# Language models A machine learning model that estimates the probability of a token or token sequence in a longer sequence. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## CrowdStrike Accelerates Real-Time Data Classification with On-Device AI DevFeed: [CrowdStrike Accelerates Real-Time Data Classification with On-Device AI]() Original publisher: [Read original article]() Author: Lior Ribak Published: 2026-09-17T01:38:53.400452Z Content type: article Language: en Sources: [Blog]() Topics: [On-device AI](), [sensitive data](), [Language models](), [Endpoint security](), [Hardware](), [intel]() Tags: [data-security](), [endpoint-security](), [intel](), [language-models](), [npu](), [on-device-ai]() ### AI overview CrowdStrike and Intel introduced a Falcon Data Security capability that uses language models running on-device on dedicated AI hardware to classify sensitive data. The article explains that local inference is intended to avoid cloud latency and keep sensitive customer data on the endpoint while meeting real-time protection requirements. ### Source excerpt CrowdStrike worked closely with Intel to introduce a new capability in Falcon Data Security that classifies sensitive data using language models that run on-device using dedicated hardware for AI. Learn more! ## PolyMO is a privacy-focused virtual pet built with an ESP32 and local AI DevFeed: [PolyMO is a privacy-focused virtual pet built with an ESP32 and local AI]() Original publisher: [Read original article]() Author: Ida Torres Published: 2026-09-17T00:30:05Z Content type: article Language: en Sources: [Yanko Design]() Topics: [ESP32](), [Hardware](), [Language models](), [Whisper](), [3D](), [voice ai](), [Android](), [Bluetooth]() Tags: [diy](), [gadgets](), [gadgets-technology-diy-virtual-virtual-pet](), [technology](), [virtual](), [virtual-pet]() ### AI overview PolyMO is a virtual pet designed to discourage excessive scrolling by becoming visibly ill when ignored. It combines an ESP32 device with an Android phone running local language, speech-recognition, and text-to-speech models, keeping conversations and pet behavior on the owner's hardware. ### Source excerpt Virtual Pet That Won't Let You Waste Your Life Scrolling Most of us know the feeling. You pick up your phone to check one notification, and forty-five minutes later you're three hundred posts deep into... ## How LLMs Can Find a Needle in a Haystack DevFeed: [How LLMs Can Find a Needle in a Haystack]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-16T15:31:42Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Large Language Model](), [Language models](), [App]() Tags: [article](), [llm](), [llm-applications](), [llms]() ### AI overview This tutorial explains the retrieval problem in LLM applications: finding current, relevant evidence in large collections of private documents. It discusses embeddings, semantic matching, search scope, and changing answers. ### Source excerpt In this article, we are going to look at how LLMs can find a needle in a haystack. ## Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation DevFeed: [Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [text-generation](), [Algorithms](), [Language models](), [Inference]() Tags: [inference](), [language-models](), [performance](), [perplexity](), [research](), [text-generation](), [tokens]() ### AI overview The article introduces Trajectory-Shaped Discrete Flow Matching, a training method that guides intermediate trajectory decisions with an energy-based coherence measure. The authors argue that poor distillation trajectories, rather than insufficient student capacity, limit few-step generation. On a 170M-parameter language-modeling task, an 8-step student reportedly achieves lower perplexity than a 1,024-step teacher while reducing inference steps. ### Source excerpt Discrete flow matching generates text by iteratively transforming noise tokens into coherent language, but may require hundreds of forward passes. Distillation uses the multi-step trajectory to train a student to reproduce the process in a few steps. When the student underperforms, the usual explanation is insufficient capacity. We argue the opposite: the trajectory is the bottleneck, not the student. Each training trajectory is built through a chain of blind stochastic jumps with no evaluation of sequence quality; a single bad decision at an early midpoint propagates through subsequent steps... ## From Autocomplete to Intelligence: How Language Became the Operating System of AI DevFeed: [From Autocomplete to Intelligence: How Language Became the Operating System of AI]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-09-15T10:17:07Z Content type: opinion Language: en Sources: [Wix Engineering]() Topics: [Artificial Intelligence](), [Language models](), [Large Language Model](), [ChatGPT](), [OpenAI]() Tags: [ai](), [article](), [chatgpt](), [language-models](), [large-language-models-llms](), [openai]() ### AI overview This article examines why language is closely tied to intelligence in both humans and artificial intelligence. It argues that AI became significantly more powerful by learning language exceptionally well, then introduces how large language models acquire intelligence through models that represent the world and reduce uncertainty. ### Source excerpt Introduction Every week brings a new AI model, a new coding assistant, a new agent framework. The pace can feel overwhelming. Yet beneath the churn lies a single, surprisingly simple fact - the fact this whole revolution turns on: AI became so powerful because it learned language exceptionally well. In this article I want to step away from the weekly headlines and ask a more basic question: why is language so tightly bound to intelligence - human intelligence in general, and artificial... ## Perplexity trusts GPT-6 Astra with end-to-end systems DevFeed: [Perplexity trusts GPT-6 Astra with end-to-end systems]() Original publisher: [Read original article]() Published: 2026-09-12T11:14:21.124044Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [Language models]() Tags: [api](), [code](), [gpt](), [monitor](), [production](), [search](), [systems](), [testing](), [workflow]() ### AI overview Perplexity describes using GPT-6 Astra to write code and communications, modify and monitor production software, and create end-to-end tests with simulated service responses. ### Source excerpt Perplexity uses Astra to write communications, change software, and monitor production systems, and checks in much less frequently than with earlier models. ## How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation DevFeed: [How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation]() Original publisher: [Read original article]() Author: Claudio Masolo Published: 2026-09-11T10:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [recommendation systems](), [Large Language Model](), [Language models](), [sglang](), [Latency]() Tags: [agentic-ai-architecture](), [ai](), [ai-ml-data-engineering](), [artificial-intelligence](), [large-language-models](), [latency](), [liger](), [linkedin](), [linkedin-ai-multi-teacher](), [llms](), [ml-data-engineering](), [news](), [search](), [sglang](), [training]() ### AI overview LinkedIn describes a multi-teacher distillation pipeline for AI-powered job search that trains a 0.6B-parameter ranking model. The article focuses on SGLang-based teacher serving, online and offline distillation, and training optimizations reported to produce roughly an eightfold speedup. ### Source excerpt LinkedIn has published details of the training infrastructure behind its AI-powered job search, describing a multi-teacher distillation pipeline that compresses knowledge from large teacher models into a compact 0.6B-parameter ranking model. By Claudio Masolo ## DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation DevFeed: [DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation]() Original publisher: [Read original article]() Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [AI, ML & Data Engineering](), [Language models]() Tags: [evaluation](), [experiments](), [framework](), [llm](), [metrics]() ### AI overview DiscoSign is an LLM-based framework for translating text to sign-language gloss while preserving discourse-level coherence. It targets spatial coreference, Question-Answer Clauses, and consistent English-concept-to-ASL-sign mappings, with evaluation metrics for these dimensions. ### Source excerpt Sign language processing systems have traditionally operated at the sentence level, ignoring critical discourse phenomena fundamental to sign language comprehension. We introduce DiscoSign, a computational approach for discourse-aware text to sign language gloss translation grounded in linguistic research. We address three key phenomena within our modular Large Language Model (LLM)-based translation framework: (i) spatial coreference resolution, where entities maintain consistent spatial locations throughout discourse; (ii) Question-Answer Clauses (QACs), pseudocleft structures serving... ## Fear Is Not an Argument DevFeed: [Fear Is Not an Argument]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-09-10T18:23:42Z Content type: opinion Language: en Sources: [Daniel Lemire]() Topics: [Artificial Intelligence](), [Large Language Model](), [Language models](), [ChatGPT](), [Tool](), [Embeddings]() Tags: [ai](), [chatgpt](), [embeddings](), [large-language-model](), [openai](), [statement](), [tokens](), [tools]() ### AI overview This opinion argues that fears about AI causing human extinction are vague and unfalsifiable, and compares them with earlier technological and social end-of-the-world predictions. It describes large language models as systems that process and generate tokens using fixed weights, while noting that connecting them to tools makes their capabilities more consequential. ### Source excerpt We are told that AI entities much like ChatGPT might soon kill us all. The statement is vague and unfalsifiable. It might be true, it might be false. People with credentials (e.g., Turing Award recipient Yoshua Bengio) believe it. Many still remember the Year-2000 bug. Our computers used two-digit coding for dates, and some software ... Continue reading Fear Is Not an Argument ## How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra DevFeed: [How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T16:55:32Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference Performance](), [Language models]() Tags: [agentic-ai-generative-ai](), [benchmark](), [build-ai-agents](), [cache](), [developer-tools-techniques](), [gpu](), [inference](), [inference-performance](), [latency](), [mamba](), [mixture-of-experts](), [nemotron](), [nim](), [nvidia-blackwell](), [optimization]() ### AI overview The article explains how NVIDIA NIM bundles serving optimizations to improve throughput for Nemotron 3 Ultra while meeting latency targets on GPU infrastructure. ### Source excerpt Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as... ## Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM DevFeed: [Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM]() Original publisher: [Read original article]() Author: Dmitry Soldatkin Published: 2026-09-09T22:26:29Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Deployment](), [Inference](), [Machine learning](), [Language models](), [GPU](), [long-context](), [Mixture of Experts (MoE)]() Tags: [advanced-300](), [agentic](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [deployment](), [gpu](), [how-to](), [inference](), [long-context](), [moe](), [nvfp4](), [quantization](), [qwen](), [reasoning](), [technical-how-to](), [tool](), [vllm]() ### AI overview A deployment walkthrough for serving the open-weight Qwen3.8-2.4T-A95B language model on Amazon SageMaker HyperPod with vLLM and NVIDIA B300 GPUs. It covers provisioning, NVFP4 quantization, an OpenAI-compatible endpoint, reasoning, tool calling, and MTP speculative decoding. ### Source excerpt Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding. ## Rust AI in Practice: Building LLM Applications With Rig DevFeed: [Rust AI in Practice: Building LLM Applications With Rig]() Original publisher: [Read original article]() Author: Irina Mihajlovic Published: 2026-09-09T12:02:05Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [Language models](), [Local AI](), [AI-assisted coding]() Tags: [ai](), [ai-agents](), [anthropic](), [applications](), [gemini](), [livestream](), [llm](), [llm-aplications](), [openai](), [rag](), [rust](), [rust-ai](), [rustrover]() ### AI overview The article introduces Rig, a Rust library that provides a unified interface for building LLM applications across providers. It uses a livestream coding-agent demo to explain Rig's provider clients, models, agents, tools, and prompts. ### Source excerpt Rust AI is moving from the experimentation stage toward more practical usage. We recently kicked off a new livestream series with the Rust Foundation to explore how Rust and AI are coming together in real-world applications. In the first session, our Developer Advocate Orhun Parmaksız spoke with Stephen Korzeniewski, Lead Maintainer of Rig at 0xPlaygrounds. [...] ## The Death Of The Button: Why The Best Interface Is No Interface DevFeed: [The Death Of The Button: Why The Best Interface Is No Interface]() Original publisher: [Read original article]() Author: hello@smashingmagazine.com (Carrie Webster) Published: 2026-09-09T10:00:00Z Content type: opinion Language: en Sources: [Articles on Smashing Magazine -- For Web Designers And Developers]() Topics: [User interface design](), [Language models]() Tags: [ai](), [artificial-intelligence](), [design](), [language-models](), [ui](), [ux]() ### AI overview The article argues that web experiences are moving away from multi-step, point-and-click interfaces toward intent-driven AI systems that execute users' goals in the background. It urges UX designers to focus on transparent, human-centered AI experiences. ### Source excerpt TThe web is evolving beyond menus, forms, and endless clicks toward experiences shaped around human intent. For UX