zigzag312

Even if the purchasing entity is backed by a foreign country?

But many of the listed hypotheticals are not dependent (on top) on others, and since there are multiple that actually increases probability of an undesirable outcome.

Yes these can be used as alternatives sometimes, but they are not really the same thing. We need a streamlined way for reusable libraries (written in different languages) to be consumed by different languages while not being limited to a specific runtime or platform. When code from different languages needs to be able to run inside the same process, with low calling overhead and avoiding copying when possible.

Your answer just throws in anything that enables programs to communicate somehow, ignoring all the differences and tradeoffs to what is being discussed here. Many of your solutions lock you in to a specific platform, a language or add a non-trivial overhead like message serialization or add an unnecessary complexity to a program. Also, IR does not solve the same problem as ABI.

Exactly! It's surprising to me how little development is happening in this area.

What about a better ABI abstraction?

Can you share source of this data? I have my doubts about the quality of the data, since OAuth2 is such a complex system with so many footguns.

In the end there is always some long lived secret. What changes is just where and how it is stored, secured and used.

I bet we can generalize to say that data shows that you will likely fail to properly secure any secret (including the ones used in OAuth2).

EDIT: An example: https://news.ycombinator.com/item?id=37973937

I agree with what you said. I just wanted to add that intelligent models probably need to have some notion embedded (but not everything), as some information retrieval is not trivial. Too few embedded notions will hurt it's ability to solve problems but from some point onward you'll get diminishing returns (where it starts to make sense to rely just on information retrieval).

For example, you if you instruct a model to create decoder for some data type users will upload to your website. The intelligent model without notions will retrieve information about that data type and build a working decoder, but it might miss from context that users uploading to a website means untrusted input and thus won't even try to gather information about what it needs to be done to securely handle such uploaded data.

Or if you give it a task to translate text to a language it didn't encounter during training. You can provide it with grammar rules and a dictionary for information retrieval, but I guess it won't perform as well as inteligent model that already has some fundamental notions of that language and only needs a dictionary to expand its vocabulary.

Gpt-4.1 only knows a lot of patterns, but doesn't have reasoning intelligence that would help it properly use that knowledge. So, a small reasoning model can easily beat it in a lot of tasks. The question is how will, 14 months from now, new small reasoning models compare to current big reasoning models.

How much information needs to be embedded is not yet clear, but currently, bigger reasoning models are still better at complex tasks than small reasoning models. Either sweet spot of embedded notions is higher that what current small models have or information retrieval ability needs to improve.

Understanding of a specific problem space can be a prerequisite to be able to form a proper query (i.e. to ask the correct question).

Model doesn't know what it doesn't know.

Ah, now I see what you meant.

Of course you can have reactive state, your complaint however was:

"The react in react stands for reactivity, however it is not." [because] "Its entire state management is not reactive"

React is primarily an UI library, not full state management library. And its UI is reactive.

UI is reactive, not state. You push changes to state and UI reacts to it.

It roughly compares with GPT-4.1 (!!), released 14 months ago

I think the mayor win for coding was reasoning. That's why such a small model can match GPT-4.1 in coding, but I suspect that GPT-4.1 still wins in general world knowledge due to bigger size.

It's not arguing that predictive power is bad. Just that people often mistakenly believe some phenomenon is understood more deeply than it really is, because a model can fit data and generate accurate predictions.

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).

pg_probackup seems to be another one.

This project looks nice, albeit a bit young for a backup tool.

Did you encounter any issues or limitations?

Is that info up-to-date? Their readme states:

  **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

https://github.com/databasus/databasus/issues/411

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.

Reference counting is a form of garbage collection.

Type unions only at first, but there's more being planned.

You can use dependencies that aren't using nullable reference types in projects that use it. You can enable/disable nullable reference types per file, as it only influences static analysis. There's no runtime difference between a non-nullable reference type and a nullable reference type.

.NET JIT supports dynamic PGO.

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.

Which part of the ecosystem is blocking your projects from using nullable references? I find them very helpful, but the projects were all newer or migrated to new SDK.

From what I've read, this is for the first implementation of unions, to reduce amount of compiler work they need to do. They have designed them in a way they can implement enhancements like this in the future. Things like non-boxing unions and tagged unions / enhanced enums are still being considered, just not for this version.

I personally like the direction C# is taking. A multi-paradigm language with GC and flexibility to allow you to write highly expressive or high performance code.

Better than a new language for each task, like you have with Go (microservices) and Dart (GUI).

I'm using F# on a personal project and while it is a great language I think the syntax can be less readable than that of C#. C# code can contain a bit too much boilerplate keywords, but it has a clear structure. Lack of parenthesis in F# make it harder to grasp the structure of the code at a glance.

There are more than 10x more users than lines of code

Being less efficient is also a problem, because if majority becomes less efficient (lower productivity), the overall wealth and economic growth of that society are going to decline significantly.

We do have evidence that when money is not a problem, we become less efficient. For example, monopolies or state run companies.

Just the first result from google: https://www.mdpi.com/2227-7390/11/3/657

Another problem with UBI is that, if we want for UBI to cover basic costs of living, these expenses are actually quite big as UBI essentially would need to cover things like rent, food and health services. Otherwise we will still have plenty of homeless people with UBI.

I think this further proves that the hypothesis of decoupling content from presentation is flawed. The question is how many more data points do we need before we admit that?