# MLX MLX is an array framework for efficient and flexible machine learning on Apple silicon, with Python, C++, C, and Swift APIs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Viability of local models for coding DevFeed: [Viability of local models for coding]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-07-07T12:34:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [coding](), [Large Language Model](), [agentic-coding](), [Hardware](), [quantization](), [MLX](), [Tooling]() Tags: [agentic-coding](), [coding](), [hardware](), [llms](), [local](), [mlx](), [quantization](), [tooling]() ### AI overview This memo examines how viable local language models are for coding, with particular attention to agentic coding rather than autocomplete. It discusses hardware constraints, model size, context windows, response speed, quantization, runtimes, and tooling. The author reports that tool calling remains unreliable but that models can often recover from failures. ### Source excerpt Birgitta Böckeler recently spent some time trying out running local LLMs for some programming tasks. In this memo she outlines the factors that influence how viable they are for the job. more... ## WWDC 2026 Viewing Guide DevFeed: [WWDC 2026 Viewing Guide]() Original publisher: [Read original article]() Author: Keith Harrison Published: 2026-06-15T09:16:45Z Content type: tutorial Language: en Sources: [K. Harrison]() Topics: [Swift](), [SwiftUI](), [Xcode](), [WebAssembly](), [gRPC](), [MLX](), [NumPy](), [Testing]() Tags: [2026](), [apple](), [auto-layout](), [comparison](), [ios](), [ios27](), [macos27](), [objective-c](), [swift](), [swiftui](), [webassembly](), [wwdc](), [xcode]() ### AI overview A guide to planning and watching WWDC 2026 sessions, highlighting platform themes and selected updates in Swift, SwiftUI, Xcode, WebAssembly, gRPC, and MLX. ### Source excerpt My WWDC 2026 viewing guide to help you plan the sessions you want to watch. Where Do I Start? The two big themes for the year are again Liquid Glass and Apple Intelligence. Start by watching the Platforms State of the Union (SOTU) for a summary of what's new this year. If you only have five minutes try the SOTU recap. You don't have to learn everything new today! There are over 100 sessions and a lot of new stuff to learn. You may feel like you're getting left behind. Don't let it overwhelm you. You've got time. Don't burn yourself out trying to keep up. Watching The Sessions I watch the videos using the Apple Developer app. It's available on macOS and iOS. The video player supports from 0.5x to 2x playback, most videos have transcripts and summaries, and allow copying of the onscreen sample code. You can also watch on YouTube. There are a lot of sessions, but many are short (15-20 minutes). There's no padding and Apple's engineers get to the point quickly. Swift Swift 6.4 is here with some quality of life improvements: What's new in Swift Condense long @available attributes with anyAppleOS, @diagnose to control compiler deprecation warnings. Double-colon module selector. Standard Library task cancellation shield, dictionary mapKeyedValues, new FilePath type. XCTest interop with Swift Testing. Subprocess 1.0. New ProgressManager. @C attribute for Swift-C interop. Compile to WebAssembly. New @inline(always) and @specialized attributes. Iterable protocol for noncopyable elements. Build real-time apps and services with gRPC and Swift gRPC Swift package to generate and deploy network code for remote procedure call services. Explore numerical computing in Swift with MLX NumPy-like API. Examples of computing the Mandlebrot set, heat distribution, and curve fitting. SwiftUI Liquid Glass changes and some welcome improvements: What's new in SwiftUI Liquid Glass is now mandatory. Minimal menu icons by default. Resize live previews in Xcode. Prominent tab role. Toolbar visibil ## What's new in Swift: May 2026 Edition DevFeed: [What's new in Swift: May 2026 Edition]() Original publisher: [Read original article]() Author: Dave Lester Published: 2026-06-03T21:45:00Z Content type: article Language: en Sources: [Swift.org]() Topics: [Swift](), [SDKs](), [Back end](), [iOS](), [MLX](), [wasm](), [WebAssembly](), [AWS Lambda]() Tags: [aws](), [aws-lambda](), [backend](), [community](), [conferences](), [ios](), [local](), [mlx](), [news](), [releases](), [serverless](), [swift](), [wasm](), [webassembly]() ### AI overview A curated May 2026 digest of Swift community activity, including meetup videos, backend development resources, the Temporal Swift SDK, AWS Lambda, MLX for iOS, and Swift for WebAssembly updates. ### Source excerpt Welcome to "What's new in Swift," a curated digest of releases, videos, and discussions in the Swift project and community. To start, we're focusing on some of the many local meetup groups sharing Swift content: Around the world, local meetup groups and conferences