AI in Software Engineering - AI models have solved coding, but not software engineering, as production speed is crucial [4] - Building AI to accelerate production is challenging due to reliability requirements and the need for multi-agent orchestration [5][6][7] - Resolve AI focuses on using AI to address the complexities of production systems, which involves more than just code [13] Resolve AI's Solution - Resolve AI provides AI site reliability engineer agents to troubleshoot alerts and incidents [11] - Resolve's agents can understand production systems from code to backend databases, offering a faster solution [11] - Customers are using Resolve AI for "vibe debugging," indicating usage beyond incidents and alerts, leading to increased product usage [12] Talent Acquisition - Resolve AI competes with companies like Meta, OpenAI, and Anthropic for AI engineers [14] - Resolve AI attracts top researchers by offering the opportunity to significantly impact the company and change software engineering [16] Future of Software Engineering - Humans will operate at a higher level of abstraction, with agents handling much of the work [17] - Underlying infrastructure and tools will adapt to be more suitable for agents [17]

AI in Software Engineering - AI models have solved coding, but not software engineering, as production speed and tribal knowledge are key [4] - Building AI to accelerate production is challenging due to reliability requirements and the need for multi-agent orchestration [5][6][7] - Resolve AI focuses on using AI agents to troubleshoot alerts and incidents, acting as an AI site reliability engineer [11] - The company's AI agents can replace significant amounts of work, offering value exceeding coding agents [10] Resolve AI's Business and Technology - Resolve AI was founded to address the problem of increasing data and work for humans caused by existing observability tools [9] - Resolve AI's agents utilize human tools and understand production systems from code to backend databases [11] - Large enterprises are adopting Resolve AI's product in production with success, using it for "vibe debugging" beyond incidents and alerts [12] - Resolve AI differentiates itself by understanding the entire production system, not just code, and extracting knowledge unique to each company [13] Talent Acquisition and Future Vision - Resolve AI competes with major AI labs like Meta, OpenAI, and Anthropic for AI engineering talent [14] - Resolve AI attracts talent by offering the opportunity to have a significant impact on the company and the enterprise software engineering landscape [16] - The future of production engineering involves humans operating at a higher level of abstraction, with agents handling much of the underlying work [17]

Key Optimization Goal - Aims to achieve Time To First Byte (TTFB) below 150 milliseconds for voice models [1] Technology and Tools - Leverages open-source TTS models like Orpheus, which have an LLM backbone [1] - Employs tools and optimizations such as TensorRT-LLM and FP8 quantization [1] Production Challenges - Client code, network infrastructure, and other outside-the-GPU factors can introduce latency [1] - Common pitfalls exist when integrating TTS models into production systems [1] Scalability and Customization - Focuses on scaling TTS models in production [1] - Extends the system to serve customized models with voice cloning and fine-tuning [1]