# Ollama Ollama is a platform for running and building with open models locally or in the cloud, with a REST API and Python and JavaScript libraries. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## 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. ## 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 ## 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. ## 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. ## 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. ## Redpanda open-sources top 16 AI connectors DevFeed: [Redpanda open-sources top 16 AI connectors]() Original publisher: [Read original article]() Author: Mike Broberg Published: 2025-08-18T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Redpanda-Connect](), [Open Source](), [Artificial Intelligence](), [stream-processing](), [OpenAI](), [Streaming](), [Amazon Bedrock](), [Embeddings](), [Retrieval Augmented Generation (RAG)](), [cohere](), [Ollama]() Tags: [ai](), [ai-connector-platforms](), [ai-connectors-for-commercial-products](), [ai-connectors-open-source](), [ai-data-streaming](), [ai-integration-tools](), [announcement](), [bedrock](), [cloud-native-ai-services](), [cohere](), [embeddable-ai-capabilities](), [embeddings](), [ollama](), [open-source](), [open-source-ai-for-business](), [openai](), [openai-connectors](), [rag](), [real-time-ai-streaming](), [redpanda-ai-connectors](), [redpanda-connect](), [stream-processing](), [streaming](), [streaming-ai-processors]() ### AI overview Redpanda announces the open-source release of its top AI connectors under the Apache 2.0 license. The connectors integrate Redpanda Connect with destinations and models including OpenAI, Cohere, Amazon Bedrock, Ollama, and Google Cloud Vertex AI, supporting streaming pipelines and use cases such as generation, summarization, classification, translation, and text embeddings for RAG. ### Source excerpt Redpanda open-sources top AI connectors to the most used destinations, including OpenAI, Cohere, Bedrock, Ollama, and Vertex AI. Learn more. ## Welcome GPT OSS, the new open-source model family from OpenAI! DevFeed: [Welcome GPT OSS, the new open-source model family from OpenAI!]() Original publisher: [Read original article]() Author: Vaibhav Srivastav; Pedro Cuenca; Lewis Tunstall; Clem 🤗; Matthew Carrigan; Clémentine Fourrier; Célina Hanouti; Lucain Pouget; Marc Sun; Simon Pagezy Published: 2025-08-05T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [gpt-oss](), [Open Source](), [OpenAI](), [Artificial Intelligence](), [inference-providers](), [llama.cpp](), [moe](), [Ollama](), [Python](), [quantization](), [Transformers](), [vllm]() Tags: [api](), [artificial-intelligence](), [community](), [gpt](), [gpt-oss](), [inference-providers](), [javascript](), [llama-cpp](), [llm](), [moe](), [ollama](), [open-source](), [openai](), [python](), [quantization](), [transformers](), [vllm]() ### AI overview Hugging Face welcomes OpenAI's gpt-oss open-source model family. The article describes the models' Apache 2.0 licensing, local deployment options, reasoning and tool-use capabilities, MoE architecture, quantization, supported inference implementations, and access through Inference Providers and the Responses API. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Gemma 3n fully available in the open-source ecosystem! DevFeed: [Gemma 3n fully available in the open-source ecosystem!]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Pedro Cuenca; Sergio Paniego; Vaibhav Srivastav; Christopher Fleetwood; Joshua; Steven Zheng; Kashif Rasul Published: 2025-06-26T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gemma](), [Open Source](), [AI Development](), [llama.cpp](), [MLX](), [Ollama](), [transformers.js](), [GPU](), [Hardware](), [timm](), [asr](), [Embeddings]() Tags: [ai](), [artificial-intelligence](), [audio](), [community](), [embeddings](), [gemma](), [gpu](), [hardware](), [implementation](), [llama-cpp](), [llm](), [mlx](), [multimodal](), [ollama](), [open-source](), [release](), [research](), [speech](), [timm](), [transformers](), [transformers-js](), [translation](), [vision](), [vlm]() ### AI overview This article announces the availability of Gemma 3n in major open-source libraries and presents practical usage and fine-tuning examples. It describes the model variants, memory-efficient hardware requirements, multimodal audio and vision encoders, speech-to-text and translation capabilities, and architectural features including MatFormer and Per-Layer Embeddings. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Inner Loop Agents DevFeed: [Inner Loop Agents]() Original publisher: [Read original article]() Published: 2025-04-19T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [Large Language Model](), [Tool](), [Parsing](), [Ollama](), [vllm](), [JSON](), [Reinforcement learning](), [XML]() Tags: [agents](), [anthropic](), [api](), [json](), [llm](), [ollama](), [openai](), [parsing](), [reinforcement-learning](), [tools](), [vllm](), [xml]() ### AI overview This article explains "inner loop agents," a design in which an LLM can parse and execute tool calls within the model-serving loop instead of returning control to a client. It discusses how software such as Ollama and vLLM handles model-specific tool-call formats and suggests that concurrent tool use during generation is a key motivation. ### Source excerpt What if an LLM could use tools directly? In this post I discuss a potentially divergent view of agents, where agents are less like systems and more like LLMs specially trained to solve problems with a specific set of tools. ## Not Everything is an Agent DevFeed: [Not Everything is an Agent]() Original publisher: [Read original article]() Published: 2025-04-01T06:47:17Z Content type: opinion Language: en Sources: [Ariya Hidayat]() Topics: [Large Language Model](), [Artificial