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The evolution of this is to use agents, and have users "chat with the data"

Yes, you can actually do this already because we expose a REST API and TypeScript SDK functions to execute the queries.

The idea, as I understand it, is not to run edgejs multitenant in the sense that have multiple tenants under the same edgejs process. Instead, you spawn one edgejs process for each tenant. So in the openclaw example each sandboxed call would be a new edgejs process.

You mean the gateway? I see, but what I concern not only multitenant or gateway process, agents need tools, that brings more challenge to entire runtime.

It's there, you just need to use it with the responses API. Set model field to 'gpt-5.3-codex'


Replace "Agent" with "Employee" and apply the same algorithm. Evaluate employee efficiency. Profit?


I'd unironically (and privately) want to do that with the code of both myself and those around me - to maybe see who I should listen more to, as well as who maybe less (ideally down to the feature level), because everyone has opinions, sometimes loud ones, but some approaches lead to a lot of churn and issues over the years.


I do wonder what the outcome would've been had the 4 hours been spent in perfecting the input to the AI-generator. It's not a fair comparison if the same amount of time is not spent on both.

How good mesh can a human produce in the time that it took for the gen-AI?


Another approach is to spec functionality using comments and interfaces, then tell the LLM to first implement tests and finally make the tests pass. This way you also get regression safety and can inspect that it works as it should via the tests.


I've never gotten incorrect answers faster than this, wow!

Jokes aside, it's very promising. For sure a lucrative market down the line, but definitely not for a model of size 8B. I think lower level intellect param amount is around 80B (but what do I know). Best of luck!


Make it for Qwen 2.5 and I'd buy it.

You don't actually need "frontier models" for Real Work (c).

(Summarization, classification and the rest of the usual NLP suspects.)


I completely agree. So many things can benefit from having "smart classifiers".

Like, give me semantic search that can detect the difference between SSL and TLS without needing to put a full LLM in the loop.


As someone with a 3060, I can attest that there are really really good 7-9B models. I still use berkeley-nest/Starling-LM-7B-alpha and that model is a few years old.

If we are going for accuracy, the question should be asked multiple times on multiple models and see if there is agreement.

But I do think once you hit 80B, you can struggle to see the difference between SOTA.

That said, GPT4.5 was the GOAT. I can't imagine how expensive that one was to run.


Amazing! It couldn't answer my question at all, but it couldn't answer it incredibly quickly!

Snarky, but true. It is truly astounding, and feels categorically different. But it's also perfectly useless at the moment. A digital fidget spinner.


does no one understand what a tech demo is anymore? do you think this piece of technology is just going to be frozen in time at this capability for eternity?

do you have the foresight of a nematode?


Yeah, two p’s in the word pepperoni …


There is an ongoing lobbying push for "Made in EU" [0] which is unrelated to OPs article. The winds sure are blowing towards European sovereignty. Thanks, Trump!

[0]: https://www.euronews.com/business/2026/02/19/made-in-europe-...


Inspiring! I'll likely pursue the same thing.


I don't see the point nor the hype for these models anymore. Until the price is reduced significantly, I don't see the gain. They've been able to solve most tasks just fine for the past year or so. The only limiting factor is price.


Efficiency matters too. If a model is smarter so it solves the same task with fewer tokens, that matters more than $/Mtok


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