐ฉ๐ซ๐จ๐œ๐ž๐ฌ๐ฌ๐ข๐ง๐ , ๐ž๐ฆ๐›๐ž๐๐๐ž๐ ๐ฌ๐ฒ๐ฌ๐ญ๐ž๐ฆ๐ฌ, ๐š๐ง๐ ๐†๐๐”/๐…๐๐†๐€-๐ฅ๐ž๐ฏ๐ž๐ฅ ๐ญ๐ฎ๐ง๐ข๐ง๐ . Python abstracts hardware complexity, favoring easeโ€”but ๐ฅ๐ž๐ฌ๐ฌ ๐ฌ๐ฎ๐ซ๐ ๐ข๐œ๐š๐ฅ ๐œ๐จ๐ง๐ญ๐ซ๐จ๐ฅ. ๐Ÿง‘โ€๐Ÿ’ป ๐ƒ๐ž๐ฏ๐ž๐ฅ๐จ๐ฉ๐ฆ๐ž๐ง๐ญ ๐’๐ฉ๐ž๐ž๐ Python is ๐Ÿ๐š๐ฌ๐ญ๐ž๐ซ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ž, debug, and iterate. C requires more effort but shines when ๐ฉ๐ซ๐ž๐œ๐ข๐ฌ๐ข๐จ๐ง ๐š๐ง๐ ๐ž๐Ÿ๐Ÿ๐ข๐œ๐ข๐ž๐ง๐œ๐ฒ outweigh developer convenience. ๐ŸŒ ๐„๐œ๐จ๐ฌ๐ฒ๐ฌ๐ญ๐ž๐ฆ Python boasts massive libraries: TensorFlow, PyTorch, Hugging Face, and a thriving ML community. C has fewer high-level AI librariesโ€”but underpins many Python packages with ๐œ๐จ๐ซ๐ž ๐œ๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ž ๐ฅ๐ข๐›๐ซ๐š๐ซ๐ข๐ž๐ฌ ๐ฅ๐ข๐ค๐ž ๐„๐ข๐ ๐ž๐ง, ๐Ž๐๐๐— ๐‘๐ฎ๐ง๐ญ๐ข๐ฆ๐ž, ๐š๐ง๐ ๐‹๐ข๐›๐“๐จ๐ซ๐œ๐ก. ๐ŸŽฏ ๐”๐ฌ๐ž ๐‚๐š๐ฌ๐ž๐ฌ C : ๐‡๐ข๐ ๐ก-๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ๐š๐ง๐œ๐ž ๐ข๐ง๐Ÿ๐ž๐ซ๐ž๐ง๐œ๐ž ๐ž๐ง๐ ๐ข๐ง๐ž๐ฌ, ๐ž๐ฆ๐›๐ž๐๐๐ž๐ ๐€๐ˆ, ๐ซ๐จ๐›๐จ๐ญ๐ข๐œ๐ฌ, ๐€๐‘/๐•๐‘, ๐š๐ฎ๐ญ๐จ๐ง๐จ๐ฆ๐จ๐ฎ๐ฌ ๐ฏ๐ž๐ก๐ข๐œ๐ฅ๐ž๐ฌ Python: ๐‘๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก, ๐ž๐ฑ๐ฉ๐ž๐ซ๐ข๐ฆ๐ž๐ง๐ญ๐š๐ญ๐ข๐จ๐ง, ๐ฆ๐จ๐๐ž๐ฅ ๐ญ๐ซ๐š๐ข๐ง๐ข๐ง๐ , ๐๐‹๐, ๐‚๐•, ๐ฉ๐ซ๐จ๐ญ๐จ๐ญ๐ฒ๐ฉ๐ข๐ง๐  ๐ŸŽ“ ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐‚๐ฎ๐ซ๐ฏ๐ž Python wins in ๐ž๐š๐ฌ๐ž ๐จ๐Ÿ ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐š๐ง๐ ๐ซ๐ž๐š๐๐š๐›๐ข๐ฅ๐ข๐ญ๐ฒ. C demands ๐ฆ๐จ๐ซ๐ž ๐ฅ๐จ๐ฐ-๐ฅ๐ž๐ฏ๐ž๐ฅ ๐ฎ๐ง๐๐ž๐ซ๐ฌ๐ญ๐š๐ง๐๐ข๐ง๐  ๐š๐ง๐ ๐๐ž๐›๐ฎ๐ ๐ ๐ข๐ง๐  ๐ฌ๐ค๐ข๐ฅ๐ฅ๐ฌ. ๐Ÿ”— ๐ˆ๐ง๐ญ๐ž๐ ๐ซ๐š๐ญ๐ข๐จ๐ง Best of both worlds: use ๐๐ฒ๐ญ๐ก๐จ๐ง ๐Ÿ๐จ๐ซ ๐ฆ๐จ๐๐ž๐ฅ ๐๐ž๐ฏ๐ž๐ฅ๐จ๐ฉ๐ฆ๐ž๐ง๐ญ, export to ๐‚ ๐Ÿ๐จ๐ซ ๐๐ž๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐ž๐ง๐ญ. Tools like ONNX, Pybind11, and TorchScript bridge the gap. ๐Ÿ’ฌ ๐๐จ๐ญ๐ญ๐จ๐ฆ ๐‹๐ข๐ง๐ž: Donโ€™t just ask โ€œPython vs. C โ€โ€”ask ๐ฐ๐ก๐ž๐ง ๐š๐ง๐ ๐ฐ๐ก๐ž๐ซ๐ž. Because in the real world of AI, ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ฉ๐ซ๐จ๐ญ๐จ๐ญ๐ฒ๐ฉ๐ž๐ฌ. ๐‚ ๐ฉ๐จ๐ฐ๐ž๐ซ๐ฌ ๐ฉ๐ซ๐จ๐๐ฎ๐œ๐ญ๐ข๐จ๐ง. ๐ŸŒ pcdoctorsnet.com ๐Ÿ“ž 1 (346) 355-6002 #CppInAI #PythonVsCpp #AIEngineering #EdgeAI #MachineLearningTools #AIFrameworks #HighPerformanceAI #EdgeAI #DeepLearningDeployment #CppForML #PythonAndCpp #BrainAndBuild #AIDevelopment #MachineLearning #PythonAndCpp #SmartDeployment #texas #usa #UnitedStates #pcdoctorsnet #canada #india
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25 Apr 2025
With so many AI tools out there, itโ€™s hard to know where to begin. Shaheer Airaj, Head of ML & AI Projects at SuperDataScience, highlights 3 tools to help you get started with AI. #DXBToday #DubaiOneTv #MachineLearningTools #AIEducation #AICommunity
