flâneur

Lâm Vũ

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on the atlas — 90

highlights — 419

  • In speed running (particularly TASes), a video game pretends to be made out of things like ‘walls’ and ‘speed limits’ and ‘levels which must be completed in a particular order’, but it’s really again just made out of bits and memory locations, and messing with them in particular ways, such as deliberately overloading the RAM ⁠to cause ⁠memory allocation errors, can give you infinite ‘velocity’ or shift you into ⁠alternate coordinate systems in the true physics, allowing enormous movements in the supposed map, giving shortcuts to the ‘end’⁠⁠5⁠ of the game.
    On Seeing Through and Unseeing: The Hacker Mindset · Gwern.net
  • Activity diagram is essentially an advanced version of flow chart that modeling the flow from one activity to another activity.
    What is Activity Diagram?
  • The visual representation of a document in Notion reflects the structure of the information it contains.
    The data model behind Notion's flexibility
  • In other words, when you indent something in Notion, you are manipulating relationships between blocks and their content, not just adding a style.
    The data model behind Notion's flexibility
  • Each block defines the position and order in which its content blocks are rendered. We call this hierarchical relationship between blocks and their render children the “render tree.” But, it doesn't look like a tree with branches — different block types render their children in different ways
    The data model behind Notion's flexibility
  • The content attribute of a block is what stores the array of block IDs (or pointers) referencing those nested blocks.
    The data model behind Notion's flexibility
  • Changing the type of a block doesn’t change the block’s properties or content — it only changes the type attribute. The information is just rendered differently, or even ignored if the property isn’t used by that block type.
    The data model behind Notion's flexibility
  • The block type is what specifies how the block is rendered in Notion’s UI — and depending on that type, we interpret the block’s properties and content differently
    The data model behind Notion's flexibility
  • Content — an array (or ordered set) of block IDs representing the content inside this block, like nested bullet items in a bulleted list or the text inside a toggle. Parent — the block ID of the block’s parent. The parent block is only used for permissions.
    The data model behind Notion's flexibility
  • Properties — a data structure containing custom attributes about a specific block. The most common property is title, which stores the text content of block types like paragraphs, lists, and of course, the title of a page. More elaborate block types require additional or different properties, like a page block in a database with user-defined properties.
    The data model behind Notion's flexibility
  • Much like LEGO blocks in a LEGO set, Notion blocks are the singular pieces that represent all units of information inside the Notion editor. The attributes of a block determine how that information is rendered and organized.
    The data model behind Notion's flexibility
  • Everything you see in Notion is a block. Text, images, lists, a row in a database, even pages themselves — these are all blocks, dynamic units of information that can be transformed into other block types or moved freely within Notion. They’re the LEGOs we use to build and model information. And when put together, blocks are like LEGO sets, creating something much greater than the sum of their parts.
    The data model behind Notion's flexibility
  • A physical data model (PDM) is a data model that represents relational data objects. It describes the technology-specific and database-specific implementation of the data model and is the last step in transforming from a logical data model to a working database. A physical data model includes all the needed physical details to build a database.
    Conceptual vs Logical vs Physical Data Models
  • Cardinality Defines the numerical attributes of the relationship between two entities or entity sets
    ER Diagram (ERD) - Definition & Overview | Lucidchart
  • In the conceptual data model, the entities and relationships were all defined.
    Conceptual vs Logical vs Physical Data Models
  • The LDM includes the specific attributes of each entity, the relationships between entities, and the cardinality of those relationships.
    Conceptual vs Logical vs Physical Data Models
  • A logical data model (LDM) contains representations that fully defines relationships in data, adding the details and structure of essential entities. It’s important to note that the LDM remains data platform agnostic because it focuses on business needs, flexibility, and portability.
    Conceptual vs Logical vs Physical Data Models
  • These essential concepts are usually captured in an Entity Relationship Diagram (ERD) and the accompanying entity definitions.
