01What Ornith-1.0 Is and How It Works

Ornith-1.0 is a new, free, local AI coding model that you can run on your own computer. It's an open-source model from a company called Deep Reinforce, and it's designed to write code and solve tasks. The model is self-learning, meaning it improves itself over time without needing a fixed human-written recipe.

Here's how it works: when you give Ornith-1.0 a task, it first writes a plan for solving it, then follows that plan to write the code. After the result is graded, the score teaches both the code and the plan, so both improve. This process, called self-scaffolding, creates a positive feedback loop.

The model gets better at writing plans and code, which is why a 9B model can outperform much larger models.

02Main Features of Ornith-1.0

Ornith-1.0 comes with several standout features that make it appealing for local AI use:

  • Free and open-source: You can download and use it without any cost.
  • Runs locally: Works on your own hardware, so your data stays private and you don't need an internet connection.
  • Self-improving: It uses reinforcement learning to learn from each task and improve for the next.
  • Agentic coding: Designed specifically for coding tasks, not creative writing.
  • Works with agent systems: You can plug it into tools like Hermes and your agent operating system.
  • High benchmark scores: It outperforms models like Qwen 3.7 Max and GM4-31B on certain tests.

In the episode, Julian points out that Ornith-1.0 is based on the Jemma 4 and Qwen 3.5 setups, which gives it a solid foundation.

03How to Set Up Ornith-1.0

Setting up Ornith-1.0 is straightforward, as Julian demonstrates. Here are the steps to get it running on your machine:

  1. Install Olama on your computer. Make sure you have it open.
  2. Download Ornith-1.0 by using one simple prompt command in Olama.
  3. Once downloaded, you can plug it into whatever application you want, like Hermes or your agent OS.
  4. If using Hermes, create a separate profile for Ornith-1.0 and switch to it.
  5. Test it with some tasks to see how it performs.

Julian notes that it's easy to set up and ran well on his system, so you don't need advanced technical skills.

04How to Use Ornith-1.0 for Building

Once Ornith-1.0 is installed, you can use it for a variety of coding tasks. In the episode, Julian shows examples of creations made with Ornith-1.0, including apps, games, tools, and visual stuff like a sleek landing page.

You can switch between models anytime within your agent operating system. In Hermes, you can set up a separate profile and run Ornith-1.0 agentically. This means you can combine a self-learning agent (Hermes) with a self-learning model (Ornith-1.0) for more powerful results.

There are also systems built for Ornith-1.0:

  • Local Hermes engine: An offline agent you can talk to, driven by Ornith-1.0.
  • Agent Kanban: Use teams of local models like Ornith-1.0 working together inside a Kanban board.
  • Local chat system: Use local models, see what they've built, save workspaces, and preview results.

05Benchmark Results and Comparisons

The episode shows several benchmark comparisons for Ornith-1.0. It outperforms GM4-31B on benchmarks and even beats Qwen 3.7 Max on some tests. Specifically, on TerminalBench 2.1 and SWE Bench Verified, Ornith-1.0 shows strong results.

Julian compares Ornith-1.0 to other local models like Gemma 12B, which he found too slow, and Quifos, which broke sometimes. Ornith-1.0 seems more reliable. He also notes that Ornith-1.0 is designed for agentic coding, while Quifos is more for creative writing and reasoning.

Julian says he doesn't pay much attention to benchmarks, but the results are impressive for a 9B model. He encourages listeners to test it themselves.

06Tips, Limits, and Mistakes to Avoid

Based on the episode, here are some tips and limitations to keep in mind when using Ornith-1.0:

  • It's not for creative writing: Ornith-1.0 is made for coding, so don't expect it to excel at creative tasks. Use models like Quifos for that.
  • Benchmarks aren't everything: Julian advises testing models yourself rather than relying solely on benchmark scores.
  • It may not beat frontier models: For agentic tasks, it's great, but it won't perform as well as top-tier models on all tasks.
  • Context window differences: Ornith-1.0 has a different context window compared to other models, which could affect performance on long tasks.

Mistakes to avoid: don't expect it to be a general-purpose model. Also, make sure Olama is installed correctly and you're using the right command to download.

07Who Ornith-1.0 Is For and Why It Matters

Ornith-1.0 is for anyone who wants to run AI locally without paying for cloud services. It's especially useful for developers and tinkerers who care about privacy, speed, and cost. The benefits are clear: it's free, private, fast, and local AI is getting better all the time.

In the episode, Julian shows how you can build useful things with it, and he provides a local leaderboard to compare models fairly. Ornith-1.0 matters because it makes powerful AI accessible to everyone, even on a laptop.

If you want access to the full agent operating system and systems for using Ornith-1.0, you can join the AI Profit Boardroom community at theaiprofitboard.com. There, you get daily updates, training, and support.