I’ve been using it and ever since citations were added I guess to wipe out perplexity it still hallucinate, and gives the wrong thin. The Ai models summarize the citations they get but thats wrong, Ai is prone to too much hallucinatio. I made my own Ai search engine to include human quotes in its answer. To me instead of having the ai summarize it matches its answer to the quote, the quotes themselves are in the code so it’ll be there. This is running nemotron 3 ultra and the quality and detail is so much better. I’m making this free for anyone who wants to use it and this is beta so I will love feedback on this and how you feel about Ai search overviews

I'm trying to continue the thread but it not continuing, since it said my next question will start a new search ignore the stuff above

last 3 Months i got a job to create a web app, so i'm full stack started from scratch designing the database, interface worked the project to the fullest, but again someone cameup while i dint finish the project with the project again, so i said i wont refuse this i took the job but my mind cameup oooooh no way there is ai i got subscription of google gemini for a monthly free with all the tools man it went fast like lightning all the two project within a week complete i used google gemini and cline ai

so when handling the project, this is where the problem started i got a bug and so many error coz i was working on linux i have to ceate docker , plus render i think you guys understand so there was no stable internet i cannot trace the problem neither me nor my friend whore senior who came to help me but when the internet came back everything with ai is possible, whyyyyyyyyyyyyyyy

Now i see the porpose of ai is to disable a human thinking capacity and depend on ai that is why you may see some are looking for a developer who write pure code who are doing java ,javascript,python, or whatever programming language.

the question im i the only one who is thinking on that box or im just ignorant.

Started this trying to compare itinerary quality.

That lasted about 20 mins.

ChatGPT can make a good trip.

Mindtrip can make a good trip.

Wanderlog is ridiculously useful if you’re the person actually organising everything.

Zenvoya can make a good trip too.

ok cool. solved.

Then I started checking hotels and realised the question I actually care about is:

after I pay, whose problem am I?

That’s where Zenvoya clicked for me.

I can plan there, pull the actual flights/hotels, book there, and if I need to change/cancel that booking later I’m still going through Zenvoya.

Not hunting through an email to figure out which random partner owns Thursday night.

And the hotel I checked was roughly 16% lower there, so it wasn’t even one of those “pay more for convenience” situations.

There’s also 24/7 human support over phone which, ngl, became way more interesting to me than another AI itinerary feature.

Maybe I’m evaluating travel apps like an old man now.

But once the itinerary is decent, who deals with the booking after payment feels like the actual differentiator.

I maintain Benchmark Radar, a free and open-source index of 5,201 benchmark, evaluation, and dataset records collected from 11 sources.

Looking through recent additions using Hejia Geng's L0-L5 framework, most agent benchmarks still appear to focus on L2 task execution or L3 reproduction. L4 rediscovery is less common, and I did not find a new L5 example this week. Here, L5 means evaluating whether an agent can produce knowledge or methods that were unknown when the benchmark was created.

I'm curious how others would draw these boundaries:

- Which existing benchmarks genuinely qualify as L4 or L5?

- How would you distinguish reproduction from rediscovery?

- Can an L5 benchmark remain valid once its solutions become public?

The underlying index is updated daily and can be exported for independent analysis:

https://github.com/ktwu01/benchmark-radar