---
title: "Tables: alignment, code, math inline"
url: https://mdfy.app/hjlgK4OA
updated: 2026-05-14T18:15:49.480Z
source: "mdfy.app"
---
# Tables: alignment, code, math inline

> Showing the table-rendering primitives. Tables are where most documents fall apart — wrong alignment, wide cells, code formatting eats the layout. The patterns below work.

## Right-align numerics, left-align text

| Provider | Embedding model | Dim | Latency p95 (ms) | $/1M tokens |
|---|---|---:|---:|---:|
| OpenAI | text-embedding-3-small | 1536 | 8 | $0.02 |
| OpenAI | text-embedding-3-large | 3072 | 14 | $0.13 |
| Voyage | voyage-3-large | 1024 | 12 | $0.18 |
| Cohere | embed-v4 | 1024 | 28 | $0.10 |

The `---:` alignment marker is the part most authors miss. Numbers want to be right-aligned so columns line up at the decimal.

## Inline code inside cells

| Endpoint | Method | Auth | Description |
|---|---|---|---|
| `/api/docs/{id}` | GET | optional | Fetch a doc by id |
| `/api/docs` | POST | editToken | Create a new doc |
| `/api/bundles/{id}/graph` | POST | editToken or owner | Trigger AI analysis |
| `/api/hub/{slug}/recall` | POST | optional | Hybrid recall against a public hub |

## Math inside cells

| Concept | Notation | Where it shows up |
|---|---|---|
| Identity | $e^{i\pi}+1=0$ | Slide 0 of any math lecture |
| 2-norm | $\|x\|_2 = \sqrt{\sum_i x_i^2}$ | Vector normalisation in recall |
| Cosine similarity | $\cos\theta = \frac{x \cdot y}{\|x\|\|y\|}$ | Every retrieval call |
| Softmax | $\sigma(x_i) = \frac{e^{x_i}}{\sum_j e^{x_j}}$ | Reranker score normalisation |

## When to break out of a table

If a row needs more than ~80 chars of prose in any one cell, the table is the wrong shape. Use a bulleted list or a short heading hierarchy instead. Tables earn their formatting by enabling comparison; once a single row needs paragraphs, the comparison is no longer the point.