# Ollama Published articles for Ollama. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Quiz: How to Get Started With Ollama DevFeed: [Quiz: How to Get Started With Ollama]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-15T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Ollama](), [Python](), [Code](), [Hardware]() Tags: [chat](), [generate](), [hardware](), [how-to](), [install](), [internet](), [local](), [models](), [ollama](), [python](), [running]() ### AI overview An interactive 10-question quiz tests understanding of installing Ollama, pulling local models, and calling chat and generate functions from Python. It also covers multi-turn conversations, local hardware, and data privacy. ### Source excerpt Check your understanding of installing Ollama, pulling local models, and calling the chat and generate functions from your Python code. ## Perplexity's new agent runs entirely on your GPU -- with one expensive catch DevFeed: [Perplexity's new agent runs entirely on your GPU -- with one expensive catch]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-14T18:21:44Z Content type: news Language: en Sources: [The New Stack]() Topics: [GPU](), [Nvidia](), [Windows](), [Orchestration](), [qwen](), [Cloud](), [browser](), [Ollama]() Tags: [ai-agents](), [ai-infrastructure](), [gpu](), [hardware](), [infrastructure](), [news](), [nvidia](), [ollama](), [orchestration](), [qwen](), [windows]() ### AI overview The article reports that Perplexity's Portable Computer, a local version of its Computer agent, is available in the Perplexity app for Windows on compatible Nvidia GeForce RTX and RTX PRO GPUs. It requires at least 24GB of VRAM and combines local models, orchestration, a browser, tool calling, and a proprietary SPACE sandbox. The article also discusses platform-specific engineering, external service connectors, and the boundary between local and cloud computing. ### Source excerpt Running an LLM on your PC is easy enough, but putting an agent to work there is a different story. The post Perplexity's new agent runs entirely on your GPU -- with one expensive catch appeared first on The New Stack. ## 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. ## EP223: Ollama vs vLLM vs SGLang DevFeed: [EP223: Ollama vs vLLM vs SGLang]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-22T15:31:34Z Content type: comparison Language: en Sources: [ByteByteGo]() Topics: [Ollama](), [sglang](), [vllm](), [Large Language Model](), [API](), [Cache](), [Agent Skills]() Tags: [ai-agents](), [api](), [cache](), [models](), [ollama](), [sglang](), [vllm]() ### AI overview This comparison explains how Ollama, vLLM, and SGLang handle requests for open-weight models. Ollama uses a FIFO queue and pre-quantized GGUF models for local development and prototyping; vLLM uses continuous batching and PagedAttention for high-traffic serving; and SGLang uses prefix-aware scheduling and RadixAttention for agents, multi-turn chats, and structured outputs. ### Source excerpt To use open-weight models on your machine, you have three main options: Ollama, vLLM, and SGLang. But each engine handles requests differently. ## 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 ## Self-Hosted LLM Inference: Serving, Benchmarking and Agent Guardrails DevFeed: [Self-Hosted LLM Inference: Serving, Benchmarking and Agent Guardrails]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-08-07T07:00:00Z Content type: article Language: en Sources: [Philipp Lehmann]() Topics: [Self-hosted](), [Large Language Model](), [Inference](), [model-serving](), [benchmarking](), [LLM evaluation / benchmarking](), [API]() Tags: [agents](), [api](), [benchmarking](), [guardrails](), [inference](), [llm](), [ollama](), [security](), [self-hosted](), [testing]() ### AI overview This article examines self-hosted LLM inference for infrastructure work, focusing on data locality, local-versus-hosted model tradeoffs, repeatable benchmarking on personal tasks, model serving, and guardrails for agents that can propose or apply changes. ### Source excerpt Ollama OpenAI-compatible /v1/chat/completions: a repeatable benchmark on your own tasks and a dry-run plus --approve guardrail for infrastructure agents. ## Running Ollama Locally with Podman on Fedora Linux DevFeed: [Running Ollama