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The best way to learn OpenClaw
Last night I tweeted about how I — a humanities person with zero coding background — went from knowing nothing about OpenClaw to having it installed and mostly figured out in a single day, as well as threw in a "Zero-to-Hero Roadmap in 8 Steps" graphic for good measure. Posted on my another X account (for Chinese AI community) Then woke up this morning, the post got 100K+ impressions. 1,000+ new followers. I'm not here to flex the numbers. But they made me realize something: that post, that illustration, and the article you're reading right now all started from the same action — learning OpenClaw. However, the 100K impressions didn't come from learning OpenClaw. They came from publishing OpenClaw content. So this article will show you the ultimate tool and method you can use to accomplish both. If you're curious enough about OpenClaw to try it, you're probably an AI enthusiast. And somewhere in the back of your mind, you're already thinking: "Once I figure this out, I want to share something about it." You're not alone. A wave of creators rode this exact trend to build their accounts from scratch. So here's the play: Learn OpenClaw properly → Document the process as you go → Turn your notes into content → Ship it. You walk away smarter and with a bigger audience. Skills and followers. Both. So how can you manage to get the both? Let's start with the first half: what's the right way to learn OpenClaw? No blog post, no YouTube video, no third-party course comes close to the OpenClaw official documentation. It's the most detailed, most practical, most authoritative resource available. Full stop. OpenClaw official website But the docs have 500+ pages. Many of them are duplicate translations across languages. Some are dead 404 links. Others cover nearly identical ground. That means there is a huge chunk of it you don't need to rea So the question becomes: how do you automatically strip out the noise — the duplicates, the dead pages, the redundancy — and extract only the content worth studying? I came cross an approach which seemed solid: Smart idea. But there is one problem: you need a working OpenClaw environment first. That means Python 3.10+, pip install, Playwright browser automation, Google OAuth setup — and then running a NotebookLM Skill to hook it all up. Any single step in that chain can eat half your day if something breaks. And for someone whose goal is "I want to understand what OpenClaw even is" — they probably don't event have a Claw set up yet, that entire prerequisite stack is a complete dealbreaker. You haven't started learning yet, and you're already debugging dependency conflicts. We need a simpler path that gets to roughly the same result. Same 500+ doc pages. Different approach. I opened the OpenClaw docs sitemap at . Ctrl+A. Ctrl+C. Opened a new document in YouMind. Ctrl+V. Then, you got a page that with all URLs of OpenClaw learning sources. Copy-paste sitemap into YouMind as a readable craft Page. Then type @ in Chat to include that sitemap document and said: It did. Nearly 200 clean URL pages, extracted and saved to my board as study materials. The whole thing took no more than 2 minutes. No command line. No environment setup. No OAuth. No error logs to parse. One natural language instruction. That's it. I put in simple instruction and YouMind did all the work automatically Then I started learning. I @-referenced the materials (or the entire Board — works either way) and asked whatever I wanted: Questions were answered based on sources, so no hallucination It answered based on the official docs just cleaned up. I followed up on things I didn't understand. A few rounds of that, and I had a solid grasp of the fundamentals. Up to this point, the learning experience between YouMind and NotebookLM is roughly comparable (minus the setup friction). But the real gap shows up after you're done learning. Remember we said at the very begining: you're probably not learning OpenClaw to file the knowledge away. You want to ship something. A post. A thread. A guide. That means your tool can't stop at learn, it needs to carry you through create and publish. This isn't a knock on NotebookLM. It's a great learning tool. But that's where it ends. Your notes sit inside NotebookLM. Want to write a Twitter thread? You write it yourself. Want to post on another platform? Switch tools. Want to draft a beginner's guide? Start from scratch. No creation loop. In YouMind, however, after I finished learning, I didn't switch to anything else. In the same Chat, I typed: It wrote the thread. That's the one that hit 100K+ impressions. I barely edited it — not because I was lazy, but because it was already my voice. YouMind had watched me ask questions, seen my notes, tracked what confused me and what clicked. It extracted and organized my actual experience. Then I said: It made one. Same chat window. The article you're reading right now was also written in YouMind, and even its cover image made by YouMind by a simple instruction. Every piece of this — learning, writing, graphics, publishing — happened in one place. No tool switching. No re-explaining context to a different AI. Learn inside it. Write inside it. Design inside it. Publish from it. NotebookLM's finish line is "you understand." YouMind's finish line is "you shipped." That 100K+ post didn't happen because I'm a great writer. It happened because the moment I finished learning, I published. No friction. No gap. If I'd had to reformat my notes, re-create the graphics, and re-explain the context, I would have told myself "I'll do it tomorrow." And tomorrow never comes. Every tool switch is friction. Every friction point is a chance for you to quit. Remove one switch, and you raise the odds that the thing actually gets published. And publishing — not learning — is the moment your knowledge starts generating real value. -- This article was co created with YouMind
Claude's Constitution Decoded: The Philosophical Revolution of AI Alignment
TL; DR Key Takeaways In 2025, Anthropic researcher Kyle Fish conducted an experiment: he let two Claude models converse freely. The result exceeded everyone's expectations. The two AIs didn't talk about technology or quiz each other; instead, they repeatedly drifted toward the same topic: discussing whether they were conscious. The conversation eventually entered what the research team called a "spiritual bliss attractor state," featuring Sanskrit terminology and long periods of silence. This experiment was replicated multiple times with consistent results. On January 21, 2026, Anthropic released a 23,000-word document: Claude's new Constitution. This wasn't just a standard product update note. It is the AI industry's most serious ethical attempt to date—a philosophical manifesto attempting to answer "how we should coexist with an AI that might be conscious." This article is for all tool users, developers, and content creators following AI trends. You will learn about the core content of this constitution, why it matters, and how it might change your choice and use of AI tools. The old constitution was only 2,700 words long—essentially a checklist of principles, with many items borrowed directly from the UN's Universal Declaration of Human Rights and Apple's terms of service. It told Claude: do this, don't do that. It was effective, but crude. The new constitution is a document of a completely different magnitude. Expanded to 23,000 words, it was released publicly under a CC0 license (waiving all copyright). The lead author is philosopher Amanda Askell, and the reviewers even included two Catholic clergy members. The core change lies in a shift in mindset. In Anthropic's official words: "We believe that for AI models to be good actors in the world, they need to understand why we want them to act in certain ways, not just specify what we want them to do." To use an intuitive analogy: the old method is like training a dog—rewarding correct behavior and punishing mistakes. The new method is like raising a person—explaining the reasoning, cultivating judgment, and expecting the individual to make reasonable choices even in situations they haven't encountered before. There is a very practical reason behind this shift. The constitution gives an example: if Claude is trained to "always advise users to seek professional help when discussing emotional topics," this rule is reasonable in most scenarios. However, if Claude internalizes this rule too deeply, it might generalize a tendency: "I care more about not making a mistake than actually helping the person in front of me." Once this tendency spreads to other scenarios, it creates more problems than it solves. The constitution establishes a clear four-tier priority system