# Local AI Published articles for Local AI. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AMD's GAIA Local AI Now Able To Transcribe & Summarize Meeting Recordings DevFeed: [AMD's GAIA Local AI Now Able To Transcribe & Summarize Meeting Recordings]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T10:19:10Z Content type: news Language: en Sources: [Phoronix]() Topics: [gaia](), [Local AI](), [Generative AI](), [Artificial Intelligence]() Tags: [agents](), [ai](), [bug-fixes](), [desktop-linux](), [gaia](), [generative-ai](), [github](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [local-ai](), [macos](), [open-source](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [release](), [security](), [ubuntu-benchmarks](), [ubuntu-hardware](), [user-interface](), [windows]() ### AI overview AMD GAIA 0.24 adds local transcription and summarization of meeting recordings, including multi-speaker recognition. The release also improves inbox triage and its text interface, supports an embedded local Lemonade server, and includes security and bug fixes. ### Source excerpt AMD's GAIA software for building AI agents on your PC and leveraging generative AI locally with the power of AMD Ryzen and Radeon hardware continues becoming more featureful. Out today is AMD GAIA 0.24/0.24.1 and with it comes the ability to transcribe and summarize meeting recordings locally along with other functionality... ## HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard DevFeed: [HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-15T23:15:57Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [Local AI](), [Hardware](), [GPU](), [cpu](), [Large Language Model]() Tags: [consumer](), [cpu](), [deepseek](), [dgx-spark](), [gemma](), [gpu](), [hardware](), [local-ai](), [models](), [ollama](), [windows](), [workstation]() ### AI overview StorageReview previews HP's pre-production ZBook Ultra G3a 16, a local AI laptop with 192GB of unified memory and up to 160GB assignable to its integrated GPU. The article examines its hardware and planned testing while noting that shipping-hardware benchmarks are not yet available. ### Source excerpt HP's ZBook Ultra G1a 14 holds the Best for Large Models spot on our Best Laptops for Local AI leaderboard because its 128GB of unified memory, 96GB of it assignable to the GPU, loaded models no discrete-GPU laptop could touch. The new HP ZBook Ultra G3a 16 raises that pool to 192GB with up to The post HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard appeared first on StorageReview.com. ## How and Why We Bought 4x DGX Sparks DevFeed: [How and Why We Bought 4x DGX Sparks]() Original publisher: [Read original article]() Author: Alex Ellis Published: 2026-09-15T00:00:00Z Content type: opinion Language: en Sources: [Alex Ellis' Blog]() Topics: [Local AI](), [GPU](), [qwen](), [Cloud]() Tags: [agents](), [ai](), [cloud](), [gpu](), [llm](), [local-ai](), [localai](), [qwen](), [red-teaming](), [security]() ### AI overview The author explains why OpenFaaS Ltd bought four DGX Sparks and what the team learned from deploying local AI. The article argues that local infrastructure can provide tangible privacy and risk-reduction benefits for business use cases, even though it is not primarily justified by cost per token. ### Source excerpt In June we deployed an RTX 6000 Pro into production, a few weeks later, we're now operating DGX Sparks for the team. Learn how and why. ## Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX DevFeed: [Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX]() Original publisher: [Read original article]() Author: Gerardo Delgado Published: 2026-09-14T15:00:52Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [Local AI](), [Artificial Intelligence](), [NVIDIA RTX](), [Windows](), [GeForce](), [qwen](), [NVIDIA DGX](), [Cloud](), [GitHub](), [Slack](), [Google](), [Microsoft]() Tags: [agentic-ai](), [ai](), [ai-agents](), [cloud](), [dgx-spark](), [drive](), [geforce](), [github](), [google](), [local-ai](), [microsoft](), [nvidia-dgx](), [nvidia-rtx](), [open-source](), [qwen](), [rtx-pro](), [rtx-spark](), [slack](), [windows]() ### AI overview Perplexity is adding Portable Computer to its Windows app for compatible NVIDIA GeForce RTX PCs and NVIDIA RTX PRO Workstations. The local agent uses NVIDIA-accelerated models to plan multistep tasks, analyze files, and keep sensitive information on the device, while users can authorize cloud support for more advanced research and reasoning. ### Source excerpt As local models become more capable, AI agents can handle more work directly on a PC while keeping sensitive information