# Blog Posts

Use this file for public explainers and blog-style summaries.

## Assume This Reader

- Assume general familiarity with LLMs, training, tokens, and transformers.
- Do not assume deep knowledge of the specific technique.

## Optimize For

- Explain what the work is and why it matters.
- Preserve technical accuracy without turning the post into a paper.
- Make the result legible to a strong ML engineer outside the niche.

## Write This Way

- Explain new or non-standard concepts before leaning on them. Linking to explanations elsewhere is acceptable.
- Favor intuition, framing, and observed results over derivations.
- Keep implementation details light unless they are central to the point.
- Use examples and concrete comparisons instead of abstract claims.
- Lead paragraphs with the main takeaway, not setup.
- Prefer concrete numbers or comparisons over qualitative claims.

## Avoid These Failure Modes

- Do not drown the reader in internal terminology.
- Do not hide the result behind long context-setting.
- Do not turn the post into marketing copy.
- Do not imply broader conclusions than the data supports.

## Test

- Ask whether a strong ML engineer outside the subarea could follow the piece without looking things up.
