From d082b585d5c224fd73e7b897eaacaad627d634b6 Mon Sep 17 00:00:00 2001 From: vukrosic Date: Sun, 19 Oct 2025 15:55:26 +0200 Subject: [PATCH 1/6] Improve readability by adding blank lines in convert_pdf_figures.py, QUICKSTART.md, README.md, and convert_qerl.py. --- .../page.tsx | 359 ++++++++++++++++++ .../content.md | 103 +++++ 2 files changed, 462 insertions(+) create mode 100644 app/blog/roadmap-to-open-superintelligence/page.tsx create mode 100644 public/content/roadmap-to-open-superintelligence/content.md diff --git a/app/blog/roadmap-to-open-superintelligence/page.tsx b/app/blog/roadmap-to-open-superintelligence/page.tsx new file mode 100644 index 0000000..57a40cd --- /dev/null +++ b/app/blog/roadmap-to-open-superintelligence/page.tsx @@ -0,0 +1,359 @@ +'use client'; + +import Link from "next/link"; +import { useLanguage } from "@/components/providers/language-provider"; +import { MarkdownRenderer } from "@/components/markdown-renderer"; +import { useEffect, useState } from "react"; + +interface HeroData { + title: string; + subtitle: string; + tags: string[]; +} + +export default function RoadmapToOpenSuperintelligence() { + const { language } = useLanguage(); + const [markdownContent, setMarkdownContent] = useState(''); + const [heroData, setHeroData] = useState(null); + const [isLoading, setIsLoading] = useState(true); + const [copySuccess, setCopySuccess] = useState(false); + + useEffect(() => { + const fetchMarkdownContent = async () => { + try { + const filename = language === 'zh' ? 'content-zh.md' : 'content.md'; + const response = await fetch(`/content/roadmap-to-open-superintelligence/${filename}`); + const content = await response.text(); + + // Parse frontmatter + const frontmatterMatch = content.match(/^---\n([\s\S]*?)\n---\n([\s\S]*)$/); + if (frontmatterMatch) { + const frontmatterContent = frontmatterMatch[1]; + const markdownBody = frontmatterMatch[2]; + + // Parse YAML-like frontmatter (simple parsing for our use case) + const heroData: HeroData = { + title: "Roadmap To Open Superintelligence", + subtitle: "A Strategic Path Forward for Building AGI Through Open Collaboration", + tags: ["🚀 Roadmap", "🎯 Vision", "🔬 Research"] + }; + + // Extract values from frontmatter + const lines = frontmatterContent.split('\n'); + let currentKey = ''; + let currentArray: string[] = []; + + for (const line of lines) { + const trimmedLine = line.trim(); + if (trimmedLine.startsWith('hero:')) continue; + + if (trimmedLine.includes(':')) { + const [key, ...valueParts] = trimmedLine.split(':'); + const value = valueParts.join(':').trim().replace(/^["']|["']$/g, ''); + + switch (key.trim()) { + case 'title': + heroData.title = value; + break; + case 'subtitle': + heroData.subtitle = value; + break; + case 'tags': + currentKey = 'tags'; + currentArray = []; + break; + } + } else if (trimmedLine.startsWith('- ')) { + if (currentKey === 'tags') { + const tagValue = trimmedLine.substring(2).replace(/^["']|["']$/g, ''); + currentArray.push(tagValue); + } + } else if (trimmedLine === '' && currentArray.length > 0) { + if (currentKey === 'tags') { + heroData.tags = currentArray; + currentArray = []; + currentKey = ''; + } + } + } + + // Handle final array + if (currentArray.length > 0 && currentKey === 'tags') { + heroData.tags = currentArray; + } + + setHeroData(heroData); + setMarkdownContent(markdownBody); + } else { + // Fallback if no frontmatter + setMarkdownContent(content); + } + } catch (error) { + console.error('Failed to fetch markdown content:', error); + setMarkdownContent('# Error loading content\n\nFailed to load the article content.'); + } finally { + setIsLoading(false); + } + }; + + fetchMarkdownContent(); + }, [language]); + + const handleCopyArticle = async () => { + try { + // Get the raw markdown content without frontmatter + const filename = language === 'zh' ? 'content-zh.md' : 'content.md'; + const response = await fetch(`/content/roadmap-to-open-superintelligence/${filename}`); + const content = await response.text(); + + // Remove frontmatter if present + let contentWithoutFrontmatter = content.replace(/^---\n[\s\S]*?\n---\n/, ''); + + // Remove image paths (markdown image syntax: ![alt text](image-path)) + contentWithoutFrontmatter = contentWithoutFrontmatter.replace(/!\[.*?\]\(.*?\)/g, ''); + + await navigator.clipboard.writeText(contentWithoutFrontmatter); + setCopySuccess(true); + setTimeout(() => setCopySuccess(false), 2000); + } catch (error) { + console.error('Failed to copy article:', error); + } + }; + + if (isLoading) { + return ( +
+
+
+

Loading article content...

