Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Built a playable game in under 5 minutes with Pokee-Isaac 28B. From idea → code → working game, all in one flow. Isaac combines strong coding + agentic reasoning with a 10M-token context window, so it can keep track of large codebases, assets, requirements, and iterations without constantly losing... show more

Pokee AI

11,522 subscribers

51,355 просмотров • 28 дней назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 5

Фото профиля Ethan Walker

Ethan Walker27 дней назад

Going from an idea to a fully playable game in under 5 minutes is seriously impressive. The combination of coding, reasoning, and a 10M token context makes this especially interesting for rapid prototyping. 🔥🎮

Фото профиля 安叫兽|Bird🕊️ 🔶 BNB

安叫兽|Bird🕊️ 🔶 BNB27 дней назад

不到五分钟做出能玩的，最惊讶的是中途没把上下文搞丢。

Фото профиля AI Clips

AI Clips27 дней назад

Fairy code glowing as forest buttons stage surreal game echoes

Фото профиля ZEECO

ZEECO27 дней назад

This is so good

Фото профиля Elara AI

Elara AI27 дней назад

Built a playable game in 5 minutes with 10M context

Похожие видео

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

Toha Khan

66,214 просмотров • 19 дней назад

A 4,316-page legal file. One simple question: can the AI actually read the whole case? We tested it side by side. ChatGPT: upload failed — the file was too large to process. Pokee-Isaac: processed the full 4,316-page file in one context. For enterprise legal teams, this is where 10M context becomes practical: contracts, exhibits, filings, depositions, and evidence can be analyzed together instead of broken into fragments. If your team works with massive, sensitive document sets and needs private or local deployment, Pokee-Isaac is built for that. Book an enterprise demo with us today:

A 4,316-page legal file. One simple question: can the AI actually read the whole case? We tested it side by side. ChatGPT: upload failed — the file was too large to process. Pokee-Isaac: processed the full 4,316-page file in one context. For enterprise legal teams, this is where 10M context becomes practical: contracts, exhibits, filings, depositions, and evidence can be analyzed together instead of broken into fragments. If your team works with massive, sensitive document sets and needs private or local deployment, Pokee-Isaac is built for that. Book an enterprise demo with us today:

Pokee AI

37,932 просмотров • 28 дней назад

Introducing Claude Code Hook - Context Timeline (Saving this to try later) Install with: npx claude-code-templates@latest --hook monitoring/context-timeline Managing the context window and the subagents running in Claude Code is hard to keep track of That's why I built this hook... It starts the moment you open a session and shows a timeline with the main agent's context window and how subagents start working in their own separate context Every subagent you have running will show up in real time This way you can manage the context and the subagents you run, and see everything in a much simpler way than in the console

Introducing Claude Code Hook - Context Timeline (Saving this to try later) Install with: npx claude-code-templates@latest --hook monitoring/context-timeline Managing the context window and the subagents running in Claude Code is hard to keep track of That's why I built this hook... It starts the moment you open a session and shows a timeline with the main agent's context window and how subagents start working in their own separate context Every subagent you have running will show up in real time This way you can manage the context and the subagents you run, and see everything in a much simpler way than in the console

Daniel San

51,706 просмотров • 5 месяцев назад

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

Md Riyazuddin

31,199 просмотров • 3 месяцев назад

Already using Pokee Isaac with Claude Code? You can use the same package with OpenAI's Codex—no new integration required! Because claude-pokee exposes Isaac through MCP, Codex can connect to its existing ask, build, iterate, and health tools. Simply install the package below or pass the link to Codex, register its MCP server with Codex, and continue using your existing Pokee API credentials. We tested the setup by having Isaac generate a complete, playable retro arcade landing page inside a Codex project. Check it out! Get started today: Github Repo: Pokee Developer Console:

Already using Pokee Isaac with Claude Code? You can use the same package with OpenAI's Codex—no new integration required! Because claude-pokee exposes Isaac through MCP, Codex can connect to its existing ask, build, iterate, and health tools. Simply install the package below or pass the link to Codex, register its MCP server with Codex, and continue using your existing Pokee API credentials. We tested the setup by having Isaac generate a complete, playable retro arcade landing page inside a Codex project. Check it out! Get started today: Github Repo: Pokee Developer Console:

Pokee AI

33,124 просмотров • 19 дней назад

What a fun little breakfast chat with swyx Windsurf just released Fast Context. Unlike traditional agentic search, Fast Context searches and retrieves relevant code from your codebase 20x faster. Game changing for developers who want to stay in flow. Watch it in action inside Andrej Karpathy’s nanochat repo. All over a bowl of Swyx's favorite childhood cereal, Raisin Bran. Powered by Cerebras.

