# LLMs Large language models are neural networks trained on broad text corpora to predict token sequences and generate, summarize, translate, and classify natural language. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train DevFeed: [Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train]() Original publisher: [Read original article]() Published: 2026-09-15T20:00:35Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI search](), [Reinforcement learning](), [Artificial Intelligence](), [Google](), [LLMs](), [Algorithms & Theory]() Tags: [ai](), [ai-search](), [algorithms-theory](), [data-mining-modeling](), [diffusion](), [generative-ai](), [google](), [icml](), [icml-2026](), [reinforcement-learning](), [research](), [retrieval](), [rl](), [search]() ### AI overview Google Research presents Retrieve-for-Train, a framework that uses offline reinforcement learning to compile reward-aligned query fan-outs into training data for a lightweight diffusion retriever. The approach is intended to produce diverse, complementary, and coherent search-result sets in a single inference pass, reducing reliance on expensive inference-time reasoning. ### Source excerpt Algorithms & Theory ## A theoretical separation between quantum computers & LLMs DevFeed: [A theoretical separation between quantum computers & LLMs]() Original publisher: [Read original article]() Author: Srinivasan Arunachalam; Arkopal Dutt; Hari Krovi; Rik Sengupta; Ryan Mandelbaum Published: 2026-09-15T04:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [LLMs](), [Computer science]() Tags: [ai](), [computer-science](), [llms](), [quantum](), [research](), [science]() ### AI overview The article discusses research showing theoretical separations between shallow quantum circuits and restricted large language models. The work identifies computational problems involving function computation and sampling where shallow quantum circuits have a provable advantage, while emphasizing that the results are theoretical rather than immediately practical. ### Source excerpt Recent research further demonstrates the theoretical abilities of quantum computing ## LLMs as a Judge: How to Know if Your LLM is Healthy DevFeed: [LLMs as a Judge: How to Know if Your LLM is Healthy]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-14T15:31:02Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [LLM evaluation / benchmarking](), [Testing](), [LLMs]() Tags: [article](), [evaluation](), [llms](), [testing]() ### AI overview This article explains how to evaluate large language model applications, including why conventional software tests are insufficient, how LLM-as-a-Judge works, and how to combine automated checks, curated datasets, human review, and production monitoring. ### Source excerpt In this article, we are going to look at the process of LLM evaluation in detail. ## The Real Python Podcast - Episode #311: Django Developers Survey Results & Reproducible Python Builds DevFeed: [The Real Python Podcast - Episode #311: Django Developers Survey Results & Reproducible Python Builds]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-11T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Django](), [Python](), [LLMs](), [releases]() Tags: [2026](), [developers](), [django](), [llms](), [news](), [podcast](), [python](), [reproducible-builds](), [survey]() ### AI overview Episode 311 discusses findings from the 2026 Django Developers Survey, including how Django users are adopting LLMs in their development workflows. It also covers reproducible Python builds, recent Python and Django ecosystem releases, community news, and projects such as matchify and pydantic-pint. ### Source excerpt What are the latest trends uncovered in the 2026 Django Developers Survey? How are Django users employing LLMs in their development process? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects. ## From MIT to IBM, expediting AI and quantum deployment DevFeed: [From MIT to IBM, expediting AI and quantum deployment]() Original publisher: [Read original article]() Author: Lauren Hinkel | MIT-IBM Computing Research Lab Published: 2026-09-02T20:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [ibm](), [Reinforcement learning](), [Computing](), [LLMs]() Tags: [academic](), [ai](), [ai-agents](), [algorithms](), [alumni-ae](), [aram-harrow](), [artificial-intelligence](), [atari-games](), [careers](), [collaboration](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [computing](), [data](), [electrical-engineering-and-computer-science-eecs](), [fair-ai](), [graduate-postdoctoral](), [graduate-students](), [ibm](), [industry](), [irene-ko](), [isaac-chuang](), [language-models](), [learning](), [luca-daniel](), [machine-learning](), [mit-ibm-computing-research-lab](), [mit-ibm-watson-ai-lab](), [mit-schwarzman-college-of-computing](), [pulkit-agrawal](), [quantum-computing](), [quantum-machine-learning](), [research](), [school-of-engineering](), [srinivasan-arunachalam](), [trustworthy-ai](), [vllm-hook](), [zhang-wei-hong]() ### AI overview MIT graduate students and a former postdoc who moved to IBM describe how work with the MIT-IBM Computing Research Lab helped translate rigorous research into industry applications. Their areas include quantum machine learning, reinforcement learning, AI agents, and trustworthy and fair AI. ### Source excerpt MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems. ## Leverage Android skills and Gemma 4 in Android Studio Quail 4 DevFeed: [Leverage Android skills and Gemma 4 in Android Studio Quail 4]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-09-01T15:00:00Z Content type: article Language: en Sources: [Android Developers Blog]() Topics: [Agent Skills](), [Android Studio](), [Artificial Intelligence](), [LLMs](), [Google](), [gemma4]() Tags: [agent-skills](), [ai](), [android-skills](), [android-studio](), [development](), [gemma-4](), [google](), [llms]() ### AI overview Android Studio Quail 4 is stable and includes Android skills bundled directly into the IDE. The curated, preloaded skills help its agent handle complex Android workflows, including API changes, migrations, configuration, profiling, navigation upgrades, and adaptive UI work. ### Source excerpt Posted by Amman Fasil Asfaw, Product Manager, Android Studio Android Studio Quail 4 is now stable and ready for you to use in production. This is the final stable release for Android Studio Quail. The new features in Android Studio enable you to build premium apps with AI efficiently and effectively. Check out the video below to see the most helpful new features from the last 4 releases that can help improve and speed up your development. Here is a deep dive into what's new in Android Studio Quail 4: Android skills bundled into Android Studio While LLMs are incredibly capable at generic coding queries, they frequently write incorrect or outdated code when confronted with rapidly evolving Android APIs, platform-specific migrations, or complex configuration structures.To solve this, we bundle Android skills that have been curated by the team who builds Android, directly into Android Studio. Following the open-standard agent skills specification, these are modular, AI-optimized instructions designed specifically to guide LLMs through complex Android workflows. Android skills are now pre-loaded directly into the IDE, so you can start using them without having to manually download additional files. When you prompt the Android Studio agent, we analyze your prompt and search against the metadata for installed skills, automatically invoking them when they're most relevant. Your agent gains instant domain expertise, applying Google's best practices with less overhead spent on long, manual setup prompts. Android Studio comes preloaded with 23 curated skills, including: Need help upgrading your build? You have the Android Gradle Plugin (AGP) 9 Upgrade skill. Want to profile your app for any performance issues? You have the Android Profiler skill. Ready for a Jetpack Navigation framework upgrade? You have the Navigation3 skill. Adapting your app UI to different Android devices? You have the Adaptive skill. We also encourage you to create your own custom skills to extend Agent M ## Why KV Cache Stores K and V Vectors But Never Q? DevFeed: [Why KV Cache Stores K and V Vectors But Never Q?]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-08-30T20:15:33Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [LLMs](), [Parsing](), [Inference]() Tags: [gpu](), [interview](), [llms](), [parsing](), [pdf](), [technical]() ### AI overview The document discusses Marker v2, an open-source PDF parsing pipeline that uses shared inference serving and batching to improve GPU utilization. It also begins explaining autoregressive LLMs and why their forward pass is relevant to KV-cache behavior, but the supplied text ends before the explanation is complete. ### Source excerpt A popular technical LLM interview question. ## Fast and Hard Code DevFeed: [Fast and Hard Code]() Original publisher: [Read original article]() Author: Armin Ronacher Published: 