Reliability, Evaluation, and Grounding
Hallucinations, jagged capabilities, validation, source-grounded systems, model behavior testing, and limits of AI outputs.
36%
Best tweets about Generative AI
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
Original Xholic analysis
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
- Median score
- 7.48
- Leading format
- Other
- Recent posts
- 32%
44% of posts
All-time engagement
100% of posts
Published in 90 days
Conversation map
The themes creators return to
Enterprise Adoption and ROI
Organizational deployment, workflow consistency, employee use patterns, productivity, implementation failures, and measurable business value.
24%
Model Research and Scaling
Generative-model architectures, training methods, world models, reasoning, capability advances, benchmarks, and compute-efficient scaling.
20%
Agents and Autonomous Workflows
The transition from content generation to reasoning, tool use, planning, execution, coding agents, and agentic systems.
18%
Content Quality and Creator Economics
AI-generated content performance, AI slop, human creator displacement, search visibility, advertising outcomes, and editorial quality.
18%
Creative Media Generation
Image, video, 3D, gaming, neural rendering, marketing creative, avatars, and production workflows enabled by generative AI.
16%
Building with GenAI
Practical implementation with APIs, RAG, fine-tuning, LoRA, orchestration, model customization, and open-source developer tooling.
14%
GenAI Education
Open courses, GitHub curricula, roadmaps, tutorials, terminology explainers, and skill-building materials for generative AI.
10%
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
Consensus and debate
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.
What performs
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
- View standout post 1 Score 2204.2 · 294.68× median
- View standout post 2 Score 1746.2 · 233.45× median
- View standout post 3 Score 216.6 · 28.95× median
- View standout post 4 Score 155.7 · 20.81× median
- View standout post 5 Score 132.1 · 17.66× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Eric Seufert
@eric_seufert
2 posts
-
2. Justin Thomas
@JustinThomasAI
2 posts
-
3. MIT Sloan School of Management
@MITSloan
2 posts
-
4. Ryan Law
@thinking_slow
2 posts
-
5. Tom Goodwin
@tomfgoodwin
2 posts
-
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.
Best Generative AI tweets
Top Generative AI tweets from 45 creators
Ranked 01–50
- 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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- 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 for three years every major lab told you the same story. scale is all you need. just throw more GPUs at it. just train on more tokens. eventually the model will "wake up" and understand the world. it was a lie. or at minimum, a very expensive bet that just lost. LeCun kept saying generative AI is a dead end. predicting the next pixel or the next token is fundamentally wasteful, the model burns trillions of parameters memorizing surface details instead of learning how reality actually works. he proposed JEPA instead. predict abstract concepts in a compressed thought space. don't paint the world pixel by pixel, understand it. the problem was JEPA kept collapsing. left to its own devices the model would cheat, mapping a dog, a car, and a human to the same point in latent space. technically minimizes the loss. learns absolutely nothing. every fix was ugly. seven loss terms. frozen encoders. EMA tricks. stop-gradients. the kind of duct-tape engineering that should have been a red flag. then LeCun's team dropped LeWorldModel. they replaced all the hacks with one regularizer that forces the latent space into a gaussian distribution. the model can no longer cheat. to make accurate predictions it has to actually encode physics. 15 million parameters. single GPU. trains in hours. plans 48x faster than foundation world models. detects physically impossible events on its own. meanwhile OpenAI is raising another $40B to train GPT-6 on a data center the size of manhattan. the entire scaling thesis just got embarrassed by a model that fits on a gaming PC.
- 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 scrolling and realized this isn't a links page. it's an actual school. > a full 10-week "Applied LLMs Mastery" course, week by week, foundations to deployment > 90+ free GenAI courses from Stanford, Princeton, ETH Zurich, Karpathy, all indexed in one place > 60 of the most common GenAI interview questions, written out with answers > 5-day roadmaps for LLM foundations, for RAG, for agents > jupyter notebooks you can actually run the woman who made it is Aishwarya Naresh Reganti. she's an applied scientist. this is not a dropshipper repackaging a youtube playlist. it's a researcher writing the syllabus she wished existed and handing it to strangers. there are even free courses on it with actual certification at the end. evals. agent-building. the stuff bootcamps charge four figures for. 5,500 people forked it. that's 5,500 copies of an entire AI education that can't be unpublished now. and the part that gets me is the math. people are paying for $2,000 "become an AI engineer" cohorts built on a worse version of what this repo gives away at zero cost with the receipts attached. 100% Open Source. MIT License. repo: https://t.co/TZ9hNi6KDF
- 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 dropped DLSS 5 at GTC today, and yes, it is actually real-time generative neural rendering. Now I to was worried about hallucinating frames. But DLSS 5 takes the ground truth of the game (the 3D geometry and motion vectors) and uses a generative AI model to completely redraw the lighting, shadows, skin subsurface scattering, and materials. Akin to a Hollywood-tier VFX filter over every single frame as you play!
