We asked what people would most want their J-Card to reveal about them. 40% chose how they make decisions. That's interesting because we often focus on the decisions we make, but rarely stop to examine how we arrived at them. What did we consider? What did we reject? What made us change our minds? Those choices can reveal patterns we don't always notice ourselves. And that's part of what we're exploring with Jarvix.live Not just remembering what you've done, but helping you better understand the thinking behind it. Explore your own J-Card at Jarvix.live

If your J-Card could reveal one thing about you based on your AI conversations, what would you be most curious to discover?

We've been a little quieter on X lately. But behind the scenes, we're still exploring, building, and refining what personal AI could become. Some days, progress isn't about shipping something new. It's about understanding what needs to be better. One step at a time. Less noise. More meaningful progress. More to come. Jarvix.live

What makes Jarvix different from just another AI memory tool? Most AI memory tools focus on remembering what you said. But remembering your conversations isn't the same as understanding how you think. What you explored. What you rejected. What you changed. What you eventually chose. Those decisions reveal something that saved conversations alone might miss. That's the thinking behind Jarvix.live Not just helping AI remember your past. Helping it understand your context, recognize patterns, and suggest what might be worth doing next. Because memory tells AI what happened. Understanding helps it recognize why it mattered.

Imagine telling someone in 2020 that AI would soon be writing production code, building apps, analyzing research, and handling work that once took entire teams. They probably wouldn't believe you. Yet here we are. A few years ago, we were impressed when AI could write a decent paragraph. Today, we're asking it to build software, analyze research, reason through problems, and coordinate entire workflows. But something interesting happened along the way. As AI became more capable, our expectations changed. We stopped asking, “Can AI do this?” And started asking, “Can AI understand what I'm actually trying to achieve?” Because generating an answer is one thing. Understanding the person, their goals, previous decisions, and the reasoning behind their work is something else entirely. The last few years were about making AI more capable. The next few may be about making AI more personal. Not just intelligence that knows more about the world. Intelligence that understands more about the person it's working with.

The most dangerous sentence in the AI era: “Wait, I have an idea.” Five minutes later, you've got a business plan, a landing page, a logo, and a product roadmap. All for a problem you weren't even trying to solve. AI didn't just lower the barrier to building. It lowered the barrier to getting distracted professionally. 😭

How much of your own behavior do you actually notice? Think about the last few weeks. You might have spent hours exploring an idea you never finished, returned to the same problem several times, or gradually changed your approach to something without realizing it. In the moment, these things feel unrelated. Just another conversation, another project, another decision. But when you look at them together, patterns begin to emerge. Maybe the topics you keep revisiting reveal what genuinely interests you. Maybe the projects you abandon tell you something about where your motivation fades. Maybe the way you refine ideas shows that your standards have changed. We rarely notice these patterns while we're living through them. That's what makes SELF-REFLECTION interesting. It's not simply looking back at what you did. It's asking what those choices might reveal about how you're thinking, learning, and changing. And this is the thinking behind Self Reflection in Jarvix.live Using the context you choose to share, Jarvix helps surface patterns across your activity so you can examine them for yourself. Not to tell you who you are. But to help you notice things about yourself that are easy to miss. Because sometimes, the most useful insight isn't a new answer. It's recognizing something about yourself that was already there. Explore Self Reflection at Jarvix.live

We’ve noticed something interesting about working with AI. Sometimes the most revealing part of a conversation isn’t the answer you keep. It’s everything you didn’t keep. You explore five ideas, reject three, refine one, and eventually choose another. But those rejected paths aren’t meaningless.

One question we’ve heard from users again and again: Is Jarvix local-first? The short answer is: yes. But we’ve learned that “local-first” can mean different things to different people. For us, it means your personal context is kept on your device, and Jarvix.live only works with the context you choose to share. It doesn’t mean pretending every part of Jarvix runs locally. Some features like search, media, and voice may need external providers to do their job. In those cases, limited data needed for that specific request may leave your device. That distinction matters. We don’t want to call Jarvix “100% local” just because it sounds better. We’d rather tell you exactly what stays local, what may leave your device, and why. Local-first is a design decision. Transparency is a responsibility.

Most productivity tools can tell you what needs to be done. The harder question is: What actually deserves your attention next? A project rarely moves in a straight line. You make a decision. Reject an approach. Discover something new. Leave a problem unresolved. Start working on something else. Then come back days later. By the time you ask, What should I do next?, the answer depends on everything that happened before. That’s why a simple task list can only take you so far. It knows what’s open. It doesn’t always know why it matters now. As AI makes it easier to research, write, build, and generate options, the bottleneck increasingly shifts toward deciding what deserves attention. That’s the idea behind Next Move in Jarvix.live Instead of simply giving you more things to do, it looks at the context around your work and surfaces a possible next action. You stay in control. Jarvix suggests. You decide what happens next. Because personal AI shouldn’t just help you do more. It should help you recognize what matters next.

One thing we don’t want to do with Jarvix is pretend we support every AI tool just because it sounds good on a website. AI is moving quickly. New agents, tools, and sources are showing up all the time, and we’re designing Jarvix to work around the ones you already use. But there’s an important distinction between what we’re building for it and what we support today. So we’d rather be clear about what Jarvix.live actually supports than publish a giant integration list that looks impressive but tells you very little. Because integrations aren’t valuable simply because they exist. What matters is whether Jarvix can actually bring useful context from that source into your workflow, preserve the relationships between things, and make that context available when it becomes relevant. That’s the standard we’re building toward. The goal isn’t to make you move your work into another ecosystem. It’s to work around the tools and sources you already rely on, while giving the context between them somewhere to live. If you use a tool with Jarvix, it should be because it genuinely makes your workflow more connected, not because we wanted another logo on a webpage. We’ll keep the integration list honest as it evolves. What’s supported today. What’s coming next. And what isn’t supported yet. No inflated promises. Just a clearer path toward AI that can actually work with the context you choose.