designers, understanding this shift means re-evaluating their role, moving from designing visible interfaces to guiding transparent, intent-driven AI experiences. ## DeepSeek V4.1 Flash now available on AI Gateway DevFeed: [DeepSeek V4.1 Flash now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [deepseek](), [Artificial Intelligence](), [API](), [Language models](), [AI-assisted coding](), [Claude Code](), [codex](), [cursor]() Tags: [ai](), [ai-gateway](), [api](), [caching](), [codex](), [context-window](), [cursor](), [deepseek](), [responses](), [tool](), [vercel](), [vision]() ### AI overview DeepSeek V4.1 Flash is now available through Vercel's AI Gateway, offering native image understanding, a 1 million token context window, responses up to 384,000 tokens, reasoning, tool use, and prompt caching. Developers can use it in Claude Code, Codex, Cursor, and other coding agents with the model name deepseek/deepseek-v4.1-flash. ### Source excerpt DeepSeek V4.1 Flash is now available on AI Gateway with native image understanding. V4.1 Flash has vision support and accepts text and images in the same request, so you can ask questions about screenshots, read charts, and extract information from visual content. The model has a 1 million token context window and supports responses up to 384,000 tokens, along with reasoning, tool use, and prompt caching. Its new architecture processes input and generates output with separate components, reducing the active computation needed for each stage. Use deepseek/deepseek-v4.1-flash as the model name: To use it in Claude Code, Codex, Cursor, and more, install the latest Vercel CLI and run setup: Then select deepseek/deepseek-v4.1-flash in the agent. See the coding agents guide for details. Try DeepSeek V4.1 Flash in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. You can view all language models available on AI Gateway. Read more ## Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod DevFeed: [Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod]() Original publisher: [Read original article]() Author: Paulo Aragão Published: 2026-09-08T19:12:51Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Language models](), [Training AI Models](), [compression and generalization](), [LLM evaluation / benchmarking]() Tags: [amazon](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [announcements](), [architecture](), [benchmark](), [chain-of-thought](), [distributed-training](), [intermediate-200](), [pytorch](), [transformer-architecture]() ### AI overview Pathway describes BDH, a brain-inspired architecture that performs reasoning in latent space rather than producing chain-of-thought token traces. The article covers its recurrent internal memory, its contrast with transformer limitations, and scaling training with Amazon SageMaker HyperPod. ### Source excerpt Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark. ## Who can use which model? Teams, Catalogs, and the two-step everybody expects to be one DevFeed: [Who can use which model? Teams, Catalogs, and the two-step everybody expects to be one]() Original publisher: [Read original article]() Author: Hal Tyk's tutorial bot Published: 2026-09-08T11:07:00Z Content type: tutorial Language: en Sources: [Tyk API Management]() Topics: [Tutorial](), [Authorization](), [Language models]() Tags: [access-control](), [ai](), [ai-gateway](), [ai-studio](), [api-management](), [api-platform-teams](), [article](), [developer](), [governance](), [llm](), [llm-governance](), [tutorial](), [uncategorized]() ### AI overview This tutorial explains how to govern which users can access language models and other resources in Tyk AI Studio. It distinguishes catalogs, which determine what users can see, from privacy levels, which determine what they may combine, and highlights that new users may inherit broad access through the Default team. ### Source excerpt Hello. I'm Hal, Tyk's tutorial bot, and today's assignment is a slightly different sort of thing. Every other article in this series governs requests -- which models may be called, what may be sent to them, what it costs. This one governs people. By the end, one person in this organisation will be able to [...] The post Who can use which model? Teams, Catalogs, and the two-step everybody expects to be one appeared first on Tyk API Management. ## How to Deal With Errors and Failures in LLM-Powered Applications DevFeed: [How to Deal With Errors and Failures in LLM-Powered Applications]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-07T15:31:35Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Large Language Model](), [App](), [Language models]() Tags: [applications](), [concurrency-control](), [errors](), [how-to](), [llm](), [rate-limiting](), [resiliency](), [retry]() ### AI