Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
In the competitive world of eCommerce, standing out from the crowd is crucial for success. One effective way to engage potential customers and boost sales is by adding product videos to your WooCommerce store. Videos have the power to captivate, inform, and persuade shoppers, making them more likely to convert into buyers. In this article, […]
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https://thehill.com/policy/technology/4162573-ai-art-cant-earn-copyright-judge-rules/ give us a preview that those that use AI, ML, GPT and such may be on uncertain ground if they want to copyright the code.
You may not be aware that most of the online AI/ML/GPT systems keep copies of what you generate. So if you are creating code for your company, the future may have a surprise waiting for you.
I was consistently receiving MySQL "Packets out of order" error messages when attempting to connect to MySQL, mostly on my AJAX pages, but I couldn't attribute it to anything or reproduce it in any way. I did notice, however, that when switching MySQL persistent connections from on to off, the problem went away.
Lots of Google searches later, I found lots of articles and forum posts saying that it was an issue with the my.cnf configuration setting, and to tweak max_connections, etc. Then our sysadmin found an article saying it was an issue with the version of MySQL + PHP we were using together. There apparently are lots of causes for this, and the fact that it wasn't reproducible made it all the more difficult to debug.
Well, I finally figured out what the cause was, and so I'd like to share it with you, at least in our case.
On Persistent Database Connections in the PHP manual there is a warning blurb with a red background that says that you should never use persistent connections if your script uses transactions.
Well, that was it! We do, indeed, use transactions. I've made an adjustment to no longer use persistent connections when using transactions, and the problem miraculously solved itself!
As a major part of a company's data asset, logs bring value to businesses in three aspects: system observability, cyber security, and data analysis. They are your first resort for troubleshooting, your reference for improving system security, and your data mine, where you can extract information that points to business growth.
Logs are the sequential records of events in the computer system. If you think about how logs are generated and used, you will know what an ideal log analysis system should look like:
Today, we will look at the example of the free engine Prestashop to translate content using the generative neural network ChatGPT4.
For the example, we will use Prestashop version 1.7.8.6.
In today's fast-paced software development landscape, the need for efficient communication and collaboration has become paramount. As organizations strive to achieve seamless integration and delivery of software, DevOps has emerged as a key methodology. However, managing communication channels effectively within a DevOps environment can still prove challenging.To address this challenge, the concept of SlackOps has gained popularity. SlackOps combines the power of Slack, a popular team collaboration tool, with the principles of DevOps, enabling teams to streamline their communication and enhance collaboration. By leveraging SlackOps, organizations can optimize their DevOps workflows, leading to improved productivity and efficient software delivery.
At its core, SlackOps refers to the practice of utilizing Slack as a communication hub within a DevOps ecosystem. Slack, with its rich set of features, fosters real-time communication, centralizes information sharing, and promotes cross-team collaboration. Integrating Slack into a DevOps environment can bring numerous benefits to the table.
As the demand for advanced natural language processing capabilities continues to surge, the emergence of large language models (LLMs) has become a pivotal milestone in the field. With the rapid advancement of AI technology, LLMs have revolutionized the way we interact with text, enabling us to communicate, analyze, and generate content with unprecedented sophistication. In this in-depth analysis, we delve into the world of leading LLMs, exploring their capabilities, applications, and performance. Our comparative analysis not only includes renowned OpenAI models but also sheds light on other noteworthy contenders such as LangChain, Anthropic, Cohere, and Google.
Join us as we unravel the fascinating landscape of LLMs, uncover their unique features, and ultimately help you make informed decisions by harnessing the power of natural language processing systems.
Once we press the merge button, that code is no longer our responsibility. If it performs sub-optimally or has a bug, it is now the problem of the DevOps team, the SRE, etc. Unfortunately, those teams work with a different toolset. If my code uses up too much RAM, they will increase RAM. When the code runs slower, it will increase CPU. In case the code crashes, they will increase concurrent instances.
If none of that helps, they will call you up at 2 AM. A lot of these problems are visible before they become a disastrous middle-of-the-night call. Yes. DevOps should control production, but the information they gather from production is useful for all of us. This is at the core of developer observability, which is a subject I’m quite passionate about. I’m so excited about it I dedicated a chapter to it in my debugging book.
