I think you have it backwards. Systems engineering is the big picture discipline of designing & managing complex systems while config management is a specific process within that.
The amount of paths in the wrong direction are infinitely more than then number in the right direction. You'll quickly realize this doesn't actually scale.
I'm a bit confused by this; are you referring to vanishing/exploding gradients during training or iteration at inference? If the former, this is only true if you take too many steps. If the latter, we already know this works and scales well.
The latter, and I would disagree that “this works and scales well” in the general sense. It clearly has very finite bounds by the fact we haven’t achieved agi by running an llm in a loop..
The approach of “try a few more things before stopping” is a great strategy akin to taking a few more stabs at RNG. It’s not the same as saying keep trying until you get there - you won’t.
> It clearly has very finite bounds by the fact we haven’t achieved agi by running an llm in a loop..
That's one hell of a criterion. Test-time inference undergoes a similar scaling law to pretraining, and has resulted in dramatically improved performance on many complex tasks. Law of diminishing returns kicks in of course, but this doesn't mean it's ineffective.
> akin to taking a few more stabs at RNG
Assuming I understand you correctly, I disagree. Scaling laws cannot appear with glassy optimisation procedures (essentially iid trials until you succeed, the mental model you seem to be implying here). They only appear if the underlying optimisation is globally connected and roughly convex. It's no different than gradient descent in this regard.
But test-time inference leads to better data to train better models that can generate better test-time inference data.
There's an obvious trend going on here, of course we're still just growing these systems and going with whatever works.
It's worked well so far, even if it's more convoluted than elegant...
What puts my mind at ease is that the current state of these AI systems isn't going to go backwards because of the data they generate which contributes to the pool of possible knowledge for more advanced systems.
I never made a claim that it's ineffective, just that it's of limited effectiveness. The diminishing returns kick in quickly, and it's not applicable in more domains than it is applicable.
It's a hyperparameter much like learning rate. If the learning rate is too high, the training process would not work either. Addressing this is just a matter of a grid search.
Sorry to break it to you but 10% profit on 6.5M rev is very low and will absolutely not fetch a high multiple, especially considering this is a mature 10 year old business. This is not a high growth business and you may have grown overly rose colored glasses by thinking it could be priced as one.
So much more. What assets/patents do they own? How much money is in the bank? What does their liability sheet look like? How “hot” is their industry right now?
Some time ago I found a good formula to plugin numbers and get a valuation multiple. The questions above were the ones that really moved the multiplier. A major lot of “startups” are in the 1-2x range. The hot ones will peak at 7-12x.
I suppose the industry is not hot right now. EdTech was never really very hot. It was 'luke warm' at best, a decade ago. They own a lot of software, also, they publish their own math textbook (both digital and print). They have licenses with thousands of schools across multiple countries. I don't recall they have any debt.
I feel like they could easily bump up profits by $2 million just by letting go of people... But they could probably double the license cost per student. Although schools don't have much money, they are kind of slow and bureaucratic; set in their ways. It's a small cost for them anyway, once a system is part of the curriculum, they'll probably pay extra to avoid reorganizing the lessons.
As you describe this is largely a cash flow business and the bulk of the value should be extracted via dividends to the benefit of major shareholders.
A tech enabled business needs gross margins north of 70% to be attractive from a leverage standpoint, unless revenue is scaling very rapidly. Without these there’s no attractive exit opportunities.
There are border towns where some workers do daily crossings. Without refund infrastructure in place it looks like this would add $250/day fee for all of these individuals
If all you do is run games and discord on your home PC memory consumption won't matter.
If you have multiple uses or work from home ... Discord expanding to 4 G to display the meme channel with all those cat photos will be annoying to say the least.
Case in point, I stopped running Discord on my laptop. Still run it on a desktop to keep in touch with some people, but it's not my default goto for any communication.
Also, just because most users don't know better, it doesn't mean that Electron apps aren't basically disrespecting the user's resources and passing needless costs to them. Especially if you have hundreds of million users the extra cost they pay dwarfs whatever you the app developer would have paid for a working native application.
The whole thesis OP is making is that this isn’t really true, evidenced by real world behaviours. Electron apps have some of the highest market share, even the worlds most popular ide is an electron app.
The amount of people who won’t adopt based on pricipal is exceedingly small.
I think your benchmark may miss the mark a bit if this is your angle.
20m records and 9k/sec isn’t very impressive. I would imagine most prospective customers have larger workloads, as you could throw this behind Postgres and call it a day.
FWIW I was interested but your metrics made me second guess and wonder what was wrong.
Fair point. Thanks for calling it out! To clarify, we’re focused on a specific use case: Kafka to ClickHouse pipelines with exactly-once guarantees. Kafka can’t provide exactly-once out of the box when writing to external systems like ClickHouse. You could use something like Flink, but there’s no native Flink-to-ClickHouse connector and Flink requires certain ops effort from the teams.
Our goal was to show users a very easy-to-reproduce load test to validate the results. As a next step, we’re actively working on a Kubernetes-ready version that will scale horizontally and plan to share those higher-throughput results with the HN community soon.
More than the team with no product. There's a huge difference between large entrenched businesses and early-stage startups. Turnover in large corporations is huge, the team has turned over multiple times already, they can do so again.
An early stage company that barely has any code? Yes, in that case, it's the team that matters because they haven't fulfilled the vision yet.
Are there any difficulties in obtaining TN Visas related to the stage of the sponsor company?
More specifically, is it feasible for a Canadian operating startup to open a US entity and employ Canadian engineers under the TN Visa category? This would be an early stage non-VC backed company, but profitable and paying above prevailing market wage.
