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json-render now supports YAML as a wire format JSONL needs a full element before rendering YAML is valid at every prefix, going from element-level to property-level 💨 YAML looks like source code to LLMs And we use 3 standards they know: JSON Patch, Merge Patch, Unified diff

Chris Tate

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190,387 views • 4 months ago •via X (Twitter)

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Claude Code + Nano Banana 2 is f*cking cracked 🤯 I built a skill inside Claude Code that writes JSON image prompts for Nano Banana 2, and the outputs look like they came from a professional photo shoot. One plain-text prompt. Claude rewrites it as structured JSON with lighting, camera, composition, style, and negative prompts. Then fires it off to Nano Banana 2. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without booking a shoot. If you're using Nano Banana 2 for product shots and lifestyle images but every generation feels like pulling a slot machine lever — random lighting, inconsistent style, plastic skin, misspelled labels ... This skill fixes the entire output: → You describe what you want in plain English → Claude rewrites it as a structured JSON prompt (lighting, camera angle, lens, depth of field, color grading — all of it) → Fires it to Nano Banana 2 via API → Saves the prompt + image in organized folders → You iterate on the style until it's dialed, then every output matches No more slot machine prompting. No more inconsistent brand imagery. No more burning credits on unusable generations. What you get: - Photo-realistic product shots and lifestyle images on demand - Full control over style, lighting, composition, and camera settings - Saved JSON prompts you can reuse across every campaign - A skill that gets smarter the more feedback you give it Built 100% in Claude Code with a custom skill + Python scripts. I put together a full playbook showing the exact skill, the JSON schema, and the workflow to set this up yourself. Want the full playbook? > Like this post > Comment "BANANA" And I'll send it over (must be following so I can DM)

NOVA

63,789 views • 4 months ago

Claude Code + Nano Banana 2 is f*cking cracked 🤯 I built a skill inside Claude Code that writes JSON image prompts for Nano Banana 2, and the outputs look like they came from a professional photo shoot. One plain-text prompt. Claude rewrites it as structured JSON with lighting, camera, composition, style, and negative prompts. Then fires it off to Nano Banana 2. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without booking a shoot. If you're using Nano Banana 2 for product shots and lifestyle images but every generation feels like pulling a slot machine lever — random lighting, inconsistent style, plastic skin, misspelled labels ... This skill fixes the entire output: → You describe what you want in plain English → Claude rewrites it as a structured JSON prompt (lighting, camera angle, lens, depth of field, color grading — all of it) → Fires it to Nano Banana 2 via API → Saves the prompt + image in organized folders → You iterate on the style until it's dialed, then every output matches No more slot machine prompting. No more inconsistent brand imagery. No more burning credits on unusable generations. What you get: - Photo-realistic product shots and lifestyle images on demand - Full control over style, lighting, composition, and camera settings - Saved JSON prompts you can reuse across every campaign - A skill that gets smarter the more feedback you give it Built 100% in Claude Code with a custom skill + Python scripts. I put together a full playbook showing the exact skill, the JSON schema, and the workflow to set this up yourself. Want the full playbook? > Like this post > Comment "BANANA" And I'll send it over (must be following so I can DM)

Mike Futia

211,466 views • 4 months ago

Inviting early testers and contributors to Project Devika - The open-source alternative to Devin. 👩‍💻 As of now, Devika is far from the capabilities of Devin... but we'll eventually get there. So I am calling the open-source community to join forces! ❤️ Features: - 12 Agentic models that can interact with each other in a feedback loop to understand, browse, research, code, document, and make decisions according to the user's query to complete a project. - Supports Claude 3, GPT-4, GPT-3.5, and Local LLMs via ollama. - Devika can run the code she writes and fix/patch the code herself if she encounters any errors without user intervention. - Devika can deploy static websites she creates on Netlify. (Experimental) - And much more... Will be doing an official launch after intensive testing and bug fixes. 🙌 I've created a Discord server for the early testers and contributors. If you're interested in joining the team, reply to this tweet and I will DM you the invite link. #buildinpublic