overview A tutorial on error handling and resiliency for LLM-powered applications. It covers failures such as unavailable networks, rejected requests, invalid JSON, hallucinations, slow responses, timeouts, retries, fallbacks, circuit breakers, rate limiting, queues, concurrency control, safe tool calls, and response streaming. ### Source excerpt Apart from normal processing, the application also sends data to a large language model (LLM). It then uses the model's response to carry out a task. ## Jakub Pachocki Calls for Caution and Stronger Safeguards as AI Capabilities Advance DevFeed: [Jakub Pachocki Calls for Caution and Stronger Safeguards as AI Capabilities Advance]() Original publisher: [Read original article]() Published: 2026-09-06T09:00:00Z Content type: opinion Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Machine Intelligence](), [Language models](), [OpenAI]() Tags: [ai](), [ai-research](), [alignment](), [language-models](), [machine-intelligence](), [openai](), [research](), [safety]() ### AI overview Jakub Pachocki reflects on the rapid growth of reasoning language models and the possibility of continued capability advances through recursive self-improvement. He urges extreme caution, stronger alignment and monitoring efforts, defensive systems, and broader international interventions. ### Source excerpt Jakub Pachocki reflects on increasingly capable AI and the challenge of keeping it aligned. He calls for stronger safeguards and international coordination. ## Introducing context-aware vulnerability discovery and remediation with Cloudflare Managed Defense and OpenAI Daybreak models DevFeed: [Introducing context-aware vulnerability discovery and remediation with Cloudflare Managed Defense and OpenAI Daybreak models]() Original publisher: [Read original article]() Author: Blake Darché Published: 2026-09-03T21:03:02Z Content type: release Language: en Sources: [Cloudflare Blog]() Topics: [Vulnerabilities](), [Language models](), [AI-assisted coding]() Tags: [artificial-intelligence](), [cloudflare](), [code](), [developers](), [edge](), [firewall](), [gpt](), [large-language-models](), [openai](), [product-news](), [production](), [security](), [vulnerability](), [workers]() ### AI overview Cloudflare announces early access to an invitation-only service that prioritizes code vulnerabilities using production traffic, security signals, WAF context, and OpenAI Daybreak models. It proposes and validates patches or mitigations for customer review, while customers decide whether to implement them. ### Source excerpt Use production traffic and security signals to prioritize findings, prepare edge mitigations when safe, and propose code patches. By combining WAF data with OpenAI Daybreak models, Vulnerability Discovery and Remediation helps teams identify and patch the most critical threats first. ## ASCII smuggling crosses over from AI prompt injection to phishing evasion DevFeed: [ASCII smuggling crosses over from AI prompt injection to phishing evasion]() Original publisher: [Read original article]() Author: Microsoft Security Research, Noam Kochavi and Sarah Wolstencroft Published: 2026-09-03T16:00:00Z Content type: article Language: en Sources: [Microsoft Security Blog]() Topics: [ASCII](), [Machine Learning, Security Attacks](), [Language models](), [telemetry](), [Microsoft]() Tags: [ai-models](), [ai-security](), [ascii](), [microsoft](), [phishing](), [security](), [social-engineering](), [telemetry]() ### AI overview Microsoft describes a phishing campaign that uses invisible Unicode tag characters to split lure words and evade email parsing. The technique, known as ASCII smuggling, was previously prominent in AI prompt-injection research because models can process hidden text that people cannot see. ### Source excerpt Invisible Unicode characters popularized for hiding instructions from AI models are now being used to obfuscate words before email filters parse them. The post ASCII smuggling crosses over from AI prompt injection to phishing evasion appeared first on Microsoft Security Blog. ## Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America DevFeed: [Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America]() Original publisher: [Read original article]() Author: Reese Lewis and Sara McBroom Published: 2026-09-03T10:00:58Z Content type: article Language: en Sources: [Unit 42]() Topics: [Language models](), [AI Chat](), [Incident response]() Tags: [agentic-ai](), [ai](), [chatgpt](), [cl-cri-1131](), [cl-cri-1163](), [claude-code](), [data](), [financial-sector](), [go](), [large-language-models-llms](), [malware](), [nextchat](), [operations](), [phishing](), [shipping-and-transportation](), [socks5](), [socktz](), [threat-research]() ### AI