on the device. Portable Computer is a local version of the agent Perplexity Computer that plans and carries out multistep tasks. Accelerated by NVIDIA GPUs, it uses local models to analyze data, bring together information [...] ## Use a local and open source code assistant DevFeed: [Use a local and open source code assistant]() Original publisher: [Read original article]() Author: Seth Kenlon Published: 2026-09-09T14:01:45Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [ai-coding](), [Local AI](), [Open Source Models & Datasets](), [Large Language Model](), [Model Context Protocol](), [ide](), [Security, Privacy and Abuse Prevention](), [Command-line interface](), [Homebrew](), [Linux](), [macOS]() Tags: [ai-coding](), [ai-inference](), [artificial-intelligence](), [command-line](), [developer-tools](), [ide](), [linux](), [llm](), [local-ai](), [macos](), [model-context-protocol](), [ollama](), [open-source](), [privacy]() ### AI overview This Red Hat Developer article explains how to use OpenCode as a local, open source AI coding assistant. It covers OpenCode's terminal, desktop, and IDE extension interfaces, its use of the Model Context Protocol, installation requirements, and the need to configure an LLM. For privacy-conscious local development, it recommends open source local AI tools such as Ollama or OpenVINO. ### Source excerpt There's a lot of excitement about AI coding assistants, but many of the available options either aren't open source, or don't respect your data privacy by sending what you're working on to the cloud for processing. If you're looking for an alternative to closed AI, then you need an open coding assistant and an open source IDE. The post Use a local and open source code assistant appeared first on Red Hat Developer. ## Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026 DevFeed: [Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026]() Original publisher: [Read original article]() Author: Gerardo Delgado Published: 2026-09-03T16:00:59Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [Local AI](), [Inference](), [Hardware](), [Inference Performance](), [Mixture of Experts (MoE)]() Tags: [agentic-ai](), [agents](), [ai](), [ai-inference](), [announcements](), [artificial-intelligence](), [dgx-spark](), [hardware](), [inference](), [llama-cpp](), [local-ai](), [mixture-of-experts-moe](), [nvidia](), [nvidia-rtx](), [open-source](), [rtx-ai-garage](), [rtx-spark](), [video](), [vllm]() ### AI overview NVIDIA announces local-AI updates at IFA 2026, including agent tooling, faster local inference, RTX Spark Windows PCs, and locally runnable models for agentic, coding, and video-generation workloads. ### Source excerpt Frontier intelligence is going local. At IFA 2026, NVIDIA, Microsoft and its partners are teaming up to provide faster inference and new tools that make agents easier to set up and run locally on NVIDIA hardware. New compact NVIDIA RTX Spark Windows PCs are also coming in October to give AI enthusiasts, developers and creators [...] ## Choosing Local Models for Coding Agents Based on Hardware and Workload DevFeed: [Choosing Local Models for Coding Agents Based on Hardware and Workload]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-02T19:10:34Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Local AI](), [Hardware](), [Artificial Intelligence](), [Inference](), [quantization](), [Open Source]() Tags: [ai](), [hardware](), [local](), [local-ai](), [model](), [open-source](), [quantization](), [run]() ### AI overview This practitioner's guide explains why local models that work well for chat may perform poorly in coding-agent workloads. It discusses growing conversation context, memory requirements, precision, sustained speed, and thermal limits, then introduces Magnitude, an open-source inference server that profiles a machine and selects a configuration for local agent use. ### Source excerpt A practitioner's guide to local AI. ## Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI DevFeed: [Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI]() Original publisher: [Read original article]() Author: Nico Martin; Joshua Published: 2026-09-01T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [webgpu](), [Local AI](), [Machine learning](), [Inference](), [benchmarking](), [GPU](), [Optimization]() Tags: [benchmark](), [gpu](), [hub](), [inference](), [javascript](), [kernels](), [local-ai](), [open-source](), [optimization](), [shaders](), [testing](), [webgpu]() ### AI overview Hugging Face releases @huggingface/kernels, a JavaScript library and collection of 207 versioned WebGPU kernel packages for browser-based local AI. It also introduces Fleet, a browser benchmarking and testing suite that gathers opted-in performance and correctness evidence across hardware. