DESIGN.md: Google Stitch's Most Underestimated Feature
TL; DR Key Takeaways
- DESIGN.md is an agent-friendly Markdown file used to document and synchronize design rules (color palettes, fonts, spacing, component patterns), allowing AI to automatically maintain brand consistency when generating UIs.
- Its logic is similar to Agents.md in the development world: it sets rules for AI using a file that both humans and AI can read and write.
- Google Stitch released 5 major feature upgrades in March 2026, and DESIGN.md is the most inconspicuous yet strategically valuable among them in the long term.
- DESIGN.md can automatically extract design systems from any URL and can be imported/exported between projects, completely eliminating the wasted time of repeatedly setting up design tokens.
- This upgrade garnered over 15.9 million views on Twitter, and Figma's stock price dropped 8.8% on the day.
Why did a Markdown file cause Figma's stock price to drop 8.8%?
On March 19, 2026, Google Labs announced a major upgrade to Stitch. Immediately after the news broke, Figma's stock price fell 8.8% 1. Related discussions on Twitter exceeded 15.9 million views.
This article is suitable for product designers, front-end developers, entrepreneurs who are using or following AI design tools, and all content creators who need to maintain brand visual consistency.
Most reports focused on "visible" features like infinite canvas and voice interaction. But what truly changed the industry landscape might be the most inconspicuous thing: DESIGN.md. This article will delve into what this "most underestimated feature" actually is, why it is crucial for design workflows in the AI era, and practical methods you can start using today.
Google Stitch 2026 Upgrade: A Full Overview of 5 Major Features
Before diving into DESIGN.md, let's quickly understand the full scope of this upgrade. Google has transformed Stitch from an AI UI generation tool into a complete "vibe design" platform 2. Vibe design means you no longer need to start from wireframes; instead, you can describe business goals, user emotions, and even inspiration sources using natural language, and AI directly generates high-fidelity UIs.
The five core features include:
- AI-Native Canvas: A new infinite canvas that supports mixed input of images, text, and code, providing ample space for ideas to evolve from early concepts to interactive prototypes.
- Smarter Design Agent: Capable of understanding the evolution history of an entire project, reasoning across versions, and managing multiple parallel design directions through an Agent Manager.
- Voice: Based on Gemini Live, you can speak directly to the canvas, and AI provides real-time design reviews, generates variations, and adjusts color schemes.
- Instant Prototypes: One-click conversion of static designs into clickable interactive prototypes, with AI automatically generating the next screen based on user clicks.
- DESIGN.md (Design System File): An agent-friendly Markdown file for importing and exporting design rules.
The first four features are exciting; the fifth makes you think. And it's often the things that make you think that truly change the game.
What is DESIGN.md, and why is it as important as Agents.md?
If you are familiar with the development world, you must know Agents.md. It's a Markdown file placed in the root directory of a code repository that tells AI coding assistants "what the rules of this project are": code style, architectural conventions, naming conventions. With it, tools like Claude Code and Cursor won't "freely improvise" when generating code but will follow the team's established standards 3.
DESIGN.md does exactly the same thing, but the object changes from code to design.
It is a Markdown-formatted file that records a project's complete design rules: color schemes, font hierarchies, spacing systems, component patterns, and interaction specifications 4. Human designers can read it, and AI design agents can also read it. When Stitch's design agent reads your DESIGN.md, every UI screen it generates will automatically follow the same visual rules.
Without DESIGN.md, 10 pages generated by AI might have 10 different button styles. With it, 10 pages look like they were made by the same designer.
This is why AI Business analyst Bradley Shimmin points out that when enterprises use AI design platforms, they need "deterministic elements" to guide AI's behavior, whether it's enterprise design specifications or standardized requirement datasets 5. DESIGN.md is the best carrier for this "deterministic element."
Why DESIGN.md is the most underestimated feature
On Reddit's r/FigmaDesign subreddit, users enthusiastically discussed Stitch's upgrade. Most focused on the canvas experience and AI generation quality 6. But Muzli Blog's in-depth analysis pointed out incisively: the value of DESIGN.md is that it eliminates the need to rebuild design tokens every time you switch tools or start a new project. "This isn't theoretical efficiency improvement; it genuinely saves a day of setup work" 7.
Imagine a real scenario: you are an entrepreneur and have designed the first version of your product's UI using Stitch. Three months later, you need to create a new marketing landing page. Without DESIGN.md, you would have to tell AI again what your brand colors are, what font to use for titles, and how much corner radius your buttons should have. With DESIGN.md, you just need to import this file, and AI immediately "remembers" all your design rules.
More critically, DESIGN.md doesn't just circulate within Stitch. Through Stitch's MCP Server and SDK, it can connect to development tools like Claude Code, Cursor, and Antigravity 8. This means that visual specifications defined by designers in Stitch can also be automatically followed by developers when coding. The "translation" gap between design and development is bridged by a Markdown file.
How to start using DESIGN.md: A 3-step guide
The barrier to entry for using DESIGN.md is extremely low, which is also part of its appeal. Here are three main ways to create it:
Method 1: Automatic extraction from existing websites
Enter any URL in Stitch, and AI will automatically analyze the website's color scheme, fonts, spacing, and component patterns to generate a complete DESIGN.md file. If you want the visual style of your new project to be consistent with an existing brand, this is the fastest method.
