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Levlex, the AI Agent Operating System for Your Computer

Hacker News

Levlex, the AI Agent Operating System for Your Computer

I built Levlex, the AI Agent Operating System for your computer For a quick overview, you can read the linked thread, but I’d love for you to hear the full story if you have time. Early Inspiration When ChatGPT first emerged, I realized it could output JSON (albeit somewhat unreliably) and call functions. This sparked the idea of AI systems deeply integrated into our digital lives—what people now call “AI agents.” My initial vision for Levlex was a cloud-based platform that scanned your digital ecosystem and provided hourly, daily, or weekly updates. Pivoting to Local As Levlex evolved through multiple pivots, I noticed two trends: (1) Cloud usage costs were skyrocketing (over $100 per user), and (2) serious users were increasingly interested in running AI locally for performance and privacy. Switching to a local app model introduced some hardware requirements (16GB–32GB RAM), but it also brought major benefits: you’re not dependent on any single provider, you can use custom local models, and you avoid worries about cloud servers discontinuing models. Although you can still use cloud providers if you wish. Why Not a Subscription? I decided on a one-time purchase model rather than a subscription because, as hardware gets cheaper and more powerful, more people will be able to run sophisticated AI locally. If you already buy powerful GPUs, this model likely suits you. It also differentiates Levlex from the many subscription-based services out there. What Is Levlex? Levlex addresses the fragmentation in AI tools. Instead of paying monthly for multiple AI services, you can run one local app that does (almost) everything—and offers innovative AI experiences that go beyond repackaging existing ideas. You can also install or build custom extensions, creating an ecosystem of Levlex “apps” similar to how iOS or Slack apps extend their platforms. Selected Key Features: Workflows: A generalized AI agent system to create, run, and schedule tasks—think Zapier, but with AI Agents. ARTIE: A reasoning agent engine I built that enables advanced sequential reasoning, tool integration, and open-ended problem solving. This powers what I like to call AGS agents. Custom Agents: Easily build agents for specific tasks, from custom system prompts to running CLI commands to AGS Agents, with a no-code tool for non-technical users. Graph Generator Agent: Generate beautiful interactive graphs from natural language. Chambers: Think notebookLM, but you can configure the number of AI participants, the specific models they use, and you can participate in the conversation. BrainIDs: Customizable, separate memory stores for different contexts that can be shared across chats and features (e.g., “Work” vs. “Personal”). Knowledge Discovery: A persistent AI agent that continues iterating until it finds the answer you need, ensuring deeper exploration and results. Spaces: A UI builder that allows you to combine multiple Levlex features and use in one dashboard. Levlex is built for users who want complete control over their AI—developers and non-developers alike can harness its power and extend it through custom tools. The Vision As AI models become more efficient and capable of running locally, Levlex positions itself as the operating system for this shift. This isn’t just another AI tool—it’s a rethinking of how we use AI in our daily lives. The moonshot goal of Levlex is for everybody to be able to say "we have AGI at home." I’ve spent over a year building, pivoting, and iterating on this project, and I’m excited to share it with you. Feedback and questions are greatly appreciated. Check it out and let me know what you think!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para, ios · Missing: supports, reddit linkedin, podcasting
98%98% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, efficient, builder · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, personal, apps · Missing: mobile apps, entrepreneurs, video
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription, active · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, introduce · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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