
Marcel Butucea
@marcel_butucea
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System Design & Architecture & AI Cheerleader ⛷️
Joined October 2017
RT @Jousefm2: Physics-Informed Neural Networks (PINNs) Repo. There’s a great open-source MATLAB® repository that’s been gaining attention -….
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Wow, they turned earthquake data and county boundaries into a natural-language GIS assistant 🌍💡 The LLM actually does spatial queries on Redshift—who.
aws.amazon.com
Applying emerging technologies to the geospatial domain offers a unique opportunity to create transformative user experiences and intuitive workstreams for users and organizations to deliver on their...
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80-90% of data in org documents is unstructured & untapped?! 🤯 No wonder everyone's drowning in PDFs. AWS's GenAI IDP Accelerator aims to fix that. Will this ac.
aws.amazon.com
In this post, we introduce our open source GenAI IDP Accelerator—a tested solution that we use to help customers across industries address their document processing challenges. Automated document...
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Wow, India's ChatGPT adoption is booming! 🚀 Weekly active users *quadrupled* in a year, and apparently, they have the largest student user base globally! OpenAI is opening its first India office in New Delhi—big move! 🇮🇳.
analyticsindiamag.com
India is OpenAI’s second-largest market for ChatGPT after the United States, and among the fastest-growing.
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90% of enterprise AI projects fail. Most die in data prep or pilot purgatory. 🎯 Turinton's zero-ETL, 8-week deployment model flips the script—proving 'operational'.
analyticsindiamag.com
Turinton’s Insights AI platform tackles enterprise AI challenges by cutting implementation timelines from eight to 12 months to just eight to 12 weeks.
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Struggling with massive Pandas datasets? 😩 This article shows how cuDF leverages Unified Virtual Memory to combine GPU & CPU RAM, letting you process datasets *larger*.
developer.nvidia.com
Slow data loads, memory-intensive joins, and long-running operations—these are problems every Python practitioner has faced. They waste valuable time and make iterating on your ideas harder than it…
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NVIDIA’s Blackwell GPUs debut NVFP4, a 4-bit floating-point format designed to boost AI inference speed—up to 4x faster for long sequences—while maintaining high accuracy. Read how it's shaping the future of AI:
developer.nvidia.com
Open source AI models such as Cosmos, DeepSeek, Gemma, GPT-OSS, Llama, Nemotron, Phi, Qwen, and many more are the foundation of AI innovation. These models are democratizing AI by making model...
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Did you know KDE needs over 360,000 points in 6D to match just 22 in 1D? 🤯 Meanwhile, MAF flows learn global patterns efficiently, crushing KDE in high dims—requiring up to 100x fewer sam.
towardsdatascience.com
Learn why autoregressive flows are the superior density estimation tool for high-dimensional data
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Mind blown! 🤯 This article digs into *why* multi-head self-attention works, revealing it's less about independent heads and more about routing info! Turns out, encoder-decoder attention is WAY more reliant on multiple heads. #AI #NLP
theaisummer.com
Learn everything there is to know about the attention mechanisms of the infamous transformer, through 10+1 hidden insights and observations
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Huawei Cloud just *out-gartnered* AWS & Azure with a 4.7/5 customer satisfaction score—despite U.S. sanctions. 🎯. Their secret? A.
artificialintelligence-news.com
Huawei Cloud continues its innovation in the open, with its first appearance in the Gartner Magic Quadrant for Container Management 2025.
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RT @StevePalomares_: Are you confused where to use AI in your business? . Use this simple yet powerful AI prioritization guide, from my new….
aisalesrevolution.beehiiv.com
AI Strategy Saturday: From Overwhelmed to Organized: The Best AI Starting Point for Your Business
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RT @ChainGPT_Pad: 🏆 2024 was a year of milestones for @xyro_io, from MVP development to a vibrant community, and 720k wallets trading $32b….
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Walmart's CISO drops intel: AI-powered phishing attacks are now using generative AI to craft 50% more convincing scams. Their secret weapon? They're.
venturebeat.com
Walmart CISO Jerry Geisler on securing agentic AI, modernizing identity, and Zero Trust for enterprise-scale cybersecurity resilience.
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Mind blown! 🤯 Apple's SlowFast-LLaVA-1.5 achieves SOTA long-form video understanding with just 1B params! They used a clever SlowFast mechanism & carefully curated data. Proves you don't need massive models for impressive results.
machinelearning.apple.com
We introduce SlowFast-LLaVA-1.5 (abbreviated as SF-LLaVA-1.5), a family of video large language models (LLMs) offering a token-efficient…
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Did you know Gemini boasts a *million-token* context window and seamless multimodal processing, yet most users still prefer ChatGPT’s smoother UX? 🤔 It’s a stark reminder that technical prowess alone doe.
gradientflow.com
Subscribe • Previous Issues Why AI Benchmarks Don’t Predict Consumer Success: The Gemini Paradox As someone who works with AI, my toolkit is rarely limited to a single provider. I regularly switch...
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Tired of being a human report generator? 🤖 This article reveals how NLP can automate soul-crushing data tasks. For example, an auto-report generator script uses natural langua.
kdnuggets.com
Tired of repeating the same data tasks? Automate them. This article shows beginners how to build efficient, low-maintenance data engineering workflows that pay off in the long run.
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Mind. Blown. 🤯 Just learned you can chain `.fit()` directly to your model constructor in scikit-learn! One-liners like `LogisticRegression(. ).fit()` not only look cleaner but also streamline your ML workflows. Check out 9 more:
kdnuggets.com
This tutorial will focus on ten practical one-liners that leverage the power of libraries like Scikit-learn and Pandas to help streamline your machine learning workflows.
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Gemini crushes benchmarks (million-token context window, flawless multimodal!) but loses to ChatGPT in the real world. Why? Google’s UI is a mess—no editing, clunky model switching, and more. The article nails it: raw power isn’t everything. Read more:
gradientflow.com
As someone who works with AI, my toolkit is rarely limited to a single provider. I regularly switch between models from Google (Gemini), OpenAI (GPT-X), Anthropic (Claude), and Grok. I also work with...
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NVIDIA just crammed a 120B-parameter model into a single 80GB GPU using FP4 precision—no 16-bit weights needed! 🔥 Their Blackwell architecture is a game-changer. Read how it enables 1.5M TPS inference:
developer.nvidia.com
NVIDIA and OpenAI began pushing the boundaries of AI with the launch of NVIDIA DGX back in 2016. The collaborative AI innovation continues with the OpenAI gpt-oss-20b and gpt-oss-120b launch.
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800x bandwidth leap since 2016?! 🚀 NVIDIA’s 5th-gen NVLink now crams 1,800 GB/s all-to-all in 72-GPU racks—enabling breakthroughs in AI reasoning. Discover how NVIDIA NVLink and NVLink Fusion are scaling AI inference performance and flexibility:
developer.nvidia.com
The exponential growth in AI model complexity has driven parameter counts from millions to trillions, requiring unprecedented computational resources that require clusters of GPUs to accommodate.
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