- 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 study by Goldberg and Lam. They analyzed what happened to the stock image market after generative AI was introduced. I’ve added an image showing what the data looked like month by month. Every single month after AI entered the market, 1 in 4 creators exited because the economics stopped working for them. Now, it wasn’t bad for everyone. Total consumption on the platform went up by 39% per month. But sales of human-made content dropped every month. The platform won. The buyers won. The human creators… didn’t. Think about what that would look like on YouTube. AI can already generate scripts, voiceovers, thumbnails, talking-head videos, and entire channels at a fraction of the cost and time it takes a human creator to produce a single video. The algorithm doesn’t care whether a video was made by a person who spent three days filming and editing or by a system that generated it in 20 minutes. This isn’t about being anti-AI. AI helps a lot of creators improve their output. But it is about protecting the type of content people actually want to watch. And I’m not just talking about removing AI slop. I’m talking about making sure human-driven channels are protected and prioritized over AI-driven content. The stock image market already ran the experiment that YouTube hasn’t fully felt yet. Don't ignore that. Here’s my ask to one person: @nealmohan I know you’ve seen some of my work (algorithm, platform changes, etc). I respect what you’re doing for the platform, but this is your moment to stand for human creators. It’s time to put public effort into protecting them. Not just AI labeling or low-lift solutions, they don't solve the problem in a meaningful way. No, we need a genuine, real, and public effort to preserve what YouTube was built on. You’re one of the few people in a position to set a precedent for every platform. Let’s talk. For those interested in the actual research, check out Samuel G. Goldberg and H. Tai Lam's paper called "Generative AI In Equilibrium: Evidence From A Creative Goods Marketplace"
- 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 until ChatGPT put a user interface around it that generative AI took off.” The model existed. The capability was live. Nobody moved. The breakthrough was buried in a terminal only researchers could read. ChatGPT did not invent the technology. It gave the technology a face. The algorithm is never the product. The interface is the product. OpenAI did not win because they had the best model. They won because they had the best door. Then the second shift. Huang: “Internal consumption is thinking, which led to reasoning.” This is the line that reprices every AI company on Earth. The model stopped spending all its compute talking to you. It started spending compute talking to itself. Checking its own logic. Stress-testing its own answers. Running the problem to ground before it opens its mouth. That is not autocomplete. That is cognition. When a machine thinks before it speaks, you are no longer paying for text. You are paying for judgment. Huang: “We started seeing the revenues and the economic model of OpenAI start to inflect.” Revenue does not follow generation. Revenue follows reasoning. The market pays for a machine that thinks. It will never pay for a machine that guesses. Then the third shift. The one nobody comes back from. Huang: “Claude Code. The first agentic system that was very useful. Really revolutionary stuff.” Not generative. Not reasoning. Agentic. A system that does not answer questions. It completes objectives. Reads your codebase. Makes decisions. Takes action. Ships. Huang confirmed 100% of Nvidia is already using it. But Anthropic kept it behind the enterprise wall. Most people never saw what it could do. Then OpenClaw blew the wall down. Huang compared OpenClaw’s adoption to Linux. Called it the most successful open-source project in human history. OpenClaw did what ChatGPT did three years earlier. It handed a new category of AI to everyone. The moment people watched an AI agent work autonomously, the old conversation died. You are no longer asking the machine for answers. You are handing it objectives. And it delivers. Three inflection points. Three walls broken. Generation gave you a machine that writes. Reasoning gave you a machine that thinks. Agents gave you a machine that works. Each one felt like the ceiling. Each one turned out to be the floor. Huang just told you you are standing on the third floor. Looking up. The only question left is what you aim it at.
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- 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 four sections covered in this post, giving you a complete yet simple roadmap to mastering generative AI. 1. GenAI Terminologies Lays the foundation with concepts like transformers, diffusion models, and large language models powering modern generative AI systems. 2. Using the Model APIs Explains accessible tools like OpenAI, Hugging Face, and Vertex AI that help scale and integrate AI into real-world applications. 3. Making Models Your Own Covers methods like fine-tuning, knowledge injection, and lightweight techniques such as LoRA to personalize models effectively. 4. Advanced GenAI Techniques Showcases strategies like model distillation, orchestration, and multi-agent systems that enhance efficiency and handle complex workflows. This isn’t theory, it’s the foundation shaping how AI learns, adapts, and creates.
