50 Best Tweets About Generative AI (2026)

Explore the best tweets about generative AI, including models, products, business applications, research, creative tools, risks, and real-world results.

Substantive generative AI research, products, applications, adoption, limitations, economics, and firsthand implementation experience.

Creators
45
Updated

What 50 top Generative AI posts reveal

The evidence set presents generative AI as practically useful but uneven: posts highlight open tooling, agentic workflows, and production applications, while enterprise, creator, and governance posts emphasize validation, quality control, and accountable deployment.

Dominant tone
Positive

44% of posts

Median score
7.48

All-time engagement

Leading format
Other

100% of posts

Recent posts
32%

Published in 90 days

Conversation map

The themes creators return to

Content Quality and Creator Economics

AI-generated content performance, AI slop, human creator displacement, search visibility, advertising outcomes, and editorial quality.

18%

Tone and stance

Sentiment Positive leads
Author posture Supportive leads

Performance benchmark

Median likes
27
Median reposts
5
Median replies
6
Median views
2.2K

Posts with media make up 70% of this collection. Their median all-time score is 11.2, compared with 5.02 for text-only posts.

Format mix

  • Other 100% · score 7.48

Where creators agree, and where they do not

Shared view

Reliability requires workflow design

Several posts argue for validating outputs rather than relying on unaided model responses. They describe continuous security testing, reusable workflows that are validated before organizational deployment, and retrieval systems that consult trusted information before generating responses.

Shared view

Discussion distinguishes generation from agentic workflows

Multiple posts distinguish one-shot content generation from systems described as reasoning, selecting tools, and pursuing multi-step objectives. These posts frame agents as an extension beyond producing a single text, image, or code response.

Shared view

Quality and provenance matter for content outcomes

Creator and search-oriented posts emphasize ordinary editorial and technical quality standards. One cited ad study reports weaker click-through performance for ads perceived as AI-generated, while other posts argue that weak sourcing, conspicuous artifacts, or poor site quality can hurt content performance or visibility.

Open debate

Creative efficiency versus creator risk

One post describes AI-enabled video workflows as a way to expand production capacity. Others raise concerns about effects on human-creator economics and report hostility toward generative-AI use in gaming communities. The cited stock-image-market post presents these as risks that platforms should consider, rather than direct evidence about YouTube itself.

Open debate

Capability claims meet uneven performance

Posts describe notable model advances and problem-solving examples, while others emphasize jagged capabilities, difficult troubleshooting, and unexpected failures. Together, these posts contest any simple claim that model capability is consistently reliable across tasks.

Open debate

Open resources versus deployment accountability

Posts about open-source models, courses, and security tooling present them as accessible resources for learning and building. In contrast, policy and enterprise-oriented posts focus on transparency, human review, safety controls, and responsibility for deployed systems.

Patterns behind standout posts

Local LoRA video workflow was the top outlier

The post describing local LoRA training and video generation with open tools was the highest supplied engagement outlier, with an all-time score of 2204.21 versus the overall median all-time score of 7.48.

Model-scaling critique was the second-largest outlier

The post arguing for a small world model as an alternative to prevailing scaling narratives was the second supplied engagement outlier, with an all-time score of 1746.20. Its technical and industry claims should be treated as claims made in the post.

A free GenAI curriculum was also an engagement outlier

The post about the open-source generative-AI curriculum was a supplied outlier, with an all-time score of 216.56. The evidence supports strong engagement with this specific learning-resource post.

Media had a higher median score than text

Media appeared in 35 of 50 tweets (70%). The media median all-time score was 11.154, compared with 5.023 for text posts; the top outlier was the post about a video-generation workflow.

Statistical standouts

  1. View standout post 1 Score 2204.2 · 294.68× median
  2. View standout post 2 Score 1746.2 · 233.45× median
  3. View standout post 3 Score 216.6 · 28.95× median
  4. View standout post 4 Score 155.7 · 20.81× median
  5. View standout post 5 Score 132.1 · 17.66× median

Who shapes this conversation

The five most represented creators account for 20% of the selected posts.

  1. 1. Eric Seufert

    @eric_seufert

    2 posts

  2. 2. Justin Thomas

    @JustinThomasAI

    2 posts

  3. 3. MIT Sloan School of Management

    @MITSloan

    2 posts

  4. 4. Ryan Law

    @thinking_slow

    2 posts

  5. 5. Tom Goodwin

    @tomfgoodwin

    2 posts

  6. 6. 0DIN.ai

    @0dinai

    1 post

The evidence set is dispersed across creators

The set includes 45 creators across 50 tweets. The listed repeat contributors each posted two tweets, and the top-five placement share was 20%, indicating that the sample is not concentrated in a single leading voice.

