# Contributing to DeepLossless

DeepLossless is an experimental inference-aware runtime for long-running AI coding sessions.

The project focuses on:
- execution reuse
- semantic memory
- replayable inference
- context compaction
- long-session stability

This is still an early-stage systems project, and contributions are very welcome.

---

# Philosophy

DeepLossless is NOT trying to:
- become another chatbot wrapper
- maximize prompt size
- blindly stuff more context into models

Instead, the goal is to explore:

> Long context windows are not memory.

We treat AI execution more like:
- incremental compilation
- replayable runtime systems
- execution state management

---

# Good First Contribution Areas

You do NOT need to understand the entire runtime to contribute.

Especially welcome:

## Dashboard / Visualization (HIGH PRIORITY)

We currently have almost no visualization tooling.

Ideas:
- execution timeline viewer
- cache hit/miss dashboard
- replay inspector
- semantic DAG explorer
- token usage graphs
- execution provenance viewer
- failure-memory timeline

This is one of the most impactful areas for contributors right now.

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## Benchmarking & Metrics

Help improve:
- benchmark scenarios
- token accounting
- runtime statistics
- cache effectiveness reporting
- long-session simulations

---

## CLI / Developer Experience

Examples:
- better logs
- colored output
- trace formatting
- replay debugging tools
- config UX improvements

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## Tree-sitter / Language Support

Help expand:
- AST extraction
- symbol tracking
- semantic compression
- language adapters

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## Documentation

Especially helpful:
- architecture diagrams
- execution flow explanations
- benchmark walkthroughs
- replay examples
- tutorial sessions

---

# Development Setup

```bash
git clone ...
cargo test
cargo bench
```

Useful tests:

```bash
cargo test --test simulated_session -- --nocapture
cargo test --test long_session_benchmark -- --nocapture
```

---

# Project Structure

High-level overview:

```text
Client
  ↓
Proxy / Runtime Interception
  ↓
Execution Normalization
  ↓
Cache / Replay / Memory Layer
  ↓
Upstream LLM
```

Key components:

| Component         | Purpose                        |
| ----------------- | ------------------------------ |
| Runtime           | interception + replay          |
| DAG memory        | semantic execution graph       |
| Cache layer       | tool reuse                     |
| Failure memory    | prevent repeated bad reasoning |
| Snapshot system   | replayable execution           |
| Compression layer | entropy-aware compaction       |

---

# Areas That Are Still Experimental

Some systems are intentionally evolving rapidly:

* replay architecture
* mutation engine
* execution snapshots
* memory evolution
* context injection policies

Please open discussions before large refactors.

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# Contributor-Friendly Areas

These are especially safe to work on:

* dashboards
* visualization
* metrics
* documentation
* CLI improvements
* benchmark tooling

---

# Before Opening Large PRs

Please open:

* an issue
* discussion
* design sketch

especially for:

* replay semantics
* DAG mutation
* cache invalidation
* execution scheduling

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# Why This Project Exists

Long-running AI coding sessions slowly degrade over time.

Agents:

* reread the same files
* repeat failed fixes
* rebuild the same plans
* waste reasoning and tokens

DeepLossless explores whether AI execution can become:

* incremental
* replayable
* reusable
* memory-aware

rather than stateless prompt reconstruction.

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Thanks for checking out the project.
Even small contributions help a lot.
