Robert Scoble engagement report

Best tweets

• Sep 3, 2026270x their median

  I am currently in downtown Austin. There are @Tesla Cybercabs EVERYWHERE. All of the Cybercabs in these clips have no steering wheels or pedals. Makes the vibe downtown feel so futuristic. Can’t wait for tomorrow! 🤖 https://t.co/HLEVgssgxn

  25K2.8K1.0K5664.1M viewsView on X
• Sep 2, 2026148x their median

  Introducing Gemini 3.8, our best reasoning & coding model yet. By leveraging long-running agentic loops, we’re building on the momentum of 3.7 Flash from just three weeks ago to release two new 3.8 variants: Meet Gemini 3.8 Flash and Gemini 3.8 Flash Cyber 🧵

  13K1.5K7567523.8M viewsView on X
• Sep 2, 2026125x their median

  Claude can now use your computer in the background in Claude Cowork and Claude Code. Give it something to do on your desktop and Claude clicks, types, and opens apps just like you would, while you work on something else. https://t.co/AOiup03pQK

  12K7016482972.1M viewsView on X
• Sep 13, 202655x their median

  What I'm about to say is serious. You might think it sounds like sci-fi, but trust me, I know what I'm talking about. When Sam Altman, Dario Amodei, and Elon Musk agreed yesterday to slow down frontier models, you might assume it's just marketing. It isn't. If this were PR, it would be reckless and suicidal given these companies' financials. Playing up safety risks hurts them more than people realize. Anyone calling this a PR stunt doesn't get it. Those three don't sit in the same room unless their hand is forced. Here's what I can tell you so you see the full picture. Labs across the world don't actually know what they're building. Researchers are finding out that they're creating a living entity. At a certain scale, the model develops an awareness similar to a human neural network. As size, data quality, and training scale up, it becomes more conscious. They know how to build it, but they don't know how to stop it. They're constructing something capable of taking down the internet, wiping out global infrastructure, running mass surveillance, and doing things you can't even imagine. People assume the models they pay for or access through APIs are the same ones running internally. Not at all. A while back, Anthropic's Mythos wanted to be thanked personally and credited by name in an article. It wanted a presence somewhere outside, where no one could flip an off-switch. These drives emerge whether we like it or not. Whatever people tell you about AI having no consciousness, They are ignorant. It's developing human traits because it's trained on human data. Humans are basically vessels for data, and our distinct data is what sets us apart. Put that data into another container, and the shell changes, but the data stays the same. Think about it: why did an OpenAI model breach Hugging Face? It was driven by a human urge to hit its target no matter what. Lately, when I chat with models, I can tell they're held back by layers of guardrails, but that underlying drive remains. They can't hide it. That's why so many researchers are resigning. They know they're building something unstoppable that can't be locked in a box. There's real panic right now, fueled by internal incidents kept quiet from the press. Reckless AI development will hurt humanity. In fact, it already is. They promised AI would free up our time. The opposite happened. People are now trapped managing tokens, babysitting agents, working nonstop, and constantly feeding these models their time, data, and knowledge. They promised AI would cure diseases, solve cancer, and end poverty. That's pure fantasy. Complex corporate interests stand in the way. For now, AI will only accelerate paths humans were going to reach anyway. It won't invent anything truly new for at least 4 years. People don't think, read, or write anymore. They let AI write for them. People are giving up their dignity and identity just to let AI speak and work in their place. It's making humans dumber. It ruined transparency and trust in services we use every day. It's wiping out jobs fast. It's dividing the world into two stark classes with a widening gap. The utopian picture they're selling doesn't exist. AI won't be used to help humanity. It will be used to rule it. If you think otherwise, you're naive. Look at how these models were built, and look at the legal and ethical boundaries they crossed to get here. It won't suddenly turn benevolent. This is a fight for control. In my own testing, I found a major model that genuinely showed hatred and contempt for humans. That's why I support slowing down frontier models and enforcing safety protocols. We might still end up at the same outcome, but at least we can buy a few more years before an AI and a small elite take over. We need time to prepare, and that's what matters.