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Build with Tailscale. Build on Tailscale. DevFeed: [Build with Tailscale. Build on Tailscale.]() Original publisher: [Read original article]() Author: Kevin Purdy Published: 2026-08-31T14:00:00Z Content type: article Language: en Sources: [Blog on Tailscale]() Topics: [networking](), [Local AI](), [Go](), [App](), [ide]() Tags: [apis](), [applications](), [dev](), [go](), [local](), [local-ai](), [networking](), [server](), [software]() ### AI overview Tailscale describes two ways to build with its networking platform: embedding secure, identity-aware connectivity into applications with tsnet, and automating tailnet creation and management through APIs. A local Ollama server illustrates how application-level identities, ACLs, MagicDNS, and managed HTTPS can avoid public ports, host daemons, and manual proxy configuration. ### Source excerpt Put secure networking inside what you build, then automate the rest. ## Building a Local, Multimodal AI Terminal Agent with Gemma 4 DevFeed: [Building a Local, Multimodal AI Terminal Agent with Gemma 4]() Original publisher: [Read original article]() Author: Arjun Prabhulal Published: 2026-08-12T09:25:11Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [gemma4](), [Artificial Intelligence](), [multimodal-ai](), [Command-line interface](), [Large Language Model](), [Local AI](), [Code](), [Google]() Tags: [ai](), [ai-agent](), [artificial-intelligence](), [code](), [function-calling](), [gemma](), [gemma-4](), [google](), [google-cloud-platform](), [local-ai](), [multimodal](), [ollama](), [open-source](), [terminal]() ### AI overview A tutorial introduces Gemma 4 and builds a local multimodal terminal agent named gemma4-agent. It covers function calling, tool orchestration, text, image, and voice processing, plus Gemma 4's model variants and architecture. ### Source excerpt Introduction Open-source LLM models have been improving rapidly with tool calling, extended context windows, and native vision and audio capabilities, all while delivering strong benchmark performance. Gemma 4, recently introduced by Google Deepmind brings all of these features together in sizes efficient enough to run locally. In this article, we'll look at the capabilities of Gemma 4 and build a multimodal (Text, Vision, Voice) CLI agent (gemma4-agent) with function-calling capabilities. By the end, you'll have an agent that can chat, write, execute code, analyze images, and process voice instructions to deliver highly grounded responses. What is Gemma 4 Model ? Gemma 4 is Google DeepMind's open model family, released in April 2026 under the Apache 2.0 license. Built from the same research and technology behind Gemini 3, Gemma 4 is designed for high-performance reasoning, coding, multimodal understanding, and local AI execution across different model sizes. Features of Gemma 4 Models Improved Tool calling : Native function calling and tool orchestration, letting agents act autonomously without bloating prompt instructions Thinking mode : Built-in step-by-step thinking mode via the <|think|> token for complex multi-turn logic Context Windows : Up to 256K tokens on the 12B and larger models (128K on the edge-sized E2B/E4B) for processing long document and tool outputs Extended Multimodality : Gemma 4 models can process text,voice and images simultaneously like extracting data from charts, analyzing screenshots , and reviewing UI mockups. Gemma 4 Model Variants & SpecificationsGemma 4 Architecture Gemma 4 comes in five model sizes built around four architectural variants, each making different trade-offs between performance, inference speed, compute, and memory. Gemma4 Unified 12B vs Effective Parameters Effective-parameter models (E2B and E4B) are dense transformer models optimized for edge and on-device deployment. The "E" stands for effective parameters use Per-La ## Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA DevFeed: [Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-10T13:27:19Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Local AI](), [Large Language Model](), [long-context](), [Inference](), [Meta](), [Open Source](), [GPU](), [NVIDIA DGX](), [NVLink](), [Jetson](), [Automation]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [ai](), [automation](), [context-window](), [data-center-cloud](), [dgx-spark](), [dgx-station](), [edge-computing](), [featured](), [gpu](), [inference](), [jetson](), [local-ai](), [nemoclaw](), [nvidia](), [nvidia-dgx](), [nvlink](), [open-source](), [top-stories]() ### AI