I have a new technique for reliably vibecoding apps:

First, you write your requirements in an unambiguous specification language. This is the prompt, but to disambiguate it from less precise prompts, we will call it the source of truth encoding, or source code for short. You then feed it to an agent that will create an of outputs by applying some heuristic-driven transforms that are likely (but not guaranteed) to improve performance. This agent compiles a load of information about how to transform the code into a single pipeline, so we’ll call it a ‘compiler’. This then feeds to the next agent that finds missing parts of the program and tries to fill them in with existing implementations. This is more efficient than simply generating new code and more reliable since the existing implementations are better tested. This agent has a knowledge base of existing code organised in grouping that I’ll refer to as ‘libraries’. It creates links in that web of knowledge between the outputs of the first agent and these existing ‘libraries’ and so we’ll call it a ‘linker’.

I think it might catch on. VCs: I think we can build this thing for only a couple of hundred million dollars! And the compute requirements are far lower than for existing agentic workflows, so we can sell it as a service and become profitable far sooner than other AI startups. Sign up now for our A round! We have a working proof of concept that can output the Linux kernel, LibreOffice, and many other large codebases from existing prompts!

I have a new technique for reliably vibecoding apps:

First, you write your requirements in an unambiguous specification language. This is the prompt, but to disambiguate it from less precise prompts, we will call it the source of truth encoding, or source code for short. You then feed it to an agent that will create an of outputs by applying some heuristic-driven transforms that are likely (but not guaranteed) to improve performance. This agent compiles a load of information about how to transform the code into a single pipeline, so we’ll call it a ‘compiler’. This then feeds to the next agent that finds missing parts of the program and tries to fill them in with existing implementations. This is more efficient than simply generating new code and more reliable since the existing implementations are better tested. This agent has a knowledge base of existing code organised in grouping that I’ll refer to as ‘libraries’. It creates links in that web of knowledge between the outputs of the first agent and these existing ‘libraries’ and so we’ll call it a ‘linker’.

I think it might catch on. VCs: I think we can build this thing for only a couple of hundred million dollars! And the compute requirements are far lower than for existing agentic workflows, so we can sell it as a service and become profitable far sooner than other AI startups. Sign up now for our A round! We have a working proof of concept that can output the Linux kernel, LibreOffice, and many other large codebases from existing prompts!

I have a new technique for reliably vibecoding apps:

First, you write your requirements in an unambiguous specification language. This is the prompt, but to disambiguate it from less precise prompts, we will call it the source of truth encoding, or source code for short. You then feed it to an agent that will create an of outputs by applying some heuristic-driven transforms that are likely (but not guaranteed) to improve performance. This agent compiles a load of information about how to transform the code into a single pipeline, so we’ll call it a ‘compiler’. This then feeds to the next agent that finds missing parts of the program and tries to fill them in with existing implementations. This is more efficient than simply generating new code and more reliable since the existing implementations are better tested. This agent has a knowledge base of existing code organised in grouping that I’ll refer to as ‘libraries’. It creates links in that web of knowledge between the outputs of the first agent and these existing ‘libraries’ and so we’ll call it a ‘linker’.

I think it might catch on. VCs: I think we can build this thing for only a couple of hundred million dollars! And the compute requirements are far lower than for existing agentic workflows, so we can sell it as a service and become profitable far sooner than other AI startups. Sign up now for our A round! We have a working proof of concept that can output the Linux kernel, LibreOffice, and many other large codebases from existing prompts!

I have a new technique for reliably vibecoding apps:

First, you write your requirements in an unambiguous specification language. This is the prompt, but to disambiguate it from less precise prompts, we will call it the source of truth encoding, or source code for short. You then feed it to an agent that will create an of outputs by applying some heuristic-driven transforms that are likely (but not guaranteed) to improve performance. This agent compiles a load of information about how to transform the code into a single pipeline, so we’ll call it a ‘compiler’. This then feeds to the next agent that finds missing parts of the program and tries to fill them in with existing implementations. This is more efficient than simply generating new code and more reliable since the existing implementations are better tested. This agent has a knowledge base of existing code organised in grouping that I’ll refer to as ‘libraries’. It creates links in that web of knowledge between the outputs of the first agent and these existing ‘libraries’ and so we’ll call it a ‘linker’.

