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>> Topics to Discuss
I recently read a short book titled “智变时代:AI驱动的新工业革命与人类未来” It mentions/quotes two concepts that left a deep impression on me, which I will briefly list here.
Large Models as Operating Systems
We can use large language models (now more appropriately referred to as large multimodal models) in the same way we use operating systems. The core of this system is the LMM, which processes incoming data like a CPU and provides results after computation. The difference is that while a computer's CPU accepts hexadecimal assembly instructions, the LMM accepts natural language.
The system's memory is represented by the LMM's context window, which indicates the maximum number of tokens that can be processed in a single inference operation.
Outside of the model, there are other system components typical of an operating system, such as I/O for perception through modalities like voice and vision; and a file system that grants the model the ability for infinite memory. After all, the model is not a database; its memory is an aid to computation.
Cheap Induced Demand
This paradox states that when something becomes more efficient, people tend to consume more of it. Jevons observed that as the efficiency of the steam engine improved, it required less coal to accomplish more tasks, yet coal consumption increased.
By increasing the supply of potential demand, such as intelligent services, people will use these services more frequently.
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Markdown-Based TODO Software
A self-deployable TODO management software that uses Markdown for storage.
After a quick look post-deployment, it retrieves all lanes (TODO group areas) via a lanes.json file. Each lane is a folder containing Markdown files for each TODO item.
lanes.json -> Assume it as ['Today', 'Week', 'Month']
tasks directory
├─Today
│ ├─http://XXX1.md
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├─Week
└─Month
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Open Source Community Forum
An open-source community forum repository used by OpenAI, currently with over 40K stars.
I just discovered that the OpenAI Community is built using this open-source repository 🫡
Upon closer inspection, I found that many well-known websites use it, which explains the familiarity~
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"Bottom Fishing"
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In conclusion, the author summarizes the lessons learned from this experience, including the importance of setting boundaries to prevent burnout, using "sales agents" to streamline the transaction process, and maintaining friendliness in customer service. Although the side project did not yield the expected financial returns, it provided valuable experience for future projects.
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