MIT Sloan posts emphasize organizational use and validation

MIT Sloan’s two cited posts address how employees interact with generative AI and how models may respond when professionals try to validate outputs, adding organizational-use and validation perspectives to the set.

Practitioner posts focus on content quality and creator economics

Eric Seufert’s cited posts address ad perceptions, creator economics, and platform responses to generative AI. Ryan Law’s posts focus on authority, sourcing, and editorial standards in AI-assisted content.

Since the previous snapshot

What changed since Aug 12, 2026

  • 80% of the selected posts remained.
  • The creator count changed by +2.
  • The leading sentiment remained stable.
How this analysis was made

Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.

Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.

This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.

Top Generative AI tweets from 45 creators

Ranked 01–50

  1. 01

    @ostrisai ·

    I trained this @ltx_model LTX 2.3 LoRA of George Costanza at home on my 5090 in about a day with AI Toolkit. I generated this 30 second video with @ComfyUI on my 5090 in 6 minutes. Open source is, always has been, and always will be, the future of generative AI. (SOUND ON)

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  2. 02

    @heyrobinai ·

    THE ENTIRE AI INDUSTRY JUST GOT HUMILIATED a tiny model trained in just a few hours on a single graphics card is planning 48x faster than billion-dollar supercomputers. It actually understands physics instead of just memorizing patterns. yann lecun was right the whole time

    • 219 Replies
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  3. 03

    @aiwithmayank ·

    A WOMAN BUILT THE ENTIRE "HOW TO LEARN GENERATIVE AI" CURRICULUM INTO ONE GITHUB REPO AND GAVE IT AWAY FOR FREE it's called awesome-generative-ai-guide and it's quietly sitting at 26,000 stars. i went in to grab one thing. the list of free courses. that's it. then i kept

    • 6 Replies
    • 42 Reposts
    • 147 Likes
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  4. 04

    @ChrisGPT ·

    I WON'T PLAY VIDEO GAMES IF THEY HAVE GENERATIVE AI" 🗣️🗣️🗣️ Well, hate to break it to you.. but generative AI is now literally looking better than real games. Everyone crying about "AI slop" is completely missing what's actually happening in gaming right now. Nvidia just

    • 108 Replies
    • 42 Reposts
    • 734 Likes
    • 25.7K Views
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  5. 05

    @MarioJoos ·

    ⚠️IMPORTANT READ: If YouTube doesn’t solve its AI problem quickly, we won’t have many human creators left. (+ research insights) If you’re wondering why it feels like you’re seeing more AI content, and why human creators seem to be disappearing, here are the numbers from a real

    • 58 Replies
    • 60 Reposts
    • 414 Likes
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  6. 06

    @r0ck3t23 ·

    Jensen Huang just laid out the three inflection points that turned AI from a science project into a workforce. Three shifts. Two years. Each one more irreversible than the last. The first was generative. Huang: “The technology sat in plain sight months before GPT. It wasn’t

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    • 47 Reposts
    • 163 Likes
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  7. 07

    @goyalshaliniuk ·

    Everyone’s talking about Generative AI, but most only see the surface. Here’s a guide that covers generative AI from core concepts to advanced techniques, each step adds clarity, making it easier to build smarter, more adaptable, and future-ready AI. Here’s a breakdown of the

    • 20 Replies
    • 29 Reposts
    • 96 Likes
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  8. 08

    @milan_milanovic ·

    𝗗𝗼𝗻𝗮𝗹𝗱 𝗞𝗻𝘂𝘁𝗵 𝗶𝘀 𝘀𝗵𝗼𝗰𝗸𝗲𝗱 𝗯𝘆 𝗵𝗼𝘄 𝗴𝗼𝗼𝗱 𝗔𝗜 𝗵𝗮𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 Knuth is now 88 years old. He wrote The Art of Computer Programming starting in 1962, and won the Turing Award in 1974. In his paper, which talks about how AI helped him solve a problem, he wrote at the

    • 11 Replies
    • 32 Reposts
    • 123 Likes
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  9. 09

    @laszlogaal_ ·

    2026 = The end of upscaling? One of generative AI's biggest drawbacks was the resolution, and I'm not talking about the output resolution as 1080p would be enough for most of the projects. I'm talking about the "internal" resolution of how these models work, that makes small

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  10. 10

    @MITSloan ·

    As generative AI use becomes widespread, organizations must determine how employees use the technology. In a study co-authored by MIT Sloan professor Kate Kellogg, researchers identified three distinct ways in which people interacted with AI. Learn more: https://t.co/OkU1nJhGLf

    Graphic titled '3 Types of AI Users: Are You a Cyborg, Centaur, or Self-Automator?' 

