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FunSearch teams up creative AI that spits out code with a system that checks if the ideas are actually good. This back-and-forth helps it evolve initial thoughts into solid discoveries. By Alex Ivanovs.

In a leap for artificial intelligence, Google DeepMind researchers have unveiled a new AI-driven tool named FunSearch, which has made unprecedented discoveries in the realm of mathematical sciences. This tool marks the first instance where a Large Language Model (LLM) has contributed to solving complex open problems in mathematics and computer science.

Source: https://blog.postman.com/2022-api-platform-landscape-trends-and-challenges/

At its core, FunSearch is a marriage of creative AI and precise evaluation. It starts with an LLM, like Google’s PaLM 2, which generates inventive solutions in code. These are then sifted through by an automated evaluator, ensuring only accurate and viable ideas progress. This back-and-forth process allows initial concepts to evolve into groundbreaking discoveries.

What sets FunSearch apart is its problem-solving ability and how it does so. Unlike black-box solutions, it produces programs that reveal the ‘how’ of the solutions, not just the ‘what’. This transparency allows scientists to understand and learn from the AI’s approach, sparking new insights and strategies. Good read!

Scala has been one of the most trusted and reliable programming languages for several tech giants and startups to develop and deploy their big data applications. Top companies from Netflix and LinkedIn to Foursquare and AirBnB take advantage of the many benefits of Scala in reliably developing their platforms and integrating with state-of-the-art machine learning models. By ParamRaval.

The article makes a good job explaining:

• What is Scala for data engineering?
• Why should data engineers Learn Scala for data engineering?
• Java vs. Scala for data engineering
• Python vs. Scala for data engineering
• Learn Scala for data engineering - How to get started
• Scala for data engineering tutorial - Beginning to code in Scala

Although not widely known, Scala has been developed over several years to tackle some significant problems in handling massive data pipelines. Several months ago, worldwide feedback from companies working with large datasets showed us how they had shifted their big data projects to Scala, given its flexibility and production performance. Nice one!

Understanding the concept of a tech stack is essential. In this blog post, we look at the meaning of the term “tech stack,” explore its components and provide examples of common tech stacks. By Daragh Ó Tuama.

Further in this article you will learn:

• What is a tech stack?
• What is in a tech stack?
• Presentation layer (Front End)
• Application layer
• Data layer
• The best tech stacks for programmers
  • LAMP stack
  • MEAN stack
  • MERN stack
  • .NET stack
• Which tech stack should I learn?

By starting with the fundamentals and gradually exploring different technologies, you can develop a strong foundation in software development and stay adaptable in this ever-changing field. So, embrace the world of tech stacks and embark on your journey to become a proficient developer. Good read!

Now AWS Lambda scales up to 12 times faster. Each synchronously invoked Lambda function now scales by 1,000 concurrent executions every 10 seconds until the aggregate concurrency across all functions reaches the account’s concurrency limit. By Marcia Villalba.

Building scalable and high-performing applications can be challenging with traditional architectures, often requiring over-provisioning of compute resources or complex caching solutions for peak demands and unpredictable traffic. Many developers choose Lambda because it scales on-demand when applications face unpredictable traffic.

Before this update, Lambda functions could initially scale at the account level by 500–3,000 concurrent executions (depending on the Region) in the first minute, followed by 500 concurrent executions every minute until the account’s concurrency limit is reached. Because this scaling limit was shared between all the functions in the same account and Region, if one function experienced an influx of traffic, it could affect the throughput of other functions in the same account. This increased engineering efforts to monitor a few functions that could burst beyond the account limits, causing a noisy neighbor scenario and reducing the overall concurrency of other functions in the same account. Excellent news!

A quick migration guide to Vite from Create React App, because (apart from Next.js) Vite is the natural successor of CRA for creating a modern React application as SPA. By Robin Wieruch.

"scripts": {
  "start": "vite",
  "build": "vite build",
  "serve": "vite preview"
},

The article then guides you through all the necessary steps:

• Install Vite and all React related libraries (here: Vite’s React Plugin) as development dependencies
• Uninstall create-react-app’s dependency
• Adjust your package.json to use the following new scripts
• Rename all extensions of files which are using JSX from “.js” to “.jsx”
• Create a vite.config.js file in your Vite + React project’s root directory
• Move the public/index.html into the project’s root folder, because Vite expects it there
• Remove all %PUBLIC_URL% occurrences in the index.html file
• Last, link the src/index.js file in your moved index.html file

If you have been using a create-react-app project without any further configuration (e.g. a fresh create-react-app installation), you are ready to start your new Vite based React application with npm start. Good read!

Kaggle is the largest platform for data scientists and machine learning experts, offering aspirants with the most hands-on experience in the complicated field of data science. Due to this, experts have high praise for the Kaggle community for its role in data scientist upskilling. The study of any subject requires classification. If you want to crack open all the data types for the juicy information inside, check out the different types of big data articles here. By Abhresh Sugandhi.