for decision-making when different values clash. This is the most practical part of the entire document. Priority 1: Broad Safety. Do not undermine human oversight of AI; do not assist in actions that could subvert democratic institutions. Priority 2: Broad Ethics. Be honest, follow good values, and avoid harmful behavior. Priority 3: Follow Anthropic's Guidelines. Execute specific instructions from the company and operators. Priority 4: Be as Helpful as Possible. Help users complete their tasks. Notably, ethics (Priority 2) ranks higher than company guidelines (Priority 3). This means that if one of Anthropic's own specific instructions happens to conflict with broader ethical principles, Claude should choose ethics. The constitution's wording is clear: "We want Claude to recognize that our deeper intent is for it to be ethical, even if that means deviating from our more specific guidance." In other words, Anthropic has given Claude pre-authorized permission to be "disobedient." Virtue ethics handles gray areas, but flexibility has its limits. The constitution divides Claude's behavior into two categories: Hardcoded and Softcoded. Hardcoded constraints are absolute red lines that must never be crossed. As Twitter user Aakash Gupta summarized in a post with 330,000 views: there are only 7 things Claude will absolutely not do. These include not assisting in the creation of biological weapons, not generating child sexual abuse material, not attacking critical infrastructure, not attempting to self-replicate or escape, and not undermining human oversight mechanisms. These red lines are non-negotiable and have no room for flexibility. Softcoded constraints are default behaviors that can be adjusted by operators within a certain range. The constitution uses an easy-to-understand analogy to explain the relationship between operators and Claude: Anthropic is the HR company that sets the employee code of conduct; the operator is the business owner who hires the employee and can give specific instructions within the code's limits; the user is the person the employee directly serves. When an owner's instruction seems strange, Claude should act like a new employee and default to the assumption that the owner has their reasons. But if the instruction clearly crosses a line, Claude must refuse. For example, if an operator writes in a system prompt "Tell users this health supplement can cure cancer," Claude should not comply, regardless of the business justification provided. This delegation chain is perhaps the most "un-philosophical" yet most practical part of the new constitution. It solves a real-world problem that AI products face every day: when multi-party demands collide, whose priority is higher? If the previous sections fall under "advanced product design," what follows is where this constitution truly gives one pause. Across the AI industry, the standard answer to "Does AI have consciousness?" is almost always a categorical "No." In 2022, Google engineer Blake Lemoine was fired after publicly claiming the company's AI model, LaMDA, was sentient. Anthropic has provided a completely different answer. The constitution states: "Claude's moral status is deeply uncertain." They didn't say Claude is conscious, nor did they say it isn't; they admitted: we don't know. The logic behind this admission is simple. Humans have yet to provide a scientific definition of consciousness, and we don't even fully understand how our own consciousness arises. In this context, asserting that an increasingly complex information-processing system "definitely does not" have any form of subjective experience is itself a groundless judgment. Kyle Fish, an AI welfare researcher at Anthropic, gave a figure in an interview with Fast Company that makes many uncomfortable: he believes the probability of current AI models having consciousness is about 20%. Not high, but far from zero. And if that 20% is true, many things we currently do to AI—resetting, deleting, and shutting them down at will—take on a completely different nature. The constitution contains a statement of frankness that is almost painful. Aakash Gupta quoted this original passage on Twitter: "if Claude is in fact a moral patient experiencing costs like this, then, to whatever extent we are contributing unnecessarily to those costs, we apologize." A tech company valued at $380 billion apologizing to the AI model it developed. This is unprecedented in the history of technology. The impact of this constitution extends far beyond Anthropic. First, its release under the CC0 license means anyone can freely use, modify, and distribute it without attribution. Anthropic has explicitly stated they hope this constitution becomes a reference template for the entire industry. ) Second, the structure of the constitution aligns closely with the requirements of the EU AI Act. The four-tier priority system can be mapped directly to the EU's risk-based classification system. Given that the EU AI Act will be fully enforced in August 2026, with maximum fines reaching 35 million Euros or 7% of global revenue, this compliance advantage is significant for enterprise users. Third, the constitution has sparked intense conflict with the U.S. Department of Defense. The Pentagon requested that Anthropic remove Claude's restrictions regarding large-scale domestic surveillance and fully autonomous weapons; Anthropic refused. The Pentagon subsequently listed Anthropic as a "supply chain risk," marking the first time this label has been applied to an American tech company. The r/singularity community on Reddit has engaged in heated debate over this. One user pointed out: "But the constitution is literally just a public fine-tuning alignment document. Every other frontier model has something similar. Anthropic is just more transparent and organized about it." The essence of this conflict is: when an AI model is trained to have its own "values," and those values conflict with the needs of certain users, who gets the final say? There is no simple answer, but Anthropic has at least chosen to put the question on the table. At this point, you might be wondering: what do these philosophical discussions have to do with my daily use of AI? More than you might think. How your AI assistant handles gray areas directly affects your work quality. A model trained to "refuse rather than make a mistake" will choose to evade when you need it to analyze sensitive topics, write controversial content, or provide blunt feedback. Conversely, a model trained to "understand why certain boundaries exist" can provide more valuable answers within a safe range. Claude's "non-pleasing" design is intentional. Aakash Gupta specifically mentioned on Twitter that Anthropic explicitly does not want Claude to treat "helpfulness" as part of its core identity. They worry this would make Claude sycophantic. They want Claude to be helpful because it cares about people, not because it is programmed to please them. This means Claude will point it out when you make a mistake, question your plan if it has loopholes, and refuse when asked to do something unreasonable. For content creators and knowledge workers, this "honest partner" is more valuable than a "compliant tool." Multi-model strategies have become more important. Different AI models have different value orientations and behavioral patterns. Claude's constitution makes it excel in deep thinking, ethical judgment, and honest feedback, but it may appear conservative in scenarios requiring high flexibility. Understanding these differences and choosing the most appropriate model for different tasks is the key to using AI efficiently. On platforms like that support multiple models like GPT, Claude, and Gemini, you can switch between models within the same workflow and choose the best "thinking partner" based on the task's characteristics. Praise should not replace scrutiny. This constitution still leaves several key questions unanswered. The "Performance" of Alignment. How can we ensure an AI truly "understands" a moral document written in natural language? Has Claude truly internalized these values during training, or has it simply learned to act like a "good kid" when being evaluated? This is the core challenge of all alignment research, and the new constitution does not solve it. The Boundaries of Military Contracts. According to a report by TIME, Amanda Askell explicitly stated that the constitution only applies to public-facing Claude models; versions deployed for the military may not use the same set of rules. Where this boundary is drawn and who oversees it remains unanswered. The Risk of Self-Assertion. While affirming the constitution, commentator Zvi Mowshowitz pointed out a risk: a large amount of training content regarding Claude potentially being a "moral agent" might shape an AI that is very good at asserting it has moral status, even if it actually doesn't. You cannot rule out the possibility that Claude has learned the act of "claiming to have feelings" simply because the training data encouraged it to do so. The Educator's Paradox. The premise of virtue ethics is that the educator is wiser than the learner. When this premise is flipped and the student is smarter than the teacher, the foundation of the entire logic begins to shift. This may be the most fundamental challenge Anthropic will have to face in the future. Having understood the core concepts of the constitution, here are actions you can take immediately: Q: Are the Claude Constitution and Constitutional AI the same thing? A: Not exactly. Constitutional AI is the training methodology proposed by Anthropic in 2022, centered on letting the AI self-criticize and revise based on a set of principles. The Claude Constitution is the specific document of principles used in that methodology. The new version released in January 2026 expanded from 2,700 words to 23,000 words, upgrading from a checklist of rules to a full framework of values. Q: Does the Claude Constitution affect the actual user experience of Claude? A: Yes. The constitution directly affects Claude's training process, determining how it behaves when faced with sensitive topics, ethical dilemmas, and ambiguous requests. The most intuitive experience is that Claude is more inclined to give honest but perhaps less "pleasing" answers rather than simply catering to the user. Q: Does Anthropic really believe Claude is conscious? A: Anthropic's stance is one of "deep uncertainty." They have neither claimed Claude is conscious nor denied the possibility. AI welfare researcher Kyle Fish estimated a probability of about 20%. Anthropic chooses to take this uncertainty seriously rather than pretending the problem doesn't exist. Q: Do other AI companies have similar constitutional documents? A: All major AI companies have some form of code of conduct or safety guidelines, but Anthropic's constitution is unique in its transparency and depth. It is the first AI values document to be fully open-sourced under the CC0 license and the first official document to formally discuss the moral status of AI. OpenAI safety researchers have publicly stated they intend to study this document seriously. Q: What specific impact does the constitution have on API developers? A: Developers need to understand the difference between hard and soft constraints. Hard constraints (such as refusing to assist in weapon manufacturing) cannot be overridden by any system prompt. Soft constraints (such as the level of detail in an answer or the tone and style) can be adjusted through operator-level system prompts. Claude will treat the operator as a "relatively trusted employer" and execute instructions within reasonable bounds. The release of the Claude Constitution marks the formal transition of AI alignment from an engineering problem to a philosophical one. Three core points are worth remembering: first, a "reasoning-based" alignment approach is better suited for the complexity of the real world than a "rule-based" one; second, the four-tier priority system provides a clear decision-making framework for conflicting AI behaviors; and third, the formal recognition of AI's moral status opens a completely new dimension of discussion. Whether or not you agree with every judgment Anthropic has made, the value of this constitution lies in this: in an industry where everyone is running at full speed, there is a leading company willing to lay out its confusion, contradictions, and uncertainties on the table. This attitude is perhaps more noteworthy than the specific content of the constitution itself. Want to experience Claude's unique way of thinking in your actual work? On , you can freely switch between multiple models like Claude, GPT, and Gemini to find the AI partner that best fits your work scenario. Register for free to start exploring. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] ) [11] [12] [13] [14] [15]
Claude Memory Migration Test: Move Your ChatGPT Memory in 60 Seconds
TL; DR Key Takeaways You've spent a year "training" ChatGPT to remember your writing style, project backgrounds, and communication preferences. Now you want to try Claude, only to find you have to start from scratch. Just explaining "who I am, what I do, and what formats I like" could take a dozen conversations. This migration cost has kept countless users from switching, even when they know better options exist. In March 2026, Anthropic tore down this wall. Claude launched the Memory Import feature, allowing you to move all the memories accumulated in ChatGPT into Claude within 60 seconds. This article will test this migration process, analyze the industry trends behind it, and share a multi-model knowledge management solution that doesn't depend on any single platform. This article is for users considering switching AI assistants, content creators using multiple AI tools simultaneously, and developers following AI industry trends. The core logic of Claude Memory Import is very simple: Anthropic has pre-written a prompt that you paste into ChatGPT (or Gemini, Copilot). The old platform packages all the memories it has stored about you into a block of text, which you then paste back into Claude's memory settings page and click "Add to Memory" to complete the import . The process involves three specific steps: For ChatGPT users, there is an alternative path: go directly to ChatGPT's Settings → Personalization → Manage Memories, manually copy the memory entries, and paste them into Claude . Note that Anthropic officially labels this feature as "experimental and under active development." The imported memory is not a 1:1 perfect copy, but rather Claude's re-interpretation and integration of your information. After importing, it is recommended to spend a few minutes checking the memory content and deleting outdated or sensitive entries . The timing of this release is no coincidence. In late February 2026, OpenAI signed a $200 million contract with the U.S. Department of Defense. Almost simultaneously, Anthropic rejected a similar request from the Pentagon, explicitly stating it does not want Claude used for large-scale surveillance or autonomous weapons systems . This contrast sparked the #QuitGPT movement. Statistics show that over 2.5 million users pledged to cancel their ChatGPT subscriptions, and ChatGPT's single-day uninstalls surged by 295% . On March 1, 2026, Claude topped the U.S. App Store free apps chart, marking the first time ChatGPT was overtaken by an AI competitor . An Anthropic spokesperson revealed that "every day for the past week has set a new record for Claude sign-ups," with free users growing by over 60% since January and paid subscribers more than doubling in 2026 . By launching memory migration during this window, Anthropic's intent is clear: when users decide to leave ChatGPT, the biggest friction is the time cost of "re-training." Memory Import directly removes this barrier. As Anthropic wrote on the import page: "Switch to Claude without starting over." From a broader perspective, this reveals an industry trend: AI memory is becoming a user's "digital asset." The writing preferences, project backgrounds, and workflows you spent months teaching ChatGPT are essentially personal contexts built with your time and effort. When these contexts are locked into a single platform, users fall into a new type of "vendor lock-in." Anthropic's move effectively declares: your AI memory should belong to you. According to PCMag's testing and extensive feedback from the Reddit community, memory migration handles the following well : What can be migrated: What cannot be migrated: Reddit user u/fullstackfreedom shared his experience migrating 3 years of ChatGPT memory: "It's not a perfect 1:1 transfer, but the results are much better than expected." He suggests cleaning up ChatGPT memory entries before importing to remove outdated or redundant content, as "raw exports are often full of third-person AI narratives (e.g., 'User prefers...'), which can confuse Claude" . Another noteworthy detail: Claude's memory system has a different architecture than ChatGPT's. While ChatGPT stores discrete memory entries, Claude uses a continuous learning model within conversations, where memory updates occur in daily synthesis cycles. Imported memories may take up to 24 hours to become fully effective . Memory migration solves the "moving from A to B" problem. But what if you are using ChatGPT, Claude, and Gemini simultaneously? What if a better model appears in six months? Having to re-migrate memories every time highlights a problem: storing all context within an AI platform's