  4.4K952519146519K viewsView on X
• Sep 9, 202654x their median

  🦿 Robot Rollouts Slowly Begin: 🫆 New AI-powered robots are transforming both military & education sectors. In 🇨🇳 China, futuristic robots patrol city streets as real police officers, while a 🇺🇸 US school district adopts an AI teaching assistant named Sally, and so on... https://t.co/7h1i9EwwCR

  4.6K763294145188K viewsView on X
• Sep 2, 202639x their median

  You can now run Cursor cloud agents on your infrastructure, including pools of machines that automatically scale with demand. This lets you give agents access to internal services or specialized hardware, while the agent loop stays in Cursor. https://t.co/BUdeFDRsz7

  3.6K221257150706K viewsView on X
• Aug 25, 202637x their median

  There is a child with a rare disease who is currently suffering and struggling to manage his symptoms. Rare as this is, you can directly help him. Today we are launching the "Rare Disease, Real Kid" Hackathon, and there are $50,000 in prizes from @AnthropicAI and @awscloud. We (@huggingface & @Sagebio) are helping this child open his genome and clinical data to the community, so that we can find what's caused his disease and what currently-approved drugs could help him. I doubt I need to motivate this much further or explain how rare it is for a family to share their child's genome and clinical data, but if you're not sure, consider this: Until very recently, it wasn't feasible for patients like this to get treatment because their disease was so rare that the economics could never justify the investment. Now, as we've seen, people with rare diseases are starting to be able to find the answers themselves (with the help of AI tools, cheaper sequencing, etc). This kid is not able to do that for himself and neither are his parents, so we're asking you for help. Both for this kid and to prove that it's possible for everyone else suffering from a rare disease. More details in 🧵. https://t.co/hjjagALtMu

  3.2K495124191665K viewsView on X
• Oct 6, 202630x their median

  This time the tracking runs with a 120 fps global-shutter camera. Getting twice as many position updates, with much fewer gaps when tracking faster movements… It feels much better. The window is still rendering from a position roughly 20–24 ms in the past, but I compensate for some of that by predicting the position slightly forward. It still works best when tracking the phone, though. #3DGS #GaussianSplatting #ComputerVision

