Every day the world is becoming increasingly powered by artificial intelligence. In fact, you’d struggle to find tech companies that have not announced AI integrations into their tech stack in one way or another. Cynics might say this is a passing phase, but the reason AI is so popular is that it’s a versatile set of capabilities that can help solve a lot of problems.

Search engines are one of those things in life we take for granted. Whenever we’re looking for something, we throw a word or phrase into a search engine and, like magic, it gives us a list of matching results. It might not feel as magical nowadays because it’s something we do every day. But anyone who remembers the days of Alta Vista should understand how well we have it now.

When investing in technology, organizations should look for products and services that get them the best bang for their buck. So, who comes out on top when investing in search and logging solutions? Elasticsearch® surpasses OpenSearch in cost efficiency.

In today's digital landscape, applications are at the heart of both our personal and professional lives. We've grown accustomed to these applications being perpetually available and responsive. This expectation places a significant burden on the shoulders of developers and operations teams.

Large language models (LLMs) like GPT-4 and LLaMA have created a whole world of possibilities over the past couple of years. It’s heralded a boom in AI tools and applications, and ChatGPT has become a household name seemingly overnight. But this boom wouldn’t be possible without the powerful tools and frameworks created to facilitate this new generation of apps. One of these frameworks is LangChain, which makes it easy to build new apps using existing LLMs.

Artificial intelligence tools are everywhere, and it’s no mystery why. They can carry out a huge variety of tasks and find the solutions to many everyday problems. But these apps are only as good as their AI search algorithm. In simple terms, an AI search algorithm is the decision-making formula an AI tool uses to find the optimal solution to your specific problem. Search algorithms may make trade-offs between speed, relevance, or another weighted factor.

After an incredibly fast-moving 2023, what does the future hold for AI and search? Conversational generative AI leapt into the public consciousness over the past year, and organizations scrambled to define their strategy for capitalizing on the trend. AI-boosted relevance is reshaping the way users experience search — and elevating their expectations for the quality of the interaction.

Elastic APM for native apps provides auto-instrumentation of outgoing HTTP requests and view-loads, captures custom events, errors, and crashes, and includes pre-built dashboards for data analysis and troubleshooting purposes Elastic® APM for iOS and Android native apps is generally available in the stack release v8.12. The Elastic iOS and Android APM agents are open-source and have been developed on-top, i.e., as a distribution of the OpenTelemetry Swift and Android SDK/API, respectively.

A while ago, we posted a blog that detailed how we imported over 4 billion chess games with speed using Python and optimized the code leveraging our Universal ProfilingTM. This was based on Elastic Stack running on version 8.9. We are now on 8.12, and it is time to do a second part that shows how easy it is to observe compiled languages and how Elastic®’s Universal Profiling can help you determine the benefit of a rewrite, both from a cost and environmental friendliness angle.

Developers and SREs choose Microsoft Azure to run their applications because it is a trustworthy world-class cloud platform. It has also proven itself over the years as an extremely powerful and reliable infrastructure for hosting business-critical applications. Elastic Observability offers over 25 out-of-the-box integrations for Microsoft Azure services with more on the way. A full list of Azure integrations can be found in our online documentation.