+
+
+ ); + } + + return ( + <> + {/* Hero Section */} +
+ {/* Background effects */} +
+
+
+
+ + {/* Animated background particles */} +
+
+
+
+
+
+ +
+
+
+

+ + {heroData?.title || 'Roadmap To Open Superintelligence'} + +

+
+ {heroData?.subtitle || 'A Strategic Path Forward for Building AGI Through Open Collaboration'} +
+ + {/* Tags */} + {heroData?.tags && heroData.tags.length > 0 && ( +
+ {heroData.tags.map((tag, index) => ( + + {index > 0 && •} + + {tag.includes('🚀') && ( + + + + )} + {tag.includes('🎯') && ( + + + + )} + {tag.includes('🔬') && ( + + + + )} + {tag.replace(/[🚀🎯🔬]/g, '').trim()} + + + ))} +
+ )} + + {/* Glow effect for the title */} +
+ + {heroData?.title || 'Roadmap To Open Superintelligence'} + +
+
+
+
+
+ + {/* Main Content */} +
+
+ {/* Article Container */} +
+ {/* Content Card */} +
+ {/* Copy Button at Top */} +
+
+
+ + + {/* Tooltip */} +
+ {language === 'en' + ? 'Perfect for pasting into AI chatbots for self-studying! 🤖' + : '非常适合粘贴到AI聊天机器人进行自学！🤖' + } + {/* Tooltip arrow */} +
+
+
+
+
+ + {/* Article Body */} +
+
+ +
+
+ + {/* Article Footer */} +
+
+
+ + + + + Open Superintelligence Lab + +
+
+ Share + + {/* Copy Article Button */} +
+ + + {/* Tooltip */} +
+ {language === 'en' + ? 'Perfect for pasting into AI chatbots for self-studying! 🤖' + : '非常适合粘贴到AI聊天机器人进行自学！🤖' + } + {/* Tooltip arrow */} +
+
+
+ + + + + + + + + + + +
+
+
+
+ + {/* Navigation */} +
+ + + + + {language === 'en' ? 'Back to Home' : '返回首页'} + + +
+ Scroll to + +
+
+
+
+
+ + ); +} + diff --git a/public/content/roadmap-to-open-superintelligence/content.md b/public/content/roadmap-to-open-superintelligence/content.md new file mode 100644 index 0000000..261db36 --- /dev/null +++ b/public/content/roadmap-to-open-superintelligence/content.md @@ -0,0 +1,103 @@ +--- +hero: + title: "Roadmap To Open Superintelligence" + subtitle: "A Strategic Path Forward for Building ASI Through Open Collaboration" + tags: + - "🚀 Roadmap" + - "🎯 Vision" + - "🔬 Research" +--- + +Nobody knows what superintelligence will look like or how to make it. + +Therefore, our roadmap will not be a linear path from A to B. It will be a **strategic framework for exploration, learning, and collective progress.** It's about creating a machine that discovers the path. + +**The Mission:** To create open and transparent Superintelligence that benefits everyone. +1. **Core Principles:** + * **Radical Openness:** All code is open source (e.g., MIT/Apache 2.0 license). All research discussions happen in public ([Discord](https://discord.gg/6AbXGpKTwN)). All findings are shared ([YouTube](https://www.youtube.com/channel/UC7XJj9pv_11a11FUxCMz15g), [blog posts](https://opensuperintelligencelab.com/), pre-prints). + * **Distributed Collaboration:** Intelligence is everywhere. We welcome contributors from all backgrounds. A great PR is a great PR, regardless of its author's credentials. + * **Educational Core:** Our primary output is not just the code, but a generation of researchers and engineers who build it. + * **Pragmatic Exploration:** We follow clear principles but stay flexible. We’ll explore different directions — LLMs, World Models, Video Generation, and JEPA — and choose based on what works best. +2. **The Grand Challenge:** Our goal is not to execute a known plan, but to build a community and a research engine capable of discovering it. + +--- + +#### **Phase 1: Foundational Capabilities & Community Bootstrapping (The First 6-12 Months)** + +The goal here is not to invent ASI, but to build the tools, skills, and community that *can*. This phase is perfect for training our new workforce. + +* **Objective:** Achieve SOTA on well-understood problems and build a robust, open-source ML infrastructure. +* **Projects:** + 1. **Replication Projects:** Pick 3-5 seminal papers and replicate them from scratch in a clean, well-documented repository. This is an incredible learning tool. + * *Examples:* GPT-2/3 (nanoGPT style), a Diffusion Model (DDPM), AlphaZero, a Vision Transformer (ViT). + * **GitHub Workflow:** Create a repo `open-superintelligence/nanoGPT-replication`. Create issues like `[Task] Implement Multi-Head Attention`, `[Task] Set up data loading pipeline for OpenWebText`, `[Bug] Loss is not converging`. Contributors pick these up. + 2. **"The Stack" Project:** Build your lab's core toolkit. A standardized, reusable library for training, data loading, and evaluation. This is a force multiplier. + 3. **Educational Content:** Your YouTube videos are the fuel for this phase. Each issue you work on can be a video. + * *Video Title Idea:* "Let's Code a Transformer from Scratch! (Open Superintelligence Lab - Day 5)" + * *Content:* Explain the theory, show the live coding, push the code, and end with a call to action: "If you want to help, check out issue #47 on our GitHub to implement the AdamW optimizer. Link in the description!" + +#### **Phase 2: Principled Exploration (The Next 1-2 Years)** + +Now that your community has skills and you have a stable codebase, you can start exploring the frontier. Instead of "do everything," frame it as distinct but potentially interconnected **Research Thrusts**. This allows people to specialize and you to hedge your bets. + +* **Objective:** Investigate promising but less-certain avenues for intelligence. +* **Proposed Research Thrusts:** + 1. **Thrust A: Predictive World Models:** This is where your **V-JEPA** idea fits perfectly. The hypothesis is that a system that can accurately predict the future (in some abstract representation space) will have learned a fundamental understanding of the world. + * *Projects:* Implement and scale I-JEPA, V-JEPA. Try to combine it with language models (e.g., can a text prompt influence a video prediction?). + 2. **Thrust B: Scalable Reasoning & Planning:** This is where your **hierarchical reasoning** idea fits. The hypothesis is that raw scaling of transformers is not enough; we need explicit