Dimitri Lovato

How black are u exactly? Juz curious 🤔

🚨BREAKING: Researchers just discovered that AI video generators secretly understand 3D space and physics. Nobody taught them to. They learned it on their own. Every time a video model generates a realistic scene — water flowing, objects falling, a camera moving through a room — it has to understand how the physical world actually works. Otherwise the video looks wrong. Objects clip through each other. Lighting doesn't match. Motion breaks. To generate correctly, the model had to learn physics. Implicitly. Silently. As a side effect of doing its actual job. Researchers from Huazhong University just figured out how to extract that hidden knowledge and plug it directly into other AI systems. It's called VEGA-3D. Here's the problem it solves. Every multimodal AI — GPT-4o, Gemini, Claude — has a well-documented blind spot. Ask it to reason about 3D space and it struggles. Where is this object relative to that one? How far away is this wall? What happens physically when these two things interact? These models are semantically brilliant and spatially blind. The standard fix requires 3D training data — depth maps, point clouds, explicit geometric annotations. That data is expensive, scarce, and hard to generalize. Every lab working on embodied AI and robotics hits this wall. VEGA-3D skips the wall entirely. Here's how it works: Video diffusion models already contain rich 3D understanding locked in their internal representations. Not in the output — in the noise. In the intermediate layers. In the process of generation itself. VEGA-3D taps directly into those intermediate layers during generation. It extracts the spatial and physical features the model built to make the video look real. Then it injects those features into any multimodal language model as a plug-and-play module. No 3D training data needed. No depth sensors. No geometric scaffolding. Just the hidden physics knowledge that video models already learned — now accessible to AI systems that never had it. Here's the wildest part: It works as a drop-in upgrade. You don't retrain the language model. You don't change the architecture. You attach VEGA-3D and the model gains spatial reasoning it never had before. The results across 3D scene understanding, spatial reasoning, and robot manipulation benchmarks: state-of-the-art across the board. The implication is larger than one paper. Every video model trained on internet-scale data has been quietly learning physics this whole time. We've been using that knowledge only to generate pretty videos. VEGA-3D is the first framework that asks: what if we used it for everything else too? Robotics. Autonomous driving. Medical imaging. Any AI system that needs to understand the physical world — now has a new source of spatial knowledge that's been sitting unused inside video generators for years. Code is publicly available. #1 paper on Hugging Face today.

GOODBYE FIGMA⚠️ GOOGLE JUST LAUNCHED STITCH. TYPE A VIBE. GET A FULL UI IN SECONDS. NO WIREFRAMES. NO DESIGN SYSTEM SETUP. NO "LET ME SEND THIS TO THE DESIGNER". Copy these 5 prompts to build pro UI without touching Figma:

GOODBYE COPY-PASTING INTO CHATGPT⚠️ CLAUDE COWORK JUST CHANGED HOW WORK GETS DONE. IT READS YOUR FILES. WRITES REAL DOCS. RUNS WHILE U SLEEP. NO PROMPT ENGINEERING. NO MANUAL UPLOADS. NO "SOUNDS LIKE GENERIC AI". Here's the full Cowork playbook to set it up in 30 minutes:

🚨 OpenClaw just became the most important software most people have never heard of. Not because of the hype. Because of what it's actually doing to real businesses right now. A founder with a $2.5M ARR company spent one week with OpenClaw. Here's what his team built: → An end-to-end ad creative pipeline that goes from idea to published Meta ads — living inside Slack, open to the whole team → A data analytics agent connected to BigQuery that answers any business question in real time. Nobody writes SQL anymore → A recruiting agent that found 30 ranked candidates with portfolios and email addresses from a single job description One week. Three agents. Material impact on a real business. And he said he has twenty more stories. Here's why this matters more than another AI tool launch. OpenClaw isn't polished. It's raw. It takes work to figure out. The people calling it hype are the people who tried it once and bounced. That's exactly what the early internet felt like. The people who figured out the early internet didn't wait for it to get easier. They got in while it was hard and built things nobody else could see yet. OpenClaw is that moment for agentic AI. Here's the wildest part: The shipping velocity is unlike anything in the space right now. Native PDF tools. Telegram live streaming. External secrets. Better routing. ACP subagents on by default. 100+ stability and security fixes. All in a single update cycle. This is what a team building like they're running out of time looks like. OpenClaw may not win the agentic race. There will be competitors. The big labs are already converging on this. But it doesn't matter who wins. OpenClaw already opened Pandora's box. Every personal self-improving agent you'll use in five years traces back to what this tool revealed. And right now, while most people are still deciding whether it's real — a small group of founders is quietly automating their entire operations with it. This is the shittiest it will ever be. That's not a warning. That's an invitation.

DURING A JOB INTERVIEW, if they ask: "Why are you leaving your current job?" USE THE GOLDEN RESPONSE:

@Tanishawithx Buying islands 😂😂😂

web devs are fighting over which AI wrapper to build next while the guys writing C++ and managing memory allocation for the GPUs are quietly buying islands.

