QR codes encode data using several error correction levels. Higher error correction allows for more damage or obstruction while still remaining readable, but requires more modules (the black and white squares). Uppercase letters, numbers, and some symbols use the alphanumeric mode, which is more efficient than the byte mode used for lowercase letters and other characters. Since alphanumeric mode requires fewer bits to encode the same information, a QR code with uppercase letters can achieve the same error correction level with fewer modules, making it smaller.
The Substack post details how DeepSeek, a video search engine with content filtering, can be circumvented by encoding potentially censored keywords as hexadecimal strings. Because DeepSeek decodes hex before applying its filters, a search for "0x736578" (hex for "sex") will return results that a direct search for "sex" might block. The post argues this reveals a flaw in DeepSeek's censorship implementation, demonstrating that filtering based purely on keyword matching is easily bypassed with simple encoding techniques. This highlights the limitations of automated content moderation and the potential for unintended consequences when relying on simplistic filtering methods.
Hacker News users discuss potential censorship evasion techniques, prompted by an article detailing how DeepSeek, a coder-focused search engine, appears to suppress results related to specific topics. Several commenters explore the idea of encoding sensitive queries in hexadecimal format as a workaround. However, skepticism arises regarding the long-term effectiveness of such a tactic, predicting that DeepSeek would likely adapt and detect such encoding methods. The discussion also touches upon the broader implications of censorship in code search engines, with some arguing that DeepSeek's approach might hinder access to valuable information while others emphasize the platform's right to curate its content. The efficacy and ethics of censorship are debated, with no clear consensus emerging. A few comments delve into alternative evasion strategies and the general limitations of censorship in a determined community.
This post details the process of creating a QR Code by hand, using the example of encoding "Hello, world!". It breaks down the procedure into several key steps: data analysis (determining the appropriate encoding mode and error correction level), data encoding (converting the text into a bit stream), error correction coding (adding redundancy for robustness), module placement in the matrix (populating the QR code grid with black and white modules based on the encoded data and fixed patterns), data masking (applying a mask pattern for optimal readability), and format and version information encoding (adding metadata about the QR Code's configuration). The post thoroughly explains each step, including the relevant algorithms and calculations, ultimately demonstrating how the final QR Code image is generated from the initial text string.
HN users largely praised the article for its clarity and detailed breakdown of QR code generation. Several appreciated the focus on the underlying principles and math, rather than just abstracting it away. One commenter pointed out the significance of explaining Reed-Solomon error correction, highlighting its crucial role in QR code functionality. Another user found the interactive demo particularly helpful for visualizing the process. Some discussion arose around alternative encoding schemes and their potential benefits, along with mention of a similar article focusing on PDF417 barcodes. A few commenters shared personal experiences using the article's information for practical projects.
Summary of Comments ( 73 )
https://news.ycombinator.com/item?id=43149077
Hacker News users discussed the trade-off between QR code size and error correction level. Several commenters pointed out that uppercase letters require less data than lowercase due to fewer bits needed in the alphanumeric mode. This smaller data size allows for a smaller QR code with the same error correction level or a higher error correction level for the same size. One commenter highlighted the importance of the QR code standard's details in understanding this phenomenon. Some also mentioned practical considerations, like the prevalence of uppercase URLs in certain contexts and the lack of visual difference in small QR codes. A few users suggested that the blog post's explanation was overly simplified, failing to fully explain the encoding mechanism and the impact of error correction. Finally, a commenter noted that different QR code generators may have varying implementations impacting resulting size.
The Hacker News post titled "Why are QR Codes with capital letters smaller than QR codes with lower case?" has generated several comments discussing the article's findings. The core idea discussed revolves around the alphanumeric encoding mode of QR codes being case-sensitive and how that affects the size of the resulting QR code.
Several commenters expand on the article's explanation regarding character encoding. They highlight that uppercase letters have a lower numeric value in the alphanumeric mode specification, resulting in fewer bits required to encode them. This efficiency in encoding translates to a smaller data payload, which in turn allows for a smaller QR code. One commenter explains that the savings comes from encoding two uppercase characters with 11 bits, whereas two lowercase characters require 11 bits each (22 total). Another points out the distinction between the encoding method and the size of the resulting graphic, emphasizing that encoding fewer bits leads to a smaller data matrix, which is then rendered visually as a smaller QR code.
Some commenters go deeper into the technical details of the alphanumeric mode. One commenter mentions how the article's example of encoding "HELLO" versus "hello" demonstrates this efficiency clearly. Another commenter provides further insight into the encoding specification, detailing the numeric values assigned to each alphanumeric character and how the encoding process concatenates and converts these values into binary data.
A few commenters offer practical perspectives on the issue. One points out that mixed-case encoding is almost always less efficient than all-uppercase or all-numeric encoding. Another highlights the importance of considering the target scanner and its ability to interpret different QR code sizes and complexities.
One commenter offers a related observation about micro QR codes and their limited error correction capability. Another suggests exploring alternative encoding schemes, like Base45, which can potentially offer better compression and smaller QR code sizes.
Finally, one commenter praises the article's clarity and conciseness, appreciating its effective explanation of a seemingly counter-intuitive phenomenon.