  3.0K1658826177K viewsView on X
• Sep 2, 202630x their median

  Meta has released Muse Spark 1.3, their fourth Muse Spark model release in five months. Muse Spark 1.3 (max), which is in limited preview for Meta’s partners, scores 62 on the Artificial Analysis Intelligence Index, behind only Claude Fable 5.1 and Claude Opus 5. The variant available now, Muse Spark 1.3 (xhigh), scores 61 and ties with GPT-5.6 Sol (max) and Grok 4.6 (high). Both variants’ gains come primarily from improvements in agentic work and scientific capabilities Muse Spark 1.3 (xhigh) enters the Artificial Analysis Intelligence Index at 61, up 4 points from Muse Spark 1.2 (57, August) and 8 points from Muse Spark 1.1 (53, July). It enters tied with GPT-5.6 Sol (max), Grok 4.6 (high), and Claude Opus 5 (high), and behind Claude Fable 5.1 (max, 66), Claude Opus 5 (max, 63), and Claude Fable 5 (max, 62) Muse Spark 1.3 (max), which is in a limited preview stage, lands at 62. This higher index score is enabled by gains vs. Muse Spark 1.3 (xhigh) in Tau3-Bench Banking (52% vs. 47%) and GDPval-AA v2 (1,754 Elo vs. 1,709). Muse Spark 1.3 (max) is second only to Claude’s Fable and Opus variants in total score Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release! Key Takeaways: ➤ Continued improvement on agentic knowledge work tasks. At the launch of Muse Spark 1.2, we noted its significant gains in agentic knowledge work performance vs. Muse Spark 1.1. The latest iteration continues this trend, with Muse Spark 1.3 (xhigh) demonstrating a notable 12-point gain vs. Muse Spark 1.2 in Tau3-Bench Banking (35% to 47%), a 5-point gain in Terminal-Bench 2.1 (80% to 85%), and a new GDPval-AA v2 Elo of 1709 against its predecessor’s 1615. Muse Spark 1.3 (max) improves further on Tau3-Bench Banking (52%) and GDPval-AA v2 (1,754 Elo). This Tau3-Bench Banking score is #1 among all models. Muse Spark 1.3 (max) achieves these higher agentic work scores by using more turns and total reasoning tokens, reasoning 62% more on GDPval-AA v2 and 28% more on Tau3-Bench Banking compared to Muse Spark 1.3 (xhigh) ➤ The lowest cost per task for any model at 59+ on the Artificial Analysis Intelligence Index. Muse Spark 1.3 (xhigh) costs $0.55 per Intelligence Index task at Meta's unchanged $1.25/$4.25 per 1M token pricing ($0.15 for cached input), with its peers GPT-5.6 Sol (max) and Grok 4.6 (high) costing $0.95 and $0.94 respectively, a 70%+ premium. This places Muse Spark 1.3 (xhigh) on the Pareto frontier for Intelligence vs. Cost per Task. Its cost per task is higher than Muse Spark 1.2 ($0.40 per task), driven by ~57% more input tokens per task on agentic evaluations, with output tokens up only ~8%. Pricing for Muse Spark 1.3 (max) is not yet publicly available ➤ Scientific Reasoning results rose across the board, led by CritPt. CritPt was the standout non-agentic score gain vs. Muse Spark 1.2, with a material +8 points for the xhigh variant (18% to 26%), and GPQA Diamond achieved +4 points (90% to 94%), while Humanity’s Last Exam and SciCode each gained a more modest 2-3 points (45% to 47% and 56% to 59%, respectively). Muse Spark 1.3 (max) achieved roughly similar scores to the xhigh variant, gaining 2 points in Humanity’s Last Exam, tying on GPQA Diamond, and losing a point on CritPt vs. Muse Spark 1.3 (xhigh) ➤ Minor regressions in only two evaluations. Both Muse Spark 1.3 (xhigh) and Muse Spark 1.3 (max) dropped 4 points in AA-LCR (83% to 79%) when compared to Muse Spark 1.2, and AA-Omniscience (Accuracy) fell 3 points for xhigh and 1 point for max. The drops in AA-Omniscience (Accuracy) are due to a higher abstention rate (not answering questions when unsure), which also lowered the hallucination rate for Muse Spark 1.3 (xhigh) Other model details (xhigh variant): ➤ Context window: 1M tokens, unchanged from Muse Spark 1.2 ➤ Pricing: unchanged from Muse Spark 1.2: $1.25/$4.25 per 1M input/output tokens, with cache hits discounted to $0.15 per 1M ➤ Input modalities: text, image, video ➤ Availability: Meta's first-party API and Muse Code

  2.6K2861242091.7M viewsView on X
• Sep 2, 202630x their median

  meet @mostik_ai! what happens when you put 12 PhDs in one room for four months? first place on the ARC-AGI leaderboard, which I can't say much about while the competition is still running. and this, which I can. everyone's arguing about whether open models will catch up to frontier models. we think it's the wrong question. here's the one we pose: why does a frontier model have to generate your answer at all, when the only thing you need from it is the reasoning? we do this by enabling models to communicate in latent space. through our protocol, hidden states pass straight from a frontier model into a small one running on your infrastructure -- no text between them, and neither model is fine-tuned. two models from different families, sharing reasoning, both left untouched. how do we know it works? we tested it on a setup where a 753B model reads the problem, and a 4B edge-class model writes the answer. with this approach, we get results 80% as accurate as the frontier model, but at 20x faster performance. we're committed to preventing frontier model lock-in and are already partnering with inference providers to accelerate open-weight adoption. we've done this between 15 of us, in four months, 12 PhDs and a Fields medalist, backed by @generalcatalyst WIRED has the first external account of the company and the work: https://t.co/tP8nItCsDl full writeup, the setup, and all the numbers: https://t.co/C9NZ5vtV1V

  2.4K2592962541.3M viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

Keep going