What a fun little breakfast chat with swyx Windsurf just released Fast Context. Unlike traditional agentic search, Fast Context searches and retrieves relevant code from your codebase 20x faster. Game changing for developers who want to stay in flow. Watch it in action inside Andrej Karpathy’s nanochat repo. All over a bowl of Swyx's favorite childhood cereal, Raisin Bran. Powered by Cerebras.

Sarah Chieng

397,034 просмотров • 10 месяцев назад

NVIDIA built the best free AI API platform and nobody's talking about it. 100+ models and no credit card need and best thing is credits don't expire. Meta, Google, DeepSeek, Microsoft all in one place. I was looking for a free reasoning model API for a project and everything was paywalled. Then I found → Pick your model (chat, code, vision, reasoning it's all there) → Click "View Code" → Generate API key → Drop it in your project OpenAI-compatible endpoints with zero billing setup.

NVIDIA built the best free AI API platform and nobody's talking about it. 100+ models and no credit card need and best thing is credits don't expire. Meta, Google, DeepSeek, Microsoft all in one place. I was looking for a free reasoning model API for a project and everything was paywalled. Then I found → Pick your model (chat, code, vision, reasoning it's all there) → Click "View Code" → Generate API key → Drop it in your project OpenAI-compatible endpoints with zero billing setup.

Hasan Toor

18,499 просмотров • 3 месяцев назад

I used Grok 4.5 in Grok Build to create a game engine for agentic coding, then built an ARPG on it, with all game assets generated via Grok Imagine in Grok Build. AI agents can build worlds, combat, loot, progression, maps, tests, and playable games. ⚒️🎮

I used Grok 4.5 in Grok Build to create a game engine for agentic coding, then built an ARPG on it, with all game assets generated via Grok Imagine in Grok Build. AI agents can build worlds, combat, loot, progression, maps, tests, and playable games. ⚒️🎮

tetsuo

199,823 просмотров • 2 месяцев назад

Claude Code just shipped v2.1.69 with a new built-in skill: /claude-api It detects your language, picks the right surface (direct API, tool use, or Agent SDK), and loads the relevant reference docs directly into context. Covers streaming, batches, files API, structured outputs, tool use patterns, MCP, also sets model defaults so you're not guessing. Useful when you're building on top of Claude and don't want to context-switch to the docs every 5 minutes.

Claude Code just shipped v2.1.69 with a new built-in skill: /claude-api It detects your language, picks the right surface (direct API, tool use, or Agent SDK), and loads the relevant reference docs directly into context. Covers streaming, batches, files API, structured outputs, tool use patterns, MCP, also sets model defaults so you're not guessing. Useful when you're building on top of Claude and don't want to context-switch to the docs every 5 minutes.

Daniel San

152,831 просмотров • 6 месяцев назад

94% OF ENGINEERING ORGS USE AI. ONLY 6% SCALE IT The missing link is context and governance. That’s exactly what Atlassian is going after with its new agentic engineering stack: Code Context + governed agent loops in Atlassian Jira + DX measurement. The idea is simple: make agents a native part of your engineering system instead of a side quest in a terminal. > Code Context grounds them in your architecture > Governed loops take work from backlog to pull request > DX measures the impact And the data backs up the context thesis: DX found context-rich AI teams shipped ~64% more per developer 👊 A pretty strong signal that context is becoming one of the biggest leverage points in agentic engineering.

94% OF ENGINEERING ORGS USE AI. ONLY 6% SCALE IT The missing link is context and governance. That’s exactly what Atlassian is going after with its new agentic engineering stack: Code Context + governed agent loops in Atlassian Jira + DX measurement. The idea is simple: make agents a native part of your engineering system instead of a side quest in a terminal. > Code Context grounds them in your architecture > Governed loops take work from backlog to pull request > DX measures the impact And the data backs up the context thesis: DX found context-rich AI teams shipped ~64% more per developer 👊 A pretty strong signal that context is becoming one of the biggest leverage points in agentic engineering.