Intelligence](), [LangChain](), [Ollama](), [coding assistant](), [anthropic]() Tags: [agent](), [agentic](), [ai](), [anthropic](), [code](), [coding-assistant](), [langchain](), [llm](), [ollama](), [routing]() ### AI overview This opinion argues that not every LLM-based program should be called an agent. It distinguishes simple LLM calls, retrieval, memory, chaining, and routing from systems handling complex real-world tasks, and recommends relying on successful evaluations rather than anecdotes or hype. ### Source excerpt "Agent" is likely going to be the word that will cause existential dread to true LLM enthusiasts. ## Using Ollama and Kotlin to migrate multiple files into a new library DevFeed: [Using Ollama and Kotlin to migrate multiple files into a new library]() Original publisher: [Read original article]() Author: Leonidas Partsas Published: 2025-03-09T16:26:35Z Content type: tutorial Language: en Sources: [le0nidas]() Topics: [Ollama](), [Kotlin](), [migration](), [Large Language Model](), [Library](), [API]() Tags: [ai](), [api](), [kotlin](), [llm](), [migration](), [okhttp](), [ollama](), [til]() ### AI overview The article describes using Ollama with Kotlin to migrate a project from LoganSquare to kotlinx.serialization across multiple files. It outlines a workflow that loads file contents, sends them with prompts to a local LLM, and writes the results back, using OkHttp and kotlinx.serialization to communicate with Ollama. ### Source excerpt At work, there is a need to migrate our project from using LoganSquare to kotlinx.serialization. Part of the work involves replacing the annotations the first library is using with the ones from the second. Unfortunately some cases are not as simple as replacing foo with boo. For example, a property must be annotated with @JsonField(name ... Continue reading Using Ollama and Kotlin to migrate multiple files into a new library -> ## Run Large Language Models Locally on a Mac with Ollama DevFeed: [Run Large Language Models Locally on a Mac with Ollama]() Original publisher: [Read original article]() Author: Mattt (mattt@nshipster.com) Published: 2025-02-14T08:00:00Z Content type: tutorial Language: en Sources: [NSHipster]() Topics: [Ollama](), [Large Language Model](), [llama.cpp](), [llama3](), [Containers](), [Homebrew]() Tags: [artificial-intelligence](), [containers](), [homebrew](), [inference](), [latency](), [llama-cpp](), [llama3](), [llms](), [miscellaneous](), [oci](), [ollama]() ### AI overview This tutorial explains how to use Ollama to download, run, and manage large language models locally on a Mac. It covers installation, llama3.2, llama.cpp, Modelfiles, OCI-based model distribution, and the practical benefits of local inference, including privacy, cost, latency, control, and reliability. ### Source excerpt While we wait for Apple Intelligence to arrive on our devices, something remarkable is already running on our Macs. Think of it as a locavore approach to artificial intelligence: homegrown, sustainable, and available year-round. ## How to Try DeepSeek-R1 for Coding in Zed DevFeed: [How to Try DeepSeek-R1 for Coding in Zed]() Original publisher: [Read original article]() Author: Richard Feldman Published: 2025-01-27T00:00:00Z Content type: tutorial Language: en Sources: [Zed Industries - Blog]() Topics: [deepseek](), [coding](), [Large Language Model](), [Ollama](), [Open Source](), [Tutorial](), [API keys]() Tags: [ai](), [coding](), [deepseek](), [getting-started](), [large-language-model](), [ollama](), [open-source]() ### AI overview This tutorial explains how to use DeepSeek-R1 for coding in the Zed code editor through the DeepSeek API or locally with Ollama. It covers installing Zed, adding an API key, selecting a model, and configuring locally hosted Ollama models. ### Source excerpt How to try DeepSeek-R1 for coding via the Zed code editor's out-the-box support. ## Local RAG with Ollama, Mistral, and Turso DevFeed: [Local RAG with Ollama, Mistral, and Turso]() Original publisher: [Read original article]() Author: Jamie Barton Published: 2025-01-08T00:00:00Z Content type: tutorial Language: en Sources: [Turso Blog]() Topics: [Retrieval-Augmented Generation](), [Ollama](), [Turso](), [SQLite](), [Embeddings](), [libSQL](), [Local-First](), [Chat Bot](), [pdf]() Tags: [build](), [cloud](), [data](), [embeddings](), [llm](), [local](), [local-first](), [mistral](), [offline](), [ollama](), [on-device](), [open-source](), [rag](), [sql](), [sqlite](), [text-generation](), [turso](), [vector]() ### AI overview A tutorial for building a fully local and offline Retrieval-Augmented Generation pipeline using Ollama, Mistral, and Turso. It stores documents and vector embeddings in a SQLite file, generates embeddings locally, and retrieves relevant content without cloud calls or network access. ### Source excerpt Build a fully local RAG pipeline with Ollama, Mistral, and Turso. Store data and vector embeddings in one SQLite file, with no cloud calls and no network. ## Entrapix: You Should Have A ConfusedAgentError DevFeed: [Entrapix: You Should Have A ConfusedAgentError]() Original publisher: [Read original article]() Published: 2024-12-08T00:00:00Z Content type: article Language: en Sources: [Tim Kellogg]() Topics: [Ollama](), [Large Language Model](), [systems](), [API]() Tags: [agent](), [api](), [llm](), [ollama](), [rag]() ### AI overview The article introduces Entrapix, a fork of Ollama that detects high entropy and varentropy in an LLM and exits through the API when the model appears uncertain. It contrasts this approach with entropix's dynamic sampling and discusses handling confused agents with clarification or retrieval. ### Source excerpt I just released entrapix, which is a fork of Ollama that raises a ConfusedAgentError, so to speak, when the LLM becomes unsure of itself. [Next page]()