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Finding the right large language model for your needs

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Finding the right large language model for your needs
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  • Inference speed: Smaller models generally provide quicker inference times, enabling real-time processing and increasing energy efficiency and cost savings.
  • Accuracy: Larger models enhanced with retrieval-augmented generation, aka RAG, often yield higher accuracy.
  • Deployability: Smaller models are well-suited for edge devices and mobile applications, while larger models run ideally in a cloud or data center.
  • Cost: Larger models require more compute infrastructure to run.

Developers should also consider which languages the AI model must support, based on who will use it and where it will be applied. This is particularly important in modern workplaces, where employees may speak many different languages. Ensuring the model can seamlessly translate languages is vital for effective communication and collaboration across its users.

Additionally, with the growing importance of sovereign AI, many countries are building proprietary models trained on local languages and data sets. This allows nations to maintain control and autonomy over AI, ensuring the development and application of these technologies align with their unique cultural, ethical, and legal standards.

How companies are using LLMs

LLMs are powering AI applications, including chatbots and predictive analytics tools, that are delivering breakthroughs and efficiencies across industries.

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Node.js previews network inspection support

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Node.js previews network inspection support
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Node.js v20.18.0, a just-introduced update to the Long-Term Support (LTS) version of the popular asynchronous, event-driven JavaScript runtime, features experimental network inspection support.

Introduced October 3, Node.js 20.18.0 enables users to inspect network activities occurring within a JavaScript application. Still in active development, this capability is initially limited to HTTP and HTTPS modules only. To use this feature, Node.js must be started with the command $ node --inspect-wait --experimental-network-inspection index.js.

Other highlights of Node.js 20.18.0 include a new option for the tls.createSecureContext API. Developers can use tls.create.SecureContext({allowPartialTrustChain:true}) to treat non-self-signed certificates in the trust CA certificate list as trusted.

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Google ships Gemini 1.5 Flash-8B AI model

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Google ships Gemini 1.5 Flash-8B AI model
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Google’s Gemini 1.5 Flash-8B AI model is now production-ready. The company said the stable release of Gemini 1.5 Flash-8B has the lowest cost per intelligence of any Gemini model.

Availability was announced October 3. Developers can access gemini-1.5-flash-8B for free via Google AI Studio and the Gemini API. Gemini 1.5 Flash-8B offers a 50% lower price compared to 1.5 Flash and twice the rate limits. Lower latency on small prompts also is featured.

An experimental version of Gemini 1.5 Flash-8B had been released in September as a smaller, faster variant of 1.5 Flash. Flash-8B nearly matches the performance of the 1.5 Flash model launched in May across multiple benchmarks and performs well on tasks such as chat, transcription, and long context language translation, Google said.

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Visual Studio Code 1.94 improves file search

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Visual Studio Code 1.94 improves file search
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For Python, developers now can run Python tests with coverage and get rich results in the editor, Microsoft said. To run tests with coverage, users must select the coverage run icon in Test Explorer or “Run with coverage” from any menu that triggers test runs. The Python extension will run coverage by using the pytest-cov plugin if developers are using pytest, or by using coverage.py if using unittest. Once the coverage is complete, lines are highlighted in the editor for line-level coverage. The Python extension also has added a default problem matcher, simplifying issue tracking in Python code and providing more contextual feedback.

The Source Control Graph in Visual Studio Code 1.94 features a new history item reference picker in the view title, allowing developers to use the reference picker to filter the history items shown in the graph to a different branch or to view multiple branches. The Source Control Graph also expands the list of actions available in the context menu for source control history items. Actions have been added to create a new branch/tag from a history item, cherry-pick a history item, and check out an item.

Elsewhere in Visual Studio Code 1.94:

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Why cloud security outranks cost and scalability

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Why cloud security outranks cost and scalability
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Business reputation and trust are always vulnerable; companies recognize that a security breach can instantly damage both. Indeed, you’ll get yourself on the morning news and watch your stock drop by 50%. By prioritizing security, businesses aim to safeguard their reputation and customer relationships.

Long-term cost implications mean that focusing initially on cost and scalability might seem feasible, but the long-term financial impact of security incidents can be severe. Most people in the cybersecurity space understand that risk equals money. The more risk, the less your systems are worth, considering the potential for a breach. Prioritizing security can prevent costly breaches and downtime.

Innovation and agility mean that to remain competitive, businesses need to innovate rapidly. A secure cloud infrastructure enables this by providing a reliable foundation for building and deploying new services without compromising data integrity or security.

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Visual Studio Code 1.94 boosts file finding

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Visual Studio Code 1.94 boosts file finding
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For Python, developers now can run Python tests with coverage and get rich results in the editor, Microsoft said. To run tests with coverage, users must select the coverage run icon in Test Explorer or “Run with coverage” from any menu that triggers test runs. The Python extension will run coverage by using the pytest-cov plugin if developers are using pytest, or by using coverage.py if using unittest. Once the coverage is complete, lines are highlighted in the editor for line-level coverage. The Python extension also has added a default problem matcher, simplifying issue tracking in Python code and providing more contextual feedback.

