Why Byblos
Common Use Cases
Researching a Topic
Alice is performing a literature review on whether poor oral hygiene influences the risk of Alzheimer's disease. She creates a workspace and begins collecting papers on neuroinflammation, oral health, dementia, and the blood-brain barrier. As she reads, she leaves reactions on papers she finds important and records observations in page discussions.
As the workspace grows, finding important papers becomes easier rather than harder. When Alice wants to revisit a source, she filters pages that she rated highly or marked with a heart. Her notes remain attached to the papers that inspired them, making it easy to recover not only the source material but also what she was thinking when she read it.
One well-cited paper on P. gingivalis has a significant impact on her thinking. She creates a new tree and begins exploring both the paper's references and the papers that cite it. That investigation eventually branches into another tree focused on the oral microbiome, where she begins searching for other microbial candidates that may influence neurodegeneration.
A few weeks later, her advisor Bob becomes interested in the project. Alice shares the workspace with him. Instead of sending dozens of PDFs, bookmarks, notes, and links, she shares one workspace containing the papers, trees, annotations, discussions, and reasoning that led her to the current hypothesis. Bob leaves comments on several papers and suggests additional directions for investigation.
By plotting annotated pages over time, Alice can see how her interests evolved throughout the project. Early work focused on neuroinflammation. Later work concentrated on oral microbiology and host immune response. After a month, Alice realizes that oral microbiome research has become the center of the investigation. What began as a question about Alzheimer’s disease has evolved into a potential review article focused on microbial drivers of neurodegeneration. More importantly, it preserves the reasoning that connected them.
Streaming and Community Exploration
Alice is a Twitch streamer. Most evenings she explores the web with her viewers. Some nights she looks at indie games. Other nights she reviews AI tools, startup ideas, scientific papers, politics, or whatever else catches the community's interest.
Normally these streams generate thousands of chat messages. Interesting ideas appear, people share links, debates break out, and recommendations are made. By the next minute, most of that context is effectively gone. New viewers have no idea what was discussed last week, and even regular viewers struggle to remember which pages generated the most interest week to week.
Instead of sharing only her screen, Alice shares a Byblos workspace with her entire audience. Every page she visits becomes part of a growing community workspace. Viewers can react to pages, leave notes, create discussions, and contribute related pages of their own to communal trees. Conversations happen around the pages themselves rather than being squeezed through a single stream of chat messages.
Over time, the workspace becomes a collective persistent memory of the community. A discussion about an indie game remains attached to the game's Steam page. A debate about a startup remains attached to the company's website. A useful explanation on a scientific paper can still be found months later. New viewers can explore previous streams, understand why certain pages became important, and participate in conversations long after the original stream ended. Streamers can make more informed decisions about what type of content their audience responds to.
The result is not just a stream. It is a persistent community built around a shared collection of webpages. The community is no longer limited to reacting to what the streamer finds. It can collectively discover, discuss, organize, and build on that information over time. Six months later, new viewers are still discovering, discussing, and expanding trees created during the community’s earliest streams.
Living Publications
Alice and Bob are investigative journalists researching allegations that a city council member steered public contracts toward companies owned by close associates. They create a workspace and begin collecting evidence. News articles grow into procurement records. Procurement records grow into company registrations. Company registrations grow into campaign finance disclosures. Interviews, archived webpages, public statements, and court filings gradually expand into a large investigation tree.
As the investigation develops, Alice and Bob leave annotations, discussions, reactions, and notes directly on the evidence. Some pages strengthen the allegation. Others weaken it. Several claims remain unresolved. Contradictory evidence is preserved alongside supporting evidence rather than disappearing during editing.
In a traditional newsroom, the final product would be a linear article. The investigation would be compressed into a narrative, most of the supporting work would remain invisible, and readers would have little ability to inspect the reasoning process behind the story.
In Byblos, the workspace itself becomes the publication. Readers can follow the investigation at their own pace. They can inspect the evidence supporting a claim, examine counterarguments, review annotations left by reporters, and trace conclusions back to their original sources. Different readers can explore different paths through the same body of evidence.
Publication is no longer the end of the investigation. Readers can leave comments on specific pieces of evidence. Experts can challenge claims directly on the pages that support them. New information can be attached to existing nodes. Corrections, updates, and follow-up reporting become part of the same workspace rather than separate articles published months later.
The result is not an interactive article. It is a living publication: a shared workspace where evidence, discussion, interpretation, and publication remain connected long after the story is released. Two years later, new evidence is attached to the original investigation rather than spawning an entirely separate story.
Planning a Vacation
A family is planning a vacation to either Lisbon, Athens, Paris, or Barcelona. Alice creates a workspace and shares it with husband Bob and kids Carol and Dave. Bob creates a tree for flights. He records the ticket prices of different flights and their durations to candidate cities. Alice creates a tree and looks at Airbnb listings in each city. She samples ten three-bedroom listings in each city and estimates the average accommodation cost for a week. Carol and Dave search for activities in their own trees. They find museums, beaches, amusement parks, and restaurants. They both rate each from one to five stars.
By next week, the family has collected 200 pages. Flights, listings, attractions, restaurants, and travel guides are all connected to the same decision of which city to visit.
Carol sees Dave's tree and notices that he keeps giving high ratings to Barcelona's tapas restaurants. Dave notices that Carol loves Barcelona's beaches and attractions. They agree on Barcelona. Alice showed that Barcelona had the most expensive accommodation. Bob showed that flights to Barcelona were second-most expensive. Sorting by cost, the cheapest options for both were in Athens. Sorting flights by durations showed that Athens saved 4 hours of layover each way. That tipped the scales, and they chose Athens.