mechanisms for long-horizon planning and abstract thought. + * *Projects:* Explore neuro-symbolic integrations (e.g., an LLM that can write and execute code in a formal logic solver). Implement search algorithms like MCTS on top of learned models. + 3. **Thrust C: Extreme-Scale Foundation Models:** This is the LLM / Video Gen track. The hypothesis is that many desired capabilities will emerge from pure scale, and our job is to push that boundary. + * *Projects:* Find a niche you can compete in. Don't try to train a GPT-4. Maybe a highly specialized science LLM, or a model trained on all of Wikipedia's edit history to understand concept evolution. + 4. **Thrust D: Agency & Embodiment:** The hypothesis that true intelligence must be grounded in action and interaction with an environment. + * *Projects:* Build agents in simulated environments (e.g., MineRL, Procgen). Focus on long-term memory and self-motivated exploration. + +#### **Phase 3: Synthesis & Integration** + +This is the speculative, long-term phase. The goal is to take the most successful outcomes from Phase 2 and begin combining them into novel, unified architectures. + +* *Example:* What if the World Model from Thrust A could serve as the "imagination" for the planning agent in Thrust B, which is all orchestrated by a large language model from Thrust C? + +--- + +### Part 3: The Daily Workflow - From Idea to YouTube Video + +This is how you make the roadmap a reality. + +1. **The "Lab Meeting" (Weekly YouTube Stream/Video):** + * You, as the lab director, set the high-level focus for the week. + * "This week, in our World Models thrust, we're focusing on scaling up our V-JEPA implementation. Our main goal is to get it running on 4 GPUs without crashing. Here are the three main challenges..." + * Shout out key contributors from the past week. Review an interesting Pull Request live. + +2. **GitHub as the "Source of Truth":** + * Every task, from "fix a typo in the documentation" to "design a new loss function," is a GitHub issue. + * Use tags to organize: `[Thrust A: World Models]`, `[good first issue]`, `[help wanted]`, `[discussion]`. This makes it easy for newcomers to find a way to contribute. + +3. **The "Daily Showcase" (Your YouTube Videos):** + * Don't just vlog. Make it a structured lab update. Pick one interesting commit or PR from the day. + * **Format:** + * **Hook (15s):** "Today, we finally figured out why our model's loss was exploding. The answer is surprisingly simple and it's a lesson for anyone training large models." + * **Context (60s):** "We're working on our V-JEPA implementation (part of our World Models thrust) and we were stuck on issue #123..." + * **The Work (2-3 min):** Show the code. Explain the bug. Explain the fix. Credit the contributor who found it. + * **The Result (30s):** Show the new, stable loss curve. + * **Call to Action (30s):** "This unblocks us for the next step: implementing multi-block masking. If that sounds interesting, check out issue #145. Link below. See you all tomorrow." + +### Answers to Your Specific Doubts: + +* **"Should I read papers for new architectures?"** + * **YES, absolutely.** But your role is to be the *curator and translator*. Read papers not just to implement them, but to place them within your roadmap. A paper on causal reasoning fits into Thrust B. A new self-supervised learning technique fits into Thrust A. Your job is to explain *why* it's important to your community. + +* **"Should I combine V-JEPA and some hierarchical reasoning?"** + * This is a fantastic Phase 3 goal. Frame it that way. First, build excellent, standalone implementations in Phase 2. This creates a strong foundation. Then, you can propose the grand synthesis project, which will be much more achievable with the tools and skills you've already built. + +* **"Should we do LLMs or video gen models or everything?"** + * Use the **Thrusts** model. You're not doing "everything" chaotically. You are running a portfolio of principled bets on the future of AI. LLMs are in Thrust C, Video Gen in Thrust A. This organizes the chaos and lets different people work on different parts of the puzzle in a coordinated way. + +### Your First Steps: + +1. **Write and publish your Manifesto.** This is your founding document. +2. **Set up the infrastructure:** A GitHub organization, a Discord server, your YouTube channel branding. +3. **Announce Phase 1.** Choose your first replication project (nanoGPT is a great choice because it's so well-understood). +4. **Create the first 10-20 GitHub issues.** Make 5 of them `[good first issue]` (e.g., documentation, setting up requirements.txt). +5. **Record and post your first video:** "I'm launching the Open Superintelligence Lab. Here's our plan, and here's how you can make your first contribution, right now." + +You are not just a YouTuber. You are the founder and director of a distributed, open-source research lab. Your product is not just videos; it's a community, a codebase, and a shared journey of discovery. This is an incredibly powerful and inspiring vision. Embrace the uncertainty and make the process of exploration the core of your content. \ No newline at end of file From fad4a61b02b2651d7d3846824f02a225a0864c3b Mon Sep 17 00:00:00 2001 From: vukrosic Date: Sun, 19 Oct 2025 18:25:15 +0200 Subject: [PATCH 2/6] Update footer text to display 'Budapest' and revise roadmap content for clarity and focus on foundational capabilities and lab bootstrapping, including new project objectives and descriptions. --- components/footer.tsx | 2 +- .../content.md | 81 +++---------------- 2 files changed, 11 insertions(+), 72 deletions(-) diff --git a/components/footer.tsx b/components/footer.tsx index b67173a..4a448f2 100644 --- a/components/footer.tsx +++ b/components/footer.tsx @@ -73,7 +73,7 @@ export function Footer() { • - {language === 'en' ? 