The Source Control Graph in Visual Studio Code 1.94 features a new history item reference picker in the view title, allowing developers to use the reference picker to filter the history items shown in the graph to a different branch or to view multiple branches. The Source Control Graph also expands the list of actions available in the context menu for source control history items. Actions have been added to create a new branch/tag from a history item, cherry-pick a history item, and check out an item.

Elsewhere in Visual Studio Code 1.94:

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SingleStore acquires BryteFlow to boost data ingestion capabilities

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SingleStore acquires BryteFlow to boost data ingestion capabilities
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Additionally, it also has a separate tool, SAP Data Lake Builder, that can be used to ingest data from SAP. Other specific data ingesting tools on offer include BryteFlow for Oracle, BryteFlow for SQL, BryteFLow for SQL Server, BryteFlow for PostgreSQL, and integrations with Databricks, Teradata, Google BigQuery, and Apache Kafka.

What effect will the acquisition have?

BryteFLow’s existing customers may have to look elsewhere for help with data integration if they use databases other than SingleStoreDB: “Our number one priority is to integrate BryteFlow into SingleStore and bring value to our customers through SingleConnect. We have no plan to sell BryteFlow independently at this time, apart from some special cases,” SingleStore CEO Raj Verma said via email.

But there will be opportunities for SingleStore customers, said Duncan Van Kouteren, research analyst at Nucleus Research. “The acquisition will enable customers to integrate data from various sources while maintaining real-time data analytics functionalities by utilizing BryteFlow’s capabilities such as change data capture (CDC),” he said.

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OpenAI updates API with model distillation, prompt caching abilities

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OpenAI updates API with model distillation, prompt caching abilities
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“Many developers use the same context repeatedly across multiple API calls when building AI applications, like when making edits to a codebase or having long, multi-turn conversations with a chatbot,” OpenAI explained, adding that the rationale is to reduce token consumption when sending a request to the LLM.

What that means is that when a new request comes in, the LLM checks if some parts of the request are cached. In case it is cached, it uses the cached version, otherwise it runs the full request.

OpenAI’s new prompt caching capability works on the same fundamental principle, which could help developers save on cost and time.

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Understanding VBS Enclaves, Windows’ new security technology

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Understanding VBS Enclaves, Windows’ new security technology
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Putting a trusted execution environment on a PC is useful for more than securing AI. It protects sensitive data, adding a new level of protection beyond at rest and in motion: in use. While it does require more work to define and use a VBS Enclave, it’s worth it to have more security with only limited performance impact.

With Windows 11’s memory integrity tools, a VBS Enclave uses Windows’ integral hypervisor to create a new, isolated, high-privilege area of system memory: Virtual Trust Level 1. Most of your code, and Windows itself, continues to run at Virtual Trust Level 0. VTL 1 is used by a secure version of the Windows kernel, with its own isolated user mode. This is where your VBS Enclave runs, as part of an application that appears to cross the boundary between the two zones. In reality, you’re separating off the VTL 1 enclave and using secure channels to communicate with it from the rest of your application in VTL 0.

Using VBS Enclaves in your applications

So how do you build and use VBS Enclaves? First, you’ll need Windows 11 or Windows Server 2019 or later, with VBS enabled. You can do this from the Windows security tool, via a Group Policy, or with Intune to control it via MDM. It’s part of the Memory Integrity service, so you should really be enabling it on all supported devices to help reduce security risks, even if you don’t plan to use VBS Enclaves in your code.

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How to use extension methods in C#

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How to use extension methods in C#
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public static class MyListExtensions
{
    public static T GetLastElement(this List list)
    {
        if(list.Count > 0)
            return list[list.Count - 1];
        return default(T);
    }
}

The GetLastElement is an extension method that returns the last element of a list. You can invoke this extension method using the following code snippet.


List integers = new List { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
int element = integers.GetLastElement();
Console.WriteLine(element);

Overloading an extension method in C#

Similar to other methods, you can also overload an extension method. The following code snippet shows how you can overload the Substring method of the string class to return a substring of a string. This overloaded Substring method takes the starting and ending index and a Boolean as parameters. The Boolean denotes if the returned string should be converted to upper case. If you pass true in this parameter when calling the extension method, the returned string will be converted to upper case.

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OpenAI previews Realtime API for speech-to-speech apps

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OpenAI previews Realtime API for speech-to-speech apps
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OpenAI has introduced a public beta of the Realtime API, an API that allows paid developers to build low-latency, multi-modal experiences including text and speech in apps.

Introduced October 1, the Realtime API, similar to the OpenAI ChatGPT Advanced Voice Mode, supports natural speech-to-speech conversations using preset voices that the API already supports. OpenAI also is introducing audio input and output in the Chat Completions API to support use cases that do not need the low-latency benefits of the Realtime API. Developers can pass text or audio inputs into GPT-4o and have the model respond with text, audio, or both.

With the Realtime API and the audio support in the Chat Completions API, developers do not have to link together multiple models to power voice experiences. They can build natural conversational experiences with just one API call, OpenAI said. Previously, creating a similar voice experience had developers transcribing an automatic speech recognition model such as Whisper, passing text to a text model for inference or reasoning, and playing the model’s output using a text-to-speech model. This approach often resulted in loss of emotion, emphasis, and accents, plus latency.

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