Strengthen Off the Grid claim

FIELD_NOTES.md

@@ -188,11 +188,11 @@ Decision: ship MiniCPM5-1B LoRA v8 as the default model because it clears the br
 
 ## 2026-06-09 Off the Grid Claim
 
-The field guide says Off the Grid is about no cloud APIs: "The whole thing runs on the model in front of you." Jawbreaker meets the no-external-LLM-API version of that badge:
+The field guide says Off the Grid is about no cloud APIs: "The whole thing runs on the model in front of you." Jawbreaker meets that badge:
 
 - the Space loads `openbmb/MiniCPM5-1B` and the Jawbreaker LoRA adapter directly through Transformers
 - inference happens in the app runtime on Hugging Face ZeroGPU
 - there is no OpenAI, Anthropic, hosted MiniCPM, or other external LLM API call in the scam-analysis path
 - local/eval tooling also supports GGUF experiments through `llama-cpp-python`
 
-Precise wording matters: claim Off the Grid as no external LLM API / small open model running directly in the app runtime. Do not describe the public Space as a fully offline laptop demo, because it is hosted on Hugging Face for judging.
+Precise wording matters: claim Off the Grid confidently as small open model inference inside the app runtime with no external LLM API. Hugging Face ZeroGPU is the judge-facing compute layer, not a hosted model dependency.

HONEST_SUBMISSION.md

@@ -10,7 +10,7 @@ Jawbreaker should compete hard without gaming the hackathon.
 - OpenBMB targeting: MiniCPM is central to the deployed scam analysis path.
 - Codex usage: the GitHub repo has Codex-attributed commits and the build logs document Codex's role.
 - Custom UI: the app uses a custom Gradio interface, not the default demo look.
-- Off the Grid: the app does not call an external LLM API; it loads the open MiniCPM base model and Jawbreaker LoRA adapter directly in the app runtime. The public demo uses Hugging Face ZeroGPU compute, not a hosted model API.
+- Off the Grid: the app does not call an external LLM API; it loads the open MiniCPM base model and Jawbreaker LoRA adapter directly in the app runtime. Hugging Face ZeroGPU is the demo compute layer, not a hosted model API.
 - Eval work: the repo includes hand-curated synthetic/sanitized eval cases plus generated synthetic train/dev/test data.
 - Public data page: sanitized/synthetic eval and training artifacts are published at `build-small-hackathon/jawbreaker-scam-defense-data`.
 - Well-Tuned: a MiniCPM LoRA adapter was trained on Modal, published to Hugging Face, and evaluated before deployment.
@@ -23,7 +23,7 @@ Jawbreaker should compete hard without gaming the hackathon.
 - Do not claim the generated synthetic dataset is real user data.
 - Do not claim benchmark superiority unless we publish the eval command, dataset, and result.
 - Do not claim llama.cpp as the deployed runtime unless the live Space actually uses the llama.cpp path.
-- Do not claim the public Space is a fully offline laptop demo. It is Off the Grid in the no-external-LLM-API sense, while hosted for judging on ZeroGPU.
+- Do not confuse transport with model dependency. The hosted Space is legitimate judge-facing ZeroGPU compute; the Off the Grid claim is about avoiding hosted/external LLM APIs.
 - Do not hide fallback behavior. If MiniCPM fails and Jawbreaker uses deterministic safety fallback, document that as a reliability layer.
 
