I've been using both and as far as I can tell with ccusage, the $ equivalent budgets are about the same between them now. This may have been true before anthropic doubled their quotas and openai 2x promo expired last month.
I have _yet_ to hit a time where TSA can make multiple hours disappear. Precheck w/ touchless ID lines are virtually empty at most airports, the actual security screen itself is quite fast given almost nothing needs to be removed from your bag these days. I still tend to arrive early, but I don't mind getting work done at the airport, especially at a lounge - though I've arrived very close to departure other times and still make it to the gate with plenty to spare.
On international returns, both Global Entry or MPC lines are virtually empty when I arrive (SFO)
The worst part is international arrivals in foreign countries, where immigration can soak up a lot of time, and you have no choice but to stand in line. Luckily I don't have to fly internationally too many times a year.
While I agree with some of these observations - the research cited in the article really do not match the claims at all from what I can tell.
> 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.
I’ve seen fake accounts created by bad actors attempting to pose as others for gaining remote employment. It’s possible that is what was happening, and the takedown was from LI taking down the profile from the bad actor.
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.
I'm actually curious on the evolution of models as well over time - I created a "created at" group by option to try to broadly visualize how models over time (half calendar years) drift in terms of pareto efficiency. There's a clear trend going up which is good.
I hope to build some better visualizations down the line on model evolution instead of just hacking it onto the current scatterplot.
There's plenty of evidence that good prompts (prompt engineering, tuning) can result in better outputs.
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)
Sure, but tricks & techniques that work with one model often don't translate or are actively harmful with others. Especially when you compare models from today and 6 or more months ago.
Keep in mind that the first reasoning model (o1) was released less than 8 months ago and Claude Code was released less than 6 months ago.
We think Grafana is still a great tool and many teams are heavily invested in the Grafana ecosystem. We'll continue to invest in Grafana support via ClickHouse's official Grafana plugin and that won't be changing at all with this release.
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.
Ah sorry I missed that part of the question, yes MongoDB and ClickHouse are the two stateful services. We'll be looking to see if we can offer some mode to simplify it down to just ClickHouse but that'll take a bit more work.
fwiw it seems like “reduce white point” overrides any of these effects - which is good.