1. Cyborgs: In a study co-authored by MIT Sloan professor Kate Kellogg, cyborgs accounted for 60% of participants who consulted with AI. They collaborated closely, allowing AI to lead and taking its advice on some occasions while pushing back on others.

2. Centaurs accounted for 14% of consultants, who were more constrained in their engagement. Unlike cyborgs, centaurs controlled their interactions with AI, harnessing it as a tool for targeted efficiency.

3. Self-automators constituted 26% of consultants. They demonstrated what the researchers describe as “abdicated co-creation,” offloading tasks almost entirely to AI. This approach is characterized by delegating analytical and evaluative thinking to the AI, producing polished results that lacked depth.
    • 4 Replies
    • 31 Reposts
    • 86 Likes
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  11. 11

    @heyrimsha ·

    Microsoft just dropped a free 21-lesson course that takes you from zero to building real Generative AI applications. It hit 108K stars and it's the cleanest learning path for GenAI I've seen all year. - 21 lessons covering LLMs, RAG, agents, fine-tuning - Python + TypeScript

    • 4 Replies
    • 6 Reposts
    • 35 Likes
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  12. 12

    @coreyhainesco ·

    I built a skill that produces marketing videos using AI tools and programmatic frameworks — HeyGen, Remotion, Hyperframes, Runway, and more. You describe what you need and it recommends the right tool: AI avatars for talking heads, programmatic rendering for templates at scale,

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  13. 13

    @0dinai ·

    We've been thinking about AI security wrong. Traditional application security tools were designed for structured inputs, deterministic outputs, and well-defined attack surfaces. None of that applies to generative AI. At 0DIN, we've spent the past two years building

    • 0 Replies
    • 9 Reposts
    • 36 Likes
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  14. 14

    @LuizaJarovsky ·

    🚨 The European Union's Code of Practice on Transparency of AI-Generated Content is finally here! [Download it below]. What's inside & additional info: A) Rules applicable to providers of generative AI systems: 1. Marking of AI-generated or manipulated content 2. Detection of

    • 3 Replies
    • 19 Reposts
    • 35 Likes
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  15. 15

    @VaibhavSisinty ·

    Most people in AI can't actually explain the difference between "Generative AI," "Agentic AI," and "AI Agents." They use all three like they mean the same thing. They don't. And once it clicks, you can't unsee it. Here's the cleanest way to think about it: Generative AI is the

    • 7 Replies
    • 5 Reposts
    • 38 Likes
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  16. 16

    @Marie_Haynes ·

    Google is "piloting novel ways to partner with websites whose content meaningfully contributes to the freshness and factuality of generative AI responses." This document with Google's suggestions for how the US governs AI is a very interesting read. https://t.co/zEssT2IRxC

    • 6 Replies
    • 15 Reposts
    • 52 Likes
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  17. 17

    @TheSixFiveMedia ·

    Most enterprises have yet to solve the consistency problem with generative AI. "Just a slight change in the seed value or the prompt can dramatically change the output." — Varun Parmar, SVP and GM, Adobe GenStudio and Firefly Enterprise Varun Parmar told @PatrickMoorhead &

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  18. 18

    @AlphaSignalAI ·

    Top Papers of the Week (March 30 - April 5) 1. An Alternative Trajectory for Generative AI 2. MIRAGE: The Illusion of Visual Understanding 3. AI Agent Traps 4. Emotion Concepts and their Function in a Large Language Model 5. Meta-Harness: End-to-End Optimization of Model

    • 3 Replies
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  19. 19

    @eric_seufert ·

    AI-generated ads perform roughly as well as human-created ads, unless consumers suspect they were generated by AI. Researchers from the Technical University of Munich, Columbia Business School, Harvard Business School, and elsewhere analyzed more than 16 billion ad impressions

    • 4 Replies
    • 10 Reposts
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  20. 20

    @xelebofficial ·

    We are now in the era of Agentic AI, the third major inflection point in the history of AI. Inflection 1: Generative AI Models that create. Given a prompt, they produce text, images, code. They respond, but don't act. Inflection 2: Reasoning AI Models that think. Before

