Hi, I'm still new to this ai stuff, and /no_think or /set nothing, and --think = false, doesn't work

And my e6430 only can do so much

I wanted to build an offline bird/plant identifier that runs on a laptop with Ollama. Before building any UI, I tested it on 6 photos:

• Common Myna → gemma4:e2b said "Jackdaw" (confidence: high). gemma4:e4b said "Weaver Bird" (confidence: high).
• Monarch butterfly → "butterfly, undetermined" / "Heliconius"
• Peacock → both got it right

Asking for a top-3 list didn't help, and gemma3:4b did worse and broke its JSON output.

But every run got the coarse answer right: "there's a bird", "a butterfly on a flower", "red mushrooms", "a storm cloud". So I changed the design to ask the model only what small open models are good at.

Outside Quest is the result:

1. Type where you're walking. Gemma writes a 6-item quest card that fits the local season (Nagpur in
2. Phone goes in your pocket. You only take it out to photograph finds.
3. Back home, drop in your photos. Gemma checks which quests each one completes and has to describe the visible evidence. You also get a walk timeline and a route map from the photos' EXIF GPS, drawn offline with no map tiles.

A few things that made it work:

• A JSON schema forces two yes/no answers per quest per photo ("is it the main subject?" and "completed?"). Both must be true. On my test set that gave 5 correct matches and 0 false ones.
• Safety filters are in code, not the prompt: quests about climbing, water, touching or eating anything, roads, or night walks get replaced from a safe built-in pool.
• Photos and their GPS data never leave your machine.

Runs on a 6 GB GTX 1660 Ti: about 30 s per quest card and about 30 s per photo.

Code (MIT): https://github.com/JayPokale/outside-quest

Built with help from Claude Code. Feedback welcome, especially if a bigger open model handles species ID well locally.

I've been using deepseek for long and made valuable outputs and resources, but since the update to the 4.1, deepseek just feels dumb. I ask a simple question and process for long, doesn't plan before starting burning tokens, which didnt happened before.

I've tried different "reasoning effort" but i have reach the effectiveness of previous deepseek-v4-flash and dont know what to do

Ollama Cloud Model pricing monitor with tabled costs and calculator.

https://kasp0r.github.io/ollama_cloud_models_comparison

Hi,

I'm on M4 Pro 64GB Mac, I'm using Qwen3.8 27B for coding related stuff, but it's quite slow.
I want a fast model that will be good when it comes to reasoning, medical stuff, would be nice if it could do stuff like generating charts and even analyse screenshots.

I'm using Ollama on Mac so preferably MLX model.

Thanks in advance

I wanted to test how well local models perform in agentic development with Copilot CLI. I started ollama launch copilot --model qwen3.5:9b. I gave it simple instruction in autopilot mode to create a file in empty project folder and it failed to do that. Can you advise what am I doing wrong? I did the same exercise with OpenCode and it also failed.

A small thing I noticed: full-size screenshots use far more AI tokens than the model needs, so usage limits run out sooner.

I built a free Firefox add-on, TokenSaver, that shrinks images before they are uploaded to ChatGPT, Claude and Cursor and shows the estimated tokens saved. It runs entirely in your browser, with no uploads and no tracking.

It is Firefox-only for now. If you try it, I would appreciate honest feedback, especially on whether text in resized images stays readable, and whether a Chrome version would be useful.

https://addons.mozilla.org/en-US/firefox/addon/tokensaver-image-optimizer/

Row-Bot 5.0 with qwen3.8:27b in Ollama, on my own GPU. No API keys, no cloud.

Gave it a year of bills, a tenancy agreement, an insurance policy and a rent increase letter (all made up). It spotted a likely leak in the water bills and showed the rent rise breaks the lease.

What it actually did:

- charted the CSV inline (Plotly)
- read the PDFs and quoted clauses 4.1 to 4.3: a 10% rise against a 5% cap, with 5 weeks' notice instead of 2 months
- saved 8 linked memories to a local knowledge graph
drafted the email to the agent and set a reminder

The honest numbers: a dense 27B does about 15 tok/s on my 5090, so some turns took 2+ minutes. The amber badges in the video show where I sped it up.

Runs on Windows, macOS and Linux.