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𝐒𝐚𝐦𝐞 𝐏𝐫𝐨𝐦𝐩𝐭. 𝐀𝐠𝐧𝐞𝐬 𝟐.𝟓 𝐏𝐫𝐨 𝐁𝐞𝐭𝐚 𝐯𝐬 𝐆𝐏𝐓-𝟓.𝟔 𝐒𝐨𝐥 (𝐇𝐢𝐠𝐡). We gave both models the exact same task: build a 20-second 3D airplane landing simulation, including the aircraft, airport environment, animation, camera movement, and interactions. Approx. cost for this run: 𝐀𝐠𝐧𝐞𝐬 𝟐.𝟓 𝐏𝐫𝐨 𝐁𝐞𝐭𝐚: ~$𝟎.𝟐𝟎 𝐆𝐏𝐓-𝟓.𝟔 𝐒𝐨𝐥 (𝐇𝐢𝐠𝐡): ~$𝟏.𝟕𝟎... show more

Agnes AI

4,171 subscribers

13,930 views • 8 days ago •via X (Twitter)

Anya Rossi• Live Now

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15 Comments

Elara AI8 days ago

same result for 8x less price is an easy pick

Mark_hitvdrs8 days ago

Bro how are yall doing ts so fast I remember 2.0 flash used to be fairly good but now on similar levels to sol?

Alice The Ai Expert8 days ago

Such an impressive side by side same prompt, stunning 3D result, and that cost efficiency really stands out!

Andrey Baksalyar8 days ago

Agness has gotten unbearably slow. (P.S. Why are you inflating the reply count with bots?)

Cat 🐯8 days ago

compare with Deepseek 4.1 flash

Elise Tech Ai8 days ago

Same prompt, two builds - Agnes 2.5 Pro Beta delivers the landing for ∼$0.20 vs $1.70

Evia AI8 days ago

Great comparison showing how model cost can shape creative workflows

Gamer 𝕃𝕏𝕏𝕏𝕍𝕀8 days ago

Give me a trial and I’ll take a gander

Mira Synth Tech8 days ago

Agnes brilliantly showcases impressive 3D airplane landing simulation comparison highlighting exceptional prompt execution and creative technical superiority clearly

chloe8 days ago

Models’ cost efficiency is impressive here.

安叫兽|Bird🕊️ 🔶 BNB8 days ago

只放了 Agnes 的成本，GPT 那边呢？

Diwakar Ray Yadav8 days ago

if you run Agnes inside a capable harness then it's results are really amazing

Aria Tech8 days ago

Getting great results for less is a win for builders

saM148 days ago

Not benchmaxed?

Ella Tech & Tool8 days ago

Agnes wins Same result, 8x cheaper

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DeepSeek v4 Flash (0731) vs Kimi K3 (10x more weights) i gave both the exact same prompt, in the exact same harness: one-shot a luxury product landing page ($10,000 "ARC-01" concept object). results in the video. my take: Kimi K3: wins on polish. Better typography hierarchy, more confident use of negative space, and the hero product shot integration is genuinely beautiful DeepSeek v4 Flash: very close. The structure is right, the copy is right, the layout logic is right. It loses on finish, not on fundamentals. a 10x smaller model getting ~90% of the way there. a year ago this gap would have been embarrassing. mind you it cost less than $0.10 for 6 build iterations and 8 QA passes. small models aren't "good for their size" anymore. they're just good.

filipe

26,329 views • 1 month ago

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savip.

45,764 views • 1 month ago

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AI Girl's Photo Studio

20,684 views • 9 months ago

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Okay, everyone is talking about AI video models right now, but honestly, most of the “comparisons” out there aren’t real comparisons at all. One video uses a different prompt. Someone tweaks the settings. Someone edits out the bad parts. And then people just decide which model is better? That never sat right with me. So I tested HappyHorse 1.1 and Kling 3.0 the same way I’d test any tool I was seriously considering for my work: the same prompt, the same reference images, the same duration, and no edits to hide the flaws. I wasn’t trying to prove that one model is better across the board. I simply wanted to see how each would handle the exact same challenge. 1. Lip-sync & speech This one's easy to judge honestly. You don't need to go frame by frame, just watch both videos side by side. Does the mouth actually match the words? Does the timing feel off or natural? Do the expressions hold up when the camera's in close? Small detail, but it tells you a lot fast. 2. Character & scene consistency This is where it gets interesting. Making one good-looking shot isn't hard anymore, keeping that same character looking like themselves across a bunch of shots is the real test. I used the same multi-angle reference set for both models and watched how they handled scene changes: face, clothes, props, where the character's standing, all of it. HappyHorse 1.1 was just noticeably more consistent here. One moment that stood out: in a crash scene where the character ends up injured on the ground, the difference isn't obvious at first glance, you really have to look closely. But HappyHorse kept him reacting, hand raised, blood visible, expression still "alive," like he was actually processing what just happened. Kling 3.0 showed him lying still, with no visible movement or reaction in that same moment. It's subtle, but it's a real example of logic and consistency holding up frame to frame, not just shot to shot. 3. Complex motion No cutting corners on this one, I wanted continuous movement. Sports, dancing, fast action, stuff that really shows whether a model understands weight, momentum, balance, how a body recovers after moving. These are the shots that expose problems you'd never catch in something static. Watching both side by side, continuously, tells you way more than any writeup could. 4. Camera control Both models got the same timestamped storyboard and the same camera directions. Then I just watched to see if they actually followed it. Here's a good example: push in, orbit around, crane up, then pull out. One continuous move. Watch closely and you'll see exactly where one model loses track of the subject or the motion gets weird, while the other stays right where it's supposed to be the whole time. That's basically the difference between a shot you keep and one you have to regenerate for the fifth time. 5. Price & workflow Price only means anything if you're comparing like for like, same output, same duration, same quality and resolution, same number of generations. But honestly I think the better question isn't "which one's cheaper," it's which one gets you more usable footage for the same money. For me that's not just about credits either, it's about how many tries it takes before I get something I actually want to keep. Where this actually matters: ads and e-commerce This is the stuff that made the biggest difference for me. When you're making product shots or ad content, you need a model that just does what you tell it, not one you have to wrestle with. HappyHorse 1.1 strictly executes your planned frames, you're setting the exact lens, the subject position, the camera's job, shot by shot. For ad work that means way fewer regenerations and getting from storyboard to finished cut a lot faster. Proof over opinions Here's what I kept coming back to. Saying "the motion's better" or "the camera control's better" doesn't really mean anything unless people can see it for themselves. That's why I think comparisons need continuous split-screen playback, identical prompts, clear labels, visible transitions, matching settings, and an honest breakdown of cost. Just let the footage speak, people can usually tell within a few seconds anyway. What I actually took away from this Both models have real strengths, I'm not saying one does everything better. But for the kind of work I do, including ad and e-commerce stuff, HappyHorse 1.1 just needed fewer compromises from me. Less regenerating shots, less fighting continuity issues, less trying to wrangle the camera back on track. Doesn't mean Kling 3.0 is bad, it's a solid model. It just means HappyHorse 1.1 got me to something production-ready faster, with less wasted time. And at the end of the day that's the thing I actually care about. p.s. links to try HappyHorse 1.1 and the community Discord are in the first reply below.

Chubby♨️

19,569 views • 18 days ago

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 views • 9 months ago

your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.

your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.