bring Swift developers together, and some even predate Swift itself! Many have YouTube channels where they share videos from their events, so you can tune in remotely. Let's highlight a few. In May the SF Swift meetup hosted a talk by Dan Federman, Agentify Your Swift Repo, which covers building an agent for CI and review feedback. Swift Barcelona has a dedicated YouTube presence, while some groups, like the new MLX India meetup, post event playlists, including a recent talk about using MLX Swift in iOS apps that's worth checking out. And there's even a community-organized Swift Community Meetups YouTube channel, which hosts meetups online and is home to a series of cross-platform Swift talks. These groups support developers in a few ways worth calling out: Local community: they're building local connections among Swift developers and organizations. Room for experimentation: by being less formal and sometimes featuring works in progress, meetups create space to share ideas, get feedback, and learn from one another. I encourage more folks to get involved in their local Swift meetup. And if your group starts publishing content and videos, please share on the Swift Forums in the Community Showcase category. -- Dave Lester Now on to other news about Swift: Videos to watch Interested in using Swift for backend server development? Mohammad Azam posted a livestream recording of Introduction to Hummingbird, a walkthrough of the web framework covering installation and development basics. Meet the Temporal Swift SDK, from Replay 2026, introduces the SDK that brings Temporal's durable workflows (long-running processes that survive crashes, retries, and restarts without losing state) to Swift. The SDK recently reached ## The PR you would have opened yourself DevFeed: [The PR you would have opened yourself]() Original publisher: [Read original article]() Author: Pedro Cuenca; Awni Hannun Published: 2026-04-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Pull Request](), [Open Source](), [Artificial Intelligence](), [AI Development](), [Language models](), [MLX]() Tags: [agent](), [agents](), [announcement](), [artificial-intelligence](), [best-practices](), [bugs](), [claude](), [code](), [community](), [contributors](), [language-models](), [llm](), [mlx](), [open-source](), [performance]() ### AI overview The article examines how agent-generated pull requests are changing open-source contribution, using the Transformers library as an example. It explains why maintainers must preserve human-readable design, implicit contracts, performance, and project context, and introduces a Skill and test harness to help port language models from Transformers to mlx-lm while supporting contributors and reviewers rather than automating contribution. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Welcome Gemma 4: Frontier multimodal intelligence on device DevFeed: [Welcome Gemma 4: Frontier multimodal intelligence on device]() Original publisher: [Read original article]() Author: merve; Pedro Cuenca; Sergio Paniego; ben burtenshaw; Steven Zheng; Alvaro Bartolome; Nathan Habib Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gemma4](), [multimodal](), [Open Source](), [long-context](), [quantization](), [llama.cpp](), [MLX](), [Rust](), [webgpu]() Tags: [audio](), [benchmarks](), [fine-tuning](), [gemma](), [gemma4](), [generation](), [google](), [guide](), [llama](), [llama-cpp](), [local](), [long-context](), [mlx](), [models](), [moe](), [multimodal](), [on-device](), [open](), [open-source](), [quantization](), [science](), [speed]() ### AI overview Gemma 4 is presented as an open, Apache 2-licensed family of multimodal models supporting image, video, text, and, for selected variants, audio inputs. The article highlights on-device deployment, long-context support, compatibility across libraries and devices, quantization, benchmark results, and implementation options including llama.cpp, MLX, WebGPU, and Rust. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Granite 4.0 Nano: Just how small can you go? DevFeed: [Granite 4.0 Nano: Just how small can you go?]() Original publisher: [Read original article]() Author: Kate Soule; Rameswar Panda Published: 2025-10-28T14:59:38Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [ibm](), [Large Language Model](), [Model Development](), [Open Source Models & Datasets](), [llama.cpp](), [MLX](), [vllm](), [Transformer](), [Loop Engineering](), [gemma](), [qwen](), [Google]() Tags: [apache](), [architecture](), [benchmarks](), [code](), [data](), [development](), [gemma](), [google](), [ibm](), [language-models](), [llama](), [llama-cpp](), [math](), [mlx](), [model](), [model-development](), [models](), [on-device](), [qwen](), [training]() ### AI overview IBM introduces Granite 4.0 Nano, a family of compact language models for edge and on-device applications. The release includes hybrid-SSM and