Uploaded February 2026 | Updated September 2026, 2 weeks ago
I built a code review app powered by @ArceeAI's new Trinity Large model and deployed it to a @HuggingFace Space.
⭐️⭐️⭐️ More content on Substack at julsimon.substack.com ⭐️⭐️⭐️
In this video, I walk through a live demo — paste a GitHub PR URL, hit review, and watch it catch real issues: security flaws, logic bugs, missing edge cases. It's fast, free to try, and the 512K context window lets it process entire files without chunking.
Code Review App (HF Space): huggingface.co/spaces/juliensimon/trinity-code-reviewer
Trinity Large is a 400B parameter Mixture-of-Experts model trained in 33 days for $20M — a fraction of what frontier labs spend. It has 256 experts but activates only 4 per token, so only 13B active parameters do the work. That translates to 2-3x faster inference than anything in its weight class, which you can feel in the demo when reviews come back in seconds.
In this video:
- 🔴 Live demo: code review app catching real issues on real PRs
- 🏗️ How I built the app — stack, prompting strategy, handling large diffs
- 🧠 Architecture deep dive: 256 experts, 4 active, extreme sparsity
- 📦 Why three checkpoints exist: Preview, Base, and TrueBase
- ⚡ Why MoE makes this practical to actually deploy and run at scale
Try the app yourself and let me know what it catches in your code.
Hugging Face model page: huggingface.co/arcee-ai/Trinity-Large-Preview
OpenRouter model page: openrouter.ai/arcee-ai/trinity-large-preview
Arcee AI blog post: arcee.ai/blog/trinity-large
Arcee AI technical report: github.com/arcee-ai/trinity-large-tech-report
VentureBeat article: venturebeat.com/technology/arcees-u-s-made-open-source-trinity-large-and-10t-checkpoint-offer-rare-look
I built a code review app powered by @ArceeAI's new Trinity Large model and deployed it to a @HuggingFace Space.
⭐️⭐️⭐️ More content on Substack at julsimon.substack.com ⭐️⭐️⭐️
In this video, I walk through a live demo — paste a GitHub PR URL, hit review, and watch it catch real issues: security flaws, logic bugs, missing edge cases. It's fast, free to try, and the 512K context window lets it process entire files without chunking.
Code Review App (HF Space): huggingface.co/spaces/juliensimon/trinity-code-reviewer
Trinity Large is a 400B parameter Mixture-of-Experts model trained in 33 days for $20M — a fraction of what frontier labs spend. It has 256 experts but activates only 4 per token, so only 13B active parameters do the work. That translates to 2-3x faster inference than anything in its weight class, which you can feel in the demo when reviews come back in seconds.
In this video:
- 🔴 Live demo: code review app catching real issues on real PRs
- 🏗️ How I built the app — stack, prompting strategy, handling large diffs
- 🧠 Architecture deep dive: 256 experts, 4 active, extreme sparsity
- 📦 Why three checkpoints exist: Preview, Base, and TrueBase
- ⚡ Why MoE makes this practical to actually deploy and run at scale
Try the app yourself and let me know what it catches in your code.
Hugging Face model page: huggingface.co/arcee-ai/Trinity-Large-Preview
OpenRouter model page: openrouter.ai/arcee-ai/trinity-large-preview
Arcee AI blog post: arcee.ai/blog/trinity-large
Arcee AI technical report: github.com/arcee-ai/trinity-large-tech-report
VentureBeat article: venturebeat.com/technology/arcees-u-s-made-open-source-trinity-large-and-10t-checkpoint-offer-rare-look
