The hottest Code generation Substack posts right now

GPT-4 has significantly improved in Python code generation, including data visualizations like choropleth maps.
Choropleth maps are a type of thematic map that can be generated through GPT-4 prompting.
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AI is making it easier for people to program by allowing them to describe tasks in English and having the computer figure out the code.
Computers need precise instructions and struggle with understanding context, making programming challenging.
Programmers are rare, expensive, and building software is costly, but AI is helping automate coding, making programmers more productive.

Google DeepMind's AlphaGeometry AI system solves complex geometry problems at human Olympiad level.
Codium AI's AlphaCodium improves code generation in LLMs with test-based iterative flow.
Meta is working on open-source AGI and Microsoft Research made progress in AI-driven drug discovery.

Virtual developer tooling uses LLMs to generate code based on human prompts.
Innovations like self-healing code and retrieval augmentation are enhancing the performance of virtual developer tools.
The current challenge for virtual developer projects is ensuring reliability in code generation by improving heuristics.

GPT Engineer is an open platform for developers to experiment with AI code generation tools.
The platform is focused on improving, stability, setting benchmarks, and community involvement.
Interested individuals can find more information and get involved with the project on GitHub.

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Human intelligence learns from mistakes through self-reflection to improve performance.
Applying Reflexion framework helps AI agents iterate solutions for problems without definitive answers.
Using Reflexion to refine test generation can shift focus from code generation to improving test accuracy.

ChatGPT is being used in the world of Python coding for prompt engineering, especially in the area of data visualization.
The author, a Python programmer with over 20 years of experience, has been leveraging ChatGPT to enhance prompt engineering skills.
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Using ChatGPT, Home Assistant's capabilities can be expanded with code generation.
LLMs like ChatGPT can interact with the real world by generating code to control devices.
Balancing generality and specificity in prompt engineering can enhance the effectiveness of using LLMs with smart home systems.

Start with defining the API schema instead of API-first approach as it allows for early feedback, unblocks consumer development, and enables incremental progress.
An API schema specifies all endpoints, interactions, and responses, and can be visualized in tools like Swagger, aiding in documentation and code generation.
Working schema-first has benefits like enabling real-time collaboration, source-controlled contract, and code generation capabilities based on the formal specification.

Machine learning model predicts activation energies of hydrogen atom transfer in proteins
CodeBERTScore evaluates code generation using pretrained models
SWARM parallelism offers efficient communication for training large models