- 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 start: "Shock! Shock!" Here is what happened: 𝟭. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Knuth was stuck for weeks on an open graph theory problem he was preparing for a future volume of TAOCP. The problem involves a 3D grid of points, think of it as an m×m×m cube. Each point connects to three neighbors. The challenge is to find a single rule that traces three distinct paths through the entire cube, each visiting every point exactly once. That kind of path is called a Hamiltonian cycle. Knuth had worked it out for a 3×3×3 cube. His friend Filip Stappers confirmed it worked up to a 16×16×16 cube by running it on a computer. But no one could find a general rule that worked for any size. 𝟮. 𝗧𝗵𝗲 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 Stappers gave the problem to Claude Opus 4.6 with one strict rule: after every attempt, write down what you tried and what you learned before moving on. Claude worked through 31 explorations over about an hour. It tried simple formulas, brute-force search, geometric patterns, and statistical methods. Most hit dead ends. At attempt 25, it essentially told itself: "The search approach won't get us there. This needs actual mathematical reasoning." At attempt 31, it found a construction that worked. 𝟯. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 Claude found a surprisingly simple rule for navigating the cube. At each point, look at where you are and follow a small set of conditions to decide which direction to move next. That's it. No complex formula, no special cases beyond a handful of boundary checks. Stappers ran the resulting program against every odd cube size from 3 to 101. It produced perfect results every time. Then, Knuth wrote a formal proof, generalized the construction, and showed that there are exactly 760 valid solutions of this type for all odd cube sizes. Claude found one of them. Knuth found all of them. 𝟰. 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝗻𝗲𝘅𝘁 The even-sized cubes were still unsolved. Then a friend fed that version of the problem to GPT-5.4 Pro and got back a 14-page proof that required no further work. Then another researcher used GPT and Claude together as collaborating agents and found an even better solution that covered both cases. The problem that had been open for years, odd and even sizes, is now fully solved. Knuth's reaction was: "We are living in very interesting times indeed." His closing line: "It seems I'll have to revise my opinions about generative AI one of these days." From Donald Knuth, that sentence lands differently than it would from anyone else
- 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 faces distorted in your generations, the ones that will make a wide-shot impossible because the small details will morph and of course with some models text rendering is not possible. Upscaling with AI was always a strange case, local upscalers sometimes lacked the quality compared to the closed source competitors, but those can be pretty pricey and the results are sometimes way too "creative". So I was very, very interested when @Kling_ai announced native, not upscaled 4K generation but wanted to have a good method test it. Again, I never missed the 4K output resolution with genAI, but I missed the nuanced detail rendering of the engines and the lack of that made it impossible to create for example very wide shots with tiny details. So I fired up some image models to generate stills inspired by Morocco and see how Kling behaves, now in 4K. Some notable moments: 00:22 - Skin and eye texture, the movements, the little coins in the headdress 01:18 - The reflection in the water 01:39 - The reflection in the eye, and the scarf textures 02:10 - Check all the details on the rails 02:20 - Water particles and leaves 02:29 - The rendering of the rusty parts of the rail 02:50 - How the waves change the reflection in the foreground 02:55 - The cloud movement in the background
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- 10
- 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 code samples in every build lesson - Translated into 50+ languages including Hindi, Arabic, Japanese - Works with Azure OpenAI, OpenAI API, and GitHub Models 100% Opensource. MIT License. Link in comments.
