Technical Documentation

Design considerations

Artificial Intelligence is capable of producing a greater amount of code compared to human coders.

Hash-based storage presents a significant opportunity for cost reduction in data lakes, primarily due to the mitigation of data duplication.

Code copilots have the ability to apply generic code, thereby eliminating the need for author bookkeeping.

As the volume of code increases and the time for verification decreases, the reliability of hash-based storage becomes increasingly crucial.

For safety purposes, it is more secure to reference code using its complete hash rather than a file name, date, or version.

With the decreasing cost of storage, maintaining simple consistency becomes more important than achieving perfectly optimized disk usage.

Hashing individual segments of a complete file effectively addresses the issue of block repetition, facilitating efficient burst read returns.

The concept of change ordering has become obsolete as Generative AI is capable of working on parallel versions.

With the non-linear nature of coding, the importance of time stamps has diminished.

Maintaining a stable codebase is crucial, with a hash serving to identify an entire repository rather than a single difference.

All revisions must undergo testing and review prior to utilization.

The authorship of these revisions becomes irrelevant in the context of AI. The importance shifts to the auditor instead.

The most frequently utilized version of a stable codebase is often more important than the most recent version.

The storage of all revisions takes precedence over the complete push updates of repository history.

There may still be a requirement for a secure method to iterate through all versions for backup purposes by administrators.

An API key, particularly one that can only be set by the server owner, is generally sufficient for administrators.

API keys can be further secured with the addition of 2FA wrappers and monitoring solutions.

The implementation of limited retention effectively addresses privacy law concerns.

If the majority of systems operate on auto clean, identifying the source of personal data becomes a straightforward process.

Data is typically cleaned up within a specific timeframe, such as ten minutes or two weeks.

A typical response to a privacy query could be "If you used the site more than two weeks ago, your data has been deleted."

Secondary backups can continue to iterate and store data for longer periods, while maintaining a fixed cache container size.

Most systems operate on auto clean. The last backup can be used to retrieve or delete private data.

Streaming workloads are favored, with an emphasis on limiting the buffer size used.

Streaming smaller blocks enables the prefetching of content in advance, enhancing both reliability and security.

Clustering requires any replicas to be managed within applications. We use a shared nothing approach reflecting hardware reliability of 2020s.

Clustering uses the hash of the value to identify the location of blocks.

Clustering uses the key to identify the location of block of a read-write key-value pair.

Security considerations