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I asked Perplexity Computer one question: “If Nvidia’s AI data center revenue doubles again, map exactly where every incremental dollar flows across the semiconductor supply chain with a chart” It generated this full supply chain chart and video in minutes. It shows exactly who benefits - memory, foundry, packaging,... show more

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101,207 views • 4 months ago •via X (Twitter)

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Running operations used to take me 10+ hours/week. Now it takes 30 minutes. Here’s what changed: → I stopped using AI as a chatbot → I started using Perplexity Computer as a system In one workflow, it: • Reads my Slack updates • Checks my calendar • Pulls data from sheets • Generates a full weekly report • Summarizes key decisions • Flags risks automatically No switching tools. No manual work. This is the shift most people are missing: AI isn’t for asking questions anymore. It’s for executing workflows. If you’re still “chatting” with AI… you’re already behind. see what you’ve been missing—start now, explore key features in minutes, and feel the difference . Try it now:

Running operations used to take me 10+ hours/week. Now it takes 30 minutes. Here’s what changed: → I stopped using AI as a chatbot → I started using Perplexity Computer as a system In one workflow, it: • Reads my Slack updates • Checks my calendar • Pulls data from sheets • Generates a full weekly report • Summarizes key decisions • Flags risks automatically No switching tools. No manual work. This is the shift most people are missing: AI isn’t for asking questions anymore. It’s for executing workflows. If you’re still “chatting” with AI… you’re already behind. see what you’ve been missing—start now, explore key features in minutes, and feel the difference . Try it now:

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Making a 30s video used to take me 2 hours… Now it takes me 3 minutes. OpenArt Story just changed the game. You can use it to turn any idea into a stunning, AI-generated video, with motion, music, and a full narrative arc. Here’s how it works + a great opportunity to win some $$

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CoreWeave and Nebius are not tech companies. They are equipment leasing businesses. Jim Chanos on iConnections: strip the AI narrative and you have companies that buy GPUs from $NVDA and rent them to hyperscalers. The economics confirm it - mid-to-high single-digit pre-tax returns on capital where the data is available. His rule: a middleman in a supply chain should never trade at a higher multiple than the company that controls its supply. Nvidia sets GPU prices. CoreWeave takes them. TSMC sets chip supply. Neo clouds depend on it. The market has this relationship inverted. That is the compression trade. Full supply chain breakdown and what it means for neo cloud valuations: Source: iConnections -

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It used to take weeks to make a LoFi animation like this. Now, with AI, you can create one in just 15 minutes. Music included. Full workflow with tools and prompts 🧵👇

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Dan Gambardello

29,090 views • 1 month ago

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43,739 views • 4 months ago

so without telling it exactly what to say, i nudged the AI in the direction of this lame, tired old joke and it kinda got there a couple times 💪🪢 ai generated video and dialogue 🔊

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Jacob Klug

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American Rancher Alliance/Hufeisen Ranch

10,734 views • 3 months ago

The children of Auchtertool made this video to tell us how they feel about the hyper scale data center planned for our village. Please take a few minutes to watch it. But this isn't an Auchtertool problem - it's a Scottish one. The proposed data centre would need roughly half the power currently used by every home in Scotland combined. Who is paying to build all that extra capacity? It would also swallow huge amounts of water, and take much-needed farming land out of the food chain, which we all rely on. And to be clear - this isn't about being "anti-Al". A data centre like this powers the everyday: your photo storage, your online shopping, and yes, Al too. The question isn't whether we need infrastructure. It's how we build it safely, and sustainably. And how the people of Scotland benefit from it. At this scale, the only obvious winner is the developer. It's not obvious what the tangible benefits are for the people of Scotland. If you think a project this big deserves to be properly thought through, visit our village website to find out more and object.

