# pi-context-zone 🧠⚠️🧟

**Visual context health bar for the [Pi coding agent](https://github.com/badlogic/pi-mono) — see your smart/warm/dumb zone at a glance.**

Inspired by [Dex Horthy's "No Vibes Allowed"](https://www.youtube.com/watch?v=rmvDxxNubIg) talk and the concept of **context rot** in AI coding agents.

```
🧠 ████░░░░│░░░░░│░░░░░ smart 36% left
```

## Install

```bash
pi install npm:pi-context-zone
```

Or load directly:

```bash
pi -e ./index.ts
```

## What It Does

Adds a single visual status line to your Pi footer showing:

- **Progress bar** with zone dividers at 40% and 70%
- **Color gradient** — green → yellow → red as context fills
- **Zone label** — which zone you're in (smart / warm / dumb)
- **Remaining %** — how much room before the next zone

Updates automatically after each turn, compaction, and session start.

## Why This Matters

### The Context Rot Problem

LLMs don't degrade gracefully as their context fills up — they hit cliffs. This isn't about forgetting a fact buried in the middle (needle-in-a-haystack); it's about **reasoning quality collapse**. The model starts cutting corners, ignoring instructions, repeating mistakes, and hallucinating with full confidence.

Dex Horthy (HumanLayer) coined the term **"Dumb Zone"** after analyzing 100,000+ developer sessions. His framework identifies ~40% context utilization as the inflection point where AI coding agents transition from sharp, capable assistants to confused, error-prone machines.

### The Zones

| Zone | Context Used | What Happens |
|---|---|---|
| 🧠 **Smart** | 0 – 40% | Peak reasoning. Follows instructions, catches edge cases, accurate tool selection. |
| ⚠️ **Warm** | 40 – 70% | Degrading. F1 scores drop ~45%. Instruction drift, shallow pattern matching, starts relying on pre-training over your actual context. |
| 🧟 **Dumb** | 70%+ | Broken. Hallucination rates spike to 40%. Infinite debug loops. Confidently wrong. Auto-compaction triggers here but it's lossy. |

### How Models Actually Perform (March 2026)

The "dumb zone" threshold varies by model. Here's how current frontier models handle long context on the MRCR v2 (8-needle) benchmark — the gold standard for measuring **reasoning quality** (not just retrieval) across context lengths:

| Model | Context Window | MRCR @ 128K | MRCR @ 256K | MRCR @ 1M | Smart Zone Ends |
|---|---|---|---|---|---|
| Claude Opus 4.6 | 1M | ~94% | 93% | **78%** | ~70% (most resilient) |
| Claude Sonnet 4.6 | 1M | — | — | 65% | ~50-60% |
| GPT-5.4 | 1M | 86% | 79% | 37% | ~30-40% |
| Gemini 3.1 Pro | 2M | 85% | ~50% | 26% | ~25-30% |
| MiniMax M2.1 | 1M | ~73% | — | ~32% | ~30-40% |
| Grok 3 | 1M | — | — | — | ~50% (severe distractor susceptibility) |
| DeepSeek V3 | 128K | **95%** | N/A | N/A | Near 100% (within its window) |
| Llama 4 Scout | 10M | — | — | — | Unknown (no MRCR published) |

> **Key insight**: Claude Opus 4.6's Context Compaction architecture genuinely resists context rot better than any other model — it maintains 78% reasoning accuracy at 1M tokens where GPT-5.4 drops to 37% and Gemini 3.1 to 26%. But even Opus degrades. The 40% rule is conservative and works well as a universal default.

### What Causes Context Rot

1. **Attention dilution** — Transformer attention is a fixed budget. More tokens = less focus per token.
2. **Lost in the middle** — Models remember the beginning and end of context but forget the middle (U-shaped curve).
3. **Trajectory poison** — Your conversation history is full of the model's own mistakes and your corrections. The model learns to predict more mistakes.
4. **KV cache compression** — At high utilization, models compress older context, losing the "why" behind decisions.

### What To Do About It

When the bar turns yellow or red:

1. **Compact** — Use `/compact` or let auto-compaction handle it
2. **New session** — Start fresh with a clean context (RPI workflow: Research → Plan → Implement)
3. **Sub-agents** — Delegate heavy exploration to isolated contexts

This extension simply makes the problem **visible** so you can act before quality degrades.

## Configuration

The extension uses sensible defaults based on the research:

| Setting | Value | Rationale |
|---|---|---|
| Smart → Warm | 40% | Dex Horthy's inflection point, validated across models |
| Warm → Dumb | 70% | Where hallucination rates spike and auto-compaction triggers |
| Bar width | 20 chars | Fits comfortably in most terminal widths |

These thresholds are intentionally model-agnostic. While Claude Opus 4.6 can push further into the warm zone without degrading, the 40% threshold is the safe universal default that works across all providers.

## References

- **"No Vibes Allowed: Solving Hard Problems in Complex Codebases"** — Dex Horthy, AI Engineer 2025 ([YouTube](https://www.youtube.com/watch?v=rmvDxxNubIg))
- **Context Rot: How Increasing Input Tokens Impacts LLM Performance** — Chroma Research, 2025
- **MRCR v2 (8-needle) benchmark** — Multi-Round Coreference Resolution, the gold standard for long-context reasoning evaluation
- **"Lost in the Middle"** — Stanford, 2023 — U-shaped retrieval accuracy in long-context LLMs
- **Claude Opus 4.6 technical report** — Anthropic, Feb 2026 — Context Compaction architecture
- **12-Factor Agents** — Dex Horthy / HumanLayer — Framework for building reliable AI agents

## License

MIT