'Advancing AI research and development' : '推进AI研究和开发'} + Budapest diff --git a/public/content/roadmap-to-open-superintelligence/content.md b/public/content/roadmap-to-open-superintelligence/content.md index 261db36..19a6386 100644 --- a/public/content/roadmap-to-open-superintelligence/content.md +++ b/public/content/roadmap-to-open-superintelligence/content.md @@ -22,82 +22,21 @@ Therefore, our roadmap will not be a linear path from A to B. It will be a **str --- -#### **Phase 1: Foundational Capabilities & Community Bootstrapping (The First 6-12 Months)** +#### **Phase 1: Foundational Capabilities & Lab Bootstrapping (The First 6-12 Months)** The goal here is not to invent ASI, but to build the tools, skills, and community that *can*. This phase is perfect for training our new workforce. -* **Objective:** Achieve SOTA on well-understood problems and build a robust, open-source ML infrastructure. -* **Projects:** - 1. **Replication Projects:** Pick 3-5 seminal papers and replicate them from scratch in a clean, well-documented repository. This is an incredible learning tool. - * *Examples:* GPT-2/3 (nanoGPT style), a Diffusion Model (DDPM), AlphaZero, a Vision Transformer (ViT). - * **GitHub Workflow:** Create a repo `open-superintelligence/nanoGPT-replication`. Create issues like `[Task] Implement Multi-Head Attention`, `[Task] Set up data loading pipeline for OpenWebText`, `[Bug] Loss is not converging`. Contributors pick these up. - 2. **"The Stack" Project:** Build your lab's core toolkit. A standardized, reusable library for training, data loading, and evaluation. This is a force multiplier. - 3. **Educational Content:** Your YouTube videos are the fuel for this phase. Each issue you work on can be a video. - * *Video Title Idea:* "Let's Code a Transformer from Scratch! (Open Superintelligence Lab - Day 5)" - * *Content:* Explain the theory, show the live coding, push the code, and end with a call to action: "If you want to help, check out issue #47 on our GitHub to implement the AdamW optimizer. Link in the description!" +* **Objective:** Achieve SOTA on well-understood problems and build a robust, open-source ML infrastructure - [LLM](https://github.com/Open-Superintelligence-Lab/blueberry-llm), JEPA, Video Gen, World Models, TRM (HRM) +* +1. Creating custom optimizer for each part of a model - [Manifold Muon](https://thinkingmachines.ai/blog/modular-manifolds/) -#### **Phase 2: Principled Exploration (The Next 1-2 Years)** +2. **V-JEPA:** The hypothesis is that a system that can accurately predict the future (in some abstract representation space, by learning to predict videos) will have learned a fundamental understanding of the world. +*Project:* Implement V-JEPA-2. Come up with research questions and directions. Make videos, blog posts and encourage community exploration. -Now that your community has skills and you have a stable codebase, you can start exploring the frontier. Instead of "do everything," frame it as distinct but potentially interconnected **Research Thrusts**. This allows people to specialize and you to hedge your bets. +3. **3D-World Generation:** Following "The Bitter Lesson" (general algorithm + compute scaling eventually wins) - understand how to generate virtual worlds efficiently where AI models can learn on their own by interacting with them. -* **Objective:** Investigate promising but less-certain avenues for intelligence. -* **Proposed Research Thrusts:** - 1. **Thrust A: Predictive World Models:** This is where your **V-JEPA** idea fits perfectly. The hypothesis is that a system that can accurately predict the future (in some abstract representation space) will have learned a fundamental understanding of the world. - * *Projects:* Implement and scale I-JEPA, V-JEPA. Try to combine it with language models (e.g., can a text prompt influence a video prediction?). - 2. **Thrust B: Scalable Reasoning & Planning:** This is where your **hierarchical reasoning** idea fits. The hypothesis is that raw scaling of transformers is not enough; we need explicit mechanisms for long-horizon planning and abstract thought. - * *Projects:* Explore neuro-symbolic integrations (e.g., an LLM that can write and execute code in a formal logic solver). Implement search algorithms like MCTS on top of learned models. - 3. **Thrust C: Extreme-Scale Foundation Models:** This is the LLM / Video Gen track. The hypothesis is that many desired capabilities will emerge from pure scale, and our job is to push that boundary. - * *Projects:* Find a niche you can compete in. Don't try to train a GPT-4. Maybe a highly specialized science LLM, or a model trained on all of Wikipedia's edit history to understand concept evolution. - 4. **Thrust D: Agency & Embodiment:** The hypothesis that true intelligence must be grounded in action and interaction with an environment. - * *Projects:* Build agents in simulated environments (e.g., MineRL, Procgen). Focus on long-term memory and self-motivated exploration. +#### **Phase 2: The Next Models(Long-term Vision)** -#### **Phase 3: Synthesis & Integration** +This is the speculative, long-term phase. The goal is to take the most successful outcomes from Phase 1 and Phase 2 and begin combining them into novel, unified architectures. -This is the speculative, long-term phase. The goal is to take the most successful outcomes from Phase 2 and begin combining them into novel, unified architectures. - -* *Example:* What if the World Model from Thrust A could serve as the "imagination" for the planning agent in Thrust B, which is all orchestrated by a large language model from Thrust C? - ---- - -### Part 3: The Daily Workflow - From Idea to YouTube Video - -This is how you make the roadmap a reality. - -1. **The "Lab Meeting" (Weekly YouTube Stream/Video):** - * You, as the lab director, set the high-level focus for the week. - * "This week, in our World Models thrust, we're focusing on scaling up our V-JEPA implementation. Our main goal is to get it running on 4 GPUs