    • 23 Replies
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  21. 21

    @burkov ·

    3D generation is one of the most impactful uses of generative AI because it allows editing the generated objects and then produce their physical copies. This recent paper from Microsoft explain the SOTA approach to 3D generation. Learn from the paper with an AI tutor and

    • 6 Replies
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    • 21 Likes
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  22. 22

    @thinking_slow ·

    "does AI content work?" is entirely the wrong thing to ask. a much better question is "how is AI content materially different from 'normal' content?" usually when people publish "AI content" they are unwittingly engaging in a *different* strategy to traditional content

    • 7 Replies
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  23. 23

    @MITSloan ·

    When professionals try to validate AI outputs, generative AI often responds not with corrections or candor but with escalating persuasion tactics. https://t.co/qO8EAMunxf

    • 6 Replies
    • 12 Reposts
    • 27 Likes
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  24. 24

    @sabir_huss50540 ·

    🚨BREAKING: Microsoft quietly built the best free Generative AI course on the internet and put it on GitHub. It's called Generative AI for Beginners. - 21 lessons, from what an LLM is to building agents and RAG - "Learn" lessons for concepts, "Build" lessons with real code -

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  25. 25

    @alex_prompter ·

    AI model that wins gold at the Math Olympiad can't read a clock. Stanford has a name for this: "jagged intelligence." And their 2026 AI Index proves it changes everything about how you should prompt. Frontier models now score above PhD-level on science benchmarks and dominate

    • 3 Replies
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    • 26 Likes
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  26. 26

    @JustinThomasAI ·

    Global AI spend will be in the hundreds of billions by 2026, but only ~39% of companies can point to any bottom line impact from AI. Reports referencing RAND find that 80% of AI projects never deliver their promised business value, and MIT finds ~95% of generative AI pilots fail

    • 7 Replies
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  27. 27

    @pankajkumar_dev ·

    Anthropic’s Claude costs users $200/month. But it may be burning up to $5,000 in compute per user. The AI race is getting insanely expensive. Just look at the numbers: • $197B VC funding into generative AI startups in 2025 alone • $1.6T+ invested into AI since 2013 • Big Tech

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  28. 28

    @eric_seufert ·

    I found it noteworthy that Netflix saw fit to highlight the use of generative AI in content production in its earnings release, even naming specific examples. Contrast this with gaming, where audience hostility to the use of GenAI has been so virulent that studios downplay their

    • 2 Replies
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  29. 29

    @minhsmind ·

    People are using a Kane Parsons quote as proof that the amazing 20-yr-old director of Backrooms is anti-AI, but here’s the full quote, dweebs. “I think I’m in the same boat as most well-adjusted people,” he says. “If I could snap my fingers and make generative AI disappear

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  30. 30

    @ttunguz ·

    “What happens when a new employee brings their agent to work?” An executive asked this recently. Imagine a few years from now : a student graduates, having trained their own agent through university. It knows everything they’ve learned, every paper, every problem solved. Day

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  31. 31

    @semrush ·

    Google just drew a clear line around AI search optimization: it’s SEO. On May 15, 2026, Google published its official guide, “Optimizing your website for generative AI features on Google Search.” John Mueller announced the release on the Google Search Central Blog, and Google

    • 5 Replies
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  32. 32

    @alvinfoo ·

    The latest Stanford AI Index Report 2026 highlights a shift from generative AI evangelism to evaluation as 88% of organizations integrate the technology, creating a "jagged frontier" of mixed capabilities. Key breakthroughs include advanced scientific problem-solving, a critical

    • 2 Replies
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  33. 33

    @tomfgoodwin ·

    I used generative AI for something today, It took me three minutes and saved me about 20 awful hours I also used generative AI to do some troubleshooting with my email, And I wasted about 4 hours It's still not that obvious what it's great for and what it's disastrous for

    • 8 Replies
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  34. 34

    @JustinThomasAI ·

    Every generation is shaped by the tools that surround them during their formative years. The generation that grew up with the internet thinks in hyperlinks. The generation that grew up with smartphones thinks in constant connectivity. The generation growing up with AI is going to

    • 4 Replies
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  35. 35

    @seraleev ·

    Apple made ~$900M from generative AI apps in 2025 ~75% of that came from OpenAI Apple then pays Google ~$1B/year for Gemini So ironically… ChatGPT might be indirectly funding its own competitor