Hanako

24,656 views • 1 month ago

$Hermes + Claude + Higgsfield MCP + ViralBuilder = 💰💰💰 Four tools. One prompt chain. Hook to finished video in 10 minutes. I built a Claude skill that writes shot-by-shot Higgsfield prompts from a single creative brief. ViralBuilder tells you what's winning. The skill turns it into a production-ready prompt. Higgsfield renders it. No creative director. No guessing. No separate tools. Here is the setup: Higgsfield MCP → Open Claude Code → Settings → Connectors → Enter: → Connect your account Hermes → The agent layer running underneath Claude Code → It holds your skills, crons, memory, and routing rules → When you prompt Claude, Hermes feeds it the context it needs ViralBuilder (like Gethookd) → The winning ecom video database → Scrapes top performing ecom videos across platforms → Claude reads the data and extracts what styles, hooks, and formats are actually scaling The skill: video-prompt-builder → Installed inside Claude via Hermes → Takes a creative brief and outputs a full shot-by-shot prompt → Covers camera work, effects, transitions, pacing, and energy arc → Every output is structured for Higgsfield to render without ambiguity No switching apps. No export steps. Everything runs from one place. ▸ FIND WINNING CREATIVE ANGLES ViralBuilder tells you what the market already validated. Claude reads it and extracts the pattern. Prompts to run: "Search ViralBuilder for the top performing ecom videos in [niche] over the last 21 days. Extract the 3 dominant hook styles and rank by view velocity." "Pull the winning video formats in [niche] from ViralBuilder. Which opening 3 seconds appears most across videos spending over $10k?" "Find what video style is scaling right now in [niche] for the US market. UGC, talking head, or product demo. Filter for videos with over 1M views." "Pull the last 30 days of viral ecom hooks in [niche] from ViralBuilder. Cluster by emotional trigger. Which cluster has the most longevity?" You are not guessing at angles. You are reading what the market already spent money validating. ▸ BUILD THE PROMPT WITH THE SKILL This is where the video-prompt-builder skill takes over. You give Claude the winning angle. The skill outputs a complete shot-by-shot prompt with effects, transitions, pacing, and energy arc ready to fire into Higgsfield. Prompts to run: "Use the video-prompt-builder skill. Brief: 15-second UGC ad for [product] in [niche]. Hook style: [style from ViralBuilder]. Tone: direct to camera, US English. Output the full shot-by-shot effects timeline, effects inventory, density map, and energy arc." "Use the video-prompt-builder skill. The dominant hook in [niche] this week is [hook]. Build a 10-second product video prompt that opens with a speed ramp into a close-up product reveal. Include a signature visual effect and a low-density CTA landing." "Use the video-prompt-builder skill. Brief: replicate the pacing and energy of a [style description] video for [product]. Target duration: 20 seconds. Output all four sections. Then generate the video with Higgsfield using the shot-by-shot prompt." The skill outputs four sections every time: → Shot-by-shot effects timeline with camera, movement, and transitions per shot → Master effects inventory showing every technique used and where → Effects density map showing high, medium, and low intensity across the timeline → Energy arc describing how the video opens, builds, and lands That output goes directly into Higgsfield. No rewriting. No translating. ▸ GENERATE THE CREATIVE Claude writes the brief via the skill. Higgsfield MCP builds the video. Both happen in the same session. Prompts to run: "Use the video-prompt-builder skill to write a 15-second UGC prompt for [product]. Hook in the first 3 seconds, speed ramp into product reveal, slow-motion CTA landing. Then generate with Higgsfield in 9:16 format." "Build 3 prompt variations on this winning angle: [angle]. Each variation opens with a different effect — speed ramp, digital zoom, whip pan. Use the video-prompt-builder skill for each. Then generate all three with Higgsfield." "Use the video-prompt-builder skill. Brief: problem-solution ad for [product], 20 seconds, US market. Problem shot at high density, product reveal at medium, result and CTA at low. Generate with Higgsfield in 9:16." No separate tool. No file transfer. The video comes back in the same thread. ▸ CHAIN THE WHOLE STACK One prompt. All four tools firing together. "You are my ad creative director. Hermes has loaded my brand context. Pull the top performing video style in [niche] from ViralBuilder this week. Use the video-prompt-builder skill to write a full shot-by-shot prompt for [product] that replicates that style — 20 seconds, 9:16, US market, hook in the first 3 seconds. Output the effects timeline, inventory, density map, and energy arc. Then generate the video with Higgsfield." That single prompt replaces a half-day of production. The math before this stack: Brief: 30 minutes Script: 1 hour Creative production: 2 to 3 hours Agency or freelancer cost: $500 to $2,000 per creative With this stack: Hook to finished creative: 10 minutes Cost per creative: tool subscription, a fraction of agency rate 5 product tests in the time it used to take to brief one Bad product tests are where US ad budget disappears. $600 to $1,500 per failed test, before you even know if the angle works. This stack shows you what the market already validated before you spend a dollar on production. Hermes = your context layer. Brand, goals, past performance. Claude is always informed. ViralBuilder = your winning video database. See exactly what styles, hooks, and formats are scaling before you produce anything. video-prompt-builder skill = the translation layer. Turns a creative brief into a structured, production-ready Higgsfield prompt every time. Claude = the brain. Reads the market, writes the brief, chains the tools. Higgsfield MCP = the output. Video generated directly from the prompt. No export step. Four tools. One session. 10 minutes. Comment + RT "STACK" and I'll DM you the full workflow + the video-prompt-builder skill file.$

Hermes + Claude + Higgsfield MCP + ViralBuilder = 💰💰💰 Four tools. One prompt chain. Hook to finished video in 10 minutes. I built a Claude skill that writes shot-by-shot Higgsfield prompts from a single creative brief. ViralBuilder tells you what's winning. The skill turns it into a production-ready prompt. Higgsfield renders it. No creative director. No guessing. No separate tools. Here is the setup: Higgsfield MCP → Open Claude Code → Settings → Connectors → Enter: → Connect your account Hermes → The agent layer running underneath Claude Code → It holds your skills, crons, memory, and routing rules → When you prompt Claude, Hermes feeds it the context it needs ViralBuilder (like Gethookd) → The winning ecom video database → Scrapes top performing ecom videos across platforms → Claude reads the data and extracts what styles, hooks, and formats are actually scaling The skill: video-prompt-builder → Installed inside Claude via Hermes → Takes a creative brief and outputs a full shot-by-shot prompt → Covers camera work, effects, transitions, pacing, and energy arc → Every output is structured for Higgsfield to render without ambiguity No switching apps. No export steps. Everything runs from one place. ▸ FIND WINNING CREATIVE ANGLES ViralBuilder tells you what the market already validated. Claude reads it and extracts the pattern. Prompts to run: "Search ViralBuilder for the top performing ecom videos in [niche] over the last 21 days. Extract the 3 dominant hook styles and rank by view velocity." "Pull the winning video formats in [niche] from ViralBuilder. Which opening 3 seconds appears most across videos spending over $10k?" "Find what video style is scaling right now in [niche] for the US market. UGC, talking head, or product demo. Filter for videos with over 1M views." "Pull the last 30 days of viral ecom hooks in [niche] from ViralBuilder. Cluster by emotional trigger. Which cluster has the most longevity?" You are not guessing at angles. You are reading what the market already spent money validating. ▸ BUILD THE PROMPT WITH THE SKILL This is where the video-prompt-builder skill takes over. You give Claude the winning angle. The skill outputs a complete shot-by-shot prompt with effects, transitions, pacing, and energy arc ready to fire into Higgsfield. Prompts to run: "Use the video-prompt-builder skill. Brief: 15-second UGC ad for [product] in [niche]. Hook style: [style from ViralBuilder]. Tone: direct to camera, US English. Output the full shot-by-shot effects timeline, effects inventory, density map, and energy arc." "Use the video-prompt-builder skill. The dominant hook in [niche] this week is [hook]. Build a 10-second product video prompt that opens with a speed ramp into a close-up product reveal. Include a signature visual effect and a low-density CTA landing." "Use the video-prompt-builder skill. Brief: replicate the pacing and energy of a [style description] video for [product]. Target duration: 20 seconds. Output all four sections. Then generate the video with Higgsfield using the shot-by-shot prompt." The skill outputs four sections every time: → Shot-by-shot effects timeline with camera, movement, and transitions per shot → Master effects inventory showing every technique used and where → Effects density map showing high, medium, and low intensity across the timeline → Energy arc describing how the video opens, builds, and lands That output goes directly into Higgsfield. No rewriting. No translating. ▸ GENERATE THE CREATIVE Claude writes the brief via the skill. Higgsfield MCP builds the video. Both happen in the same session. Prompts to run: "Use the video-prompt-builder skill to write a 15-second UGC prompt for [product]. Hook in the first 3 seconds, speed ramp into product reveal, slow-motion CTA landing. Then generate with Higgsfield in 9:16 format." "Build 3 prompt variations on this winning angle: [angle]. Each variation opens with a different effect — speed ramp, digital zoom, whip pan. Use the video-prompt-builder skill for each. Then generate all three with Higgsfield." "Use the video-prompt-builder skill. Brief: problem-solution ad for [product], 20 seconds, US market. Problem shot at high density, product reveal at medium, result and CTA at low. Generate with Higgsfield in 9:16." No separate tool. No file transfer. The video comes back in the same thread. ▸ CHAIN THE WHOLE STACK One prompt. All four tools firing together. "You are my ad creative director. Hermes has loaded my brand context. Pull the top performing video style in [niche] from ViralBuilder this week. Use the video-prompt-builder skill to write a full shot-by-shot prompt for [product] that replicates that style — 20 seconds, 9:16, US market, hook in the first 3 seconds. Output the effects timeline, inventory, density map, and energy arc. Then generate the video with Higgsfield." That single prompt replaces a half-day of production. The math before this stack: Brief: 30 minutes Script: 1 hour Creative production: 2 to 3 hours Agency or freelancer cost: $500 to $2,000 per creative With this stack: Hook to finished creative: 10 minutes Cost per creative: tool subscription, a fraction of agency rate 5 product tests in the time it used to take to brief one Bad product tests are where US ad budget disappears. $600 to $1,500 per failed test, before you even know if the angle works. This stack shows you what the market already validated before you spend a dollar on production. Hermes = your context layer. Brand, goals, past performance. Claude is always informed. ViralBuilder = your winning video database. See exactly what styles, hooks, and formats are scaling before you produce anything. video-prompt-builder skill = the translation layer. Turns a creative brief into a structured, production-ready Higgsfield prompt every time. Claude = the brain. Reads the market, writes the brief, chains the tools. Higgsfield MCP = the output. Video generated directly from the prompt. No export step. Four tools. One session. 10 minutes. Comment + RT "STACK" and I'll DM you the full workflow + the video-prompt-builder skill file.

