diff --git a/README.md b/README.md
index 4109d5f..86c3009 100644
--- a/README.md
+++ b/README.md
@@ -17,6 +17,32 @@ $ ./coli chat
◆ Ciao! 😊 Come posso aiutarti oggi?
```
+
+## See it running
+
+
+ 
+
+The web dashboard (./coli web): a 744B model answering at 4+ tok/s end-to-end on 6× RTX 5090 —
+with live token metrics, the hardware panel, and the VRAM/RAM/disk expert tiers.
+
+
+ 
+
+The Brain page: all 19,456 experts as a living cortex — colour is the storage tier,
+brightness is routing heat, and every expert routed in a turn flashes white. Hovering shows the expert's
+measured topic affinity.
+
+## Contents
+
+- [The idea](#the-idea)
+- [See it running](#see-it-running)
+- [What's implemented](#whats-implemented)
+- [Honest numbers](#honest-numbers-wsl2-12-cores-25-gb-ram-nvme-via-vhdx)
+- [Download the model](#download-the-model)
+- [Web dashboard](#web-dashboard)
+- [Got a better machine?](#got-a-better-machine-try-it--heres-what-to-expect)
+
## The idea
A 744B Mixture-of-Experts model activates only ~40B parameters per token — and only ~11 GB of those change from token to token (the routed experts). So:
@@ -501,6 +527,23 @@ thrashing. Persistent `.coli_usage` remains the long-term signal and is not deca
**Conversations reopen warm** (`.coli_kv`, since 2026-07-10): `coli chat` persists the compressed MLA KV-cache to disk after every turn (~182 KB/token, appended incrementally, crash-safe). Close the chat, reopen it tomorrow — the model still remembers the whole conversation and **zero re-prefill happens**: validated byte-identical to an uninterrupted session. `:reset` clears it, `KVSAVE=0` disables it.
+## Web dashboard
+
+One command serves the OpenAI-compatible API **and** the web console on the same port, then opens your browser when the engine is ready:
+
+```bash
+cd web && npm install && npm run build # once
+./coli web --model
+```
+
+What you get:
+
+- **Chat** with live metrics: a flashing token counter while generating, then tok/s, time-to-first-token, prompt→completion counts and queue wait;
+- **Runtime panel**: your hardware (CPU, GPUs + VRAM, RAM, cores), the scheduler, and the live expert-tier bar — how many of the 19,456 experts sit in VRAM / RAM / disk right now;
+- **Brain**: the whole model as a 76×256 cortex, one cell per expert. Colour = tier, brightness = routing heat, and the experts routed in each turn flash white and decay — you watch the model think. Hover any cell for its tier, heat and [measured topic affinity](https://github.com/JustVugg/colibri/issues/175) (specialists for code, Chinese, math, law… live in layers 11–22).
+
+The dashboard talks to the engine over two tiny protocol lines (`TIERS`, `EMAP`/`HITS`) and plain JSON endpoints — nothing heavier than the engine itself.
+
## Got a better machine? Try it — here's what to expect
colibrì was built on deliberately humble hardware (12 cores, 25 GB RAM, an older DRAM-less NVMe behind a WSL2 VHDX that measured ~1 GB/s random on *this* drive — note WSL2 VHDX is not inherently slow: a community 5090 box measured 10.5 GB/s O_DIRECT through one, [#101](https://github.com/JustVugg/colibri/issues/101)). **Every one of those constraints is a knob your machine can turn up.** The engine needs: Linux (or WSL2), macOS, or **Windows 11 natively (MinGW-w64)**; gcc with OpenMP, AVX2, ≥16 GB RAM, and the ~370 GB int4 model on a local NVMe (ext4/NTFS — never a network/9p mount).
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