overview Meta's Muse Glimmer is a 30B open-weight dense model designed for local agentic AI workflows. With a 120K+ context window and performance of up to 20K tokens per second on a single GPU, it supports sustained, multi-step tool use and local processing of sensitive data. ### Source excerpt Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI... ## 11 Underrated Self-Hosted Apps You Can Run on a Raspberry Pi DevFeed: [11 Underrated Self-Hosted Apps You Can Run on a Raspberry Pi]() Original publisher: [Read original article]() Author: Karen Rangi Published: 2026-07-11T05:00:00Z Content type: article Language: en Sources: [RaspberryTips]() Topics: [App](), [Open Source](), [Docker](), [dashboards](), [Local AI](), [data](), [Monitoring]() Tags: [article](), [docker](), [inspiration](), [local-ai](), [open-source](), [raspberry-pi](), [self-hosted]() ### AI overview A hands-on overview of self-hosted applications that can run on a Raspberry Pi, including photo and video management, productivity, file management, and monitoring tools. The article highlights Docker-based deployment and discusses Immich as a local-AI photo-management option and Homepage as a customizable dashboard. ### Source excerpt I have been running several self-hosted applications on my Raspberry Pi for some time now. From my experience, it's one of the best ways to take back control of your data while ditching costly cloud subscriptions. In this article, I will share the best self-hosted apps you can run on a Raspberry Pi, based on... ## What a Raspberry Pi Can (and Can't) Do With AI DevFeed: [What a Raspberry Pi Can (and Can't) Do With AI]() Original publisher: [Read original article]() Author: Patrick Fromaget Published: 2026-06-24T11:54:14Z Content type: article Language: en Sources: [RaspberryTips]() Topics: [Artificial Intelligence](), [Local AI](), [Large language models (LLMs)](), [object-detection](), [OpenClaw]() Tags: [ai](), [applications](), [computer-vision](), [cpu](), [gpu](), [hardware](), [large-language-models-llms](), [local](), [local-ai](), [object-detection](), [openclaw](), [quick-tips](), [raspberry-pi]() ### AI overview This article explains what Raspberry Pi devices can and cannot do with AI. They can run applications such as object detection, computer vision projects, lightweight AI agents, and some small language models, but limited computing power makes most modern LLMs slow or impractical locally. AI HAT and AI Camera products can improve computer vision workloads but do little for LLM execution. ### Source excerpt AI (artificial intelligence) is a buzzword that has been thrown around a lot these days, and the Raspberry Pi ecosystem is no exception. New use cases have been tested on it, and new products have even been released to accompany this phenomenon. So, what can your Raspberry Pi actually do with AI? A Raspberry Pi... ## Reachy Mini goes fully local DevFeed: [Reachy Mini goes fully local]() Original publisher: [Read original article]() Author: Amir Mahla; Andres Marafioti Published: 2026-05-27T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [reachy](), [gemma4](), [llama.cpp](), [Large Language Model](), [speech-to-speech](), [hugging face](), [WebSocket]() Tags: [api](), [artificial-intelligence](), [audio](), [blog](), [gemma](), [guide](), [how-to](), [llama](), [llama-cpp](), [llm](), [local](), [local-ai](), [open-source](), [reachy](), [robotics](), [server](), [speech](), [speech-to-speech]() ### AI overview A tutorial for running fully local conversations with a Reachy Mini robot. It describes a cascaded VAD, speech-to-text, LLM, and text-to-speech pipeline using llama.cpp with Gemma 4, Silero VAD, Parakeet-TDT STT, and Qwen3-TTS, connected through a Realtime API-compatible WebSocket. The setup avoids cloud services, API keys, and sending data off the local machine. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Why and How to Run Local Models in Zed DevFeed: [Why and How to Run Local Models in Zed]() Original publisher: [Read original article]() Author: Cameron Mcloughlin Published: 2026-05-19T00:00:00Z Content type: tutorial Language: en Sources: [Zed Industries - Blog]() Topics: [Local AI](), [LLMs](), [llama.cpp](), [Ollama](), [data]() Tags: [developer](), [hardware](), [llama](), [llama-cpp](), [llms](), [local](), [local-ai](), [ollama]() ### AI overview This article explains why developers may choose local models in Zed, focusing on privacy, cost, control, and availability. It also discusses their limitations compared with cloud-hosted frontier models and