Method 2: Generate from brand assets
Upload your brand logo, VI manual screenshots, or any visual references, and Stitch's AI will extract design rules from them and generate DESIGN.md. For teams that don't yet have systematic design specifications, this is equivalent to AI performing a design audit for you.
Method 3: Manual writing
Advanced users can directly write DESIGN.md using Markdown syntax, precisely specifying each design rule. This method offers the strongest control and is suitable for teams with strict brand guidelines.
If you prefer to collect and organize a large amount of brand assets, competitor screenshots, and inspiration references before starting, YouMind's Board feature can help you save and retrieve all these scattered URLs, images, and PDFs in one place. After organizing your materials, use YouMind's Craft editor to directly write and iterate on your DESIGN.md file. Native Markdown support means you don't need to switch between tools.
Common error reminders:
- Do not write DESIGN.md as a "vision document." It requires specific values (e.g.,
primary-color: #1A73E8), not vague descriptions (e.g., "use brand blue").
- Update regularly. DESIGN.md is a living document, and design rules should evolve synchronously with product iterations.
- Do not try to cover all scenarios in one file. Start with core colors, fonts, and spacing, then gradually expand.
AI Design Tool Comparison: Which one is best for you?
Google Stitch's upgrade has made the AI design tool landscape even more crowded. Here's a comparison of the positioning of several mainstream tools:
Tool | Best Use Case | Free Version | Core Advantage |
|---|---|---|---|
AI-native UI design + prototyping | ✅ | DESIGN.md design system + MCP ecosystem | |
Professional team collaboration design | ✅ | Mature component library and plugin ecosystem | |
AI-assisted coding | ✅ | Code generation + context understanding | |
Design asset collection + specification writing | ✅ | Board multi-source integration + Craft Markdown editing | |
Rapid generation of front-end components | ✅ | React/Next.js ecosystem integration |
It's important to note that these tools are not mutually exclusive. A complete AI design workflow might involve: using YouMind Board to collect inspiration and brand assets, using Stitch to generate UI and DESIGN.md, and then connecting to Cursor for development via MCP. The interoperability between tools is precisely where the value of standardized files like DESIGN.md lies.
FAQ
Q: What is the difference between DESIGN.md and traditional design tokens?
A: Traditional design tokens are usually stored in JSON or YAML format, primarily for developers. DESIGN.md uses Markdown format, catering to both human designers and AI agents, offering better readability and the ability to include richer contextual information such as component patterns and interaction specifications.
Q: Can DESIGN.md only be used in Google Stitch?
A: No. DESIGN.md is essentially a Markdown file and can be edited in any Markdown-supported tool. Through Stitch's MCP Server, it can also seamlessly integrate with tools like Claude Code, Cursor, and Antigravity, enabling synchronization of design rules across the entire toolchain.
Q: Can non-designers use DESIGN.md?
A: Absolutely. Stitch supports automatic extraction of design systems from any URL and generation of DESIGN.md, so you don't need any design background. Entrepreneurs, product managers, and front-end developers can all use it to establish and maintain brand visual consistency.
Q: Is Google Stitch currently free?
A: Yes. Stitch is currently in the Google Labs phase and is free to use. It is based on Gemini 3 Flash and 3.1 Pro models. You can start experiencing it by visiting stitch.withgoogle.com.
Q: What is the relationship between vibe design and vibe coding?
A: Vibe coding uses natural language to describe intent for AI to generate code, while vibe design uses natural language to describe emotions and goals for AI to generate UI designs. Both share the same philosophy, and Stitch integrates them through MCP, forming a complete AI-native workflow from design to development.
Summary
Google Stitch's latest upgrade, seemingly a release of 5 features, is essentially Google's strategic move in the AI design field. The infinite canvas provides space for creativity, voice interaction makes collaboration more natural, and instant prototypes accelerate validation. But DESIGN.md does something more fundamental: it addresses the biggest pain point of AI-generated content, which is consistency.
A Markdown file transforms AI from "random generation" to "rule-based generation." This logic is exactly the same as Agents.md's role in the coding domain. As AI capabilities grow stronger, the ability to "set rules for AI" becomes increasingly valuable.
If you are exploring AI design tools, I recommend starting with Stitch's DESIGN.md feature. Extract your existing brand's design system, generate your first DESIGN.md file, and then import it into your next project. You'll find that brand consistency is no longer an issue that requires manual oversight but a standard automatically ensured by a file.
Want to manage your design assets and inspiration more efficiently? Try YouMind to centralize scattered references into one Board, and let AI help you organize, retrieve, and create.
References
[1] Figma Stock Falls After Google Labs Updates Stitch Design Tool
[2] Google Official Blog: AI design with Stitch
[3] What makes a good Agents.md?
[4] New AI Design Standard: What is DESIGN.md? How to write it?
