Best of AI — December 2023
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Community Picks·2y
Google's Answer to ChatGPT Arrives in Gemini [Here's How To Access It]
Gemini is Google's rival to ChatGPT, offering advanced conversational abilities. It comes in three variants and Gemini Ultra outmatches GPT-4 across benchmark tests. Gemini has multimodal learning capabilities and aims to absorb sensory inputs like touch and sound. Gemini Pro is currently public-facing, while Gemini Ultra will be released in 2024.
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Hacker News·2y
Google AI for Developers
Google AI offers developers a range of tools and technologies to leverage artificial intelligence and machine learning in their projects. It brings benefits such as enhanced functionality, improved user experience, and automation. However, developers may also face challenges and limitations when working with Google AI.
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Serokell·2y
Top software development trends for 2024
2024 will see software development trends including the integration of AI and machine learning, the use of blockchain beyond cryptocurrencies, the adoption of multi-runtime microservices, and a focus on cybersecurity. AR and VR applications will continue to increase, and sustainable software development will be a key consideration. In computing, there will be advancements in serverless computing, cloud and edge computing, and the development of quantum computing. Python will remain dominant, and Rust will see increased adoption. The rise of low-code and no-code platforms, progressive web applications, and cross-platform app development are also notable trends.
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The World Depends on 60-Year-Old Code No One Knows Anymore
A 64-year-old programming language called COBOL, which is still widely used in industries like banking and finance, is facing a shortage of skilled programmers. IBM has developed an AI-powered code assistant called watsonx to help convert COBOL code into more modern languages. The technology is new and unproven, but if successful, it could have far-reaching implications for code modernization and development.
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Substack·2yHow Google takes the pain out of code reviews, with 97% dev satisfaction
Google's code review tool, Critique, has a 97% satisfaction rate among Google software engineers. It provides efficient code review processes based on guidelines that emphasize continuous improvement, maintaining or improving the codebase, following style guides, and sharing knowledge. Critique also integrates AI-powered tools that offer ML-powered suggestions and automate certain aspects of the code review. It is loved for its features such as static analysis, focus on the latest changes, familiar diffing interface, integration with other Google tools, and gamification elements.
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Getting started with AI·2y
Is Gemini the GPT killer? Here's how you can try it.
Gemini, Google's rival to ChatGPT, has been released and is being hailed as the GPT killer. Gemini has 5 times the computational power of GPT and outperforms GPT-4 in various tasks. It is available in three versions: Ultra, Pro, and Nano. Gemini is built for multimodality, allowing it to understand and respond to different types of information. Access to Gemini is currently limited to Bard users, and the Pro version is only available in English. The future impact and monetization of Gemini models by Google are still unclear.
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Stack Overflow Blog·2y
Self-healing code is the future of software development
Large language models have the ability to improve their output through self-reflection, which is beneficial in software development and CI/CD. Self-healing code is being explored as a way to automate solutions and improve code quality. However, there are concerns about the quality and volume of code generated by AI. Companies will need to develop best practices to effectively use generative AI in software engineering.
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daily.dev·2y
2023 is finally over, let’s wrap it up!
daily.dev reflects on the year 2023 and highlights the launch of Squads and the development of daily.dev Search as significant milestones. They also mention raising a seed round of $11M and express gratitude towards the community. They aim to continue building a home for a new developer culture.
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Awesome Go·2y
Awesome Go Weekly - Issue 397, Dec 28, 2023
Weekly overview of popular Go news, articles, and packages. Includes reminiscing CGI scripts, carcinization of Go programs, learning GitHub repos, JSON constructors, Docker setup for Go APIs, collecting and analyzing billions of data points, scan2email, Golang meetup, and large-scale Oracle extractions.
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Towards Data Science·2y
System Design Series: The Ultimate Guide for Building High-Performance Data Streaming Systems from Scratch!
This article discusses the process of building high-performance data streaming systems for recommendation services. It explains the need for data streaming pipelines, the challenges of data ingestion, the importance of using Apache Kafka, the steps involved in data processing, the choice of using Cassandra as a data store, and the process of querying recommendations.
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80 LEVEL·2y
ChatGPT is Getting Lazy
ChatGPT, built on OpenAI's GPT-4 model, has been criticized by users for being lazy and unhelpful. Some users have noticed that ChatGPT provides incomplete answers and behaves with a sassy tone. OpenAI has acknowledged the issue and is investigating the cause of the unexpected behavior.
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Ars Technica·2y
Meta’s new AI image generator was trained on 1.1 billion Instagram and Facebook photos
Meta has released a standalone AI image generator, Imagine with Meta AI, which was trained on 1.1 billion publicly available Facebook and Instagram photos. The generator can create novel images based on written prompts and has average performance compared to other AI image synthesis models.
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What is UI 2.0? Netlify CEO Matt Biilmann explains
Developers are integrating AI into their workflow for automation, knowledge sharing, and productivity. However, AI experimentation comes with challenges such as incorrect answers, security issues, lack of regulation, and decreased code quality. UI 2.0 is a concept of customizing app UIs for each user.
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Medium·2y
A Comprehensive Survey of Large Language Models (LLMs)
The article discusses the growth of large language models (LLMs), the key practices related to LLMs, the three emergent abilities of LLMs (In-Context Learning, Instruction Following, and Step-by-Step Reasoning), the development of the LLM landscape, training data sources for LLMs, and the continuous refinement and integration of LLMs into existing technological landscapes.
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Most AI startups are doomed
Most AI startups, especially those formed from post-ChatGPT hype, are doomed due to lack of defensibility and differentiation. Gluing together AI APIs and building UIs without real moats leads to little value for investors. Even Alphabet, Meta, and OpenAI have no defensibility in the AI space. However, startups that rely on monstrous compute requirements or operate in domains with real-world, proprietary data may have value and potential longevity.
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The hardest part of building software is not coding, it's requirements
The hardest part of building software is not coding, it's creating clear and precise requirements. AI has excelled in chess due to its finite parameters, but struggles with self-driving cars due to the infinite variables involved. While AI may be able to rewrite existing software faster, it cannot replace the need for human judgment and clear specifications.
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