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Rust types team moves forward on next-gen trait solver

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The Rust types team has made significant progress on the next-generation trait solver, according to a June 26 blog post.

Work done to stabilize the use of the next-generation trait solver in coherence checking surfaced small behavior regressions and hangs, which caused delays. But the team is close to compiling the standard library and the compiler with the new solver enabled everywhere. They expect a long tail of minor issues and behavioral differences from the existing implementation. There also are open design questions that will need to be resolved prior to stabilizing the new implementation. The goal is to use the next-generation trait solver everywhere by default.

Announced in January 2023, the Rust types team deals with parts of the Rust language and compiler involving the type system including type checking, trait solving, and borrow checking. Goals include working to make the type system sound, fast, consistent, and extensible. Priorities of the types team include making the type system sound, fast, consistent, and extensible. The types team is a subteam of the language and compiler teams.

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Messy data is holding enterprises back from AI

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Overall, the report underscores that although enterprises are eager to harness the potential of generative AI, significant infrastructure and data management groundwork is required to realize its benefits and ensure sustainable, long-term success.

A CIO’s to-do list from hell

Most enterprises knew they had data issues long before AI started to impact the market in significant ways. Indeed, most have avoided AI and business intelligence investments due to their lack of confidence in their data. Nobody in the company completely understands where the data is and what it means. Silo leaders own and manage the data, so there is no single source of truth for things as simple as what a customer is and where customer data should come from. Redundancy is common in sales, production tracking, and other areas where the data is mismanaged.

How did things get this bad? Most enterprises spent years focused on new, shiny objects such as ERP and CRM systems, which contain important data, but it’s locked up in proprietary data stores. After ERP and CRM came data warehousing, distributed systems, data integration, and now cloud. Through it all, data has gotten more complex, distributed, and heterogeneous, with a lack of centralized control. Too many companies don’t understand the metadata and can’t trace data properly through the business processes. Also, acquisitions have driven some data redundancy; many enterprises still operate the older systems that came with the businesses they acquired. Now, we’re facing AI, where the meaning, structure, and truthfulness of data are not optional.

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Developer productivity poorly understood, report says

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Developer productivity is neither well-understood nor enabled, according to Atlassian’s just-released State of Developer Experience Report 2024. The report also found that interest in the developer experience, or DX, is growing but efforts are lagging.

Announced July 15 and accessible at atlassian.com, the State of Developer Experience Report 2024 is based on a survey of 1,250 engineering leaders in the US, Germany, France, and Australia, and 900 developers around the world. The survey was intended to find out what practices keep software development work flowing smoothly and what practices introduce friction. Feelings about work environments in the age of generative AI and microservices also were assessed. Among the findings were that less than half of developers believe their organizations prioritize developer experience and two out of three developers lose more than eight hours a week due to inefficiencies in their roles. Also, two out of three developers are not seeing significant productivity gains from using AI tools yet.

The top five contributors to developer role complexity, according to engineering leaders, include understaffing, expansion of the developer role, new technology, switching context between tools, and collaboration with other teams. The top five contributors to developer time loss cited include technical debt, insufficient documentation, build processes, lack of time for deep work, and lack of clear direction. Almost all engineering leaders surveyed, 99%, acknowledge that the developer role has become more complex. The top five practices leaders believe will improve developer productivity and satisfaction include AI automation, provision of new collaboration tools, risk taking and experimentation, streamlining decision-making, and hosting hackathons.

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Talk of GitLab sale highlights growing importance of DevSecOps platforms

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By connecting these two capabilities, he said, development teams can release and monitor their applications with a single platform. “Whether that makes sense from a business perspective is a matter for those two companies to decide, but most definitely, enterprise platform teams are linking those capabilities together on their own.”

Platform compatibility

John Annand, research practice lead at Info-Tech Research Group, also thinks that, if a Datadog deal occurred, it could be a good match.

“GitLab is a very popular DevSecOps tool that lets enterprises optimize their pre-production activities rather than having to build their own CI/CD and testing pipelines from multiple vendors’ products and from scratch,” he said in an email. “Datadog’s product is all the information about how a deployed application behaves once it’s left the dev environment and is forced to deal with real-world conditions.”

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How to use HybridCache in ASP.NET Core

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The key benefits of HybridCache are greater simplicity, improved performance, and enhanced efficiency.

HybridCache is an abstract class pertaining to the Microsoft.Extensions.Caching.Hybrid namespace. To store data to the cache and retrieve data from the cache, you use the GetOrCreateAsync method of the HybridCache class. This method takes advantage of the cache key to retrieve an object from the primary cache.

Now, if the object requested by the application is not available in the cache, i.e., in the case of a cache miss, the method checks the secondary cache if it is configured. If the data is not available there, the method stores the object in both the primary and secondary caches.

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Building next-generation applications with the Windows Application SDK

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Download channels show upcoming features

The Windows App SDK is available in three different channels; you can choose between the current stable release, a preview release for features that should ship soon, or an experimental channel where new features are trialed. The current supported release is Version 1.5.5, released early in July 2024. The next major release will be in six months or so. The current preview release predates the current build, so a new version should be available soon. Experimental builds are based on the development tree of the planned 1.6 release, with a second release in July 2024.