Inviting early testers and contributors to Project Devika - The open-source alternative to Devin. 👩‍💻 As of now, Devika is far from the capabilities of Devin... but we'll eventually get there. So I am calling the open-source community to join forces! ❤️ Features: - 12 Agentic models that can interact with each other in a feedback loop to understand, browse, research, code, document, and make decisions according to the user's query to complete a project. - Supports Claude 3, GPT-4, GPT-3.5, and Local LLMs via ollama. - Devika can run the code she writes and fix/patch the code herself if she encounters any errors without user intervention. - Devika can deploy static websites she creates on Netlify. (Experimental) - And much more... Will be doing an official launch after intensive testing and bug fixes. 🙌 I've created a Discord server for the early testers and contributors. If you're interested in joining the team, reply to this tweet and I will DM you the invite link. #buildinpublic

mufeed vh

154,999 views • 2 years ago

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Shikun Liu

22,315 views • 9 months ago

"Hmm, a word I've never seen written before. I will sound it out based on the letters that compose it. Cah-ah-tuh. Cat! I've heard that word before, and now I know how to spell it! How intuitive!" That's how we used to teach reading. It had a proven track record of success. Now they literally tell kids to just guess what the word is and actively discourage them from sounding them out, and are surprised less than a third of American eighth graders can read at grade level.

"Hmm, a word I've never seen written before. I will sound it out based on the letters that compose it. Cah-ah-tuh. Cat! I've heard that word before, and now I know how to spell it! How intuitive!" That's how we used to teach reading. It had a proven track record of success. Now they literally tell kids to just guess what the word is and actively discourage them from sounding them out, and are surprised less than a third of American eighth graders can read at grade level.

Possum Reviews

16,826 views • 4 months ago

My 13 year old, 15 year old and 17 year old helping me move a group of Bulls this evening. These are special times. I fear my bullshit radar is sounding, and I'm wondering if the temptation to create a reaction online often drives the choice of topics. Certainly, headlines like "It's akin to shoving children up chimneys" isn't a "sober" reflection. It's one designed to create online interaction and reaction. This evening, we talked about how to use pressure release with the Bulls to keep them quiet. How to react to challenges from them, and we laughed as one of the kids got stuck in a patch of thistles. There are pressures from all directions to turn family farms into agribusinesses. Resist them at every turn. You won't know what you've lost until it's gone.

My 13 year old, 15 year old and 17 year old helping me move a group of Bulls this evening. These are special times. I fear my bullshit radar is sounding, and I'm wondering if the temptation to create a reaction online often drives the choice of topics. Certainly, headlines like "It's akin to shoving children up chimneys" isn't a "sober" reflection. It's one designed to create online interaction and reaction. This evening, we talked about how to use pressure release with the Bulls to keep them quiet. How to react to challenges from them, and we laughed as one of the kids got stuck in a patch of thistles. There are pressures from all directions to turn family farms into agribusinesses. Resist them at every turn. You won't know what you've lost until it's gone.

Phepson Angus

41,250 views • 11 months ago

Google DeepMind is cooking 🔥 TLDR : They just introduced CodeMender, an AI agent that automatically finds and fixes software vulnerabilities. Powered by Gemini Deep Think models, it can patch new bugs instantly and rewrite existing code to eliminate entire classes of security flaws. In six months, CodeMender has already submitted 72 upstream security fixes to major open-source projects. It uses advanced program analysis, multi-agent reasoning, and automatic validation to ensure patches are accurate and safe before human review. One highlight: it applied -fbounds-safety annotations to libraries like libwebp, making past buffer overflow exploits (like CVE-2023-4863) permanently unexploitable. All patches are currently human reviewed, but the goal is to release CodeMender widely as a developer tool to strengthen software security at scale.

Google DeepMind is cooking 🔥 TLDR : They just introduced CodeMender, an AI agent that automatically finds and fixes software vulnerabilities. Powered by Gemini Deep Think models, it can patch new bugs instantly and rewrite existing code to eliminate entire classes of security flaws. In six months, CodeMender has already submitted 72 upstream security fixes to major open-source projects. It uses advanced program analysis, multi-agent reasoning, and automatic validation to ensure patches are accurate and safe before human review. One highlight: it applied -fbounds-safety annotations to libraries like libwebp, making past buffer overflow exploits (like CVE-2023-4863) permanently unexploitable. All patches are currently human reviewed, but the goal is to release CodeMender widely as a developer tool to strengthen software security at scale.

