Unlocking the Future: The Best AI Career Path Revealed! @juliensimonfr

Uploaded August 2024 | Updated September 2026, 2 weeks ago

Discover why transforming company knowledge into actionable datasets is the ultimate AI career move! In today's rapidly evolving tech landscape, it's crucial to leverage information stored in minds and files, converting it into valuable data for model evaluation and fine-tuning. This innovative approach not only enhances organizational efficiency but also establishes a competitive advantage that models alone simply can't achieve. Learn how this strategy can reshape your professional journey in AI and prepare you for success in this high-demand field. Don't miss out on this insightful discussion!

Unlocking the Future: The Best AI Career Path Revealed!

$Trinity Large: An Open 400B MoE Model I built a code review app powered by @ArceeAIs new Trinity Large model and deployed it to a @HuggingFace Space. ⭐️⭐️⭐️ More content on Substack at https://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. Its fast, free to try, and the 512K context window lets it process entire files without chunking. Code Review App (HF Space): https://huggingface.co/spaces/juliensimon/trinity-code-reviewer Trinity Large is a 400B parameter Mixture-of-Experts model trained in 33 days for $20M — a fraction of what frontier labs spend. It has 256 experts but activates only 4 per token, so only 13B active parameters do the work. That translates to 2-3x faster inference than anything in its weight class, which you can feel in the demo when reviews come back in seconds. In this video: - 🔴 Live demo: code review app catching real issues on real PRs - 🏗️ How I built the app — stack, prompting strategy, handling large diffs - 🧠 Architecture deep dive: 256 experts, 4 active, extreme sparsity - 📦 Why three checkpoints exist: Preview, Base, and TrueBase - ⚡ Why MoE makes this practical to actually deploy and run at scale Try the app yourself and let me know what it catches in your code. Hugging Face model page: https://huggingface.co/arcee-ai/Trinity-Large-Preview OpenRouter model page: https://openrouter.ai/arcee-ai/trinity-large-preview Arcee AI blog post: https://www.arcee.ai/blog/trinity-large Arcee AI technical report: https://github.com/arcee-ai/trinity-large-tech-report VentureBeat article: https://venturebeat.com/technology/arcees-u-s-made-open-source-trinity-large-and-10t-checkpoint-offer-rare-look$

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