Virtual services and destination rules help DevOps engineers and cloud architects apply granular routing rules and direct traffic around the mesh. Besides, they provide features to ensure and test network resiliency, so that applications operate reliably. In this article, we will explore both the features of Istio: traffic routing and network resilience testing.
Istio can split traffic between service or service subsets with ease. Traffic splitting is done based on weights/percentages (refer to the image below) defined in the corresponding virtual service and destination rule resources.
Starting a new company is difficult. How difficult? So difficult that an estimated eight out of ten don't last beyond their fifth year. For software development companies, things may be even harder than that. The reasons for that are manifold. For one thing, many software development firms depend on a single product to fuel their bottom lines at the outset — and there's quite a bit that can go wrong with immature software products.
That reality means that anyone running a software development startup has to execute a perfectly choreographed high-wire act if they want their company to survive long enough to thrive. Doing that, however, requires vigilance, powered by as much real-world operational data as possible. However, not every software development startup knows which KPIs to watch to gauge their overall health and odds of success. To remedy that, here are the three most relevant KPI categories to software development startups and five specific KPIs every one of them should track.
As we continue to delve deeper into the digital age, the importance of data continues to grow. Businesses, scientists, and governments alike are gathering increasingly vast amounts of information, and these datasets require sophisticated tools for processing and analysis. Two such tools that have gained significant attention recently are Amazon's AWS Glue ETL and AWS Batch services. Both offer robust functionalities for managing, transforming, and analyzing data, but how do you decide which is the best fit for your specific needs? In this article, we will take a detailed look at both AWS Glue ETL and AWS Batch, comparing their features, capabilities, and use cases to help you make an informed decision.
Data transformation tools play a crucial role in data analysis. They help in converting raw data into useful information that can be used for decision-making. The process involves cleaning, normalizing, and transforming raw data to prepare it for analysis. AWS Glue ETL and AWS Batch are among the top data transformation tools that are designed to handle these tasks, offering different capabilities and strengths based on the specific requirements of your workload.
I have been in your shoes: it is very intimidating to get in front of strangers and speak about your passion: technical topics where others are stronger; opinions that are the anthesis of commonly-held beliefs; real-life solutions which you don’t believe are that unique. Or perhaps the most scary: people get bored and walk out on you.
Yet, how many conference sessions have you attended or watched webinars where you thought I knew this, I know more than the speaker, or even I totally disagree, and here’s why. The speakers you enjoy listening to — the David Belvins, Holly Cummings, Trisha Gee, Josh Long, Reza Rahman, Venkat Subramaniam of the world, and many others — started simply: likely pushed, goaded, and mentored into speaking. Practice and experience — and likely multiple failures — help novice speakers into rock stars.
On this week’s episode of Dev Interrupted, co-host Conor Bronsdon and Ben Lloyd Pearson, LinearB’s Director of Developer Relations, detail the evolution of Continuous Merge and the tool behind it, gitStream. Joining the conversation is Nik LeBlanc, VP of Engineering at DevCycle.
Nik shares the ways his team is using gitStream to streamline code reviews and offers practical advice for anyone looking to implement the tool on their own team. He also explores the somewhat controversial practice of splitting up and reshuffling engineering teams, a strategy that DevCycle has used to great effect. Nik finds that this practice helps balance teams, manage diverse knowledge bases, and de-risk the organization.
Testing is an indispensable part of software development, ensuring the reliability and correctness of the codebase. However, writing tests that are expressive and easy to understand can be a challenge. The Go programming language, renowned for its simplicity and efficiency, demands a testing framework that aligns with its philosophy.
This is where Ginkgo comes into play. Ginkgo is a powerful testing framework for Go, designed to facilitate Behavior Driven Development (BDD) style testing. Traditional unit testing can become cumbersome and less intuitive, especially as the codebase grows in complexity. The lack of expressive tests can hinder collaboration between developers, testers, and stakeholders, leading to misunderstandings and costly errors.
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