Locally with Podman on Fedora Linux]() Original publisher: [Read original article]() Author: Yazan Monshed Published: 2026-08-05T08:00:00Z Content type: article Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Large Language Model](), [Linux](), [Open Source Models & Datasets](), [API](), [cURL](), [GPU]() Tags: [ai-development](), [api](), [curl](), [fedora-project-community](), [gpu](), [large-language-models-llms](), [linux](), [llama](), [nvidia](), [offline](), [ollama](), [open-source](), [podman](), [privacy](), [python](), [terminal](), [using-software]() ### AI overview This article explains how to run Ollama locally in a Podman container on Fedora Linux. It covers installing or verifying Podman, creating persistent storage for model weights, running the Ollama container, downloading Llama 3, optionally enabling Nvidia GPU acceleration, and using Ollama's local REST API with curl. ### Source excerpt Running Large Language Models (LLMs) locally has become increasingly popular for development, privacy, and offline testing. Ollama makes this incredibly straightforward, allowing you to run models like Llama 3 or Mistral directly on your machine. By leveraging Podman on Fedora Linux, you can isolate Ollama inside a container. This approach keeps your host system clean [...] ## Fully Automated AI Inference on AWS, Azure, and Google Cloud with Pulumi DevFeed: [Fully Automated AI Inference on AWS, Azure, and Google Cloud with Pulumi]() Original publisher: [Read original article]() Author: Engin Diri Published: 2026-06-30T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [Ollama](), [Cloud](), [Infrastructure as code](), [model-serving](), [GPU](), [Artificial Intelligence](), [OpenID connect (OIDC)](), [Ubuntu]() Tags: [ai](), [aws](), [azure](), [cloud](), [gcp](), [google-cloud](), [gpu](), [inference](), [infrastructure-as-code](), [model-serving](), [oidc](), [ollama](), [pulumi-esc](), [tutorials](), [typescript](), [ubuntu]() ### AI overview This tutorial shows how to use Pulumi to provision GPU virtual machines on AWS, Azure, and Google Cloud, install drivers, run Ollama, and pull an open-source model without manual setup. It also uses Pulumi ESC with OIDC instead of a long-lived access key and avoids treating model-download completion as an infrastructure resource. ### Source excerpt Putting Ollama on a cloud GPU is something I keep coming back to. A while ago I wrote up running open-source LLMs on an AWS EC2 box with Ollama and Pulumi, and the shape never really changes: a GPU instance, a model server, and a firewall rule in front. Infrastructure as code earned its place by making that kind of setup predictable and repeatable, and AI infrastructure is no exception. A GPU box serving a model is still a VM, a disk, and a firewall rule, and it should be declared like one. Thorsten Hans made exactly that case in his Akamai post, Fully Automated AI Infrastructures with Terraform and Akamai Cloud, which stands up a single GPU instance on Linode, installs the drivers, runs Ollama, and pulls a model, with no manual steps after terraform apply. I liked the shape of it, so this post ports the same idea to Pulumi and runs it across AWS, Azure, and Google Cloud instead of one. The result is one program shape per cloud: a single pulumi up brings up a GPU box that installs its own driver, runs Ollama, and pulls a model with no manual steps, and a single pulumi destroy takes it back down. Along the way it drops the two imperative bits the Terraform version leans on: a static access token sitting in an environment variable, and a null_resource running a shell loop to wait for the model. The first becomes an OIDC login from a Pulumi ESC environment, so no long-lived key lives anywhere. The second turns out not to be a resource at all. What you are building Strip away the per-cloud naming and every version of this is the same three things: a GPU virtual machine, a firewall in front of it, and a cloud-init script that turns a bare Ubuntu box into a running inference server. The model serving runs on Ollama, which exposes an HTTP API on port 11434 and keeps the model resident in GPU memory between requests. flowchart LR Dev([Your machine / curl]) -->|"HTTP :11434"| FW["Firewall / security group (allow 11434, optional 22)"] FW --> VM["GPU