    Conceptual vs Logical vs Physical Data Models
  • A conceptual data model (CDM) operates at a high level, providing an overarching perspective on the organization's data needs. It defines a broad and simplified view of the data a business utilizes or plans to utilize in its daily operations
    Conceptual vs Logical vs Physical Data Models
  • Demonstrate the essential components as they relate to each other
    3 Examples of Conceptual Data Models | ThoughtSpot
  • Two primary artifacts arise from the process of creating a conceptual data model—the actual CDM diagram and the definitions of the essential concepts.
    3 Examples of Conceptual Data Models | ThoughtSpot
  • A shared understanding of the business ensures that everyone has the same definitions of the essential concepts including which are included or excluded. Essential concepts mean roles, people, places, or processes, that are an integral part of the business model
    3 Examples of Conceptual Data Models | ThoughtSpot
  • A conceptual data model is a technology-agonistic, high-level, abstract representation of the data an organization uses, or intends to use, in its business operations
    3 Examples of Conceptual Data Models | ThoughtSpot
  • Plus, MCP only addresses context portability, not memory. I’ll repeat - the accumulated history of interactions, the learned preferences, the refined understanding of each user—that's not portable. And that's where the real lock-in lives.
    The Next Great Distribution Shift - Brian Balfour
  • This creates a compounding advantage. More integrations make ChatGPT more valuable. More value attracts more users and deepens engagement. More users attract more integrations. We've seen this movie before.
    The Next Great Distribution Shift - Brian Balfour
  • Anthropic's Model Context Protocol was a clever defensive move. In theory, it commoditizes integrations—if every AI can access the same context through standardized protocols, no single platform can monopolize it
    The Next Great Distribution Shift - Brian Balfour
  • If Apple suddenly shipped a compelling AI assistant that leveraged all that context, integrated with all their services, and opened it to developers, they could leapfrog everyone. They have the users, the developer relationships, and the platform experience. But "if" is doing heavy lifting here. Apple's AI efforts have been underwhelming (and that is putting it politely). Their culture prioritizes privacy over the data collection that powers AI. Their famous secrecy conflicts with the developer openness platforms require. OpenAI clearly sees this threat—hence their reported device collaboratio…
    The Next Great Distribution Shift - Brian Balfour
  • Apple is the sleeping giant that could change everything. They own the device layer—both mobile and desktop. They see every interaction, every app, every moment of your digital life. They have the ultimate context and could build the ultimate memory.
    The Next Great Distribution Shift - Brian Balfour
  • Because in platform shifts, there are only three types of companies: those who move too early and waste resources, those who move too late and miss the window, and those who see the shift coming and position themselves to win.
    The Next Great Distribution Shift - Brian Balfour
  • If I’m right, in six months, it’s likely every SaaS product and consumer application will be rushing to complete a ChatGPT integration
    The Next Great Distribution Shift - Brian Balfour
  • The companies that survived previous platform shifts didn't just integrate—they integrated strategically. They used Facebook's viral channels while building email lists. They leveraged Google's traffic while developing brand loyalty. They sold through the App Store while creating web experiences.
    The Next Great Distribution Shift - Brian Balfour
  • Most importantly: How do you use the platform while building your own moat? This is where product strategy matters. You need to create value that persists even when platform access gets restricted. Build direct relationships. Capture first-party data. Create moats within your own product.
    The Next Great Distribution Shift - Brian Balfour
  • This is the prisoner's dilemma of platform shifts.
    The Next Great Distribution Shift - Brian Balfour
  • Every product leader faces the same dilemma. In isolation, integrating with ChatGPT makes no sense. Why would HubSpot want to become a database to ChatGPT's interface? Why would Notion want to feed their replacement? Why would any company voluntarily feed the next platform monopoly? But we don't operate in isolation. We operate in competitive markets. And markets are ruthless about efficiency. When your competitor integrates with ChatGPT and their users love the AI-powered experience, what choice do you have? When customers start asking why your product doesn't work with ChatGPT like your riva…
    The Next Great Distribution Shift - Brian Balfour
  • The only other times I have seen this pattern is in retention curves of products like Facebook, LinkedIn, and Slack. I’ll repeat, it does not matter that Google / Meta have access to larger distribution if the retention & engagement disparity is this large.