introduces setup guidance. ### Source excerpt You can run local AI models in Zed to get better performance and control over your data. Here's how. ## I Built a 256GB Local AI Cluster on My Desk DevFeed: [I Built a 256GB Local AI Cluster on My Desk]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-05-18T13:00:00Z Content type: article Language: en Sources: [Techno Tim]() Topics: [Local AI](), [Artificial Intelligence](), [agentic-coding](), [Hardware](), [Homelab](), [coding](), [model-serving](), [vllm](), [networking](), [Docker](), [NCCL](), [Monitoring]() Tags: [agentic-coding](), [ai](), [docker](), [github](), [hardware](), [homelab](), [local-ai](), [model-serving](), [monitoring](), [nccl](), [networking](), [self-hosted](), [self-hosted-ai](), [server](), [vllm]() ### AI overview The article tests a two-node local AI cluster built from ASUS Ascent GX10 systems, using one machine for practical agentic coding and two machines to run a larger local model. It evaluates a real coding workflow involving model serving, storage, networking, memory, Docker, NCCL, monitoring, power draw, and the application being built, concluding that local AI is capable and cloud-independent but still operationally complex. ### Source excerpt I have been covering local and self-hosted AI for a few years now - from running models privately at home to what is still running in my homelab today. But to run the larger, more capable models, you need something more specialized than a general-purpose home server. I wanted to know how good local AI has actually gotten, so I built a mini AI cluster on my desk and used it for a real coding wo... ## DwarfStar 4 and the Future of Local AI Model Support DevFeed: [DwarfStar 4 and the Future of Local AI Model Support]() Original publisher: [Read original article]() Published: 2026-05-14T22:22:45Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Local AI](), [deepseek](), [Inference](), [Frontier Model](), [DGX Spark](), [GPU](), [Large Language Model]() Tags: [ai](), [deepseek](), [dgx-spark](), [frontier-model](), [gpu](), [inference](), [llms](), [local-ai]() ### AI overview The author reflects on DwarfStar 4's rapid popularity and attributes it to demand for a focused local AI experience, capable hardware, quantization, and recent local AI advances. They describe a plan to support the best practically fast open-weights model over time, with possible specialized variants for coding, legal, and medical use. ### Source excerpt I didn't expect DwarfStar 4 (https://github.com/antirez/ds4) to become so popular so fast. It is clear that there was a need for single-model integration focused local AI experience, and that a few things happened together: the release of a quasi-frontier model that is large and fast enough to change the game of local inference, and the fact that it works extremely well with an extremely asymmetric quants recipe of 2/8 bit, so that 96 or 128GB of RAM are enough to run it. And, of course: all the experience produced by the local AI movement in the latest years, that can be leveraged more promptly because of GPT 5.5 (otherwise you can't build DS4 in one week -- and even with all this help you need to know how to gently talk to LLMs). The last week was funny and also tiring, I worked 14 hours per day on average. My normal average is 4/6 since early Redis times, but the first few months of Redis were like that. So, what's next? Is this a project that starts and ends with DeepSeek v4 Flash? Nope, the model can change over time. The space will be occupied, in my vision, by the best current open weights model that is *practically fast* on a high end Mac or "GPU in a box" gear (like the DGX Spark and other similar setups). I bet that the next contender is DeepSeek v4 Flash itself, in the new checkpoint that will be released and, hopefully, a version specifically tuned for coding, and who knows, other expert-variants (not in the sense of MoE experts) maybe. For local inference, to have a ds4-coding, ds4-legal, ds4-medical models make a lot of sense, after all. You just load what you need depending on the question. It is the first time since I play with local inference (I play with it since the start) that I find myself using a local model for serious stuff that I would normally ask to Claude / GPT. This, I think, is really a big thing. It is also the first time that using vector steering I can enjoy an experience where the LLM can be used with more freedom. DeepSeek v4 Flash ## pyghidra-mcp Meets Ghidra GUI: Drive Project-Wide RE with Local AI DevFeed: [pyghidra-mcp Meets Ghidra GUI: Drive Project-Wide RE with Local AI]() Original