Since we’ve established “AI Inspiration Collision Forum,” there’s been a lot of random content being created, with people experimenting with AI to record and publish things, but thoughtful reflection is becoming increasingly scarce. Moving forward, it would be beneficial to slightly control the output of this section and consolidate it into a monthly magazine format – releasing one article per month would suffice.

github-readme-stats is a GitHub profile statistics generator that allows users to display various statistics and charts within their GitHub profiles. It offers multiple customization options to tailor it to user needs.

I manage my repository habits by grouping them by project; GitHub doesn’t support repository grouping, so I have to achieve this by splitting repositories across different organizations. The latest branch of github-readme-stats cannot support statistics for repositories spanning different organizations; I forked a branch and merged the corresponding code.

• Script for batch modifying Git history commit author information, rewriting Git history records using git filter-branch.

• Last month, we experimented with cursor, but due to the limitations of the free quota, we didn’t develop overly complex features; we just did some basic testing. We discovered then that Byte also released similar products, both using the same large models – Claude-3.5 – at their core. Byte’s product is called Trae, initially launched in the Mac version and finally released its Windows version in February of this year. Big companies are good because you can freely “white嫖” (literally translates to “free-eat”), without having to pay, with unlimited use of Claude-3.5 – this model performs quite well.

Artificial Intelligence (AI) has undoubtedly been one of the most hotly debated topics in the technology field in recent years, particularly over the past two years, with AI technology experiencing rapid advancements. Whether it’s deep learning, natural language processing, or computer vision and automated decision systems, the application scenarios for AI are constantly emerging. However, despite continuous technological breakthroughs, AI still faces a bottleneck similar to that of a Docker release – a lack of a killer app to truly ignite the market.

The US stock market has three trading sessions: pre-market, live market, and post-market. The logic for pushing data – whether it’s full data or numerical increments – is optimized to conserve bandwidth (sending as little data as possible). Initially, only the full dataset is sent in the first transmission; subsequent transmissions are incremental updates of all fields.

Why not use the optimal solution? This involves multiple project teams, some of which have been live for many years. As we’re a new integration, we can only strive for compatibility.

On the eve of the 2024 National Day holiday, China’s stock market experienced a remarkable surge, attracting considerable attention. However, following the holiday, the market unexpectedly turned to a dramatic plunge. This “polarization” of the stock market – a rollercoaster ride between soaring highs and plummeting lows – not only subjected investors to a thrilling yet nerve-wracking experience, but also prompted deep reflection within the market regarding policy, economic conditions, and fundamental market rules.

Business Model: The backend service establishes a connection with the group’s market data gateway using TCP. Each time a connection is established, it must first send an authorization request and then continuously send heartbeat packages to maintain the connection status.

However, one day, an alert message was received indicating that the service had disconnected. After carefully examining the logs, it was discovered that the backend service was continuously sending heartbeat packages, but the other party did not respond at all, yet the connection remained open.