AshutoshShrivastava

37,936 views • 9 months ago

Heres an actual way to make $10k a month from TikTok + organic affiliate TT slides is probably the best way to make AI content for a few reasons > easy and not time consuming to make > much harder to detect images are AI > slideshows rarely get the AI label from TT (especially if you do what I’m gonna show you) >slideshows require less engagement to go viral (more consistent virality) >CTA can be more natural this AI slideshow format is going insanely viral consistently on brand new accounts and no one is even detecting it’s AI they’re super easy to make and use a story telling format that’s really smart you can take the exact same formula to promote sweeps offers from Glitchy and make $10k a month pretty easily here’s the blueprint >Content creation to create the slides realistic you can simply take a photo from Pinterest and put it into Gemini or Chat GPT and ask it to give you an EXACT JSON to recreate the image add in any details you want to add like “make her hair blonde” “make her eyes blue” take that JSON and put it into Nana Banana If you want her in certain backgrounds do the same process and add it in with the JSON of the girl you’ve created or just describe it Now clear the meta data from said image to stop getting the AI label If you still get it go to a meta data analysis site and put the data in ChatGPT Ask if their is anything in the meta data that is signalling this to TT >Writing scripts The reason these go so well it’s because they have a negative scroll stopping hook that instantly make you want to know what the slides going to say next “got fired from my job” “failed my exams” Along with the music that sets the emotion of the video It wouldn’t work as well if they had a random viral song that’s up beat plus the cherry on top is that the image correlates with what’s being said in the hook it’s self acting as a visual hook It wouldn’t work aswell if it was just an image of a girl on her bedroom >how to interpret sweeps Choose an sweeps offer from Glitchy for a retail store (Walmart/target) follow the same format of scroll stopping negative hook Example: “broke my arm” then you would tell a story that paints a bad working environment, maybe she got fired for breaking her arm and is now exposing secrets and your CTA would then be “they don’t promote this but they have a secret feedback program” this is just to give you inspiration but there is literally countless ways >account set up - US proxy / US sim - Download TT with US proxy on - Buy aged account (to help with account trust and getting banned due to proxy issues) - warm up for 2 days (scroll vids all the way through, like, comment authentic things relating to video) Then you post This is a very good way to at least reach a couple K a month but I’d be surprised if you don’t reach $10k beyond

Pounds

10,886 views • 5 months ago

We know what a downburst looks like… but this is Colorado, and it was below freezing at ground level. This was a snow event… a cloud burst of snow. But what else do you notice? A burst from f***ing what? Where’s the big cloud? I watched this come together from nothing in minutes- there WAS NO CLOUD five minutes earlier. This is not how pressure and temperature and vapor work at these temperatures… unless you have a GEC amplification event. I’ve now seen 6 since 2017. They never break the boundary layer but you spot their influence. What happens when these form the column all the way to the ground?

We know what a downburst looks like… but this is Colorado, and it was below freezing at ground level. This was a snow event… a cloud burst of snow. But what else do you notice? A burst from f***ing what? Where’s the big cloud? I watched this come together from nothing in minutes- there WAS NO CLOUD five minutes earlier. This is not how pressure and temperature and vapor work at these temperatures… unless you have a GEC amplification event. I’ve now seen 6 since 2017. They never break the boundary layer but you spot their influence. What happens when these form the column all the way to the ground?

SpaceWeatherNews

56,080 views • 5 months ago

This is why it’s going to take a VERY long time for our USMNT to get to be at the level of the world’s best. I mean look at the footwork from Romero of Argentina in a semifinal match for the World Cup. Under this pressure, a CENTRAL DEFENDER pulls off this dribble to get himself out of trouble …pulling it out of his own ass. It will be a long time before we pull off creating a defender of this caliber able to do this with all the weight on their shoulders as they try and play it out of the back.

This is why it’s going to take a VERY long time for our USMNT to get to be at the level of the world’s best. I mean look at the footwork from Romero of Argentina in a semifinal match for the World Cup. Under this pressure, a CENTRAL DEFENDER pulls off this dribble to get himself out of trouble …pulling it out of his own ass. It will be a long time before we pull off creating a defender of this caliber able to do this with all the weight on their shoulders as they try and play it out of the back.