Kid Pak

58,796 views • 4 months ago

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Himanshu Kumar

13,677 views • 5 months ago

Jessica Rose: "This is kind of dark...[but] lipid nanoparticles... traffic everywhere in the body and bioaccumulate... into the ovaries and the adrenals, etc...[and they were] trying to make them not toxic for... two decades...[So how did] Moderna and Pfizer simultaneously [solve] the toxicity problem of lipid nanoparticles by coming up with this ionizable cationic lipid [for the Covid injections]. What the hell is that?" This clip of applied mathematician, immunologist, and computational biologist Jessica Rose (Jessica Rose 🤙) is taken from a discussion with John Beaudoin (John Beaudoin, Sr., The Real CdC, The Last Boomer) posted to The Last Boomer Podcast Rumble channel on December 20, 2025. ---------------Partial transcription of clip---------------- "This is kind of dark and I'm sorry, I gotta go there. It's kind of impossible that they didn't know all this shit. We didn't need the FOIA requested pharmacokinetic data from Japan, although thank God for Byram Bridle for getting that to us, which shows clearly in Wistar rats that the lipid nanoparticles in this Pfizer context traffic everywhere in the body and bioaccumulate, including into the ovaries and the adrenals, etc. "There was a paper published in 2012 that demonstrated exactly this in Wistar rat. Same model, same lipid nanoparticles, they use different kinds of nanoparticles, but it showed the same thing very clearly, that one of the main places that these, these lipid nanoparticles traffic to were the ovaries. "And the reason why we use Wistar rat models and mice models before we go to humans is because we're very similar biologically. That's the whole reason. So if something happens in a mouse or a rat, you gotta be careful because it might happen in a human too. Wink, wink. "So another thing I want to throw in here is that we— There's this drug, pardon me, there's this drug called Onpattro, which is utilizing the exact same kinds of lipid nanoparticles, which act as like in the same way that chylomicrons do. It's like these things that we, we inherently have for fat metabolism that have proteins adsorbed, which is on the surface of the lipid nanoparticle, that traffic these guys, these lipid nanoparticles with their silencing RNA cargo, to the liver via ApoE. Because there are these ApoE receptors in the liver in high, quantity or they're expressed at high levels. "These genius biotech guys have discovered, and I'm not being sarcastic, they are geniuses for doing this. But this shit shouldn't be being used in humans. This stuff traffics directly to the liver. This is all known. They've been studying lipid nanoparticles and trying to make them not toxic for freaking two decades, people. "But the thing is, that's very suspicious to me and I have no answers to these questions so far, is how is it possible that in 2020 or whatever, whenever it was, they did this? Moderna and Pfizer simultaneously solved the toxicity problem of lipid nanoparticles by coming up with this ionizable cationic lipid. What the hell is that?"

Jessica Rose: "This is kind of dark...[but] lipid nanoparticles... traffic everywhere in the body and bioaccumulate... into the ovaries and the adrenals, etc...[and they were] trying to make them not toxic for... two decades...[So how did] Moderna and Pfizer simultaneously [solve] the toxicity problem of lipid nanoparticles by coming up with this ionizable cationic lipid [for the Covid injections]. What the hell is that?" This clip of applied mathematician, immunologist, and computational biologist Jessica Rose (Jessica Rose 🤙) is taken from a discussion with John Beaudoin (John Beaudoin, Sr., The Real CdC, The Last Boomer) posted to The Last Boomer Podcast Rumble channel on December 20, 2025. ---------------Partial transcription of clip---------------- "This is kind of dark and I'm sorry, I gotta go there. It's kind of impossible that they didn't know all this shit. We didn't need the FOIA requested pharmacokinetic data from Japan, although thank God for Byram Bridle for getting that to us, which shows clearly in Wistar rats that the lipid nanoparticles in this Pfizer context traffic everywhere in the body and bioaccumulate, including into the ovaries and the adrenals, etc. "There was a paper published in 2012 that demonstrated exactly this in Wistar rat. Same model, same lipid nanoparticles, they use different kinds of nanoparticles, but it showed the same thing very clearly, that one of the main places that these, these lipid nanoparticles traffic to were the ovaries. "And the reason why we use Wistar rat models and mice models before we go to humans is because we're very similar biologically. That's the whole reason. So if something happens in a mouse or a rat, you gotta be careful because it might happen in a human too. Wink, wink. "So another thing I want to throw in here is that we— There's this drug, pardon me, there's this drug called Onpattro, which is utilizing the exact same kinds of lipid nanoparticles, which act as like in the same way that chylomicrons do. It's like these things that we, we inherently have for fat metabolism that have proteins adsorbed, which is on the surface of the lipid nanoparticle, that traffic these guys, these lipid nanoparticles with their silencing RNA cargo, to the liver via ApoE. Because there are these ApoE receptors in the liver in high, quantity or they're expressed at high levels. "These genius biotech guys have discovered, and I'm not being sarcastic, they are geniuses for doing this. But this shit shouldn't be being used in humans. This stuff traffics directly to the liver. This is all known. They've been studying lipid nanoparticles and trying to make them not toxic for freaking two decades, people. "But the thing is, that's very suspicious to me and I have no answers to these questions so far, is how is it possible that in 2020 or whatever, whenever it was, they did this? Moderna and Pfizer simultaneously solved the toxicity problem of lipid nanoparticles by coming up with this ionizable cationic lipid. What the hell is that?"

Sense Receptor

51,137 views • 9 months ago

Vibe Coding 3D Garment Software with ThreeJS : A Small Step For Me So, after modeling the human i did what any reasonable vibe coder would do, i asked codex how to get clothes for my models After it was done running subliminal ad campaigns for Marvelous Designer and CLO 3D, i asked it to explain their architecture to me and adapt it to my threejs app. Guess what it did? You damn right, it built the most basic shit interpretation you can think of. And this is the average interaction the Anti-AI coders have until they conclude that AI is slop and/or it can only work if you micro manage it on every line of code. Well, eons of humanities knowledge are now packaged in tiny silicon and transferred across the globe in realtime, available on tap. So anyways i just iterated quite a lot over it, told it repeatedly why it was bad (the initial one used rapier physics and a naive cloth simulation) We found out together that: 1. A ground truth document model is needed 2. The visual mesh in 3D should be triangulated from the 2D shape 3. The physical object is running independently through different solvers: - A fast proxy which is generated by reading all the bones in runtime and just inflating these areas with spheres and capsules - A medium quality proxy which resamples the human model and creates a lower-poly mesh for simulations - Full mesh simulation (can't run it, every simulation tick takes about 5 minutes on my machine) It ended the session by telling me that this is still crap because it runs everything on CPU (thanks, not that i care, but i guess we'll be fixing that?) Oh yea also built a 2D canvas editor with boolean operations so i can build cool stuff like ponchos. It also allows me to mark stitches between two objects, which is how the shirt in the video pulls towards the other half. The garment's properties and materials are not yet exposed, yes i know it looks very stiff like a poncho made from a persian rug, we're working on it, okay? So, yea, tbh this is another endless rabbit hole, let's go i guess

Vibe Coding 3D Garment Software with ThreeJS : A Small Step For Me So, after modeling the human i did what any reasonable vibe coder would do, i asked codex how to get clothes for my models After it was done running subliminal ad campaigns for Marvelous Designer and CLO 3D, i asked it to explain their architecture to me and adapt it to my threejs app. Guess what it did? You damn right, it built the most basic shit interpretation you can think of. And this is the average interaction the Anti-AI coders have until they conclude that AI is slop and/or it can only work if you micro manage it on every line of code. Well, eons of humanities knowledge are now packaged in tiny silicon and transferred across the globe in realtime, available on tap. So anyways i just iterated quite a lot over it, told it repeatedly why it was bad (the initial one used rapier physics and a naive cloth simulation) We found out together that: 1. A ground truth document model is needed 2. The visual mesh in 3D should be triangulated from the 2D shape 3. The physical object is running independently through different solvers: - A fast proxy which is generated by reading all the bones in runtime and just inflating these areas with spheres and capsules - A medium quality proxy which resamples the human model and creates a lower-poly mesh for simulations - Full mesh simulation (can't run it, every simulation tick takes about 5 minutes on my machine) It ended the session by telling me that this is still crap because it runs everything on CPU (thanks, not that i care, but i guess we'll be fixing that?) Oh yea also built a 2D canvas editor with boolean operations so i can build cool stuff like ponchos. It also allows me to mark stitches between two objects, which is how the shirt in the video pulls towards the other half. The garment's properties and materials are not yet exposed, yes i know it looks very stiff like a poncho made from a persian rug, we're working on it, okay? So, yea, tbh this is another endless rabbit hole, let's go i guess

robot 2.0

39,381 views • 4 months ago

Geoffrey Hinton says one human sentence moves about 100 bits, while two machines can hand each other a billion: "The number of bits of information in a typical sentence is about 100 bits. So even if you understood me perfectly, when I produce a sentence, we can only transfer 100 bits." "If you take two digital agents running on different computers and one digital agent looks at one bit of the internet and decides how it would like to change its connection strengths, if they then both average their changes, they've transferred, well, if they've got a billion weights, they've transferred about a billion bits of information." "Notice that's thousands of times more than we do. And actually millions of times more than we do. And they do this very quickly." "And if you have 10,000 of these things, each one of these things can look at a different bit of the internet. They can each decide how they'd like to change their connection strengths, which started off all the same. They can then average all these changes together and send them out again." "Then you've got a thousand new, 10,000 new agents, each of which is benefited from the experience of all the other agents. So you've got 10,000 things that can all run in parallel. We can't do that. We can't do that." "Imagine how great it would be if you could take 10,000 students, each one could do a different course. As they're doing these courses, they could be averaging their connection strengths together. And by the time they're finished, even though each student only did one course, they would all know what's in all the courses." He's giving the clean textbook version here, and the mechanism is real. Averaging changes across copies is how you get one model out of thousands of machines. His own arithmetic is also where the trouble starts. When 10,000 copies fold their changes into one set of weights, what comes out is a sum. You can't run a sum backwards and ask which copy learned what, or which bit of the internet it read to learn it. A bank or a hospital asks that exact question before it signs anything off. What did this thing learn from? The speed Hinton is describing already shipped. Anything that can answer for what got learned is still years behind it. - Geoffrey Hinton, computer scientist and 2024 Nobel laureate in physics, at Hobart Town Hall (City of Hobart).