[5] Google Stitch and the shift to AI-driven development
[6] Reddit: Google just dropped Stitch and it might actually threaten Figma
[7] Google just introduced Vibe Design, here's what it means for UI designers
[8] Google unveils voice-driven Vibe Design tool to build UIs
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Why Do AI Agents Always Forget Things? A Deep Dive into the MemOS Memory System
You've probably encountered this scenario: you spend half an hour teaching an AI Agent about a project's background, only to start a new session the next day, and it asks you from scratch, "What is your project about?" Or, even worse, a complex multi-step task is halfway through, and the Agent suddenly "forgets" the steps already completed, starting to repeat operations. This is not an isolated case. According to Zylos Research's 2025 report, nearly 65% of enterprise AI application failures can be attributed to context drift or memory loss . The root of the problem is that most current Agent frameworks still rely on the Context Window to maintain state. The longer the session, the greater the Token overhead, and critical information gets buried in lengthy conversation histories. This article is suitable for developers building AI Agents, engineers using frameworks like LangChain / CrewAI, and all technical professionals who have been shocked by Token bills. We will deeply analyze how the open-source project MemOS solves this problem with a "memory operating system" approach, and provide a horizontal comparison of mainstream memory solutions to help you make technology selection decisions. To understand what problem MemOS is solving, we first need to understand where the AI Agent's memory dilemma truly lies. Context Window does not equal memory. Many people think that Gemini's 1M Token window or Claude's 200K window is "enough," but window size and memory capacity are two different things. A study by JetBrains Research at the end of 2025 clearly pointed out that as context length increases, LLMs' efficiency in utilizing information significantly decreases . Stuffing the entire conversation history into the Prompt not only makes it difficult for the Agent to find critical information but also causes the "Lost in the Middle" phenomenon, where content in the middle of the context is recalled the worst. Token costs expand exponentially. A typical customer service Agent consumes approximately 3,500 Tokens per interaction . If the full conversation history and knowledge base context need to be reloaded every time, an application with 10,000 daily active users can easily exceed five figures in monthly Token costs. This doesn't even account for the additional consumption from multi-turn reasoning and tool calls. Experience cannot be accumulated and reused. This is the most easily overlooked problem. If an Agent helps a user solve a complex data cleaning task today, it won't "remember" the solution next time it encounters a similar problem. Every interaction is a one-off, making it impossible to form reusable experience. As an analysis by Tencent News stated: "An Agent without memory is just an advanced chatbot" . These three problems combined constitute the most intractable infrastructure bottleneck in current Agent development. was developed by the Chinese startup MemTensor. It first released the Memory³ hierarchical large model at the World Artificial Intelligence Conference (WAIC) in July 2024, and officially open-sourced MemOS 1.0 in July 2025. It has now iterated to v2.0 "Stardust." The project uses the Apache 2.0 open-source license and is continuously active on GitHub. The core concept of MemOS can be summarized in one sentence: Extract Memory from the Prompt and run it as an independent component at the system layer. The traditional approach is to stuff all conversation history, user preferences, and task context into the Prompt, making the LLM "re-read" all information during each inference. MemOS takes a completely different approach. It inserts a "memory operating system" layer between the LLM and the application, responsible for memory storage, retrieval, updating, and scheduling. The Agent no longer needs to load the full history every time; instead, MemOS intelligently retrieves the most relevant memory fragments into the context based on the current task's semantics. This architecture brings three direct benefits: First, Token consumption significantly decreases. Official data from the LoCoMo benchmark shows that MemOS reduces Token consumption by approximately 60.95% compared to traditional full-load methods, with memory Token savings reaching 35.24% . A report from JiQiZhiXing mentioned that overall accuracy increased by 38.97% . In other words, better results are achieved with fewer Tokens. Second, cross-session memory persistence. MemOS supports automatic extraction and persistent storage of key information from conversations. When a new session is started next time, the Agent can directly access previously accumulated memories, eliminating the need for the user to re-explain the background. Data is stored locally in SQLite, running 100% locally, ensuring data privacy. Third, multi-Agent memory sharing. Multiple Agent instances can share memory through the same user_id, enabling automatic context handover. This is a critical capability for building multi-Agent collaborative systems. MemOS's most striking design is its "memory evolution chain." Most memory systems focus on "storing" and "retrieving": saving conversation history and retrieving it when needed. MemOS adds another layer of abstraction. Conversation content doesn't accumulate verbatim but evolves through three stages: Stage One: Conversation → Structured Memory. Raw conversations are automatically extracted into structured memory entries, including key facts, user preferences, timestamps, and other metadata. MemOS uses its self-developed MemReader model (available in 4B/1.7B/0.6B sizes) to perform this extraction process, which is more efficient and accurate than directly using GPT-4 for summarization. Stage Two: Memory → Task. When the system identifies that certain memory entries are associated with specific task patterns, it automatically aggregates them into Task-level knowledge units. For example, if you repeatedly ask the Agent to perform "Python data cleaning," the relevant conversation memories will be categorized into a Task template. Stage Three: Task → Skill. When a Task is repeatedly triggered and validated as effective, it further evolves into a reusable Skill. This means that problems the Agent has encountered before will likely not be asked a second time; instead, it will directly invoke the existing Skill to execute. The brilliance of this design lies in its simulation of