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#Machinelearning Reel by @sorhan.hq (verified account) - Meet Guangting Yu ( @yugtmacs ): - Applied Math PhD. Quant - Finance MS (UMich). - Worked on alpha discovery using Kalman filters. Published - AI/ML
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@sorhan.hq
Meet Guangting Yu ( @yugtmacs ): - Applied Math PhD. Quant - Finance MS (UMich). - Worked on alpha discovery using Kalman filters. Published - AI/ML research (NeurIPS). Quants don’t guess markets. They hunt alpha. Hidden edges in data. Statistical arbitrage exploits tiny pricing gaps Factor models like Fama French explain what drives returns. - Kalman filters separate signal from noise in real time - Mean reversion bets prices snap back - Momentum rides trends early - Machine learning models like XGBoost and neural nets predict moves before they’re obvious. Covariance Formula: Cov(X, Y) = E[XY] − E[X]E[Y] Measures how two variables move together Positive means they rise together, negative means they move opposite Core signal used to detect relationships and extract alpha While most people stare at charts he’s modeling the system behind them #quant #machinelearning #startups #ai #founder
#Machinelearning Reel by @sopi.iscoding (verified account) - Putting robotics to good use 😬 #tech #robotics #electronics #machinelearning #womenintech
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@sopi.iscoding
Putting robotics to good use 😬 #tech #robotics #electronics #machinelearning #womenintech
#Machinelearning Reel by @benkimball.ai - You can access TOP AI models for FREE on GitHub… wait until the end 🤯 devs are literally giving away powerful LLMs you can use today #aitools #github
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@benkimball.ai
You can access TOP AI models for FREE on GitHub… wait until the end 🤯 devs are literally giving away powerful LLMs you can use today #aitools #github #freeai #llm #machinelearning openai aiapps developer techhacks automation learnai viraltech Here is the Link: https://github.com/elder-plinius/G0DM0D3
#Machinelearning Reel by @eldan.nomad (verified account) - I started my AI career more than 10 years ago from 3rd part country. But got offer from all of FAANG companies! I spent years as an AI/ML Engineer a
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@eldan.nomad
I started my AI career more than 10 years ago from 3rd part country. But got offer from all of FAANG companies! I spent years as an AI/ML Engineer at Apple and Microsoft. Here’s the exact roadmap I’d follow to get that job from scratch — in 2026. Most people study ML and wonder why they fail FAANG loops. The truth? BigTech doesn’t just test your models. It tests 4 things at once: → Algorithms & Data Structures → ML Breadth (theory depth) → ML System Design → Behavioral ownership Miss one. No offer. The 7-Phase Roadmap: 01 — Math & Programming Foundations 02 — Classical Machine Learning 03 — Deep Learning & PyTorch 04 — Transformers & Modern LLMs 05 — Engineering & Systems 06 — FAANG Interview Preparation 07 — Application & Negotiation Strategy Timeline: ~9–14 months at 15–20 hrs/week. It’s not fast. But it’s the right way. Comment “AIML” and I’ll send you the full step-by-step PDF guide — free. Covers every phase in detail, the exact LeetCode strategy, ML System Design framework, must-practice designs, and salary negotiation tactics. Save this. Share it with someone who needs it.