2026-08-22T00:00:00Z Content type: opinion Language: en Sources: [Armin Ronacher]() Topics: [Rust](), [LLMs](), [Zig](), [Artificial Intelligence](), [Programming](), [WebAssembly](), [Cloudflare](), [Vercel](), [eBPF]() Tags: [ai](), [cloudflare](), [ebpf](), [llms](), [performant](), [programming](), [rust](), [thoughts](), [vercel](), [webassembly](), [zig]() ### AI overview An opinion article argues that LLM-assisted programming makes language choice less consequential, allowing developers to use unfamiliar or traditionally difficult languages. It focuses on growing interest in fast, small software built with Rust and Zig, while noting examples involving WebAssembly, Cloudflare, Vercel, and other demanding technologies. ### Source excerpt One of the memes on Twitter is that "programming is solved now." I'm not sure to what degree it is, but one thing is pretty clear: the act of familiarizing yourself with a language no longer matters and some of the friction that mattered for humans does not matter for agents. As a result, LLMs make language choice much less consequential than it used to be. If you don't like the choice, you can seemingly rewrite it in another language and you can make it pick a language that you, as a programmer, are entirely unfamiliar with. Which in turn means that people can, and do, choose based on the marketing of languages much more. As a long-term Rust programmer I found it quite fascinating to see people now ship Rust code who previously might not have chosen it. I attribute at least one part of this to two recent vibe shifts: there is a lot more talk about wanting fast software, and about LLMs being exceptional at optimizing code without regressing behavior. Folks like Mitchell Hashimoto, Charlie Marsh, Jarred Sumner, Daniel Lemire and quite a few others always carried a certain level of obsession with fast and performant software and they also all happen to be receptive to agents writing code. Maybe as a result, or unrelated others are now joining in. That's because with things like autoresearch you don't even necessarily need to know all the tricks: you just need to put an agent on it -- though knowledge greatly helps! If you look around, there are plenty of projects that want to be fast and small, and they increasingly pick "hard languages". And it's not just Rust that is benefiting. Even Zig -- despite the fact that the creators and parts of the core community are pretty negative on the whole AI thing -- is too. For instance Cloudflare's new Artifacts service uses a pure-Zig Git-protocol engine, compiled to a roughly 100 KB WebAssembly module and Vercel released fx, a Zig coding agent advertised to be small and fast. From what I can tell, all these projects are largely LLM ## The Real Python Podcast - Episode #308: Navigating Silent Failures in AI: Strategies for Effective Oversight DevFeed: [The Real Python Podcast - Episode #308: Navigating Silent Failures in AI: Strategies for Effective Oversight]() Original publisher: [Read original article]() Author: Real Python Published: 2026-08-21T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Artificial Intelligence](), [Large Language Model](), [AI-assisted coding](), [LLMs](), [Command-line interface](), [audit trail](), [Embeddings](), [Markdown](), [Script](), [Python]() Tags: [agentic-ai](), [agents](), [ai](), [api](), [audit-trail](), [cli](), [coding-agents](), [embeddings](), [gemini](), [markdown](), [opencode](), [tokens]() ### AI overview This podcast episode examines why AI systems can fail silently and how to improve oversight through context, checklists, validation, hooks, skills, scripts, and audit trails. It discusses document parsing problems such as unsupported formats, dropped attachments, and silent truncation, along with coding agents, CLI tools, embeddings, model selection, and token usage. The episode also highlights a course on AI-assisted Python coding with OpenCode and Gemini. ### Source excerpt Why do AI systems silently fail? How can you set up a system that produces results while also reviewing and validating the work? This week on the show, Calvin Hendryx-Parker returns to discuss his recent talk "Orchestrate Agentic AI: Context, Checklists, and No-Miss Reviews." ## Optimizing prompt cache DevFeed: [Optimizing prompt cache]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-08-08T14:07:13Z Content type: article Language: en Sources: [Alex Ewerlof Notes]() Topics: [Caching](), [Cache](), [LLMs]() Tags: [cache](), [caching](), [cost](), [token]() ### AI overview This visual article