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Introducing RL Environment Creator Skill Now any one can create RL environments $ npx skills add adithya-s-k/RL_Envs_101 > You can create environments across multiple frameworks like OpenEnv, OpenReward, Verifiers, NemoGym ... > the repo has live working examples of environments that your coding agent can reference > The skill...

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Adithya S K

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46,556 просмотров • 2 месяцев назад •via X (Twitter)

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LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.
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LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

elvis

31,213 просмотров • 3 месяцев назад

Excited to announce AI Research Skills - an open-source library of 82 specialized skills for AI coding tools. One command gives your agent expert knowledge in: → Model training & fine-tuning (TRL, Unsloth ...) → Distributed systems (DeepSpeed, FSDP ...) → Inference optimization (vLLM, TensorRT ...) → Agent building ? (Langchain, AutoGPT ...) Works with Claude Code, Cursor, Gemini CLI, Windsurf, and Codex with one click interactive installation @ npx orchestra-research/ai-research-skills If you found the ML paper writing skill useful, check out the comprehensive collection
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Excited to announce AI Research Skills - an open-source library of 82 specialized skills for AI coding tools. One command gives your agent expert knowledge in: → Model training & fine-tuning (TRL, Unsloth ...) → Distributed systems (DeepSpeed, FSDP ...) → Inference optimization (vLLM, TensorRT ...) → Agent building ? (Langchain, AutoGPT ...) Works with Claude Code, Cursor, Gemini CLI, Windsurf, and Codex with one click interactive installation @ npx orchestra-research/ai-research-skills If you found the ML paper writing skill useful, check out the comprehensive collection

Zechen Zhang

75,466 просмотров • 5 месяцев назад

The concept of creating an exact digital replica of the physical world has always fascinated me: environments that look and behave exactly like our everyday reality, precisely captured in the digital domain. This is the essence of 𝐖𝐨𝐫𝐥𝐝 𝐌𝐨𝐝𝐞𝐥𝐬, simulated realities indistinguishable from our own. Generating these models is the core mission behind what we are building at SpAItial AI. True World Models must capture both photorealistic appearance and underlying physics, spatially-consistent across the environment. For static scenes, current models already deliver impressive results, unlocking downstream applications from gaming to 3D design. However, the true frontier lies in modeling dynamics, which will enable the training of AI agents whose learned behaviors can bridge the sim-to-real gap, thus unlocking countless real-world applications.
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The concept of creating an exact digital replica of the physical world has always fascinated me: environments that look and behave exactly like our everyday reality, precisely captured in the digital domain. This is the essence of 𝐖𝐨𝐫𝐥𝐝 𝐌𝐨𝐝𝐞𝐥𝐬, simulated realities indistinguishable from our own. Generating these models is the core mission behind what we are building at SpAItial AI. True World Models must capture both photorealistic appearance and underlying physics, spatially-consistent across the environment. For static scenes, current models already deliver impressive results, unlocking downstream applications from gaming to 3D design. However, the true frontier lies in modeling dynamics, which will enable the training of AI agents whose learned behaviors can bridge the sim-to-real gap, thus unlocking countless real-world applications.

Matthias Niessner

21,586 просмотров • 5 месяцев назад

RL is painfully slow 😭 — bottlenecked by super-long CoT rollout. 🔭 Sparse attention should help, but naive sparse rollout hits a brutal efficiency–stability tradeoff: A tedious trial-and-error sparsity sweep for each dense policy is required before an actual RL run. 🐤Sparrow chirps no more pain! Introduce Sparrow: Sparse Rollout for stable and efficient long-context RL. Sparrow finds that: 💡As long as we keep the tail distribution mismatch throughout the sparse rollout above a critical threshold, the RL training will be stable. 💡Even cooler! Through comprehensive control studies of Qwen3-1.7B, 4B, 8B thinking models RL with 40K rollout max length, the critical threshold stays constant across model sizes. 💡Sparrow then finds the optimal dynamic sparse schedule to reach the threshold with minimal cost. 💡Sparrow's findings are empirically validated to generalize in Qwen3-14B, and hold on both Math and Coding RL. 🐤Sparrow empirically helps achieve 2.2× / 2.4× / 2.0× rollout speedup on Qwen3 1.7B / 4B / 8B thinking models, while keeping training stability over extended RL steps. We release the 🐤bird in the following formats. [1/n] Paper: Code: Blog:
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RL is painfully slow 😭 — bottlenecked by super-long CoT rollout. 🔭 Sparse attention should help, but naive sparse rollout hits a brutal efficiency–stability tradeoff: A tedious trial-and-error sparsity sweep for each dense policy is required before an actual RL run. 🐤Sparrow chirps no more pain! Introduce Sparrow: Sparse Rollout for stable and efficient long-context RL. Sparrow finds that: 💡As long as we keep the tail distribution mismatch throughout the sparse rollout above a critical threshold, the RL training will be stable. 💡Even cooler! Through comprehensive control studies of Qwen3-1.7B, 4B, 8B thinking models RL with 40K rollout max length, the critical threshold stays constant across model sizes. 💡Sparrow then finds the optimal dynamic sparse schedule to reach the threshold with minimal cost. 💡Sparrow's findings are empirically validated to generalize in Qwen3-14B, and hold on both Math and Coding RL. 🐤Sparrow empirically helps achieve 2.2× / 2.4× / 2.0× rollout speedup on Qwen3 1.7B / 4B / 8B thinking models, while keeping training stability over extended RL steps. We release the 🐤bird in the following formats. [1/n] Paper: Code: Blog:

Infini-AI-Lab

78,080 просмотров • 1 месяц назад

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.
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We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

elvis

47,678 просмотров • 3 месяцев назад

GPT-5.5 by Reasoning Effort: I've asked it in Codex to create a physics-based visualisation of RL cycles for different sized models (70b, 1t, 10t), to demonstrate how the amount of RL you can do differs by model size. My assessment of each: - Low: weird slop - Medium: kinda cooked - High: sort of tried but ultimately incoherent - Extra High: elite - really nice idea and well executed Obviously this is just one shot, but worth trying different reasoning levels for the new models, medium seems to be pretty good for GPT-5.5 and it was really bad for many previous GPT models.
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GPT-5.5 by Reasoning Effort: I've asked it in Codex to create a physics-based visualisation of RL cycles for different sized models (70b, 1t, 10t), to demonstrate how the amount of RL you can do differs by model size. My assessment of each: - Low: weird slop - Medium: kinda cooked - High: sort of tried but ultimately incoherent - Extra High: elite - really nice idea and well executed Obviously this is just one shot, but worth trying different reasoning levels for the new models, medium seems to be pretty good for GPT-5.5 and it was really bad for many previous GPT models.

Peter Gostev (SF: 22-26 June)

209,258 просмотров • 3 месяцев назад

NEW HTML VIDEO SKILL: Claude can now make photo-grid promo video ads like these. These are very popular offer-style ads that work well for bottom-of-funnel conversions. Brands will often run these when they're doing seasonal sales, for example. It's pure HTML, so it's very cheap to make because there's no video generation cost. You can create dozens of variants very fast and then test them in Meta. All you have to do is install the skills and give Claude your brand website. This skill is insane - it'll make the entire Ad and give you a link where you can download the mp4 file. > If you want more variants, just mention in the prompt > If you have a specific concept in mind, just mention in the prompt > If you want any edits on the generated ad, just mention in the prompt Comment Goose and I'll DM you the skill (must be following so I can DM you)
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NEW HTML VIDEO SKILL: Claude can now make photo-grid promo video ads like these. These are very popular offer-style ads that work well for bottom-of-funnel conversions. Brands will often run these when they're doing seasonal sales, for example. It's pure HTML, so it's very cheap to make because there's no video generation cost. You can create dozens of variants very fast and then test them in Meta. All you have to do is install the skills and give Claude your brand website. This skill is insane - it'll make the entire Ad and give you a link where you can download the mp4 file. > If you want more variants, just mention in the prompt > If you have a specific concept in mind, just mention in the prompt > If you want any edits on the generated ad, just mention in the prompt Comment Goose and I'll DM you the skill (must be following so I can DM you)

Shiv

91,036 просмотров • 10 дней назад

Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.
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Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Google AI

22,487 просмотров • 8 месяцев назад

Figure is aiming to develop the world’s largest and most diverse real-world humanoid pretraining dataset. For this purpose, they’re partnering with Brookfield, a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. The data collected from this collaboration will be used to train Figure’s Helix AI model, enabling humanoids to perform tasks autonomously in real-world environments designed for humans. In addition to data collection, the partnership will explore support for next-generation GPU data centers, real estate for robotic training environments, and commercial use cases across Brookfield’s global footprint.
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Figure is aiming to develop the world’s largest and most diverse real-world humanoid pretraining dataset. For this purpose, they’re partnering with Brookfield, a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. The data collected from this collaboration will be used to train Figure’s Helix AI model, enabling humanoids to perform tasks autonomously in real-world environments designed for humans. In addition to data collection, the partnership will explore support for next-generation GPU data centers, real estate for robotic training environments, and commercial use cases across Brookfield’s global footprint.