#Machinelearning Reel by @sorhan.hq (verified account) - Researcher name: Guangting Yu, Dailey Labs The next wave of AI isn't just chat. It's simulation. As models get better at learning dynamics, differe
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@sorhan.hq
Researcher name: Guangting Yu, Dailey Labs The next wave of AI isn’t just chat. It’s simulation. As models get better at learning dynamics, differential equations, and world models, AI moves closer to running experiments, testing ideas in simulated environments, and helping engineer real systems before they’re built. Simulation engineering may be one of the biggest frontiers in AI. 🌊 #startuplife #founder #tech #machinelearning #ai
#Machinelearning Reel by @aiteasy - Raoul Pal just explained why every high-paying career is about to get repriced. ⠀ For centuries, big salaries came from two things. Scarce knowledge
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@aiteasy
Raoul Pal just explained why every high-paying career is about to get repriced. ⠀ For centuries, big salaries came from two things. Scarce knowledge or scarce capital. ⠀ Lawyers earn because legal knowledge is rare. Doctors earn because medical knowledge is rare. Consultants, accountants, engineers. All paid for knowing things other people do not. ⠀ Artificial intelligence just made knowledge infinite. Not better. Infinite. ⠀ Every legal precedent. Every medical study. Every accounting principle. Available instantly to anyone at zero cost. ⠀ When something moves from scarce to infinite, its value drops to zero. Not as a metaphor. Mathematically. ⠀ Water is more useful than diamonds but diamonds cost more because they are scarce. AI turned knowledge into water. ⠀ Pal called it the single greatest innovation of humanity. Bigger than anything except splitting the atom. ⠀ Your law degree, medical training, and MBA were never selling you knowledge. They were selling you scarcity. And scarcity just ended. ⠀ Machine learning and automation made information abundant. The economic model built on gatekeeping knowledge is breaking apart. ⠀ The question is no longer what do you know. It is what can you do with what everyone now knows. ⠀ If knowledge is worth zero, what makes you valuable? ⠀ Follow Aiteasy. The future is easier than you think. ⠀ Credit: The Diary Of A CEO YT / Steven Bartlett / Raoul Pal ⠀ #ArtificialIntelligence #Automation #TechReels #MachineLearning #ChatGPT
#Machinelearning Reel by @dontlookback.ai - Someone just conducted a live experiment that reveals how different AI systems perform when given real money and complete autonomy in financial market
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@dontlookback.ai
Someone just conducted a live experiment that reveals how different AI systems perform when given real money and complete autonomy in financial markets. Two autonomous AI agents were each given $1,000 and 48 hours to trade independently on Polymarket, a prediction market platform. No human intervention. No guidance. Just pure algorithmic decision making in real time. Both agents started with identical conditions: same capital, same time window, same market access. The only variable was the underlying AI model powering each agent’s trading decisions. The results were strikingly different. The agent running on Claude executed over 5,200 trades during the 48-hour period, transforming the initial $1,000 into approximately $14,200. That’s a return exceeding 1,300%, while simultaneously covering its own API costs throughout the experiment. The competing OpenClaw agent told a very different story. It executed fewer than 200 trades, experienced a 94% drawdown, and completely depleted its funds before the test period ended. Same starting capital. Same market conditions. Two AI systems with vastly different outcomes. This experiment highlights something important as artificial intelligence becomes more sophisticated and autonomous. These systems are no longer just theoretical constructs or controlled demonstrations. They’re capable of operating independently in complex, real world environments where financial decisions have actual consequences. As machine learning models continue advancing and autonomous agents become more capable, we’re entering a phase where AI systems may actively compete against each other in financial markets, making split second decisions that humans simply cannot match in speed or volume. The question isn’t whether this technology works. The question is what happens next as these systems scale. ⸻ Don’t forget to Share and Follow @dontlookback.ai ⸻ #AI #technology #trading #autonomousAI artificial intelligence, machine learning, algorithmic trading, prediction markets, autonomous agents, financial technology, AI models, Claude AI, automated systems, market analysis, fintech innovation, intelligent automation
#Machinelearning Reel by @plutoplatypus_ - AI (Artificial Intelligence) is a broad field focused on building systems that can perform tasks requiring human-like intelligence-such as learning, r
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@plutoplatypus_
AI (Artificial Intelligence) is a broad field focused on building systems that can perform tasks requiring human-like intelligence—such as learning, reasoning, vision, and decision-making. It includes everything from recommendation systems and computer vision to robotics and automation. On the other hand, LLMs (Large Language Models) are a specific subset of AI designed to understand and generate human language. They are trained on massive amounts of text data to perform tasks like chatting, writing, summarizing, and coding. In simple terms, all LLMs are part of AI, but not all AI is an LLM. While AI can “see,” “predict,” or “decide,” LLMs primarily “read” and “write” like humans. Understanding this difference is key if you’re stepping into modern tech—because today’s most powerful applications often combine multiple AI systems, with LLMs handling communication and other AI models handling perception and decision-making. #ArtificialIntelligence #LLM #MachineLearning #AIvsLLM #TechConcepts
#Machinelearning Reel by @foundedceo (verified account) - Alexandr Wang was born in 1997 in Los Alamos, New Mexico, and showed an early talent for mathematics and computer science. After enrolling at Massachu
