Stack Overflow Survey Finds Most-Proven Technologies: Open Source, Cloud Computing, Machine Learning (stackoverflow.blog) 70
Stack Overflow explored the "hype cycle" by asking thousands of real developers whether nascent tech trends have really proven themselves, and how they feel about them. "With AI-assisted technologies in the news, this survey's aim was to get a baseline for perceived utility and impact" of various technologies, writes Stack Overflow's senior analyst for market research and insights.
The results? "Open source is clearly positioned as the north star to all other technologies, lighting the way to the chosen land of future technology prosperity." Technologies such as blockchain or AI may dominate tech media headlines, but are they truly trusted in the eyes of developers and technologists? On a scale of zero (Experimental) to 10 (Proven), the top proven technologies by mean score are open source with 6.9, cloud computing with 6.5, and machine learning with 5.9. The lowest scoring were quantum computing with 3.7, nanotechnology with 4.5, and low code/no code with 4.6....
[When asked for the next technology that everyone will use], AI comes in at the top of the list by a large margin, but our three top proven selections (open source, machine learning, cloud computing) follow after....
It's one thing to believe a technology has a prosperous future, it's another to believe a technology deserves a prosperous future. Alongside the emergent sentiment, respondents also scored the same technologies on a zero (Negative Impact) to 10 (Positive Impact) scale for impact on the world. The top positive mean scoring technologies were open source with 7.2, sustainable technologies with 6.6 and machine learning with 6.5; the top negative mean scoring technologies were low code/no code, InnerSource, and blockchain all with 5.3. Seeing low code/no code and blockchain score so low here makes sense because both could be associated with questionable job security in certain developer careers; however it's surprising that AI is not there with them on the negative end of the spectrum. AI-assisted technology had an above average mean score for positive impact (6.2) and the percent positive score is not that far off from those machine learning and cloud computing (28% vs. 33% or 32%).
Possibly what we are seeing here as far as why developers would not rate AI more negatively than technologies like low code/no code or blockchain but do give it a higher emergent score is that they understand the technology better than a typical journalist or think tank analyst. AI-assisted tech is the second highest chosen technology on the list for wanting more hands-on training among respondents, just below machine learning. Developers understand the distinction between media buzz around AI replacing humans in well-paying jobs and the possibility of humans in better quality jobs when AI and machine learning technologies mature. Low code/no code for the same reason probably doesn't deserve to be rated so low, but it's clear that developers are not interested in learning more about it.
Open source software is the overall choice for most positive and most proven scores in sentiment compared to the set of technologies we polled our users about.
One quadrant of their graph shows three proven technologies which developers still had negative feelings about: biometrics, serverless computing, and rapid prototyping tools. (With "Internet of Things" straddling the line between positive and negative feelings.)
And there were two technologies which 10% of respondents thought would never be widely used in the future: low code/no code and blockchain. "Post-FTX scandal, it's clear that most developers do not feel blockchain is positive or proven," the analyst writes.
"However there is still desire to learn as more respondents want training with blockchain than cloud computing. There's a reason to believe in the direct positive impact of a given technology when it pays the bills."
The results? "Open source is clearly positioned as the north star to all other technologies, lighting the way to the chosen land of future technology prosperity." Technologies such as blockchain or AI may dominate tech media headlines, but are they truly trusted in the eyes of developers and technologists? On a scale of zero (Experimental) to 10 (Proven), the top proven technologies by mean score are open source with 6.9, cloud computing with 6.5, and machine learning with 5.9. The lowest scoring were quantum computing with 3.7, nanotechnology with 4.5, and low code/no code with 4.6....
[When asked for the next technology that everyone will use], AI comes in at the top of the list by a large margin, but our three top proven selections (open source, machine learning, cloud computing) follow after....
It's one thing to believe a technology has a prosperous future, it's another to believe a technology deserves a prosperous future. Alongside the emergent sentiment, respondents also scored the same technologies on a zero (Negative Impact) to 10 (Positive Impact) scale for impact on the world. The top positive mean scoring technologies were open source with 7.2, sustainable technologies with 6.6 and machine learning with 6.5; the top negative mean scoring technologies were low code/no code, InnerSource, and blockchain all with 5.3. Seeing low code/no code and blockchain score so low here makes sense because both could be associated with questionable job security in certain developer careers; however it's surprising that AI is not there with them on the negative end of the spectrum. AI-assisted technology had an above average mean score for positive impact (6.2) and the percent positive score is not that far off from those machine learning and cloud computing (28% vs. 33% or 32%).
