Everyone's Racing to Scale Models. DDC's Working on Something Else — Notes from ETH Summer Shanghai

The AI wave shows no sign of slowing, and Web3 keeps looking for ways to build alongside it.

Every summer, ETHPanda and LXDAO run "ETH Summer," a series of in-person Ethereum community events across multiple Chinese cities, bringing local Web3 builders together to talk through what's happening in the ecosystem right now.

On July 5, 2026, the event came to Shanghai, this time centered on AI and the economics of the AI ecosystem, drawing enthusiasts from across the space. DDC co-founder and CTO Li Yingxuan took part as both a speaker and a panelist, sharing our team's work and thinking on Web3 + AI from two angles, the product side and the data economy.

In this piece, we've pulled together the core ideas from that talk, for builders and AI enthusiasts who couldn't make it in person.

Keynote | AI's missing piece is you

During the keynote, our co-founder shared his research and views on "Training AI That Actually Knows You: How to Distill Your Personal Context".

For the past few years, almost every AI project has been pouring energy into the model, making it smarter with each iteration.

But when you actually use AI, you want it to get what you're saying and handle things in a way that fits you. A smarter model doesn't get you there. It needs data about you before it can understand you or do the job right.

There's a distinction here that's easy to miss. What big labs feed their models is massive general data, and what comes out is a general-purpose ability, built for everyone, a little bit of knowledge about everything. The problem with general data is that it can never fully land on you as an individual. So in day-to-day use, you still need to bring your own personal context, the part that's only about you.

That's what DDC works on.

Once AI big labs have handed us a smart assistant, the next step is that we need to bring our own personal context, so that assistant can grow into a real partner that's actually yours. But this is exactly what individual users are missing, because that data sits trapped inside different apps, with no easy way to pull it out into something readable.

So, at the event, DDC shared our new AI product Life Capsule, for the first time. This tool can help you take the real digital traces you leave scattered across platforms and distill them into a readable, portable Markdown memory file you can hand to any AI.

The whole thing turns on that word, distill. It pulls the substance out of messy raw traces, drops the noise, and shapes what's left into context a person can read and a machine can use. The raw material might be a screen recording of you scrolling TikTok, or an Amazon order. After distilling, it becomes clear information: what you've been paying attention to lately, what you keep buying, which tools you reach for. With this context in hand, AI doesn't have to guess who you are. It can actually read the traces you've really left behind.

To deliver on that "easy," DDC built Life Capsule as a very lightweight tool. Distilling takes just three steps:

• Capture — upload the real digital traces you've generated, catching your actual online behavior.
• Review — the vision model reads your uploaded images or screen recordings frame by frame, and you're free to look them over and fine-tune.
• Capsule — confirm the file and turn it into Markdown, ready to import into your notes app or an AI memory layer anytime.

There's no denying the big labs are working on memory and connections too, but this kind of approach has two built-in limits.

First, the distilling is unstable. The system pulls things out of your conversations automatically, and you have little control over what gets picked up or how accurate it is. Second, what it distills is only the version of you that shows up in the chat box. The much larger part, the things you never said to it but actually do day to day, stays invisible to it, so the picture is always partial.

Some people might worry that personal behavioral data reveals so much that handing it to Life Capsule raises safety concerns. In our co-founder's words, what a tool refuses to do matters more than what it can do.

For now, Life Capsule is set to read only the traces users upload themselves. It doesn't scrape anything without permission, and it doesn't use that data to label users or build any kind of profile or diagnosis. Data stays on the user's side by default, under their own control. Whether to export it, and which AI to hand it to, is entirely the user's call.

This way, users can distill their context with privacy intact, and use AI more intelligently as a result.

Panel | Data is an asset you own.

In the panel, the conversation moved from the product to something bigger, the question of how data should be used once AI agents actually start doing things on people's behalf. Below are a few of our co-founder's takes from the discussion. We'll lay out the fuller version in a follow-up piece.

First, for an agent to go from a smart tool to a real participant in the economy, everything hinges on whether it can get authorized access to genuine data. An agent that doesn't know the user, even with hundreds of tools at its disposal, can only give generic answers, and may even make the wrong calls. So if you want it to truly act for you, step one is understanding the user.

Second, understanding the user means using the user's data, and this is exactly where blockchain comes in. The chain needs to handle a few things at the base layer, authorization, proof, settlement, and revenue distribution. The raw data still stays on the user's side with privacy protected, while the record of who granted access, what scope the agent used, whether it went beyond its permissions, and who the resulting value should go to, all of that goes on-chain, where it can be verified and traced.

Third, the role data plays is shifting in the agent era. It used to be more like fuel for training models, with platforms collecting it, models consuming it, and users getting almost none of the upside. Once data becomes the context an agent uses to carry out tasks, it moves closer to an asset. Users can grant access on their own terms, deciding which agent gets it, for how long, within what scope, and at what price.

Following these three points, DDC's goal is to make personal context a data asset that can be authorized, verified, and settled. This is where DDC is headed longer term, beyond Life Capsule itself.

The labs have your assistant ready. The rest is on you.

Sooner or later, everyone's going to need a portable AI memory layer they actually own. It doesn't belong to any single platform, you can export it, review it, grant access to it, and pull that access back whenever you want.

Life Capsule is where this starts, distilling the digital traces scattered across your apps into a memory file you control. The further step is making that personal context something that can be authorized, verified, and settled, a real data asset that belongs to the user.

It's a road DDC hopes to walk with more developers, agent teams, and data partners. As for how personal context actually gets distilled and used, and how the AI data economy gets working, we'll get into it in the research and pieces to come. Stay tuned.

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