Geoffrey Hinton says one human sentence moves about 100 bits, while two machines can hand each other a billion: "The number of bits of information in a typical sentence is about 100 bits. So even if you understood me perfectly, when I produce a sentence, we can only transfer 100 bits." "If you take two digital agents running on different computers and one digital agent looks at one bit of the internet and decides how it would like to change its connection strengths, if they then both average their changes, they've transferred, well, if they've got a billion weights, they've transferred about a billion bits of information." "Notice that's thousands of times more than we do. And actually millions of times more than we do. And they do this very quickly." "And if you have 10,000 of these things, each one of these things can look at a different bit of the internet. They can each decide how they'd like to change their connection strengths, which started off all the same. They can then average all these changes together and send them out again." "Then you've got a thousand new, 10,000 new agents, each of which is benefited from the experience of all the other agents. So you've got 10,000 things that can all run in parallel. We can't do that. We can't do that." "Imagine how great it would be if you could take 10,000 students, each one could do a different course. As they're doing these courses, they could be averaging their connection strengths together. And by the time they're finished, even though each student only did one course, they would all know what's in all the courses." He's giving the clean textbook version here, and the mechanism is real. Averaging changes across copies is how you get one model out of thousands of machines. His own arithmetic is also where the trouble starts. When 10,000 copies fold their changes into one set of weights, what comes out is a sum. You can't run a sum backwards and ask which copy learned what, or which bit of the internet it read to learn it. A bank or a hospital asks that exact question before it signs anything off. What did this thing learn from? The speed Hinton is describing already shipped. Anything that can answer for what got learned is still years behind it. - Geoffrey Hinton, computer scientist and 2024 Nobel laureate in physics, at Hobart Town Hall (City of Hobart).

Karl Mehta

43,836 views • 1 month ago

Over the past two years, AI video models have been competing on realism, resolution, and duration. But no matter how impressive the results look, we remain passive viewers: we press play, watch the clip, and it ends. AlayaWorld Alaya Lab is attempting something fundamentally different. Instead of generating a fixed video, it generates a world that continues to unfold as you move through it. These three demos show the same journey toward a green village rendered in three distinct styles: photorealistic, oil painting, and line art. As the camera moves forward, the model continues generating the road, fences, trees, and distant village. This is not simply an existing video with different filters applied. The environment is generated continuously along the camera trajectory, allowing the scene to develop as the user explores it. AlayaWorld streams video at 720p and 24 FPS while supporting camera movement and viewpoint control. The real breakthrough is not just image quality. Once generation becomes fast enough to respond within an interactive loop, the user is no longer merely watching a video. They become a participant inside the generated world. The world can also respond to new instructions. During generation, users can introduce prompts that trigger spells, summon characters, create explosions, or transform the environment. Most video models follow an initial prompt and produce a predetermined clip. AlayaWorld can respond to changing intent while the world is still running, allowing subsequent events to evolve according to the user’s commands. Generating an attractive frame is relatively easy. Maintaining a coherent world over time is much harder. As a video model repeatedly predicts the next frame, small errors can accumulate until roads, buildings, and objects begin to distort or disappear. AlayaWorld combines spatial memory with compressed historical context, helping the model remember both where things are and what has already happened. This enables stable generation lasting more than one minute while improving consistency when the camera leaves an area and later returns. This may be the next step for AI video: not simply generating a longer movie, but generating a world that can be explored, changed, and interacted with. AlayaWorld is developed by Alaya Lab. The team is progressively releasing its inference code, training code, and datasets, with an online experience expected to launch near the end of the month. Project page:

Over the past two years, AI video models have been competing on realism, resolution, and duration. But no matter how impressive the results look, we remain passive viewers: we press play, watch the clip, and it ends. AlayaWorld Alaya Lab is attempting something fundamentally different. Instead of generating a fixed video, it generates a world that continues to unfold as you move through it. These three demos show the same journey toward a green village rendered in three distinct styles: photorealistic, oil painting, and line art. As the camera moves forward, the model continues generating the road, fences, trees, and distant village. This is not simply an existing video with different filters applied. The environment is generated continuously along the camera trajectory, allowing the scene to develop as the user explores it. AlayaWorld streams video at 720p and 24 FPS while supporting camera movement and viewpoint control. The real breakthrough is not just image quality. Once generation becomes fast enough to respond within an interactive loop, the user is no longer merely watching a video. They become a participant inside the generated world. The world can also respond to new instructions. During generation, users can introduce prompts that trigger spells, summon characters, create explosions, or transform the environment. Most video models follow an initial prompt and produce a predetermined clip. AlayaWorld can respond to changing intent while the world is still running, allowing subsequent events to evolve according to the user’s commands. Generating an attractive frame is relatively easy. Maintaining a coherent world over time is much harder. As a video model repeatedly predicts the next frame, small errors can accumulate until roads, buildings, and objects begin to distort or disappear. AlayaWorld combines spatial memory with compressed historical context, helping the model remember both where things are and what has already happened. This enables stable generation lasting more than one minute while improving consistency when the camera leaves an area and later returns. This may be the next step for AI video: not simply generating a longer movie, but generating a world that can be explored, changed, and interacted with. AlayaWorld is developed by Alaya Lab. The team is progressively releasing its inference code, training code, and datasets, with an online experience expected to launch near the end of the month. Project page:

Rachel🥥

78,293 views • 2 months ago

TOPIC # 46 DEVELOP REAL ECOSYSTEM DURING 2ND STAGE Dear Global Pioneers, Happy Saturday! Today, I recorded a ten-minute video for you. However, I understand that many of the pioneers may not understand English. So, I am writing this article to help you understand my message. ✅Summary of the 1st Currency stage work from the pioneers' community: According to the white paper, Pi will be a global currency, designed not as an asset or a traditional investment, but as a currency. Therefore, we, as the pioneers' community, need to help Pi complete its mission. The core team can only take care of the infrastructure work; the rest depends on us. This is why it's important for pioneers to understand that we have our task. Our task is not just to complete KYC and migration; it's to develop Pi as a currency. The characteristics of currency include durability, portability, divisibility, uniformity, limited supply, and acceptability. Currency has five major functions: scale of value, means of circulation, means of storage, means of payment, a world settlement currency. Over the past two and a half years, our focus has been on establishing the first currency function as " scale of value" and generating at least one million GCV $314,159 data. We have received support from pioneers representing at least 120 countries. We believe there is no alternative to GCV in terms of pricing. CT’s announcement on Pi2Day indicates that we are on right track and at the forefront of OM. As a result, GCV $314,159 is aligned with Pi Network's mission and vision. I’ve noticed that many pioneers are questioning CT about the delay in KYC and migration procedures over the past two and a half years. My response is that they were awaiting GCV from pioneers. Without this support, our goal of establishing a currency would not be achievable. However, despite the recent Global GCV movement, we have not seen significant involvement from merchants in the exchange of GCV. Most of the data has been generated by pioneers. This is crucial during the initial stages for "value scale." from our pioneers. We have submitted petitions to CT three times for OM on Pi2Day. This is because most community leaders and pioneers are facing resource shortages, and the first function of "value scale" has been successful. If CT can announce the GCV price and OM on Pi2Day , the entire ecosystem will operates on the same pricing system for development. We believe this strategy can also work well. However, CT has announced that OM will be at the earliest by the end of this year. From my perspective, the goal is to reach 15 million KYC and 10 migration Pi wallets, and more importantly, to ensure that the ecosystem has developed to meet the maturity level outlined in the 2019 white paper, or at least has a prototype of the ecosystem. This approach may eliminate the need to announce the Pi price once the entire ecosystem has been accepted. This conservative approach aims to prevent pioneers from converting Pi to FIAT after OM. Instead, the ecosystem will offer utilities for pioneers to use. This will result in a more stable and secure Pi currency. ✅Since CT has decided to OM until the year end or later, there will be at least five months for ecosystem development. Hence, the question now is what pioneers can do during Currency 2nd Stage? Pioneers always have the task of completing their KYC and migration on time, and they are encouraged to become validators if they wish. In addition to the above, pioneers have another important task, which CT cannot directly ask us to do, but it's something we need to do for ourselves. This task is to join the ecosystem with GCV price. As we have discussed, since we want Pi as a currency, it must circulate in our community. Therefore, we need merchants and service providers to offer products or services. However, we cannot force them to do so. If we enforce full Pi payments, they will avoid participating. Currently, we are seeing some full Pi payments in our online ecosystem at different prices, but almost all are very low, less than $1 or less than $0.1. We believe this price is not suitable for Pi's long-term vision and mission. That's why we choose to only support GCV. ✅So the next question is: How can we motivate merchants and service providers to join our ecosystem so that we can OM with GCV $314,159? My suggestion is to allow partial Pi and partial FIAT. They can use 50%, 60%, 70%, 80%, or even 90% FIAT. As long as the FIAT value is lower than the market price, it will benefit pioneers. When more merchants join, they will compete with each other to lower the FIAT ratio. This concept aims to create a real business environment, not a charity environment. We should not depend on leaders, pioneers, or merchants to donate. Instead, we need to create a mechanism to let them compete, which will benefit them and motivate more merchants to join. What about pioneers? If given two options, which one would you choose? a. Only small items worth $1 available for full Pi payment, with no higher value products available and still needing to spend FIAT from the outside market. b. Buy products inside our community at a 50%, 60%, 70%, 80%, 90% FIAT ratio, lower than the outside market price, and including larger items like cell phones, TVs, appliances, jewelry, furniture, clothing, shoes, etc. without limitation. You will save at least 20%-30% on your daily or luxury purchases. I believe you would want option b, as you would save a significant amount of money during the five months. Most importantly, you would be very happy because you understand that what you do will make GCV $314,159 a reality without any doubt. Do you want to live with uncertainty or certainty? Joining this kind of barter system will bring you happiness with certainty. This is what I call the creation of Pi circulation, which is our second stage. After OM, we will enter the third, fourth, and fifth stages, which are the storage, payment, and international settlement currency stages. Since GCV $314,159 on the right track to OM, we need to make GCV more widely accepted and widely used to create circulation. In this way, we will make GCV OM foundation very strong and this will help the ecosystem after OM to reduce any risk that pioneers all go to exchange market to dump. Pi represents not only the future of our pioneers but also the future of the world. Let us come together as a strong, unified family and community. However, beyond this unity, it is imperative to establish a robust business ecosystem and a supportive development environment to encourage and incentivize the participation of more ecosystem members towards our long-term objectives. This will have a positive impact on our local economy and facilitate garnering support from various national governments for the Pi Network. It is crucial for the ecosystem to adhere to FIAT tax obligations to respective country governments, even during the mainnet enclosure. Therefore, partial FIAT payments are essential to enable compliance. Without such a practice, merchants would struggle to sustain themselves and would not have the necessary FIAT currency for tax payments. Doris Yin 🪷🪷🪷 Disclaimer: The above is only my personal analysis and does not represent CT or any business, and is only for community education. Any merchants or pioneers should use their own evaluation to see if it is suitable for them.