- 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, or generative AI for creative footage. Then it handles the full workflow. Video is the highest-converting content format but most teams don't have the production capacity. AI tools and programmatic frameworks change that equation entirely. It's called /video and it's part of Marketing Skills — a free, open source collection of 40 marketing skills for AI agents like Claude Code, Cursor, and Codex. npx skills add coreyhaines31/marketingskills
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- 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 infrastructure to find, track, and understand vulnerabilities in frontier AI models. Our bug bounty program has surfaced real jailbreaks, prompt injection chains, and agentic attack patterns that never show up in benchmark evals. We built the 0DIN Scanner to enable continuous validation of model behavior. Today, we're making it open source under Apache 2.0. The scanner is built on top of NVIDIA's GARAK LLM vulnerability scanner. We extend GARAK with a graphical UX, scheduling, our proprietary probe library, enterprise-grade reporting, and the vulnerability intelligence that flows directly from our bug bounty program. The repo is live now: https://t.co/BIBo4k5zYD And if you need the full hosted platform, continuous monitoring, interactive dashboards, cross-model benchmarking, and structured reporting - Schedule a demo and we'll walk you through it: https://t.co/QBp7NthVrG Read the full announcement: https://t.co/h5k0WbIEp7
- 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 Markings of AI-generated or manipulated content 3. Measures to meet the requirements for marking and detection solutions 4. Testing, verification, and compliance B) Rules applicable to deployers of generative AI systems: 1. Disclosure of Deepfakes and published text 2. Internal processes 3. Disclosure for artistic, creative, and similar works 4. Human review and editorial control for published text - Additional information: - The relevant AI Act rules on transparency will start applying on August 2nd 2026. - Once the EU Commission and the AI Board approve the Code as adequate, providers and deployers who sign it will be able to demonstrate compliance with the relevant AI Act obligations. - There will still be EU Commission guidelines on these obligations. - A reminder that the code of practice is voluntary and is now open for signatures. - I haven't seen any company publicly announce plans to sign it yet; I'll keep you posted. - 👏 Congratulations to the six working group chairs (and the input from over 180 stakeholders!) who made this Code possible: @kbontcheva, @DinoPedreschi, @G_De_Gregorio, Anja Bechmann, Madalina Botan, and Christian Riess - well done! - 👉 Download the code below. 👉 To learn more about AI's legal and ethical challenges, join my newsletter's 96,700+ subscribers below.
- 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 brain that creates. You ask, it produces. Text, images, code. Then it stops and waits. Brilliant, but passive. It answers, it doesn't act. This is GPT and DALL-E. Agentic AI is the brain that decides. It adds judgment on top, picking tools, calling APIs, looping through steps, correcting itself, all to reach a goal instead of just spitting out one answer. Generative AI gives you a response. Agentic AI works toward an outcome. AI Agents are the brain that acts in the real world. The full loop: fetch data, plan the steps, take actions, check results, update memory, adapt next time. The key word is autonomy. It touches live systems and learns from them. Think self-driving cars and robots, not just software returning text. The simplest way to lock it in: → Generative AI creates. → Agentic AI decides. → AI Agents do. And each one wraps the last. Every agent has a generative brain at its core, it just stacks decision-making and action on top. So next time someone pitches you an "AI agent," ask one thing: does it create, decide, or actually do? Most stop at the first.
- 16
- 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 & @danielnewmanUV how @Adobe is addressing it: creative technologists build the workflow once, validate the output quality, then deploy it across the entire organization. Marketers in every geography get access to personalized campaigns they never had the budget or resources to run before.
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- 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 Harnesses More details on each paper in the thread.
- 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 and 116 million clicks from Tabola. In a novel quasi-experiment, they compared the performance of AI-generated and human-generated "sibling ads": ads from the same campaigns, for the same products, and with the same launch dates. They find that CTR performance for AI-generated ads is statistically "indistinguishable" from human-created ads when controlling for campaign-level factors. But they also reached an equally important conclusion: ads that look AI-generated perform worse. The researchers identified a set of visual traits that consumers identify as "AI-like," and found that perceived artificiality reduced click-through rates even when the ads were not explicitly flagged as AI-generated. Ironically, the authors find that a not-insignificant number of human-generated ads were perceived as AI-generated for possessing those visual traits. This is an important result. The cost of creative production through generative AI is decreasing rapidly, especially for static images. But not all AI-generated creative performs equivalently. Teams need to consider an optimization for their AI-enabled workflow beyond mere volume, which is the perceived visual fidelity and provenance of the ads being generated. Link to paper below. Thanks to @garjoh_canuck for flagging this for me.
- 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 responding, they break down problems and work through logic step by step. Slower, but fundamentally more capable. Inflection 3: Agentic AI Models that execute. As Jensen Huang, CEO of NVIDIA, put it, we are entering the era of agentic systems, where AI no longer just responds, but begins to act. It receives a goal, plans the steps, uses tools, checks results, and keeps going without waiting for human input at every turn. Each shift changed not just what AI can do, but how it behaves. And like every major computing shift before it, the real impact won't come from the models themselves, it will come from the infrastructure and applications built on top of them.
