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In addition to all of these, maintaining a consistent set of tools, and keeping the local development on laptops up to date is a constant struggle (e.g., non-atomicity of updates; opaqueness of the steps taken).

We recognize improved performance, nearly zero setup and security to be primary advantages of remote development environments, with the first two being productivity drivers. We greatly improve security by running everyone on the latest stable version. Vulnerable applications can be patched and updated during non-work hours. It is fairly trivial to monitor a lot of different aspects of the remote development environment, and in theory it allows us to detect and identify malicious behavior. Since Kubernetes pods running in the cloud do not have laptop battery constraints, it is trivial to scan disk for malicious artifacts or activity during off-hours.

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Moving from laptop to cloud enabled us to experiment with machines that have 90+ cores and hundreds of Gigabytes of RAM. On top of that we decided to go with kubernetes since it provided us with all the low-level building blocks we needed:

As we started using Kubernetes to run Devpods, it made perfect sense for us to start using Custom Resource Definition, which is a way of extending Kubernetes by providing a data structure that would be stored inside the control plane. That removed the need for an internal database for Devpod metadata. By using a custom controller, we were able to create and access Devpods using kubectl.

In a space that demands rapid execution, small changes can have a big impact. The Uber Freight Parcel TMS runs at the speed of your needs, allowing you to optimize your entire parcel cycle with an advanced suite of tools, complete with 24/7 support from a passionate team of domain experts.

Let me start with a few things that might sound surprising. First, none of these designs used any of the standard software architecture planning tools. We did not use UML, nor the 4+1 model, nor ADR, nor C4, nor dependency diagrams. We created plenty of diagrams, but none of them followed any strict rules. Just plain old boxes and arrows, similar this one describing information flow or this one outlining class structure and relationships between components. Two diagrams within the same design document often had a different layout and were often added and modified by different engineers.

Why was our approach different from what is commonly referred to in software architecture literature? Actually, our approach is not that different in principle, to most architecture guides. Almost all guides suggest starting with the business problem and outlining solutions and tradeoffs: which is also what we do. What we don't do is use many of the more complex tools that many architects or architecture books advocate for. We document the design as simple as we can, using the most straightforward tools: tools like Google Docs or Office365.

I know of banks and automotive companies where developers are actively discouraged from making any architecture decisions without going up the chain, getting signoff from architects a few levels up, who are overseeing several teams. This becomes a slower process, and architects can get overwhelmed with many requests. So these architects create more formal documents, in hopes of making the system more clear, using much more of the tools the common literature describes. These documents also reinforce a top-down approach, as it is more intimidating for an engineer, who is not an architect, to question or challenge the decisions that have already been documented using formal methods, that they are not that well-versed in. So they usually don't do so. To be fair, these same companies often want to optimize for developers to be more as exchangeable resources, allowing them to re-allocate people to work on a different project, on short notice. It should be no surprise that different tools work better in different environments.

Architecture patterns were born after engineers observed how similar design choices were made in some cases, and those design choices were implemented similarly. These choices were then named, written down, and extensively talked about. Architecture patterns are tools that came after the solution was solved, in hopes of making the lives of others easier. As an engineer, your goal should be more about solving solutions and learning through them rather than picking a shiny architecture pattern, in hopes that that will solve your problem.

Software architecture best practices, enterprise architecture patterns, and formalized ways to describe systems are all tools that are useful to know of and might come in handy one day. But when designing systems, start simple and stay as simple as you can. Try to avoid the complexity that more complex architecture and formal tools inherently introduce.

Kingsbury started with his motivation in undertaking a study of the vast landscape of chemical evaluation tools, which stemmed from his vision for combining the rigorous science of academic toxicology with the market-driven demand for information.

See

many more

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New Metrics '14!

For the study, Kingsbury and his team evaluated 32 tools (out of 60+ potential tools) on a number of criteria including the number of hazard endpoints, transparency, cost, ease of use, process review, and many others (interested parties can find the results of his study in an upcoming issue of the Journal of Integrated Assessment and Environmental Management).

For readers looking to skip to the good part, the highest-scoring tools were Targeted Risk Assessment (TRA), Stoffenmanager, Lowell Center LCSP, and Greensuite, but many of the lessons from the session came from understanding the underperformers: Did tools simply list acceptable chemicals are describe the criteria which rejected certain chemicals? Did the tools take applications into account (Ex: whether a chemical is used as a carpet backing or a cosmetic will have vastly different toxicology impacts)?

In this live video, you'll learn about UberCloud's latest innovation: the fully automated and self-service "UberCloud Engineering Simulation Platform" and "High-Performance Computing Containers" in action on Microsoft's Azure Kubernetes Service. You experience its major features and benefits for engineers, IT experts, and decision makers:

This website is an independent, advertising-supported comparison service. We want to help you make personal finance decisions with confidence by providing you with free interactive tools, helpful data points, and by publishing original and objective content.

Nearly half of fintech users say their finances are better due to fintech and save more than $50 a month on interest and fees. Fintech also arms small businesses with the financial tools for success, including low-cost banking services, digital accounting services, and expanded access to capital.

Companies can also create carefully refined marketing profiles and therefore, finely tune their services to the specific need. Open Banking platforms like Klarna Kosma also provide a unique opportunity for businesses to overlay additional tools that add real value for users and deepen their customer relationships.

Overall, we see fintech as empowering people who have been left behind by antiquated financial systems, giving them real-time insights, tips, and tools they need to turn their financial dreams into a reality.

Additionally, personalized portfolio management will become available to more people with the implementation and advancement of AI. Sophisticated financial advice and routine oversight, typically reserved for traditional investors, will allow individuals, including marginalized and low-income people, to maximize the value of their financial portfolios. Moreover, when coupled with NLP technologies, even greater democratization can result as inexperienced investors can interact with AI systems in plain English, while providing an easier interface to financial markets than existing execution tools.

For companies that have been forced to go DIY, building these platforms themselves does not always require forging parts from raw materials. DBS has incorporated open-source tools for coding and application security purposes such as Nexus, Jenkins, Bitbucket, and Confluence to ensure the smooth integration and delivery of ML models, Gupta said.

Intuit has also used open-source tools or components sold by vendors to improve existing in-house systems or solve a particular problem, Hollman said. However, he emphasized the need to be selective about which route to take.

In the context of Java, microservices are typically implemented using frameworks and tools that support the development of distributed systems. Some popular Java-based frameworks for building microservices include Spring Boot, Dropwizard, and Micronaut. These frameworks provide the necessary abstractions and utilities to simplify the development, deployment, and management of microservices.

Use container orchestration tools like Kubernetes to manage the allocation of system resources for each microservice. This step helps keep everything organized and ensures the entire system works as a whole.

Hence, in this context, an uber-jar is an "over-jar", one level up from a simple JAR (a), defined as one that contains both your package and all its dependencies in one single JAR file. The name can be thought to come from the same stable as ultrageek, superman, hyperspace, and metadata, which all have similar meanings of "beyond the normal".

In addition, please consider delivering an uber-jar is sometimes quite useful, if you really want to distribute a software and don't want customer to download dependencies by themselves. As a drawback, if their own policy don't allow usage of some library, or if they have to bind some extra-components (slf4j, system compliant libraries, architecture specialize libraries, ...) this will probably increase difficulties for them. ff782bc1db