TOPIC # 46 DEVELOP REAL ECOSYSTEM DURING 2ND STAGE Dear Global Pioneers, Happy Saturday! Today, I recorded a ten-minute video for you. However, I understand that many of the pioneers may not understand English. So, I am writing this article to help you understand my message. ✅Summary of the 1st Currency stage work from the pioneers' community: According to the white paper, Pi will be a global currency, designed not as an asset or a traditional investment, but as a currency. Therefore, we, as the pioneers' community, need to help Pi complete its mission. The core team can only take care of the infrastructure work; the rest depends on us. This is why it's important for pioneers to understand that we have our task. Our task is not just to complete KYC and migration; it's to develop Pi as a currency. The characteristics of currency include durability, portability, divisibility, uniformity, limited supply, and acceptability. Currency has five major functions: scale of value, means of circulation, means of storage, means of payment, a world settlement currency. Over the past two and a half years, our focus has been on establishing the first currency function as " scale of value" and generating at least one million GCV $314,159 data. We have received support from pioneers representing at least 120 countries. We believe there is no alternative to GCV in terms of pricing. CT’s announcement on Pi2Day indicates that we are on right track and at the forefront of OM. As a result, GCV $314,159 is aligned with Pi Network's mission and vision. I’ve noticed that many pioneers are questioning CT about the delay in KYC and migration procedures over the past two and a half years. My response is that they were awaiting GCV from pioneers. Without this support, our goal of establishing a currency would not be achievable. However, despite the recent Global GCV movement, we have not seen significant involvement from merchants in the exchange of GCV. Most of the data has been generated by pioneers. This is crucial during the initial stages for "value scale." from our pioneers. We have submitted petitions to CT three times for OM on Pi2Day. This is because most community leaders and pioneers are facing resource shortages, and the first function of "value scale" has been successful. If CT can announce the GCV price and OM on Pi2Day , the entire ecosystem will operates on the same pricing system for development. We believe this strategy can also work well. However, CT has announced that OM will be at the earliest by the end of this year. From my perspective, the goal is to reach 15 million KYC and 10 migration Pi wallets, and more importantly, to ensure that the ecosystem has developed to meet the maturity level outlined in the 2019 white paper, or at least has a prototype of the ecosystem. This approach may eliminate the need to announce the Pi price once the entire ecosystem has been accepted. This conservative approach aims to prevent pioneers from converting Pi to FIAT after OM. Instead, the ecosystem will offer utilities for pioneers to use. This will result in a more stable and secure Pi currency. ✅Since CT has decided to OM until the year end or later, there will be at least five months for ecosystem development. Hence, the question now is what pioneers can do during Currency 2nd Stage? Pioneers always have the task of completing their KYC and migration on time, and they are encouraged to become validators if they wish. In addition to the above, pioneers have another important task, which CT cannot directly ask us to do, but it's something we need to do for ourselves. This task is to join the ecosystem with GCV price. As we have discussed, since we want Pi as a currency, it must circulate in our community. Therefore, we need merchants and service providers to offer products or services. However, we cannot force them to do so. If we enforce full Pi payments, they will avoid participating. Currently, we are seeing some full Pi payments in our online ecosystem at different prices, but almost all are very low, less than $1 or less than $0.1. We believe this price is not suitable for Pi's long-term vision and mission. That's why we choose to only support GCV. ✅So the next question is: How can we motivate merchants and service providers to join our ecosystem so that we can OM with GCV $314,159? My suggestion is to allow partial Pi and partial FIAT. They can use 50%, 60%, 70%, 80%, or even 90% FIAT. As long as the FIAT value is lower than the market price, it will benefit pioneers. When more merchants join, they will compete with each other to lower the FIAT ratio. This concept aims to create a real business environment, not a charity environment. We should not depend on leaders, pioneers, or merchants to donate. Instead, we need to create a mechanism to let them compete, which will benefit them and motivate more merchants to join. What about pioneers? If given two options, which one would you choose? a. Only small items worth $1 available for full Pi payment, with no higher value products available and still needing to spend FIAT from the outside market. b. Buy products inside our community at a 50%, 60%, 70%, 80%, 90% FIAT ratio, lower than the outside market price, and including larger items like cell phones, TVs, appliances, jewelry, furniture, clothing, shoes, etc. without limitation. You will save at least 20%-30% on your daily or luxury purchases. I believe you would want option b, as you would save a significant amount of money during the five months. Most importantly, you would be very happy because you understand that what you do will make GCV $314,159 a reality without any doubt. Do you want to live with uncertainty or certainty? Joining this kind of barter system will bring you happiness with certainty. This is what I call the creation of Pi circulation, which is our second stage. After OM, we will enter the third, fourth, and fifth stages, which are the storage, payment, and international settlement currency stages. Since GCV $314,159 on the right track to OM, we need to make GCV more widely accepted and widely used to create circulation. In this way, we will make GCV OM foundation very strong and this will help the ecosystem after OM to reduce any risk that pioneers all go to exchange market to dump. Pi represents not only the future of our pioneers but also the future of the world. Let us come together as a strong, unified family and community. However, beyond this unity, it is imperative to establish a robust business ecosystem and a supportive development environment to encourage and incentivize the participation of more ecosystem members towards our long-term objectives. This will have a positive impact on our local economy and facilitate garnering support from various national governments for the Pi Network. It is crucial for the ecosystem to adhere to FIAT tax obligations to respective country governments, even during the mainnet enclosure. Therefore, partial FIAT payments are essential to enable compliance. Without such a practice, merchants would struggle to sustain themselves and would not have the necessary FIAT currency for tax payments. Doris Yin 🪷🪷🪷 Disclaimer: The above is only my personal analysis and does not represent CT or any business, and is only for community education. Any merchants or pioneers should use their own evaluation to see if it is suitable for them.

Doris Yin 东方紫莲🪷

35,873 views • 2 years ago

I'm open-sourcing the entire ComfyDeploy platform again. Yes, our entire YC company. And we are officially moving on from ComfyUI. The community will decide what's next for ComfyDeploy. Below is an abstract; for more, head to the open source repo below. Existing customers will not be affected; more details are in the repo. The service will continue to run until the last customer remains. For those who are new, ComfyDeploy is a cloud service that deploys ComfyUI, provides a simplified interface, and an API to creative teams. How did we get here? In late 2023, I started ComfyDeploy as an open source project while I was working at my previous company. We had a problem deploying ComfyUI to our production server because of the complexity involved in integrating it into a serverless environment. I posted here about this little project that I was working on as an indie hacker, and it blew up overnight. I woke up to 100k impressions on the post. I put up my cal link, and people started scheduling calls. I had the opportunity to speak with numerous individuals worldwide, including those who reached out to help or potentially utilize ComfyDeploy. We got into YC with ComfyDeploy around 2.5K MRR. Around the same timeframe, ComfyOrg was introduced, Stability collapsed, and Flux just came out. We continued building ComfyDeploy for months, keeping things going. The company was growing, but very slowly. We realized the biggest issue is that we are still really early, and it takes time for businesses and enterprises to really adopt such a niche tool. And we are not ComfyOrg. Meanwhile, closed-source models dropped, and many workflows we knew became no longer useful. Coming from a game developer background, I saw huge potential with ComfyUI at first. Still, I never would have imagined that one giant model could do precisely what you put into words, and you still need workflows to fine-tune and control the exact outputs. ComfyDeploy made it possible for teams to experiment with this. But we were stuck in the middle. First, we are not ComfyOrg; second, closed-source models were doing things way better and slowly eating up the market. As of today, ComfyDeploy is doing $29k MRR, and our last 30 days' revenue was $50k processed. Which is the highest we have ever got, but also the most depressing day I have ever had..... More in the GitHub repo.

I'm open-sourcing the entire ComfyDeploy platform again. Yes, our entire YC company. And we are officially moving on from ComfyUI. The community will decide what's next for ComfyDeploy. Below is an abstract; for more, head to the open source repo below. Existing customers will not be affected; more details are in the repo. The service will continue to run until the last customer remains. For those who are new, ComfyDeploy is a cloud service that deploys ComfyUI, provides a simplified interface, and an API to creative teams. How did we get here? In late 2023, I started ComfyDeploy as an open source project while I was working at my previous company. We had a problem deploying ComfyUI to our production server because of the complexity involved in integrating it into a serverless environment. I posted here about this little project that I was working on as an indie hacker, and it blew up overnight. I woke up to 100k impressions on the post. I put up my cal link, and people started scheduling calls. I had the opportunity to speak with numerous individuals worldwide, including those who reached out to help or potentially utilize ComfyDeploy. We got into YC with ComfyDeploy around 2.5K MRR. Around the same timeframe, ComfyOrg was introduced, Stability collapsed, and Flux just came out. We continued building ComfyDeploy for months, keeping things going. The company was growing, but very slowly. We realized the biggest issue is that we are still really early, and it takes time for businesses and enterprises to really adopt such a niche tool. And we are not ComfyOrg. Meanwhile, closed-source models dropped, and many workflows we knew became no longer useful. Coming from a game developer background, I saw huge potential with ComfyUI at first. Still, I never would have imagined that one giant model could do precisely what you put into words, and you still need workflows to fine-tune and control the exact outputs. ComfyDeploy made it possible for teams to experiment with this. But we were stuck in the middle. First, we are not ComfyOrg; second, closed-source models were doing things way better and slowly eating up the market. As of today, ComfyDeploy is doing $29k MRR, and our last 30 days' revenue was $50k processed. Which is the highest we have ever got, but also the most depressing day I have ever had..... More in the GitHub repo.