The Humanoid Hub

88,600 просмотров • 10 месяцев назад

In the past week, I was able to build the following games: - An endless runner - A tower defense - A Mario Party-like mini-game - An arena-based shooter The key unlock here was the ThreeJS skill With Opus 4.5 & GPT 5.2 - Claude Code / Codex CLI have much better end results now. With each game, I continued to update the skill based on bugs and issues faced - so it progressively got better and better at building the gameplay I was looking for. I've been doing this for a while now - and I can say that the leap in building games with vibe coding is real.
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In the past week, I was able to build the following games: - An endless runner - A tower defense - A Mario Party-like mini-game - An arena-based shooter The key unlock here was the ThreeJS skill With Opus 4.5 & GPT 5.2 - Claude Code / Codex CLI have much better end results now. With each game, I continued to update the skill based on bugs and issues faced - so it progressively got better and better at building the gameplay I was looking for. I've been doing this for a while now - and I can say that the leap in building games with vibe coding is real.

Chong-U

264,966 просмотров • 6 месяцев назад

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!
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OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

171,340 просмотров • 1 год назад

Tried something new today. I made a character based environment and color direction board. First extracting the character’s palette, then translating it into an environment palette, and finally mapping where those colors should appear on the background, midground, foreground, lighting, shadows, and accents. Hard to tell if it’s fully working in this clip, and this might not be the best example, so I’ll test it again with a different style and a longer video, perhaps. This is just me overcomplicating things, 100% not needed. You can get same results with simple prompts and random environments. Prompts below.
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Tried something new today. I made a character based environment and color direction board. First extracting the character’s palette, then translating it into an environment palette, and finally mapping where those colors should appear on the background, midground, foreground, lighting, shadows, and accents. Hard to tell if it’s fully working in this clip, and this might not be the best example, so I’ll test it again with a different style and a longer video, perhaps. This is just me overcomplicating things, 100% not needed. You can get same results with simple prompts and random environments. Prompts below.

Glitter Gal

23,724 просмотров • 2 месяцев назад

more frontend vibecoding tips (results below): WHY YOUR VIBECODED FRONTENDS ALL LOOK THE SAME AND SUCK: when asked to make a frontend, the agent/llm will default to the center/average of its training data (in a very loose sense). through the training process, the model essentially converges on some default UI style. it's very capable of doing things that are different from this style, but you have to ask! for instance, ChatGPT tends to reply in the same tone for all users untill you interact with it and instruct it differently ("be sassy", "eli5"). the second reason is that most of us are not good at coming up with designs and describing them precisely (see my tweet on a crash course in common components, which i'll link below). treat frontend generation just like any other eng task! you need to provide a good detailed spec. TIPS: 1. give ur agent screenshots of designs you like (you may not know the right words to describe them but the agent will! a pic = 1000 words) where to find ui inspo? Behance, Dribbble, Mobbin (Mobbin is paid but worth it!) 2. ask ur agent for proposals, this helps "seed" different directions so the final frontend stands out. don't be afraid to go back and forth. 3. ban certain tendencies: no Inter/Roboto, no shadcn (controversial), no gradients, no emojis 4. encourage the agent to be extreme and make bold decisions, not safe ones. i think that the underlying models tend to get taught during RL/fine-tuning to make conservative choices that produce reasonable but boring frontends 5. give ur agent Figma MCP. the best results will come if you mockup your vision in Figma first. 6. Ideally choose an agent with vision capabilities TLDR: Most people are tremendously underusing agents for frontend design. They are much better than you might expect.
0:12