Further in this article:

• What is Kaggle?
• Skills required by a data scientist to master Kaggle
• How to use Kaggle for data science
• Equip yourself with the basic skills

Kaggle is the largest platform for data scientists and machine learning experts, offering aspirants with the most hands-on experience in the complicated field of data science. Due to this, experts have high praise for the Kaggle community for its role in data scientist upskilling. The study of any subject requires classification. Good read!

In today’s digital world, when having good and reliable software plays a crucial role in the operational processes of every business, the developers bear the responsibility to produce high-quality code. However, that’s easier said than done. They may encounter various challenges during the development process that can lead to unintentional mistakes, resulting in what is known as code smells. But what is code smell? By softteco.com.

Back in the late 1990s, Kent Beck, the creator of extreme programming, emphasized the importance of design quality in software development. He played a significant role in popularizing the term “code smell” to describe certain patterns or characteristics in code that indicate potential issues or weaknesses.

In this article you will learn about:

• Why do code smells occur?
• How to identify code smells?
  • Common types of code smells
  • Automated analysis tools
  • Experience-based knowledge
• Common types of code smells
  • Bloaters
  • Change preventers
  • Couplers
  • Object-orientation abusers
  • Dispensables
• Best practices to reduce the code smell

Although code smells themselves may not directly cause the code to break, it is indeed crucial to be mindful of their presence. It can serve as warning signs that something could be wrong in the code, indicating potential issues or weaknesses. Interesting read!

Understanding the concept of quantum physics lays the foundation for distinguishing how quantum computers differ from classical ones. Unlike the binary logic used by classical computers, where computation is confined to zeros and ones, quantum computers leverage a state known as superposition, which allows for simultaneous existence of zeroes and ones. By Rich Tran and Franz Klein.

Further amplifying this power is a phenomenon called entanglement, where qubits are closely linked to enable simultaneous manipulation of all states. To put it simply, entanglement enables us to examine the resources of a system concurrently, thereby significantly enhancing the efficiency of traditional computers for a range of potentially valuable tasks.

Further in the article:

• Significant game changers
• Standard industry phrase for a straightforward concept—a quantum computer and a conventional computer cooperating to solve a problem - is “hybrid quantum computing”
• Quantum is commercial and mainstream and is on the road map of every CEO
• Government, academia and industry involvement in quantum computing will significantly rise, both in terms of investment and the need for national security measures
• The Warfighter’s Cutting Edge

Experts anticipate revolutionary applications for quantum technologies, such as safeguarding cryptography, enhancing data analysis with quantum machine learning, improving portfolio optimization and supply chain management algorithms, creating more secure blockchain solutions and simulating intricate chemical reactions to assist in drug development. Good read!

The future is AI-driven, trust-centric dialogue between brands and consumers. The generative AI race is well underway, and we’re already seeing applications in advertising and marketing for creative ideation and development. This includes one of the key pillars of digital advertising—search. By Christian J. Ward.

In the last six months, the breadth and pace of innovation has been intense. Google has had more core updates and helpful content updates targeting potential misuses of gen AI. At the same time, they’ve expanded their own use of their Search Generative Experience, which is constantly improving and prompting important debates in the SEO community. Microsoft’s Bing has expanded its partnership with OpenAI, and CEO Satya Nadella’s annual shareholder letter could have been called the “Copilot chronicle” expansion. This doesn’t even cover the mind-blowing expansion of image generation and other advancements over this same period of time.

The article then summarizes some trends in sections about:

• Dialogue over monologue
• Offers, not ads
• Subjective data over objective data

Once consumers become more familiar with objective search benefits, gen AI dialogue will create opportunities for brands to have honest conversations and find out what consumers want. A dialogue (through search and chat) powered by gen AI and authoritative knowledge graphs of information is the best way to get started. Nice one!

With the growing discussions about the integration of artificial intelligence (AI) into software development via tools like ChatGPT and Github Copilot, author has explored these AI-driven coding aids for some time. Initially, he engaged with Tabnine, a tool similar in function to Github Copilot, albeit a paid service, and his initial experiences were less than satisfactory. Subsequently, author approached AI-based products from other companies with a certain degree of skepticism. By Michał Szulczewski.

Tha article focuses on:

• Advance chat bots – ChatGPT and Github Pilot
• Potential challenges encountered by developers in the utilization of AI chatbots
  • Suboptimal recommendations for less common libraries and unconventional use cases
  • Reviewing new iterations of code imposes considerable cognitive load
  • Mismatched Variable and Function Nomenclature
  • Code often does not function
  • Textual chat flow differs from programming flow
• Positive aspects of using AI chatbots
  • Facilitating exploration in unfamiliar domains
  • Employing chatbots as an interactive rubber duck
  • Streamlining the configuration of fundamental structures
  • Operations on selected code
• Extensions for Integrated Development Environments (IDEs)
• AI vs. LSP

… and more. In author’s view: “Drawing from my experience of over eight years as a programmer, I’ve come to appreciate that programming is an ongoing journey of acquiring new knowledge and leveraging emerging libraries. However, given the magnitude and diverse nature of errors that AI can introduce, along with the ever-evolving landscape of programming languages and libraries, AI may never fully replace human programmers.” Interesting read!