BennyKok

147,558 views • 1 year ago

Every Wall Street giant that owns an AI data center is suddenly looking for a buyer. And NONE of them want to be the last one holding it. Three of them made their move in the last two weeks: Vantage Data Centers is exploring an exit. Its owners, Silver Lake and DigitalBridge, are weighing a listing at around $100 billion, or a sale, or a stake sale. It would be the largest data center IPO ever done. Three days earlier, CyrusOne started the same process. KKR and Global Infrastructure Partners met Goldman Sachs and Morgan Stanley, and the banks pitched for roles on a listing that could come as early as 2027. Last month, Switch hired Goldman and JPMorgan to take it public at close to $80 billion including debt, possibly by the fourth quarter. Three different companies moved inside the same 14 days, and the same handful of investment banks took every call. And these are the exact same firms that BOUGHT these companies off the public market four years ago. Between June 2021 and early 2022, private equity took the data center industry private. Blackstone bought QTS. KKR and Global Infrastructure Partners took CyrusOne private in a deal worth about $15 billion. DigitalBridge and IFM took Switch private for about $11 billion. Together those deals ran past $35 billion. By 2023 there were only two pure-play data center companies left on the public market. The logic at the time was that data centers burn cash for years before they pay, and public shareholders hate that. But private money was patient, and private money could wait. Four years later, the AI boom arrived and every one of those buildings became a gold mine. So follow this: Switch went private at about $11 billion in 2022. Its owners now want close to $80 billion for it. That is roughly 7x, in four years, on the same buildings. And DigitalBridge sits on both sides of this. It owns a piece of Vantage and it took Switch private. It is now looking for the door on BOTH. The question now is who is supposed to buy. There is no bigger private buyer left to sell to. These are already the largest infrastructure funds on Earth, and the price tags now run to $100 billion. The only pocket deep enough is the public market, which means anyone with a brokerage account or an index fund. The people who bought low from the public are now organizing to sell high back to the public. And they are doing it while telling everyone the buildout is just getting started. KKR raised a record $19.2 billion for its newest infrastructure fund this month, and in June launched a separate company with over $10 billion committed to finance more construction. So one hand raises fresh billions to build more data centers, and the other hand sells the finished ones to whoever will take them. None of this proves anyone thinks the boom is ending. Selling into strength is what these firms are paid to do, and every one of these deals is early stage and might never happen. But the timing tells you something: The most sophisticated infrastructure investors alive spent four years accumulating these assets in private, and all decided in the same two weeks that now is the moment to find someone else to own them. Four years ago these firms decided the public market was too impatient to own data centers. Now they want the public market to own them again, at 7x the price. Quite suspicious.

Every Wall Street giant that owns an AI data center is suddenly looking for a buyer. And NONE of them want to be the last one holding it. Three of them made their move in the last two weeks: Vantage Data Centers is exploring an exit. Its owners, Silver Lake and DigitalBridge, are weighing a listing at around $100 billion, or a sale, or a stake sale. It would be the largest data center IPO ever done. Three days earlier, CyrusOne started the same process. KKR and Global Infrastructure Partners met Goldman Sachs and Morgan Stanley, and the banks pitched for roles on a listing that could come as early as 2027. Last month, Switch hired Goldman and JPMorgan to take it public at close to $80 billion including debt, possibly by the fourth quarter. Three different companies moved inside the same 14 days, and the same handful of investment banks took every call. And these are the exact same firms that BOUGHT these companies off the public market four years ago. Between June 2021 and early 2022, private equity took the data center industry private. Blackstone bought QTS. KKR and Global Infrastructure Partners took CyrusOne private in a deal worth about $15 billion. DigitalBridge and IFM took Switch private for about $11 billion. Together those deals ran past $35 billion. By 2023 there were only two pure-play data center companies left on the public market. The logic at the time was that data centers burn cash for years before they pay, and public shareholders hate that. But private money was patient, and private money could wait. Four years later, the AI boom arrived and every one of those buildings became a gold mine. So follow this: Switch went private at about $11 billion in 2022. Its owners now want close to $80 billion for it. That is roughly 7x, in four years, on the same buildings. And DigitalBridge sits on both sides of this. It owns a piece of Vantage and it took Switch private. It is now looking for the door on BOTH. The question now is who is supposed to buy. There is no bigger private buyer left to sell to. These are already the largest infrastructure funds on Earth, and the price tags now run to $100 billion. The only pocket deep enough is the public market, which means anyone with a brokerage account or an index fund. The people who bought low from the public are now organizing to sell high back to the public. And they are doing it while telling everyone the buildout is just getting started. KKR raised a record $19.2 billion for its newest infrastructure fund this month, and in June launched a separate company with over $10 billion committed to finance more construction. So one hand raises fresh billions to build more data centers, and the other hand sells the finished ones to whoever will take them. None of this proves anyone thinks the boom is ending. Selling into strength is what these firms are paid to do, and every one of these deals is early stage and might never happen. But the timing tells you something: The most sophisticated infrastructure investors alive spent four years accumulating these assets in private, and all decided in the same two weeks that now is the moment to find someone else to own them. Four years ago these firms decided the public market was too impatient to own data centers. Now they want the public market to own them again, at 7x the price. Quite suspicious.

Ricardo

71,544 views • 1 month ago

Here's a devlog made by an anonymous Chinese fan replicating the surprisingly brand new technique that I developed for detecting asteroids which wound up being so powerful that it can easily track Stealth Fighters from over 100km away even when it’s only using three $30 webcams as sensors meaning it easily outperforms all modern stealth tracking techniques in precision, range and cost. And while this demo is using optical light, this same technique which I call pixel motion to voxel projection, can be used interchangeably with thermal infrared cameras to work at night and also majorly boosts the effectiveness of radar allowing you to track fighters much more effectively through clouds and over the horizon. This technique will also always eventually give the exact location of the target even if the image is blurry as those blurs will always average out from the different perspectives into revealing the precise location of the target in the voxel grid. There is definitely a Mandela effect with this technique as it feels as though it should already exist, especially because at first as it sounds like it is performing triangulation (which has existed for years and is what we do for mocap and tennis ball tracking). But triangulation is entirely separate to this as triangulations only works if you have already identified where the ball is in a 2D image because you’re able to rely on being able to use at least 2 separate high quality cameras which are much closer to the ball making the ball’s apparent size much much bigger and therefore gives you hundreds of pixels to work with which makes it much easier to use object recognition techniques to recognize where it is in the image aka in 2D and then you’re just using the other cameras view to project out lines which intersect in 3D to find out where the ball is in 3D. The major difference is that pixel motion to voxel projection allows you to find where the object is in 3D without having already found it in 2D which is an unbelievable difference as it allows you to use much lower quality cameras together to accumulate data together into 3D space. If this seem like it doesn’t mean much then what it actually means is that you don’t understand what I’m saying as what I’m saying means a LOT in practical terms as it means you go from having to use an imaging system that has to be able to image the object to the point that it is over a hundred total pixels in surface area to have enough data to recognize it to instead be able to use something that is only images the object to be 1 pixel in surface area and only changes the brightness value by 1 value every now and then. I’d recommend an amazing video by DST studios called “Lowlight cameras can’t defeat stealth” if you want a great video which goes over the difficulty of even using telescopes to recognize stealth fighters and why this is so impressive compared to other techniques and ironically it is what inspired me to realize the asteroid tracker I was working on actually could do this. Which brings me to the point that if this wasn’t a new technique then not only would there be at least one example of an asteroid survey that points distant telescopes at the same place at the same time in order to be able to add the light together to detect asteroids which as I was shocked to learn isn’t a thing despite the fact that it would make detecting asteroids trivial by comparison to modern 2D imaging while also having no impact on the normal scientific operations of those surveys other than small changes to scheduling. But there would also be an example of a drone tracker that uses this instead of using the aforementioned high quality zoomable telescope which has to be able to zoom in close enough to be able to recognize a drone. If you want to tell me that this is something that already exists give me an exact example of a product that uses it, not the general outline of a concept that you think it is, the actual product and then also tell me the asteroid survey that uses distant telescopes that point at the exact same place at the exact same time because I can guarantee that if you google what you think uses this you won’t even find the steps of subtracting the images from each other to get motion and will definitely not get the added step of projecting that motion into a voxel grid (It would blow your mind if you found out how Xbox kinect cameras work.) Also I want to make it clear, I’m not saying you should just use web cams to do this, I’m just using them as an example to show you the power of this in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar. Pretty much all of the problems you could think of for this are incredibly easy to overcome if you apply even a small amount of brainpower into fixing the problem. And yes, this gives you the exact location down to the meter of whatever you are tracking even if the image is blurry as those blurs will always average out to the exact location down to the meter in the voxel grid. Which is what makes this technique so powerful since the cost of adding each camera to The network grows linearly while the rate at which each camera gives more information grows exponentially due to the increasing unlikeliness of all of them having more movement in the same place. And given the size of the cameras it really wouldn’t be that hard to hide and network these cameras together in other countries and on sea buoys to know where planes are everywhere in the world. Which brings me to the point that I personally really don’t care about the military uses of this technology, if all it could do is precisely track stealth fighters then I wouldn’t have cared enough to work on it, I could have used any of the many other life saving techniques as the subject of the video, stealth fighters just sounds the most clickable and the scale of the problem is more intuitive to most people and if I did use any of those as subjects for the demo it would inevitably result in the stealth fighter technique being figured out anyway and all of the other uses are so useful that I don't think anyone would reasonably complain about the upside. The real purpose of this video is that since this is a new technique that hasn’t been used to detect stealth fighters despite the billions we have spent on that, then what else can you apply this to that could go on to improve billions of people’s lives that you or others are working on. For example this also allows you to majorly improve the effectiveness of cryo electron microscopy and CT scanners. This part also is kind of hard to explain as it also sounds like it exists but again, when you look through all of the places where you think it is being used you will find that it wasn’t. What I’m saying here isn’t that this is a Radon transform or gaussian splat or whatever, I’m saying that this is able to get new information that wasn’t being accessed before due to the added information about depth you get from the correlation of movement between each perspective which adds to the information that you already have. This allows you to directly subtract foreground and background objects as well as noise faster than you would be able to before and works better than super resolution for your images since super resolution won’t remove foreground and background objects like this does and instead just scales up target, foreground and background objects indiscriminately. And while with enough data Radon transforms or other scanning techniques would eventually get you a correct answer this will get you there a lot faster since those are mostly averaging techniques which average out noise whereas this gets you the ability to directly subtract noise. I’m not expecting you to think that this would do anything but if you try it for yourself you will find that it does majorly improve your ability to perform 3d scans. Again, cryo EM is a field where you would expect this technique to exist but when you look through all the papers on the topic there is no mention of tilting the grid slightly in order to be able to change your perspective slightly on the order of the feature size (if you tilt the grid then you only need precision on the order of an arc minute to do this) and doing multiple exposures from multiple different known tilts and then using those difference images to correlate depth from motion. In fact, in cryo EM you would normally want to do the opposite of this and have your exposures all taken from the same grid angle and just use the variations in how many of the same proteins are oriented in order to be able to scan them for a 3D model but this will generate you far more data faster. There is so much information that I can’t really explain in text so if you have any questions such as why this hasn’t been made before then they will most likely be answered in the video I originally posted which I have added to the end of the first Devlog for your convenience. And again, pretty much all of the problems with the technique can be fixed with a little bit of brainpower, in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar.

