You're being deliberately obtuse. Canvas has many many features. Wikis and discussion boards and quizzes (with some anticheat) and groups and the list goes on and on. Furthermore, while it was never the flashiest thing, it did it better than many of its predecessors. Yes, an individual class may not use all of these features, and yes canvas has suffered feature creep even over my time as a student and yes canvas is not doing anything technically challenging, but there is enough of it that each school rolling their own everything would be a drastic waste of everybody's time and money.
Those are the exact features I think are stupid and wastes of time both on the educators trying to bake a lesson into these clumsy interfaces, and also the students who have to use this feature once and only once and never again for this dumb assignment in one class. They merely serve to entrench educators into using the canvas system but they offer no added functionality to education.
Canvas quizzes cannot beat the anticheat of having another screen out of view. Lock down the browser all you want, it is pure security theater.
Best anticheat is still taking the quiz on paper in the classroom. Best way to engage with students is to still speak with them and ask that they might speak up.
"Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute"
Really curious how many people actually get close to that level of usage? Their general business plan only offers the $100 version, with pay-as-you-go above that.
If 95% of people are using $100 of value a month, the whales may not be hurting them that badly.
I wrote my own "harness" and it exposes the api dollar cost since those come back in the responses even while using my sub. The conversations are typically $40-$60 and the longs ones with multiple compactions get to $100+
I say "Harness" because it's just a web interface that uses `cluade -p` so I can run it in containers and remotely access it.
That’s based on Anthropic’s retail price right? Not a fair comparison, like saying that Netflix must be losing money because every movie rental is $4 and a Netflix subscriber can watch 20 movies in a month.
Yes, my thoughts exactly. Productivity by definition creates things, hopefully valuable things. Is all the extra burn on chatbots worth the cost? Has Uber somehow gotten dramatically more efficient and effective due to this massive budget overrun? Or have they just given people shiny and expensive ways to push the same work around?
> If it was actually productive, then the revenue would increase and affordability wouldn't be a question.
Revenue has increased. Have you seen Meta's latest earnings? +33% revenue - in this economy.
Affordability is not a question. There is a reason companies like Meta have no issue with their engineers spending $1k/day on tokens. It's just not that much compared to how much they make per employee.
It sounds like this has a pretty falsifiable claim here - is the revenue attributed to a tax thing? Then it's clearly not attributable to code.
I agree that the macro picture would speak for itself. Can you point to any macro level detail that is indeed cleanly showing benefits from increased productivity from LLMs?
I think the lack of evidence for LLM productivity is not an indictment on LLMs… it’s an indictment on the industry still having no real way to measure developer productivity in general.
I agree that you can't draw any conclusions about AI, but their revenue increased by 33% percent. That's just straight income before any taxes or costs are applied.
I completely agree with you. I pointed out replying to the same person that in the same report their ad impressions were up 20% and the price per ad was up 12%, which account for a huge amount chunk of that revenue increase.
All I was saying here was that tax breaks wouldn't impact revenue since revenue is reported before taxes, operating costs and anything else.
That means absolutely nothing in the context of this conversation. It says right in their release ad impressions are up almost 20% and cost per add is up 12%. Those two metrics alone account for most of the increase in their revenue. Absolutely no conclusion can be drawn regarding the impact of AI on those numbers one way or the other.
It's not like they used AI to crank out some new revenue generating piece of software, or massively reduce operating costs. In fact their operating costs rose by 35%.
> It says right in their release ad impressions are up almost 20% and cost per add is up 12%
Have you wondered why this is the case? How do you think they increased impressions so much at their scale? How they did this despite losing 20M users?
To put it clearly, AI at every part of the pipeline: writing software, product features/experiments, A/B testing them, and pushing them out to users. Even before you get to something like LLM driven recommendations, you can virtually entirely automate the process of finding more "engagement alpha" with AI.
If you have an evidence from their financial releases showing a correlation between AI usage and increased revenue I'd love to see it. Otherwise you're just making wild ass guesses.
Edit: Also, historically Meta has been growing revenue by 30 to 50 percent for the last decade. With the only exception being 2022 and 2023. So it's not like recent performance is an outlier.
That is not true at all. No matter how "productive" a company is means nothing if people aren't buying your product. And using LLMs to be more productive will not convince anyone to buy your product. Human creativity and intuition to make a product that people want to use is what sells. Productivity for productivity's sake doesn't really move the needle at all, and can make things worse.
I'd argue it's often the contrary -- since it's easy to ship features and fixes, people often ship things without questioning if it makes business sense to support a use case, or if the design is solid. Now you have exactly the same revenge but more things to maintain
On this side of the equation I think you start pulling in customer context and risk analysis on the downside. What is the churn risk for operation at 99% vs 99.9% availability.
If your site is for B2B and impacts customers own operations or revenue, you'll likely be wanting to chase the 99.9%, customers won't tolerate the 1.5 hours per week of downtime and will churn.
However, if the value you're site creates is tolerant to those sorts of disruptions, someone is just inconvenienced and can come back later, a large investment to move from 99% to 99.9% wouldn't be justified. There is literally no impact from the investment. The harder part will be the reality, most investments will be somewhere in the middle with ambiguity on the impact. IIRC, SRE principles do talk about this when setting SLOs in different terms.
I've heard some companies refer to the concept as economical thinking, which is I think a great way to think about it. Doesn't mean you'll always get it right, more so that we embed being conscious about the ROI in our work.
I also believe this is an area that I've observed several engineers really struggle with, especially when moving from big tech to startups, where it's really easy to import culture from another company, and in earlier stages of startup life... if you don't have product-market-fit, it doesn't matter how good you're availability is. Attention is a resource, make sure it's allocated to what creates value for the customer.
Depending if the site has a direct competitor and non-sticky customers, you can often get accurate loss estimates from outages. For example, friends of mine at Doordash would know when UberEats was down by the corresponding spike in traffic to their app. The competitor captures all the lost traffic.
Most enterprises will have a harder time quantifying losses, as some percentage of customers will come back later. To understand that, you need to look for a drop in completed purchase rates compared to site visits.
For a SaaS, it's even more difficult, as customers are often held captive by long contracts and might tolerate SLA breaches up to a certain point. A reasonable, though fictional, proxy would be the revenue for the contract pro-rated against the uptime during that period.
This is my thought too. The eggheads in accounting set budgets, and we produce products within that budget. I could be twice as productive with twice as many people, and maybe 50% more productive with good AI, but if it's not budgeted for it's an issue (especially short-term before the product is released).
This is why people still need to know how to write code and why it is asinine to have an LLM write code without a human reading it. Good developers should know what good code looks like and push back when what they're fed is wrong.
Any sufficiently competent typescript developer can build out an adhoc wrapper (that just inherits the type definition and passes along whatever it is passed after altering it however needed) in under a hour. It doesn't scale in the sense that you don't expose a configuration, but config as code is king.
(Source: have built out much more scuffed variants of this than the one I just described like https://github.com/boehs/ajar)
I guess a LLM can do as well. Although that's not something I'm quite ready to admit.
I've wrapped fetch a few times but i don't think I'd blame someone if they got tired of wrapping it and wanted a consistent interface across all the projects they work on.
Does this support Fusion as well? I've done photo editing using a fusion workflow before and while clunky it was the only program that could reasonably accommodate my needs at the time.
reply