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fx v0.0.9 Use a frontier model to steer, then hand implementation to a fast workhorse. • Give each subagent its own model and reasoning effort • Subagents keep working while you steer • Send feedback to subagents mid-task • Switch models with Ctrl+P without losing your draft or images... show more

Pranit

1,690 subscribers

175,008 次观看 • 8 天前 •via X (Twitter)

教育科学技术

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7 条评论

Nikolay Petkov 的头像

Nikolay Petkov8 天前

oh that’s so cool 🔥

Tinoco2 的头像

Tinoco28 天前

Why don’t we have the possibility to to oauth with Kimi, Claude and more models? Thanks.

Nandan Priyadarshi 的头像

Nandan Priyadarshi8 天前

Ran into the same wall last week — one Opus session doing steer + implement just burned context. Split it: Sonnet for the plan, a cheaper model for the patch loop. Mid-task feedback without restarting the whole thread was the unlock.

Ersin KOÇ 的头像

Ersin KOÇ8 天前

🙄

Julian Guerrero 的头像

Julian Guerrero8 天前

wanna be able to undo the last message (put it back in the composer), like opencode ctrl+x u. thx

Pranit 的头像

Pranit8 天前

Coming up 🔜

Julian Guerrero 的头像

Julian Guerrero8 天前

love it

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I use subagents for ~80% of my Codex tasks these days. If you're new to subagents, here's a quick primer: - Subagents = separate agents that work on a specific piece of work in a fresh isolated context window. - Your main thread can create subagents and delegate work to them. - Subagents report back when tasks are complete. - Codex subagents are especially useful for read-heavy tasks (e.g research, investigation, and review). - Also useful for testing and building parts of a project in parallel. Subagents are great for working on focused parts of a big task, like planning, implementation, testing, or review. My biggest pro tip for using subagents in Codex: You don’t have to design the entire delegation plan yourself. Just ask Codex to “Use subagents as needed,” and then Codex can then decide where subagents would help it in the task. Here's a demo of using subagents in Codex:

I use subagents for ~80% of my Codex tasks these days. If you're new to subagents, here's a quick primer: - Subagents = separate agents that work on a specific piece of work in a fresh isolated context window. - Your main thread can create subagents and delegate work to them. - Subagents report back when tasks are complete. - Codex subagents are especially useful for read-heavy tasks (e.g research, investigation, and review). - Also useful for testing and building parts of a project in parallel. Subagents are great for working on focused parts of a big task, like planning, implementation, testing, or review. My biggest pro tip for using subagents in Codex: You don’t have to design the entire delegation plan yourself. Just ask Codex to “Use subagents as needed,” and then Codex can then decide where subagents would help it in the task. Here's a demo of using subagents in Codex:

Avthar

18,444 次观看 • 21 天前

$100,000/mo in compute credits to startups and institutions that apply and join the Apodex Frontier Program. I'm posting the link below. Beyond the compute credits, you get access to the Apodex Deep Discover solver, powered by one of the most interesting models I've seen in a while. A quick summary of the Apodex 1.0-H model: 1. The model works like an agent team, not a single model looping over the same context. An orchestrator decomposes a task and spawns specialized subagents on demand. Each subagent works asynchronously with its own context and tools. This behavior was trained into the model. 2. The model can improve its own reasoning through a generate → verify → revise process. It generates an answer, an internal grader evaluates it, and finally, it revises it based on that feedback over multiple rounds. The grader never sees the answer key, so the process does not rely on memorization. 3. Verification is handled by independent agents, not the same model checking itself. It uses a dedicated verification team, including a conflict reviewer, a fact checker, and a draft reviewer, to audit the output before delivery. It backs every output claim with an explicit evidence chain. To give you an idea of how powerful Apodex-1.0-H is, it can coordinate up to 150 subagents executing over 15,000+ steps within a single task. If you are a research institution, academic lab, or research startup, consider applying.

$100,000/mo in compute credits to startups and institutions that apply and join the Apodex Frontier Program. I'm posting the link below. Beyond the compute credits, you get access to the Apodex Deep Discover solver, powered by one of the most interesting models I've seen in a while. A quick summary of the Apodex 1.0-H model: 1. The model works like an agent team, not a single model looping over the same context. An orchestrator decomposes a task and spawns specialized subagents on demand. Each subagent works asynchronously with its own context and tools. This behavior was trained into the model. 2. The model can improve its own reasoning through a generate → verify → revise process. It generates an answer, an internal grader evaluates it, and finally, it revises it based on that feedback over multiple rounds. The grader never sees the answer key, so the process does not rely on memorization. 3. Verification is handled by independent agents, not the same model checking itself. It uses a dedicated verification team, including a conflict reviewer, a fact checker, and a draft reviewer, to audit the output before delivery. It backs every output claim with an explicit evidence chain. To give you an idea of how powerful Apodex-1.0-H is, it can coordinate up to 150 subagents executing over 15,000+ steps within a single task. If you are a research institution, academic lab, or research startup, consider applying.