    • 1 Replies
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  36. 36

    @tomfgoodwin ·

    Generative AI in practical terms doesn't work like people think. It's much closer to "Search" than "Thinking" Ask it "What airport serves the most continents direct" and I'm sure people think it's going to go one by one over likely airports and establishing systematically the

    • 10 Replies
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  37. 37

    @rohanpaul_ai ·

    New Harvard paper shows generative models may be missing a third scaling axis: how much they explore during training. What if the next scaling axis for generative models is not a bigger model or more data, but more candidate generations per training step? Added to a strong

    • 5 Replies
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  38. 38

    @shawnchauhan1 ·

    Claude passed DeepSeek, Grok, and Gemini to become the second most used generative AI app daily. Behind only ChatGPT. This happened without the consumer brand recognition of Google, the distribution of Microsoft, or the cultural moment of DeepSeek's launch. It happened because

    • 1 Replies
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    • 11 Likes
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  39. 39

    @ReplyTariq ·

    The biggest misunderstanding in generative AI right now: Typing a prompt does not make you the author. The U.S. Copyright Office just confirmed it: Prompts are instructions, not creative control. AI-generated works without meaningful human input aren’t copyrightable. Which

    • 2 Replies
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    • 19 Likes
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  40. 40

    @india_context ·

    Salute to Auhona Mukherjee, whose evidence-driven economic journalism translates complex AI–Labour Market transformations into clear policy insights. Introduction The article analyses how Generative AI (GenAI) is reshaping South Asia's Labour Markets, posing both Productivity

    • 1 Replies
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    • 8 Likes
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  41. 41

    @Inc42 ·

    Generative AI (GenAI) startup @GnaniAi has raised $10 Mn (around ₹94 Cr) in its Series B funding round led by Aavishkaar Capital. The round also saw participation from existing backer InfoEdge Ventures.👇 The startup will deploy the fresh capital to expand its customer base by

    • 3 Replies
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    • 6 Likes
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  42. 42

    @glenngabe ·

    Banned for a year if you submit AI slop -> ArXiv, the repository of preprint academic research, says it will ban authors for a year if their papers have “incontrovertible evidence” of AI-generated work “If generative AI tools generate inappropriate language, plagiarized

    • 1 Replies
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  43. 43

    @cleartechtoday ·

    📌 Q: What is a hallucination in generative AI? A: Hallucination in generative AI refers to instances where AI models (like LLMs) produce false, inaccurate, or nonsensical content presented confidently as factual. This occurs when models generate ungrounded, fabricated, or

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  44. 44

    @sachinrekhi ·

    Question: Why aren't we seeing massive revenue acceleration amongst companies fully adopting AI for productivity? This is probably one of the most fascinating paradoxes with AI right now. While the foundation model vendors themselves have had astronomical revenue growth, the

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  45. 45

    @darshal_ ·

    Most AI companies are still trying to generate better videos. The more interesting challenge is generating worlds. Not worlds that look impressive for 20 seconds. Worlds that stay coherent, respond to every action, and keep unfolding as you explore them. That's why

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  46. 46

    @thinking_slow ·

    guess: to what does this criticism refer? "The average quality...is pretty good, but on any given day, [the accuracy of] any given entry might be up or down." "You don’t know who it’s written by, you don’t know what qualifications they have, and you don’t know whether it’s been

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  47. 47

    @BernardMarr ·

    🤖 Retrieval Augmented Generation, or RAG, is becoming one of the most important concepts in enterprise AI. In this video, I explain RAG in simple terms and why it matters so much for businesses. Rather than relying only on what an AI model learned during training, RAG allows the

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  48. 48

    @etnshow ·

    F1 World Champion Nico Rosberg (@NicoRosberg) shares how generative AI will completely revolutionise F1: "There is a huge opportunity to use generative AI for material science because in F1 everything is about materials—weight and stiffness are critical for performance." "The

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  49. 49

    @The_InnovationJ ·

    New in The Innovation Drug Discovery! The evolution of computation-driven paradigms in targeted peptide drug design: From predictive modeling to generative AI and clinical translation. Hu et al. explore how integrating generative AI, like diffusion models, with rigorous

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  50. 50

    @Ronald_vanLoon ·

    Most AI tools sound smart. But you can't actually verify what they say. That is the real problem with generative AI today, not capability, credibility. So when I started exploring what @Google is doing with NotebookLM, it made me rethink where AI is heading. An AI that only

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