explains prompt caching in LLM APIs and its effect on token cost per task. It describes how requests are tokenized, prefetched into a key-value cache, and decoded, then discusses optimizing the cache to reduce costs. ### Source excerpt One of the best ways to save token cost ## Token-budget-aware LLM reasoning: cut costs in 2026 DevFeed: [Token-budget-aware LLM reasoning: cut costs in 2026]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-07-28T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Large Language Model](), [LLMs](), [Chain-of-thought](), [Caching](), [Routing (disambiguation)](), [Benchmark](), [Claude](), [OpenAI]() Tags: [caching](), [chain-of-thought](), [claude](), [cost](), [evaluation](), [llm](), [models](), [openai](), [reasoning](), [routing](), [tech-de](), [techniques](), [tokens]() ### AI overview This guide explains token-budget-aware LLM reasoning, a technique for matching a model's reasoning-token budget to problem complexity. It covers the cost of reasoning and output tokens, prompt-level methods such as chain-of-thought and Chain of Draft, and architectural approaches including caching, routing, and memory. ### Source excerpt Reasoning models think before they answer, and those reasoning tokens are usually part of what you pay for. They're billed as output tokens, the expensive kind, and a single request can generate a few hundred of them depending on the problem. If your ... ## In-House LLM Serving at Netflix DevFeed: [In-House LLM Serving at Netflix]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-07-17T21:32:39Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [LLMs](), [Netflix](), [Inference](), [AI Platform](), [model-deployment](), [autoscaling](), [GPU](), [gRPC]() Tags: [ai](), [ai-platform](), [autoscaling](), [deployment](), [gpu](), [inference](), [llm](), [model-serving](), [netflix](), [platform-engineering]() ### AI overview Netflix describes its in-house LLM serving stack, covering deployment, inference, API access paths, and production trade-offs. ### Source excerpt By AI Platform's Model Runtime team and Inference team Introduction Most organizations consume LLMs through hosted APIs. Netflix went further -- we run the full stack ourselves, from model deployment through inference, inside our existing production environment rather than a separate ML silo. Some of those decisions weren't obvious, and a few revealed their trade-offs only under production load. This post focuses on the choices where alternatives were seriously considered: engine selection, model packaging, API surface design, deployment strategy, and output constraints enforcement. The goal is to share not just what was built, but why -- and what production revealed that the design phase didn't anticipate. Architecture Overview Member-scale ML at Netflix is fronted by a unified JVM-based serving system that handles the end-to-end flow for downstream consumers: routing and A/B test logic, candidate generation, feature fetching, inference, post-processing, and logging at each stage. Both real-time and cached batch paths are supported. Figure 1 shows the two ways callers reach inference today: the gRPC path through this serving system and a direct HTTP path used by newer LLM-driven applications. Where inference runs depends on the model. Small CPU models run in-process, avoiding remote-call overhead. Larger models need GPUs -- the serving system handles pre- and post-processing locally but delegates inference to a remote service, Model Scoring Service (MSS). MSS is the shared inference backend, supporting XGBoost, TensorFlow, PyTorch, and LLMs behind a unified interface, with NVIDIA Triton Inference Server underneath managing model loading, batching, and GPU scheduling. On top of Triton sits a Java control plane that handles deployment, versioning, health checking, autoscaling, and multi-region rollout. Model authors package their artifacts and configure the deployment; the control plane provisions GPU instances, configures Triton, and orchestrates zero-downtime upgrades ## A Local-to-Backend Workflow for Iterating on AI Features in Kotlin Multiplatform Apps DevFeed: [A Local-to-Backend Workflow for Iterating on AI Features in Kotlin Multiplatform Apps]() Original publisher: [Read original article]() Author: Paul Woitaschek Published: 2026-07-17T11:49:09Z Content type: tutorial Language: en Sources: [YAZIO Engineering - Medium]() Topics: [Artificial Intelligence](), [Kotlin Multiplatform](), [LLMs](), [Kotlin](), [Mobile]() Tags: [ai](), [kotlin](), [kotlin-multiplatform](), [llm](), [llms](), [mobile](), [mobile-app-development]() ### AI overview The article presents a workflow for building AI features in a Kotlin Multiplatform mobile app: define an app interface, implement and iterate on the prompt locally with Koog, then move the completed logic to a backend. It uses a voice-based nutrition-tracking example that converts natural-language input into typed proposed actions. ### Source excerpt TLDR: Define the interface in the app, iterate locally with Koog, move the logic to the backend when it's done. AI features have a weird reputation among mobile developers. The model sits behind an API, the prompt lives on a server someone else owns, and every change means a ticket, a review, a deploy. So the whole topic feels like backend territory. But an AI feature is just a feature. Text in, structured data out, some logic in between. The only unusual part: the core of that logic is a prompt. And prompts are not designed, they are iterated. You try an input, look at the output, adjust, try again. Dozens of times. So the real problem is not building the feature. It's making that loop fast. The pattern I would like to show: define an interface in the app, build the real UI against it, implement it locally with Koog, iterate until the output is solid, then move the implementation to the backend. In Kotlin that last step is mostly copy paste. The example: track everything in one sentence We build Yazio, a nutrition tracking app, as Kotlin Multiplatform: one shared codebase for iOS and Android. That matters here, because everything below is written once and runs on both platforms. Tracking today is many small interactions: search a food, pick a portion, add water, log a workout. Now imagine the user just says what happened: "I had two scrambled eggs and a coffee for breakfast, drank half a liter of water, went for a 30 minute run, and for lunch I ate the same as yesterday."Users can track using voice The app answers with a list of proposed actions. The user deletes the wrong ones, speaks again to add what's missing, and confirms everything at once. Speech to text is solved on both platforms, so from our side the input is just a string. Messy natural language in, exact typed data out. That's what LLMs are good at. It's also the kind of logic you won't get right on the first try. The constraint: the key can't live in the app You can't ship an OpenAI or Anthropic API ke ## Building AI Features in Kotlin Multiplatform Apps with Local Prompt Iteration DevFeed: [Building AI Features in Kotlin Multiplatform Apps with Local Prompt Iteration]() Original publisher: [Read original article]() Author: Paul Woitaschek Published: 2026-07-17T11:49:08Z Content type: tutorial Language: en Sources: [Stories by Paul Woitaschek on Medium]() Topics: [Kotlin Multiplatform](), [LLMs](), [Mobile](), [API](), [Back end](), [voice ai](), [anthropic](), [OpenAI]() Tags: [ai](), [anthropic](), [api](), [backend](), [kotlin](), [kotlin-multiplatform](), [llm](), [llms](), [mobile](), [mobile-app-development](), [openai](), [speech](), [voice]() ### AI overview This tutorial presents a workflow for building AI features in Kotlin Multiplatform mobile apps. It recommends defining the app interface first, implementing the feature locally with Koog to iterate quickly on prompts, and moving the implementation to a backend once the output is reliable. The example uses natural-language and voice-based nutrition tracking, with LLM output converted into proposed structured actions. ### Source excerpt TLDR: Define the interface in the app, iterate locally with Koog, move the logic to the backend when it's done. AI features have a weird reputation among mobile developers. The model sits behind an API, the prompt lives on a server someone else owns, and every change means a ticket, a review, a deploy. So the whole topic feels like backend territory. But an AI feature is just a feature. Text in, structured data out, some logic in between. The only unusual part: the core of that logic is a prompt. And prompts are not designed, they are iterated. You try an input, look at the output, adjust, try again. Dozens of times. So the real problem is not building the feature. It's making that loop fast. The pattern I would like to show: define an interface in the app, build the real UI against it, implement it locally with Koog, iterate until the output is solid, then move the implementation to the backend. In Kotlin that last step is mostly copy paste. The example: track everything in one sentence We build Yazio, a