VM (Ubuntu 24.04) ## Using local LLMs for agentic coding DevFeed: [Using local LLMs for agentic coding]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-06-04T09:01:34Z Content type: tutorial Language: en Sources: [Alex Ewerlof Notes]() Topics: [coding](), [agentic-coding](), [Large Language Model](), [GitHub Copilot CLI](), [Linux](), [llama.cpp](), [Ollama](), [NVIDIA RTX]() Tags: [agentic-coding](), [agents](), [ai](), [claude](), [coding](), [github](), [linux](), [llama](), [llama-cpp](), [llms](), [local](), [local-llms](), [microsoft](), [nvidia](), [nvidia-rtx](), [ollama](), [quality](), [rocm](), [state]() ### AI overview A practical guide to using local language models for agentic coding. It covers running local models, configuring coding agents such as Copilot and Pi, and evaluating local models against cloud models in terms of cost, privacy, tooling, and performance. ### Source excerpt AI honeymoon pricing is over, but your work is not ## AI, OAuth, And Other Platform APIs In The Core DevFeed: [AI, OAuth, And Other Platform APIs In The Core]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-05-31T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [Artificial Intelligence](), [OAuth](), [OpenID connect (OIDC)](), [WebAuthn](), [Passkeys](), [Framework](), [API](), [Large Language Model](), [Ollama](), [llama.cpp](), [vllm](), [SQLite]() Tags: [agent-skill](), [ai](), [api](), [authentication](), [function-calling](), [llama-cpp](), [llm](), [oauth](), [oidc](), [ollama](), [sqlite](), [streaming](), [vllm]() ### AI overview This follow-up release article describes platform APIs moved into the framework core, including a first-class LLM client and ChatView, OAuth and OIDC authentication, WebAuthn passkeys, WiFi and connectivity APIs, and share-sheet callbacks. It also outlines streaming chat, tool calls, embeddings, image generation, and local-model support through Ollama-compatible endpoints. ### Source excerpt Deeper AI integration in the framework core, modern authentication via OAuth / OIDC and WebAuthn passkeys driven from the system browser, and a few smaller additions alongside. ## 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. ## Technology Short Take 195 DevFeed: [Technology Short Take 195]() Original publisher: [Read original article]() Author: Scott Lowe Published: 2026-05-08T14:00:00Z Content type: article Language: en Sources: [Scott's Weblog]() Topics: [Artificial Intelligence](), [Large Language Model](), [Security](), [API keys](), [Automation](), [Kubernetes](), [networking](), [Ollama](), [Local AI]() Tags: [ai](), [api-keys](), [article](), [aws](), [blog](), [cilium](), [cli](), [cloud](), [cni](), [containers](), [cri-o](), [devops](), [docker](), [go](), [iac](), [k8s](), [kubernetes](), [linux](), [llm](), [networking](), [oci](), [ollama](), [security]() ### AI overview Technology Short Take 195 is a curated roundup of technology articles and posts, with an unplanned emphasis on AI and large language models. It covers API-key storage, AI tools and assistants, AI workflow automation, AI-agent security, Kubernetes security, networking, Ollama and local LLM experiments, Terraform versus OpenTofu, and cloud management. ### Source excerpt Welcome to Technology Short Take #195! It wasn't planned this way, but it seems like this Tech Short Take is heavily slanted toward AI/LLM-related articles and posts. Topics like security concerns around improper storage of API keys, how developers are using AI tools, spyware getting installed with AI assistants, and how AI/LLMs might be creating barriers to entry for new IT profesionals are all on tap this time around. I hope this unintentional focus doesn't prevent you from finding something useful! Networking Ivan Pepelnjak takes readers through the process of generating partial devices configurations with netlab. It's an older blog post, but it checks out--have a look at this walkthrough of Containerlab and Netlab. (Hat tip to Ivan for the link. Also, bonus points if you understood the reference at the start of this paragraph.) Ah, MTU issues...they don't go away if you migrate to Kubernetes. Security Sean Gallagher and Omid Mirzaei from Cisco Talos discuss how threat actors are misusing AI workflow automation. Before this article on vulnerability