human learning: from specific experiences to abstract rules, and then to automated skills. The MemOS paper refers to this capability as "Memory-Augmented Generation" and has published two related papers on arXiv . Actual data also confirms the effectiveness of this design. In the LongMemEval evaluation, MemOS's cross-session reasoning capability improved by 40.43% compared to the GPT-4o-mini baseline; in the PrefEval-10 personalized preference evaluation, the improvement was an astonishing 2568% . If you want to integrate MemOS into your Agent project, here's a quick start guide: Step One: Choose a deployment method. MemOS offers two modes. Cloud mode allows you to directly register for an API Key on the , and integrate with a few lines of code. Local mode deploys via Docker, with all data stored locally in SQLite, suitable for scenarios with data privacy requirements. Step Two: Initialize the memory system. The core concept is MemCube (Memory Cube), where each MemCube corresponds to a user's or an Agent's memory space. Multiple MemCubes can be uniformly managed through the MOS (Memory Operating System) layer. Here's a code example: ``python from memos.mem_os.main import MOS from memos.configs.mem_os import MOSConfig # Initialize MOS config = MOSConfig.from_json_file("config.json") memory = MOS(config) # Create a user and register a memory space memory.create_user(user_id="your-user-id") memory.register_mem_cube("path/to/mem_cube", user_id="your-user-id") # Add conversation memory memory.add( messages=[ {"role": "user", "content": "My project uses Python for data analysis"}, {"role": "assistant", "content": "Understood, I will remember this background information"} ], user_id="your-user-id" ) # Retrieve relevant memories later results = memory.search(query="What language does my project use?", user_id="your-user-id") `` Step Three: Integrate the MCP protocol. MemOS v1.1.2 and later fully support the Model Context Protocol (MCP), meaning you can use MemOS as an MCP Server, allowing any MCP-enabled IDE or Agent framework to directly read and write external memories. Common pitfalls reminder: MemOS's memory extraction relies on LLM inference. If the underlying model's capability is insufficient, memory quality will suffer. Developers in the Reddit community have reported that when using small-parameter local models, memory accuracy is not as good as calling the OpenAI API . It is recommended to use at least a GPT-4o-mini level model as the memory processing backend in production environments. In daily work, Agent-level memory management solves the problem of "how machines remember," but for developers and knowledge workers, "how humans efficiently accumulate and retrieve information" is equally important. 's Board feature offers a complementary approach: you can save research materials, technical documents, and web links uniformly into a knowledge space, and the AI assistant will automatically organize them and support cross-document Q&A. For example, when evaluating MemOS, you can clip GitHub READMEs, arXiv papers, and community discussions to the same Board with one click, then directly ask, "What are the benchmark differences between MemOS and Mem0?" The AI will retrieve answers from all the materials you've saved. This "human + AI collaborative accumulation" model complements MemOS's Agent memory management well. Since 2025, several open-source projects have emerged in the Agent memory space. Here's a comparison of four of the most representative solutions: A Zhihu article from 2025, "AI Memory System Horizontal Review," performed a detailed benchmark reproduction of these solutions, concluding that MemOS performed most stably on evaluation sets like LoCoMo and LongMemEval, and was the "only Memory OS with consistent official evaluations, GitHub cross-tests, and community reproduction results" . If your need is not Agent-level memory management, but rather personal or team knowledge accumulation and retrieval, offers another dimension of solutions. Its positioning is an integrated studio for "learning → thinking → creating," supporting saving various sources like web pages, PDFs, videos, and podcasts, with AI automatically organizing them and supporting cross-document Q&A. Compared to Agent memory systems which focus on "making machines remember," YouMind focuses more on "helping people manage knowledge efficiently." However, it should be noted that YouMind currently does not provide Agent memory APIs similar to MemOS; they address different levels of needs. Selection Advice: Q: What is the difference between MemOS and RAG (Retrieval-Augmented Generation)? A: RAG focuses on retrieving information from external knowledge bases and injecting it into the Prompt, essentially still following a "look up every time, insert every time" pattern. MemOS, on the other hand, manages memory as a system-level component, supporting automatic extraction, evolution, and Skill-ification of memory. The two can be used complementarily, with MemOS handling conversational memory and experience accumulation, and RAG handling static knowledge base retrieval. Q: Which LLMs does MemOS support? What are the hardware requirements for deployment? A: MemOS supports calling mainstream models like OpenAI and Claude via API, and also supports integrating local models via Ollama. Cloud mode has no hardware requirements; Local mode recommends a Linux environment, and the built-in MemReader model has a minimum size of 0.6B parameters, which can run on a regular GPU. Docker deployment is out-of-the-box. Q: How secure is MemOS's data? Where is memory data stored? A: In Local mode, all data is stored in a local SQLite database, running 100% locally, and is not uploaded to any external servers. In Cloud mode, data is stored on MemOS's official servers. For enterprise users, Local mode or private deployment solutions are recommended. Q: How high are the Token costs for AI Agents generally? A: Taking a typical customer service Agent as an example, each interaction consumes approximately 3,150 input Tokens and 400 output Tokens. Based on GPT-4o pricing in 2026, an application with 10,000 daily active users and an average of 5 interactions per user per day would have monthly Token costs between $2,000 and $5,000. Using memory optimization solutions like MemOS can reduce this figure by over 50%. Q: Besides MemOS, what other methods can reduce Agent Token costs? A: Mainstream methods include Prompt compression (e.g., LLMLingua), semantic caching (e.g., Redis semantic cache), context summarization, and selective loading strategies. Redis's 2026 technical blog points out that semantic caching can completely bypass LLM inference calls in scenarios with highly repetitive queries, leading to significant cost