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README.md

@@ -18,6 +18,23 @@ secret**, and it can be **fine-tuned on *your* private repo** so it writes in yo
 A **niche specialist**, not a general model — tuned to beat a frontier model *in one lane* (agentic
 coding + design for **TS/JS/Python/Rust/Go/HTML/CSS** + Postgres) by out-*verifying* it, not out-knowing it.
 
+## My AI-Engineer / Full-Stack / Data-Science / ML build
+This is the version I run wearing all four hats — **one on-device model, no cloud key**, tooled for the
+whole stack of those roles (strongest in the coding/agentic lane, deliberately so):
+- **AI Engineer** — *builds and ships agentic AI locally*: the 47-tool ReAct agent, **verified +
+  constrained decoding**, grammar-constrained tool I/O, MLX-native serving + the speed/stability work
+  (prompt-cache, continuous batching, frontier-grade serving). The model that *makes* AI products.
+- **Full-Stack** — front-to-back in **TS/JS/Python/Rust/Go/HTML/CSS + Postgres**, the **compiler steering
+  every line**, a **design soul** (render-and-see critic: WCAG / type-scale / OKLCH) for the UI, and
+  **SQL-on-a-real-schema** for the API — plus edit→test→fix agentic loops on *your* repo.
+- **Data Science** — stateful **REPL**, **SymPy / pandas / numpy / sklearn**, arXiv-RAG, competition-grade
+  math (GSM8K-style), and **code-rendered figures** (matplotlib / manim / TikZ).
+- **Machine Learning** — it *is* applied ML end-to-end: **REAP expert-pruning** (256→77), **mixed-precision
+  quantization**, **LoRA healing**, **distillation**, **MTP self-speculation**, GRPO/RLVR experiments — the
+  build itself is a working reference — plus a local **Lean-4 formal-math** lane (correct-by-construction).
+
+One model, fully local, **verify-everything**: the four hats I wear, on a MacBook.
+
 ## How it was made
 1. **Pruned** the MoE experts 256 → 77 by **router-weighted saliency (REAP** = `router_weight ×
    activation_norm`, padding-masked), streaming layer-by-layer (~5 GB working set — it never fits in RAM).