triage, I'd never heard of "brocards." Kelby Ludwig reminds folks that you don't want long-lived keys. Davi Ottenheimer tackles the claims about risk from Anthropic's Mythos preview. (Hint: the words "missing", "failed", "fluffy bunny", and "FUD" are involved.) William Collins reminds readers that they still need to pay attention to where they're storing sensitive data like API keys. Copy Fail has been making the rounds on lots of articles and blogs. Clément Nussbaumer looks at Copy Fail from the perspective of going from unprivileged Pod to root on a Kubernetes Node. Here's another one on Copy Fail, this time looking at blocking Copy Fail with Tetragon. Security is an arms race, and trying to secure AI agents is no different. The latest round in the race is a Falco project called Prempti. Cloud Computing/Cloud Management It's a good thing I came across this article about why friends don't let friends use Ollama. I had in ## Building a Local AI Task Manager with PydanticAI and Ollama DevFeed: [Building a Local AI Task Manager with PydanticAI and Ollama]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-03-22T14:03:18Z Content type: tutorial Language: en Sources: [Neural Bits]() Topics: [Ollama](), [Local AI](), [Pydantic](), [App](), [Large Language Model](), [Python](), [Code](), [Development]() Tags: [ai](), [code](), [dev](), [examples](), [guide](), [introduction](), [local](), [models](), [ollama](), [practical](), [tools]() ### AI overview A practical tutorial on building a local AI task manager with Ollama and PydanticAI. The application uses local language models and typed agent tools to handle tasks such as adding work, marking tasks complete, and listing overdue items. ### Source excerpt A practical introduction to agent-based architectures using typed models, tools, and runtime context in Pydantic AI. ## Building a Local Voice Dictation Device with Raspberry Pi, Whisper, and Ollama DevFeed: [Building a Local Voice Dictation Device with Raspberry Pi, Whisper, and Ollama]() Original publisher: [Read original article]() Author: Ishan Khanna Published: 2026-03-16T20:55:45Z Content type: tutorial Language: en Sources: [Ishan Khanna]() Topics: [Raspberry Pi](), [Whisper](), [Ollama](), [ASGI](), [Python](), [GPU](), [Windows](), [CircuitPython]() Tags: [ai](), [audio](), [circuitpython](), [fastapi](), [gpu](), [hardware](), [large-language-models](), [llms](), [ollama](), [python](), [raspberry-pi](), [transcription](), [whisper](), [windows]() ### AI overview This tutorial describes a local voice dictation device built with a Raspberry Pi Zero W, Raspberry Pi Pico, and an INMP441 microphone. Audio is sent over Wi-Fi to a Windows PC for Whisper transcription and Ollama text cleanup, then returned through a USB keyboard interface via a KVM switch. The author reports about $40 in hardware costs and under 700 milliseconds of end-to-end latency. ### Source excerpt I spend most of my day talking to AI agents in the terminal. Claude Code, ChatGPT, aider -- you name it. And every time I have to type out a long, detailed prompt explaining what I want refactored, I think: why am I typing this when I could just say ## Building AI Agent Applications: Lessons on Model Size, Context, MCP, and RAG DevFeed: [Building AI Agent Applications: Lessons on Model Size, Context, MCP, and RAG]() Original publisher: [Read original article]() Author: Mai Lubega Published: 2026-02-16T00:00:00Z Content type: tutorial Language: en Sources: [Deliveroo]() Topics: [AI Agent](), [Large Language Model](), [Model Context Protocol](), [Retrieval-Augmented Generation](), [Ollama](), [Go Language]() Tags: [ai](), [ai-agent](), [go](), [llms](), [mcp](), [ollama](), [rag](), [software]() ### AI overview This tutorial shares lessons from an Ardan Labs workshop on building AI agent applications. It covers using smaller local models with Ollama, managing conversation context, generating vector embeddings, building RAG applications, and creating an MCP client and server. ### Source excerpt LLMs, MCPs, RAG. There are lots of acronyms in the AI space, but what do they all mean? Dear reader, despite being a software engineer who works in the machine learning space, I confess there was a time I wasn't really sure. Fortunately, with the financial support of