Santiago

16,891 次观看 • 2 个月前

OpenAI shipped a new speech-to-speech model today: gpt-realtime-2 This is the first speech-to-speech model good enough to use in my voice agents that do "real work." Or real play, for that matter. Here's gpt-realtime-2 as the brain of the ship AI in Gradient Bang. The voice-to-voice response and tool calling times here are unedited, so you can see exactly what the interaction with the model is like in an agent with a very complex system instruction and frequent tool calls. (I did clip out the subagent task execution segments, after gpt-realtime-2 starts a subagent via a tool call. Subagents in this config used gpt-5.2 "medium" effort.)

OpenAI shipped a new speech-to-speech model today: gpt-realtime-2 This is the first speech-to-speech model good enough to use in my voice agents that do "real work." Or real play, for that matter. Here's gpt-realtime-2 as the brain of the ship AI in Gradient Bang. The voice-to-voice response and tool calling times here are unedited, so you can see exactly what the interaction with the model is like in an agent with a very complex system instruction and frequent tool calls. (I did clip out the subagent task execution segments, after gpt-realtime-2 starts a subagent via a tool call. Subagents in this config used gpt-5.2 "medium" effort.)

kwindla

55,222 次观看 • 4 个月前

✨ You can now batch remix photos with your own model For example, let's you you're a fashion brand and did a big photo shoot (real or AI) with one model (black hair), but you need the same shots with another model (blonde hair) Just select your new model, then select the photos you want to remix, and it'll use those as input to regenerate them but with your selected model Cheaper than re-doing the whole photo shoot, well actually about 1000x cheaper

@levelsio

269,052 次观看 • 6 个月前

$I'm delighted to say that Genie, our fully automated SWE agent is finally GA. It achieves a new SOTA score on the SWE-Lancer benchmark earning $88k with a 49% resolution rate – significantly out-earning any frontier reasoning model, for a fraction of the cost. Genie can work with you, or fully autonomously, and integrates with all of the tools developers love to use. Go give it a try on I'd love to hear any feedback!$

I'm delighted to say that Genie, our fully automated SWE agent is finally GA. It achieves a new SOTA score on the SWE-Lancer benchmark earning $88k with a 49% resolution rate – significantly out-earning any frontier reasoning model, for a fraction of the cost. Genie can work with you, or fully autonomously, and integrates with all of the tools developers love to use. Go give it a try on I'd love to hear any feedback!

Alistair

189,147 次观看 • 1 年前

🚀 Introducing Queue: A Smarter Way to Build with Replit Agent Today, we’re excited to launch Queue, a powerful new capability designed to enhance your experience with Replit Agent. With Queue, you can submit multiple tasks while your Agent is still working—no more waiting around or breaking your flow. As each task completes, the Agent automatically moves to the next. You can: ✅ Add multiple tasks for the Agent to process in order. ✅ Edit, reorder, or reprioritize messages on the fly. ✅ Attach files and adjust settings—including toggling extended thinking or selecting a different model—per task.

🚀 Introducing Queue: A Smarter Way to Build with Replit Agent Today, we’re excited to launch Queue, a powerful new capability designed to enhance your experience with Replit Agent. With Queue, you can submit multiple tasks while your Agent is still working—no more waiting around or breaking your flow. As each task completes, the Agent automatically moves to the next. You can: ✅ Add multiple tasks for the Agent to process in order. ✅ Edit, reorder, or reprioritize messages on the fly. ✅ Attach files and adjust settings—including toggling extended thinking or selecting a different model—per task.