The children of Auchtertool made this video to tell us how they feel about the hyper scale data center planned for our village. Please take a few minutes to watch it. But this isn't an Auchtertool problem - it's a Scottish one. The proposed data centre would need roughly half the power currently used by every home in Scotland combined. Who is paying to build all that extra capacity? It would also swallow huge amounts of water, and take much-needed farming land out of the food chain, which we all rely on. And to be clear - this isn't about being "anti-Al". A data centre like this powers the everyday: your photo storage, your online shopping, and yes, Al too. The question isn't whether we need infrastructure. It's how we build it safely, and sustainably. And how the people of Scotland benefit from it. At this scale, the only obvious winner is the developer. It's not obvious what the tangible benefits are for the people of Scotland. If you think a project this big deserves to be properly thought through, visit our village website to find out more and object.

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Mario Nawfal

861,456 views • 7 months ago

$TAO isn't dead. They called it dead at $300. It's at $200 and they're calling it dead again. Here is what the chart is not showing you: $2.2B market cap. $200M+ in 24-hour volume. 11M circulating supply with the halving already executed. 67% of supply staked and locked. Emissions cut in half. 7,200 $TAO per day down to 3,600. Every new subnet requires buying and staking more $TAO to activate. Subnet token valuations already hit $1.5B earlier this year. Grayscale filed for a spot $TAO ETF. Bitwise filed too. Decision window still open. This is not a dead asset. This is a misread asset. Real decentralized compute. Real supply shock mechanics. Real institutional positioning in the background. While the crowd prices it like a hype token that missed its cycle. The gap between on-chain reality and chart noise is not closing slowly. It just widened again. The people calling $TAO dead right now will be explaining themselves when the next catalyst hits. The people who read the docs are already accumulating. Screenshot this. Come back in 3 months. The market still has not priced this in.

$TAO isn't dead. They called it dead at $300. It's at $200 and they're calling it dead again. Here is what the chart is not showing you: $2.2B market cap. $200M+ in 24-hour volume. 11M circulating supply with the halving already executed. 67% of supply staked and locked. Emissions cut in half. 7,200 $TAO per day down to 3,600. Every new subnet requires buying and staking more $TAO to activate. Subnet token valuations already hit $1.5B earlier this year. Grayscale filed for a spot $TAO ETF. Bitwise filed too. Decision window still open. This is not a dead asset. This is a misread asset. Real decentralized compute. Real supply shock mechanics. Real institutional positioning in the background. While the crowd prices it like a hype token that missed its cycle. The gap between on-chain reality and chart noise is not closing slowly. It just widened again. The people calling $TAO dead right now will be explaining themselves when the next catalyst hits. The people who read the docs are already accumulating. Screenshot this. Come back in 3 months. The market still has not priced this in.

2xnmore

21,206 views • 1 month ago

This week, I’ve seen a lot of videos about AI generated artist @xania_monet whose records are charting on Billboard and who signed a $3 million record deal. But here’s the thing, I just made a Xania Monet song in five minutes using the exact same program, Suno. So should I be able to sign a $3 million deal too? And that’s the problem. There’s no protection stopping anyone from using these AI-generated voices because they aren’t real. This is the fundamental flaw in AI-generated artists. It was too easy. It took me five minutes. So that’s why Black artists are asking the Black community don’t support these AI-generated artists. Every play, every stream, every dollar spent on them takes money out of the mouths of real songwriters, real producers, real mix and mastering engineers, and the real people who built this industry with authenticity and soul.

This week, I’ve seen a lot of videos about AI generated artist @xania_monet whose records are charting on Billboard and who signed a $3 million record deal. But here’s the thing, I just made a Xania Monet song in five minutes using the exact same program, Suno. So should I be able to sign a $3 million deal too? And that’s the problem. There’s no protection stopping anyone from using these AI-generated voices because they aren’t real. This is the fundamental flaw in AI-generated artists. It was too easy. It took me five minutes. So that’s why Black artists are asking the Black community don’t support these AI-generated artists. Every play, every stream, every dollar spent on them takes money out of the mouths of real songwriters, real producers, real mix and mastering engineers, and the real people who built this industry with authenticity and soul.