I think it might catch on. VCs: I think we can build this thing for only a couple of hundred million dollars! And the compute requirements are far lower than for existing agentic workflows, so we can sell it as a service and become profitable far sooner than other AI startups. Sign up now for our A round! We have a working proof of concept that can output the Linux kernel, LibreOffice, and many other large codebases from existing prompts!

I have a new technique for reliably vibecoding apps:

First, you write your requirements in an unambiguous specification language. This is the prompt, but to disambiguate it from less precise prompts, we will call it the source of truth encoding, or source code for short. You then feed it to an agent that will create an of outputs by applying some heuristic-driven transforms that are likely (but not guaranteed) to improve performance. This agent compiles a load of information about how to transform the code into a single pipeline, so we’ll call it a ‘compiler’. This then feeds to the next agent that finds missing parts of the program and tries to fill them in with existing implementations. This is more efficient than simply generating new code and more reliable since the existing implementations are better tested. This agent has a knowledge base of existing code organised in grouping that I’ll refer to as ‘libraries’. It creates links in that web of knowledge between the outputs of the first agent and these existing ‘libraries’ and so we’ll call it a ‘linker’.

I think it might catch on. VCs: I think we can build this thing for only a couple of hundred million dollars! And the compute requirements are far lower than for existing agentic workflows, so we can sell it as a service and become profitable far sooner than other AI startups. Sign up now for our A round! We have a working proof of concept that can output the Linux kernel, LibreOffice, and many other large codebases from existing prompts!

I have a new technique for reliably vibecoding apps:

First, you write your requirements in an unambiguous specification language. This is the prompt, but to disambiguate it from less precise prompts, we will call it the source of truth encoding, or source code for short. You then feed it to an agent that will create an of outputs by applying some heuristic-driven transforms that are likely (but not guaranteed) to improve performance. This agent compiles a load of information about how to transform the code into a single pipeline, so we’ll call it a ‘compiler’. This then feeds to the next agent that finds missing parts of the program and tries to fill them in with existing implementations. This is more efficient than simply generating new code and more reliable since the existing implementations are better tested. This agent has a knowledge base of existing code organised in grouping that I’ll refer to as ‘libraries’. It creates links in that web of knowledge between the outputs of the first agent and these existing ‘libraries’ and so we’ll call it a ‘linker’.

I think it might catch on. VCs: I think we can build this thing for only a couple of hundred million dollars! And the compute requirements are far lower than for existing agentic workflows, so we can sell it as a service and become profitable far sooner than other AI startups. Sign up now for our A round! We have a working proof of concept that can output the Linux kernel, LibreOffice, and many other large codebases from existing prompts!

@david_chisnall
I wish the market cared about reliability

@wolf480pl @david_chisnall

> I wish the market cared about reliability

Nobody has cared enough about FOSS maintainers to make sure they can eat. Why would they start caring about the purity ideals of noncontributing users?

I have a new technique for reliably vibecoding apps:

First, you write your requirements in an unambiguous specification language. This is the prompt, but to disambiguate it from less precise prompts, we will call it the source of truth encoding, or source code for short. You then feed it to an agent that will create an of outputs by applying some heuristic-driven transforms that are likely (but not guaranteed) to improve performance. This agent compiles a load of information about how to transform the code into a single pipeline, so we’ll call it a ‘compiler’. This then feeds to the next agent that finds missing parts of the program and tries to fill them in with existing implementations. This is more efficient than simply generating new code and more reliable since the existing implementations are better tested. This agent has a knowledge base of existing code organised in grouping that I’ll refer to as ‘libraries’. It creates links in that web of knowledge between the outputs of the first agent and these existing ‘libraries’ and so we’ll call it a ‘linker’.

I think it might catch on. VCs: I think we can build this thing for only a couple of hundred million dollars! And the compute requirements are far lower than for existing agentic workflows, so we can sell it as a service and become profitable far sooner than other AI startups. Sign up now for our A round! We have a working proof of concept that can output the Linux kernel, LibreOffice, and many other large codebases from existing prompts!