布萊嗯研究所

在開發 AI 多智能體（Multi-Agent）模擬時，開發者面臨的最大痛點是： 「環境定義的極高成本」 。傳統方式需要為每個場景編寫大量冗長的 Prompt、硬編碼物理規則，並を手動處理複雜的資訊不對稱（例如：誰能看到什麼）。如果你想從「辦公室政治」場景切換到「茶館間諜」場景，幾乎得重寫整個邏輯層。 WorldSeed 的出現正是為了擊碎這種僵局。它將「世界規則」與「執行引擎」徹底解耦，讓開發者只需通過簡單的 YAML 配置即可生成一個具有物理規則、資訊隔離且能自主演化的 AI 世界。在 LLM 邁向自主 Agent 的今天，WorldSeed 提供了將 AI 從「聊天機器人」轉化為「社會參與者」的關鍵基礎設施。 When developing Multi-Agent AI simulations, developers face a critical pain point: 'The exorbitant cost of environment definition.' Traditional methods require writing exhaustive prompts for every scene, hard-coding physical rules, and manually managing complex information asymmetry (e.g., who sees what). Switching from an 'office politics' scenario to a 'teahouse espionage' setting would typically mean rewriting the entire logic layer. WorldSeed is designed to shatter this bottleneck. By completely decoupling 'world rules' from the 'execution engine,' it allows developers to generate an autonomous, evol...

[CN] 在 AI 驅動開發的時代，開發者面臨的最大痛點不再是寫代碼，而是如何精準地向 AI 描述「視覺風格」。傳統上，我們需要手動分析對手的 CSS、記錄顏色數值、定義間距，再將這些碎片化資訊餵給 AI，結果往往是 AI 生成的界面與預期相去甚遠。 design-md-chrome 徹底擊碎了這個過程，它能將任何網站的視覺基因直接轉化為標準化的 DESIGN.md 檔案。這讓 AI 擁有了一套「視覺指令集」，讓 AI 生成的 UI 不再是隨機的，而是具備精準設計系統的工業級產出。 [EN] In the era of AI-driven development, the biggest pain point for developers is no longer writing code, but accurately describing "visual styles" to AI. Traditionally, we had to manually analyze CSS, record color values, and define spacing, then feed these fragmented bits to an AI, often resulting in interfaces that missed the mark. design-md-chrome shatters this workflow by extracting the visual DNA of any website and converting it into a standardized DESIGN.md file. This provides AI with a "visual instruction set," ensuring that AI-generated UIs are no longer random but professional outputs based on a precise design system. 🛠️ 核心功能 | Key Features [CN] 一...

[CN] 在當前 LLM 的開發中，開發者面臨著一個巨大的痛點：如何在不增加參數總量的情況下，提升模型處理複雜推理的能力？傳統的 Transformer 依賴於堆疊數百層不同的權重，這導致了巨大的顯存壓力與計算冗餘。OpenMythos 旨在擊碎這一困境，它基於對 Claude Mythos 架构的理論重建，探索『遞歸深度 Transformer (RDT)』的可能性——讓模型在單次前向傳播中通過權重循環來『深思』，而非單純依靠增加層數。這正是當前 AI 領域追求『計算自適應推理』的核心熱點。 [EN] In current LLM development, developers face a massive pain point: how to enhance a model's complex reasoning capabilities without exponentially increasing the total parameter count? Traditional Transformers rely on stacking hundreds of unique layers, leading to immense memory pressure and computational redundancy. OpenMythos shatters this limitation by theoretically reconstructing the Claude Mythos architecture, exploring the potential of Recurrent-Depth Transformers (RDT). It enables the model to 'think deeper' via weight loops within a single forward pass rather than simply adding more layers, hitting the current AI hotspot of 'compute-adaptive reasoning'. 🛠...

[中文] 在 AI 時代，內容生產已不再是「寫作」的問題，而是「工作流」的問題。許多開發者與運營者面臨著巨大的痛點：為了提升 AI 搜索能見度 (GEO)，他們必須在-多個 AI 接口、雜亂的素材庫、手動的校對流程以及繁瑣的 SEO 設置之間頻繁切換。這種「碎片化」的生產方式導致了效率極低，且難以維持高品質的內容一致性。GEOFlow 應運而生，旨在擊碎這種低效的手動循環，將 AI 生成、素材管理與發布審核整合為一條標準化的工業流水線。 [English] In the AI era, content production is no longer a problem of 'writing,' but a problem of 'workflow.' Many developers and operators face a significant pain point: to improve AI Search Engine visibility (GEO), they must constantly switch between multiple AI APIs, cluttered asset libraries, manual proofreading processes, and tedious SEO settings. This 'fragmented' production method leads to extreme inefficiency and difficulty in maintaining high-quality content consistency. GEOFlow was born to shatter this inefficient manual cycle, integrating AI generation, asset management, and publishing reviews into a standardized industrial pipeline. 🛠️ 核心功能 | Key Features [中文] 多模型統一接入...