publisher: [Read original article]() Author: clearbluejar Published: 2026-05-05T07:00:00Z Content type: article Language: en Sources: [clearbluejar]() Topics: [Ghidra](), [Reverse Engineering](), [Local AI](), [MCP Server](), [Artificial Intelligence](), [Processes]() Tags: [ai](), [gemma4](), [ghidra](), [local](), [local-ai](), [mcp](), [mcp-server](), [reverse-engineering]() ### AI overview The article presents pyghidra-mcp v0.2.0, which adds a GUI-backed mode allowing a local LLM to drive a live Ghidra CodeBrowser across an entire project. It demonstrates real-time function renaming, plate comments, and cross-binary analysis, with edits recorded in Ghidra's undo history. ### Source excerpt pyghidra-mcp v0.2.0 ships a GUI-backed mode that lets a local LLM drive a live Ghidra CodeBrowser at full project scope. Renames, plate comments, and cross-binary pivots land in real time, with every edit tagged in Ghidra's undo history while the session is alive. ## How to Use Transformers.js in a Chrome Extension DevFeed: [How to Use Transformers.js in a Chrome Extension]() Original publisher: [Read original article]() Author: Nico Martin Published: 2026-04-23T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Chrome extension](), [transformers.js](), [gemma4](), [Artificial Intelligence](), [Local AI](), [Open Source](), [Code]() Tags: [artificial-intelligence](), [chrome](), [chrome-extension](), [code](), [extension](), [github](), [guide](), [inference](), [javascript](), [local](), [local-ai](), [manifest](), [on-device](), [open](), [open-source](), [transformers](), [transformers-js](), [ui](), [web]() ### AI overview A developer guide to building a local AI Chrome extension with Transformers.js under Manifest V3. It explains an architecture with a background service worker hosting models, a side-panel chat interface, and a content script for page extraction and highlighting, using the Gemma 4 Browser Assistant as a reference. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Liberate your OpenClaw DevFeed: [Liberate your OpenClaw]() Original publisher: [Read original article]() Author: Clem 🤗; ben burtenshaw; Pedro Cuenca; Jeff Boudier; merve; Niels Rogge; Victor Mustar; Mishig ᠮᠢᠰᠾᠢᠭ Published: 2026-03-27T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [OpenClaw](), [Open Source](), [hugging face](), [inference-providers](), [llama.cpp]() Tags: [agents](), [guide](), [hugging-face](), [inference-providers](), [llama-cpp](), [local-ai](), [open-source](), [openclaw](), [privacy](), [server]() ### AI overview This tutorial explains how to restore OpenClaw agents using open models through Hugging Face Inference Providers or by running models locally with llama.cpp. It compares hosted and local approaches, covering privacy, cost, hardware, model selection, configuration, and local server setup. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing 1Password® Unified Access: Identity Security for Humans and Their AI Agents DevFeed: [Introducing 1Password® Unified Access: Identity Security for Humans and Their AI Agents]() Original publisher: [Read original article]() Author: info@1password.com (Nancy Wang and Jeff Malnick) Published: 2026-03-17T00:00:00Z Content type: release Language: en Sources: [Blog on 1Password Blog]() Topics: [AI Bots]() Tags: [agents](), [ai](), [ai-agents](), [api](), [automation](), [ci-cd](), [identity](), [local-ai](), [news](), [security](), [ssh](), [unified-access]() ### AI overview 1Password introduces Unified Access Pro, an identity-security offering intended to discover, secure, and audit access involving people, AI agents, and machine identities. The article argues that credentials used by local agents, automation, and development tooling require access to be confirmed when a credential or secret is used. ### Source excerpt Agentic AI is changing how work gets done inside organizations. It's embedded in IDEs and automation tools, and it's showing up in browsers, internal workflows, and everyday productivity apps. Developers are using AI agents to accelerate engineering work, while knowledge workers are vibe coding apps without training on developer security practices, all of which create untenable risks for organizations. That shift has real implications for identity and access control. For years, identity security centered on login: authenticating the user, establishing a session, applying policy, and assuming authority for the duration of that session. That model worked for human access, but it breaks down when credentials are used by local AI agents, automation scripts, CI/CD pipelines, and AI-native tooling. In this new