- 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 quizzes for better retention: https://t.co/U13jYaNzGB PDF: https://t.co/HihW6V6vPh
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@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 marketing, and creating something *different* from traditional content. they create obvious hallmarks of AI use. for example: - publishing content much faster than usual, often on newer domains with little authority, no branded search demand, etc. - relying entirely on an AI model's internal knowledge, without sourcing information from a range of external sources - failing to include internal and external links, images, visual interest, first-person experiences - leaving obvious artefacts of AI use in the article, like obviously AI-generated imagery, obvious AI turns-of-phrase - AI writing patterns and watermarks that haven't been "humanised" by anchoring text generation in specific writing examples. some of these hallmarks make the content WORSE than normal (and hence contribute to poor performance), others are very DIFFERENT from normal (and make it easy to single out content as likely AI-generated) - both of which can contribute to that content not performing well. i obviously don't know the exact mechanisms at play when Google sinks an AI-generated blog after 3-months, but i DO know that many of these aforementioned signals are very obvious to Google: indexing requests, branded search demand, AI content detection (even if only directionally accurate), user engagement signals. we use generative AI a lot at Ahrefs, and i'm happy to do this because we do not compromise on our editorial standards. our AI process mirrors our human editorial process, step for step; it is better and more detailed than the human equivalent in many areas, because LLMs are more tireless researchers, more thorough adherents to brand voice. we are substituting one tool for another, one method of construction for another, but the end product is the same. we have even now published AI-generated content that is subjectively BETTER than our previously human-made content, because AI removed the data, design and updating constraints that previously limited our team. i am excited for the new experiences we can build for readers. you need to determine your own risk tolerance, but in my opinion, using AI to create content is not a problem - but using AI to create something that is WORSE or DIFFERENT to "normal" content marketing is. "creating bad content" or "scaling content too soon" is where problems emerge, and many people do this unwittingly when they use AI. if you know that you are compromising on your content through your use of AI, or trying to scale content on a site that barely exists in Google's consciousness, you should probably feel a bit nervous. if you want to win, change your framing and use AI to make content that is cooler and better than you were able to do before ✌
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@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 - Every code sample in both Python and TypeScript - Runs on Azure OpenAI, GitHub Models, or the OpenAI API - Translated into 50+ languages, auto-updated 112k stars. MIT License. The people shipping AI apps aren't smarter than you. They just started here.
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@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 competition mathematics Those same models read analog clocks correctly 50.1% of the time. It's a structural feature of how these systems work. The models don't have consistent capability levels. They have sharp peaks surrounded by unpredictable blind spots. Whether you get the genius version or the broken version depends entirely on how you frame the problem before the model touches it. Meanwhile, the gap between the top models has nearly vanished. US and Chinese labs have traded the #1 ranking multiple times since early 2025. Anthropic currently leads by 2.7%. Google, OpenAI, xAI, DeepSeek, and Alibaba are all within striking distance. The performance ceiling is converging. The operator skill floor is not. 53% of the global population adopted generative AI in under 3 years. Faster than the personal computer. Faster than the internet. 88% of organizations now use AI in at least one core function. But here's the number that matters: the Foundation Model Transparency Index dropped from 58 to 40 this year. The companies building these models are disclosing less about how they work, not more. Training data, parameter counts, compute budgets: increasingly classified. You can't rely on documentation that's disappearing. You have to build your own understanding through structured testing and thinking frameworks. On the coding benchmark SWE-bench Verified, model performance went from 60% to near-perfect in a single year. The ceiling for what AI can produce is no longer the constraint. The constraint is the operator's ability to define the problem clearly enough for the model to perform at its peak instead of its valley. The report also found that the estimated value of generative AI to US consumers reached $172 billion annually, with the median value per user tripling between 2025 and 2026. The people capturing that value aren't using better models. They're using better thinking applied through better prompts. Stanford's data confirms the thesis I've been building on. LLMs don't think. You do. The model is 20% of your result. Your thinking framework is the other 80%. That gap is widening, not closing.
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@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 to generate measurable financial returns or scale beyond pilot. The problem is almost never the model. It’s that companies: • Buy tools before defining the business problem • Approve budgets on projected value no one measures • Treat AI as an IT project, not a business transformation Buying an AI tool is not an AI strategy any more than buying a gym membership is a fitness plan. The tool should be the last decision you make, not the first.
