What is vectorware's business model? Are you planning to sell support/consulting to companies using your stack? Or are you looking to sell licenses to your tool? Or something else?
The tentative plan is to open source all the compiler and `std` bits with our products built on top (compilers are not good businesses). More about our products coming in the next couple of months!
This is really cool! It sounds like y'all have a compiler fork that you are using to make this work. I wanna tinker with this, is your compiler available?
The company I work for is in the process of replacing jira with an in home grown solution. They ran through a bunch of trial with pretty much every vendor under the sun looking for a replacement for jira, but none of them really sparked joy. Someone rolled their own a year or so ago, and it started gaining adoption, and people liked it enough that the whole company is switching to it.
I don't think that means saas is dead. There is a strong preference to buy instead of building if there is something worth buying.
What I find so funny about heads of AI companies coming out saying things like this, is their own career pages suggest they don't actually feel that way.
Hell yeah! I have been looking for something like this.
On the home page when I search for something like `Southeast` it shows 0 shops, but if I actually select it I get 223. This was confusing and made me unsure of how many relevant hits I would find.
It would be really nice if you could search in smaller geographic areas. State boundaries would be really nice to start. As you continue having city / intra-state regions would also be huge for the cases where you want to go in person to these places.
I looked at it more last night. It would be great if you expanded into other forms of manufacturing and related services.
For example, if I am having a complex part machined, I probably need some electronics to drive that things. Having a supplier for electronic parts, circuit board printing, plastic molding for housing those electronics would be needed to finish the job.
One of the reasons people choose webp is file size. If your images are smaller then you transfer and store fewer bytes. That can have benefits on cost and speed of transferring those to the client.
That is interesting. I have worked in the space too, and have seen similar attitudes.
I think it often comes down to who is responsible for making decisions with that data. If a product or business person is the one driving a feature, and looking for adoption, the engineers likely aren't going to be invested in building out sophisticated metrics. They get the metrics they are responsible for from their cloud provider (resource use/latency/scale).
I think that problem is compounded by the perception that these integrations are going to tank your products perf (may hurt the metrics engineers care about).
I think all of those dynamics change in really big companies with thousands of engineers. Then you can often end up in a situation where engineers are now required to maximize product metrics, and need visibility into their small slice of the pie.
So, I think its largely incentive, which is why I see all of the metrics vendors targeting product and sales people in small/mid sized companies.
That’s an interesting point. One thing I’ve noticed, though, is that even the people who are directly exposed to incentives (usually product and marketing) tend to focus almost exclusively on the final KPIs they’re measured on, like revenue or conversion rate.
Because of that, the analytics layer is often seen as something secondary. As long as the top line numbers are moving, there’s little perceived urgency to invest in a structured analytics foundation that explains why those numbers move.
So even when incentives exist, they’re often too outcome focused. Analytics that helps understand mechanisms, not just results, struggles to justify itself until something breaks or growth stalls.
I think that is because they are being judge by their outcomes.
In the space I was in (ads) users were highly mistrustful of the data. They felt everything was kind of fuzzy (eg how well are you measuring unique users and their actions).
They would end up using multiple vendors (and we would have to spend a lot of time comparing and contrasting results). They really really want "apples to apples" comparisons.
At the end of the day they were trying to answer, does what I am spending my money on give me the results the business needs? To your point there is a lot of nuanced data, but their bosses definitely only cared about the top line, did it move the needle.
I have really liked fq for binary visualization.