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15 Mar 2025
When SpaCy meets PyInstaller, is it love at first byte or just a friendly handshake across the virtual void? ๐ŸŒŒ Source: devhubby.com/thread/how-to-uโ€ฆ #CodingLife #PyData #NLP (Natural Language Processing) #MachineLearningTools
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Today, I'm diving into advanced cluster analysis in my #MachineLearning course! I'll be covering density-based clustering with DBSCAN and showcasing the process using my interactive #Python dashboard built with @matplotlib. Feel free to check it out and experiment with the interactive visualization on GitHub: ๐Ÿ”—github.com/GeostatsGuy/DataSโ€ฆโˆ€. #DataScience #Clustering #DBSCAN #PythonDashboard #InteractiveLearning #MachineLearningTools
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Shape the future with NeuralFilter.com! ๐ŸŒ Ideal for AI research platforms, innovative startups, or tech hubs. Secure this domain today. #NeuralNetworks #AIStartup #TechDomainForSale #InnovationHub #MachineLearningTools #TechBusiness #AIInnovation #PremiumDomain #Future
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23 Oct 2024
I personally find this incredible. Anthropic is introducing real-time computer navigation in the video below: Claude 3.5 now autonomously moves the cursor, clicks, and interacts with systems. How does it work? It leverages machine learning to mimic human-like actions, automating repetitive workflows. Imagine this: filling out forms, navigating websites, all without you touching the keyboard. This is a game changer, especially for building intelligent agents. In their video (below) they show it in action and provide real world examples. Like if you want to support me and follow me on my journey. โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€”โ€” #AIautomation #IntelligentAgents #AItools #FutureOfWork #TechInnovation #AIAgents #MachineLearningTools #WorkAutomation #TechBreakthrough #NextGenAI #AIpoweredAgents #Claude3 #AutomationRevolution #AIforBusiness #DigitalAgents #AIWorkflow #ClaudeAutomation #AIinnovation #TechForBusiness #AgentBuilding #AIAgentTools #Claude3AI
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Machine learning lets machines learn and improve, but what tools do they use? ๐Ÿš€ Learn more about the comprehensive curriculum of our LLM Bootcamp: hubs.la/Q02wn1pK0 Head over to the full blog post โžก๏ธ hubs.la/Q02wn1SL0 #MachineLearningTools #ML #Python #AI
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