Possibly what we are seeing here as far as why developers would not rate AI more negatively than technologies like low code/no code or blockchain but do give it a higher emergent score is that they understand the technology better than a typical journalist or think tank analyst. AI-assisted tech is the second highest chosen technology on the list for wanting more hands-on training among respondents, just below machine learning. Developers understand the distinction between media buzz around AI replacing humans in well-paying jobs and the possibility of humans in better quality jobs when AI and machine learning technologies mature. Low code/no code for the same reason probably doesn't deserve to be rated so low, but it's clear that developers are not interested in learning more about it.
Open source software is the overall choice for most positive and most proven scores in sentiment compared to the set of technologies we polled our users about.
One quadrant of their graph shows three proven technologies which developers still had negative feelings about: biometrics, serverless computing, and rapid prototyping tools. (With "Internet of Things" straddling the line between positive and negative feelings.)
And there were two technologies which 10% of respondents thought would never be widely used in the future: low code/no code and blockchain. "Post-FTX scandal, it's clear that most developers do not feel blockchain is positive or proven," the analyst writes.
"However there is still desire to learn as more respondents want training with blockchain than cloud computing. There's a reason to believe in the direct positive impact of a given technology when it pays the bills."
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Spam? That's proven already: it works well for the 1%.
microservices? (Score:2, Interesting)
How come "microservices" isn't listed? Our dumbass architect made a mess with it and ran off to another company, probably using his "experience" with microservices to do it. That's borderline fraud: Resume Oriented Programming. I can't even get a clear consistent definition of microservices, other than forcing JSON into everything until it bleeds.
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Not sure if serious...
See e.g. https://www.martinfowler.com/b... [martinfowler.com] This has been more true than not in my experience with things that are relatively new. By the time you're implementing the 4th order management system or messaging app, then sure, you might cut it up up front, but otherwise... chances of getting it right are pretty low.
And especially if you have to maintain synced state (like anything with moving inventory or balances from place to place or have to implement hard business rules on transa
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With enough skill and knowledge, one can write anything in Brainfuck language. Doesn't make it the right tool, especially if takes rocket science to debug a "Hello World" app.
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Well, i do hear that Mongo DB is web scale.
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", microservices are the ideal in 100% of cases"
LOL! :)
Lets play spot the evangelist.
People like you said the same damn thing about every software tech thats ever come along. Guess what - it isn't true. Horses for courses my friend. Microservices certainly have their place but its not everywhere. Unless for example you can give an explanation of why microservices would work well for a hardware driver? Or maybe a disk utility. Or perhaps an ftp daemon.
Take your time.
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Wow , look at those goalposts move!
"Microservice-based architecture is radically superior to monolithic in relation to development cadence"
Ignoring the buzzword BS "cadence" , are you saying its only radically superior where its radically superior?
Truly you're a savant!
LOL :)
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I don't think you know what cadence means , but it sounded cool saying it, right?
And some features may be quick to develop but debug forever.
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Its all buzzword bullshit mate. But kid yourself it makes you sound intelligent if you want. You're only fooling yourself.
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"You talk a lot to say nothing of use."
Ah the irony :)
I bet you really stun them in whiteboarding sessions when orchestrate your action points and scope out deliverable stakeholder benchmarks on your results driven roadmap eh? While drinking the kool-aid by the gallon.
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Well newsflash - despite what you may think working in your bubble, cloud is still a minority development activity, so your original statement that microservices are 100% the best solution everywhere is bullshit as I originall said. Next time qualify your statements.
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I will agree that because it's cheaper for a cloud provider to make services that can be used by any programming languages and DB platform, JSON and/or XML web calls are more economical for them rather than to code up multiple language-specific API's. Thus, their interfaces will often be web services (JSON and/or XML over http) whether cloud users like them or not.
But that doesn't necessarily mean your own application suite needs to use only web services to talk among it's parts. One-communication-approach-
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How Agile is your programming? Is it Xtreme?
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100% LOL! If *any* technology is the right tool 100%, then shoot me now and put my corpse into a flying AI car.
You probably write the mess using templates then move off to another company, leaving the maintenance headaches to somebody else.
Easy to author is NOT the same as easy to maintain.
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> The entire principle is that when things need changing or fuck up, you can have someone new come in and author a very tiny piece of the overall system.
