Recently, there has been a subtle shift in the AI programming landscape. As discussions about the "end goal of AI applications" become more intense, while many vendors are still locked in the "red ocean" battle of L2-level code completion, the two-year-old technical team AIGCode is trying to achieve an era leap through a full-stack technology solution.

AIGCode's core insight is that the shortcomings of large models often lie not in fine-tuning, but in the base model itself. Its core product AutoCoder.cc aims to transcend the L2 "assisted driving" stage and directly move toward the L3 level of "autonomous driving." In AIGCode's vision, true AI programming should be able to complete the entire closed loop of front-end, back-end, and database development based on natural language requirements, and also have the ability to manage operations and deployment.

To achieve this goal, AIGCode has built a positive feedback loop: producing high-quality samples through the practical application of L3-level products, then using these samples to train stronger base models. This concept, known as the "3No Paradigm"—"no reliance on NVIDIA's single path, surpassing L2 assistance, and rejecting blind scaling"—makes computing power utilization a key competitive barrier.

In terms of domestic computing power, AIGCode has taken an unusual step. Amid tight computing power resources, the team achieved a 65% computing power utilization (MFU) for MoE architecture on domestic chip clusters through extreme software optimization and deep engineering practices. This achievement not only demonstrates its deep expertise in high-performance optimization, but also proves through real cases that the domestic hardware ecosystem can completely bridge the gap with mainstream hardware when supported by advanced software.

Besides the "grunt work" of computing power, fundamental research is another major focus for AIGCode. The tree-structured position encoding (TPE), developed over 18 months, has shown better performance than the industry-standard RoPE approach in handling long texts and extrapolation capabilities. Additionally, innovations such as TPA (lossless linear attention mechanism) and PLE (expert decoupling architecture) further enrich its underlying technology matrix. These research achievements have received recognition from top academic conferences and demonstrated actual efficiency in commercial applications, enabling developers to build complex applications within minutes.