Reading
Supertech is an experimental AI-driven software company prototype that achieves an end-to-end automated workflow from task triggering to code merging through GitHub Actions, multi-role AI agents, and Telegram integration. This article deeply analyzes its architectural design, agent role division, and automated workflows.
Section 01
Supertech is an experimental AI-driven software company prototype. It achieves an end-to-end automated workflow from task triggering to code merging through GitHub Actions, multi-role AI agents, and Telegram integration, exploring the application boundaries of AI in the entire software engineering process and demonstrating a new software development paradigm.
Section 02
With the improvement of large language model capabilities, Supertech explores the possibility of AI agents running a software company completely autonomously. Inspired by xqliu's AI company concept, its goal is to verify the application boundaries of AI in the entire software engineering process, and build a fully automated prototype through GitHub Actions (orchestration layer), multi-role AI agents (execution layer), and Telegram (interaction layer).
Section 03
Supertech's architecture is divided into four layers:
Section 04
Supertech includes 13 automated workflows covering core scheduling, daily operations, analysis research, strategic planning, release integration, and other scenarios. A typical AI execution process:
Section 05
Key technical implementations:
Section 06
Applicable Scenarios: Content-based products, data-driven applications, standardized tools, prototype verification; Current Limitations: Limited ability in complex architecture decisions, difficulty completing innovative requirements independently, need for human review for security-sensitive operations, need for human intervention in deep fault debugging.
Section 07
Future Trends: Specialization of AI agent roles, process automation, human-machine collaboration model (AI execution + human supervision); Summary: Supertech is a bold experiment that proves highly automated development is feasible in specific scenarios. Its value lies in proposing a new paradigm of AI as team members, providing references for AI applications in software engineering.
Keep going with more reads from the same topic.
Splinter is a minimalist, high-performance key-value (KV) and vector storage system enabling zero-latency inter-process communication via shared memory and atomic operations. With only 766 lines of core code, it supports millions of operations per second and 768-dimensional vector storage, offering a new architectural approach for local LLM inference and data-intensive applications.
Folkering OS is the world's first AI-native bare-metal operating system, entirely written in Rust no_std without relying on Linux, POSIX, or libc. It can generate commands from scratch, compile them into WASM, and run them in 10 seconds, achieving true self-evolution.
This project explores how to use Large Language Models (LLMs) to detect logical vulnerabilities in Ethereum smart contracts, especially business logic flaws that are difficult for traditional static analysis tools to capture, such as economic manipulation and execution order errors.
This article introduces an open-source project for building modern large language models from scratch. The project evolves from a GPT-2-style basic architecture to a Llama 2/3-style production-level implementation, covering complete tutorial implementations of key technologies such as RMSNorm, RoPE, SwiGLU, GQA, and MoE.