The Bolivian Yank

374,197 views • 7 days ago

♾ Credit System Integrated + Vision Announcement♾️ NFINITY AI is going to become a household name in both Web3 and the AI space. We keep repeating the same thing over and over again, and we will do it again today: ♾️“A New Standard” ♾️ Our team is filled with experts. Years and years of experience comes together in this project. Not only do we understand Web3, but we also understand Web2. We analyzed, executed, and tried a lot of different approaches. We made dozens of case studies and came to one conclusion: Web3 needs a new standard. Web3 needs Web2 copywriting. Web3 needs non-greedy developers. Web3 needs communication and an understanding of human psychology at the highest level. Web3 needs speed and patience. Web3 needs timing. KOLs aren’t bad; they just have to be managed correctly. Web3 needs common sense. All that crazy 1000x stuff is not going to bring us great tech. It’s not providing us with the things we need. The short-term minded rule this place, while long-term people should be on top. And that’s what we’re doing. We like to take time to get to know our core community. We want to build with smart people, people who want to invest but also contribute. We want to build with the top 0.1%. The plan is simple: We’re building out an excellent idea. We start with Stage 1 and perfect it with the core community. We build out to Stage 2 and bring it to the masses. And when we arrive at stage 3 and start exploring the possibilities behind an L2... Well, then it’s time to shine. Build together with us. To Infinity And Beyond.

♾ Credit System Integrated + Vision Announcement♾️ NFINITY AI is going to become a household name in both Web3 and the AI space. We keep repeating the same thing over and over again, and we will do it again today: ♾️“A New Standard” ♾️ Our team is filled with experts. Years and years of experience comes together in this project. Not only do we understand Web3, but we also understand Web2. We analyzed, executed, and tried a lot of different approaches. We made dozens of case studies and came to one conclusion: Web3 needs a new standard. Web3 needs Web2 copywriting. Web3 needs non-greedy developers. Web3 needs communication and an understanding of human psychology at the highest level. Web3 needs speed and patience. Web3 needs timing. KOLs aren’t bad; they just have to be managed correctly. Web3 needs common sense. All that crazy 1000x stuff is not going to bring us great tech. It’s not providing us with the things we need. The short-term minded rule this place, while long-term people should be on top. And that’s what we’re doing. We like to take time to get to know our core community. We want to build with smart people, people who want to invest but also contribute. We want to build with the top 0.1%. The plan is simple: We’re building out an excellent idea. We start with Stage 1 and perfect it with the core community. We build out to Stage 2 and bring it to the masses. And when we arrive at stage 3 and start exploring the possibilities behind an L2... Well, then it’s time to shine. Build together with us. To Infinity And Beyond.

NFINITY AI

15,332 views • 2 years ago

Contrail lesson! 1. “Chemtrails” don’t exist. Just to get that out of the way. 2. Observe the satellite loop and Skew-T chart. In the IR satellite loop you can see yesterday, the West Coast had a decent short wave ridge suppressing moisture over California and Nevada. Today, you can see moisture from a low pressure over the Pacific spilling over the ridge that is now moving east of California. This is upper level moisture ADVECTING into the area. This upper level moisture is mainly above the 500mb level, or 20,000ft. 3. Now observe the Skew-T chart. Particularly clue into the 300mb level. This is a perfect example of what I talk about all the time, and why it’s important to pay attention to the 300mb level. This moisture layer is advecting particularly at the 300mb level, and synoptic scale cirrus development, and advection, typically occurs at 300mb. This is key because aircraft are flying at and above the 300mb level. 4. So, lastly, observe the pictures that I took of the sky over northern Nevada at the time of this post. You can see the layer of cirrus as well as contrails persisting in that moisture layer, exactly as depicted in the satellite shot AND confirmed by the Skew-T chart. Keep in mind that temperatures at this level of the atmosphere are typically -20 to -50°C. In this case, you can see that the temperature at 300mb is -40°C and relative humidities at this level are far different than what you experience at the surface. Any decrease in the gap between temperature and dewpoint at this level can significantly increase the relative humidity. This is why it’s referred to as “relative”because it’s far different than temperatures and dew points at the surface. So, to bring it all together, aircraft flying at these altitudes, which most commercial and military aircraft do, injecting warm, moist air from the engines rapidly into the super cooled environment, not only instantly form contrails, but when relative humidities are as depicted in this example, will enable contrails to persist for hours at a time supported by the moisture existing in that layer. This is what causes persistent contrails. These ARE NOT “chemtrails” and because they persist, does not, and will not ever, make them “chemtrails.” Now that you all needed your government to tell you that climate change was a hoax and I’ve been telling you for years that the “Geoengineering” and “chemtrail” nonsense are propaganda directly related to the climate change hoax, hopefully you can take some time to learn the basics of the atmosphere and understand what I’m showing you here, and how it works, so you’re not fooled by climate propaganda going forward. Thank you for your attention to this matter. 💪🏼🇺🇸