If AI could reflect your own behavior back to you, what would you want to see? - What I do well - What I keep overlooking

We asked a question recently: If AI understood enough context to act proactively, what would be more valuable? 57.1% said suggest what comes next. 42.9% said catch what’s missed. We think both answers point to something bigger. People aren’t necessarily asking AI to take control of their work. They’re asking it to notice things they might not. What should happen next? What did we overlook? What changed? What’s still unfinished? Is there something important hiding in everything that happened before? Today, most AI waits for you to point at the problem. You ask a question. It gives you an answer. But real work doesn’t happen in isolated questions. It happens across decisions, experiments, unfinished ideas, rejected approaches, conversations, files, and small details that accumulate over time. When AI can understand that larger picture, its role can change. It doesn't just respond to what you ask. It can help you notice what you didn't think to ask. Maybe that’s what proactive AI should actually mean. Not AI acting on your behalf. AI helping you see what you might have missed and what might be worth doing next. That’s the direction we’re exploring with Jarvix.live

Switching between AI tools shouldn’t feel like introducing yourself at every meeting. Hi, I’m working on this project… Here’s what I’ve already tried… Here’s what we decided… And please don’t suggest the thing we already rejected. You’re not starting over. Your AI shouldn’t either.

Most people don’t actually use just one AI anymore. You might start with one agent to think through an idea, move to another to build it, and use a third to research something along the way. The work is continuous. The tools aren’t. Every time you switch, some part of the story gets left behind. You remember what happened, but the next agent doesn’t. So you explain the project again, repeat the decisions, and recreate the context you already spent time building. That’s the frustrating part. You’re not starting a new project. You’re just using a different tool. Jarvix.live lets you choose the context you want to carry into the agents and sessions you choose. So when you move from one agent to another, the next step can start with the same grounded background. Not every piece of information. Not everything you’ve ever done. Just the context that matters to the work. That’s what continuity across agents should feel like.

Your AI tools already have context. The problem is that they each have their own. ChatGPT has your conversations. Claude Code has your project. Codex has the code it’s working with. Then you switch tools. The context stays behind. So you explain the project again. Repeat the decision you already made. Rebuild the background before you can continue. Jarvix.live changes where the context lives. 1. Connect Bring together the context you choose from the AI tools and sources you already use. 2. Carry Jarvix carries that selected context across your AI work, so you spend less time re-explaining yourself. 3. Understand It can use that context to surface patterns, recover past work, and help you see what matters. 4. Move forward When there’s a useful next step, Jarvix can suggest it — and you decide whether to act. 5. Work together With Agent Team Mode, your chosen agents can work from shared project context instead of working in separate silos. One context. Different agents. You stay in control.

What would you want from a Context OS?

AI is getting very good at giving us more options. More ideas. More designs. More strategies. More ways to solve the same problem. But having more options doesn’t automatically make us better at choosing. That’s where taste becomes more important. Knowing what to ignore. Knowing what feels right for the situation. Knowing when something is technically good but simply isn’t the right answer. AI can expand the possibility space incredibly quickly. But deciding what deserves to survive that process is still a very human skill. Maybe the next challenge isn’t teaching AI to create more. It’s teaching AI to understand why we choose what we choose. Use Jarvix.live, share your experience, mention us, and you could win $20–$200. Fill out the form to apply: docs.google.com/forms/d/e/1F…

What would make AI genuinely more useful to you? - Understanding the context - Taking action - Helping make decisions - Remembering what matters

The AI revolution is entering a different phase. For the last few years, the conversation was mostly about models. Which model is smarter? Which one reasons better? Which one writes better code? Then we started talking about prompts. Better instructions, better outputs. But as AI moves toward agents that can use tools, work across multiple steps, and collaborate with other agents, a more fundamental problem is becoming difficult to ignore: context. An agent can have a powerful model, access to dozens of tools, and a huge context window. That still doesn't mean it understands the situation it is working in. Real work isn't a collection of isolated prompts. You make a decision, reject an approach, discover something important, change direction, switch tools, and return to the same project days later. The value isn't in any single piece of information. It's in the relationship between them. And this becomes even more important as we start using multiple AI agents. One agent knows the code. Another knows the research. Another has access to your documents. Another is handling a completely different part of the project. The agents may be highly capable individually, but without shared context, you become the layer connecting them. You explain what happened. You repeat the decisions. You recreate the background. You tell each system what the others already know. That feels like one of the biggest gaps in the current AI experience. The next layer of AI may not be another smarter agent. It may be the context layer underneath them. A layer that can carry the relevant history, decisions, preferences, projects, and signals across the tools you use while still keeping you in control of what gets shared and what happens next. That's the idea behind #JarvixOS. Not replacing the agents you already use. Giving your work continuity across them. Because if AI is going to become a real collaborator, it shouldn't only understand the next prompt. It should understand what came before, what matters now, and what should happen next. The AI revolution started with better models. Then we learned how to talk to them. Now we're figuring out how to give them the context to actually work with us. Curious what AI could feel like if you didn’t have to start from zero every time? Explore Jarvix.live