without crashing. Here are the three main challenges..." - * Shout out key contributors from the past week. Review an interesting Pull Request live. - -2. **GitHub as the "Source of Truth":** - * Every task, from "fix a typo in the documentation" to "design a new loss function," is a GitHub issue. - * Use tags to organize: `[Thrust A: World Models]`, `[good first issue]`, `[help wanted]`, `[discussion]`. This makes it easy for newcomers to find a way to contribute. - -3. **The "Daily Showcase" (Your YouTube Videos):** - * Don't just vlog. Make it a structured lab update. Pick one interesting commit or PR from the day. - * **Format:** - * **Hook (15s):** "Today, we finally figured out why our model's loss was exploding. The answer is surprisingly simple and it's a lesson for anyone training large models." - * **Context (60s):** "We're working on our V-JEPA implementation (part of our World Models thrust) and we were stuck on issue #123..." - * **The Work (2-3 min):** Show the code. Explain the bug. Explain the fix. Credit the contributor who found it. - * **The Result (30s):** Show the new, stable loss curve. - * **Call to Action (30s):** "This unblocks us for the next step: implementing multi-block masking. If that sounds interesting, check out issue #145. Link below. See you all tomorrow." - -### Answers to Your Specific Doubts: - -* **"Should I read papers for new architectures?"** - * **YES, absolutely.** But your role is to be the *curator and translator*. Read papers not just to implement them, but to place them within your roadmap. A paper on causal reasoning fits into Thrust B. A new self-supervised learning technique fits into Thrust A. Your job is to explain *why* it's important to your community. - -* **"Should I combine V-JEPA and some hierarchical reasoning?"** - * This is a fantastic Phase 3 goal. Frame it that way. First, build excellent, standalone implementations in Phase 2. This creates a strong foundation. Then, you can propose the grand synthesis project, which will be much more achievable with the tools and skills you've already built. - -* **"Should we do LLMs or video gen models or everything?"** - * Use the **Thrusts** model. You're not doing "everything" chaotically. You are running a portfolio of principled bets on the future of AI. LLMs are in Thrust C, Video Gen in Thrust A. This organizes the chaos and lets different people work on different parts of the puzzle in a coordinated way. - -### Your First Steps: - -1. **Write and publish your Manifesto.** This is your founding document. -2. **Set up the infrastructure:** A GitHub organization, a Discord server, your YouTube channel branding. -3. **Announce Phase 1.** Choose your first replication project (nanoGPT is a great choice because it's so well-understood). -4. **Create the first 10-20 GitHub issues.** Make 5 of them `[good first issue]` (e.g., documentation, setting up requirements.txt). -5. **Record and post your first video:** "I'm launching the Open Superintelligence Lab. Here's our plan, and here's how you can make your first contribution, right now." - -You are not just a YouTuber. You are the founder and director of a distributed, open-source research lab. Your product is not just videos; it's a community, a codebase, and a shared journey of discovery. This is an incredibly powerful and inspiring vision. Embrace the uncertainty and make the process of exploration the core of your content. \ No newline at end of file +* *Example:* What if V-JEPA's world understanding could serve as the "thinking" for agents navigating 3D-generated worlds? How can LLMs be integrated here? From 0534b7f54dc90627ae94769c17eeddde4d3d2583 Mon Sep 17 00:00:00 2001 From: vukrosic Date: Sun, 19 Oct 2025 18:28:03 +0200 Subject: [PATCH 3/6] rename --- .../page.tsx | 16 ++++++++-------- .../content.md | 4 ++-- 2 files changed, 10 insertions(+), 10 deletions(-) rename app/blog/{roadmap-to-open-superintelligence => path-to-open-superintelligence}/page.tsx (96%) rename public/content/{roadmap-to-open-superintelligence => path-to-open-superintelligence}/content.md (95%) diff --git a/app/blog/roadmap-to-open-superintelligence/page.tsx b/app/blog/path-to-open-superintelligence/page.tsx similarity index 96% rename from app/blog/roadmap-to-open-superintelligence/page.tsx rename to app/blog/path-to-open-superintelligence/page.tsx index 57a40cd..9b04687 100644 --- a/app/blog/roadmap-to-open-superintelligence/page.tsx +++ b/app/blog/path-to-open-superintelligence/page.tsx @@ -22,7 +22,7 @@ export default function RoadmapToOpenSuperintelligence() { const fetchMarkdownContent = async () => { try { const filename = language === 'zh' ? 'content-zh.md' : 'content.md'; - const response = await fetch(`/content/roadmap-to-open-superintelligence/${filename}`); + const response = await fetch(`/content/path-to-open-superintelligence/${filename}`); const content = await response.text(); // Parse frontmatter @@ -33,9 +33,9 @@ export default function RoadmapToOpenSuperintelligence() { // Parse YAML-like frontmatter (simple parsing for our use case) const heroData: HeroData = { - title: "Roadmap To Open Superintelligence", + title: "Path To Open Superintelligence", subtitle: "A Strategic Path Forward for Building AGI Through Open Collaboration", - tags: ["🚀 Roadmap", "🎯 Vision", "🔬 Research"] + tags: ["🚀 Path", "🎯 Vision", "🔬 Research"] }; // Extract values from frontmatter @@ -103,7 +103,7 @@ export default function RoadmapToOpenSuperintelligence() { try { // Get the raw markdown content without frontmatter const filename = language === 'zh' ? 'content-zh.md' : 'content.md'; - const response = await fetch(`/content/roadmap-to-open-superintelligence/${filename}`); + const response = await fetch(`/content/path-to-open-superintelligence/${filename}`); const content = await response.text(); // Remove frontmatter if present @@ -154,7 +154,7 @@ export default function RoadmapToOpenSuperintelligence() {