As I mention elsewhere, one can do that using the RDBMS as the communication hub between "sub-apps" (for lack of a better name). And you get ACID and other benefits that JSON-over-http lacks. And that technique has been around since the dawn of client-server, which is roughly 4 decades (and probably longer). It didn't need a new term, and if it does, "mi
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You're going to need to look in a mirror on that one. Microservice-based architecture is radically superior to monolithic in relation to development cadence, long-term maintainability, scalability, etc. That said, if you don't understand it you don't understand it, and you made that much clean even if you hadn't admitted to not even knowing the definition of it.
You are just another clueless priest who doesn't know anything about any particular problem or system yet nonetheless sees themselves fit to proselytize their "radically superior" god on faith alone. Architectural decisions should be made on the basis of merit not faith.
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Provit! First we'd have to find a clear definition. I've been avoiding "overly large EXE's" for decades, mostly by leveraging RDBMS; that's nothing new. Although it's not clear microservices are really about that since it's a fuzzy notion wrapped up in buzzwords.
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That's called "modularization". The term/concept has been around since at least the late 1950's; it didn't need a new name, and STILL doesn't.
And there's many different ways to do it with various trade-offs since real apps are multi-dimensional (overlapping) in concerns. Just saying "be modular" is not specific enough to be practical. Almost no system these days is non-modular; it's a matter of being modular in a way that best fits the application type, team environment, etc. The key question is HOW to be m
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That's "web services", which were a buzzword of the early 2000's. If there is a CLEAR distinction between microservices and web services, I haven't found it yet.
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Update: See this Slashdot story [slashdot.org]
is there really any information there? (Score:5, Interesting)
The spread between items is pretty small in all of their analyses, are they even meaningful?
I'm glad they tried, but idk that there's much info or insight in there.
But i learned about "InnerSource" which is new term to me. Basically means to treat your internal projects and code bases as if they were open source. It's "interesting".
https://about.gitlab.com/topic... [gitlab.com]
https://opensource.com/article... [opensource.com]
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This is also Stack Overflow. I don't put much credence in their polling base. Even if a straight up intelligent site instead, the polling is only asking for quick off-the-cuff opinions.
If anything what it shows is that open source enthusiasts frequent stackoverflow in larger numbers than AI enthusiasts.
They seem clueless (Score:2)
In current terms at least ML and AI are synonyms. Technically AI is inclusive of but not limited to ML but in practice all functioning "AI" today is some from of ML. If you distinguish AI from ML then AI is something we don't have the slightest clue how to start building.
"Open source is clearly positioned as the north star to all other technologies, lighting the way to the chosen land of future technology prosperity"
Yeah... that was an insightful prediction 20 years ago. Open source is already the dominant
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You mostly have that first part backwards. Machine Learning includes a bunch of techniques like support vector machines, Markov modeling, principal component analysis, and neural networks -- but the vast majority of the buzzwordy uses today are some variety of deep neural network geared towards AI. We don't have any good way to generate artificial general intelligence (AGI) except huge (hundred-billion parameter) deep neural networks.
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"We don't have any good way to generate artificial general intelligence (AGI) except"
Your statement would have been good if you'd stopped at 'except.' We do not have any real artificial general intelligence at all, all we have is the ability to dupe people by grouping like and related by probability. We might be able to grow those models and do great things, more complicated things and cover more subjects as we scale them but there is no deterministic path in that process. The probability that scaling up th
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Artificial intelligence as a field is a lot like stage magic. Once the audience understands how it works it is often a lot more boring - it's just a trick! With artificial intelligence the response is instead that it's not real AI. For example, expert systems: very much classical AI, except that people know how it works and therefore it's boring and clearly not "real AI". Web search is very much derived from AI, but so boring these days...
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I disagree with your initial statement ai is more like the black box in front of us while machine learning is more like the remote control.
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That is a semantics debate. AI is the effort to replicate the black box that is human or even animal sentient intelligence, an autonomous, general, self-directed and actualized learning system. ML is a set of programming tools that through a number of tricks revolving around statistics slowly nudge closer to correct answers. If logic were coins traditional programming would be hand rolling whereas ML is a coin sorter.
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If you distinguish AI from ML then AI is something we don't have the slightest clue how to start building.
AI includes a ton of stuff that is functioning and has nothing to do with machine learning. For instance Expert systems and various widely used visual recognition systems. You seem to be referring to AGI?
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"You seem to be referring to AGI?"
No, the term AI predates the relatively recent invention of the term 'AGI' the older coining of synonym machine learning and certainly the yet more recent attempt to redefine machine learning to refer to a subset of machine learning techniques.
"AI includes a ton of stuff that is functioning and has nothing to do with machine learning."