savings . These methods can be used in conjunction with MemOS. The AI Agent memory problem is essentially a system architecture problem, not merely a model capability problem. MemOS's answer is to free memory from the Prompt and run it as an independent operating system layer. Empirical data proves the feasibility of this path: Token consumption reduced by 61%, temporal reasoning improved by 159%, and SOTA achieved across four major evaluation sets. For developers, the most noteworthy aspect is MemOS's "conversation → Task → Skill" evolution chain. It transforms the Agent from a tool that "starts from scratch every time" into a system capable of accumulating experience and continuously evolving. This may be the critical step for Agents to go from "usable" to "effective." If you are interested in AI-driven knowledge management and information accumulation, you are welcome to try for free and experience the integrated workflow of "learning → thinking → creating." [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
Lenny Opens 350+ Newsletter Dataset: How to Integrate It with Your AI Assistant Using MCP
You might have heard the name Lenny Rachitsky. This former Airbnb product lead started writing his Newsletter in 2019 and now boasts over 1.1 million subscribers, generating over $2 million in annual revenue, making it the #1 business Newsletter on Substack . His podcast also ranks among the top ten in tech, featuring guests from Silicon Valley's top product managers, growth experts, and entrepreneurs. On March 17, 2026, Lenny did something unprecedented: he made all his content assets available as an AI-readable Markdown dataset. With 350+ in-depth Newsletter articles, 300+ full podcast transcripts, a complementary MCP server, and a GitHub repository, anyone can now build AI applications using this data . This article will cover the complete contents of this dataset, how to integrate it into your AI tools via the MCP server, 50+ creative projects already built by the community, and how you can leverage this data to create your own AI knowledge assistant. This article is suitable for content creators, Newsletter authors, AI application developers, and knowledge management enthusiasts. This is not a simple "content transfer." Lenny's dataset is meticulously organized and specifically designed for AI consumption scenarios. In terms of data scale, free users can access a starter pack of 10 Newsletter articles and 50 podcast transcripts, and connect to a starter-level MCP server via . Paid subscribers, on the other hand, gain access to the complete 349 Newsletter articles and 289 podcast transcripts, plus full MCP access and a private GitHub repository . In terms of data format, all files are in pure Markdown format, ready for direct use with Claude Code, Cursor, and other AI tools. The index.json file in the repository contains structured metadata such as titles, publication dates, word counts, Newsletter subtitles, podcast guest information, and episode descriptions. It's worth noting that Newsletter articles published within the last 3 months are not included in the dataset. In terms of content quality, this data covers core areas such as product management, user growth, startup strategies, and career development. Podcast guests include executives and founders from companies like Airbnb, Figma, Notion, Stripe, and Duolingo. This is not randomly scraped web content, but a high-quality knowledge base accumulated over 7 years and validated by 1.1 million people. The global AI training dataset market reached $3.59 billion in 2025 and is projected to grow to $23.18 billion by 2034, with a compound annual growth rate of 22.9% . In this era where data is fuel, high-quality, niche content data has become extremely scarce. Lenny's approach represents a new creator economy model. Traditionally, Newsletter authors protect content value through paywalls. Lenny, however, does the opposite: he opens his content as "data assets," allowing the community to build new value layers on top of it. This has not only not diminished his paid subscriptions (in fact, the dataset's spread has attracted more attention) but has also created a developer ecosystem around his content. Compared to other content creators' practices, this "content as API" approach is almost unprecedented. As Lenny himself said, "I don't think anyone has done anything like this before." The core insight of this model is: when your content is good enough and your data structure is clear enough, the community will help you create value you never even imagined. Imagine this scenario: you're a product manager preparing a presentation on user growth strategies. Instead of spending hours sifting through Lenny's historical articles, you can directly ask an AI assistant to retrieve all discussions about "growth loops" from 300+ podcast episodes and automatically generate a summary with specific examples and data. This is the efficiency leap brought by structured datasets. Integrating Lenny's dataset into your AI workflow is not complicated. Here are the specific steps. Go to and enter your subscription email to get a login link. Free users can download the starter pack ZIP file or directly clone the public GitHub repository: ``plaintext git clone https://github.com/LennysNewsletter/lennys-newsletterpodcastdata.git `` Paid users can log in to get access to the private repository containing the full dataset. MCP (Model Context Protocol) is an open standard introduced by Anthropic, allowing AI models to access external data sources in a standardized way. Lenny's dataset provides an official MCP server, which you can configure directly in Claude Code or other MCP-supported clients. Free users can use the starter-level MCP, while paid users get MCP access to the full data. Once configured, you can directly search and reference all of Lenny's content in your AI conversations. For example, you can ask: "Among Lenny's podcast guests, who discussed PLG (Product-Led Growth) strategies? What were their core insights?" Once you have the data, you can choose different building paths based on your needs. If you are a developer, you can use Claude Code or Cursor to build applications directly based on the Markdown files. If you are more inclined towards knowledge management, you can import this content into your preferred knowledge base tool. For example, you can create a dedicated Board in and batch-save links to Lenny's Newsletter articles there. YouMind's AI will automatically organize this content, and you can ask questions, retrieve, and analyze the entire knowledge base at any time. This method is particularly suitable for creators and knowledge workers who don't code but want to efficiently digest large amounts of content with