Deliveroo's Women-in-Tech Employee Resource Group, I took the Ardan Labs Building AI-Powered Applications in Go workshop that helped me understand what's really going on behind the chat interface and where software engineering meets LLM-based applications. We went through a series of modules to incrementally build a RAG (Retrieval-Augmented Generation) AI Agent application. I started with generating vector embeddings from text and eventually built an MCP Client & Server able to process image and text input and respond to simple queries, after which I felt I understood the new AI landscape much, much better. For my dearest gentle reader, I'm happy to share these three takeaways from the course: Size Matters Full-size language models are very large and can require several hundred gigabytes of memory to run, certainly much more than is available on a personal laptop. To work around this constraint, we used Ollama -- an open-source project that provides smaller, more size-efficient LLM models - that can be run locally. In the real world, some applications get around this problem by sending data directly to OpenAI/Anthropic's APIs. They don't have to host any models locally, just send and receive data (subject to usage tier and API limits, of course). But applications that need a model to run locally for proprietary reasons will have to manage the infrastructure themselves -- which usually involves a very spicy cloud bill or a server in the corner that doubles as a space heater. Don't Lose the Plot (Manage your Context) Think of Context as the LLM's short-term memory. When we chat with an AI, we don't just send the current question; we send the entire conversation history, system instructions, and any retrieve ## Query Database Using Plain English DevFeed: [Query Database Using Plain English]() Original publisher: [Read original article]() Published: 2026-02-11T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Databases](), [Large Language Model](), [SQL](), [Code](), [DuckDB](), [Ollama](), [context](), [GitHub](), [API]() Tags: [api](), [ardan-labs](), [blog](), [code](), [context](), [database](), [duckdb](), [github](), [go](), [go-programming](), [golang](), [llms](), [ollama](), [programming](), [sql]() ### AI overview This tutorial shows how to build a system that lets users query a relational database using plain English. It uses an LLM to generate SQL, executes the query against a database, and then uses the query results to generate an answer. The example uses the Austin Bike Share dataset, Kronk Model Server, and DuckDB, while noting that other model servers and SQL databases can be used. ### Source excerpt Introduction In this post you'll see how you can create a system that allows users to query a relational database using plain English. This allows users not familiar with SQL or business intelligence systems to get insights from data. Setting Up If you want to follow along, you'll need to clone the code from the GitHub repo. This will download the code, and the database file containing the data (bikes.ddb) Note: The data is from the Austin Bike Share dataset. ## Choose Your Edit Prediction Provider DevFeed: [Choose Your Edit Prediction Provider]() Original publisher: [Read original article]() Author: Max Brunsfeld Published: 2026-02-04T00:00:00Z Content type: release Language: en Sources: [Zed Industries - Blog]() Topics: [Code](), [Ollama](), [Mercury](), [GitHub Copilot](), [API]() Tags: [build](), [code](), [github-copilot](), [inference](), [integration](), [ollama](), [state-management]() ### AI overview Zed introduces support for multiple edit prediction providers, including Zeta, Mercury Coder, Sweep, Ollama, Codestral, and GitHub Copilot's Next Edit Suggestions. A new pluggable provider architecture centralizes core functionality while simplifying provider-specific integrations. ### Source excerpt Zed now supports multiple edit prediction providers: Zeta, Mercury Coder, Sweep, Ollama, and GitHub Copilot Next-Edit. ## Protect your AI workloads from supply chain attacks DevFeed: [Protect your AI workloads from supply chain attacks]() Original publisher: [Read original article]() Published: 2026-01-30T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [Containers](), [chainguard](), [Vulnerabilities](), [AI Strategy](), [Open