Lexie.ai

Listened to Bret Taylor on the latest podcast—thanks to Sandhya Hegde for the recommendation, by the way. And wow, he knows exactly what he’s talking about—no marketing BS, just pure insight. https://lnkd.in/gYvniwtz What excites us the most is his perspective on the need for a new programming model for AI agents. This is exactly what we’re building with Thought Graph at Lexie. I highly recommend giving it a listen. Here are some highlights that align with our vision at Lexie: ?? “I don’t think the jury's still out—to use that metaphor—we’re sort of in the jQuery era of agents, not the React era.” ?? We agree. AI agents today feel like early web development with jQuery—powerful but clunky. Just as React changed the game for UI, we need a new paradigm for reasoning-based AI development. Thought Graph is that next step. ?? “I'm looking forward to the day when someone comes up with a programming system that feels both like an ‘aha’ moment but completely foreign to me at the same time… recognizing that authoring code in an editor is maybe not the primary reason why a programming system exists anymore.” ?? This is precisely why we built Thought Graph. Traditional programming languages were designed as contracts between humans and machines. Now, we need a new contract between AI, humans (not necessarily coders), and the runtime. ?? “AI reviewing AI-generated Python code seems like a local maximum to me... You don’t have true multithreading. It’s convenient, but I feel like we’re stuck in a local maximum.” ?? AI reviewing AI-written Python? That’s just a patch on an old system. Instead of trying to make Python more AI-friendly, we need a programming model built for AI from the ground up—one where AI is natively involved in program generation, reasoning, and execution. We believe this shift is happening right now, and Thought Graph is leading the way. The future of AI programming isn’t about tweaking existing languages—it’s about creating an entirely new programming system designed for AI reasoning. Who else is thinking about this? ?? #AI #Programming #Automation #ThoughtGraph #AIReasoning #FutureOfSoftware

Why we built a new runtime system and programming language for AI native apps? https://lnkd.in/eYSUNeis

We need hybrid neuro-symbolic reasoning because traditional programming languages, as purely symbolic systems, were designed as contracts between the programmer and the runtime. In this arrangement, both sides operate within a shared symbolic framework. However, the advent of AI introduces a new, transformative element into this equation: the machine no longer follows deterministic rules alone but engages in probabilistic and dynamic reasoning. To fully harness the power of AI, we need a new kind of contract—one that integrates three key parties: AI, the human, and the runtime system. All three must understand and collaborate within a shared reasoning framework. This fusion enables AI to assist in sophisticated problem-solving while remaining interpretable and reliable for humans. Symbolic reasoning provides structure and clarity, while neuro-based models offer the adaptability and pattern recognition required in complex tasks. Together, they form a system that is more powerful than either alone. An interesting analogy can be drawn between hybrid reasoning and the development of jet engines, which fueled exponential growth in aviation. Just as hybrid engines combined the strengths of propellers and jet propulsion to create a more efficient and scalable technology, hybrid neuro-symbolic systems blend structured symbolic logic with the flexibility of neural networks. This combination enables breakthroughs in automation, reasoning, and decision-making, much like how jet engines revolutionized aviation. This approach is essential to bridge the gap between human and AI understanding, ensuring that both can work effectively within a runtime environment capable of executing highly complex and adaptive tasks. https://lnkd.in/gCQvsmhD

Sales assistant is a generative platform with customizable modules. Customers can leverage the existing workflows to generate, enrich, and qualify leads and then outreach to the prospects. They can also build their own workflows using Thought Graph no-code tool. https://lnkd.in/guWDtZUH

https://lnkd.in/gNJXE_Ai

Great interview with Binny Gill! His emphasis on safety in advocating for pure natural language programming is spot on. As auto-generated code proliferates, relying solely on oversight by a small fraction of the society - programmers who can understand legacy programming languages - poses significant risks. His insights align closely with our advocacy at Lexie for natural language programming. https://lnkd.in/gDDVApW9