nutrition tracking app, as Kotlin Multiplatform: one shared codebase for iOS and Android. That matters here, because everything below is written once and runs on both platforms. Tracking today is many small interactions: search a food, pick a portion, add water, log a workout. Now imagine the user just says what happened: "I had two scrambled eggs and a coffee for breakfast, drank half a liter of water, went for a 30 minute run, and for lunch I ate the same as yesterday."Users can track using voice The app answers with a list of proposed actions. The user deletes the wrong ones, speaks again to add what's missing, and confirms everything at once. Speech to text is solved on both platforms, so from our side the input is just a string. Messy natural language in, exact typed data out. That's what LLMs are good at. It's also the kind of logic you won't get right on the first try. The constraint: the key can't live in the app You can't ship an OpenAI or Anthropic API ke ## Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization DevFeed: [Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization]() Original publisher: [Read original article]() Author: Yuhui Ouyang; Di Wang; Sreedal Menon; Jie Tian Published: 2026-07-15T17:00:52Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta AI Research]() Topics: [Optimization](), [Embeddings](), [Graphs](), [recommendation systems](), [LLMs](), [Artificial Intelligence](), [Transformer](), [data]() Tags: [ads](), [ai](), [ai-research](), [data](), [embeddings](), [engineering](), [frontier-ai](), [generative](), [learning](), [meta](), [optimization](), [personalization](), [reasoning](), [research](), [retrieval](), [scale]() ### AI overview Meta describes Hierarchical Interest Representation, an upstream system that learns unified embeddings for users, advertisers, products, and services. It combines graph learning, multimodal content processed through LLMs, engagement signals, and self-supervised distillation to improve personalization, retrieval, ranking, and deep-funnel advertising optimization. ### Source excerpt Hierarchical Interest Representation is a research area for Meta Ads. We're exploring an upstream representation layer over the universe of Ads entities - users, advertisers, products, services - learning unified embeddings that connect users' inferred interests with the breadth of what advertisers offer in their deep funnel ads. The innovations in Hierarchical Interest Representation are [...] Read More... The post Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization appeared first on Engineering at Meta. ## JulyReply - Re: Stuck in draft DevFeed: [JulyReply - Re: Stuck in draft]() Original publisher: [Read original article]() Author: Harsh Shandilya Published: 2026-07-09T18:52:00Z Content type: opinion Language: en Sources: [Posts on Harsh Shandilya]() Topics: [Learning](), [Parser](), [REST API](), [Containers](), [Git](), [Cloudflare](), [Compose](), [LLMs](), [Command-line interface]() Tags: [blog](), [blog-posts](), [blogging](), [cli](), [cloudflare](), [compose](), [containers](), [git](), [julyreply](), [llms](), [rest-api](), [technical-writing]() ### AI overview The author reflects on managing unfinished blog drafts, explaining that feedback, public interest, curiosity, and motivation sometimes lead to completion while many drafts are abandoned. They invite readers to vote on several proposed technical topics, including Retrofit, an HTML parser, Git, Docker containers in NixOS, Cloudflare, Compose UI, and LLM-assisted development. ### Source excerpt On drafts and being okay with them ## Native-speed vLLM transformers modeling backend DevFeed: [Native-speed vLLM transformers modeling backend]() Original publisher: [Read original article]() Author: Harry Mellor; Lysandre Published: 2026-07-08T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vllm](), [Inference Performance](), [Inference](), [LLMs](), [Machine learning](), [Open Source]() Tags: [architecture](), [batching](), [gpu](), [inference](), [inference-performance](), [llama](), [llama-cpp](), [llms](), [mixture-of-experts](), [mlx](), [open-source](), [optimization](), [sglang](), [transformers](), [vllm]() ### AI overview This article describes the transformers modeling backend for vLLM and its role in serving Hugging Face models without requiring model authors to port their implementations. It explains how transformers supplies modeling code while vLLM contributes optimized inference techniques, and outlines comparisons with vLLM's native implementations across several Qwen3 model configurations. It also discusses inference-performance factors including GPU parallelization, compilation, fused kernels, and custom attention kernels. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## This Week In AI Research (21-30 June 26) 🗓 DevFeed: [This Week In AI Research (21-30 June 26) 🗓]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-03T02:04:12Z Content type: article Language: en Sources: [Into AI]() Topics: [Artificial Intelligence](), [AI Research](), [Large Language Model](), [LLMs](), [OpenAI](), [Meta](), [real-time](), [coding]() Tags: [agentic](), [ai](), [ai-research](), [benchmarks](), [coding](), [gpt](), [llms](), [meta](), [model](), [openai](), [real-time](), [release](), [research]() ### AI overview A weekly roundup of AI research and releases, including Brain2Qwerty v2 for real-time decoding of typed sentences from MEG recordings, OpenAI's GPT-5.6 Sol, and Sakana Fugu orchestrator LLMs. The supplied excerpt also discusses agent architectures and performance on several benchmarks. ### Source excerpt The top 10 research papers of this week: GPT-5.6, Sonnet 5, Meta's real-time brain-to-text decoder, a 35B model that beats trillion-parameter LLMs & more! ## The Pulse: a new trend, smart model routing DevFeed: [The Pulse: a new trend, smart model routing]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-07-02T18:46:24Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [Model Routing](), [AI Models](), [LLMs]() Tags: [ai-gateway](), [ai-models](), [latency](), [litellm](), [model](), [model-routing](), [openrouter]() ### AI overview The article surveys emerging smart model-routing tools that select AI models for coding and other tasks based on factors such as prompt difficulty, cost, latency, and availability. It lists offerings including Factory Router, Not Diamond, Vercel AI Gateway, OpenRouter, Kilo Gateway, Requestly.ai, LiteLLM, and others, while noting that some vendors claim cost savings. ### Source excerpt Are there any 'intelligent' router solutions out there which select the right model for the right task? I looked into it, and there are a few options. ## What's new in Swift: June 2026 Edition DevFeed: [What's new in Swift: June 2026 Edition]() Original publisher: [Read original article]() Author: Dave Lester Published: 2026-07-02T16:00:00Z Content type: news Language: en Sources: [Swift.org]() Topics: [Swift](), [releases](), [networking](), [Operating system](), [cross-platform](), [foundation-models](), [LLMs]() Tags: [2026](), [announcements](), [apple](), [cross-platform](), [developers](), [event](), [linux](), [llms](), [networking](), [releases](), [swift](), [wwdc]() ### AI overview This June 2026 digest covers Swift project and community news, including WWDC announcements. It highlights Swift adoption in parts of Apple's operating system kernel, upcoming Swift 6.4 features, a Swift rewrite of Apple's QUIC transport layer made available for cross-platform use through SwiftNIO, and Foundation Models framework utilities for working with LLMs on Apple platforms and selected Linux distributions. ### Source excerpt Welcome to "What's new in Swift," a curated digest of releases, videos, and discussions in the Swift project and community. June was an exciting month for Swift, featuring announcements at WWDC and community events around the globe. We invited the organizers of one of them to share with us: Hey, it's Mikaela and Adrian. We are organizers of CommunityKit, a community-organized conference that takes place the same week as WWDC, and iOSDevHappyHour, a monthly online meetup that keeps the community connected year-round. This is our fifth year coming out to Cupertino, and we love being able to create a place for the community to thrive, no matter where developers live. CommunityKit brought together over 250 developers in real life to geek out over the announcements, stay for the community and vibes, see what everyone is creating, and learn from each other. Some of the highlights from this year's event were the Indie Fair, where developers showcased their apps; the Watch Party, our annual gathering to watch the keynotes together; and Make Something, Ship Nothing, a hands-on postcard-making hangout to close the week. This year we also introduced workshops, including "Inclusive by Design" by Danielle Lewis, and for the Swift community: "Write Faster, Smarter Swift" by Paul Hudson. We can't wait to hear about what everyone builds and brings to next year's Indie Fair, and hope to see you at CommunityKit and