Personal Context Is the Scarcest Resource in the AI Era, and DDC Wants to Help You Distill It

Models still need pushing forward, but pushing on the model alone is no longer enough. That has been the industry's shared read for the past couple of years.

It mirrors how the consumer internet grew up. Early on, the race was about how many features you could offer. Later it became about who understood you best and held onto your attention. AI is running the same course, and the moat everyone talks about has moved from the model to the data.

Whoever holds more data, and more real data, has the stronger AI.

Gemini has it easy, wired into the flood of data across the Google ecosystem. GPT leans on acquisition and huge volumes of interaction to fill in more outside data. Look at China and it's the same story, with Doubao, Yuanbao, and Qwen all fed by the data of the internet giants behind them.

But there's a problem that's easy to overlook. What the big players stockpile is general data at massive scale, and what it feeds is a general model, one that knows everyone yet doesn't really know the particular you.

That used to be no big deal. You asked, it answered, and general was enough. But AI is moving from question-and-answer toward agents, toward carrying a task through for you from start to finish. And for an agent to act on your behalf, being smart isn't enough. It has to know you first, which means having your personal context.

Personal context isn't simple facts like your name and age, and it isn't the "likes to travel" or "big spender" label a platform pins on you. It's a file that describes your real traces, your lasting preferences, and where you are right now, readable by a person and usable by a machine. With it, an AI doesn't have to meet you from scratch every time.

And this context is exactly what's scarcest right now.

It has, in fact, become the main battleground the big players are fighting over. From Google and OpenAI to Anthropic and Apple, every one of them is working to make its AI know you better, so that once you use it, you can't easily leave. Some analysts call this race the next battlefield for big tech, but they also point out that it has barely begun, because one fundamental problem sits in the middle of it. No one can actually get hold of a complete version of you.

Your traces are scattered across dozens of ecosystems that don't talk to each other, and each one, out of its own commercial interest, keeps its data walled off inside its own garden. So any single company only ever holds one slice of you.

And you can't readily use your own either. You have the right to pull back your data from each ecosystem, but what comes out usually can't be used directly, because it's a heap of raw records, unreadable and messy. Shaping that into a clear piece of personal context you could hand straight to an AI takes so much time and effort that almost no one actually does it.

Which is why, as AI's hunger for personal context keeps growing, a handful of "distillation" tools have started to appear across the industry.

Every distillation we have so far only goes halfway

A story went around Chinese social media a while back, and it caused a real uproar. After an employee left, his company took the chat logs and work documents he had produced on the job, fed them to an AI, and reproduced a digital double of him, close enough in tone and habit to be mistaken for the man himself. It picked up his work and carried on, and it would even post in the team group on its own. People found it chilling, and they also came up with a name for it that captured it perfectly. They called it 炼化, the term Daoist alchemists used for cooking raw matter down into an elixir pill. The company, they said, had refined a person into a pill.

Set the controversy aside, and the word turns out to describe something quite precisely. The traces a person leaves over the years are scattered and messy, chat logs and documents and habits heaped up together. Refining means drawing the substance out of that pile, discarding what doesn't matter, and reducing it to something an AI can read and use directly.

That is roughly what distillation does. The only difference is that in that story, the one being refined was somebody else, and he had no say in what it was used for. The distillation we're talking about works on your own traces, and what comes out of it should stay in your hands.

People in AI are already doing this. They all call it distillation, but each of them picks different raw material to distill, so what each one can do turns out differently.

The first kind distills your conversations with AI. ChatGPT and Claude both have Memory, which picks up your preferences, your habits, and some project background from the way you talk to them. Memory layers like Mem0 and Zep do much the same, settling your back-and-forth with a model into memory that can be called up again and again. Konshus and MindLock go a step further, letting you export your ChatGPT and Claude conversations, feed them back in, and distill them into an encrypted, portable Markdown context you can paste into any AI. But this kind of distillation has only one raw material, which is the words you typed into an AI chat box. It knows the part of you that you told it about. The much larger part you never mentioned but actually lived, it never reaches. And because the AI does the distilling itself, what it kept and what it let slip is not something you fully know, or can easily go back and check.

The second kind connects to your accounts and pulls the context out automatically. Gemini can reach into your Google apps, Copilot can read your Office documents, and ChatGPT's Plugins section connects to work software, entertainment apps, travel apps, even various investment apps. Beyond the model giants, there are projects built specifically for context. Unabyss, once you authorize it, connects to the back ends of Gmail, Slack, Notion, LinkedIn and more, gathering your account data into a context that belongs to you. Pieces came earlier and grabs code snippets for developers, holding context across IDEs and chat panels. But very few players make it in this category. Unless you are the ecosystem itself or one of the giants, almost no application is willing to hand its data to another tool through an API, and the bigger data players would rather grow their own and close the commercial loop in house. And handing over read access to your own accounts is something worth weighing on its own.