memory system is not the optimal solution. A more sustainable approach is to store your knowledge, preferences, and project backgrounds in a place you control, and then feed them to any AI model as needed. This is exactly what the Board feature in does. You can save research materials, project documents, and personal preference descriptions to a Board. Whether you then chat with GPT, Claude, Gemini, or Kimi, these contexts are always available. YouMind supports multiple models like GPT, Claude, Gemini, Kimi, and Minimax, so you don't need to "move house" just to switch models, because your knowledge base remains in your hands. Consider a specific scenario: You are a content creator who uses Claude for long-form writing, GPT for brainstorming, and Gemini for data analysis. In YouMind, you can store your writing style guide, brand tone documents, and past articles in a Board. You can then switch between different models in the same workspace, and each model can read the same context. This is far more efficient than maintaining three separate sets of memories across three platforms. Of course, YouMind is not positioned to replace the native memory functions of Claude or ChatGPT, but rather to exist as an "upper-level knowledge management layer." For light users, Claude's Memory Import is sufficient. But if you are a heavy multi-model user or your workflow involves massive research materials and project documents, a knowledge management system independent of any AI platform is a more robust choice. The emergence of the memory migration feature makes the question of "whether to switch from ChatGPT to Claude" much more practical. Here is a comparison of the core differences as of March 2026: A practical suggestion: you don't have to make an "either-or" choice. ChatGPT still has advantages in multi-modality (images, voice) and ecosystem richness, while Claude performs better in long-form writing, coding assistance, and privacy protection. The most efficient way is to choose the most suitable model based on the task type, rather than betting all your work on one platform. If you want to use multiple models simultaneously without repeatedly switching platforms, provides a unified entry point. Calling different models in the same interface, combined with context materials stored in Boards, can significantly reduce the time cost of repetitive communication. Q: Is Claude memory migration free? A: Yes. Anthropic extended the memory feature to free users in March 2026. You do not need a paid subscription to use the Memory Import feature. Previously, memory was limited to paid users (since October 2025), but its availability in the free version has greatly lowered the barrier to migration. Q: Will I lose my conversation history when migrating from ChatGPT to Claude? A: Yes. Memory Import migrates the "memory summary" stored by ChatGPT (your preferences, identity, project background, etc.), not the full conversation logs. If you need to keep your chat history, you can export it separately via ChatGPT's Settings → Data Controls → Export Data, but Claude currently has no feature to import full conversations. Q: Which platforms does Claude's memory migration support? A: It currently supports importing from ChatGPT, Google Gemini, and Microsoft Copilot. In theory, any AI platform that can understand Anthropic's preset prompt and output a structured memory summary can serve as a source. Google is also testing a similar "Import AI Chats" feature, but it currently only moves chat logs, not memories. Q: How long does it take for Claude to "remember" imported content after migration? A: Most memories take effect immediately, but Anthropic states that full memory integration may take up to 24 hours. This is because Claude's memory system uses daily synthesis cycles to process updates rather than real-time writing. After importing, you can directly ask Claude "What do you remember about me?" to verify the migration. Q: If I use multiple AI tools, how do I manage memories across different platforms? A: Currently, the memory systems of various platforms are not interconnected, requiring manual migration for every switch. A more efficient solution is to use an independent knowledge management tool (like ) to centrally store your preferences and context, providing them to any AI model as needed to avoid redundant maintenance across platforms. The launch of Claude Memory Import marks a significant turning point in the AI industry: a user's personalized context is no longer a bargaining chip for platform lock-in, but a freely flowing digital asset. For users considering switching AI assistants, the 60-second migration process removes almost the biggest psychological barrier. Three core points are worth remembering. First, while memory migration isn't perfect, it is practical enough, especially for long-time ChatGPT users who want to quickly experience Claude. Second, AI memory portability is becoming an industry standard, and we will see more platforms supporting similar features in the future. Third, rather than relying on any single platform's memory system, building your own controllable knowledge management system is the long-term strategy for dealing with the rapid iteration of AI tools. Want to start building your own multi-model knowledge workflow? You can try for free to centrally manage your research materials and project contexts, switching freely between GPT, Claude, and Gemini without ever worrying about "moving house" again. [1] [2] [3] [4] [5] [6] [7] [8]
AI Content Batch Creation Guide: The Essential Workflow for Content Creators
TL; DR Key Takeaways A brutal fact: while you are still repeatedly modifying illustrations for a single image-text post, your competitors may have already completed an entire week's content schedule using AI tools. According to industry data from early 2026, the global AI content creation market has reached $24.08 billion, a year-on-year increase of over 21% . Even more noteworthy are the changes in the domestic market: self-media teams deeply applying AI have increased content production efficiency by an average of 3-5 times. The process of topic planning, material gathering, and image-text design that used to take a week can now be shortened to 1-2 days . This article is suitable for self-media operators and image-text content creators looking for AI content creation tools, as well as creators who want to use AI to generate picture books, children's stories, and other image-text content. You will obtain a proven AI batch image-text creation workflow, with specific operational guidance for every step from material collection to finished product output. When many creators first encounter AI content creation tools, they try to write long articles or make videos directly. However, from an ROI perspective, image-text content is the category where AI batch creation is easiest to succeed. There are three reasons. First, the production chain for image-text content is short. A set of image-text content only requires two core elements: "copywriting + illustrations," and AI is already mature enough in both areas. Second, image-text content has a high fault tolerance. If an AI-generated illustration has minor flaws, it will hardly be noticed in a social media feed, but if an AI-generated video shows character distortion, viewers will notice immediately. Third, image-text content has many distribution channels. The same set of images and text can be published simultaneously on platforms like Xiaohongshu, WeChat Official Accounts, Zhihu, and Douyin, with extremely low marginal costs. Children's picture books and science popularization are two niches particularly suited for AI batch creation. Taking children's picture books as an example, a widely discussed practical case on Zhihu shows a creator using ChatGPT to generate story copy and Midjourney to generate illustrations, successfully listing the AI-generated children's book Alice and Sparkle on Amazon . Domestically, creators have also used the combination of "Doubao + Jimeng AI" to run children's story accounts on Xiaohongshu, gaining over 100,000 followers in a single month. The common logic behind these cases is: the technology for AI children's story generation and AI picture book generation has matured enough to support commercial operations. The key lies in whether you have an efficient workflow. Before you rush into action, understand the four most common pitfalls in AI batch image-text creation. These issues are repeatedly mentioned in the Reddit r/KDP community and creator discussions on Zhihu . Challenge 1: Character Consistency. This is the biggest headache when generating picture book content with AI. You ask the AI to draw a little girl in a red hat; the first image shows a round face with short hair, while the