more frontend vibecoding tips (results below): WHY YOUR VIBECODED FRONTENDS ALL LOOK THE SAME AND SUCK: when asked to make a frontend, the agent/llm will default to the center/average of its training data (in a very loose sense). through the training process, the model essentially converges on some default UI style. it's very capable of doing things that are different from this style, but you have to ask! for instance, ChatGPT tends to reply in the same tone for all users untill you interact with it and instruct it differently ("be sassy", "eli5"). the second reason is that most of us are not good at coming up with designs and describing them precisely (see my tweet on a crash course in common components, which i'll link below). treat frontend generation just like any other eng task! you need to provide a good detailed spec. TIPS: 1. give ur agent screenshots of designs you like (you may not know the right words to describe them but the agent will! a pic = 1000 words) where to find ui inspo? Behance, Dribbble, Mobbin (Mobbin is paid but worth it!) 2. ask ur agent for proposals, this helps "seed" different directions so the final frontend stands out. don't be afraid to go back and forth. 3. ban certain tendencies: no Inter/Roboto, no shadcn (controversial), no gradients, no emojis 4. encourage the agent to be extreme and make bold decisions, not safe ones. i think that the underlying models tend to get taught during RL/fine-tuning to make conservative choices that produce reasonable but boring frontends 5. give ur agent Figma MCP. the best results will come if you mockup your vision in Figma first. 6. Ideally choose an agent with vision capabilities TLDR: Most people are tremendously underusing agents for frontend design. They are much better than you might expect.

andrew gao

64,212 просмотров • 4 месяцев назад

Sharing my new skill! It keeps track of high-signal X accounts for top AI news, papers, projects, etc. Total gamechanger for me. Built with X MCP tools. Give your agent the skill and tell it to generate the artifact with top stories. Works for Codex, Claude, Hermes, OpenClaw, or whatever you use. 3 steps: 1. Set up X MCP - X API: 2. Install skill here: 3. Run prompt: "Use the x-agent-intelligence skill to build a self-contained local feed from my X MCP connection; ask for my source handles if needed, save feed.html, and validate it." It should generate a nice, beautiful HTML artifact like the one shown in the clip. You can tune it however you want. You can then set a schedule/automation to do this daily or whatever cadence you prefer. I have it every 4 hours. You will need to curate the X accounts yourself, but I have shared a few good ones under the assets. You can ask your agent to tune it to however you like. I have also shared my personal feed with our community here: I understand if it gets tricky to set up. Please reach out to me in the community forum. I plan to do a little tutorial or live session soon to help others reproduce the process. You can also store the feed as a wiki, as I have in my own implementation, but that's optional. If you encounter any issues or have ideas on how to improve it, please open a PR.
0:10

Sharing my new skill! It keeps track of high-signal X accounts for top AI news, papers, projects, etc. Total gamechanger for me. Built with X MCP tools. Give your agent the skill and tell it to generate the artifact with top stories. Works for Codex, Claude, Hermes, OpenClaw, or whatever you use. 3 steps: 1. Set up X MCP - X API: 2. Install skill here: 3. Run prompt: "Use the x-agent-intelligence skill to build a self-contained local feed from my X MCP connection; ask for my source handles if needed, save feed.html, and validate it." It should generate a nice, beautiful HTML artifact like the one shown in the clip. You can tune it however you want. You can then set a schedule/automation to do this daily or whatever cadence you prefer. I have it every 4 hours. You will need to curate the X accounts yourself, but I have shared a few good ones under the assets. You can ask your agent to tune it to however you like. I have also shared my personal feed with our community here: I understand if it gets tricky to set up. Please reach out to me in the community forum. I plan to do a little tutorial or live session soon to help others reproduce the process. You can also store the feed as a wiki, as I have in my own implementation, but that's optional. If you encounter any issues or have ideas on how to improve it, please open a PR.

elvis

70,163 просмотров • 8 дней назад

Starting a new project today, building an end to end system to forecast traffic of flights across cities (starting with Mumbai) The idea is to implement > ingestion service with kafka > data etl with polars > feast for feature store // mlflow for model registry > batch inferencing // dashboard > s3 // postgres for data storage All this orchestrated across multiple DAGs built with Airflow. This year has just been Agents and LLMs all along. Not a bad idea to keep revisiting the traditional format :) Will be posting more of this in the coming days, stay tuned
0:14

Starting a new project today, building an end to end system to forecast traffic of flights across cities (starting with Mumbai) The idea is to implement > ingestion service with kafka > data etl with polars > feast for feature store // mlflow for model registry > batch inferencing // dashboard > s3 // postgres for data storage All this orchestrated across multiple DAGs built with Airflow. This year has just been Agents and LLMs all along. Not a bad idea to keep revisiting the traditional format :) Will be posting more of this in the coming days, stay tuned

Aarno

19,481 просмотров • 7 месяцев назад

The Bitcoin Bookshelf is the corner of the library where people from all backgrounds can come to discover what Bitcoin is. By taking out the intimidation, we will create an environment for us to strengthen the code, the software, and the network behind it. We have created a place for discovery, for families and friends to learn together and teach one another about the benefits of a decentralized future. I want to thank all the sponsors that helped make this dream a reality, and all the authors who donated all of the books for this project. We are working hard to bringing a bookshelf to all corners of the world, a dedicated section in all libraries so everyone can have access to financial freedom.
0:10