- {heroData?.title || 'Roadmap To Open Superintelligence'} + {heroData?.title || 'Path To Open Superintelligence'}

@@ -193,7 +193,7 @@ export default function RoadmapToOpenSuperintelligence() { {/* Glow effect for the title */}
- {heroData?.title || 'Roadmap To Open Superintelligence'} + {heroData?.title || 'Path To Open Superintelligence'}
@@ -304,7 +304,7 @@ export default function RoadmapToOpenSuperintelligence() {
- @@ -312,7 +312,7 @@ export default function RoadmapToOpenSuperintelligence() { - diff --git a/public/content/roadmap-to-open-superintelligence/content.md b/public/content/path-to-open-superintelligence/content.md similarity index 95% rename from public/content/roadmap-to-open-superintelligence/content.md rename to public/content/path-to-open-superintelligence/content.md index 19a6386..eb2fc6d 100644 --- a/public/content/roadmap-to-open-superintelligence/content.md +++ b/public/content/path-to-open-superintelligence/content.md @@ -1,7 +1,7 @@ --- hero: - title: "Roadmap To Open Superintelligence" - subtitle: "A Strategic Path Forward for Building ASI Through Open Collaboration" + title: "The Most Difficult Project In Human History" + subtitle: "Path To Open Superintelligence" tags: - "🚀 Roadmap" - "🎯 Vision" From 65a1900a2bcc59c36b0feb876f98c9b3b0e11d99 Mon Sep 17 00:00:00 2001 From: vukrosic Date: Sun, 19 Oct 2025 18:35:04 +0200 Subject: [PATCH 4/6] Add 'Path to Open Superintelligence' link to homepage and update roadmap content for clarity, including new project objectives and descriptions. --- app/page.tsx | 31 +++++++++++++++++++ .../path-to-open-superintelligence/content.md | 21 ++++++++----- 2 files changed, 44 insertions(+), 8 deletions(-) diff --git a/app/page.tsx b/app/page.tsx index c2cb92a..91dc1bc 100644 --- a/app/page.tsx +++ b/app/page.tsx @@ -170,6 +170,37 @@ export default function Home() {
+ {/* Path to Open Superintelligence */} + +
+ {getText('Vision', '愿景')} +
+
+ {getText('Strategic', '战略')} +
+ +
+

+ {getText('Path To Open Superintelligence', '开放超级智能之路')} +

+

+ {getText( + 'A strategic roadmap for building AGI through open collaboration, addressing key challenges and defining our path forward', + '通过开放协作构建AGI的战略路线图，应对关键挑战并定义我们的前进道路' + )} +