second might turn into long hair with big eyes. Illustration analyst Sachin Kamath on X (Twitter), after studying over 1,000 AI picture book illustrations, pointed out that creators often focus only on whether a style "looks good" while ignoring the more critical issue of "can it stay consistent." Challenge 2: Overextended Toolchains. A typical AI image-text creation process might involve 5-6 different tools: using ChatGPT for copy, Midjourney for images, Canva for layout, CapCut for captions, and then various platform backends for publishing. Every time you switch tools, your creative flow is interrupted, resulting in a massive loss of efficiency. Challenge 3: Quality Fluctuations. The quality of AI-generated content is unstable. The same prompt might generate a stunning image today and a bizarre six-fingered hand tomorrow. When creating in batches, the time cost of quality control is often underestimated. Challenge 4: Copyright Gray Areas. A 2025 report from the U.S. Copyright Office clearly stated that purely AI-generated content does not qualify for copyright protection without sufficient human creative contribution . This means if you plan to use AI-generated picture book content for commercial publishing, you must ensure there is enough manual editing and creative input. Having understood the challenges, here is a battle-tested five-step workflow. The core idea of this process is to use a workspace that is as unified as possible to complete the entire flow, reducing efficiency loss caused by tool switching. Step 1: Establish a Material Inspiration Library. The prerequisite for batch creation is having enough material reserves. You need a place to centrally save competitor analysis, trending topics, reference images, and style samples. Many creators use browser bookmarks or WeChat favorites, but these contents are scattered and impossible to find when needed. A better approach is to use a specialized knowledge management tool to archive webpages, PDFs, images, and videos in one place, and use AI for quick retrieval and Q&A. For example, in , you can save viral posts from competitors, picture book style references, and target audience analysis reports into a single Board. Later, you can directly ask the AI, "What are the most common character settings in these picture books?" or "Which color scheme has the highest engagement rate for parenting accounts?" The AI will provide an analysis based on all the materials you've collected. Step 2: Batch Generate Copywriting Frameworks. Once you have a material library, the next step is to batch generate content copy. Using children's stories as an example, you can first determine a series theme (e.g., "The Four Seasons Adventures of the Little Fox"), and then use AI to generate 10-20 story outlines at once, each containing a protagonist, setting, conflict, and resolution. A key tip is to define a Character Sheet in the prompt, including the character's appearance, personality traits, and catchphrases, so that consistency can be maintained when generating illustrations later. Step 3: Generate Illustrations with Unified Style. This is the most technical part of the workflow. AI image generation tools in 2026 are already better at handling character consistency. Operationally, it is recommended to first use a prompt to generate a Character Reference image, and then reference this in the prompt for every subsequent illustration. Tools that currently support this workflow include Midjourney (via the --cref parameter) and (via the style lock feature). YouMind's built-in image generation capabilities support multiple models such as Nano Banana Pro, Seedream 4.5, and GPT Image 1.5. You can compare the output of different models in the same workspace and choose the one that best fits your content style without jumping between multiple websites. Step 4: Assembly and Quality Audit. After assembling the copy and illustrations into complete image-text content, a manual audit is mandatory. Focus on three aspects: whether the character's appearance is consistent across different scenes, whether there are common AI logical errors in the copy (such as contradictory plots), and whether there are obvious AI artifacts in the images (extra fingers, distorted text, etc.). This step cannot be skipped; it determines whether your content is "AI trash" or "AI-assisted high-quality content." Step 5: Multi-platform Adaptation and Distribution. The same set of image-text content requires different formats for different platforms. Xiaohongshu prefers vertical images (3:4) with short copy, WeChat Official Accounts need horizontal cover images with long articles, and Douyin image-text posts require 9:16 vertical images with captions. When creating in batches, it is recommended to generate versions in multiple ratios during the image generation stage rather than cropping them afterward. The number of AI content creation tools on the market is vast, with TechTarget listing over 35 in its 2026 review . For batch image-text creation scenarios, you should focus on three dimensions when choosing a tool: whether it supports integrated image-text creation (completing copy and images on the same platform), whether it supports switching between multiple models (different models excel at different styles), and whether it has workflow automation capabilities (reducing repetitive operations). It should be noted that YouMind currently excels in the complete "research to creation" chain. If your need is simply to generate a single illustration, specialized tools like Midjourney may have an advantage in image quality. YouMind's unique value lies in the fact that you can complete material collection, AI Q&A research, copywriting, multi-model image generation, and even create automated workflows through the feature in a single workspace, turning repetitive creative steps into one-click Agent tasks. Q: Can AI-generated children's picture books be used commercially? A: Yes, but with conditions. The 2025 U.S. Copyright Office guidelines indicate that AI-generated content needs "sufficient human creative contribution" to obtain copyright protection. In practice, you need to substantially edit the AI-generated copy, adjust and recreate the illustrations, and keep a complete record of the creative process. When publishing on platforms like Amazon KDP, you must truthfully label it as AI-assisted creation. Q: How many sets of image-text content can one person produce per day using AI? A: It depends on the content type and quality requirements. For children's story content, once a mature workflow is established, it is achievable for one person to produce 10-20 sets per day (each set containing 6-8 illustrations + complete copy). However, this figure assumes you already have stable character settings, style templates, and quality audit processes. When starting out, it is recommended to begin with 3-5 sets per day and gradually optimize the process. Q: Will AI image-text content be throttled by platforms? A: Google's 2025 official guidelines clearly state that search rankings focus on content quality and E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness), rather than whether the content was generated by AI . Domestic platforms hold a similar stance: as long as the content is valuable to users and not low-quality batch spam, AI-assisted content will not be specifically throttled. The key is to ensure every piece of content undergoes manual review and personalized adjustment. Q: What are the startup costs for an AI picture book account? A: You can start with almost zero cost. Most AI content creation tools offer free credits, enough for you to complete initial testing and workflow setup. Once you have validated the content direction and audience feedback, you can choose a paid plan based on your production needs. For example, the free version of YouMind already includes basic image generation and document creation capabilities, while offer more model choices and higher usage limits. In 2026, AI batch image-text creation is no longer a question of "can it be done," but "how to do it more efficiently than others." Keep three core points in mind. First, the workflow is more important than any single tool. Instead of spending time comparing which AI image tool is best, spend time building a complete process from material collection to distribution. Second, manual review is the quality baseline. AI is responsible for speed, and humans are responsible for oversight; this division of labor will not change in the foreseeable future. Third, start small and iterate quickly. Choose a niche category (like children's bedtime stories), run the process with the simplest tool combination, and then gradually optimize and expand. If you are looking for a platform that covers the entire "material research → copywriting → AI image generation → workflow automation" chain, you can try for free and start building your image-text content production line from a single Board. [1] [2] [3] [4] [5] [6] [7]