The Bitcoin Bookshelf is the corner of the library where people from all backgrounds can come to discover what Bitcoin is. By taking out the intimidation, we will create an environment for us to strengthen the code, the software, and the network behind it. We have created a place for discovery, for families and friends to learn together and teach one another about the benefits of a decentralized future. I want to thank all the sponsors that helped make this dream a reality, and all the authors who donated all of the books for this project. We are working hard to bringing a bookshelf to all corners of the world, a dedicated section in all libraries so everyone can have access to financial freedom.

Alejandra Guajardo | Miss Bitcoin

12,764 просмотров • 1 год назад

Stop relying on generic AI output Train a model on your world, your character, your style, and reuse it forever That is the idea behind LTX Trainer, an open-source training framework that lets you train reusable IC-LoRAs for specific video transformations like colorization, deblur, decompression, reference sheet control, and a lot more Each LoRA gives the model a specific behavior it can repeat across clips, so your videos become more consistent, more controlled, and more connected to your own creative direction Check these amazing examples 👇🏻
0:10

Stop relying on generic AI output Train a model on your world, your character, your style, and reuse it forever That is the idea behind LTX Trainer, an open-source training framework that lets you train reusable IC-LoRAs for specific video transformations like colorization, deblur, decompression, reference sheet control, and a lot more Each LoRA gives the model a specific behavior it can repeat across clips, so your videos become more consistent, more controlled, and more connected to your own creative direction Check these amazing examples 👇🏻

Amira Zairi

28,574 просмотров • 1 месяц назад

Today's real crypto news killed me in a video game 💀 This is Crypto Crash. I built it this afternoon with the ChainGPT AI skill for Claude Code. It's a Chrome dino-style runner, but every system in it is plugged into a live source. The ground you run on is BTC's actual 24-hour price chart. Hills are the pumps. Valleys are the dumps. When the market is bearish, you literally run downhill toward the FUD. The sky and the world's color palette flip based on the market's emotional state, scored 0 to 100 by the ChainGPT LLM reading today's headlines. Anxious days look like an orange storm. Euphoric days look like a parade. The obstacles are goblins, ghouls, wolves and a flying bird. Each one carries a real bearish headline pulled live from the ChainGPT News API. When one hits you, the game-over screen tells you exactly which piece of FUD ended your run. Three ChainGPT capabilities, woven into a single experience: the LLM, the News API, and live price data. The skill stitched them together in a single afternoon. I just had the idea. Here's what's interesting beyond the game itself. Web3 products have always had access to live data. What's new is that AI can now turn that data into experiences, environments and feedback loops on demand, with one prompt. ✅ A trading dashboard that gets more aggressive when fear spikes. ✅ An NFT marketplace whose homepage matches today's mood. ✅A token site that visibly reacts when its chain is under attack. ✅A streamer overlay that changes with every breaking headline. The plumbing is done. The hard part now is deciding what you build on top of it. Open Claude Code. The skill is one install away. /plugin install ChainGPT-org/chaingpt-claude-skill
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Today's real crypto news killed me in a video game 💀 This is Crypto Crash. I built it this afternoon with the ChainGPT AI skill for Claude Code. It's a Chrome dino-style runner, but every system in it is plugged into a live source. The ground you run on is BTC's actual 24-hour price chart. Hills are the pumps. Valleys are the dumps. When the market is bearish, you literally run downhill toward the FUD. The sky and the world's color palette flip based on the market's emotional state, scored 0 to 100 by the ChainGPT LLM reading today's headlines. Anxious days look like an orange storm. Euphoric days look like a parade. The obstacles are goblins, ghouls, wolves and a flying bird. Each one carries a real bearish headline pulled live from the ChainGPT News API. When one hits you, the game-over screen tells you exactly which piece of FUD ended your run. Three ChainGPT capabilities, woven into a single experience: the LLM, the News API, and live price data. The skill stitched them together in a single afternoon. I just had the idea. Here's what's interesting beyond the game itself. Web3 products have always had access to live data. What's new is that AI can now turn that data into experiences, environments and feedback loops on demand, with one prompt. ✅ A trading dashboard that gets more aggressive when fear spikes. ✅ An NFT marketplace whose homepage matches today's mood. ✅A token site that visibly reacts when its chain is under attack. ✅A streamer overlay that changes with every breaking headline. The plumbing is done. The hard part now is deciding what you build on top of it. Open Claude Code. The skill is one install away. /plugin install ChainGPT-org/chaingpt-claude-skill