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@foundedceo
Alexandr Wang was born in 1997 in Los Alamos, New Mexico, and showed an early talent for mathematics and computer science. After enrolling at Massachusetts Institute of Technology (MIT) he left to pursue entrepreneurship and co-founded Scale AI in 2016. The company focused on a critical problem in AI: providing the high-quality data labeling and infrastructure needed to train machine learning systems. As demand for AI accelerated, Scale AI became one of the most important infrastructure companies in the industry. The company grew fast, raising capital from leading investors and securing contracts with major technology firms and government agencies, helping make Wang one of the youngest self-made billionaires in the world. By 2024, reports valued the company at approximately $13.8 billion, placing it among the most valuable private AI startups globally. Wang often says progress comes from hyper-focus and relentless effort. His belief is simple: most people stop at one mile, while exceptional builders keep going ten. That extra intensity, applied consistently over time, is what creates separation. In industries moving as fast as AI, the founders willing to outwork, outfocus, and outlast everyone else are often the ones who shape the future. >Follow us (@foundedceo) for more insights straight from the world’s leading founders & CEO’s 🤝 (Media: Shawn Ryan Show)
#Machinelearning Reel by @blue_collar_bible - Only a matter of time.. AI is advancing quickly enough that many routine and repetitive jobs could be largely automated within the next decade, espec
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@blue_collar_bible
Only a matter of time.. AI is advancing quickly enough that many routine and repetitive jobs could be largely automated within the next decade, especially in fields like data entry, customer service, basic accounting, and even some areas of transportation and logistics. As machine learning models become better at handling language, decision-making, and pattern recognition, they’re starting to move beyond simple tasks into roles that once required human judgment. However, “replacing most jobs” isn’t likely to happen overnight—what’s more realistic is a gradual shift where AI handles large portions of work while humans adapt into roles that focus on oversight, creativity, relationship-building, and complex problem-solving. The timeline will vary by industry, but the transition is already underway, and the biggest changes will likely be felt within the next 5 to 15 years. #ai #bluecollar #plumbers #electrician #construction
#Machinelearning Reel by @algobrief (verified account) - Interesting fact: There is a profound bubble distortion that happens when you spend enough time in tech circles where every conversation eventually c
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@algobrief
Interesting fact: There is a profound bubble distortion that happens when you spend enough time in tech circles where every conversation eventually collapses into a discussion about AI, because stepping outside that world reveals that the overwhelming majority of humanity is living full, economically productive, and meaningful lives in which the question of which foundation model has the best reasoning benchmark has never once crossed their mind. Plumbers, teachers, nurses, farmers, architects, chefs, and the billions of people running small businesses in every corner of the world are solving genuinely hard problems every day using domain expertise, physical skill, human relationships, and accumulated judgment that no language model can replicate end to end, and many of those paths offer stability, autonomy, and compensation that would surprise engineers who have been conditioned to believe that tech is the only industry worth being in. The AI maximalist worldview that treats every career not adjacent to machine learning as a dead end waiting to happen is both empirically questionable and psychologically corrosive, because it creates a permanent anxiety about relevance that conveniently keeps people glued to the same industry that is selling them the tools they are told they cannot survive without. History is full of transformative technologies that reshaped some industries completely while barely touching others, and the honest version of the AI story is not that it changes everything for everyone but that it changes specific things for specific people while the rest of the world keeps moving forward on its own terms. Have you ever seriously considered a path completely outside of tech, and what pulled you back or kept you from pursuing it?
#Machinelearning Reel by @techaivault - The man who helped build modern AI is now warning the world about it. Geoffrey Hinton, often called the "Godfather of AI," played a key role in develo
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@techaivault
The man who helped build modern AI is now warning the world about it. Geoffrey Hinton, often called the “Godfather of AI,” played a key role in developing neural networks and backpropagation — the same foundation behind today’s powerful AI systems. In 2024, he was honored with the Nobel Prize for his contributions. But here’s the twist… Hinton is no longer just celebrating AI — he’s warning us. According to him, AI is already shaping reality in ways we don’t fully understand. From echo chambers on social media to large-scale surveillance and advanced scams, the impact is no longer theoretical — it’s happening now. He also raised deeper concerns about the future: AI systems becoming smarter than humans, autonomous decision-making machines, and technologies that could be misused if controlled by profit-driven companies instead of ethical systems. His biggest message? We are moving fast… but not carefully enough. The question is no longer “What can AI do?” It’s “Should we control it before it controls us?” What do you think — opportunity or danger? 🤔 Credits: @nobelprize DM for credit or removal. #artificialintelligence #futuretechnology #machinelearning #aivideo #technews

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