iOSDevHappyHour! Now on to other news about Swift: WWDC26 highlights At its WWDC26 conference, Apple provided an update on its adoption of Swift and made a variety of new Swift-related announcements. Some highlights: During the Platforms State of the Union, Apple announced that parts of the core operating system kernel are being written in Swift for upcoming releases. What's new in Swift featured changes in Swift since last year, including a preview of what's coming in Swift 6.4, like up to 4x faster URL parsing and support for async code in defer blocks. The QUIC transpor ## LLM router architecture: best practices for 2026 DevFeed: [LLM router architecture: best practices for 2026]() Original publisher: [Read original article]() Author: Jim Allen Wallace Published: 2026-07-01T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Model Routing](), [LLMs](), [Latency](), [Availability](), [API](), [networking]() Tags: [architecture](), [availability](), [best-practices](), [cost](), [latency](), [llm](), [outage](), [router](), [routing](), [tech-de]() ### AI overview This guide explains model routers as middleware that selects the most suitable large language model for each request based on factors such as task complexity, cost, latency, availability, load, and safety requirements. It covers common routing strategies, production architecture patterns, proxy and recommendation-based designs, and how routing can reduce costs and improve reliability through fallback handling. ### Source excerpt You picked GPT-5 for every LLM call in your app because it was the safe call: chat, autocomplete, classification, summarization, all of it. Then the bill arrived, and you traced part of it back to queries like "what are your business hours?" getting r... ## Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction DevFeed: [Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction]() Original publisher: [Read original article]() Published: 2026-06-26T18:30:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI Inference](), [On-device AI](), [Google](), [LLMs](), [gemma]() Tags: [ai](), [efficiency](), [energy](), [gemini](), [inference](), [machine-intelligence](), [mobile-systems](), [natural-language-processing](), [on-device](), [on-device-ai](), [phones]() ### AI overview Google Research describes a method for retrofitting Multi-Token Prediction onto frozen Gemini Nano v3 production models to accelerate on-device inference on Pixel phones. The approach targets mobile energy and memory constraints, improving the speed and energy efficiency of features such as notification summaries and proofreading without requiring separate drafting models. ### Source excerpt Machine Intelligence ## The next hit: How LLMs change the way engineers work DevFeed: [The next hit: How LLMs change the way engineers work]() Original publisher: [Read original article]() Published: 2026-06-15T00:00:00Z Content type: opinion Language: en Sources: [Aha! Engineering Blog]() Topics: [LLMs](), [AI-assisted coding](), [engineering-culture]() Tags: [coding-agents](), [engineering](), [llms](), [software-engineering]() ### AI overview The article reflects on how LLMs and coding agents compress software development feedback loops, making substantial amounts of code appear within minutes. It argues that this speed can reduce the time engineers spend thinking deeply, evaluating their approach, and correcting course. ### Source excerpt Some people are drawn to software engineering, and not everyone is like that. But a common characteristic among engineers (myself included) is the thrill of building something and seeing it work. Maybe it is not the whole system, but some piece of i ## Coding Challenge #123 - Database Driven LLM Wiki DevFeed: [Coding Challenge #123 - Database Driven LLM Wiki]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-06-06T08:01:47Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Wiki](), [LLMs](), [Database](), [Langgraph](), [LangChain](), [Retrieval Augmented Generation (RAG)]() Tags: [coding](), [database](), [knowledge-base](), [langchain](), [langgraph](), [llms](), [rag]() ### AI overview A coding challenge to build a personal LLM wiki that incrementally maintains a structured Markdown knowledge base from curated sources. The proposed implementation uses Oracle AI Database for vector embeddings and full-text indexes, hybrid search for retrieval, LangGraph for workflows, and LangChain for LLM integration. ### Source excerpt This challenge is to build your own database powered LLM Wiki. [Next page]()