The third kind distills what you do on screen. Microsoft's Recall keeps snapshotting your screen in the background, so whatever you did, you can search your way back to it later. Open-source tools like Screenpipe do much the same, recording your screen and your audio together and keeping all of it on your own machine. Rewind came earlier and started out recording screen and audio around the clock, then renamed itself Limitless, moved to a recording pendant, was acquired by Meta, and shut screen and audio recording down entirely at the end of 2025. This kind captures the rawest and most complete material there is. But the way it gets that material is constant background "monitoring," and everything on your screen gets recorded, indiscriminately. Recall is the cautionary tale. It snapshotted everything and stored it in plaintext, a security researcher built a tool that pulled the whole thing straight out, the controversy was enormous, and only then did encryption and a verified opt-in get added. And after all that recording, what it has accumulated is a library full of raw records. You can search it, you can query it, but you can't take out a context and hand it to any AI. That last step, the distilling, it hasn't quite taken.

Understood, Not Just Counted

So how should distilling actually work, if it's going to produce a better personal context?

DDC's answer is that a real personal context is a file that can account for the traces of your actual life, your long-term preferences, your behavioral patterns, your judgments about what matters, and where you are right now. It's a file a person can read and an AI can use. Working from that, DDC has been building a lightweight distilling tool and named it Life Capsule. Distilling, the way Life Capsule defines it, means pulling the substance out of raw captures, dropping the noise, and organizing what's left into a Markdown file that a person can read and a machine can use. You pick the raw material yourself. It might be a screen recording of you scrolling TikTok, an order list from Amazon, a scroll through your Instagram feed, or your iOS Screen Time, your browser tabs, your desktop workspace, your calendar. All of it starts out messy, screenshots and recordings and lists and system screens piled together. After distilling, it turns into clear information. What you've been paying attention to lately, what you keep buying or saving, which tools you reach for and when, whether your interest in something runs long or was just an impulse, what you actually prefer when it comes to work, spending, learning, and travel.

This only became doable now because multimodal models finally got good enough. Turning recordings, screenshots, and system screens into high-quality text reliably used to be hard. Vision models today can read the structure, the content, the numbers, and the meaning on a screen, and the path from your life on screen to a personal memory opened up for the first time.

Using it takes three steps.

Capture. You pick a shortcut, Instagram, TikTok, Amazon, Google Maps, whichever fits, or you drag in images, upload a screen recording, upload a video of your desktop. One principle matters here. Life Capsule doesn't ask you to fill out a personality questionnaire or play a character. It only wants the digital traces you actually left, because what a person really watched, bought, searched for, and saved tends to be more accurate than what they write down under "things I like".

Review. A vision model reads your images or recordings frame by frame, then merges them into one coherent, readable draft. What it's after is what the recording actually says, not a UI description like "there's a button here, an icon there." Which content, numbers, products, posts, and settings are in there, and which of it could serve as context the next time you use an AI. At this step, you can look it all over and edit it, and confirm whether the AI really understood what you uploaded.

Life Capsule. Once you confirm, the Markdown goes into My Life Capsules. You can download it to Obsidian, or paste it into ChatGPT, Claude, or any AI, as system context or long-term memory. It keeps version history too. Every save is a revision, you can diff them, and watch how your interests, preferences, and state have shifted. It grows along with you.

Some people will worry that recordings and screenshots carry plenty of sensitive information, and that handing them to Life Capsule raises the same safety problem. In the words of DDC's cofounder, what we don't do matters more than what we can do.

Before you upload, you choose which part goes in, and you can mask anything yourself. After the vision model has read it frame by frame, you get to go over the draft that comes out, change whatever you don't want kept, and only once you confirm does it become a Life Capsule. From deciding what goes in to deciding what stays, every gate is yours.

Next, DDC will be integrating its privacy protection technology into Life Capsule. DDC's own work is infrastructure for AI data privacy and data assetization, and its product lineup includes an Ethereum Layer 2 built in house to carry data authorization, privacy proofs, and value settlement. Once that's wired into Life Capsule, your raw data still stays on your side, and what runs on chain is the authorization and the proof. Which AI, at what time, gets to read which part of you is yours to decide, and you can revoke it whenever.

In the end, the value of a personal context comes from being real, precise, and under your control, not from how much of it there is. Real, because what gets distilled is what you actually did, not a self-description you wrote on a form. Precise, because you distill what your AI needs. Want it to help you pick something out, upload your Amazon order list. Want it to be a partner at work, upload your desktop workspace, your browser tabs, your calendar. Under your control, because what goes in, what stays, and which AI gets it are yours to call from start to finish.

That's the Life Capsule DDC building. It wants AI to understand you, not just everyone.

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