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lyind

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I have used an e-ink phone for messaging and reading for a few years. It really helped with occasional headaches and the general eye strain.

Switching back to a regular OLED phone (because of OS/firmware security), I noticed eye strain again, after just a few days of use.

Usability in sunlight and battery endurance are real advantages I miss.

Waiting for a big manufacturer to design and market a secure eInk phone, targetting the people who prioritize messaging/reading/navigation over TikTok, Instagram, YouTube and the like.

The goal shouldn't be recognition but getting the bug fixed. A working system for all is reward in itself.

That said, attribution of work is a major theme in academia and business, where professors or department leads traditionally get credited for their student's or subordinates hard work.

This nice approach has at least these drawbacks:

1. Swapping drives is hard

   * may be overcome by declaring failure domain = node

2. No powerloss protection advertised to OS, ie. slow synchronous writes
   * may be overcome by software hacks and whole-system battery supply

3. Potential slowdown on continuous write load (weeks or months, depending on drive)
   * may be overcome by software in _some_ situations

At least the last two points are a no-go for enterprise use-cases, if not addressed.

If you are an organisation you'll have a Google Workspace account and can administer your groups and users as one would expect from any IAM solution.

Admin's having to reset passwords with Google is still rare, because Google asks users to keep their own recovery methods.

Honestly, I can understand the frustration from messing up one's private account access.

But don't blame Google, please.

All this ML/AI talk makes me wonder if there could be an angle for something like "discovery of principles by debugging".

Imagine, for example, training a neural net to classify random numbers into prime and non-prime.

If such a model succeeds at the task, how would one understand what the basic "theory/higher level function" is which the model "discovered/learned"?

I am definitely gonna try this, just for fun.

We use Kubernetes IPv6-only for some time now. Using Cilium CNI (software defined network) plugin.

Works great after find workarounds for some minor issues. Much less hassle than with tight/inflexbile IPv4 address space.

DenoDB 5 years ago

I have reached the same conclusion regarding ORMs after strugling with Hibernate/JPA for some time.

At least for Java and Kotlin the awesome library jdbi ( https://jdbi.org/ ) implements a very useful hybrid approach.

One creates DAOs and Repositories to abstract away the DB and map results and arguments to/from objects on the fly. All while retaining full control over the SQL and all mapping aspects.

This way SQL and mapping can be optimized to leverage the features of each database (ie. PostgreSQL's array, UUID, hash and JSON types) or be handled generically.

The SQL loading can also be customized to read SQL from pure ".sql" files in resources/files or from inline specification via annotations.

The jdbi developers have in the past reacted very fast and competent to issue reports or PRs.

If find applications built using this library more easy to understand and also better performing than ones using a full-blown ORM.