GPT-5 Can't Solve This Puzzle. An AI 100x Smaller Can.

You heard that right. A tiny 27-million parameter AI model just beat the giants on complex reasoning tests.

To put that in perspective, that’s 4 times smaller than the original GPT-1.

A startup in Singapore built a new kind of AI that thinks more like a human brain. It has two parts: a "slow" strategic thinker and a "fast" tactical worker that team up to solve problems.

And it's crushing it.

It solves Sudoku puzzles that stump GPT-5 and finds its way through giant mazes that other top models can't handle.

This isn’t just a cool science project. It proves a smarter design can beat pure size.

For us, this is huge. It means the powerful reasoning that used to cost $20 a task could become almost free. This opens the door to automations we could only dream of before.

Here are a few ideas for n8n businesses you could build with this kind of cheap, powerful reasoning:

- Hyper-Efficient Logistics Solver

Offer a service to local delivery businesses that are still planning routes by hand. This AI’s maze-solving ability is perfect for finding the fastest, cheapest routes.

Prompt: Write a step-by-step plan for an n8n workflow that helps a local delivery business optimize its routes. The workflow should read new delivery addresses from a Google Sheet, use a reasoning AI to calculate the most efficient multi-stop route, and then send the ordered route to the driver's phone via SMS.

- Automated System Auditor

Many businesses have complex software setups that fail in hard-to-spot ways. Use a reasoning AI to find the problems automatically.

Prompt: Design an n8n workflow that acts as a 'system auditor'. It should retrieve the configuration and logs from a client's software system, use a reasoning AI to check for inconsistencies against a predefined set of rules, and then generate a clear, actionable report for the client in a Google Doc.

- Advanced Resource Scheduler

Help construction companies, event planners, or consulting firms assign the right people to the right jobs instantly, avoiding costly downtime.

Prompt: Write a plan for an n8n automation that manages daily resource allocation for a small team. The workflow needs to pull tasks, deadlines, and team member availability from Airtable, use a reasoning AI to create the most efficient daily work schedule, and then populate a shared Google Calendar with the assignments.

What other "unsolvable" problems could we tackle with this kind of AI?

Source: https://github.com/sapientinc/HRM?tab=readme-ov-file

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