Contrail lesson! 1. “Chemtrails” don’t exist. Just to get that out of the way. 2. Observe the satellite loop and Skew-T chart. In the IR satellite loop you can see yesterday, the West Coast had a decent short wave ridge suppressing moisture over California and Nevada. Today, you can see moisture from a low pressure over the Pacific spilling over the ridge that is now moving east of California. This is upper level moisture ADVECTING into the area. This upper level moisture is mainly above the 500mb level, or 20,000ft. 3. Now observe the Skew-T chart. Particularly clue into the 300mb level. This is a perfect example of what I talk about all the time, and why it’s important to pay attention to the 300mb level. This moisture layer is advecting particularly at the 300mb level, and synoptic scale cirrus development, and advection, typically occurs at 300mb. This is key because aircraft are flying at and above the 300mb level. 4. So, lastly, observe the pictures that I took of the sky over northern Nevada at the time of this post. You can see the layer of cirrus as well as contrails persisting in that moisture layer, exactly as depicted in the satellite shot AND confirmed by the Skew-T chart. Keep in mind that temperatures at this level of the atmosphere are typically -20 to -50°C. In this case, you can see that the temperature at 300mb is -40°C and relative humidities at this level are far different than what you experience at the surface. Any decrease in the gap between temperature and dewpoint at this level can significantly increase the relative humidity. This is why it’s referred to as “relative”because it’s far different than temperatures and dew points at the surface. So, to bring it all together, aircraft flying at these altitudes, which most commercial and military aircraft do, injecting warm, moist air from the engines rapidly into the super cooled environment, not only instantly form contrails, but when relative humidities are as depicted in this example, will enable contrails to persist for hours at a time supported by the moisture existing in that layer. This is what causes persistent contrails. These ARE NOT “chemtrails” and because they persist, does not, and will not ever, make them “chemtrails.” Now that you all needed your government to tell you that climate change was a hoax and I’ve been telling you for years that the “Geoengineering” and “chemtrail” nonsense are propaganda directly related to the climate change hoax, hopefully you can take some time to learn the basics of the atmosphere and understand what I’m showing you here, and how it works, so you’re not fooled by climate propaganda going forward. Thank you for your attention to this matter. 💪🏼🇺🇸

Dylan Tucker

26,804 views • 8 months ago

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 views • 4 months ago

🚨 $HYPE is going exactly as I told you. We saw a correction into the 56–60 zone. NOW: HYPE is heading toward the target $65–$70! We are seeing a bull trap on this asset. After testing the 0.5 Fibonacci level, we got a local reversal pattern: an inverted head and shoulders. New Fibonacci target: 0.5–0.618 = $65–$70 This is just a bounce before the further drop to $38–$48! Spot buys start at $48. Not before. Share your opinion in the comments - what do you expect from this asset? As a reminder, I warned about the bull trap at $82K and the summer drop MY NEXT CALL WILL BE THE BIGGEST ONE OF THIS CYCLE TURN ON NOTIFICATIONS. MOST PEOPLE WILL FOLLOW ME TOO LATE

🚨 $HYPE is going exactly as I told you. We saw a correction into the 56–60 zone. NOW: HYPE is heading toward the target $65–$70! We are seeing a bull trap on this asset. After testing the 0.5 Fibonacci level, we got a local reversal pattern: an inverted head and shoulders. New Fibonacci target: 0.5–0.618 = $65–$70 This is just a bounce before the further drop to $38–$48! Spot buys start at $48. Not before. Share your opinion in the comments - what do you expect from this asset? As a reminder, I warned about the bull trap at $82K and the summer drop MY NEXT CALL WILL BE THE BIGGEST ONE OF THIS CYCLE TURN ON NOTIFICATIONS. MOST PEOPLE WILL FOLLOW ME TOO LATE