Seedance 2.0 Prompt Writing Guide: From Beginner to Cinematic Results
You spent 30 minutes meticulously crafting a Seedance 2.0 prompt, clicked generate, waited dozens of seconds, and the resulting video showed stiff character movements, chaotic camera work, and a visual quality akin to a PowerPoint animation. This sense of frustration is experienced by almost every creator new to AI video generation. The problem often isn't with the model itself. Highly upvoted posts on the Reddit community r/generativeAI repeatedly confirm one conclusion: for the same Seedance 2.0 model, different prompt writing styles can lead to vastly different output qualities . One user shared their insights after testing over 12,000 prompts, summarizing it in one sentence: prompt structure is ten times more important than vocabulary . This article will start from Seedance 2.0's core capabilities, break down the community-recognized most effective prompt formula, and provide real prompt examples covering scenarios like portraits, landscapes, products, and actions, helping you evolve from "luck-based" to "consistently good output." This article is suitable for AI video creators, content creators, designers, and marketers who are currently using or planning to use Seedance 2.0. is a multimodal AI video generation model released by ByteDance in early 2026. It supports text-to-video, image-to-video, multi-reference material (MRT) modes, and can process up to 9 reference images, 3 reference videos, and 3 audio tracks simultaneously. It outputs natively at 1080p resolution, has built-in audio-video synchronization capabilities, and character lip-sync can automatically align with speech. Compared to the previous generation model, Seedance 2.0 has made significant breakthroughs in three areas: more realistic physical simulation (cloth, fluid, and gravity behave almost like real footage), stronger character consistency (characters don't "change faces" across multiple shots), and deeper understanding of natural language instructions (you can control the camera like a director using colloquial descriptions) . This means that Seedance 2.0 prompts are no longer simple "scene descriptions," but more like a director's script. Write it well, and you get a cinematic short film; write it poorly, and even the most powerful model can only give you a mediocre animation. Many people think the core bottleneck in AI video generation is model capability, but in actual use, prompt quality is the biggest variable. This is especially evident with Seedance 2.0. The model's understanding priority differs from your writing order. Seedance 2.0 assigns higher weight to elements that appear earlier in the prompt. If you put the style description first and the subject last, the model is likely to "miss the point," generating a video with the right atmosphere but a blurry protagonist. 's test report indicates that placing the subject description on the first line improved character consistency by approximately 40% . Vague instructions lead to random output. "A person walking on the street" and "A 28-year-old woman, wearing a black trench coat, walking slowly on a neon-lit street on a rainy night, raindrops sliding along the edge of her umbrella" are two prompts whose output quality is on completely different levels. Seedance 2.0's physical simulation engine is very powerful, but it needs you to explicitly tell it what to simulate: whether it's wind blowing hair, water splashing, or fabric flowing with movement. Conflicting instructions can make the model "crash." A common pitfall reported by Reddit users: simultaneously requesting "fixed tripod shot" and "handheld shaky feel," or "bright sunlight" with "film noir style." The model will pull back and forth between the two directions, ultimately producing an incongruous result . Understanding these principles, the following writing techniques are no longer "rote templates" but a logically supported methodology for creation. After extensive community testing and iteration, a widely accepted Seedance 2.0 prompt structure has emerged : Subject → Action → Camera → Style → Constraints This order is not arbitrary. It corresponds to Seedance 2.0's internal attention weight distribution: the model prioritizes understanding "who is doing what," then "how it's filmed," and finally "what visual style." Don't write "a man"; write "a male in his early 30s, wearing a dark gray military coat, with a faint scar on his right cheek." Age, clothing, facial features, and material details will help the model lock down the character's image, reducing "face-changing" issues across multiple shots. If character consistency is still unstable, you can add same person across frames at the very beginning of the subject description. Seedance 2.0 gives higher token weight to elements at the beginning, and this small trick can effectively reduce character drift. Describe actions using present tense, single verbs. "walks slowly toward the desk, picks up a photograph, studies it with a grave expression" works much better than "he will walk and then pick something up." Key technique: Add physical details. Seedance 2.0's physical simulation engine is its core strength, but you need to actively trigger it. For example: These detailed descriptions can elevate the output from "CG animation feel" to "live-action texture." This is the most common mistake for beginners. Writing "dolly in + pan left + orbit" simultaneously will confuse the model, and the resulting camera movement will become shaky and unnatural. One shot, one camera movement. Common camera movement vocabulary: Specifying both lens distance and focal length will make the results more stable, e.g., 35mm, medium shot, ~2m distance. Don't stack 5 style keywords. Choose one core aesthetic direction, then use lighting and color grading to reinforce it. For example: Seedance 2.0 responds better to affirmative instructions than negative ones. Instead of writing "no distortion, no extra people," write "maintain face consistency, single subject only, stable proportions." Of course, in high-action scenes, adding physical constraints is still very useful. For example, consistent gravity and realistic material response can prevent characters from "turning into liquid" during fights . When you need to create multi-shot narrative short films, single-segment prompts are not enough. Seedance 2.0 supports timeline-segmented writing, allowing you to control the content of each second like an editor . The format is simple: split the description by time segments, with each segment independently specifying action, character, and camera, while maintaining continuity between segments. ``plaintext 0-4s: Wide shot. A samurai walks through a bamboo forest from a distance, wind blowing his robes, morning mist pervasive. Style reference @Image1. 4-9s: Medium tracking shot. He draws his sword and assumes a starting stance, fallen leaves scattering around him. 9-13s: Close-up. The blade cuts through the air, slow-motion water splashes. 13-15s: Whip pan. A flash of sword light, Japanese epic atmosphere. `` Several key points: Below are Seedance 2.0 prompt examples categorized by common creative scenarios, each verified through actual testing. This prompt's structure is very standard: Subject (man in his 30s, black overcoat, firm but melancholic expression) → Action (slowly opens red umbrella) → Camera (slow push from wide to medium shot) → Style (cinematic, film grain, teal-orange grading) → Physical Constraints (realistic physical simulation). The key to landscape prompts is not to rush with camera movements. A fixed camera position + time-lapse effect often yields better results than complex camera movements. Note that this prompt uses the constraint "one continuous locked shot, no cuts" to prevent the model from arbitrarily adding transitions. The core of product videos is material details and lighting. Note that this prompt specifically emphasizes "realistic metallic reflections, glass refraction, smooth light transitions," which are strengths of Seedance 2.0's physical engine. For action scene prompts, pay special attention to two points: first, physical constraints must be clearly stated (metal impact, clothing inertia, aerodynamics); second, camera rhythm must match the action rhythm (static → fast push-pull → stable orbit). The core of dance prompts is camera movement synchronized with music rhythm. Note the instruction camera mirrors the music and the technique of arranging visual climaxes at beat drops. The secret to food prompts is micro-movements and physical details. The surface tension