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He was speaking as if it was still 1980s. Now we have cloud based ML doing the optimization finding. ML will (eventually) be able to generate biz logic code, a

He was speaking as if it was still 1980s. Now we have cloud based ML doing the optimization finding. ML will (eventually) be able to generate biz logic code, and most assuredly infrastructure config (a cloud API has a limited set of possible configs of value to our sorting patterns), UI code (we gravitate towards a limited set of UX it seems), to solve much of our daily programming work. ML can’t invent future ideas, it can’t evolve itself without us making new hardware for it. But it will implode the blue collar dev job market eventually.

platz 5y ago
how many years is eventually
- ausbah 5y ago
  5-10 years, every year
Zababa 5y ago
> ML can’t invent future ideas, it can’t evolve itself without us making new hardware for it. But it will implode the blue collar dev job market eventually. I have doubts about that. Open source code seem to achieve a large part of the same function as ML (implement "boring" code for the Xth time) but it only has increased the number of blue collar devs. On the other hand, some no-code tools are opening programming to a large number of people (Excel, actions with your iPhone, things like that). Programming is one of those things that everyone would benefit from knowing, but time is limited, so anything to lower the barrier of entry will just lead to more programming. And more programming means even more programming (someone has to develop the no-code tools, the cloud infrastructure, etc).
- mlosgoodat 5y ago
  Remember; human function names and object names are for human consumption. We could write a whole lot less if not for all the programmers who need context. We know from our hardware platforms what we can and cannot compute; their spec defines the limits. We don’t need dozens of competing languages when our goal is “reserve memory, compute values in that memory in this order, free memory when done”. My startup is focusing on learning what code shapes are ok from the context of security and developing a filtering tool to avoid allowing merging commits that violate that spec. We have a lot of interest from DOD and SV companies, in the form of “pre-emptively avoid security issues via coders who write stupid code.” Eventually we’ll have tailor made hardware with little to no generally programmable surface area. Because our generation of computing is behind us. Kids now just want the thing to emit results. The thing that’s holding them back is maintenance of “career oriented job life”. Rather than build programs, software people babysit dependency lists and process. Google products are a mess because it’s about capturing worker and customer agency, not engineering novel things.
dragonwriter 5y ago
> But it will implode the blue collar dev job market eventually. There is no “blue collar dev job market”, and the end of the dev kob market that has a hint of a bluish tinge in the collar is the end that is continuously being by tooling progress. But that just expands the scope to which it is useful to apply software development, increasing jobs and wages in software. > ML will (eventually) be able to generate biz logic code, and most assuredly infrastructure config (a cloud API has a limited set of possible configs of value to our sorting patterns), UI code (we gravitate towards a limited set of UX it seems), to solve much of our daily programming work. Its not “machine learning”, but we already have tools that develop code in all of those domains from higher-level descriptions that what previous generations of coders supplied to them. And each generation of that tooling just serves as a progressively greater output multiplier on time devoted to developing software.
- mlosgoodat 5y ago
  There is a “blue collar dev” market. I dated a “web developer” who worked for the county. She used a Photoshop like tool to update the layout of an intranet, and occasionally straighten some PHP. She knows nothing of computer science, just the tools she was trained on. She knew nothing about how the hardware works, that RAM and SSD were different types of memory or which situations make one preferable to the other, cause that was already figured out in the tooling. There are a lot of people who work like that and call themselves software engineers. We’re ultimately trying to make hardware do something, but rather than that, we invented more ornate interfaces to the same old hardware for … jobs. A hardware based future where the onboard ML chip can generate a AAA game or Pixar level media will happen. Because then they don’t have to pay a bunch of coders or artists. Greers “program the perimeter, not the area” comes to mind. Most software is simple logic and network effects. As our manufacturing process allow us to make customizable silicon from step 1, why not listen to Greer, bake the best perimeter for a task into hardware?

platz 5y ago

how many years is eventually

ausbah 5y ago
5-10 years, every year

Zababa 5y ago

> ML can’t invent future ideas, it can’t evolve itself without us making new hardware for it. But it will implode the blue collar dev job market eventually. I have doubts about that. Open source code seem to achieve a large part of the same function as ML (implement "boring" code for the Xth time) but it only has increased the number of blue collar devs. On the other hand, some no-code tools are opening programming to a large number of people (Excel, actions with your iPhone, things like that). Programming is one of those things that everyone would benefit from knowing, but time is limited, so anything to lower the barrier of entry will just lead to more programming. And more programming means even more programming (someone has to develop the no-code tools, the cloud infrastructure, etc).

mlosgoodat 5y ago
Remember; human function names and object names are for human consumption. We could write a whole lot less if not for all the programmers who need context. We know from our hardware platforms what we can and cannot compute; their spec defines the limits. We don’t need dozens of competing languages when our goal is “reserve memory, compute values in that memory in this order, free memory when done”. My startup is focusing on learning what code shapes are ok from the context of security and developing a filtering tool to avoid allowing merging commits that violate that spec. We have a lot of interest from DOD and SV companies, in the form of “pre-emptively avoid security issues via coders who write stupid code.” Eventually we’ll have tailor made hardware with little to no generally programmable surface area. Because our generation of computing is behind us. Kids now just want the thing to emit results. The thing that’s holding them back is maintenance of “career oriented job life”. Rather than build programs, software people babysit dependency lists and process. Google products are a mess because it’s about capturing worker and customer agency, not engineering novel things.

dragonwriter 5y ago

> But it will implode the blue collar dev job market eventually. There is no “blue collar dev job market”, and the end of the dev kob market that has a hint of a bluish tinge in the collar is the end that is continuously being by tooling progress. But that just expands the scope to which it is useful to apply software development, increasing jobs and wages in software. > ML will (eventually) be able to generate biz logic code, and most assuredly infrastructure config (a cloud API has a limited set of possible configs of value to our sorting patterns), UI code (we gravitate towards a limited set of UX it seems), to solve much of our daily programming work. Its not “machine learning”, but we already have tools that develop code in all of those domains from higher-level descriptions that what previous generations of coders supplied to them. And each generation of that tooling just serves as a progressively greater output multiplier on time devoted to developing software.

mlosgoodat 5y ago
There is a “blue collar dev” market. I dated a “web developer” who worked for the county. She used a Photoshop like tool to update the layout of an intranet, and occasionally straighten some PHP. She knows nothing of computer science, just the tools she was trained on. She knew nothing about how the hardware works, that RAM and SSD were different types of memory or which situations make one preferable to the other, cause that was already figured out in the tooling. There are a lot of people who work like that and call themselves software engineers. We’re ultimately trying to make hardware do something, but rather than that, we invented more ornate interfaces to the same old hardware for … jobs. A hardware based future where the onboard ML chip can generate a AAA game or Pixar level media will happen. Because then they don’t have to pay a bunch of coders or artists. Greers “program the perimeter, not the area” comes to mind. Most software is simple logic and network effects. As our manufacturing process allow us to make customizable silicon from step 1, why not listen to Greer, bake the best perimeter for a task into hardware?