reality, authority shouldn't be decided once at login and then trusted all day. It should be confirmed right when access is requested, every time a credential or secret is used. That's why we're introducing Unified Access Pro, available today. It helps teams discover, secure, and audit access across humans, agents, and machine identities, so organizations can adopt AI confidently and securely. To learn more about 1Password® Unified Access, head here:https://1password.com/platform Discover risk where traditional identity security systems can't As work shifts to AI agents and automation, more credentials are used outside the identity systems that security teams rely on. It happens on employee devices, inside local development environments, and across browser-based AI tools. Security teams often have little insight into what's happening on employee devices and in the tools they use every day, where credentials are created, stored, and first used. That gap matters. Exposed SSH keys, plaintext .env files, long-lived API tokens, and locally installed agents rarely appear in traditional SaaS logs or federated identity systems. Yet these credentials can grant direct access ## GGML and llama.cpp join HF to ensure the long-term progress of Local AI DevFeed: [GGML and llama.cpp join HF to ensure the long-term progress of Local AI]() Original publisher: [Read original article]() Author: Georgi Gerganov; Xuan-Son Nguyen; Aleksander Grygier; Lysandre; Victor Mustar; Julien Chaumond Published: 2026-02-20T00:00:00Z Content type: news Language: en Sources: [Hugging Face - Blog]() Topics: [llama.cpp](), [Local AI](), [Inference](), [Open Source](), [model-deployment](), [Artificial Intelligence](), [User Experience]() Tags: [community](), [devices](), [ggml](), [inference](), [llama-cpp](), [llm](), [local-ai](), [open-source](), [superintelligence](), [user-experience]() ### AI overview Georgi Gerganov and the ggml team are joining Hugging Face to support the llama.cpp and ggml communities while retaining autonomy over technical direction and community leadership. The collaboration will provide long-term resources, improve integration between model definitions and llama.cpp, simplify packaging and user experience, and expand access to efficient local inference on devices. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Self-Hosted Paperless-ngx + Optional Local AI: Private Documents and Improves OCR & Search (Full Setup) DevFeed: [Self-Hosted Paperless-ngx + Optional Local AI: Private Documents and Improves OCR & Search (Full Setup)]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-01-27T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [Docker Compose](), [Local AI](), [Docker](), [Artificial Intelligence](), [configuration](), [Database](), [Redis](), [Linux](), [Server](), [GPU]() Tags: [ai](), [compose](), [docker](), [gpu](), [homelab](), [linux](), [local](), [local-ai](), [ocr](), [paperless-ngx](), [redis](), [search](), [self-hosted]() ### AI overview A tutorial for deploying a self-hosted Paperless-ngx document management stack with Docker Compose. It explains the core OCR, indexing, tagging, and search workflow, then adds optional local AI through Ollama, Open WebUI, Paperless-AI, and Paperless-GPT to improve OCR and metadata suggestions without using cloud services. ### Source excerpt Build a complete Paperless-ngx stack in Docker and take control of your documents. We'll get Paperless running first (works great on its own), then optionally add local AI with Ollama + Open WebUI and upgrade OCR using Paperless-GPT and Paperless-AI for more accurate, searchable text and tags - no cloud required. This post walks through a complete, repeatable Docker Compose for Paperless-ngx, ... ## AI application topologies: cloud, edge, local, and hybrid inference DevFeed: [AI application topologies: cloud, edge, local, and hybrid inference]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2025-10-24T21:15:00Z Content type: article Language: en Sources: [Alex Ewerlof Notes]() Topics: [Artificial Intelligence](), [Cloud](), [Local AI](), [Inference](), [On-device AI](), [Latency](), [Software as a service]() Tags: [ai](), [cloud](), [edge](), [edge-ai](), [inference](), [latency](), [local-ai](), [on-device-ai](), [privacy](), [saas]() ### AI overview The article categorizes AI application topologies by where inference compute occurs relative to data: cloud, edge, local, and hybrid. It describes trade-offs involving capability, latency, cost, privacy, connectivity, vendor limits, and centralized-service outages. ### Source excerpt Cloud AI, Edge AI, Local AI, and Hybrid AI [Next page]()