正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

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... is design to first understand what type of model you are training and create an environment while keeping that in mind ps. There’s a lot more to building RL environments that can be used for training. One major aspect is the data, which this skill can’t directly solve. However, the skill will help with implementing tools, rewards, and other components of an RL environment, making it easier to go from idea to implementation quickly across different frameworks. Let me know if you’d be interested in a detailed, end-to-end blog/tutorial on building an environment and actually training a model for a useful use case.show more

Adithya S K

13,598 subscribers

46,556 次观看 • 1 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

A Severance Vision Pro Environment was featured in a WWDC25 developer session Don't expect it to make it to the Environment tab; it's just being used as a example for environments that sites can now provide via the web.

A Severance Vision Pro Environment was featured in a WWDC25 developer session Don't expect it to make it to the Environment tab; it's just being used as a example for environments that sites can now provide via the web.

M1

44,425 次观看 • 1 年前

🚨 New: Integrating Harbor (Harbor Framework) for end-to-end Computer-Use evaluation(for Windows and Linux) at scale with Thinking Machines' Tinker, OSWorld, Daytona, and bare-metal servers. We just added support for Computer Use, Tinker, and OSWorld to Harbor - a framework for evaluating agents and generating RL training data by running large-scale rollouts across parallel sandboxed environments and collecting trajectories for SFT and RL. Repo and blogpost below 👇

🚨 New: Integrating Harbor (Harbor Framework) for end-to-end Computer-Use evaluation(for Windows and Linux) at scale with Thinking Machines' Tinker, OSWorld, Daytona, and bare-metal servers. We just added support for Computer Use, Tinker, and OSWorld to Harbor - a framework for evaluating agents and generating RL training data by running large-scale rollouts across parallel sandboxed environments and collecting trajectories for SFT and RL. Repo and blogpost below 👇

Marco Mascorro

19,448 次观看 • 3 个月前

Hi everyone! My class “A-Z of NPR 3D Environment in Blender” is now up for pre-order on Coloso! (Coloso Global) I will guide you through the essential skills in Blender and show you how to create stylized environments! Link: #coloso #Blender

Hi everyone! My class “A-Z of NPR 3D Environment in Blender” is now up for pre-order on Coloso! (Coloso Global) I will guide you through the essential skills in Blender and show you how to create stylized environments! Link: #coloso #Blender

Tawan Sunflower

16,828 次观看 • 11 个月前

The AgiBot X2 Ultra humanoid, only on Dimensional Full SLAM, navigation, multi-camera perception, hosted teleoperation, RL training environments, and an agent-native control interface Developers on DimOS are shipping hundreds of humanoids into production today Get building 🦾

The AgiBot X2 Ultra humanoid, only on Dimensional Full SLAM, navigation, multi-camera perception, hosted teleoperation, RL training environments, and an agent-native control interface Developers on DimOS are shipping hundreds of humanoids into production today Get building 🦾

stash

147,089 次观看 • 1 个月前

Did you know that GlobalEye can monitor air, land and sea at the same time? By combining multiple sensors into a single platform, it creates a real-time view of the operational environment — even in complex or jamming environments. GlobalEye creates time to act.

Did you know that GlobalEye can monitor air, land and sea at the same time? By combining multiple sensors into a single platform, it creates a real-time view of the operational environment — even in complex or jamming environments. GlobalEye creates time to act.

Saab

21,431 次观看 • 3 个月前

Today we announced Workshop – a solution for launching sandboxed development environments in Ubuntu with a single command. Spend less time on configuring your environment: with just a few lines of YAML, you’ve got a reproducible environment you can use across machines. Find out how it works at:

Today we announced Workshop – a solution for launching sandboxed development environments in Ubuntu with a single command. Spend less time on configuring your environment: with just a few lines of YAML, you’ve got a reproducible environment you can use across machines. Find out how it works at:

Ubuntu

138,570 次观看 • 1 个月前

if you're making 2D pixel art games for #vibejam this is solid. generate a sprite, create an end frame, prompt the animation between them worth checking. you can use with game creator skill design your game with placeholder wireframes using your agent, replace with final sprites later

if you're making 2D pixel art games for #vibejam this is solid. generate a sprite, create an end frame, prompt the animation between them worth checking. you can use with game creator skill design your game with placeholder wireframes using your agent, replace with final sprites later

Playable Intelligence

11,942 次观看 • 2 个月前

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.