+
+ {getText('Strategic Vision', '战略愿景')} + + {getText('Read More', '阅读更多')} → + +
+
+ + {/* DeepSeek Sparse Attention Project */} Date: Sun, 19 Oct 2025 18:36:58 +0200 Subject: [PATCH 5/6] Update gradient colors in 'Path to Open Superintelligence' title for improved visual appeal --- app/blog/path-to-open-superintelligence/page.tsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/app/blog/path-to-open-superintelligence/page.tsx b/app/blog/path-to-open-superintelligence/page.tsx index 9b04687..6d6163e 100644 --- a/app/blog/path-to-open-superintelligence/page.tsx +++ b/app/blog/path-to-open-superintelligence/page.tsx @@ -153,7 +153,7 @@ export default function RoadmapToOpenSuperintelligence() {

- + {heroData?.title || 'Path To Open Superintelligence'}

@@ -192,7 +192,7 @@ export default function RoadmapToOpenSuperintelligence() { {/* Glow effect for the title */}
- + {heroData?.title || 'Path To Open Superintelligence'}
From 55d2b6d4b12d6fa4c304cce63f6c3ff5a0b36b37 Mon Sep 17 00:00:00 2001 From: vukrosic Date: Sun, 19 Oct 2025 18:40:45 +0200 Subject: [PATCH 6/6] Refactor AboutPage component to redirect to the blog path, removing previous content and enhancing navigation. --- app/about/page.tsx | 247 +----------------- .../content-zh.md | 48 ++++ 2 files changed, 50 insertions(+), 245 deletions(-) create mode 100644 public/content/path-to-open-superintelligence/content-zh.md diff --git a/app/about/page.tsx b/app/about/page.tsx index 85747d1..700531e 100644 --- a/app/about/page.tsx +++ b/app/about/page.tsx @@ -1,248 +1,5 @@ -'use client'; - -import { useLanguage } from "@/components/providers/language-provider"; +import { redirect } from 'next/navigation'; export default function AboutPage() { - const { language } = useLanguage(); - - return ( - <> - {/* Hero Section */} -
- {/* Enhanced background effects */} -
-
-
-
- - {/* Enhanced animated background particles */} -
- {/* Large floating particles - some made more glowy and dreamy */} -
-
-
-
-
-
- - {/* Medium particles - enhanced with glow */} -
-
-
-
-
- - {/* Small twinkling particles - some made more dreamy */} -
-
-
-
-
-
- - {/* Floating geometric shapes - some made more ethereal */} -
-
-
- - {/* Enhanced glowing orbs - made more dreamy */} -
-
-
- - {/* Additional dreamy particles */} -
-
-
-
- -
-
-
-

- {language === 'en' ? 'About Our Lab' : '关于我们实验室'} -

-

- {language === 'en' - ? 'Advancing AI research through open collaboration and innovation' - : '通过开放协作和创新推进AI研究' - } -

-
-
-
-
- -
-
-
-
- {/* Mission Section */} -
-

- {language === 'en' ? 'Our Mission' : '我们的使命'} -

-
-

- {language === 'en' - ? 'At the Open Superintelligence Lab, we conduct open research on the best open source projects and Large Language Models (LLMs). Our mission is to advance the field of artificial intelligence through transparent, collaborative research that benefits the entire AI community.' - : '在开放超级智能实验室，我们对最好的开源项目和大语言模型(LLMs)进行开放研究。我们的使命是通过透明、协作的研究推进人工智能领域，造福整个AI社区。' - } -

-

- {language === 'en' - ? 'We believe that the future of AI should be built on open principles, where knowledge is shared freely and innovations are accessible to everyone. Our research focuses on understanding, improving, and advancing the state-of-the-art in open source AI technologies.' - : '我们相信AI的未来应该建立在开放原则之上，知识自由分享，创新对所有人开放。我们的研究专注于理解、改进和推进开源AI技术的最先进水平。' - } -

-
-
- - {/* Research Focus */} -
-

- {language === 'en' ? 'Research Focus' : '研究重点'} -

-
-
-

- {language === 'en' ? 'Open Source Projects' : '开源项目'} -

-

- {language === 'en' - ? 'We analyze and contribute to the most promising open source AI projects, identifying best practices and areas for improvement.' - : '我们分析并贡献最有前景的开源AI项目，识别最佳实践和改进领域。' - } -

-
-
-

- {language === 'en' ? 'Large Language Models' : '大语言模型'} -

-

- {language === 'en' - ? 'We conduct research on state-of-the-art LLMs, exploring their capabilities, limitations, and potential for advancement.' - : '我们对最先进的大语言模型进行研究，探索它们的能力、局限性和改进潜力。' - } -

-
-
-
- - {/* Current Projects */} -
-

- {language === 'en' ? 'Current Research Areas' : '当前研究领域'} -

-
-
-

- DeepSeek-V3.2-Exp Research -

-

- {language === 'en' - ? 'Investigating DeepSeek\'s Sparse Attention (DSA) mechanisms and long-context efficiency improvements in open source language models.' - : '研究DeepSeek的稀疏注意力机制(DSA)和开源语言模型中的长上下文效率改进。' - } -

