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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Development Containers: Use a container as a full-featured development environment. (by devcontainers)
The number of tools and functions that aim to enhance the abilities of language models (LMs) is growing rapidly. For example, the popular LM framework LangChain grew its tool catalog from three to seventy-seven in the last 15 months. However, this approach of building tools for every little thing may be misguided and ultimately counterproductive. Instead, providing AI with direct access to a terminal, where it can use the many command line tools already created, and even create its own tools, will lead to more powerful, flexible, and future-proof systems.
However, you can try it out for yourself by installing langchain-community from my fork (I opened a pull request into the official LangChain repo but it's currently blocked due to security concerns).
The next generation of LMs will need a greater degree of isolation. Cloud providers like Amazon already allow arbitrary users access to their systems without knowing whether the user is a hacker. The startup E2B brings the same secure containerization technology Amazon uses to the AI space. Treating these LMs as if they were potentially malicious human-level hackers and placing similar restrictions on them as cloud providers do on human users should be sufficient to contain the threat.
GPT-4 and equivalent LMs merely need local containerization. Just use a development container when working on your app. Development containers work in VS Code and GitHub CodeSpaces, and are a best practice in general to let others easily collaborate. Working inside a development container will ensure that oopsies like rm -rf / do minimal damage to the parent systems. These LMs don't yet seem capable of using the terminal to intentionally break out of the container.