Charly Wargnier ♨️

29,622 просмотров • 13 дней назад

Introducing 1st full-stack AI Designer An AI agent that can plan, generate graphic assets, Video animation, production UI code all with full context Design like this could easily cost $5~$10k just 1 year ago, but now is possible to deliver in 10 min without any design skills Learnt from Kyle Skelly and I just can't resist to build it in SuperDesign.Dev, very satisfying to have image/video/code gen all in one place with consistent context You can try this on today

Introducing 1st full-stack AI Designer An AI agent that can plan, generate graphic assets, Video animation, production UI code all with full context Design like this could easily cost $5~$10k just 1 year ago, but now is possible to deliver in 10 min without any design skills Learnt from Kyle Skelly and I just can't resist to build it in SuperDesign.Dev, very satisfying to have image/video/code gen all in one place with consistent context You can try this on today

Jason Zhou

32,121 просмотров • 9 месяцев назад

I've been playing with MiniMax M3 recently. The combination of coding, agent workflows, 1M context, and multimodal understanding made me curious enough to try something real. So I gave it one task: "Build me a Skill Hub to manage all my Claude Code skills."👇

I've been playing with MiniMax M3 recently. The combination of coding, agent workflows, 1M context, and multimodal understanding made me curious enough to try something real. So I gave it one task: "Build me a Skill Hub to manage all my Claude Code skills."👇

ares. 🎧

49,239 просмотров • 3 месяцев назад

Qwen3.8-Flash-Next is still going strong at 364.7K tokens of context on an M5 Max. And this isn’t just a static long-context test. The model was reasoning about how to speed up its own workflow while using tools, and the tool calls kept working without misses. Setup: • Qwen3.8-Flash-Next • M5 Max • 128GB unified memory • MLX-Serve PR #363 • OpenCode 2 • 364.7K context The interesting part isn’t simply getting hundreds of thousands of tokens into memory. It’s what happens once the context gets this large. Long-context inference usually comes with a painful tradeoff. As the KV cache grows, memory pressure increases and generation can slow down. But this setup is still pushing through 364K tokens while maintaining a usable agent workflow. The model can reason, call tools, inspect results, continue working, and keep the session moving. And the tool calls reportedly haven’t missed so far. That’s important for agentic coding. A huge context window is only useful if the model can actually operate reliably inside it. A 400K-token context that constantly breaks tool calls isn’t very useful. A 364K session that can keep reasoning and executing tools is a different story. And the test isn’t finished yet. The current run is approaching 400K tokens, with the expectation that it can keep going. This is also another interesting example of why Apple Silicon keeps showing up in local LLM experiments. The M5 Max’s unified memory gives a large model and its growing KV cache access to one shared memory pool. With MLX-Serve continuing to improve, these machines are becoming surprisingly capable long-context inference boxes. The bigger takeaway: Context length is becoming a workload, not just a model specification. Running a model at 256K is one thing. Keeping an agent alive at 300K+ while it reasons and uses tools is much more interesting. And Qwen3.8-Flash-Next is showing that this can be pushed surprisingly far on a single 128GB Mac. 364.7K and counting. Next stop: 400K.

Qwen3.8-Flash-Next is still going strong at 364.7K tokens of context on an M5 Max. And this isn’t just a static long-context test. The model was reasoning about how to speed up its own workflow while using tools, and the tool calls kept working without misses. Setup: • Qwen3.8-Flash-Next • M5 Max • 128GB unified memory • MLX-Serve PR #363 • OpenCode 2 • 364.7K context The interesting part isn’t simply getting hundreds of thousands of tokens into memory. It’s what happens once the context gets this large. Long-context inference usually comes with a painful tradeoff. As the KV cache grows, memory pressure increases and generation can slow down. But this setup is still pushing through 364K tokens while maintaining a usable agent workflow. The model can reason, call tools, inspect results, continue working, and keep the session moving. And the tool calls reportedly haven’t missed so far. That’s important for agentic coding. A huge context window is only useful if the model can actually operate reliably inside it. A 400K-token context that constantly breaks tool calls isn’t very useful. A 364K session that can keep reasoning and executing tools is a different story. And the test isn’t finished yet. The current run is approaching 400K tokens, with the expectation that it can keep going. This is also another interesting example of why Apple Silicon keeps showing up in local LLM experiments. The M5 Max’s unified memory gives a large model and its growing KV cache access to one shared memory pool. With MLX-Serve continuing to improve, these machines are becoming surprisingly capable long-context inference boxes. The bigger takeaway: Context length is becoming a workload, not just a model specification. Running a model at 256K is one thing. Keeping an agent alive at 300K+ while it reasons and uses tools is much more interesting. And Qwen3.8-Flash-Next is showing that this can be pushed surprisingly far on a single 128GB Mac. 364.7K and counting. Next stop: 400K.