Isaac Hayes III

75,158 views • 8 months ago

Stress-tested Perplexity Perplexity Finance Computer on a real equity research workflow: Map a representative AI infrastructure supply chain across 70+ companies across multiple tiers with sourced financials, bottleneck analysis, and company classifications. The kind of deliverable that might take a junior analyst days to weeks. It produced ~2,000 lines of structured research across 4 phased reports (~5,300 credits burned in ~75 minutes of work). Every data point labeled Actual, Guidance, or Estimate with inline source citations. Then it built an interactive dashboard to make the information digestible with 4 deep research .md files compiled. A cool feature is when it flagged when its own sources conflict. It caught 8 data conflicts and wrote explicit resolution notes. TSMC CoWoS capacity ranges explained as report timing differences. ASML's Q4 FCF anomaly identified as a billing artifact. Intel Foundry's loss methodology change across fiscal years. That is fundamentally different from silently picking a number and requiring an experienced SME to catch it. Other strong points: hyperscaler capex table aligned across 5 different fiscal year-ends. ORCL RPO correctly flagged as potential overstating near-term conversion (90%+ partner-funded). Power identified as the binding constraint with MSFT carrying $80B in power-constrained backlog. Some interesting analytical observations, not headline summaries. Where it fell short: never ran the numerical supply/demand gap calculation I specifically asked for (but it was a very large ask tbf). Spot checked multiple data points I know to be true but some ones that also looked off (to be expected, especially across varying degrees of company size and disclosure). Put ~30 companies into one Three Curves bucket (defeats the framework). Some debatable calls (but wouldn't expect AI to be good at this today). A few blog-tier sources where you'd want filings is always the shortcoming of a finance-focused search. Verdict: Arguably first-draft quality from a mid-tier sell-side initiation. 80% of the work done pretty well. The remaining 20% is exactly what you'd mark up before an experienced analyst or PM sees it. Not the finished product but a remarkable effort in a short amount of time. A research scaffold you can interrogate with actual domain expertise. This was a pretty genuinely cool result and I am intrigued to keep playing around with the tool. cc: Aravind Srinivas RYY Roshan

Stress-tested Perplexity Perplexity Finance Computer on a real equity research workflow: Map a representative AI infrastructure supply chain across 70+ companies across multiple tiers with sourced financials, bottleneck analysis, and company classifications. The kind of deliverable that might take a junior analyst days to weeks. It produced ~2,000 lines of structured research across 4 phased reports (~5,300 credits burned in ~75 minutes of work). Every data point labeled Actual, Guidance, or Estimate with inline source citations. Then it built an interactive dashboard to make the information digestible with 4 deep research .md files compiled. A cool feature is when it flagged when its own sources conflict. It caught 8 data conflicts and wrote explicit resolution notes. TSMC CoWoS capacity ranges explained as report timing differences. ASML's Q4 FCF anomaly identified as a billing artifact. Intel Foundry's loss methodology change across fiscal years. That is fundamentally different from silently picking a number and requiring an experienced SME to catch it. Other strong points: hyperscaler capex table aligned across 5 different fiscal year-ends. ORCL RPO correctly flagged as potential overstating near-term conversion (90%+ partner-funded). Power identified as the binding constraint with MSFT carrying $80B in power-constrained backlog. Some interesting analytical observations, not headline summaries. Where it fell short: never ran the numerical supply/demand gap calculation I specifically asked for (but it was a very large ask tbf). Spot checked multiple data points I know to be true but some ones that also looked off (to be expected, especially across varying degrees of company size and disclosure). Put ~30 companies into one Three Curves bucket (defeats the framework). Some debatable calls (but wouldn't expect AI to be good at this today). A few blog-tier sources where you'd want filings is always the shortcoming of a finance-focused search. Verdict: Arguably first-draft quality from a mid-tier sell-side initiation. 80% of the work done pretty well. The remaining 20% is exactly what you'd mark up before an experienced analyst or PM sees it. Not the finished product but a remarkable effort in a short amount of time. A research scaffold you can interrogate with actual domain expertise. This was a pretty genuinely cool result and I am intrigued to keep playing around with the tool. cc: Aravind Srinivas RYY Roshan

Shanu Mathew

42,176 views • 3 months ago