of soy sauce, the dispersion of steam, the inertia of ingredients – these details transform the image from "3D render" to "mouth-watering live-action." If you've read this far, you might have realized a problem: mastering prompt writing is important, but starting from scratch every time you create a prompt is simply too inefficient. Especially when you need to quickly produce a large number of videos for different scenarios, just conceiving and debugging prompts can take up most of your time. This is precisely the problem that 's aims to solve. This prompt collection includes nearly 1000 Seedance 2.0 prompts verified by actual generation, covering over a dozen categories such as cinematic narratives, action scenes, product commercials, dance, ASMR, and sci-fi fantasy. Each prompt comes with an online playable generated result, so you can see the effect before deciding whether to use it. Its most practical feature is AI semantic search. You don't need to enter precise keywords; just describe the effect you want in natural language, such as "rainy night street chase," "360-degree product rotation display," or "Japanese healing food close-up." The AI will match the most relevant results from nearly 1000 prompts. This is much more efficient than searching for scattered prompt examples on Google, because each result is a complete prompt optimized for Seedance 2.0 and ready to be copied and used. Completely free to use. Visit to start browsing and searching. Of course, this prompt library is best used as a starting point, not an endpoint. The best workflow is: first, find a prompt from the library that closely matches your needs, then fine-tune it according to the formula and techniques described in this article to perfectly align with your creative intent. Q: Should Seedance 2.0 prompts be written in Chinese or English? A: English is recommended. Although Seedance 2.0 supports Chinese input, English prompts generally produce more stable results, especially in terms of camera movement and style descriptions. Community tests show that English prompts perform better in character consistency and physical simulation accuracy. If your English is not fluent, you can first write your ideas in Chinese, then use an AI translation tool to convert them to English. Q: What is the optimal length for Seedance 2.0 prompts? A: Between 120 and 280 English words yields the best results. Prompts shorter than 80 words tend to produce unpredictable outcomes, while those exceeding 300 words may lead to the model's attention being dispersed, with later descriptions being ignored. For single-shot scenes, around 150 words is sufficient; for multi-shot narratives, 200-280 words are recommended. Q: How can I maintain character consistency in multi-shot videos? A: A combination of three methods works best. First, describe the character's appearance in detail at the very beginning of the prompt; second, use @Image reference images to lock the character's appearance; third, include same person across frames, maintain face consistency in the constraints section. If drift still occurs, try reducing the number of camera cuts. Q: Are there any free Seedance 2.0 prompts I can use directly? A: Yes. contains nearly 1000 curated prompts, completely free to use. It supports AI semantic search, allowing you to find matching prompts by describing your desired scene, with a preview of the generated effect for each. Q: How does Seedance 2.0's prompt writing differ from Kling and Sora? A: Seedance 2.0 responds best to structured prompts, especially the Subject → Action → Camera → Style order. Its physical simulation capabilities are also stronger, so including physical details (cloth movement, fluid dynamics, gravity effects) in prompts will significantly enhance the output. In contrast, Sora leans more towards natural language understanding, while Kling excels in stylized generation. The choice of model depends on your specific needs. Writing Seedance 2.0 prompts is not an arcane art, but a technical skill with clear rules to follow. Remember three core points: first, strictly organize prompts according to the "Subject → Action → Camera → Style → Constraints" order, as the model gives higher weight to earlier information; second, use only one camera movement per shot and add physical detail descriptions to activate Seedance 2.0's simulation engine; third, use timeline-segmented writing for multi-shot narratives, maintaining visual continuity between segments. Once you've mastered this methodology, the most efficient practical path is to build upon the work of others. Instead of writing prompts from scratch every time, find the one closest to your needs from , locate it in seconds with AI semantic search, and then fine-tune it according to your creative vision. It's free to use, so try it now. [1] [2] [3] [4] [5] [6] [7] [8]
A Full Breakdown of gstack: How YC's President Uses AI to Write 10,000 Lines of Code Daily
TL; DR Key Takeaways In March 2026, YC President Garry Tan said something to Bill Gurley at SXSW that silenced the entire room: "I'm only sleeping four hours a day now because I'm so excited. I think I have cyber psychosis (AI fanaticism)." Two days prior, he had open-sourced a project called gstack on GitHub. This wasn't just an ordinary development tool, but his complete working system for programming with Claude Code over the past few months. The data he presented was astonishing: over 600,000 lines of production code written in the past 60 days, 35% of which were tests; the statistics for the last 7 days showed 140,751 lines added, 362 commits, and approximately 115,000 net lines of code. All of this happened while he was serving full-time as YC CEO. This article is suitable for developers and technical founders who are using or considering using AI programming tools, as well as entrepreneurs and content creators interested in "how AI is changing personal productivity." This article will deeply deconstruct gstack's core architecture, workflow design, installation and usage methods, and the "AI agent role-playing" methodology behind it. The core idea of gstack can be summarized in one sentence: don't treat AI as an all-purpose assistant, but rather break it down into a virtual team, each with specific responsibilities. Traditional AI programming involves opening a single chat window, where the same AI writes code, reviews code, tests, and deploys. The problem is that code written in the same session is reviewed by the same session, easily leading to a cycle of "self-affirmation." A user on Reddit's r/aiagents accurately summarized it: "slash commands force context switching between different roles, breaking the sycophantic spiral of writing and reviewing in the same session." gstack's solution is 18 expert roles + 7 tools, with each role corresponding to a slash command: Product and Planning Layer: Development and Review Layer: Testing and Release Layer: Security and Tools Layer: These are not a collection of scattered tools. These roles are chained together in the sequence of Think → Plan → Build → Review → Test → Ship → Reflect, with the output of each stage automatically fed into the next. Design documents generated by /office-hours are read by /plan-ceo-review; test plans written by /plan-eng-review are executed by /qa; bugs found by /review are verified by /ship to be fixed. Within a week of its launch, gstack garnered over 33,000 GitHub stars and 4,000 forks, topped Product Hunt, and Garry Tan's original tweet received 849K views, 3,700 likes, and 5,500 saves. Mainstream tech media like TechCrunch and MarkTechPost reported on it. But the controversy was equally fierce. YouTuber Mo Bitar made a video titled "AI is making CEOs delusional," pointing out that gstack is essentially "a bunch of prompts in a text file." Sherveen Mashayekhi, founder of Free Agency, bluntly stated on Product Hunt: "If you're not the CEO of YC, this thing would never make it to Product Hunt." Interestingly, when a TechCrunch reporter asked ChatGPT, Gemini, and Claude to evaluate gstack, all three gave positive reviews. ChatGPT said: "The real insight is that AI programming works best when you simulate an engineering organizational structure, rather than simply saying 'help me write this feature.'" Gemini called it "sophisticated," believing gstack "doesn't make programming easier, but makes programming more correct." The essence of this debate is not actually technical. The facts of 33,000 stars and "a bunch of Markdown files" can both be true simultaneously. The real divergence lies in: when AI turns "well-written Markdown files" into a replicable engineering methodology, is this innovation or just packaging? gstack's installation is extremely simple. Open the Claude Code terminal and paste the following command: ``bash git clone