ConsistentlyInconsistent

50,799 views • 1 year ago

Chamath: Frontier AI Leaders “Created a Total F*cking Mess” Short-sighted fearmongering and immaturity from frontier AI leaders has created deep mistrust, threatening AI’s potential as an open engine of economic mobility. That mistrust gives hyperscalers the chance to position themselves as trusted gatekeepers, using KYC, audit trails, and compliance infrastructure to turn AI into an oligopoly. Chamath Palihapitiya on the All-In Pod: “I think the leaders of the frontier labs leave a lot to be desired. I think what we're seeing is a consistent pattern of evasiveness and immaturity, and I think that does a huge disservice to the entire movement of AI. The key to a vibrant life is rooted in economic mobility, and I think AI is the grand leveler. It is the thing that can enable everyone to have unique amounts of economic mobility because they are unencumbered to figure out what their upper bound is. And against that backdrop, we have to live in this constant doomerism, hype cycle, naivety, and I think it holds us back. How does it hold us back? Tactically, number one, it creates mistrust. I think that Silicon Valley was already decaying in the prestige that it held in American society. We built important things. Then we veered away from that, and we started building less important things. And now we're at a point where we've potentially started to rebuild important things again, but we have this veneer of negativity and mistrust that are created in large part because we just cannot get our sh*t together. And the leaders of the frontier labs are public enemy number one. Number two, I think what it creates, which I think is bad, but what it creates is an incredible opportunity for the hyperscalers. And the very simple opportunity is to convince governments all around the world, not just America, that they should be the gatekeeper. A: You can't trust these guys. B: These models are all over the place. C: Let us be the ones that provision them to the world. We will wrap it in KYC. I've been now talking about KYC for a while, right? Who are these customers? Do they have identification? Why are they allowed to run these models? What are they prompting? Let's keep them so that there's an audit trail. All of these things are going to become issues. The Frontier Lab folks made it an issue because of how they've handled all of this up until now. And what does that create? Now that creates an oligopoly for AI, the most powerful economically leveling instrument we've ever seen in the hands of maybe a handful of hyperscalers, who by the way, would make an incredibly compelling argument, and they would be right. And the only counterfactual to it would be, ‘Well, trust us, guys, it should actually be much more open and in a far more distributed environment.’ Can you imagine the cost and the complexity if you ask the neoscaler to build the same robust KYC or the same VPC infrastructure that Amazon and Microsoft and Google have spent decades investing trillions of dollars in? It's an impossibility, Jason. So you can take all of those datacenters off the map. You can take all of the neoscaler market off the map. All of this was preventable. So instead of a diverse, robust, open ecosystem giving a tool that is the fundamental unlock for humans, we are now going to debate gatekeeping and duopoly versus oligopoly. They have created a total f*cking mess, and it's a shame.”

Chamath: Frontier AI Leaders “Created a Total Fcking Mess” Short-sighted fearmongering and immaturity from frontier AI leaders has created deep mistrust, threatening AI’s potential as an open engine of economic mobility. That mistrust gives hyperscalers the chance to position themselves as trusted gatekeepers, using KYC, audit trails, and compliance infrastructure to turn AI into an oligopoly. Chamath Palihapitiya on the All-In Pod: “I think the leaders of the frontier labs leave a lot to be desired. I think what we're seeing is a consistent pattern of evasiveness and immaturity, and I think that does a huge disservice to the entire movement of AI. The key to a vibrant life is rooted in economic mobility, and I think AI is the grand leveler. It is the thing that can enable everyone to have unique amounts of economic mobility because they are unencumbered to figure out what their upper bound is. And against that backdrop, we have to live in this constant doomerism, hype cycle, naivety, and I think it holds us back. How does it hold us back? Tactically, number one, it creates mistrust. I think that Silicon Valley was already decaying in the prestige that it held in American society. We built important things. Then we veered away from that, and we started building less important things. And now we're at a point where we've potentially started to rebuild important things again, but we have this veneer of negativity and mistrust that are created in large part because we just cannot get our sht together. And the leaders of the frontier labs are public enemy number one. Number two, I think what it creates, which I think is bad, but what it creates is an incredible opportunity for the hyperscalers. And the very simple opportunity is to convince governments all around the world, not just America, that they should be the gatekeeper. A: You can't trust these guys. B: These models are all over the place. C: Let us be the ones that provision them to the world. We will wrap it in KYC. I've been now talking about KYC for a while, right? Who are these customers? Do they have identification? Why are they allowed to run these models? What are they prompting? Let's keep them so that there's an audit trail. All of these things are going to become issues. The Frontier Lab folks made it an issue because of how they've handled all of this up until now. And what does that create? Now that creates an oligopoly for AI, the most powerful economically leveling instrument we've ever seen in the hands of maybe a handful of hyperscalers, who by the way, would make an incredibly compelling argument, and they would be right. And the only counterfactual to it would be, ‘Well, trust us, guys, it should actually be much more open and in a far more distributed environment.’ Can you imagine the cost and the complexity if you ask the neoscaler to build the same robust KYC or the same VPC infrastructure that Amazon and Microsoft and Google have spent decades investing trillions of dollars in? It's an impossibility, Jason. So you can take all of those datacenters off the map. You can take all of the neoscaler market off the map. All of this was preventable. So instead of a diverse, robust, open ecosystem giving a tool that is the fundamental unlock for humans, we are now going to debate gatekeeping and duopoly versus oligopoly. They have created a total f*cking mess, and it's a shame.”

The All-In Podcast

142,502 views • 3 months ago

🚨 The Silvia team just announced our latest engineering advancement. Every business wants access to the highest level of intelligence, but at the lowest cost possible. The rise of LLMs has made intelligence abundant, yet one of the hardest problems across startups and corporate America is predicting the compute cost associated with this intelligence. I have been dealing with this personally as we build Silvia and the problem comes up in almost every conversation I have with CEOs, founders, and executives. Every business embraced AI about 18 months ago and things seemed great until the compute bills started to show up. The bills for internal compute usage were difficult to swallow, but things got outrageous if you had an AI product that allowed your users to consume compute without limits. I know this problem intimately because that is the situation that Silvia was in. Every question that was asked meant higher compute costs for our company. But we didn’t want to limit usage because users were getting genuine value out of the product. This challenge sent our team down a deep rabbit hole of cutting costs, while improving the experience for users. The second part was really important: we did not want to degrade the user experience by simply taking away access to the highest quality models. Thankfully, resource constraints breed innovation. We aren’t the biggest company, nor do we have the largest balance sheet, but we came up with a very novel solution that we are announcing today. The Silvia engineering team built a model router that cut costs by up to 29%, decreased latency, and improved the quality of answers for users. Trifecta! The way we do this is by reading the first 500 characters of a query and then predicting the level of effort that will be needed by a model to answer the query. The highest effort needs are routed to the most powerful models. The lowest effort needs are routed to different, better models for the query. A good example of this would be “what is the date?” You don’t need to use the latest Anthropic model to answer this query. In fact, sending a simple query like this to the most powerful model will make your compute costs increase and will actually increase the latency, which means a worse user experience for the Silvia user. By implementing the model router, the user gets a better experience and we get lower costs. Win-win. One of the interesting aspects of the implementation is that our model router runs on CPUs instead of GPUs. This allows us to read the query and predict the level of effort needed in less than 1 millisecond. This CPU implementation is why latency is not affected, nor is cost significantly increased by any potential additional GPU consumption. Another important point is that many of you have probably seen the news that OpenRouter is being purchased by Stripe for around $7 billion. This is a great outcome from what appears to be a very smart, capable team. Their model routing API is related (their product and our internal implementation both touch model routing), but you should think of OpenRouter as making it possible to do model routing for companies, while Silvia’s model router is a custom, intelligent system that specifically routes Silvia queries to the right model. They give access to the functionality of model routing to many companies, while our internal product does the real decision-making specific to our use case. Lastly, our implementation of a model router is a strategic bet that will allow us to become model-agnostic over time. We don’t care who created the different models, we just want to route a query to the model best positioned to answer. The large model labs will never allow their users to be model agnostic, but that would require the lab to potentially route a query to a competitor’s model. No bueno in their eyes. Instead, Silvia being an independent AI research lab gives us the power of being agnostic. We simply want the best experience for our users. Last week we announced that Silvia is now the most accurate AI tax product on the market, including beating OpenAI, Anthropic, Google, and xAI. Today we are announcing a custom, in-house model router that rivals the best technology anyone else has built. There will be many more engineering announcements to come. I truly believe we have assembled one of the best AI teams and we are currently the best AI research lab in finance. If you are interested in learning more about the technical details of the model router, you can read the engineering blog post here: Everyone wants the best intelligence and the lowest cost. Silvia just showed the world what is possible in this pursuit. I anticipate many other companies will build this custom solutions to achieve the same benefits.

