Ollama Reviews and Details

Social recommendations and mentions

• LocalReceipts AI: Private Receipt-to-CSV Automation for Indian Freelancers

  Requires Python 3.10+ and a recent local Ollama installation. Model memory requirements vary substantially; choose one that fits your machine. The default llama3.2-vision can be slow without sufficient GPU memory. minicpm-v and llava are alternatives. - Source: dev.to / about 1 hour ago

• LeaseCheck: a lease reader that runs on your laptop, with rule updates you can verify

  To run it yourself you need Python 3.10 or newer, plus Ollama with gemma3:4b pulled. On Windows PowerShell:. - Source: dev.to / about 1 hour ago

• MedPort: a private medical portfolio that reads your lab reports on your own laptop

  Pip install -r requirements.txt # 1. Install Ollama (https://ollama.com) and pull a small open model Ollama pull qwen2.5:3b. - Source: dev.to / about 1 hour ago

• My dad's scooter naps 9 hours a day. So I built a side hustle app to rent it out, and a local AI that settles "that scratch was already there."

  Listings, bookings and chat work for anyone, straight away. The two AI features run on your machine, so they need Ollama with gemma3:4b (the AI setup tab walks you through it). - Source: dev.to / about 2 hours ago

• HisabMate: A Local-First, AI-Assisted Digital Bahi-Khata for Indian Microbusinesses

  Optional: Ollama for natural-language transaction Proposals. - Source: dev.to / about 2 hours ago

• Listing Lens: product photos in, marketplace listing out, all on your laptop

  # 1. Install Ollama from https://ollama.com, then: Ollama pull gemma4:e4b # 2. Install and start the app Pip install -r requirements.txt Python app.py. - Source: dev.to / about 2 hours ago

• OffMarket Writer: A Local AI That Turns Property Photos Into Listings

  Built with Gemma 3 (open-weight) running through Ollama and a Streamlit interface. - Source: dev.to / about 4 hours ago

• Rewatch: finds the patterns in your journal with Gemma 4, fully local

  Requirements: Java 17+, Ollama running, and the model pulled:. - Source: dev.to / about 4 hours ago

• SellCraft AI: a listing writer that runs on the seller's laptop, not my server

  Gemma reads the photos. The app talks to Ollama on 127.0.0.1:11434 and runs Gemma's vision model. Seeing the photo is what makes or breaks the output: it has to recognize speckled stoneware with a chunky handle, because "handmade ceramic mug" is the listing every other seller already has. Any vision model you've pulled shows up in a dropdown, so a slower laptop can swap down. - Source: dev.to / about 4 hours ago

• Living devlopment workspace for agents: Nabi Station

  Open-source AI: Gemma, Google's open-weight model, served locally by Ollama. The default model is gemma4:12b, and any other installed Gemma is used if that one is missing. - Source: dev.to / about 9 hours ago

• ReceiptPilot: a local-first AI receipt assistant for freelancers

  To run the application locally, install Ollama, ensure Docker is installed, and run:. - Source: dev.to / about 9 hours ago

• khissBkr Helping you sort your books locally.

  Local Inference (Free Tier): Uses Llama 3.2 1B via Ollama for completely offline, private tagging. The model runs directly on the user's laptop — no internet required, no API costs. - Source: dev.to / about 12 hours ago

• AI-RAG-Bot — chat with your documents. 100% local, 100% private, $0 to run

  Local inference: Ollama serves Google's open-weight gemma3:1b — the smallest Gemma variant, about 1 GB, so it runs on modest laptops with no GPU. RAG keeps prompts short and grounded in retrieved chunks, which is exactly why a 1B model is enough here: it answers from the provided context and says "information not found" when the answer isn't in the documents. - Source: dev.to / about 13 hours ago

• MenuMind: From a restaurant's PDF menu to a bilingual QR menu with Gemma 3

  The model: Gemma 3 4B (gemma3:4b), an open-weight model from Google, running locally through Ollama. I picked it because it's multimodal, so it can look at a menu page, and small enough to run on my laptop. The same model does both jobs in MenuMind: reading the menu and translating it. - Source: dev.to / about 17 hours ago

• I Built a Receipt Sorter That Never Sends Your Client Data Anywhere

  Install Ollama and pull a vision model: Ollama pull gemma3:4b. - Source: dev.to / about 15 hours ago

• Simmer: Snap Your Fridge, Get 3 Recipes

  The model runs locally through Ollama (Gemma 3 4B). A fridge photo is not sent to a paid vision API, and that is why the yearly price can be small. - Source: dev.to / about 15 hours ago

• Holdfast: a webhook safety net where Gemma explains what broke and code decides what happens

  Open-weight model: Gemma 3 4B, served by Ollama in a private service next to the app. Stack: Go standard library, SQLite (pure-Go driver), server-rendered HTML, a little vanilla JS. Hosting: a single Render Blueprint (more below). - Source: dev.to / about 19 hours ago

• Inkline — notes to diagrams, with Gemma, on your machine

  Locally, the Next.js server talks to Ollama at http://127.0.0.1:11434, model gemma4. Cloud requests go to https://ollama.com with OLLAMA_API_KEY, usually gemma4:31b. The desk sends provider: "private" or provider: "cloud" on each draw, and the server resolves that to a base URL. A private request never attaches the cloud key, and the key is not in the client bundle or the health check. The same client speaks... - Source: dev.to / about 21 hours ago

• Quotely

  Quotely is a simple CLI tool that generates professional freelance quotes using a local open-weight model via Ollama. All client data stays on your machine - no third-party APIs, no data leakage, and zero ongoing costs. - Source: dev.to / about 19 hours ago

• Receipt Sorter: a freelancer's tax-season tool that never leaves the laptop

  Model: Qwen2.5 3B, an open-weight model, running locally through Ollama. - Source: dev.to / about 20 hours ago

• The $0-per-report feature: a patient-friendly radiology explainer that runs on the clinic's own laptop

  Model: Gemma 3 4B (open weights), served locally by Ollama on an RTX 4060 Laptop (8 GB VRAM). One-time ~3 GB pull; after that it runs offline. Generation takes ~30 s per report on this laptop. - Source: dev.to / about 21 hours ago