
Neo is Bhavin Turakhia’s fifth venture and his latest involving enterprise software. This time he's taking on Microsoft Office, Google Apps with AI.
An Indian entrepreneur is investing 30 million dollars of his own money to create a new enterprise software platform that combines project management, documents, file storage, and AI into a single product. His argument is that existing workplace software was designed before generative AI existed and cannot simply be upgraded with chatbots, but instead must be rebuilt from the ground up to make AI an active part of daily work. The platform, launched internally in April, is model-agnostic, meaning enterprises can switch between different AI providers rather than being locked into one. This bet comes as enterprise AI has become a highly competitive area, with major tech companies and numerous startups racing to reshape how businesses use AI in their workflows.

In this tutorial, we build a complete PDF-to-structured-data extraction workflow around Lift, with a focus on controlled evaluation rather than a simple demo run. We begin by preparing a Colab-compatible GPU environment, selecting the appropriate precision mode for the available hardware, and patching model loading to ensure the Lift backend runs reliably even on constrained 16 GB GPUs via 4-bit NF4 quantization. From there, we generate synthetic multi-page research reports with deliberately pl

SpaceX reportedly showed investors a "handset-like" AI device before going public. It could be another signal SpaceX wants to expand into wireless.
For voice AI systems, response time is a critical problem that affects how natural conversations feel. Two companies partnered to demonstrate a speech-to-speech system using open-source components where fast inference on one company's hardware allows conversations to flow with responsiveness similar to human interaction, avoiding the multi-second delays that currently frustrate users. The system works as a modular pipeline that takes speech input, processes it through speech recognition and a language model, generates text-to-speech output, and delivers a spoken response, with each component being replaceable by developers. This approach is already being used to power robots currently deployed in the field, where responsiveness is essential to making interactions feel natural.
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