#automatedcoding

[adafruit]

FULL VIDEO - Writing an Arduino driver with OpenAI ChatGPT and PDF parsing 🤖🔧📄

One of the big tasks that Ladyada still has to spend a lot of time on is writing Arduino libraries for all our devices and sensors, particularly all the I2C & SPI chips out there! These ICs use register maps and sub-byte addressing to set dozens of configurable knobs and switches, and a good driver lets folks set and get all of the noodly bits.

However, there is yet to be a standard format to get that configuration map. Instead, you have to pore over datasheets with long lists of binary tables and bit insets to figure out how to convert that into C or Python code.

It is tough. Only a few folks can write an excellent comprehensive library…. but Ladyada can & has! In fact, there are hundreds of Arduino libraries on Adafruit's GitHub https://github.com/orgs/adafruit/repositories, all in the Ladyada 'style,' using Adafruit_BusIO for I2C / SPI register addressing https://github.com/adafruit/Adafruit_BusIO/ and since ChatGPT 4 was trained on all of it, we can ask it to become a mini-Ladyada to write new drivers.

With a PDF parsing plugin, we can even upload the chip's datasheet to extract register names, values, create enum tables, and text for doxygen comments. Here's the chatGPT log for the video https://chat.openai.com/c/f740eb57-17a6-41e3-ae0a-12da959a1f4c - and here's a previous one that is more 'complete' https://chat.openai.com/share/f44dc335-7555-4758-b2f9-487f9409d556. The amount of time it takes for ChatGPT to write a driver is about the same as it would take Ladyada, and you definitely need to be eagle-eyed to redirect the AI if it starts making mistakes… but it can be done even when Ladyada is tired after a full day of baby-care, or at the same time as pumping https://www.youtube.com/watch?v=EpbH-sXRNps - plus there's a lot less continuous typing/mousing so her wrists don't ache the next day!

Do you use ChatGPT for electrical engineering or coding work? Any suggestions on how to make this even better? This is only our 3rd day using this tool, so we're getting started with how to integrate it into our workflow.

[flowres921]

The Evolution of Coding: A Journey through Manual and Automated Methods

In the ever-evolving landscape of technology, coding stands as the backbone of innovation. From its humble beginnings rooted in manual processes to the era of automation, the journey of coding has been nothing short of fascinating. In this blog, we embark on a retrospective exploration of the evolution of coding methods, tracing the transition from manual to automated approaches.

The Dawn of Manual Coding:

Before the advent of sophisticated tools and automated processes, coding was predominantly a manual endeavor. Programmers painstakingly wrote code line by line, meticulously debugging and optimizing their creations. This era witnessed the emergence of programming languages like Fortran, COBOL, and assembly language, laying the groundwork for modern computing.

Manual coding required an intricate understanding of the underlying hardware architecture and programming concepts. Developers wielded their expertise to craft intricate algorithms and applications, often pushing the boundaries of what was thought possible. However, the manual approach was labor-intensive and prone to errors, leading to the quest for more efficient methods.

The Rise of Automation:

The evolution of coding took a significant leap with the introduction of automated tools and frameworks. Languages like C, Java, and Python democratized programming, offering higher-level abstractions and built-in functionalities. Developers could now focus on solving problems rather than getting bogged down in low-level implementation details.

One of the pivotal advancements in coding automation was the rise of Integrated Development Environments (IDEs). These software suites provided a comprehensive environment for coding, debugging, and project management, streamlining the development process. IDEs like Visual Studio, Eclipse, and PyCharm became indispensable tools for developers worldwide, boosting productivity and collaboration.

Furthermore, the advent of version control systems such as Git revolutionized collaborative coding practices. Developers could now work concurrently on the same codebase, track changes, and resolve conflicts seamlessly. This fostered a culture of collaboration and accelerated the pace of software development.

The Era of AI and Machine Learning:

As technology continues to advance, coding is undergoing yet another paradigm shift with the integration of Artificial Intelligence (AI) and Machine Learning (ML). Automated code generation, predictive analytics, and intelligent debugging are becoming commonplace, augmenting the capabilities of developers.

AI-powered coding assistants, such as GitHub Copilot and TabNine, leverage vast repositories of code to provide context-aware suggestions and autocomplete functionality. These tools empower developers to write code faster and with fewer errors, unlocking new possibilities in software innovation.

Moreover, Machine Learning algorithms are being employed to automate mundane coding tasks, such as code refactoring and optimization. By analyzing patterns and best practices from existing codebases, ML models can suggest improvements and identify potential bottlenecks, saving time and effort for developers.

The evolution of coding has been a journey marked by innovation and transformation. From manual coding practices to the era of automation and AI, developers have continually adapted to embrace new technologies and methodologies. As we look towards the future, the fusion of human creativity with machine intelligence promises to redefine the boundaries of what can be achieved through coding.

[yelloblogs]

"Unlocking Efficiency: Generative AI in Software Development"

Generative AI is revolutionizing the coding landscape by automating code generation, debugging, and optimization processes.

https://www.globalinsightservices.com/request-sample/GIS26248@/?utm_source=SnehaPatil-Article

Leveraging advanced machine learning algorithms, these AI systems can understand and replicate coding patterns, significantly reducing development time and enhancing productivity. They assist developers in writing cleaner, more efficient code, and can even suggest new ways to solve complex problems. As generative AI continues to evolve, it promises to democratize programming, making it more accessible to non-experts and accelerating innovation across industries.#GenerativeAI #AIinCoding #AutomatedCoding #CodeGeneration #AIDrivenDevelopment #FutureOfProgramming #MachineLearning #TechInnovation #SoftwareDevelopment #Programming #AITechnology #EfficientCoding #AIinTech #TechRevolution #CodeOptimization

[onevoicedata1-blog]

Make sure your clients have the newest technologies in automated coding. We'll show you what you're missing today. #onevoicedata #automatedcoding #speechunderstanding #transcription #loveyourpractice (at Pinehurst, North Carolina)

[adafruit]

Writing an Arduino driver with OpenAI ChatGPT and PDF parsing 🤖🔧📄 PREMIERES LIVE at 10pm ET TONIGHT Sep 2, 2023!