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@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 spent $410B on AI infrastructure in 2025 → expected $650B in 2026 • Hyperscalers planning $700B on data centers in 2026 • Global AI spending projected to hit $2.5T in 2026 And companies are burning cash fast: • Median AI startup spends $3–$5 to generate $1 of revenue • Enterprise GenAI spending hit $37B in 2025 (3.2× YoY) • 88% of enterprise LLM spending goes to a a few leaders Revenue is growing: • OpenAI reportedly at ~$20B ARR • Anthropic around $14B run-rate By 2030: • AI market projected $1.8T → $4.8T+ • $3T–$5.2T expected to be spent on AI data centers • U.S. AI investment alone could reach $6T • AI could add $15.7T to global GDP Risks are just as big: • Data center power demand could more than double • 95% of GenAI initiatives fail • Massive infrastructure bets depend on continued breakthroughs But the real story is what's coming next. The AI race isn't just a model race anymore. It’s a capital race.
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@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 use of such tools. Netflix clearly feels no compunction to do the same.
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@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 forever, I probably would. Creatively, I get no enjoyment from using those tools. It defeats the purpose entirely for me.” There may, he concedes, be practical uses for AI-assistance in tedious VFX labour, “but right now it’s difficult to discuss objectively because there’s so much at stake and so many genuinely harmful consequences already happening”. “What interests me more is interrogating it artistically,” he says. “We already live in a world where you walk outside and there are billboards and signs that are obvious AI slop. That’s become part of our visual reality. To me, generative AI feels less like innovation than a symptom of a broader cultural and economic rot. I’m interested in using that iconography in art – not using AI to make the art itself, but examining what it represents,” he pauses. “I definitely want to explore it further in future projects.”
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@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 one, they bring it to work. It’s like bring your own device circa 2009. The iPhone launched & nobody wanted corporate Blackberries anymore. IT scrambled to adapt. But a rogue phone couldn’t sign contracts. A rogue agent can. Amazon just learned this at scale. $6.3 million in lost orders. 99% order volume drop across North America. Four severity one incidents in one week. Amazon’s AI coding assistant contributed to at least one major production incident. The response : a 90-day safety reset with mandatory two-person review for all code changes. An internal memo admitted what everyone implicitly knows : “Best practices and safeguards around generative AI usage haven’t been fully established yet.” Companies can’t hide behind hallucinations. Utah’s AI Policy Act eliminates the hallucination defense : “It is not an affirmative defense to assert that the GenAI tool made the violative statement or undertook the violative act.” Newer and larger models are smarter and more reliable. But they fail unexpectedly. There is no relationship between size and how failures change over time. AI-generated code creates 70% more issues than human code. The TRUMP AMERICA AI Act would create explicit liability pathways - allowing the US Attorney General, state AGs, & private plaintiffs to sue AI developers for defective design & unreasonably dangerous products. That new hire’s personal agent? The company bears liability for its mistakes. The contracts it signs, the code it deploys, all of it lands on the company. Like a dog or a device, you are responsible for your agent. https://t.co/1ljYYpbgs7
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@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 added a new “Generative AI fundamentals” section to Search Central documentation. The biggest takeaway: if your content doesn’t meet the quality and technical standards needed to rank in traditional search, it won’t win visibility in AI-generated answers either. Google also shut down several popular GEO tactics. According to the guide, SEOs can ignore llms.txt files, AI-only rewrites, content “chunking,” and special Markdown or schema implementations built solely for generative AI visibility. The broader message is even bigger: Google frames AEO and GEO as extensions of SEO – not separate disciplines with separate playbooks. AI visibility runs on the same foundations: strong technical SEO, authoritative content, and clear site structure. Learn more: https://t.co/Eo8oFgg5Lz.
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@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 reliance on hardware infrastructure, and the maturation of AI agents for multi-step tasks over mere chatbots. The industry is pivoting toward efficiency and trust, with major advancements from Anthropic, OpenAI, and Meta defining the year. Download the Full Report (PDF): https://t.co/OFPm0eYH70
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@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 think in ways we do not yet have a framework for. What we do know is that early research by MIT and others found that heavy reliance on generative AI reduces original thinking and information retention even when users believe the tool is helping them. We are in the early years of an experiment on an entire generation of developing minds, and we will not see the results for another twenty years. The kids who learn to use AI as a tool while still building their own cognitive foundation are going to have an enormous advantage over the ones who use it as a substitute for thinking. The difference between those two outcomes is mostly determined by what parents and educators decide to do right now.
