Microsoft revealed a new AI tool called Infra Copilot, which uses its existing GitHub Copilot to create infrastructure code.
Infra Copilot is designed to understand the context of infrastructure tasks and generate appropriate code suggestions based on natural language prompts.
The tool can streamline the coding process, enabling professionals to focus on higher-level tasks.
It also provides standardized code snippets for consistency across different environments.
Infra Copilot is available now to programmers with a recent Visual Studio Code version and a GitHub Copilot license.
Microsoft has also launched GitHub Copilot Enterprise, using data from a company's own code repositories to generate code and answer questions, priced at $39 per month per user.
Infra Copilot is designed to understand the context of infrastructure tasks and generate appropriate code suggestions based on natural language prompts.
The tool can streamline the coding process, enabling professionals to focus on higher-level tasks.
It also provides standardized code snippets for consistency across different environments.
Infra Copilot is available now to programmers with a recent Visual Studio Code version and a GitHub Copilot license.
Microsoft has also launched GitHub Copilot Enterprise, using data from a company's own code repositories to generate code and answer questions, priced at $39 per month per user.
Discusses HID over I2C, an underappreciated HID standard used in laptops.
Explains that I2C-HID is used for touchpads, touchscreens, and sensor hubs.
Distinguishes I2C-HID from non-HID I2C devices and explains its benefits.
Describes the author's experience with using I2C-HID for a laptop touchpad project.
Explains that I2C-HID is used for touchpads, touchscreens, and sensor hubs.
Distinguishes I2C-HID from non-HID I2C devices and explains its benefits.
Describes the author's experience with using I2C-HID for a laptop touchpad project.
Inside Phi 2: Microsoft's Small Language Model explores the development of tailored AI models that minimize resource usage, with a focus on small language models (SLMs). The article discusses Microsoft Research's approach to building generative AI models, highlighting their "textbooks are all you need" strategy used in training their Phi series SLMs. Key aspects include curating high-quality training data, generating synthetic content, and fine-tuning the model with domain-specific information. This innovative approach has resulted in surprisingly good outcomes, with some benchmarked SLMs performing similarly or even better than larger LLMs like GPT.
Moreover, LAMBDA is the true lambda that we know and love: a lambda can be an argument to another lambda or its result; you can define the Church numerals; lambdas can return lambdas, so you can do currying; you can define a fixed-point combinator using LAMBDA and hence write recursive functions; and so on. (Additionally, since lambdas can be named, they can directly call themselves recursively, which is much more convenient than using a fixed-point combinator.)