Source](), [Security](), [PyTorch](), [Tensorflow](), [MLOps]() Tags: [ai](), [ai-containers](), [chainguard](), [chainguard-containers](), [chainguard-for-ai](), [chainguard-libraries](), [containers](), [cudnn](), [cves](), [data-processing](), [deployment](), [kserve](), [legacy](), [libraries](), [mlops](), [nemo](), [ollama](), [python](), [pytorch](), [security](), [security-vulnerabilities](), [supply-chain](), [supply-chain-attacks](), [zero-cve-containers]() ### AI overview The article discusses security and operational challenges in AI/ML workloads, including complex dependencies, bloated artifacts, infrastructure sprawl, and unremediated CVEs. It presents Chainguard Containers' minimal images for AI workloads as a way to reduce attack surface, storage needs, and deployment overhead, and cites a 50 MB gpu-operator image compared with a 170 MB upstream equivalent. ### Source excerpt Chainguard secures AI adoption with minimal, zero-CVE containers and source-built libraries that prevent supply chain malware while keeping developers fast. ## Local AI Setup Guide for Android Developers DevFeed: [Local AI Setup Guide for Android Developers]() Original publisher: [Read original article]() Author: blundell Published: 2026-01-18T14:03:28Z Content type: tutorial Language: en Sources: [Blundell]() Topics: [android-development](), [Ollama](), [gemma](), [Large Language Model]() Tags: [android](), [android-development](), [beginner](), [beginner-reference](), [gemma](), [how-to](), [intermediate](), [llm](), [local](), [offline](), [ollama](), [reference]() ### AI overview A Day 1 setup guide for Android developers that recommends AI-focused browser resources and Ollama for running open-source models locally. It discusses Gemma 3, including its multimodal capabilities, 128k-token context window, and hardware considerations. ### Source excerpt Forget just installing Android Studio and calling it a day. To operate at scale, you need a local environment that augments your engineering intuition with LLM-powered efficiency. Here is your Day 1 guide to setting up your machine for success. The post Zero to AI: An Android Developer's Vital Local Setup first appeared on Blundell. ## Making Java a first-class AI citizen with Langchain4j DevFeed: [Making Java a first-class AI citizen with Langchain4j]() Original publisher: [Read original article]() Author: deors Published: 2025-12-18T03:03:33Z Content type: tutorial Language: en Sources: [Java Advent Calendar]() Topics: [Java](), [AI Development](), [Large Language Model](), [API](), [Resilience](), [Scalability](), [observability](), [OpenAI](), [Google AI](), [Ollama]() Tags: [2025](), [ai](), [ai-models](), [api](), [java](), [langchain4j](), [ollama](), [openai](), [vertex]() ### AI overview This article introduces LangChain4j as a framework-agnostic Java library for building and running generative AI solutions. It explains its API, integration with Spring, Jakarta, Quarkus, Micronaut, and standalone applications, and support for cloud and local models, with examples based on version 0.36.2. ### Source excerpt Introduction When the first technical solutions based on Artificial Intelligence began to be created, Python was the language and runtime platform of choice. It was not a total surprise as Python was the choice of the great majority of data scientists to analyse data, perform experiments and create AI models, so it was kind of [...] The post Making Java a first-class AI citizen with Langchain4j appeared first on JVM Advent. ## Watch the recordings from my Python + AI series DevFeed: [Watch the recordings from my Python + AI series]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2025-10-31T14:22:00Z Content type: article Language: en Sources: [Pamela Fox]() Topics: [Python](), [Large Language Model](), [Embeddings](), [Retrieval Augmented Generation (RAG)](), [Azure OpenAI](), [Model Context Protocol](), [LangChain](), [SDK](), [OpenAI](), [Ollama]() Tags: [ai](), [embeddings](), [generative-ai](), [langchain](), [large-language-models-llms](), [mcp](), [ollama](), [openai](), [python](), [rag]() ### AI overview A blog article provides recordings and materials from a nine-part Python and generative AI series. It covers language models, embeddings, retrieval-augmented