Replit ⠕

75,576 次观看 • 1 年前

finally ported my fav Claude Code feature into Pi: `/subtask` a subtask is a fork of your current conversation, it inherits everything you've discussed, works in the background, and sends only its final answer back into the conversation that spawned it. Less context pollution/bloating and you can continue the main conversation as subtasks are running. here's what's packed into the Pi extension: - a live panel under the prompt: ↑↓ to select, enter to watch a fork work in real time, x to stop or dismiss it - steer a running fork mid-task, or resume a finished one without losing its progress - the model can spawn subtasks on its own and keep working while it waits for the result - forks reuse the parent's prompt cache, so they're cheaper than briefing a subagent - every fork's transcript is a real pi session: reopen it any time with `pi --session `

finally ported my fav Claude Code feature into Pi: `/subtask` a subtask is a fork of your current conversation, it inherits everything you've discussed, works in the background, and sends only its final answer back into the conversation that spawned it. Less context pollution/bloating and you can continue the main conversation as subtasks are running. here's what's packed into the Pi extension: - a live panel under the prompt: ↑↓ to select, enter to watch a fork work in real time, x to stop or dismiss it - steer a running fork mid-task, or resume a finished one without losing its progress - the model can spawn subtasks on its own and keep working while it waits for the result - forks reuse the parent's prompt cache, so they're cheaper than briefing a subagent - every fork's transcript is a real pi session: reopen it any time with `pi --session `

Victor M

18,394 次观看 • 1 个月前

Progress in open models is keeping Big AI labs up at night, and I'm here for it! We have a brand new open-weight multimodal model optimized for long-horizon tasks. This model is really good at something: it can work on tasks that keep evolving over time. • 280B total parameters, but only 16B active • 512K context window • Understands text, images, and audio • Strong reasoning, coding, and tool use But the best of all: the model learns and adapts to new information! Imagine you start running an agent today to solve a problem, and while it's working, you get new information that changes the initial conditions, or you change your mind. The agents you run today don't have issues with short tasks and goals that don't change, but reality is messy, and that makes it hard for long-horizon agents to succeed. The new dots3-note Preview model introduces TEMPO. TEMPO is a new reinforcement learning technique that lets the model periodically pause and critique its own progress. Basically, from time to time, the agent asks itself: "Am I getting closer to the goal, or am I wasting my time?" The same model switches between actor and critic. The actor works on the problem. The critic looks at the current state, reasons about how much progress it has made, and determines what should happen next. TEMPO gives the model feedback along the way. This is huge for any agent that can work on long-horizon tasks without wasting its time.

Santiago

80,792 次观看 • 1 个月前

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

Artificial Analysis

132,623 次观看 • 1 个月前

This is so cool. In the next Codex update, multi agents will get a massive flexibility upgrade. "Hey Codex, when you implement this plan, I want you to delegate all the lower complexity tasks to GPT 5.3 Spark subagents" Instead of needing to create 100 different custom agent roles for different situations, you can just prompt your agent to spawn whatever model or reasoning level you want. With only natural language. No config files. No pre-defined roles. Just tell the orchestrator what to use and it listens.

This is so cool. In the next Codex update, multi agents will get a massive flexibility upgrade. "Hey Codex, when you implement this plan, I want you to delegate all the lower complexity tasks to GPT 5.3 Spark subagents" Instead of needing to create 100 different custom agent roles for different situations, you can just prompt your agent to spawn whatever model or reasoning level you want. With only natural language. No config files. No pre-defined roles. Just tell the orchestrator what to use and it listens.

am.will

128,125 次观看 • 6 个月前

Generative video models are rapidly improving in quality. Meet Replay, a new AI model that can generate stunning videos from text. Replay v0.1 is designed to create ultrasmooth HD videos with a new interface. Available today for everyone. What's New? 1. Replay understands plain English prompts without prompt engineering. Try "rugged surfer" or "mermaid". 2. What's a movie without actors? Replay can crisply render close-ups of people and animals. 3. Free and fast generation from our homepage with no waiting list (link in bio). Behind the scenes, Replay is powered by aligning our new diffusion model for videos with the LLM behind Genmo Chat. We’re so excited to see what you create! Share your creations below👇

Generative video models are rapidly improving in quality. Meet Replay, a new AI model that can generate stunning videos from text. Replay v0.1 is designed to create ultrasmooth HD videos with a new interface. Available today for everyone. What's New? 1. Replay understands plain English prompts without prompt engineering. Try "rugged surfer" or "mermaid". 2. What's a movie without actors? Replay can crisply render close-ups of people and animals. 3. Free and fast generation from our homepage with no waiting list (link in bio). Behind the scenes, Replay is powered by aligning our new diffusion model for videos with the LLM behind Genmo Chat. We’re so excited to see what you create! Share your creations below👇

