> An NBER study of support agents [2] found generative AI boosted novice productivity by about a third while barely helping experts. Harvard Business School researchers found the same pattern in consulting work [3].

The first work cited was a research study on GPT-3(!) from 2020. Which is a barely coherent model relative to today's SOTA.

The second HBS research study literally finds the opposite of what's claimed:

> we observed performance enhancements in the experimental task for both groups when leveraging GPT-4. Note that the top-half-skill performers also received a significant boost, although not as much as the bottom-half-skill performers.

Where bottom-half skilled participants with AI outperformed top-half skilled participants without AI. (And top-half skilled participants gained another 11% improvement when pared with AI). Again, GPT-4 model intelligence (3 years ago) is a far cry from frontier models today.

Other times they would just link to real LinkedIn profiles, but the LinkedIn profile will say that they’re not actively looking and are a victim of id fraud basically.

It’s been a huge issue spotting candidates falsifying information since remote work took off unfortunately. They payout is if they can get at least 1 or 2 paychecks before being found out, they’ve made a good profit.

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I hope to build some better visualizations down the line on model evolution instead of just hacking it onto the current scatterplot.

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Slot machines on the other hand are truly random and success is luck based with no priors (the legal ones in the US anyways)

Improving LLM output through better inputs is neither an illusion, nor as easy as learning how to google (entire companies are being built around improving llm outputs and measuring that improvement)

- Reorder columns: You should be able to do so by modifying the order of the SELECT statement at the top.

- Wrapping: totally agree - this is something we're adding in, it's on the near term todos.

- Pin fields: you're totally right, you can pin values but that doesn't prioritize the filter in general. I'm getting this one in the queue.

- Time picker: I hear you with Google Cloud's time picker, they did indeed make a really nice one. Though I'm curious to hear more about the breakdown, is it wanting to customize how that chart is grouped beyond log level?

- Autocomplete: We had a regression if you're on v2.0.0 which is what I suspect you're hitting. If you're on v2.0.1 lucene should have field value autocomplete. We don't have it yet for SQL.

- Live View: Can you clarify what is scrolling? Or is this about disabling live?

- Chart View: I just tried really quick and it seems okay to me, do you mind sharing more details?

- Sidebar: Is this related to field popularity to pin fields or something else?

This feedback is all super helpful - you can probably tell we're still early in building out the perfect experience. I'd love it if we could dive in deeper either on our discord (https://hyperdx.io/discord) or email: michael.shi@clickhouse.com. Either way this has been exceptionally helpful :)

However, there's a bit of a fundamental difference in the user experience we're targeting. Grafana has really excelled at traditional monitoring dashboards, low cardinality monitoring workflows.

ClickHouse unlocks a newer paradigm of high cardinality, high performance observability. It enables a new set of workflows/UX that allows engineers to query novel problems quickly as opposed to working off of static dashboards. That's really a big focus of ours, so you'll see we do exploration/search/syntax/UI layout is quite different from Grafana due to this.

At this point it isn't even an original realization of ours. Just as an example, Shopify built a complete custom app (only keeping the auth part of Grafana) while migrating to ClickHouse for similar reasons.

But in this case there's probably no reason for you :) These improvements will come to our cloud offering of course as we work on rolling out upgrades from HyperDX v1 to v2 in cloud.

My take is that OTel is overall the best investment, it's widely supported across the board by many companies and other vendors. It's also constantly being improved with interesting ideas like otel-arrow which will make it even more performant (and columnar friendly!)

We'll also continue invest in the OTel ecosystem ourselves in making it easier and easier to get started :)

That being said, I'm not saying that OTel collector is always the right choice, we want to meet users where they are. Some users have data that gets piped into S3 files and we ingest off of a S3 bucket just due to how they've collected data, some use Vector due to its flexibility with VRL, focus on logs, or specific integrations it provides out of the box. So the answer is always - it depends :) but I do like OTel and think the future is bright.

Beyond raw performance and cost effectiveness, which is quite important at scale, we work a lot on making sure the application layer itself is intuitive to use. You can always play around with what ours looks like at play.hyperdx.io :)

Otherwise yes you can authenticate against the other versions with a email/password (really the email doesn't do anything in the open source distribution, just a user identifier but we keep there to be consistent)