The release of Blots 0.7.4, a specialized programming language for quick calculations and JSON data transformation, has generated interesting discussions in the programming community about language design philosophy and practical usability. While Blots aims to bridge the gap between terseness and clarity in data processing tasks, community feedback reveals both appreciation for its approach and questions about specific design decisions.
Blots Language Specifications
- Current Version: 0.7.4 (released September 18, 2025)
- Supported Platforms: macOS and Linux
- Installation Methods: Homebrew (macOS) or curl-based installer
- Language Type: Expression-oriented, specialized for data transformation
- Primary Use Cases: Quick calculations, JSON processing, interactive data analysis
Comparison with Established Alternatives
The most substantial community discussion centers around how Blots compares to existing tools like Lil and jq. One developer demonstrated how the same weather data processing example could be accomplished in Lil using fewer lines of code, highlighting different approaches to list manipulation and data extraction. The comparison revealed that while Blots uses a via operator for mapping operations, Lil employs a wildcard .. syntax that some find more intuitive for accessing nested data structures.
The discussion also touched on Lil's ability to present data in SQL-like table formats, which provides a visual advantage when working with structured data. This capability allows developers to quickly scan and understand data relationships, something that traditional command-line tools often struggle to provide in a readable format.
Code Comparison: Blots vs Lil
| Feature | Blots Syntax | Lil Syntax |
|---|---|---|
| JSON Parsing | Automatic with inputs | "%j" parse |
| Array Mapping | via forecast => forecast.temperature |
..temperature (wildcard) |
| Average Calculation | avg(temps) |
(sum t)/count t |
| Data Access | inputs.properties.periods |
i.properties.periods |
Controversial Boolean Behavior
A significant point of contention emerged around Blots' handling of boolean operations on arrays. The language's decision to return a single boolean value when comparing arrays (like [4, 5, 6] > 3 returning true instead of [true, true, true]) has raised eyebrows among developers. This design choice prioritizes checking if a condition applies to all elements rather than providing element-wise results, which differs from expectations set by other programming languages and mathematical operations.
Accessibility and Learning Curve
Community members expressed both intimidation and excitement about creating custom programming languages. The discussion revealed that modern resources have made language development more accessible than in previous decades, with abundant tutorials and open-source projects available for learning. However, the creator of Blots acknowledged that tools like jq, while powerful, can be challenging to learn due to their emphasis on terseness over readability.
I wanted a tool that had a more balanced trade-off of characters vs. clarity
This philosophy reflects a broader trend in programming tool development, where developers seek solutions that don't require extensive memorization of cryptic syntax patterns.
Market Position and Use Cases
Blots positions itself specifically as a non-general-purpose language, focusing on quick calculations and data transformation tasks. This narrow scope allows it to optimize for specific workflows, particularly those involving JSON processing and interactive data exploration. The language's interactive mode and output recording capabilities suggest it's designed for exploratory data analysis rather than production systems.
The community discussion indicates that specialized languages like Blots serve an important niche for developers who need something more approachable than traditional command-line tools but don't require the full complexity of general-purpose programming languages.
Reference: The Blots Programming Language