Microsoft provides a list of the features available in each channel, along with the current support life cycle. Versions are supported for a year from initial release, so the current 1.5 release will be in support until the end of February 2025, with Version 1.4 dropping out of support at the end of August 2024. There’s one unusual caveat to the Windows App SDK support: Although it offers compatibility back to Windows 10 Version 1809, technically only in-support versions of Windows are covered.

The Windows App SDK and the Copilot Runtime

The Windows App SDK is intended to be a key component of the Copilot Runtime, hosting, among other features, artificial intelligence APIs based on the Phi Silica local generative AI model and AI-powered OCR services. However, these promised features are still missing from the current Version 1.6 experimental builds, two months after Build. If Microsoft wants to shift developers’ attention to Windows, it needs to capitalize on the capabilities of its new hardware and ship Copilot Runtime APIs as soon as possible and accelerate the transition of Version 1.6 from experimental to production.

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Google’s Genkit for Go brings AI app development to Go language

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Google has unveiled Genkit for Go, an open-source framework for building AI-powered applications and cloud services natively in Go. The project is a collaboration between Google’s Firebase and Go language teams.

Introduced July 17 and currently in an alpha state, Genkit for Go enables developers to build generative AI applications by combining Go’s performance and concurrency advantages with Genkit’s libraries and tools. Potential use cases include intelligent assistants that understand complex requests, customer support agents using retrieval-augmented generation (RAG), and data transformation tools that convert unstructured data such as natural language into structured formats (SQL, queries, tables) for deeper analysis.

A guide to getting started with Genkit for Go can be found at firebase.google.com. Genkit for Go follows the May introduction of Firebase Genkit for Node.js, for JavaScript and TypeScript developers. Genkit provides lightweight, composable abstractions to simplify development of sophisticated AI workflows without sacrificing control and customizability, Google said. The framework features:

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Google rolls out new dev tools focusing on open source and GenAI

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IDC’s Dayaratna said he was particularly intrigued by Project Vanni because it directly addresses a particularly difficult coding obstacle, especially when development teams exist in many different countries speaking many different languages. The problem can also exist in countries with many different local languages, such as when widespread Indian teams have speakers writing in, for example, Hindi, Bengali, Marathi and Kannada.

The Vanni project is dealing with two difficult challenges. One is translating coding references without missing key technical details, especially when it is being translated into multiple languages. The second, potentially more severe, problem is doing these translations within a GenAI environment, where the algorithm can hallucinate or even simply try to extrapolate meaning where it might predict the next word incorrectly. If that happens, the translations become confusing and possibly destructive.

“What (coders) need is translation that is contextual and is specific to natural language prompts in software,” Dayaratna said, adding that translation specialized for developers “does not exist” yet. 

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Deno adds workspaces for managing monorepos

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The latest update to the Deno TypeScript /JavaScript and WebAssembly runtime features workspace support along with Node.js compatibility improvements.

Deno 1.45 was introduced July 11. The release introduces workspaces for managing monorepos, simplifying dependency management, configuration sharing, and module organization across large code bases, Deno Land said. There are two forms of supported workspaces: Deno-first workspaces defined in deno.json and backwards-compatible NPM workspaces. Global configuration for these workspaces is applied to each member package but can be overridden by members. Developers can mix and match NPM and Deno workspaces, with an NPM package inside a Deno workspace or vice versa.

For Node.js compatibility, Node-API support has been revamped, fixing issues with packages such as prisma, sqlite3,  and  paper. Other Node.js compatibility improvements include working on support for dd.trace and support for fs.lutimes and fs.lutimesSync. Also, node:crypto and node:zlib were added. The node:http module was updated, with capabilities such as Server#close() now doing a graceful shutdown, allowing in-flight requests to finish. In another improvement, the node:vm will consume less memory. For NPM support, types shipped with the package are now preferred over types from @types scope.

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Salesforce previews Einstein-powered service agent

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In order to create an Einstein Service Agent customized for an enterprise, its developers need to access the Einstein 1 platform, start creating a new copilot by giving its specific name and purpose, and then expose it to service topics, Nichols explained, adding that once the service topics are mapped and a LLM is selected, developers can assign knowledge-based actions to the agent.

These knowledge-based actions, according to the company, make the agent autonomous or allow the agent or chatbot to take actions without having a human in the loop, differentiating it from regular programmed chatbots or service agents.

One of the main differences between an older predictive chatbot and the Einstein Service Agent is its ability to understand intent because of its LLM base, said Keith Kirkpatrick, research director at The Futurum Group.

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Mistral’s new Codestral Mamba to aid longer code generation

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The company tested Codestral Mamba on in-context retrieval capabilities up to 256k tokens — twice the number seen in OpenAI’s GPT4o — and found its 7B version performing better than open source models in several benchmarking tests, such as HumanEval, MBPP, Spider, and CruxE.

The larger 22B parameter version of the new model also performed significantly better than CodeLlama-34B with the exception of the CruxE benchmark.

While the 7B version is available under the Apache 2.0 license, the larger 22B version is available under a commercial license for self-deployment or community license for testing purposes.

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