ChainGPT

25,510 просмотров • 1 месяц назад

At Uber a big problem for design teams and engineering teams was design source of truth. No one knew what the app truly looked like, so weekly a bunch of designers would get into a meeting room and check that the engineers correctly implemented the figma designs that they made Now with coding agents throughput of changes has increased an order of magnitude and it has become impossible to manually keep up. Here we used the Revyl CLI to create a flow of every state in the Uber design, by navigating our mobile use agent on an cloud iOS simulator. This is something that would have taken a team of designers tens of hours to recreate manually; All done in less than an hour asynchronously with a simple prompt. Enable your team to know what your users are actually seeing and empower coding agents to give your users a delightful experience without any blindspots Get started with our new free trial and create a map for your own app 🗺️
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At Uber a big problem for design teams and engineering teams was design source of truth. No one knew what the app truly looked like, so weekly a bunch of designers would get into a meeting room and check that the engineers correctly implemented the figma designs that they made Now with coding agents throughput of changes has increased an order of magnitude and it has become impossible to manually keep up. Here we used the Revyl CLI to create a flow of every state in the Uber design, by navigating our mobile use agent on an cloud iOS simulator. This is something that would have taken a team of designers tens of hours to recreate manually; All done in less than an hour asynchronously with a simple prompt. Enable your team to know what your users are actually seeing and empower coding agents to give your users a delightful experience without any blindspots Get started with our new free trial and create a map for your own app 🗺️

Anam Hira

96,372 просмотров • 3 месяцев назад

Elon Musk just made every skill you’ve ever earned sound like a waste of time. Musk: “Down the road with a Neuralink, you can just upload any subject instantly. You wanna fly a helicopter? No problem. Any given skill, you just upload it instantly.” Not faster learning. Not better education. Instant upload. The surgeon who spent 12 years learning to cut. The pilot who logged 5,000 hours learning to fly. The attorney who gave a decade to case law. Their entire advantage erased in a software update. We built civilization on one assumption. That knowledge is earned through suffering. That the distance between who you are and who you want to be is measured in discipline and years. Neuralink doesn’t close that distance. It deletes it. And what that kills isn’t employment. It’s identity. We don’t just use skills. We become them. Ask a surgeon who they are. They don’t say “I work in medicine.” They say surgeon. Ask a pilot. They say pilot. The identity was never the skill itself. It was the cost of acquiring it. If everyone can upload surgery in seconds, no one is a surgeon anymore. The skill still exists. The meaning behind it doesn’t. For centuries we told ourselves that mastery is what builds character. That the hardest thing you ever earned is the closest thing to purpose you’ll ever find. Neuralink doesn’t threaten your career. It threatens the story you tell yourself about why your life matters. The question nobody wants to sit with isn’t whether Musk can build this. It’s who you are when the thing that took you 20 years to become can be downloaded in 20 seconds.
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Elon Musk just made every skill you’ve ever earned sound like a waste of time. Musk: “Down the road with a Neuralink, you can just upload any subject instantly. You wanna fly a helicopter? No problem. Any given skill, you just upload it instantly.” Not faster learning. Not better education. Instant upload. The surgeon who spent 12 years learning to cut. The pilot who logged 5,000 hours learning to fly. The attorney who gave a decade to case law. Their entire advantage erased in a software update. We built civilization on one assumption. That knowledge is earned through suffering. That the distance between who you are and who you want to be is measured in discipline and years. Neuralink doesn’t close that distance. It deletes it. And what that kills isn’t employment. It’s identity. We don’t just use skills. We become them. Ask a surgeon who they are. They don’t say “I work in medicine.” They say surgeon. Ask a pilot. They say pilot. The identity was never the skill itself. It was the cost of acquiring it. If everyone can upload surgery in seconds, no one is a surgeon anymore. The skill still exists. The meaning behind it doesn’t. For centuries we told ourselves that mastery is what builds character. That the hardest thing you ever earned is the closest thing to purpose you’ll ever find. Neuralink doesn’t threaten your career. It threatens the story you tell yourself about why your life matters. The question nobody wants to sit with isn’t whether Musk can build this. It’s who you are when the thing that took you 20 years to become can be downloaded in 20 seconds.

Dustin

39,062 просмотров • 2 месяцев назад