I suppose that is technically possible in that we could artificially produce an intelligence biologically or on another medium besides mechan
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I highly suggest you just read the wikipedia page about AI in full to get a bit of a grasp on how scientists define those terms, instead of making up your own definitions. You are free to use whatever meaning you want for terms like 'AI', but it's very confusing to talk with someone that is using completely different definitions. AI is a field of science and there are common meanings associated to these terms. And they have been for large part the same since the 70s. You seem to be suggesting that your defi
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"how scientists define those terms"
Those terms aren't from science and aren't theirs to redefine. But you won't find an actual scientist doing so because the scientific method is a system for developing models to predict the behavior of physical reality, artificial intelligence is not an aspect of physical reality.* It is a very impressive engineering field.
* Asterisk is definitely needed here though, I'm not denying that there is science which overlaps AI and scientists bridging that gap. For every one of
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You are saying computer science is not science?
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I'm absolutely saying that computer science is not a science but rather mathematics. While math is used in science it is not a science and computer science is just math. Which puts it solidly ahead of other not sciences like the social pseudoscience because at least math is objective and you can use the scientific method to test the coherence of your math if nothing else.
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In the "old days" when dinosaurs roamed the earth we used to call this "AI" thing a "Decision Tree" and the "ML" thing was known as "Statistical Analysis".
Not much has changed other than the buzzword market-speak.
Not even the reasons have changed: The purpose is still as positied by Barnum: The parting of money from idiots.
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All "functioning" AI often means "things we still call AI". Much of image processing used to be the domain of artificial intelligence researchers. The same with natural language processing - most people don't label translate.google.com as AI anymore, even though the heart of it is indeed straight out of AI research. I used to work at an AI research lab in the 80s and a lot of what they worked on there I dont think most would call AI anymore. Even when I went to grad school there was one professor who ju
Most-Proven Technologies ... (Score:2)
Survey Finds Most-Proven Technologies: Open Source, Cloud Computing, Machine Learning
The "wheel" says, "Hold my beer." :-)
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Kinda where I already lost any interest there when "hype" and "proven" are seemingly synonymous to them. If there was some truth to that with "real developers" that were asked for their opinions, all the pieces of toilet paper we've been using today would be logged on a blockchain.
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Now I did and looked at their data, which shows that "AI-assisted technologies" is more proven than "Real-time 3D". That does make me very skeptical.
Do the "thousands of real developers" not count video games that are built on "Real-time 3D" game engines into this? Or do they think video games engines aren't proven technology yet?
No love for ChatGPT? (Score:2)
Even IBM is incorporating it into Watson so Watson can do all kinds of amazing AI things while it brings world peace at the same time.
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Henceforth Watson is now known as Watson the Magnificent!
WTF? (Score:5, Insightful)
When did Open Source become a "technology"? It may be many things - an ecosystem, a figurative umbrella, a philosophy, an ideal, a specific kind of development approach or, dare I say it, a glowing example of functioning collectivism which even hard-core conservative capitalists see fit to use and abuse. But is it a technology? I don't think so.
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It's none of those things, it's just code you can see.
"Open Standards" means documented and interoperable, and that's what "Open Source" is. The code is the ultimate documentation, and being able to see it is the ultimate in achieving interoperability.
"Free Software" is several of those things, but not all of them. The usual poster child for "Open Source" is Linux, which is in fact not just "Free Software", but also successful because of it in the words of early contributors — who chose to contribute
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And now I'm having one of those "Doh!" moments because I conflated "Open Source" and "Free", even though I know better. Thanks for the correction.
Low Code or No Code (Score:3)
The author seems surprised that developers would rank AI highly while being distrustful of No Code when both supposedly threaten our livelihoods.
We are not distrustful or No Code because of this. We are distrustful because simply giving people the illusion that they can code does not eliminate any of the problems with code. It simply lowers the barrier to entry and allows the creation of code (disguised as something else) which is more difficult to maintain with our existing tools.
Bugs, complexity and need for configuration management/architecture will not go away. No Code will simply create a clusterf**** of code for coders to maintain. See the Inner Platform Antipattern: https://exceptionnotfound.net/... [exceptionnotfound.net]
"Machine Learning" LOL! (Score:2)
Except... (Score:2)
Open Source is not a technology. It is a type of a licence.
Obvious idiots at work (Score:2)
ML is not "proven" in any way. Rather the opposite. And the cloud still has some rather huge question marks to it as well.
Seems the people that responded to this survey just outed themselves as bright-eyed fools.
I'm convinced (Score:2)
I'm convinced that microservices is another word for IPC over HTTP