🚨 The Silvia team just announced our latest engineering advancement. Every business wants access to the highest level of intelligence, but at the lowest cost possible. The rise of LLMs has made intelligence abundant, yet one of the hardest problems across startups and corporate America is predicting the compute cost associated with this intelligence. I have been dealing with this personally as we build Silvia and the problem comes up in almost every conversation I have with CEOs, founders, and executives. Every business embraced AI about 18 months ago and things seemed great until the compute bills started to show up. The bills for internal compute usage were difficult to swallow, but things got outrageous if you had an AI product that allowed your users to consume compute without limits. I know this problem intimately because that is the situation that Silvia was in. Every question that was asked meant higher compute costs for our company. But we didn’t want to limit usage because users were getting genuine value out of the product. This challenge sent our team down a deep rabbit hole of cutting costs, while improving the experience for users. The second part was really important: we did not want to degrade the user experience by simply taking away access to the highest quality models. Thankfully, resource constraints breed innovation. We aren’t the biggest company, nor do we have the largest balance sheet, but we came up with a very novel solution that we are announcing today. The Silvia engineering team built a model router that cut costs by up to 29%, decreased latency, and improved the quality of answers for users. Trifecta! The way we do this is by reading the first 500 characters of a query and then predicting the level of effort that will be needed by a model to answer the query. The highest effort needs are routed to the most powerful models. The lowest effort needs are routed to different, better models for the query. A good example of this would be “what is the date?” You don’t need to use the latest Anthropic model to answer this query. In fact, sending a simple query like this to the most powerful model will make your compute costs increase and will actually increase the latency, which means a worse user experience for the Silvia user. By implementing the model router, the user gets a better experience and we get lower costs. Win-win. One of the interesting aspects of the implementation is that our model router runs on CPUs instead of GPUs. This allows us to read the query and predict the level of effort needed in less than 1 millisecond. This CPU implementation is why latency is not affected, nor is cost significantly increased by any potential additional GPU consumption. Another important point is that many of you have probably seen the news that OpenRouter is being purchased by Stripe for around $7 billion. This is a great outcome from what appears to be a very smart, capable team. Their model routing API is related (their product and our internal implementation both touch model routing), but you should think of OpenRouter as making it possible to do model routing for companies, while Silvia’s model router is a custom, intelligent system that specifically routes Silvia queries to the right model. They give access to the functionality of model routing to many companies, while our internal product does the real decision-making specific to our use case. Lastly, our implementation of a model router is a strategic bet that will allow us to become model-agnostic over time. We don’t care who created the different models, we just want to route a query to the model best positioned to answer. The large model labs will never allow their users to be model agnostic, but that would require the lab to potentially route a query to a competitor’s model. No bueno in their eyes. Instead, Silvia being an independent AI research lab gives us the power of being agnostic. We simply want the best experience for our users. Last week we announced that Silvia is now the most accurate AI tax product on the market, including beating OpenAI, Anthropic, Google, and xAI. Today we are announcing a custom, in-house model router that rivals the best technology anyone else has built. There will be many more engineering announcements to come. I truly believe we have assembled one of the best AI teams and we are currently the best AI research lab in finance. If you are interested in learning more about the technical details of the model router, you can read the engineering blog post here: Everyone wants the best intelligence and the lowest cost. Silvia just showed the world what is possible in this pursuit. I anticipate many other companies will build this custom solutions to achieve the same benefits.

Anthony Pompliano 🌪

76,176 views • 1 month ago

i ran 1,000 agents across the last month of the internet looking for one thing: people who are already paying real money to do a job by hand 24 days later that list has paid me $104,513. the agents cost $4,100. the workers ran on Claude Sonnet 5, which had been out for two days when i started. the verifier ran on Fable 5, back online the day before that. cheap model doing the reading, expensive model doing the judging. none of that is the interesting part. the interesting part has a name, and it is called Graph Engineering: independent work runs side by side instead of in a queue, dependent work keeps its order, and nothing is allowed to grade its own homework. one agent asking one question at a time would still be reading today. wired as a graph, the same models did this: → 1,000 workers, 16 live at once, each handed its own slice: one forum, one thread, one review section. no worker ever saw another's work → every worker hunted one phrase shape: a person admitting what they already pay. "i pay someone to do this every morning." "we keep a guy just for this task." → a separate Fable 5 verifier picked up every hit on fresh context and killed anything without a name and a number attached → all of it collected into one file 1,940,000 discussions read. 41,812 hits. 3,147 survived the verifier. that verifier killed 92% of what the workers brought back, and that number is the entire business. people complain for free. the ones naming who they pay and how much are buyers. 14 hours from launch to one finished file. one agent doing the same work end to end needs 224 hours straight and forgets the beginning by the middle. 3,147 buyers collapsed into 63 repeating jobs. the most profitable one was so boring i almost deleted it on the first pass. 412 people paying a human $740 a month to do the same dull thing by hand. nobody had built for it because nobody wants to build it. i emailed all 412 before writing a line of code. 96 replied. 34 prepaid a year at $1,400. $47,600 in the bank before the product existed. i cost six times less than the person they were already paying, and that closed every call. built it in 11 days, for people who had already written me their own spec. then the remaining 3,147 got the email: 26 more prepaid a year, 97 signed monthly at $129, and four paid $2,000 each to have it fitted to their process. $47,600 + $36,400 + $12,513 + $8,000 = $104,513 take the Graph Engineering out and there is no story. one agent runs out of memory before it finishes 1.9m discussions. a searcher grading its own findings hands you 41,812 pieces of garbage. a thousand workers sharing one context overwrite each other by hour two. fan out where the work is independent. verify on fresh context. isolate every worker. three moves, 24 days, and a month of the internet becomes one file you can sell from. i wrote the whole method down: what a node is, how to spot the dependencies that were never real, and six graphs you can run this week. free ↓ bookmark this

i ran 1,000 agents across the last month of the internet looking for one thing: people who are already paying real money to do a job by hand 24 days later that list has paid me $104,513. the agents cost $4,100. the workers ran on Claude Sonnet 5, which had been out for two days when i started. the verifier ran on Fable 5, back online the day before that. cheap model doing the reading, expensive model doing the judging. none of that is the interesting part. the interesting part has a name, and it is called Graph Engineering: independent work runs side by side instead of in a queue, dependent work keeps its order, and nothing is allowed to grade its own homework. one agent asking one question at a time would still be reading today. wired as a graph, the same models did this: → 1,000 workers, 16 live at once, each handed its own slice: one forum, one thread, one review section. no worker ever saw another's work → every worker hunted one phrase shape: a person admitting what they already pay. "i pay someone to do this every morning." "we keep a guy just for this task." → a separate Fable 5 verifier picked up every hit on fresh context and killed anything without a name and a number attached → all of it collected into one file 1,940,000 discussions read. 41,812 hits. 3,147 survived the verifier. that verifier killed 92% of what the workers brought back, and that number is the entire business. people complain for free. the ones naming who they pay and how much are buyers. 14 hours from launch to one finished file. one agent doing the same work end to end needs 224 hours straight and forgets the beginning by the middle. 3,147 buyers collapsed into 63 repeating jobs. the most profitable one was so boring i almost deleted it on the first pass. 412 people paying a human $740 a month to do the same dull thing by hand. nobody had built for it because nobody wants to build it. i emailed all 412 before writing a line of code. 96 replied. 34 prepaid a year at $1,400. $47,600 in the bank before the product existed. i cost six times less than the person they were already paying, and that closed every call. built it in 11 days, for people who had already written me their own spec. then the remaining 3,147 got the email: 26 more prepaid a year, 97 signed monthly at $129, and four paid $2,000 each to have it fitted to their process. $47,600 + $36,400 + $12,513 + $8,000 = $104,513 take the Graph Engineering out and there is no story. one agent runs out of memory before it finishes 1.9m discussions. a searcher grading its own findings hands you 41,812 pieces of garbage. a thousand workers sharing one context overwrite each other by hour two. fan out where the work is independent. verify on fresh context. isolate every worker. three moves, 24 days, and a month of the internet becomes one file you can sell from. i wrote the whole method down: what a node is, how to spot the dependencies that were never real, and six graphs you can run this week. free ↓ bookmark this

Argona

19,695 views • 1 month ago

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

Ricardo

44,177 views • 1 month ago