AI. A common misconception to note: do not try to dump all data into one AI chat window at once. A better approach is to process it in batches by topic, or let the AI retrieve it on demand via the MCP server. Lenny previously only released podcast transcript data, and the community has already built over 50 projects. Below are 5 categories of the most representative applications. Gamified Learning: LennyRPG. Product designer Ben Shih transformed 300+ podcast transcripts into a Pokémon-style RPG game, . Players encounter podcast guests in a pixelated world and "battle" and "capture" them by answering product management questions. Ben used the Phaser game framework, Claude Code, and the OpenAI API to complete the entire development, from concept to launch, in just a few weeks . Cross-Domain Knowledge Transfer: Tiny Stakeholders. , developed by Ondrej Machart, applies product management methodologies from the podcasts to parenting scenarios. This project demonstrates an interesting characteristic of high-quality content data: good frameworks and mental models can be transferred across domains. Structured Knowledge Extraction: Lenny Skills Database. The Refound AI team extracted from the podcast archives, each with specific context and source citations . They used Claude for preprocessing and ChromaDB for vector embeddings, making the entire process highly automated. Social Media AI Agent: Learn from Lenny. is an AI Agent running on X (Twitter) that answers users' product management questions based on the podcast archives, with each reply including the original source. Visual Content Re-creation: Lenny Gallery. transforms the core insights of each podcast episode into beautiful infographics, turning an hour-long podcast into a shareable visual summary. The common characteristic of these projects is that they are not simple "content transfers," but rather create new forms of value based on the original data. Facing a large-scale content dataset like Lenny's, different tools are suitable for different use cases. Below is a comparison of mainstream solutions: If you are a developer, Claude Code + MCP server is the most direct path, allowing real-time querying of the full data in conversations. If you are a content creator or knowledge worker who doesn't want to code but wishes to digest this content with AI, YouMind's Board feature is more suitable: you can batch import article links and then use AI to ask questions and analyze the entire knowledge base. YouMind is currently more suitable for "collect → organize → AI Q&A" knowledge management scenarios but does not yet support direct connection to external MCP servers. For projects requiring deep code development, Claude Code or Cursor is still recommended. Q: Is Lenny's dataset completely free? A: Not entirely. Free users can access a starter pack containing 10 Newsletters and 50 podcast transcripts, as well as starter-level MCP access. The complete 349 articles and 289 transcripts require a paid subscription to Lenny's Newsletter (approximately $150 annually). Articles published within the last 3 months are not included in the dataset. Q: What is an MCP server? Can regular users use it? A: MCP (Model Context Protocol) is an open standard introduced by Anthropic in late 2024, allowing AI models to access external data in a standardized way. It is currently primarily used through development tools like Claude Code and Cursor. If regular users are not familiar with the command line, they can first download the Markdown files and import them into knowledge management tools like YouMind to use AI Q&A features. Q: Can I use this data to train my own AI model? A: The use of the dataset is governed by the file. Currently, the data is primarily designed for contextual retrieval in AI tools (e.g., RAG), rather than direct use for model fine-tuning. It is recommended to carefully read the license agreement in the GitHub repository before use. Q: Besides Lenny, have other Newsletter authors released similar datasets? A: Currently, Lenny is the first leading Newsletter author to open up full content in such a systematic way (Markdown + MCP + GitHub). This approach is unprecedented in the creator economy but may inspire more creators to follow suit. Q: What is the deadline for the creation challenge? A: The deadline for the creation challenge launched by Lenny is April 15, 2025. Participants need to build projects based on the dataset and submit links in the Newsletter comment section. Winners will receive a free one-year Newsletter subscription. Lenny Rachitsky's release of 350+ Newsletter articles and 300+ podcast transcript datasets marks a significant turning point in the content creator economy: high-quality content is no longer just something to be read; it is becoming a programmable data asset. Through the MCP server and structured Markdown format, any developer and creator can integrate this knowledge into their AI workflow. The community has already demonstrated the immense potential of this model with over 50 projects. Whether you want to build an AI-powered knowledge assistant or more efficiently digest and organize Newsletter content, now is a great time to act. You can go to to get the data, or try using to import the Newsletter and podcast content you follow into your personal knowledge base, letting AI help you complete the entire closed loop from information gathering to knowledge creation. [1] [2] [3] [4] [5] [6] [7]
Grok Imagine Video Generation Review: Triple Crown Power vs. Five Model Comparison
In January 2026, xAI's generated 1.245 billion videos in a single month. This number was unimaginable just a year prior, when xAI didn't even have a video product. From zero to the top, Grok Imagine achieved this in just seven months. Even more noteworthy are the leaderboard statistics. In the video review operated by Arcada Labs, Grok Imagine secured three first-place rankings: Video Generation Arena Elo 1337 (leading the second-place model by 33 points), Image-to-Video Arena Elo 1298 (defeating Google Veo 3.1, Kling, and Sora), and Video Editing Arena Elo 1291. No other model has simultaneously topped all three categories. This article is suitable for creators, marketing teams, and independent developers who are currently choosing AI video generation tools. You will find a comprehensive cross-comparison of the five major models: Grok Imagine, Google Veo 3.1, Kling 3.0, Sora 2, and Seedance 2.0, including pricing, core features, pros and cons, and scenario recommendations. DesignArena uses an Elo rating system, where users anonymously blind-test and vote between the outputs of two models. This mechanism is consistent with LMArena (formerly LMSYS Chatbot Arena) for evaluating large language models and is considered by