@RituWithAI U missed FAQ.md

96% of Github repos in 2026 be like:😂

Violence against women is never acceptable. We demand accountability, support for survivors, and real justice. Stand with women. Stand against abuse. 💜

Lovely moments ♥️

A 5-day plan to set up Claude: (made for teams, not individuals) ✦ Monday (45-60 min): Set up Projects Build a separate Project for every task your team does repeatedly. Let Claude interview you. Upload ONE gold-standard example per Project. Full guide below (the article). ✦ Tuesday (15 min): Create Prompt Templates For each Project, generate a one-sentence prompt template with a single [INPUT] field. Your teammates copy-paste it and fill in their raw notes. ✦ Wednesday (20-25 min): Test for a "wow" Pick one real task you did manually this week. Run it through your Project. Screenshot the before/after. This becomes your proof for Thursday. ✦ Thursday (35 min): Convert 1 person Don't train 10 people. Pick the one who's drowning. Sit with them for 15 min. Use THEIR work. Then, make them a co-owner of the Project. ✦ Friday (60 min): Roll out to the team Send ONE message to your team channel. Lead with ONE specific result. List 3 things they can do RIGHT NOW. Then DM 2-3 people separately. @rubenhassid

Earns $400 per hour by using chatGPT Prompts I've created a guide with 24 methods to help you earn $5,000 daily Normally $199, but today, it is free. (Only For 48 Hours) So, what are you waiting for? To get it; Like & Repost Comment 'Send' Follow me so I can DM you.

If you don't have my "Claude Power User Playbook" yet... The one I built to get 10x more output from Claude every session with a complete system across settings, prompting frameworks, file creation, memory management, and advanced workflows... Just comment "CLAUDE" and I'll DM it to you for free (must follow)

SHOCKING: 99% of GTM engineers using Claude are barely scratching the surface. Right now, the entire internet is screaming "Claude, Claude, Claude"... But here's the truth: just prompting it won't build GTM infrastructure. To unlock its real power, you need to master: - Claude Code deployment with the WAT framework and CLAUDE. md self-improvement loop - MCP connections, sub-agents, and automations running 24/7 without you - Pre-built prompt systems covering every GTM function you actually run I spent 100+ hours building and documenting the most complete Claude GTM Engineering Bible and compiled every prompt, workflow, build sequence, and deployment guide into one resource. I'll give it to only 500 people. To get it: 1. Follow me MUST (so I can DM) 2. Comment "CLAUDE" 3. I'll DM you the bible If you don't follow or comment, you won't receive it.

GOODBYE $60K COPYWRITERS⚠️ CLAUDE JUST KILLED THE CONTENT AGENCY. 5 POSTS/WEEK. 30 MINUTES. $20/MONTH. NO VA. NO COPYWRITER. NO "SEND ME YOUR BRAND GUIDELINES". Copy these 5 prompts to automate your LinkedIn like an agency:

Sam Altman just posted a goodbye letter to developers. He called it a thank you note. Developers are not happy. Here's what he said and what it actually means: 🧵

🚨BREAKING: Researchers just proved that ChatGPT is failing you constantly. And you have no idea it's happening. Not the failures where it says something wrong and you catch it. The other kind. The silent ones. The ones where you walk away satisfied with an answer that was incomplete, misleading, or flat-out wrong. 78% of AI failures are invisible to the user. You never notice. You never push back. You move on. The wrong information lives in your head as fact. Researchers analyzed hundreds of thousands of real conversations from WildChat — one of the largest datasets of actual human-AI interactions ever collected. Not lab simulations. Not controlled experiments. Real people having real conversations with ChatGPT about real things that mattered to them. They categorized every failure. Then they measured how often users detected them. Most of the time, they didn't. Here's how the invisible failures work. The most dangerous failure isn't the one that sounds wrong. It's the one that sounds exactly right. Confident tone. Fluent sentences. Logical structure. Everything that makes you trust an answer — present. Everything that makes the answer correct — missing. The AI doesn't stumble when it's wrong. It doesn't hedge. It doesn't slow down. It delivers incorrect information with the same velocity and certainty it delivers correct information. There is no signal that something went wrong. So you don't check. Why would you? It sounded right. Here's what the researchers found inside those failures. The most common invisible failure: the AI answers a question you didn't ask. You asked something specific. It detected a related topic and answered that instead. The response is coherent, relevant-seeming, and completely misses your actual need. You read it. You think you got an answer. You didn't. The second most common: confident incompleteness. The AI gives you part of the answer and stops. Not because it ran out of knowledge. Because it pattern-matched to a response length that felt complete. You got 60% of what you needed and left thinking you got 100%. The third: plausible fabrication. Not hallucination in the obvious sense — a made-up name, a fake citation. Subtle fabrication. A statistic that's close but wrong. A date that's off by a year. A nuance reversed. Nothing that triggers your skepticism. Everything that embeds quietly as knowledge. Here's the part that makes it structural, not accidental. These failures aren't random. They cluster around a specific condition: when the user already has partial knowledge about the topic. If you know nothing, you ask follow-up questions. If you know a lot, you catch the errors. If you know just enough to feel informed but not enough to verify — which is the exact situation most people are in for most topics — you are maximally vulnerable to invisible failures. The AI isn't failing the experts. It isn't failing the complete beginners. It's failing the people in the middle. Which is almost everyone. Almost all the time. It gets worse. The researchers found that conversational warmth actively suppresses failure detection. When the AI is friendly, apologetic, and engaging, users rate the interaction as successful even when the output was objectively wrong. The experience of being helped overrides the reality of not being helped. You feel heard. You feel assisted. The answer was wrong. You leave happy. Now think about every decision you've made recently where ChatGPT was part of your research. Every answer you accepted because it was fluent and confident. Every time you didn't check because the response felt complete. You weren't evaluating the quality of the answer. You were evaluating the quality of the experience. Those are not the same thing. And 78% of the time, the gap between them went unnoticed.

I GAVE CLAUDE MY SALARY AND IT GAVE ME A PLAN No financial advisor. No spreadsheets. No guesswork. 7 prompts 🧵

🚨 Picsart just launched an AI agent marketplace. 130 million creators can now "hire" AI assistants the same way they'd hire a freelancer. This is what the shift from tools to agents looks like in practice 🧵