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README.md

@@ -18,39 +18,68 @@ tags:
   - achievement:fieldnotes
 ---
 
+<p align="center">
+  <img src="assets/neurobait-logo.jpg" alt="NeuroBait logo" width="420" />
+</p>
+
 # NeuroBait
 
 NeuroBait is an ADHD-friendly companion for task initiation: a warm space and a
 gentle boost for the moment when starting feels heavier than the task itself.
 
-It is built for a very specific moment: the user already knows the task matters,
-but the first move still feels too heavy. Instead of turning that friction into
-a productivity lecture, NeuroBait replies with short, warm, agency-preserving
-language. It avoids shame, streak pressure, diagnostic framing, and visible
-prompt labels such as `Micro-action`, `Hook`, or `Stakes`.
+It started from a simple observation: sometimes the hard part is not knowing
+what to do. The hard part is crossing the gap between knowing and starting. A
+to-do list can be technically correct and still feel like one more thing to
+carry. NeuroBait is built for that freeze moment.
+
+Instead of giving a productivity lecture, NeuroBait tries to offer a small
+spark: short, warm, agency-preserving language that reconnects the user with
+what matters and lowers the first move until it feels possible. No shame, no
+streak pressure, no diagnostic framing, and no visible prompt labels like
+`Micro-action`, `Hook`, or `Stakes`.
+
+This Space runs a fine-tuned model through the app itself. It does not call an
+external hosted LLM API for chat responses.
+
+## Try It
+
+- Live Space: https://huggingface.co/spaces/build-small-hackathon/NeuroBait
+- Direct app host: https://build-small-hackathon-neurobait.hf.space
+- Demo video: https://www.youtube.com/watch?v=-EJ06hOgyd0
+- Story article: https://huggingface.co/blog/build-small-hackathon/neurobait-adhd
+- Model adapter: https://huggingface.co/build-small-hackathon/NeuroBait
+- Source repo: https://github.com/Subrata15/NeuroBait-Build-Small-Model
+
+## Sample Chat
 
-This Space runs a fine-tuned small model through the app itself. It does not call
-an external hosted LLM API for the chat response.
+<p align="center">
+  <img src="assets/example-chat.png" alt="Example NeuroBait chat screenshot" width="760" />
+</p>
 
 ## Build Small Hackathon Submission
 
 - Primary track: **Backyard AI**
-- Why this track: NeuroBait focuses on a real everyday ADHD and
-  neurodivergent friction - starting the thing that already matters - and turns a
-  small model into a practical companion for that moment.
+- Why this track: NeuroBait was built from a real household problem and a real
+  daily friction - the moment when a person already knows the task matters but
+  still cannot begin.
 - Bonus quest fit: **Well-Tuned**, because the Space uses a published LoRA
   adapter fine-tuned for NeuroBait's voice and behavior.
-- Bonus quest fit: **Off-Brand**, because the app uses custom Gradio styling and
-  product copy instead of the default chatbot shell.
+- Bonus quest fit: **Off-Brand**, because the app uses custom Gradio styling,
+  product copy, mood input, and a NeuroBait-specific chat experience instead of
+  the default chatbot shell.
 - Sponsor fit: **Modal-powered**, because fine-tuning and generation evaluation
   were run on Modal GPU infrastructure.
 
 NeuroBait was fine-tuned with Modal and deployed as a Gradio app on Hugging Face
 ZeroGPU.
 
-## What The App Does
+## What NeuroBait Does
 
-The app is intentionally narrow. NeuroBait should:
+The app is intentionally narrow. NeuroBait does not try to become a therapist,
+planner, diagnosis tool, or productivity operating system. It is designed for
+one useful behavior: helping a stuck person find a lighter first move.
+
+NeuroBait should:
 
 - respond in concise, natural prose,
 - preserve the user's agency,
@@ -70,6 +99,18 @@ response style slightly:
 The mood input does not change the safety scope. It only nudges the app-level
 prompting and presentation.
 
+## Why Fine-Tune?
+
+The base model is capable, but capability alone was not the target. Out of the
+box, a model often responds with helpful-looking structure: long paragraphs,
+bold labels, bullet lists, and generic encouragement. For someone already
+overwhelmed, that can become another wall of text.
+
+The fine-tune was trained to learn NeuroBait's voice and behavior layer:
+shorter responses, warmer framing, fewer assumptions, and no leaked internal
+recipe labels. The goal was not to make a general assistant. The goal was to
+make the first sentence feel less heavy.
+
 ## Model And Runtime
 
 - Base model: `unsloth/gemma-3-12b-it`
@@ -80,12 +121,10 @@ prompting and presentation.
 - Quantization: 4-bit bitsandbytes NF4 inside the `@spaces.GPU` window
 
 Unsloth is used for training, not for Space inference. The deployed app uses the
-standard `transformers` + `peft` path so the public demo can load the Gemma 3 12B
-base model plus the NeuroBait LoRA adapter on Hugging Face ZeroGPU.
+standard `transformers` + `peft` path so the public demo can load the Gemma 3
+12B base model plus the NeuroBait LoRA adapter on Hugging Face ZeroGPU.
 
-## Runtime Configuration
-
-Expected environment variables:
+Expected runtime environment variables:
 
 ```text
 BASE_MODEL=unsloth/gemma-3-12b-it
@@ -121,6 +160,19 @@ Generation eval over 8 held-out or novel prompts:
 - Base label leaks: 5
 - Fine-tuned label leaks: 0
 
+## Community Attention
+
+After publication, NeuroBait drew organic attention from both AI community
+accounts and real users who reposted or shared the project:
+
+- LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7470126428928319488/
+- Instagram: https://www.instagram.com/p/DZZw6UKAj0N/?img_index=1
+- LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7470052176942415872/
+- LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7470087187074572288/
+- X: https://x.com/AiquestAcademy/status/2064301066733101313
+- X: https://x.com/Jorgebanuelosmx/status/2064317026664059034
+- X: https://x.com/amphetamarina/status/2064322048659279917
+
 ## Related Repos
 
 - Model adapter: https://huggingface.co/build-small-hackathon/NeuroBait