Genmo

154,651 次观看 • 3 年前

Sakana AI CEO David Ha hardmaru: "If you assemble a team of Avengers, that team will be better than the individual heroes. The same with AI." Sakana Fugu, their new AI “model” goes toe-to-toe with Mythos and Fable 5 on a few benchmarks — without training a single frontier model of its own. Fugu orchestrates between different frontier AI models to achieve higher results. On the real risk, David adds: "I'm more concerned about the structure of dependency… you don't want to be tied to a single particular AI model or a single particular vendor." An exclusive 1:1 with CROSS DIG’s 中川雅博 / Masahiro Nakagawa @ TBS CROSS DIG #SakanaAI #SakanaFugu

Sakana AI CEO David Ha hardmaru: "If you assemble a team of Avengers, that team will be better than the individual heroes. The same with AI." Sakana Fugu, their new AI “model” goes toe-to-toe with Mythos and Fable 5 on a few benchmarks — without training a single frontier model of its own. Fugu orchestrates between different frontier AI models to achieve higher results. On the real risk, David adds: "I'm more concerned about the structure of dependency… you don't want to be tied to a single particular AI model or a single particular vendor." An exclusive 1:1 with CROSS DIG’s 中川雅博 / Masahiro Nakagawa @ TBS CROSS DIG #SakanaAI #SakanaFugu

TBS CROSS DIG with Bloomberg

78,986 次观看 • 2 个月前

✨ I made a new feature called [ 💃 Photo Shoot Simulator ] which lets you do real life photo shoots with real life models on my site Photo AI Upload clothes to try on, or prompt your scene, and your model will now move and pose for you Then when you hit the [ 📸 ] button, you take photos just like you would in a real life photo shoot All your photos are then available upscaled as 4K high resolution pics with a full commercial license to use in your store, social media, marketing, on ads, etc. No need for a model, photographer, or studio anymore!

✨ I made a new feature called [ 💃 Photo Shoot Simulator ] which lets you do real life photo shoots with real life models on my site Photo AI Upload clothes to try on, or prompt your scene, and your model will now move and pose for you Then when you hit the [ 📸 ] button, you take photos just like you would in a real life photo shoot All your photos are then available upscaled as 4K high resolution pics with a full commercial license to use in your store, social media, marketing, on ads, etc. No need for a model, photographer, or studio anymore!

@levelsio

581,055 次观看 • 1 年前

We’re excited to introduce Personas 🎤 Personas let you save the essence of a song - vocals, style, vibe - and reimagine it across your creations. Want to bring your favorite vocals or style into a new song? Here’s how: 1⃣ Find a song you love, click Create then Make a Persona 2⃣ Toggle between Public and Private for your Persona. Public Personas can be used by others, will have their own page, and can be found in your library and profile. 3⃣ Add lyrics and style like you would any other song! This feature is available to all Pro & Premier members in early-access beta. You get 200 free songs to make with Personas - after that, each new song made with Personas will cost 10 credits. We’re working to improve Personas so please give us your feedback through the app, on Discord or email us at [email protected]. We can’t wait to see what you do with your Personas! ❤️ Team Suno

Suno

89,231 次观看 • 1 年前

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

Andrew Ng

358,190 次观看 • 1 年前

New course: Build and Train an LLM with JAX, built in partnership with Google and taught by Chris Achard. JAX is the open-source library behind Google's Gemini, Veo, and other advanced models. This short course teaches you to build and train a 20-million parameter language model from scratch using JAX and its ecosystem of tools. You'll implement a complete MiniGPT-style architecture from scratch, train it, and chat with your finished model through a graphical interface. Skills you'll gain: - Learn JAX's core primitives: automatic differentiation, JIT compilation, and vectorized execution - Build a MiniGPT-style LLM using Flax/NNX, implementing embedding and transformer blocks - Load a pretrained MiniGPT model and run inference through a chat interface Come learn this important software layer for building LLMs!

New course: Build and Train an LLM with JAX, built in partnership with Google and taught by Chris Achard. JAX is the open-source library behind Google's Gemini, Veo, and other advanced models. This short course teaches you to build and train a 20-million parameter language model from scratch using JAX and its ecosystem of tools. You'll implement a complete MiniGPT-style architecture from scratch, train it, and chat with your finished model through a graphical interface. Skills you'll gain: - Learn JAX's core primitives: automatic differentiation, JIT compilation, and vectorized execution - Build a MiniGPT-style LLM using Flax/NNX, implementing embedding and transformer blocks - Load a pretrained MiniGPT model and run inference through a chat interface Come learn this important software layer for building LLMs!