對於開發者來說，最痛苦的時刻莫過於拿到一個『只要參考這個網站』的指令，卻得花數小時在 Chrome DevTools 中反覆切換、手動抄寫顏色代碼、字體大小和間距。即使有了 Figma，現實中的網站實作往往與設計稿存在落差。 design-extract 擊碎了這種低效的『手動對齊』過程。在 AI 驅動開發的時代，LLM 需要精確的上下文（Context）才能生成高品質 UI，而此工具能將整個網站的設計語言轉化為 AI 可讀的 Markdown 與 Token，讓 AI 能精準地幫你重建任何視覺風格，將數天的對接時間縮短至秒級。 For developers, one of the most frustrating experiences is being told to 'just refer to this website' and spending hours toggling through Chrome DevTools, manually copying color codes, font sizes, and spacing. Even with Figma, real-world implementations often drift from original designs. design-extract shatters this inefficient 'manual alignment' process. In the era of AI-driven development, LLMs require precise context to generate high-quality UI. By transforming a website's design language into AI-optimized Markdown and Tokens, this tool allows AI to accurately recreate any visual style, reducing days of manual bridging to mere seconds. 🛠️ 核心功能 ...

[ZH] 對於獨立開發者（Indie Hackers）而言，最大的痛點並非「如何編碼」，而是「該做什麼」。每天面對海量的資訊流（Hacker News, GitHub, Reddit），開發者往往陷入『資訊過載』卻『靈感匱乏』的困境，導致花費數月開發出沒人需求的產品。BuilderPulse 誕生於此：它利用 AI 將 300 多個公共信號轉化為每日單一、高勝率的開發方向，將開發者從無止盡的市場研究中解放，直接切入最具商業價值的痛點。 [EN] For indie hackers, the biggest pain point isn't 'how to code,' but 'what to build.' Developers often find themselves trapped in 'information overload' yet 'inspiration poverty,' scrolling through endless feeds of Hacker News, GitHub, and Reddit, only to spend months building products nobody wants. BuilderPulse solves this by using AI to distill 300+ public signals into a single, high-conviction build direction every morning, liberating developers from tedious market research and pointing them directly toward the most commercially viable pain points. 🛠️ 核心功能 | Key Features [ZH] 跨平台信號聚合： 每日分析 Hacker News, GitHub Trending, Product Hunt 等 10+ 個核心來源，不再需要手動刷網頁。 [EN] Cro...

  這套配置的核心目標是： 解耦 (Decoupling) 。我們讓 n8n 跑在容器內，但透過隧道與你本地的 Python 資源對接。 1. 目錄配置 (Project Structure) 首先，在終端機建立工作目錄。良好的目錄結構是維運的第一步。 Bash mkdir -p ~/n8n-infra/data cd ~/n8n-infra 2. 撰寫 docker-compose.yml 使用你最愛的編輯器（VS Code 或 vim ），建立 docker-compose.yml 。這份檔案定義了 n8n 本身以及一個 PostgreSQL 數據庫（比起預設的 SQLite，Postgres 在長期執行大量 539 數據分析時更穩定）。 YAML version: '3.8' services: db: image: postgres:16-alpine container_name: n8n-postgres restart: always environment: - POSTGRES_USER=n8n_admin - POSTGRES_PASSWORD=n8n_secure_pass - POSTGRES_DB=n8n_metadata volumes: - ./data/postgres:/var/lib/postgresql/data n8n: image: docker.n8n.io/n8nio/n8n:latest container_name: n8n-webui restart: always ports: - "5678:5678" environment: - DB_TYPE=postgresdb - DB_POSTGRESDB_HOST=db - DB_POSTGRESDB_PORT=5432 - DB_POSTGRESDB_DATABASE=n8n_metadata - DB_POSTGRESDB_USER=n...