OG actionscript was very similar to Javascript. It only started to diverge when type hints were introduced.

AS2 was mostly following the direction of ES4 — so it wouldn’t have diverged if it hadn’t been abandoned.

Sounds like a less efficient version of the mixture of experts approach.

How does mixture of experts architecture work? Are they debating, or merely delegating?

From what I've read, for each token or input patch, the gate computes a set of probabilities (or scores) over the experts, then selects a small subset (often the top‑[k]) and routes that input only to those.

Ie each expert computes its own transformation on the same original input (or a shared intermediate representation), and then their outputs are combined at the next layer via the gate’s weights.

That’s post hoc combination, not B reasoning over A’s reasoning.

A MoE model is one model with expert parts which use less tokens. Which makes it easier for an expert to diverge to a better optimum state. Its easier to only need to know medicin instead of everything and being able to separate everything away from medicin even if certain names, concepts etc. are the same.

AI agents discussing things with each others would be more like one thinking model thinking throught the problem with different personas.

With different underlying models, you can leverage the best model for one persona. Like people said before (6 month ago, no clue if this is still valid) that they prefer GPT for planning and Claude for executing / coding.

Why are you doing meaningless microbenchmarks?

Are you claiming that this does not show the speed difference between socket vs in process communication?

Since OpenJDK was released there isn't much point maintaining GCJ.

American cities lack medium density mixed commercial-residential areas.

The problems you mentioned are policing and welfare problems, both things that America sucks at.

Or it becomes too expensive like in germany. Then it will suck too.

€58 a month for all local/regional public transport is very cheap.

Where I am, in another part of western Europe, a single-region ticket is normally ~30-50% more expensive than the all-of-Germany ticket.

It is subsidized. Through taxes you pay the full prize anyways. Just that someone else than you who decides, what do with your money, like subsidized public transport.

Nothing is more subsidised than cars and their infrastructure.

> Reason Japanese carpenters do or did that is that sea air + high humidity would absolutely rot anything with nail and screw.

The other reason was that iron was very expensive in Japan as they had only low quality iron ore.

The most useful feature of LLMs is giving sources (with URL preferably). It can cut through a lot of SEO crap, and you still get to factcheck just like with a Google search.

I like using LLMs and I have found they are incredibly useful writing and reviewing code at work.

However, when I want sources for things, I often find they link to pages that don't fully (or at all) back up the claims made. Sometimes other websites do, but the sources given to me by the LLM often don't. They might be about the same topic that I'm discussing, but they don't seem to always validate the claims.

If they could crack that problem it would be a major major win for me.

It would be difficult to do with a raw model, but a two-step method in a chat interface would work - first the model suggests the URLs, tool call to fetch them and return the actual text of the pages, then the response can be based on that.

I prototyped this a couple months ago using OpenAI APIs with structured output.

I had it consume a "deep thought" style output (where it provides inline citations with claims), and then convert that to a series of assertions and a pointer to a link that supposedly supports the assertion. I also split out a global "context" (the original meaning) paragraph to provide anything that would help the next agents understand what they're verifying.

Then I fanned this out to separate (LLM) contexts and each agent verified only one assertion::source pair, with only those things + the global context and some instructions I tuned via testing. It returned a yes/no/it's complicated for each one.

Then I collated all these back in and enriched the original report with challenges from the non-yes agent responses.

That's as far as I took it. It only took a couple hours to build and it seemed to work pretty well.

From what I have seen, a lot of what it does is read articles also written by AI or forum posts with all the good and bad that comes with that.

Delphi has been dead for 10+ years. Nobody uses it except for a few legacy applications and licenses cost $1200+.

You're kind of missing the point. Turbo Pascal has been dead for a lot longer. Or is it?

The point is that TypeScript and C# are extremely similar for a good reason, not a coincidence, and that Anders Hejlsberg knows what the fuck he's doing and talking about, and has been implementing amazing groundbreaking well designed languages and IDEs for a very long time. Turbo Pascal was so great it flummoxed Bill Gates, so Microsoft sent a limo to recruit and hire Anders Hejlsberg from Borland, then he made Visual J++, Windows Foundation Classes, C#, and TypeScript.

https://en.wikipedia.org/wiki/Turbo_Pascal

>Scott MacGregor of Microsoft said that Bill Gates "couldn't understand why our stuff was so slow" compared to Turbo Pascal. "He would bring in poor Greg Whitten [programming director of Microsoft languages] and yell at him for half an hour" because their company was unable to defeat Kahn's small startup, MacGregor recalled.

https://news.ycombinator.com/item?id=8664370

>"According to the suit, Microsoft also offered Mr. Hejlsberg a $1.5 million signing bonus, a base salary of $150,000 to $200,000 and options for 75,000 shares of Microsoft stock. After Borland's counteroffer last October, Microsoft offered another $1.5 million bonus, the complaint says."

> missing the real problem that your developers are probably using their home gaming PC for work because it's 10x faster than the garbage you gave them.

> Yes, this happens. All the time. You just don't know because you made the perfect the enemy of the good.

That only happens in cowboy coding startups.

In places where security matters (e.g. fintech jobs), they just lock down your PC (no admin rights), encrypt the storage and part of your VPN credentials will be on a part of your storage that you can't access.

In my experience, fintech companies (including ones that either belong to or own a bank) follow one of two playbooks:

- Issue high-powered laptops that the developers work on directly, then install so many security suites that Visual Studio takes three minutes to launch. The tech stack is too crusty and convoluted to move to anything else like developer VMs without major breakage. - Rely 100% on Entra ID to protect a tech stack that's either 100% Azure or 99% Azure with the remaining 1% being Citrix. You can dial in with anything that can run a Citrix client or a browser modern enough to run the AVD web client. If they could somehow move the client hardware to the Azure cloud, they would.

I don't really associate fintech with a modern, well-implemented tech stack. Well, I suppose moving everything to the cloud is modern but that doesn't mean it's particularly well done.

Microsoft, Google, or Amazon don't t care about your fintech code. Other fintechs do.

The threat isn't your cloud provider stealing your code, it's your own staff walking out the door with it and either starting their own firm or giving it to a competitor in exchange for a "job" at 2x their previous salary.

I've seen very high security fintech setups first-hand and I've got friends in the industry, including a friend that simply memorised the core algorithms, walked out, rewrote it from scratch in a few years and is making bank right now.

PS: The TV show Severance is the wet dream of many fintech managers.