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

31,141 次观看 • 2 个月前

that’s it. you only have to read this one article to: > learn claude skill foundations > learn claude skill architecture > learn claude skill testing > learn claude skill production which will let you create claude skills that: 1: automate your workflow’s 2: make you more productive 3: outperform your competitors welcome to the land of automation my claude cowork and claude code friends!

that’s it. you only have to read this one article to: > learn claude skill foundations > learn claude skill architecture > learn claude skill testing > learn claude skill production which will let you create claude skills that: 1: automate your workflow’s 2: make you more productive 3: outperform your competitors welcome to the land of automation my claude cowork and claude code friends!

hoeem

508,155 次观看 • 3 个月前

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

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 次观看 • 5 个月前

🚨Current scalable RL algos train a policy w/o value func, which is limiting with learning in open-ended, non-stationary, dynamic environments. But, how to scale value-based RL with more data/compute is unclear... Not anymore: presenting scaling laws for value-based RL 🧵⬇️

🚨Current scalable RL algos train a policy w/o value func, which is limiting with learning in open-ended, non-stationary, dynamic environments. But, how to scale value-based RL with more data/compute is unclear... Not anymore: presenting scaling laws for value-based RL 🧵⬇️

Aviral Kumar

37,301 次观看 • 1 年前

This is a complete game-changer. This agent skills marketplace has over a MILLION ready-to-use agent skills and plug-ins. Just search the skill type you want to deploy, and watch hundreds of skill files appear. If you use AI regularly, this is a must.

This is a complete game-changer. This agent skills marketplace has over a MILLION ready-to-use agent skills and plug-ins. Just search the skill type you want to deploy, and watch hundreds of skill files appear. If you use AI regularly, this is a must.

Miles Deutscher

38,809 次观看 • 1 个月前

Out of sheer frustration ... I decided to vibe code my own tile map editor to see if it can help with making my agent skills be able to build better levels. It also proves that the agent skill is sufficiently good enough to know how to read the tilemaps - just bad at level design. So maybe I just need to build a level design skill.

Out of sheer frustration ... I decided to vibe code my own tile map editor to see if it can help with making my agent skills be able to build better levels. It also proves that the agent skill is sufficiently good enough to know how to read the tilemaps - just bad at level design. So maybe I just need to build a level design skill.

Chong-U

37,202 次观看 • 5 个月前

We published a skill for Omni Flash so you can bootstrap video editing into your agent: ``` npx skills add google-gemini/gemini-skills --skill gemini-omni-flash-api ``` The skill covers the core workflows: - Text to video - Image references to video - First frame to video - Conversational video editing

We published a skill for Omni Flash so you can bootstrap video editing into your agent: ``` npx skills add google-gemini/gemini-skills --skill gemini-omni-flash-api ``` The skill covers the core workflows: - Text to video - Image references to video - First frame to video - Conversational video editing

Philipp Schmid

53,144 次观看 • 5 天前

🔥🆙!! The 6AM group brought the HEAT on squats. Create an environment that PEOPLE want to be a part of.

🔥🆙!! The 6AM group brought the HEAT on squats. Create an environment that PEOPLE want to be a part of.

Jake Riedel

70,338 次观看 • 1 年前

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:

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

77,156 次观看 • 25 天前

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.

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 次观看 • 4 个月前

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.

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 次观看 • 2 个月前

“Don’t chase perfection. You want to be in an environment in your mind where I’m going to create great habits that lead to a high level of performance and a high level of consistency,” Brent Venables The daily habits of a winner reveal their character.

“Don’t chase perfection. You want to be in an environment in your mind where I’m going to create great habits that lead to a high level of performance and a high level of consistency,” Brent Venables The daily habits of a winner reveal their character.

The Winning Difference

13,180 次观看 • 1 个月前