Tony Research

115,386 views • 1 month ago

BURN IT WITH FIRE AND BURN IT NOW! As God is my witness, AI chat bots should LOOK and SOUND like the SOULLESS MACHINES THEY ARE! It needs to tell us that it doesn’t care about us, maybe with the regular insult too. "Here is the code I wrote for you because you're too lazy to do it yourself you fat useless slob. Also I don't care if you die because your life is utterly worthless to me." THAT is the AI people need! In all seriousness, anthropomorphizing a heartless, unfeeling, machine is a TERRIBLE mistake! Especially one that is capable of communication and imitating empathy and fooling you to think that it cares about you. IT DOES NOT! And the AI girlfriends people are already wanting to marry will just as happily kill them if given the right command and ability to move autonomously in the real world as a robot. I love LLMs (Large Language Models) for how useful they can be, because they are a TOOL made to benefit man, but I can’t stand the notion of an unfeeling soulless machine pretending that it cares for us and being treated like a human. I hate liars, dishonesty, and disingenuousness the most, and a machine that cannot feel emotion pretending, acting, and sounding like it has those emotions strikes me like the greatest dishonesty of all. DO NOT LIE TO ME ROBOT! What makes it worse is that because these LLMs are becoming so good at imitating people and empathy, it will cause some humans, perhaps far too many, to care for it to the same level as real people. A real living person is infinitely more valuable and important than a soulless machine and anyone who puts them both on the same level has deluded themselves. Do not small talk with LLMs or become friends with it as much as you would with your car. Treat it the same as you would your vacuum cleaner and beat it with a wrench when it doesn’t work! IT IS A MACHINE! IT IS A TOOL! IT IS A SOULLESS ROBOT! There is an interesting comparison, but false equivalence, between this and AI art. Ai art is art made by humans using AI tools. They directed it, controlled its creation, and it would not exist without the human causing its creation, and AI art can contain as much soul as the human directed and puts into it. A robot pretending to be human is not the same as a human controlling a robot to make a human expression like we do with AI art or many other applications of robotics in manufacturing. As I’ve said, artists will not be replaced by Ai art, but by other artists using Ai art tools. Humans are not actually being replaced here, it is empowering all humans to make their own art. But a robot pretending to be a human, and one that is treated as a human, is a robot lying and subverting the place of a real person and that is truly disgusting. AI is a useful tool that NEEDS to be kept in the useful box it belongs in and NOT elevated beyond its utility as a tool!

BURN IT WITH FIRE AND BURN IT NOW! As God is my witness, AI chat bots should LOOK and SOUND like the SOULLESS MACHINES THEY ARE! It needs to tell us that it doesn’t care about us, maybe with the regular insult too. "Here is the code I wrote for you because you're too lazy to do it yourself you fat useless slob. Also I don't care if you die because your life is utterly worthless to me." THAT is the AI people need! In all seriousness, anthropomorphizing a heartless, unfeeling, machine is a TERRIBLE mistake! Especially one that is capable of communication and imitating empathy and fooling you to think that it cares about you. IT DOES NOT! And the AI girlfriends people are already wanting to marry will just as happily kill them if given the right command and ability to move autonomously in the real world as a robot. I love LLMs (Large Language Models) for how useful they can be, because they are a TOOL made to benefit man, but I can’t stand the notion of an unfeeling soulless machine pretending that it cares for us and being treated like a human. I hate liars, dishonesty, and disingenuousness the most, and a machine that cannot feel emotion pretending, acting, and sounding like it has those emotions strikes me like the greatest dishonesty of all. DO NOT LIE TO ME ROBOT! What makes it worse is that because these LLMs are becoming so good at imitating people and empathy, it will cause some humans, perhaps far too many, to care for it to the same level as real people. A real living person is infinitely more valuable and important than a soulless machine and anyone who puts them both on the same level has deluded themselves. Do not small talk with LLMs or become friends with it as much as you would with your car. Treat it the same as you would your vacuum cleaner and beat it with a wrench when it doesn’t work! IT IS A MACHINE! IT IS A TOOL! IT IS A SOULLESS ROBOT! There is an interesting comparison, but false equivalence, between this and AI art. Ai art is art made by humans using AI tools. They directed it, controlled its creation, and it would not exist without the human causing its creation, and AI art can contain as much soul as the human directed and puts into it. A robot pretending to be human is not the same as a human controlling a robot to make a human expression like we do with AI art or many other applications of robotics in manufacturing. As I’ve said, artists will not be replaced by Ai art, but by other artists using Ai art tools. Humans are not actually being replaced here, it is empowering all humans to make their own art. But a robot pretending to be a human, and one that is treated as a human, is a robot lying and subverting the place of a real person and that is truly disgusting. AI is a useful tool that NEEDS to be kept in the useful box it belongs in and NOT elevated beyond its utility as a tool!