traditional transformer variants ranging from roughly 350M to 1.5B parameters, supports vLLM, llama.cpp, and MLX, and is released under the Apache 2.0 license. The article reports strong performance across knowledge, math, code, safety, instruction-following, and tool-calling benchmarks. ### Source excerpt Today we are excited to share Granite 4.0 Nano, our smallest models yet, released as part of IBM's Granite 4.0 model family. Designed for the edge and on-device applications, these models demonstrate excellent performance for their size and represent IBM's continued commitment to develop powerful, useful, models that don't require hundreds of billions of parameters to get the job done. ## SOTA OCR with Core ML and dots.ocr DevFeed: [SOTA OCR with Core ML and dots.ocr]() Original publisher: [Read original article]() Author: Christopher Fleetwood; Pedro Cuenca Published: 2025-10-02T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [MLX](), [PyTorch]() Tags: [apple](), [architecture](), [battery](), [coreml](), [cpu](), [developers](), [framework](), [gpu](), [high-performance](), [images](), [mlx](), [model](), [ocr](), [on-device](), [parameter](), [precision](), [pytorch](), [repo](), [tools](), [vision]() ### AI overview This tutorial explains how to convert dots.ocr from PyTorch to Core ML for on-device execution on Apple hardware. It discusses the roles of the Neural Engine, GPU, MLX, and Core ML, then outlines a staged conversion process beginning with GPU execution, FLOAT32 precision, and static shapes. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Swift Transformers Reaches 1.0 - and Looks to the Future DevFeed: [Swift Transformers Reaches 1.0 - and Looks to the Future]() Original publisher: [Read original article]() Author: Pedro Cuenca; Christopher Fleetwood; Mattt; Vaibhav Srivastav Published: 2025-09-26T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [swift-transformers](), [MLX](), [Swift](), [Inference](), [Local AI](), [hugging face](), [Large Language Model](), [Open Source]() Tags: [apple](), [asr](), [coreml](), [hugging-face](), [inference](), [llms](), [local](), [mlx](), [open-source](), [swift](), [swift-transformers](), [transformers]() ### AI overview swift-transformers reaches version 1.0 as a Swift library for running local models on Apple Silicon, including iPhones. It combines input preparation, Hugging Face Hub access, local caching and offline downloads, and wrappers for running Core ML-converted LLMs. The release establishes a stable foundation for Apple-focused local inference and future MLX and agentic use cases. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Gemma 3n fully available in the open-source ecosystem! DevFeed: [Gemma 3n fully available in the open-source ecosystem!]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Pedro Cuenca; Sergio Paniego; Vaibhav Srivastav; Christopher Fleetwood; Joshua; Steven Zheng; Kashif Rasul Published: 2025-06-26T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gemma](), [Open Source](), [AI Development](), [llama.cpp](), [MLX](), [Ollama](), [transformers.js](), [GPU](), [Hardware](), [timm](), [asr](), [Embeddings]() Tags: [ai](), [artificial-intelligence](), [audio](), [community](), [embeddings](), [gemma](), [gpu](), [hardware](), [implementation](), [llama-cpp](), [llm](), [mlx](), [multimodal](), [ollama](), [open-source](), [release](), [research](), [speech](), [timm](), [transformers](), [transformers-js](), [translation](), [vision](), [vlm]() ### AI overview This article announces the availability of Gemma 3n in major open-source libraries and presents practical usage and fine-tuning examples. It describes the model variants, memory-efficient hardware requirements, multimodal audio and vision encoders, speech-to-text and translation capabilities, and architectural features including MatFormer and Per-Layer Embeddings. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The Transformers Library: standardizing model definitions DevFeed: [The Transformers Library: standardizing model definitions]() Original publisher: [Read original article]() Author: Lysandre; Arthur Zucker; Pedro Cuenca; Julien Chaumond Published: 2025-05-15T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Transformer](), [LLMs](), [interoperability](), [sglang](), [vllm](), [llama.cpp](), [tgi](), [MLX]() Tags: [announcement](), [community](), [interoperability](), [library](), [llama-cpp](), [llms](), [mlx](), [open-source](), [sglang](), [tgi](), [transformers](), [vllm](), [vlms]() ### AI overview The article presents Transformers as a standard library for defining and supporting machine learning model architectures. It describes its broad ecosystem integrations, including training frameworks and inference engines, and highlights interoperability with vLLM, SGLang, TGI, llama.cpp, and MLX. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.