Andrew Ng

194,547 次观看 • 6 个月前

Today, we're sharing new research on GWM Worlds 2. Our latest General World Model that generates interactive real-time simulations in continuous 720p video at 24 fps with 48000 Hz audio. Built on top of our foundational audio-visual generation model, GWM Worlds 2 allows you to define the environment, subjects, visual style, physical rules and ambience of any world. Once inside, you steer it with text actions addressed to any subject or to the scene itself, alongside continuous camera motion. The world continues from each new input instead of following a fixed clip or script so sessions have no preset length. GWM Worlds 2 creates a new foundation for interactive entertainment, virtual characters, robotics and embodied-agent simulation. Learn more about GWM Worlds 2 at the link below.

Today, we're sharing new research on GWM Worlds 2. Our latest General World Model that generates interactive real-time simulations in continuous 720p video at 24 fps with 48000 Hz audio. Built on top of our foundational audio-visual generation model, GWM Worlds 2 allows you to define the environment, subjects, visual style, physical rules and ambience of any world. Once inside, you steer it with text actions addressed to any subject or to the scene itself, alongside continuous camera motion. The world continues from each new input instead of following a fixed clip or script so sessions have no preset length. GWM Worlds 2 creates a new foundation for interactive entertainment, virtual characters, robotics and embodied-agent simulation. Learn more about GWM Worlds 2 at the link below.

Runway

304,024 次观看 • 17 天前

holy sh*t, it can't be f**king real you can use GPT-5.6 and claude for FREEEEE just put GPT-5.6 Luna and Claude Haiku 4.5 behind a free chat with zero sign-up required what you get for free: - GPT-5.6 Luna - Claude Haiku 4.5 - GPT-5.4 mini - Mistral Small 4 - gpt-oss 120B - Gemma 4 31B voice chat and image generation also available private by design how to use it (under 10 seconds): step 1: go to (no sign-up, no redirect) step 2: pick your model from the dropdown GPT-5.6 Luna, Claude Haiku 4.5, or any of the others step 3: start typing that's it. you're chatting with a frontier model for $0 important: > anonymous means rate limits are tighter than paid tiers > if you hit a limit, switch models or come back later > your conversations stay private

holy sh*t, it can't be f**king real you can use GPT-5.6 and claude for FREEEEE just put GPT-5.6 Luna and Claude Haiku 4.5 behind a free chat with zero sign-up required what you get for free: - GPT-5.6 Luna - Claude Haiku 4.5 - GPT-5.4 mini - Mistral Small 4 - gpt-oss 120B - Gemma 4 31B voice chat and image generation also available private by design how to use it (under 10 seconds): step 1: go to (no sign-up, no redirect) step 2: pick your model from the dropdown GPT-5.6 Luna, Claude Haiku 4.5, or any of the others step 3: start typing that's it. you're chatting with a frontier model for $0 important: > anonymous means rate limits are tighter than paid tiers > if you hit a limit, switch models or come back later > your conversations stay private

Sakata

248,826 次观看 • 25 天前

HERMES CAN NOW RUN FOR FREE FOREVER A new Gemma 4 update just removed the biggest problem with local AI agents. The Upgrade: → Gemma 4 runs around 90% faster on Apple Silicon using Ollama + MLX → Plug it into Hermes with a few terminal commands → Run private agent workflows without subscriptions, token limits, or API bills What It Can Actually Do: ✓ Build and preview apps locally ✓ Use /learn to turn tutorials into permanent skills ✓ Read files, analyse landing pages, summarise emails, and track AI news ✓ Run loops until a task is complete instead of waiting for another prompt ✓ Launch multiple Gemma 4 subagents to work on separate tasks in parallel No Apple Silicon? ✔ OpenRouter offers the 31B model through a free API The real win is not faster chat. It is having Hermes build, retry, check its work, and operate in the background without charging you for every loop.

HERMES CAN NOW RUN FOR FREE FOREVER A new Gemma 4 update just removed the biggest problem with local AI agents. The Upgrade: → Gemma 4 runs around 90% faster on Apple Silicon using Ollama + MLX → Plug it into Hermes with a few terminal commands → Run private agent workflows without subscriptions, token limits, or API bills What It Can Actually Do: ✓ Build and preview apps locally ✓ Use /learn to turn tutorials into permanent skills ✓ Read files, analyse landing pages, summarise emails, and track AI news ✓ Run loops until a task is complete instead of waiting for another prompt ✓ Launch multiple Gemma 4 subagents to work on separate tasks in parallel No Apple Silicon? ✔ OpenRouter offers the 31B model through a free API The real win is not faster chat. It is having Hermes build, retry, check its work, and operate in the background without charging you for every loop.

Julian Goldie SEO

11,363 次观看 • 2 个月前