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@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 answer. In this case, not all, It's not, it's merely finding in a corpus of data a lot of instances of people writing about this. This isn't a small thing, it means it knows nothing, understands nothing, it means it's usefulness is somewhat limited by what's been covered before* Ask it the longest non stop flight and it will "generate an answer", but it's really parroting back commonly covered terrain. Ask it which flight travels over the most water, or which flight goes over the most time zones or which flight has the greatest altitude difference, or anything few people asked before and it won't think, it will parrot back something similar to often covered material. It does indeed have the capacity "to think", you could ask it to break it into a process, tell it where to look, tell it how to get to an original answer, And it will do this FAIRLY quickly. And perhaps FAIRLY accurately, but it will burn a lot of tokens, and it's not the same magical feeling. Thats the hard stuff about this, What it's good at, how it works, and what it's terrible at are not necessarily instinctive. So when people are rude about it, they are often wrong. And when people are delighted in it, they are also often wrong.
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@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 Representation Autoencoder (RAE) image-generation recipe, exploration reaches the baseline's final performance with 6.2× fewer training samples processed and 4.1× fewer FLOPs. This paper treats best-of-K training as something bigger: a way to scale how many modes a generative model can learn without adding inference steps. Today, diffusion, flow, and autoregressive models handle multimodal targets largely by splitting generation into many easier steps, which also creates a mismatch between how they train and how they sample. Explorative Modeling moves that burden into training instead. At each update, the model tries K candidate generations and learns only from the candidate closest to the target, letting different latents specialize to different modes rather than being pulled toward an average. If the scaling trend survives larger runs, compute-optimal generative training may need to budget for exploration alongside parameters and data. – arxiv. org/abs/2607.27372 Title: "Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation"
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@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 enterprise adoption compounds quietly. The coverage of the AI race is almost entirely focused on model benchmarks and funding rounds. Daily active usage is the number that actually describes where the market is going. Anthropic is winning a race most people are not watching.
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@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 means something most people haven’t realized yet: A large share of today’s AI-generated content may legally belong to no one! 📄 Report: https://t.co/0XO9gHBezf
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@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 Opportunities and Employment Risks, especially for India's IT-BPM Outsourcing Sector. Key Insights (UPSC Enrichment) World Bank Development Report 2026 highlights AI as a major force transforming Global Labour Markets. MNCs and Global Value Chain (GVC) firms are slowing hiring faster than domestic firms due to AI Adoption. India's Comparative Advantage in IT Services, BPM, and Digitally Deliverable Services faces Automation Pressure. AI is replacing many Routine Cognitive and Entry-Level White-Collar Tasks. Job Polarisation may widen Income Inequality and create Skill Premiums. Developing Economies remain less vulnerable than advanced economies because many AI effects enhance Productivity rather than eliminate jobs. AI Applications in Agriculture, Healthcare, Education, Governance, and Weather Forecasting demonstrate significant Developmental Potential. The real challenge is AI Adaptation, Reskilling, and Institutional Readiness, not AI itself. Criticism / Perspective Excessive dependence on foreign AI Platforms may create Technological Dependence, Digital Divide, and Data Sovereignty concerns. Without Reskilling and Social Protection, AI could deepen Labour Market Inequality. Way Forward Promote AI Governance, Digital Public Infrastructure, Human Capital Development, Reskilling, Innovation Ecosystems, AI Ethics, MSME Digitalisation, Responsible AI, and Public-Private Partnerships. Conclusion India should transform its Outsourcing Economy into an AI-Augmented Knowledge Economy, combining Innovation, Skill Development, and Inclusive Growth. Ethical Keywords (GS-IV) Adaptability, Innovation, Inclusiveness, Human-Centric AI, Accountability, Transparency, Digital Ethics, Equity, Lifelong Learning, Responsibility, Public Interest. #ArtificialIntelligence #GenerativeAI #WorldBank #IndiaEconomy #FutureOfWork #DigitalEconomy #SkillDevelopment #Innovation #AIGovernance #UPSC #UPSCMains #PublicPolicy #CurrentAffairs #Technology #Employment #InclusiveGrowth #DigitalTransformation #CivilServices #KnowledgeEconomy #AIEthics
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@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 exploring new verticals and global markets. Additionally, a chunk of the funds will go towards research and development and talent acquisition. Founded in 2016 by Ganesh Gopalan and Ananth Nagaraj, https://t.co/A6fRCVveIx launched its voice-first AI model Inya during the ‘India Impact AI Summit 2026’ last month. https://t.co/A6fRCVveIx’s B2B agentic AI solutions enhance customer experience for large enterprises as well as customer-facing businesses across sectors such as BFSI, telecommunications, ecommerce, consumer internet, healthcare, among others. 🔗 Read the full article here: https://t.co/r1TJIib1Ob #Inc42 #news #AI #Startups #Startupfundings
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@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 content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in scientific works, it is the responsibility of the author(s)." https://t.co/J9vD36vgik