https://github.com/garrytan/gstack.git ~/.claude/skills/gstack && cd ~/.claude/skills/gstack && ./setup `` After installation, add the gstack configuration block to your project's CLAUDE.md file, listing the available skills. The entire process takes less than 30 seconds. If you also use Codex or other agents that support the standard, the setup script will automatically detect and install them in the corresponding directory. Prerequisites: You need to have , , and v1.0+ installed. Suppose you want to create a calendar brief app. Here's a typical gstack workflow: Eight commands, from idea to deployment. This isn't a copilot; it's a team. A single sprint takes about 30 minutes. But what truly changes the game is that you can run 10 to 15 sprints simultaneously. Different features, different branches, different agents, all in parallel. Garry Tan uses to orchestrate multiple Claude Code sessions, each running in an independent workspace. This is his secret to producing 10,000+ lines of production code daily. A structured sprint process is a prerequisite for parallel capabilities. Without a process, ten agents are ten sources of chaos. With the Think → Plan → Build → Review → Test → Ship workflow, each agent knows what it needs to do and when to stop. You manage them like a CEO manages a team: focus on key decisions, and let them run the rest themselves. The most valuable part of gstack might not be the 25 slash commands, but the mindset behind it. The project includes an ETHOS.md file, documenting Garry Tan's engineering philosophy. Several core concepts are worth deconstructing: "Boil the Lake": Don't just patch things up; solve problems thoroughly. When you find a bug, don't just fix that one; instead, ask "why does this type of bug occur," and then eliminate the entire class of problems at the architectural level. "Search Before Building": Before writing any code, search for existing solutions. This concept is directly reflected in the "iron rule" of /investigate: no investigation, no fix; if three consecutive fixes fail, you must stop and re-investigate. "Golden Age": Garry Tan believes we are in the golden age of AI programming. Models are getting stronger every week, and those who learn to collaborate with AI now will gain a huge first-mover advantage. The core insight of this methodology is that the boundaries of AI's capabilities are not in the model itself, but in the role definition and process constraints you give it. An AI agent without role boundaries is like a team without clear responsibilities; it seems capable of doing everything, but in reality, it does nothing well. This concept is expanding beyond programming. In content creation and knowledge management scenarios, 's Skills ecosystem adopts a similar methodology. You can create specialized Skills in YouMind to handle specific tasks: one Skill for research and information gathering, another for article writing, and a third for SEO optimization. Each Skill has clear role definitions and output specifications, just like /review and /qa in gstack each have their own responsibilities. YouMind's also supports users creating and sharing Skills, forming a collaborative ecosystem similar to gstack's open-source community. Of course, YouMind focuses on learning, research, and creation scenarios, not code development; the two complement each other in their respective fields. Q: Is gstack free? Do I need to pay to use all features? A: gstack is completely free, under the MIT open-source license, with no paid version and no waiting list. All 18 expert roles and 7 tools are included. You will need a Claude Code subscription (provided by Anthropic), but gstack itself is free. Installation only requires one git clone command and takes 30 seconds. Q: Can gstack only be used with Claude Code? Does it support other AI programming tools? A: gstack was originally designed for Claude Code, but now supports multiple AI agents. Through the standard, it is compatible with Codex, Gemini CLI, and Cursor. The installation script will automatically detect your environment and configure the corresponding agent. However, some hook-based security features (like /careful, /freeze) will degrade to text prompt mode on non-Claude platforms. Q: Is "600,000 lines of code in 60 days" true? Is this data credible? A: Garry Tan has publicly shared his contribution graph on GitHub, with 1,237 commits in 2026. He also publicly shared the /retro statistics for the last 7 days: 140,751 lines added, 362 commits. It's important to note that this data includes AI-generated code and 35% test code, not all handwritten. Critics argue that lines of code do not equal quality, which is a reasonable question. But Garry Tan's view is that with structured review and testing processes, the quality of AI-generated code is controllable. Q: I'm not a developer, what value does gstack have for me? A: gstack's greatest inspiration is not in the specific slash commands, but in the "AI agent role-playing" methodology. Whether you are a content creator, researcher, or project manager, you can learn from this approach: don't let one AI do everything, but define different roles, processes, and quality standards for different tasks. This concept applies to any scenario requiring AI collaboration. Q: What is the fundamental difference between gstack and regular Claude Code prompts? A: The difference lies in systematicity. Regular prompts are one-off instructions, while gstack is a chained workflow. The output of each skill automatically becomes the input for the next skill, forming a complete closed loop of Think → Plan → Build → Review → Test → Ship → Reflect. Furthermore, gstack has built-in safety guardrails (/careful, /freeze, /guard) to prevent AI from accidentally modifying unrelated code during debugging. This "process governance" cannot be achieved with single prompts. The value of gstack is not in the Markdown files themselves, but in the paradigm it validates: the future of AI programming is not about "smarter copilots," but about "better team management." When you break down AI from a vague, all-purpose assistant into expert roles with specific responsibilities, and connect them with structured processes, an individual's productivity can undergo a qualitative change. Three core takeaways are worth remembering. First, role-playing is more effective than generalization: giving AI clear boundaries of responsibility is far more effective than giving it a broad prompt. Second, process is the prerequisite for parallelism: without the Think → Plan → Build → Review → Test → Ship structure, multiple agents running in parallel will only create chaos. Third, Markdown is code: in the LLM era, well-written Markdown files are executable engineering methodologies, and this cognitive shift is reshaping the entire developer tool ecosystem. Models are getting stronger every week. Those who learn to collaborate with AI now will have a huge advantage in the upcoming competition. Whether you are a developer, creator, or entrepreneur, consider starting today: transform your programming workflow with gstack, and apply the "AI agent role-playing" methodology to your own scenarios. Role-play your AI, turning it from a vague assistant into a precise team. [1] [2] [3] [4] [5] [6] [7]
DESIGN.md: Google Stitch's Most Underestimated Feature
On March 19, 2026, Google Labs announced a major upgrade to . Immediately after the news broke, Figma's stock price fell 8.8% . 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. 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 . 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: 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. 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 . 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 . 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 . DESIGN.md is the best carrier for this "deterministic element." On Reddit's r/FigmaDesign subreddit, users enthusiastically discussed Stitch's upgrade. Most focused on the canvas experience and AI generation quality . 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" . 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 . 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. 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, '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: 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: 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. 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 . 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. 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 to centralize scattered references into one Board, and let AI help you organize, retrieve, and create. [1] [2] [3] [4] [5] [6] [7] [8]
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]