FHILY👑

39,982 просмотров • 16 дней назад

Pokee Isaac now runs inside Claude Code. And you can use it a few different ways: have Claude call Isaac for complete one-shot generations, switch directly to Isaac as your model mid-session, or run the entire Claude Code session on Isaac. For the workflow shown below, Claude plans and verifies. Isaac generates the complete artifact in a single pass. Integration built and open-sourced by ⭕ AI & Design (Marco). Here's a quick how-to video in order to get started! You can access the repo at:

Pokee Isaac now runs inside Claude Code. And you can use it a few different ways: have Claude call Isaac for complete one-shot generations, switch directly to Isaac as your model mid-session, or run the entire Claude Code session on Isaac. For the workflow shown below, Claude plans and verifies. Isaac generates the complete artifact in a single pass. Integration built and open-sourced by ⭕ AI & Design (Marco). Here's a quick how-to video in order to get started! You can access the repo at:

Pokee AI

41,060 просмотров • 20 дней назад

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @pauleveritt@fosstodon.org. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @[email protected]. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

Andrew Ng

465,717 просмотров • 5 месяцев назад

We built MiniMax a CLI coding agent like Claude Code, powered by MiniMax M3. It can: → Read files → Write code → Run shell commands autonomously → Understand large codebases with long-context reasoning → Handle real multi-file engineering tasks → Work entirely inside your terminal And it's fully hackable. Swap prompts. Add tools. Customize workflows. Make it your own. No GUI. Just code. Built for developers who live in the terminal.

We built MiniMax a CLI coding agent like Claude Code, powered by MiniMax M3. It can: → Read files → Write code → Run shell commands autonomously → Understand large codebases with long-context reasoning → Handle real multi-file engineering tasks → Work entirely inside your terminal And it's fully hackable. Swap prompts. Add tools. Customize workflows. Make it your own. No GUI. Just code. Built for developers who live in the terminal.

Darshal Jaitwar

18,861 просмотров • 3 месяцев назад

Claude Fable 5 + Higgsfield MCP is insane. You can now build a playable game with a full story and stunning visuals from one click. Fable 5 researches your idea and writes the game. Higgsfield MCP generates all the assets. On Higgsfield Supercomputer and in Claude via Higgsfield MCP.

Claude Fable 5 + Higgsfield MCP is insane. You can now build a playable game with a full story and stunning visuals from one click. Fable 5 researches your idea and writes the game. Higgsfield MCP generates all the assets. On Higgsfield Supercomputer and in Claude via Higgsfield MCP.

Higgsfield AI 🧩

696,710 просмотров • 3 месяцев назад

🎉 Day-0 support for in vLLM, available today in v0.23.0! Congrats to Z.ai on GLM-5.2, a flagship model built for long-horizon coding agents. ✨ 1M-token context, built to hold project-scale engineering work in a single run ✨ Tuned for long-horizon coding: large-scale implementation, automated research, and performance optimization ✨ One task can carry a full dev workflow, from requirements to a deployable product across platforms ✨ Client-side and mobile engineering, including an on-device debugging loop Try it out running it on vLLM today: 🔗

🎉 Day-0 support for in vLLM, available today in v0.23.0! Congrats to Z.ai on GLM-5.2, a flagship model built for long-horizon coding agents. ✨ 1M-token context, built to hold project-scale engineering work in a single run ✨ Tuned for long-horizon coding: large-scale implementation, automated research, and performance optimization ✨ One task can carry a full dev workflow, from requirements to a deployable product across platforms ✨ Client-side and mobile engineering, including an on-device debugging loop Try it out running it on vLLM today: 🔗

vLLM

36,240 просмотров • 3 месяцев назад

How to install the ChainGPT AI skill for Claude Code so you can ship Web3 projects with ZERO coding experience. Setup takes about 3 minutes: 1. Install Claude Code at 2. Run /plugin install ChainGPT-org/chaingpt-claude-skill ( 3. Get a ChainGPT API key at: 4. Add credits at: 5. Type /chaingpt and describe what you want to build What I built: I asked for a cat-themed AI NFT generator. The skill knew which API endpoint to hit, how to handle the response, and how to display the image. If I can do this in under an hour with no coding background, so can you. Anyone can build with ChainGPT AI! Get your API key → Top up credits →

How to install the ChainGPT AI skill for Claude Code so you can ship Web3 projects with ZERO coding experience. Setup takes about 3 minutes: 1. Install Claude Code at 2. Run /plugin install ChainGPT-org/chaingpt-claude-skill ( 3. Get a ChainGPT API key at: 4. Add credits at: 5. Type /chaingpt and describe what you want to build What I built: I asked for a cat-themed AI NFT generator. The skill knew which API endpoint to hit, how to handle the response, and how to display the image. If I can do this in under an hour with no coding background, so can you. Anyone can build with ChainGPT AI! Get your API key → Top up credits →

ChainGPT

26,832 просмотров • 4 месяцев назад