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$GOOGL just launched Gemini “Deep Think” an AI mode that helps engineers turn sketches into real, 3D-printable designs. If engineers start relying on this every day, Google naturally becomes part of the actual building process.

Shay Boloor

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240,260 次观看 • 5 个月前 •via X (Twitter)

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Google just did it again. They just launched "Google Skills" a free platform to turn anyone into an AI expert. 3,000+ courses, labs & certificates from Google Cloud, DeepMind, and Grow with Google all in one place. → Learn AI, data, and coding from real Google engineers → Earn credentials that actually get you hired → Build hands-on projects with Gemini Code Assist → Compete on global leaderboards and share your wins No paywalls. No BS. Just world-class AI education free. This might be the biggest AI upskilling move of the decade. 👉

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Alex Veremeyenko

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Hitem3D V2.0 just launched. It's an AI 3D model generation tool that does three things: - Photo or image to full 3D model - Model segmentation so you can isolate and refine parts - Image to STL for 3D printing Combine this with Google Nano Banana and you can build game assets, product prototypes, and printable designs in minutes. Here's what I tested. Sponsored by Hi3D 1/4

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AshutoshShrivastava

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Today, we’re rolling out an updated Deep Think mode available in the Gemini app for Google AI Ultra subscribers. Here’s what you need to know: — Gemini 3 Deep Think mode pushes the boundaries of intelligence even further, delivering meaningful improvement in reasoning capabilities — It works by using advanced parallel thinking to explore multiple hypotheses simultaneously — This builds on the Gemini 2.5 Deep Think variants that recently achieved a gold-medal standard at this year's International Mathematical Olympiad and at the International Collegiate Programming Contest World Finals Ultra subscribers can try Deep Think today by selecting “Deep Think” in the prompt bar and Gemini 3 Pro in the model selector.

Today, we’re rolling out an updated Deep Think mode available in the Gemini app for Google AI Ultra subscribers. Here’s what you need to know: — Gemini 3 Deep Think mode pushes the boundaries of intelligence even further, delivering meaningful improvement in reasoning capabilities — It works by using advanced parallel thinking to explore multiple hypotheses simultaneously — This builds on the Gemini 2.5 Deep Think variants that recently achieved a gold-medal standard at this year's International Mathematical Olympiad and at the International Collegiate Programming Contest World Finals Ultra subscribers can try Deep Think today by selecting “Deep Think” in the prompt bar and Gemini 3 Pro in the model selector.

Google AI

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AI designs functional hardware! 🔧 Claude Opus 4.7 is leading CAD benchmarks, and AI is starting to design functional hardware autonomously. The shift happening: CAD as a tool → CAD as an autonomous system. The model didn't just sketch geometry, it generated mechanically valid designs with joints and motion constraints. It optimized for real-world functionality, not just visual correctness like earlier LLMs. This mirrors what happened in coding last year. In software, engineers moved toward requirements, architecture, and orchestration while code became the compiled outcome of AI systems. Mechanical engineering may follow the same path: from designing parts → defining intent. This changes the role of engineering teams. Instead of manually designing every component, engineers will define constraints, validate outputs, and focus on system-level architecture. Engineers spend less time on geometry and more time on optimization and integration. AI handles the mechanical validity checks that traditionally required experienced designers. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Lukas Ziegler

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Swami Sivasubramanian

154,343 次观看 • 1 年前

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

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10,182 次观看 • 22 天前

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Hyperbolic

41,228 次观看 • 1 年前

$GOOG is finally integrating AI in its homepage. “AI Mode” button goes directly to Gemini. This is going to be a game changer. For reference, ChatGPT got 5.5 billion traffic last month while Gemini got 500 million. Google, on the other hand, got 85 billion. Even if it can redirect just 5% of this traffic to Gemini, it’ll catch ChatGPT in usage. Bullish on $GOOG.

$GOOG is finally integrating AI in its homepage. “AI Mode” button goes directly to Gemini. This is going to be a game changer. For reference, ChatGPT got 5.5 billion traffic last month while Gemini got 500 million. Google, on the other hand, got 85 billion. Even if it can redirect just 5% of this traffic to Gemini, it’ll catch ChatGPT in usage. Bullish on $GOOG.

Oguz Erkan

96,056 次观看 • 1 年前

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Anthropic pays $750,000 a year for engineers who can build LLMs from scratch. Not prompt engineers. Not fine-tuners. Not RAG specialists. Engineers who understand the architecture well enough to build the thing from zero. Stanford just put a 2-hour lecture on exactly that on the internet for free. Scaling laws. Data collection. Architecture design. Post-training alignment. The theoretical foundation behind every model you use daily taught by the people who research it at one of the best CS programs on earth. Free. From Stanford. Two hours. The engineers earning $750,000 at Anthropic understand everything in this lecture. Most people using AI every day understand none of it. Watch it tonight. Full implementation guide in the article below.

CyrilXBT

36,692 次观看 • 1 个月前

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Bindu Reddy

11,800 次观看 • 7 个月前

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Most PMs still think in tasks. Write a PRD. Update JIRA. Review designs. AI native PMs think differently. They think in prompts. .Mike Bal calls it "thinking in prompts" and admits it sounded silly at first. But once you integrate AI into daily work, it becomes natural. Instead of "I need to write a PRD," they think: "What do I need to get done? What are the steps? What are the best tools?" They treat AI as an extension of themselves. Breaking down tasks into instructions and steps. "If you're thinking about AI as like an extension of yourself, you're thinking like, what are the instructions? What are the steps? And if you're reflective, like you probably do that to yourself at some level anyway, you have your inner dialogue, right?" The shift is from task-oriented to process-oriented thinking. It's how you already think internally. AI native PMs just make that process explicit. Lesson: AI native PMs don't just use AI tools. They fundamentally restructure how they think about work, breaking every task into steps and instructions that AI can execute.

Aakash Gupta

16,788 次观看 • 5 个月前

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,959,598 次观看 • 1 个月前