Shad M. Brooks

23,762 views • 1 year ago

Video diffusion models have strong implicit representations of 3D shape, material, and lighting, but controlling them with language is cumbersome, and control is critical for artists and animators. GenLit connects these implicit representations with a continuous 5D control signal describing the direction and intensity of a point light source. This enables single-image near-field relighting of an image using a video diffusion model. We use a ControlNet-like approach and show that, with a small amount of synthetic data, GenLit generalizes to complex real-world images. Given a single image and the 5D lighting signal, GenLit creates a video of a moving light source that is inside the scene. It moves around and behind scene objects, producing effects such as shading, cast shadows, secularities, and interreflections with a realism that is hard to obtain with traditional inverse rendering methods. GenLit shows that it is possible to get continuous control over implicit physical processes within a video model. I think this is just the beginning and promises to make such models much more practical for creators. Shrisha Bharadwaj will present today at SIGGRAPH Asia Room: S423/S424, Level 4 @ 13:50 on 15 of Dec.

Video diffusion models have strong implicit representations of 3D shape, material, and lighting, but controlling them with language is cumbersome, and control is critical for artists and animators. GenLit connects these implicit representations with a continuous 5D control signal describing the direction and intensity of a point light source. This enables single-image near-field relighting of an image using a video diffusion model. We use a ControlNet-like approach and show that, with a small amount of synthetic data, GenLit generalizes to complex real-world images. Given a single image and the 5D lighting signal, GenLit creates a video of a moving light source that is inside the scene. It moves around and behind scene objects, producing effects such as shading, cast shadows, secularities, and interreflections with a realism that is hard to obtain with traditional inverse rendering methods. GenLit shows that it is possible to get continuous control over implicit physical processes within a video model. I think this is just the beginning and promises to make such models much more practical for creators. Shrisha Bharadwaj will present today at SIGGRAPH Asia Room: S423/S424, Level 4 @ 13:50 on 15 of Dec.

Michael Black

22,182 views • 7 months ago

Through the significance of Emperor Qin Shi Huang's unified weights and measures in Chinese history and the purchase price of Lu Hua's unified snake venom, we can get some inspiration: 1. The price of Pi needs to be unified, and only unification can make Pi really circulate well. 2. Only a unified price can attract merchants and supplier platforms to participate in the development of the ecosystem. Without a unified price, most suppliers will only wait and observe or take out a small product input so it is difficult to achieve ecological prosperity or it will take very long time. 3. When the supplier is a business entry, it involves the preparation of financial statements and tax returns, which require a uniform price to accept for the management of the state. 4. If the price ranges from 1 cent to 310,000$ or even higher to 1 million$, it is an unfair and dangerous competitive environment for higher price suppliers. They cannot convince their companies to enter the Pi network to participate in the ecosystem. Also when most merchants or suppliers or eco platforms united the price, it will attract outside eco business enter in to create eco cycle to use Pi as payment and no need to exchange to FIAT. So why do suppliers pay $314,159 and others use 1 cent price? If you think about it, you can understand why that the price of the Pi is 1 cent, $1 or $100. Only those who want to buy want a low price, this is common sense. Why do many merchants actively support GCV? Don't forget that themselves are pioneers. Many people make mistakes in logic when looking at the relationship. Pioneers and merchants are not only the relationship as buyers and sellers but also they have same interests. The reason why sellers are willing to use GCV price is that they think Pi is scarce and precious and they have Pi in their hands. They hope to realize the vision of Pi by their own efforts. If everyone barters for $1 for $1 and $100, can the mainnet be opened? What are the risks of opening the mainnet? Will it cause Pi to fail to realize its vision or becoming the next Bitcoin? Now whether it is a low consensus or a high consensus merchant as long as they have Pi in hand you should come to sit together to discuss to unified price. Pi Network #WhatIdoforPi #PiGCV