 ## Training Rules

README.md

@@ -13,22 +13,33 @@ short_description: Private scam defense for someone you love.
 tags:
 - gradio
 - build-small-hackathon
+- backyard ai
 - backyard-ai
 - openbmb
 - minicpm
 - minicpm5
+- tiny titan
 - tiny-titan
+- well tuned
 - well-tuned
+- off brand
 - off-brand
+- off the grid
 - off-the-grid
+- best demo
 - best-demo
+- bonus quest champion
 - bonus-quest-champion
+- sharing is caring
 - sharing-is-caring
+- field notes
 - field-notes
 - modal
+- best use of modal
 - best-use-of-modal
 - codex
 - openai
+- best use of codex
 - best-use-of-codex
 - local-first
 - scam-defense
@@ -57,7 +68,7 @@ Scam defense for someone you love.
 - **Off Brand / Off the Grid / Sharing is Caring / Field Notes:** custom candy-brutalist Gradio UI, no external LLM API, public [dataset/eval bundle](https://huggingface.co/datasets/build-small-hackathon/jawbreaker-scam-defense-data), and [`FIELD_NOTES.md`](FIELD_NOTES.md).
 - **Submission package:** [Live Space](https://huggingface.co/spaces/build-small-hackathon/jawbreaker), [model](https://huggingface.co/build-small-hackathon/jawbreaker-minicpm5-1b-lora-v8), [dataset](https://huggingface.co/datasets/build-small-hackathon/jawbreaker-scam-defense-data), and [collection](https://huggingface.co/collections/build-small-hackathon/jawbreaker-6a263632dcd0b6d41ca914ff).
 
-Jawbreaker is designed for local, on-device inference to protect user privacy. For this hackathon demo, it is hosted on Hugging Face ZeroGPU so judges can try the same app without local setup.
+Jawbreaker is built around direct small-model inference to protect user privacy. The public hackathon Space runs MiniCPM5-1B + Jawbreaker LoRA on Hugging Face ZeroGPU for judge access, and the repo keeps local Transformers/GGUF tooling for running without hosted LLM APIs.
 
 Jawbreaker helps a real person pause before clicking, replying, or sending money. Paste a suspicious text, email, or DM and Jawbreaker breaks it into plain-English warning signs: what the sender is pretending to be, what pressure tactic is being used, what they want, and the safest next step.
 
@@ -108,7 +119,7 @@ The deployed Space uses `openbmb/MiniCPM5-1B` through Hugging Face Transformers
 - Training: PEFT/LoRA on Modal A100
 - Eval: guarded Modal A100 run across the 632-case hard v8 suite, with earlier 320/394-case v4 comparison runs
 - Runtime: ZeroGPU in the Hugging Face Space
-- Off the Grid: the app loads and runs the small open model directly through Transformers; it does not call OpenAI, Anthropic, hosted MiniCPM, or other external LLM APIs for inference
+- Off the Grid: the app loads and runs the small open model directly through Transformers in the Space runtime; it does not call OpenAI, Anthropic, hosted MiniCPM, or other external LLM APIs for inference
 
 Why this model:
 
@@ -162,7 +173,7 @@ Training/eval artifacts:
 | Best Use of Modal | Claimed | Modal A100 was used for PEFT/LoRA training and guarded eval runs across the MiniCPM calibration path; see `training/modal_train.py`, `training/modal_eval.py`, and the committed 632/394/320-case eval report files. |
 | Tiny Titan | Claimed | The deployed model is `openbmb/MiniCPM5-1B`, well under the 4B badge threshold. |
 | Well-Tuned | Claimed | Published MiniCPM5-1B LoRA adapter, generated calibration splits, and 632-case hard eval with zero dangerous undercalls. |
-| Off the Grid | Claimed | No external LLM API: the Space runs the small open model directly through Transformers on ZeroGPU, and local GGUF/Transformers tooling is included. |
+| Off the Grid | Claimed | The Space runs the small open model directly through Transformers on ZeroGPU with no external LLM API; local GGUF/Transformers tooling is included. |
 | Off Brand | Claimed | Custom Gradio UI beyond the stock component look. |
 | Sharing is Caring | Claimed | Public dataset/eval bundle, model card, build log, Codex trace, and collection are linked from the Space. |
 | Field Notes | Claimed | `FIELD_NOTES.md` documents product decisions, model/runtime pivots, eval results, and submission tradeoffs. |