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@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 biased information based on patterns rather than truth, often appearing plausible v/ @Cloudflare #Cloudsecurity #AISecurity #AIArchitecture
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@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 story of the companies leveraging them is not quite as straightforward. Intuitively one might assume that specific companies that go all in on AI tools should see a significant increase in productivity which should translate into significantly higher revenue compared to competitors. And that might be the case when a single industry player adopts AI. But what we are actually seeing is far more nuanced than that. We are instead seeing clusters of AI-pilled industries emerge where many of the top players in that industry are adopting these generative AI capabilities all at once. Did your team accelerate product development using coding agents? Well so did your top 3 competitors, resulting in your feature release cadence not looking all that different from theirs. Similarly, customer expectations are incredibly high thanks to broadly adopted consumer and enterprise tools like ChatGPT and Claude. Did you add a chatbot to your product? That's cute, but how do I get it to work with all the tools I've already integrated with Claude? Or take Gemini for example. I find myself complaining every day of why I can't do something in Google Docs/Sheets/Slides and Gemini because Claude has set the bar so high for what I expect from these tools. Just like during the mobile revolution, mobile was initially a differentiator but then quickly became table stakes. We are seeing that same cycle transpire again with AI.
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@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 LingBot-World 2.0 stands out. The engineering isn't just about visual quality. It's about solving one of the hardest problems in generative AI: keeping an interactive world stable over long horizons without falling apart. And the fact that Robbyant decided to open-source both the model and the agentic harness makes this release even more interesting. It feels less like another AI demo and more like infrastructure for what's coming next. @robbyant_brain 💻 GitHub: https://t.co/bQBWuHXBUM 🎮 Try it: https://t.co/VJy43faBGP 🤗 Weights: https://t.co/dEmViDnNSO
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@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 reviewed by anyone who knows what they’re talking about." "Forever open to the uninformed… meddler." "The main problem is the lack of authority." "[It lacks a] habit or tradition of respect for expertise." "The premise...is that continuous improvement will lead to perfection – that premise is completely unproven." "Hyperlinks, bullet points and cut-and-paste press releases do not an encyclopedia entry make." "A flawed and irresponsible research tool." --- if you answered "generative AI", you would be wrong. this is a list of 20-year-old criticisms shared after the launch of Wikipedia. for all our concerns about accuracy and authority and legitimacy, for all the furore around regulation and fairness and compensation for creators, i think generative AI is here to stay. generative AI is a natural continuation of a process of information commodification that started a long time ago. crucially, it offers a better experience for most people. information is free and convenient and personalised to the whims of each user. that is hard to resist. i think Wikipedia is a good parallel for understanding generative AI (not least because it plays such a crucial role in training LLMs). it was untrusted and error-prone and far from perfect, but the experience it offered was such an obvious upgrade from the status quo that it weathered those complaints and became a cornerstone of the internet. and as we saw in the wake of Wikipedia's release, there was still a place for formal research, for professional accreditations and authority sources, for personal experiences and stories, for content that went above and beyond the new baseline created by Wikipedia. the same will be true of generative AI.
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@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 system to look up trusted information before generating a response. 🔹 What Retrieval Augmented Generation, or RAG, actually means 🔹 Why standard AI models can struggle with outdated or missing information 🔹 How RAG helps AI search company documents, databases, and trusted knowledge sources 🔹 Why this makes AI more accurate, relevant, and useful for business 🔹 How RAG is helping organizations build smarter AI assistants using their own data If you want to understand RAG, enterprise AI, generative AI, AI assistants, large language models, and how businesses can make AI more reliable, this video gives you a clear and practical explanation. #AI #RAG #GenerativeAI #EnterpriseAI #ArtificialIntelligence #LLM #AIAssistants #BusinessTechnology #DigitalTransformation #DataAI
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@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 most important thing is getting to the minimum weight, because 10 kilos over is three-tenths of a second per lap; if you are over, you have no chance of winning." "If you manage to use these advancements to move 5kg from a high area down to under the car with titanium ballast, that is huge performance."
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@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 physics-based validation prevents "structural hallucinations" and accelerates the translation of in silico peptide blueprints into viable clinical therapeutics. https://t.co/aAiNsb9n63 #DrugDiscovery #GenerativeAI #computationaldesign
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@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 thinks using your knowledge. Here is why that matters.
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