the industry to be the ranking method closest to actual user preferences. Grok Imagine's three Elo scores represent different capability dimensions. Video Generation Elo 1337 measures the quality of videos generated directly from text prompts; Image-to-Video Elo 1298 tests the ability to transform static images into dynamic videos; and Video Editing Elo 1291 assesses performance in style transfer, adding/removing elements, and other operations on existing videos. The combination of these three capabilities forms a complete video creation loop. For practical workflows, you not only need to "generate a good-looking video" but also need to quickly create advertising material from product images (image-to-video) and fine-tune generated results without starting from scratch (video editing). Grok Imagine is currently the only model that ranks first in all three of these stages. It's worth noting that Kling 3.0 has regained its leading position in the text-to-video category in some independent benchmark tests. AI video generation rankings change weekly, but Grok Imagine's advantage in the image-to-video and video editing categories remains solid for now. Below is a comparison of the core parameters of the five mainstream AI video generation models as of March 2026. Data is sourced from official platform pricing pages and third-party reviews. Core Features: Text-to-video, image-to-video, video editing, video extension (Extend from Frame), multi-aspect ratio support (1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3). Based on xAI's self-developed Aurora autoregressive engine, trained using 110,000 NVIDIA GB200 GPUs. Pricing Structure: Free users have basic quota limits; X Premium ($8/month) provides basic access; SuperGrok ($30/month) unlocks 720p and 10-second videos, with a daily limit of approximately 100 videos; SuperGrok Heavy ($300/month) has a daily limit of 500 videos. API pricing is $4.20/minute. Pros: Extremely fast generation speed, almost instantly returning image streams after inputting prompts, with one-click conversion of each image to video. Video editing capability is a unique selling point: you can use natural language instructions to perform style transfer, add or remove objects, and control motion paths on existing videos without having to regenerate them. Supports the most aspect ratios, suitable for producing horizontal, vertical, and square materials simultaneously. Cons: Maximum resolution is only 720p, which is a significant drawback for brand projects requiring high-definition delivery. Video editing input is capped at 8.7 seconds. Image quality noticeably degrades after multiple chained extensions. Content moderation policies are controversial, with "Spicy Mode" having attracted international attention. Core Features: Text-to-video, image-to-video, first/last frame control, video extension, native audio (dialogue, sound effects, background music generated synchronously). Supports 720p, 1080p, and 4K output. Available through Gemini API and Vertex AI. Pricing Structure: Google AI Plus $7.99/month (Veo 3.1 Fast), AI Pro $19.99/month, AI Ultra $249.99/month. API pricing for Veo 3.1 Fast is $0.15/second, Standard is $0.40/second, both including audio. Pros: Currently the only model that supports true native 4K output (via Vertex AI). Audio generation quality is industry-leading, with automatic lip-sync for dialogue and synchronized sound effects with on-screen actions. First/last frame control makes shot-by-shot workflows more manageable, suitable for narrative projects requiring shot continuity. Google Cloud infrastructure provides enterprise-grade SLA. Cons: Standard duration is only 4/6/8 seconds, significantly shorter than Grok Imagine and Kling 3.0's 15-second cap. Aspect ratios only support 16:9 and 9:16. Image-to-video functionality on Vertex AI is still in Preview. 4K output requires high-tier subscriptions or API access, making it difficult for average users to access. Core Features: Text-to-video, image-to-video, multi-shot narrative (generates 2-6 shots in a single pass), Universal Reference (supports up to 7 reference images/videos to lock character consistency), native audio, lip-sync. Developed by Kuaishou. Pricing Structure: Free tier offers 66 credits per day (approx. 1-2 720p videos), Standard $5.99/month, Pro $37/month (3000 credits, approx. 50 1080p videos), Ultra is higher. API price per second is $0.029, making it the cheapest among the five major models. Pros: Unbeatable value for money. The Pro plan costs approximately $0.74 per video, significantly lower than other models. Multi-shot narrative is a killer feature: you can describe the subject, duration, and camera movement for multiple shots in a structured prompt, and the model automatically handles transitions and cuts between shots. Supports native 4K output. Text rendering capability is the strongest among all models, suitable for e-commerce and marketing scenarios. Cons: The free tier has watermarks and cannot be used for commercial purposes. Peak-time queue times can exceed 30 minutes. Failed generations still consume credits. Compared to Grok Imagine, it lacks video editing features (can only generate, not modify existing videos). Core Features: Text-to-video, image-to-video, Storyboard shot editing, video extension, character consistency engine. Sora 1 was officially retired on March 13, 2026, making Sora 2 the sole version. Pricing Structure: Free tier discontinued as of January 2026. ChatGPT Plus $20/month (limited quota), ChatGPT Pro $200/month (priority access). API pricing: 720p $0.10/second, 1080p $0.30-$0.70/second. Pros: Physical simulation capabilities are the strongest among all models. Details such as gravity, fluids, and material reflections are extremely realistic, suitable for highly realistic scenarios. Supports video generation up to 60 seconds, far exceeding other models. Storyboard functionality allows frame-by-frame editing, giving creators precise control. Cons: The price barrier is the highest among the five major models. The $200/month Pro subscription deters individual creators. Service stability issues are frequent: in March 2026, there were multiple errors such as videos getting stuck at 99% completion and "server overload." No free tier means you cannot fully evaluate before paying. Core Features: Text-to-video, image-to-video, multimodal reference input (up to 12 files, covering text, images, videos, audio), native audio (sound effects + music + 8 languages lip-sync), native 2K resolution. Developed by ByteDance, released on February 12, 2026. Pricing Structure: Dreamina free tier (daily free credits, with watermark), Jiemeng Basic Membership 69 RMB/month (approx. $9.60), Dreamina international paid plans. API provided via