This is exactly what I did a few days ago: I created my own "small software" to solve my own need: https://news.ycombinator.com/item?id=49330939. Let me tell you: it works, it works really well, and I don't feel like, "Hey, the options out there are better."
In fact, the options available are outdated in terms of frameworks and bloated with features to please all kinds of customers. They try to do everything but end up doing everything poorly.
I see a different future. A future where software developers are approached by clients with requirements in the form of an LLM generated program. They do it because they are at a point where LLM fails to make new changes without breaking existing stuff.
Maybe it won't be a program, but just some LLM context as some data dump.
This program or context will take up the role of a PM. Developers will refer to the program, or ask context for clarifications and the developer will build the actual program with or without the help from LLMs.
the program itself is kind of useless. the client will send it anyway to demonstrate a proof of concept but hopefully the dev will build from scratch
what's important is the spec. the poc isn't a spec because the dev is being hired exactly because that software doesn't solve the problem fully - whatever it has missing is the important bits
this spec will probably be generated by a llm, but there is some noise added. if the client can send their prompt, alongside the whole llm session (maybe with sensitive tool calls redacted), the dev would have everything
As I said elsewhere: The future of tools like github is a platform for manual testing, where you write a prompt, the AI proposes a change and you can experiment with the UI and attach notes for the next iteration. AI can take user requests, prioritize, aggregate into tickets, and turn them into pull requests.
For a lot of end users, this may be enough, no programmers will be needed to get software built and shipped. For the rest, it lets programmers fill the remaining gaps, doing the manual testing to make sure the system works correctly, test for regressions and make sure the LLMs add those to the test suite, and then manage monitoring the rollouts.
I'm not as sure that this idea of plugins will pan out; AI will want to make changes to support what it produces.
This is exactly what I did a few days ago: I created my own "small software" to solve my own need: https://news.ycombinator.com/item?id=49330939. Let me tell you: it works, it works really well, and I don't feel like, "Hey, the options out there are better."
In fact, the options available are outdated in terms of frameworks and bloated with features to please all kinds of customers. They try to do everything but end up doing everything poorly.
I see a different future. A future where software developers are approached by clients with requirements in the form of an LLM generated program. They do it because they are at a point where LLM fails to make new changes without breaking existing stuff.
Maybe it won't be a program, but just some LLM context as some data dump.
This program or context will take up the role of a PM. Developers will refer to the program, or ask context for clarifications and the developer will build the actual program with or without the help from LLMs.
the program itself is kind of useless. the client will send it anyway to demonstrate a proof of concept but hopefully the dev will build from scratch
what's important is the spec. the poc isn't a spec because the dev is being hired exactly because that software doesn't solve the problem fully - whatever it has missing is the important bits
this spec will probably be generated by a llm, but there is some noise added. if the client can send their prompt, alongside the whole llm session (maybe with sensitive tool calls redacted), the dev would have everything
As I said elsewhere: The future of tools like github is a platform for manual testing, where you write a prompt, the AI proposes a change and you can experiment with the UI and attach notes for the next iteration. AI can take user requests, prioritize, aggregate into tickets, and turn them into pull requests.
For a lot of end users, this may be enough, no programmers will be needed to get software built and shipped. For the rest, it lets programmers fill the remaining gaps, doing the manual testing to make sure the system works correctly, test for regressions and make sure the LLMs add those to the test suite, and then manage monitoring the rollouts.
I'm not as sure that this idea of plugins will pan out; AI will want to make changes to support what it produces.