tinyagi-aop | Agent Oriented Programming Experiment
kandi X-RAY | tinyagi-aop Summary
kandi X-RAY | tinyagi-aop Summary
tinyagi-aop is a TypeScript library. tinyagi-aop has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.
TinyAGI AOP is a small experimental repository that aims to showcase some basic concepts behind Agent Oriented Programming (AOP), a paradigm that is somewhat synonymous to Object Oriented Programming (OOP) but at a much higher level of abstraction. While OOP will remain and still serve as one of the key programming models, AOP will aim to operate on a semantically higher level. With the introduction of Large Language Models (LLMs) and GPT, it opens up a far greater opportunity for creating AOP-based systems. LLM/GPT based systems will, however, require a change in thinking about solutions to a problem space. The future of software engineering and software-systems may as well be a Distirbuted Autonomous Agent-based one, built on the AOP principles. When thinking about AOP and LLMs, our focus will shift between deterministic and non-deterministic programming styles. While some of us may be used to non-deterministic and approximated outputs for certain classes of engineering problems, particularly in the field of Data Sciences, relying on approximations and non-deterministic outputs that serve as control flow within our system design, does feel rather strange.
TinyAGI AOP is a small experimental repository that aims to showcase some basic concepts behind Agent Oriented Programming (AOP), a paradigm that is somewhat synonymous to Object Oriented Programming (OOP) but at a much higher level of abstraction. While OOP will remain and still serve as one of the key programming models, AOP will aim to operate on a semantically higher level. With the introduction of Large Language Models (LLMs) and GPT, it opens up a far greater opportunity for creating AOP-based systems. LLM/GPT based systems will, however, require a change in thinking about solutions to a problem space. The future of software engineering and software-systems may as well be a Distirbuted Autonomous Agent-based one, built on the AOP principles. When thinking about AOP and LLMs, our focus will shift between deterministic and non-deterministic programming styles. While some of us may be used to non-deterministic and approximated outputs for certain classes of engineering problems, particularly in the field of Data Sciences, relying on approximations and non-deterministic outputs that serve as control flow within our system design, does feel rather strange.
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tinyagi-aop has a low active ecosystem.
It has 5 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
tinyagi-aop has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of tinyagi-aop is current.
Quality
tinyagi-aop has no bugs reported.
Security
tinyagi-aop has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
tinyagi-aop does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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tinyagi-aop releases are not available. You will need to build from source code and install.
Installation instructions are not available. Examples and code snippets are available.
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tinyagi-aop Key Features
No Key Features are available at this moment for tinyagi-aop.
tinyagi-aop Examples and Code Snippets
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Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install tinyagi-aop
You can download it from GitHub.
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