BytePlus, priced at approx. $0.02-$0.05/second. Pros: 12-file multimodal input is an exclusive feature. You can simultaneously upload character reference images, scene photos, action video clips, and background music, and the model synthesizes all references to generate video. This level of creative control is completely absent in other models. Native 2K resolution is available to all users (unlike Veo 3.1's 4K which requires a high-tier subscription). The entry price of 69 RMB/month is one-twentieth of Sora 2 Pro. Cons: Access experience outside of China still has friction, with the international version of Dreamina only launching in late February 2026. Content moderation is relatively strict. The learning curve is relatively steep, and fully utilizing multimodal input requires time to explore. Maximum duration is 10 seconds, shorter than Grok Imagine and Kling 3.0's 15 seconds. The core question when choosing an AI video generation model is not "which is best," but "which workflow are you optimizing?" Here are recommendations based on practical scenarios: Batch production of social media short videos: Choose Grok Imagine or Kling 3.0. You need to quickly produce materials in various aspect ratios, iterate frequently, and don't have high resolution requirements. Grok Imagine's "generate → edit → publish" loop is the smoothest; Kling 3.0's free tier and low cost are suitable for individual creators with limited budgets. Brand advertisements and product promotional videos: Choose Veo 3.1. When clients demand 4K delivery, synchronized audio and video, and shot continuity, Veo 3.1's first/last frame control and native audio are irreplaceable. Google Cloud's enterprise-grade support also makes it more suitable for commercial projects with compliance requirements. E-commerce product videos and materials with text: Choose Kling 3.0. Text rendering capability is Kling's unique advantage. Product names, price tags, and promotional copy can appear clearly in the video, which other models struggle with consistently. The $0.029/second API price also makes large-scale production possible. Film-grade concept previews and physical simulations: Choose Sora 2. If your scene involves complex physical interactions (water reflections, cloth dynamics, collision effects), Sora 2's physics engine is still the industry standard. The maximum duration of 60 seconds is also suitable for full scene previews. But be prepared for a $200/month budget. Creative projects with multiple material references: Choose Seedance 2.0. When you have character design images, scene references, action video clips, and background music, and you want the model to synthesize all materials to generate video, Seedance 2.0's 12-file multimodal input is the only choice. Suitable for animation studios, music video production, and concept art teams. Regardless of the model you choose, prompt quality directly determines output quality. Grok Imagine's official advice is to "write prompts like you're briefing a director of photography," rather than simply stacking keywords. An effective video prompt usually contains five levels: scene description, subject action, camera movement, lighting and atmosphere, and style reference. For example, "a cat on a table" and "an orange cat lazily peering over the edge of a wooden dining table, warm side lighting, shallow depth of field, slow push-in shot, film grain texture" will produce completely different results. The latter provides the model with enough creative anchors. If you want to get started quickly instead of exploring from scratch, contains 400+ community-selected video prompts, covering cinematic, product advertising, animation, social content, and other styles, supporting one-click copy and direct use. These community-validated prompt templates can significantly shorten your learning curve. Q: Is Grok Imagine video generation free? A: There is a free quota, but it's very limited. Free users get about 10 image generations every 2 hours, and videos need to be converted from images. The full 720p/10-second video functionality requires a SuperGrok subscription ($30/month). X Premium ($8/month) provides basic access but with limited features. Q: Which is the cheapest AI video generation tool in 2026? A: Based on API cost per second, Kling 3.0 is the cheapest ($0.029/second). Based on subscription entry price, Seedance 2.0's Jiemeng Basic Membership at 69 RMB/month (approx. $9.60) offers the best value. Both provide free tiers for evaluation. Q: Which is better, Grok Imagine or Sora 2? A: It depends on your needs. Grok Imagine ranks higher in image-to-video and video editing, generates faster, and is cheaper (SuperGrok $30/month vs. ChatGPT Pro $200/month). Sora 2 is stronger in physical simulation and long videos (up to 60 seconds). If you need to quickly iterate short videos, choose Grok Imagine; if you need cinematic realism, choose Sora 2. Q: Are AI video generation model rankings reliable? A: Platforms like DesignArena and Artificial Analysis use anonymous blind testing + Elo rating systems, similar to chess ranking systems, which are statistically reliable. However, rankings change weekly, and results from different benchmark tests may vary. It's recommended to use rankings as a reference rather than the sole decision-making basis, and to make judgments based on your own actual testing. Q: Which AI video model supports native audio generation? A: As of March 2026, Grok Imagine, Veo 3.1, Kling 3.0, Sora 2, and Seedance 2.0 all support native audio generation. Among them, Veo 3.1's audio quality (dialogue lip-sync, environmental sound effects) is considered the best by multiple reviews. AI video generation entered a true multi-model competitive era in 2026. Grok Imagine's journey from zero to a DesignArena triple crown in seven months proves that newcomers can completely disrupt the landscape. However, "strongest" does not equal "best for you": Kling 3.0's $0.029/second makes batch production a reality, Veo 3.1's 4K native audio sets a new standard for brand projects, and Seedance 2.0's 12-file multimodal input opens up entirely new creative avenues. The key to choosing a model is to clarify your core needs: whether it's iteration speed, output quality, cost control, or creative flexibility. The most efficient workflow often doesn't involve betting on a single model, but rather flexibly combining them based on project type. Want to quickly get started with Grok Imagine video generation? Visit the for 400+ community-selected video prompts that can be copied with one click, covering cinematic, advertising, animation, and other styles, helping you skip the prompt exploration phase and directly produce high-quality videos. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]