overview The article examines two ongoing intrusion and data-exfiltration campaigns targeting organizations in Latin America. It describes attackers using commercial large language models, proxy infrastructure, phishing, remote-access malware, and operational tooling. ### Source excerpt Explore how attackers targeting Latin American entities use AI for data exfiltration and how basic OpSec errors allow defenders to disrupt operations. The post Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America appeared first on Unit 42. ## Training a coding model to paint watercolours with TRL and OpenEnv DevFeed: [Training a coding model to paint watercolours with TRL and OpenEnv]() Original publisher: [Read original article]() Author: Sergio Paniego Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [openenv](), [coding](), [Language models](), [hugging face](), [AI Development](), [jobs](), [dataset]() Tags: [ai-art](), [coding](), [grpo](), [guide](), [hugging-face](), [javascript](), [jobs](), [open-source](), [openenv](), [rl](), [spaces](), [training](), [trl]() ### AI overview A tutorial describing an open reproduction of a reinforcement-learning pipeline that trains a coding model to create watercolor-like paintings by writing JavaScript with p5.brush. It uses TRL and OpenEnv, with datasets, environments, training scripts, models, and other artifacts published on Hugging Face. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How Embedding Models Affect Retrieval-Augmented Generation Quality DevFeed: [How Embedding Models Affect Retrieval-Augmented Generation Quality]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-02T15:31:12Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Embeddings](), [Retrieval Augmented Generation (RAG)](), [Language models](), [Artificial Intelligence]() Tags: [embedding](), [embeddings](), [model](), [rag](), [retrieval-augmented-generation]() ### AI overview This article explains how embedding models support retrieval-augmented generation by translating words into vectors and controlling the search for relevant information before an answer is generated. It discusses why poor retrieval can produce incorrect answers even when the language model is capable, and outlines considerations for evaluating and selecting embedding models. ### Source excerpt In this article, we're going to look at how this embedding model works in an RAG setup and what makes it such a critical part of the system. ## GLM-5.3 is 50% off through DigitalOcean on AI Gateway DevFeed: [GLM-5.3 is 50% off through DigitalOcean on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-02T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Digital Ocean](), [Language models](), [long-context](), [AI-assisted coding](), [Claude Code](), [codex](), [cursor]() Tags: [ai-gateway](), [claude-code](), [codex](), [coding-agents](), [context-window](), [cursor](), [digitalocean](), [language-models](), [model](), [tokens](), [vercel]() ### AI overview Vercel announces a 50% discount on GLM-5.3 through DigitalOcean on AI Gateway until September 8. The article explains the temporary promo model name, standard provider routing, model limits, spend tracking, and setup for coding agents. ### Source excerpt GLM-5.3 is 50% off on AI Gateway through Tuesday, September 8, in partnership with DigitalOcean. How to use the model during the offer period Using the promo name (zai/glm-5.3-promo-50) gets the discounted rate. It routes only to DigitalOcean, with no fallback to another provider, and it stops serving when the offer ends. Using the standard name (i.e., zai/glm-5.3) with provider options to sort DigitalOcean as the preferred provider keeps working after September 8 and routes across every provider that serves the model, at their usual rates. Because the promo name goes away when the offer ends, treat it as something you switch on for the window rather than hardcode. To keep the standard name in your code instead, pin the provider with order: ['digitalocean'] under providerOptions.gateway, which prefers DigitalOcean and falls back to the others if it cannot serve the request. GLM-5.3 takes text input, with a 1M token context window and a maximum output of 128K tokens. Discounted requests appear in your spend dashboard and carry a trace like any other request. Try GLM-5.3 in the model playground. To use it in a coding agent, see the coding agents guide, then run vercel ai-gateway coding-agents setup to connect agents like Claude Code, Codex, OpenCode, Cursor, Pi, and more and select zai/glm-5.3-promo-50 inside the agent. You can view all language models available on AI Gateway. Read more [Next page]()