    The Next Great Distribution Shift - Brian Balfour
  • If you are a Reforge alum, you should have one thing burned into your brain - those with the highest retention & engagement win categories. Retention is the god metric. It is a universal law at this point and has been proven over and over.
    The Next Great Distribution Shift - Brian Balfour
  • Once OpenAI achieves context lock-in—when switching to another AI means losing months or years of accumulated context—the platform dynamics will shift.
    The Next Great Distribution Shift - Brian Balfour
  • This is fundamentally different from previous platform moats. Facebook needed your social graph. Google needed the web's content. OpenAI needs something far more intimate: your entire digital context. Every document, every conversation, every preference, every workflow.
    The Next Great Distribution Shift - Brian Balfour
  • The key here is that MCP only addresses context portability, not memory
    The Next Great Distribution Shift - Brian Balfour
  • OpenAI has identified their moat, and it's not the model quality. It's context and memory.
    The Next Great Distribution Shift - Brian Balfour
  • Create artificial scarcity: Make organic reach feel special and exclusive Build creator dependence: Encourage people to abandon other channels Flip the switch: Once critical mass is achieved, monetize aggressively Gaslight the community: Claim the algorithm changes are about "quality"
    The Next Great Distribution Shift - Brian Balfour
  • But static profiles only reveal so much. LinkedIn needed dynamic data—what you read, what you share, what makes you engage. They needed users actively participating, not just existing.
    The Next Great Distribution Shift - Brian Balfour
  • LinkedIn's moat is professional data—not just who you are, but what you do, what you care about, and how you behave professionally. This data is gold to three lucrative customer segments: marketers targeting B2B buyers, recruiters hunting talent, and salespeople seeking warm leads.
    The Next Great Distribution Shift - Brian Balfour
  • Every platform starts by figuring out what will make them unassailable. For Facebook, it was the social graph—who knows whom. For Google, it was search data—what people want. For Apple, it was having an application ecosystem to attract premium device owners. The moat determines everything that follows.
    The Next Great Distribution Shift - Brian Balfour
  • Google's moat was elegantly circular: data and content. The more people searched, the more data they gathered. The better their results, the more people searched. But this virtuous circle had a critical dependency—they needed the entire web's content. Unlike Facebook or Apple, Google's moat required universal participation. They needed millions of websites creating content, structured in ways their crawler could understand. The open web wasn't just philosophy—it was a business requirement.
    The Next Great Distribution Shift - Brian Balfour
  • Apple recognized their moat wasn't just hardware—it was the ecosystem. If they could make the iPhone the platform with the best apps, apps you couldn't get anywhere else, they'd create switching costs so high that users would never leave. The network effect was more apps led to more iPhone users which led to more apps. But Apple couldn't build all those apps themselves.
    The Next Great Distribution Shift - Brian Balfour
  • The iPhone was revolutionary but expensive, limited to AT&T, and lacked basic features like copy-paste. Apple needed something to make the iPhone indispensable—to justify the premium price and lock in users before Android gained momentum.
    The Next Great Distribution Shift - Brian Balfour
  • Facebook's platform shift established the modern template: Use developers to build what you can't build alone Let them take the risks and validate use cases Once you've won, take back control and monetize aggressively
    The Next Great Distribution Shift - Brian Balfour
  • We’ll provide you a canvas to build anything you want on our platform Use our viral channels to grow as fast as you can Keep 100% of your app's revenue We'll just monetize the sidebar ads
    The Next Great Distribution Shift - Brian Balfour
  • The platform isn't your friend. It's not your enemy either. It's a force of nature. You can surf the wave or get crushed by it, but you can't stop it from coming
    The Next Great Distribution Shift - Brian Balfour