generation, evaluation and safety, AI agents, Model Context Protocol, and related Python examples using services including GitHub Models, Ollama, Azure OpenAI, and OpenAI models. ### Source excerpt My colleague and I just wrapped up a live series on Python + AI, a nine-part journey diving deep into how to use generative AI models from Python. I gave the english streams while my colleague Gwen gave the spanish streams (and I hung out in her live chat, working on my technical spanish!). The series introduced multiple types of models, including LLMs, embedding models, and vision models. We dug into popular techniques like RAG, tool calling, and structured outputs. We assessed AI quality and safety using automated evaluations and red-teaming. Finally, we developed AI agents using popular Python agents frameworks and explored the new Model Context Protocol (MCP). To apply the concepts, we put together code examples that run for free thanks to GitHub Models, a service that provides free models to every GitHub account holder for experimentation and education. The examples are also compatible with local models (via Ollama), Azure OpenAI, or OpenAI.com models. Even if you missed the live series, you can still access all the material using the links below! If you're an instructor, feel free to use the slides and code examples in your own classes. Python + AI: Large Language Models 📺 Watch recording In this session, we explore Large Language Models (LLMs), the models that power ChatGPT and GitHub Copilot. We use Python to interact with LLMs using popular packages like the OpenAI SDK and LangChain. We experiment with prompt engineering and few-shot examples to improve outputs. We also demonstrate how to build a full-stack app powered by LLMs and explain the importance of concurrency and streaming for user-facing AI apps. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vector embeddings 📺 Watch recording In our second session, we dive into a different type of model: the vector embedding model. A vector embedding is a way to encode text or images as an array of floating-point numbers. Vector embeddings enable similarity search across ## The Complete Guide to Ollama: Local LLM Inference Made Simple DevFeed: [The Complete Guide to Ollama: Local LLM Inference Made Simple]() Original publisher: [Read original article]() Author: The AI Merge Published: 2025-10-25T13:02:36Z Content type: tutorial Language: en Sources: [Neural Bits]() Topics: [Ollama](), [Inference](), [Large Language Model](), [Command-line interface](), [Docker](), [Python](), [API]() Tags: [api](), [cli](), [docker](), [inference](), [llm](), [local](), [local-llm](), [ollama](), [python]() ### AI overview A practical guide to Ollama for local large language model inference. It covers Ollama's role in the LLM ecosystem, its architecture, model management and customization, Hugging Face model integration, an OpenAI-compatible API, Python usage, and Docker deployment. ### Source excerpt A deep dive into Ollama's architecture, going through model management, OpenAI API schema and local inference integrations with CLI, Docker and Python. ## TL;DR for Session Recording. AI Session Summaries for SSH, Kubernetes exec, and Postgres DevFeed: [TL;DR for Session Recording. AI Session Summaries for SSH, Kubernetes exec, and Postgres]() Original publisher: [Read original article]() Author: ben@goteleport.com (Ben Arent) Published: 2025-09-24T00:00:00Z Content type: release Language: en Sources: [Teleport]() Topics: [Artificial Intelligence](), [ssh](), [Kubernetes](), [Security](), [audit](), [JSON](), [litellm](), [Ollama](), [OpenAI](), [Self-hosted]() Tags: [ai](), [audit](), [inference](), [json](), [kubernetes](), [litellm](), [ollama](), [openai](), [release](), [security](), [self-hosted](), [ssh]() ### AI overview Teleport introduces AI Session Summaries for interactive SSH, Kubernetes exec, and Postgres sessions. The feature generates concise, readable summaries with operational outcomes, security-relevant activity, and risk or impact levels, and stores summaries as JSON alongside session recordings. ### Source excerpt Teleport's AI-powered session summaries transform hours of recordings into readable digests, making security reviews and audits significantly more efficient. [Next page]()