
Reader
Displays what you're looking at
Screenshots





About this Mod

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OVERVIEW 
This datapack is inspired by the WAILA series. It is compatible with mods and has support for custom blocks from datapacks.
Notes:
Requires:
FEATURES 
- Visit the wiki for more information
Custom Delevopment Support
- Add the tag
data.blockto an entity so that its name can be displayed as a block
Gameplay
- The names of
blocks/entitiesare displayed on youractionbarwhen looking at them
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Available Versions
How to Install Reader on Your Server
Order Server
Order a Minecraft Java server with at least 3 GB RAM (4 GB recommended).
Set datapack Loader
In the panel under "Egg", select the datapack loader and matching Minecraft version (26.2).
Install Mod
Open the mod browser in the dashboard and search for "Reader". Click "Install" – done! Alternatively, upload the .jar via SFTP to the /mods folder.
Compatibility
Mod Loaders
Minecraft Versions
26.2, 26.1.2, 26.1.1 (+30 more)
Server-side
✓ RequiredRecommended RAM
4 GB(min. 3 GB)Frequently Asked Questions
Reader server crashes on startup – what to do?
Most common cause: wrong datapack version or insufficient RAM. Check the server log (latest.log) for "OutOfMemoryError" or "Mixin" errors. With Mado Hosting: ensure at least 3 GB RAM is allocated and the loader matches the mod version (26.2). You can switch loaders with one click in the panel.
Is Reader compatible with datapack and fabric and forge and neoforge and quilt?
Reader officially supports datapack, fabric, forge, neoforge, quilt for Minecraft 26.2, 26.1.2, 26.1.1. Note: Forge and Fabric mods are NOT cross-compatible – pick one loader and stick with it. The Mado dashboard automatically detects incompatible loader combinations.
Server lagging with Reader – how to optimize performance?
Recommended RAM: 4 GB (per 8 players). Use /spark profiler to check if Reader consumes the most tick time. Common fixes: reduce server view-distance to 8-10, install "performant" or "starlight" as supplementary mods on Forge. With Mado Hosting, your server runs on NVMe SSDs with dedicated CPU cores for minimal latency.
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