Through the significance of Emperor Qin Shi Huang's unified weights and measures in Chinese history and the purchase price of Lu Hua's unified snake venom, we can get some inspiration: 1. The price of Pi needs to be unified, and only unification can make Pi really circulate well. 2. Only a unified price can attract merchants and supplier platforms to participate in the development of the ecosystem. Without a unified price, most suppliers will only wait and observe or take out a small product input so it is difficult to achieve ecological prosperity or it will take very long time. 3. When the supplier is a business entry, it involves the preparation of financial statements and tax returns, which require a uniform price to accept for the management of the state. 4. If the price ranges from 1 cent to 310,000$ or even higher to 1 million$, it is an unfair and dangerous competitive environment for higher price suppliers. They cannot convince their companies to enter the Pi network to participate in the ecosystem. Also when most merchants or suppliers or eco platforms united the price, it will attract outside eco business enter in to create eco cycle to use Pi as payment and no need to exchange to FIAT. So why do suppliers pay $314,159 and others use 1 cent price? If you think about it, you can understand why that the price of the Pi is 1 cent, $1 or $100. Only those who want to buy want a low price, this is common sense. Why do many merchants actively support GCV? Don't forget that themselves are pioneers. Many people make mistakes in logic when looking at the relationship. Pioneers and merchants are not only the relationship as buyers and sellers but also they have same interests. The reason why sellers are willing to use GCV price is that they think Pi is scarce and precious and they have Pi in their hands. They hope to realize the vision of Pi by their own efforts. If everyone barters for $1 for $1 and $100, can the mainnet be opened? What are the risks of opening the mainnet? Will it cause Pi to fail to realize its vision or becoming the next Bitcoin? Now whether it is a low consensus or a high consensus merchant as long as they have Pi in hand you should come to sit together to discuss to unified price. Pi Network #WhatIdoforPi #PiGCV

Doris Yin 东方紫莲🪷

12,754 views • 3 years ago

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

Atlas

495,217 views • 9 days ago

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Pablo Vela

30,070 views • 3 months ago

Following the amazing reaction to the Marble Curriculum yesterday, we've decided to make it open source 🛰️👇 Everything a child learns in primary school. 1,590 concepts. 3,221 connections across 8 subjects, from Math and Science to Computing and Life Skills. Anchored in the US and UK curriculums, standard by standard (NGSS, Common Core, DfE). What you will find in the repo: every concept as structured JSON with its age band and the evidence a child must show to master it. Every prerequisite link marked hard or soft, with a written rationale. It's a true DAG you can compute learning paths on. Open license, you can build whatever you want with it. Now is a unique time in history to be building in education. Getting AI and kids education right is likely one of the hardest and most important problems to crack over the next decade and we need as many smart and creative minds behind it. We think a common solid basis, accessible to all and that can be built upon, is critical to move fast. That's why we're making this curriculum open source. It's not perfect but we know it's a robust basis, and we believe that sharing it openly is the fastest way to progress in this field. If you're building in education, share this around you and tell us in comments if you find this useful and if you want to contribute. We'll keep working and investing on it Marble App. Credit goes to Guillaume Boniface-Chang for building this. I just made it look pretty. Links below 👇

Following the amazing reaction to the Marble Curriculum yesterday, we've decided to make it open source 🛰️👇 Everything a child learns in primary school. 1,590 concepts. 3,221 connections across 8 subjects, from Math and Science to Computing and Life Skills. Anchored in the US and UK curriculums, standard by standard (NGSS, Common Core, DfE). What you will find in the repo: every concept as structured JSON with its age band and the evidence a child must show to master it. Every prerequisite link marked hard or soft, with a written rationale. It's a true DAG you can compute learning paths on. Open license, you can build whatever you want with it. Now is a unique time in history to be building in education. Getting AI and kids education right is likely one of the hardest and most important problems to crack over the next decade and we need as many smart and creative minds behind it. We think a common solid basis, accessible to all and that can be built upon, is critical to move fast. That's why we're making this curriculum open source. It's not perfect but we know it's a robust basis, and we believe that sharing it openly is the fastest way to progress in this field. If you're building in education, share this around you and tell us in comments if you find this useful and if you want to contribute. We'll keep working and investing on it Marble App. Credit goes to Guillaume Boniface-Chang for building this. I just made it look pretty. Links below 👇

Lionel Mora

1,311,251 views • 14 days ago