> The out-of-the-box Shotwell manages photos quite well without any intelligence.
This piqued my interest on how it does it and after briefly checking the project it seems it only has two features for automatic photo categorization. 1) it can group photos by date and 2) It has face detection and recognition that uses trained weights (so ML "intelligence").
One interesting thing about Barman is that it just uses PG's own backup utilities. It doesn't implement custom parsers and things like that. So, there's less maintenance work needed for Barman when PostgreSQL changes data-file internals. Tradeoff is that there's less custom optimization than pgBackRest/pg_probackup/WAL-G-local.
Databasus seems to be taking somewhat similar approach to Barman, but (at this time) does not appear to use pg_receivewal, which makes it less efficient than Barman.
For PG v17+, Barman seems to be the most efficient backup solution based on PG native tools, that is able to do low-RPO or even zero-RPO (if configured as a synchronous receiver).
**Backup types**
- **Logical** — Native dump of the database in its engine-specific binary format. Compressed and streamed directly to storage with no intermediate files
- **Physical** — File-level copy of the entire database cluster. Faster backup and restore for large datasets compared to logical dumps
- **Incremental** — Physical base backup combined with continuous WAL segment archiving. **Enables Point-in-time recovery (PITR)** — restore to any second between backups. Designed for disaster recovery and near-zero data loss requirements
EDIT: It seem PITR has been added this March (for PostgreSQL)
Handling of exceptions is not enforced at compile time, while ownership is.
Better example might be statically typed languages. They were harder to use at first, but now with good type inference and features like generics, they are much more ergonomic than at first. The accessibility gap between static and dynamic languages has narrowed with time and maybe we can expect that user-friendliness of ownership will also improve like that.
That's due to trimming which can be also be enabled for self-contained builds that use JIT compilation. Trimming is mandatory for AOT though. But you can use annotations to prevent trimming of specific thing.
AOT doesn't support generating new executable code at runtime (Reflection.Emit), like you can do in JIT mode.
This piqued my interest on how it does it and after briefly checking the project it seems it only has two features for automatic photo categorization. 1) it can group photos by date and 2) It has face detection and recognition that uses trained weights (so ML "intelligence").
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