zihotki

> Specs are the ground truth.

Oh, I would like to disagree. Specs are transitional and have low value in the long term. The project/product documentation which one should make based on the specs and the actual implementation and found gotcha's - are the truth.

One day the other tools would catch up, it's not the moat, it's just a missing feature.

AI is coming there soon. May be faster than one can learn it.

I wonder why there is no Github Copilot in the comparison. It also supports BYOK and is quite capable

Technically, anything what you plug into your car charger would do. The requirement is to have a licensed meter in the charging box. When setting up, you configure connection to your ERE provider and it just reports.

There are two parts in the data you supply to Jev for classification - the prompt describing your classification and the data. The data can be quite small - a simple chat message. And prompt part could be considerable since you need to describe your rubrics well.

With Jev you each time pay for your prompt, you can't cache it.

I would hold your horses to paint it as dirt cheap.. In my cases for spam detection Luna was 20% cheaper due to prompt caching, although not as fast.

That's what ADRs (architecture decision records) are for.

Documentation of the project are the projections of coded intent at a higher zoom level. C4model is an amazing concept and applies well to the context management and docs

In Netherlands you could also get paid for charging your car. There is such thing called ERE Certificates - emission reduction unit certificate is generated by offsetting emissions. Pollutters have to buy them to offset the harm. It's up to 0.1 euro per KWh (electricity costs 0.28 euro per KWh), depending on the demand.

And don't underestimate the complexity and importance of eval suite. It's a lot of work to get a good dataset. Your agents are as good as your dataset is.

My bet is that humans will kill humans faster than AI. AI enables not only good actors, but also bad actors: Russians, Iranians, Houthis, other terrorist groups - they all now effectively use AI to speed-up development of their weapons as well as hybrid warfare.

Landing page screams llm-isms, and after reading and looking around, I still can't identify what problems it tries to solve. The features described are just standard integration glue, they have little relevance to context or why's.

Why does it worth investing time into adopting this one instead building an in-house solution tailored to existing process?

Have you tried reading/reviewing the code claude generates? That could have prevented the issue with the accounts.

If everyone starts applying cooldowns, won't it postpone the problem? So now there is a considerable amount of users who are affected and someone from the affected group discovers the infection and reports it.

But if everyone will be delaying updates, won't be there less chances to catch it in time? I'm not fully sure if it's possible to preventively scan all NPM packages or how much compute it would require.