-
-
-

- GPT-OSS Research -

-

- {language === 'en' - ? 'Exploring OpenAI\'s open-source Mixture of Experts (MoE) language models with advanced reasoning capabilities and safety features.' - : '探索OpenAI的开源专家混合(MoE)语言模型，具有先进的推理能力和安全特性。' - } -

-
-
-
- - {/* Values */} -
-

- {language === 'en' ? 'Our Values' : '我们的价值观'} -

-
-
-
- 🔓 -
-

- {language === 'en' ? 'Openness' : '开放性'} -

-

- {language === 'en' - ? 'Transparent research and open collaboration' - : '透明研究和开放协作' - } -

-
-
-
- 🚀 -
-

- {language === 'en' ? 'Innovation' : '创新'} -

-

- {language === 'en' - ? 'Pushing the boundaries of AI research' - : '推动AI研究的边界' - } -

-
-
-
- 🤝 -
-

- {language === 'en' ? 'Collaboration' : '协作'} -

-

- {language === 'en' - ? 'Building a stronger AI community together' - : '共同建设更强大的AI社区' - } -

-
-
-
- - {/* Call to Action */} -
-

- {language === 'en' ? 'Join Our Research' : '加入我们的研究'} -

-

- {language === 'en' - ? 'Interested in contributing to open AI research? Explore our projects and learn how you can get involved in advancing the field of artificial intelligence.' - : '有兴趣为开放AI研究做贡献吗？探索我们的项目，了解如何参与推进人工智能领域的发展。' - } -

- -
-
-
-
-
- - ); + redirect('/blog/path-to-open-superintelligence'); } diff --git a/public/content/path-to-open-superintelligence/content-zh.md b/public/content/path-to-open-superintelligence/content-zh.md new file mode 100644 index 0000000..b80fe5f --- /dev/null +++ b/public/content/path-to-open-superintelligence/content-zh.md @@ -0,0 +1,48 @@ +--- +hero: + title: "人类历史上最艰难的项目" + subtitle: "通往开放超级智能之路" + tags: + - "🚀 路线图" + - "🎯 愿景" + - "🔬 研究" +--- + +没有人知道超级智能会是什么样子，也不知道如何创造它。 + +因此，我们的路线图不会是从A到B的线性路径。它将是一个**用于探索、学习和集体进步的战略框架。** 它关乎创造一台能够发现路径的机器。 + +**使命：** 创造开放透明的超级智能，造福所有人。 +1. **核心原则：** + * **极度开放：** 所有代码都是开源的（例如，MIT/Apache 2.0许可证）。所有研究讨论都在公开进行（[Discord](https://discord.gg/6AbXGpKTwN)）。所有发现都会分享（[YouTube](https://www.youtube.com/channel/UC7XJj9pv_11a11FUxCMz15g)、[博客文章](https://opensuperintelligencelab.com/)、预印本）。 + * **分布式协作：** 智慧无处不在。我们欢迎来自各种背景的贡献者。一个优秀的PR就是优秀的PR，无论其作者的资历如何。 + * **教育核心：** 我们的主要产出不仅是代码，而是一代构建它的研究人员和工程师。 + * **务实探索：** 我们遵循明确的原则，但保持灵活性。我们将探索不同的方向——LLM、世界模型、视频生成、JEPA等等。 +2. **重大挑战：** 我们的目标不是执行一个已知的计划，而是建立一个能够发现它的社区和研究引擎。 + +--- + +#### **第一阶段：基础能力与实验室启动（前6个月）** + +第一阶段的目标不是发明ASI，而是构建将会发明它的工具、技能和社区。这个阶段非常适合培训我们的新生力量。 + +* **目标：** 在充分理解的问题上达到最先进水平，并构建强大的开源机器学习基础设施 - [LLM](https://github.com/Open-Superintelligence-Lab/blueberry-llm)、JEPA、视频生成、世界模型、TRM (HRM) + +1. 为模型的每个部分创建自定义优化器 - [Manifold Muon](https://github.com/Open-Superintelligence-Lab/blueberry-llm/issues/36)。[更多研究任务在这里](https://github.com/Open-Superintelligence-Lab/blueberry-llm/issues) + +2. **V-JEPA：** 假设是，一个能够准确预测未来的系统（在某个抽象表示空间中，通过学习预测视频）将学会对世界的基本理解。 +*项目：* 实现V-JEPA-2。提出研究问题和方向。制作视频、博客文章，鼓励社区探索。 + +3. **3D世界生成：** 遵循"痛苦的教训"（通用算法+计算扩展最终获胜）——理解如何高效生成虚拟世界，让AI模型可以通过与之交互来自主学习。 + +#### **第二阶段：下一代模型（长期愿景）** + +这是长期阶段。目标是从第一阶段中挑选最成功的成果，开始将它们组合成新颖的统一架构。 + +* *例如：* 如果V-JEPA的世界理解可以作为代理在3D生成世界中导航的"思考"呢？LLM如何整合到这里？ + +**更多信息即将发布：** + +- [Discord](https://discord.gg/6AbXGpKTwN) +- [YouTube](https://www.youtube.com/channel/UC7XJj9pv_11a11FUxCMz15g) +