Hear your agent suffer through your code
91–97 of 97 posts
Re: Hear your agent suffer through your code
#92Earlier quoted context omitted.
You're being over-opinionated for something you don't understand. You should really try these tools out with an open mind. I know you won't take that last bit of advice, so this makes you not worth my time. But I can tell you this - these tools make people productive in ways you aren't understanding.
You're funny mate :) Read a bit through my comments' history. I've been using "these tools" before folks like you even heard of the term LLM. But I guess I am not easily impressed.
Sucks that people like you are on hacker news to be honest.
Re: Hear your agent suffer through your code
#93Earlier quoted context omitted.
You're funny mate :) Read a bit through my comments' history. I've been using "these tools" before folks like you even heard of the term LLM. But I guess I am not easily impressed.
Skimmed your comment history and you honestly just look like a huge asshole. Sucks that people like you are on hacker news to be honest.
Re: Hear your agent suffer through your code
#94Hi Hacker News, I'm Andrew, the CTO of Endless Toil. Endless Toil is building the emotional observability layer for AI-assisted software development. As engineering teams adopt coding agents, the next challenge is understanding not just what agents produce, but how the codebase feels to work inside. Endless Toil gives developers a real-time signal for complexity, maintainability, and architectural strain by translati…
This sounds like a cheeky joke project, but assuming it's not, it got me thinking: I wonder if coding AI can be effectively and reliably prompted into minimizing its own anguish. Like, "don't write code that is going to make you (or I) suffer." And along those lines, do we know if the things that make AIs suffer are the same things that make human developers suffer? Perhaps the least-agonizing code for an LLM to inge…
Re: Hear your agent suffer through your code
#95Hi Hacker News, I'm Andrew, the CTO of Endless Toil. Endless Toil is building the emotional observability layer for AI-assisted software development. As engineering teams adopt coding agents, the next challenge is understanding not just what agents produce, but how the codebase feels to work inside. Endless Toil gives developers a real-time signal for complexity, maintainability, and architectural strain by translati…
This sounds like a cheeky joke project, but assuming it's not, it got me thinking: I wonder if coding AI can be effectively and reliably prompted into minimizing its own anguish. Like, "don't write code that is going to make you (or I) suffer." And along those lines, do we know if the things that make AIs suffer are the same things that make human developers suffer? Perhaps the least-agonizing code for an LLM to inge…
Respectfully, the reason you think “AIs suffer” is because of a shortcoming in your understanding of what an LLM actually is.
This scenario is no different than considering if a shovel gets tired after using it all day to dig holes in the ground.
Re: Hear your agent suffer through your code
#96Hi Hacker News, I'm Andrew, the CTO of Endless Toil. Endless Toil is building the emotional observability layer for AI-assisted software development. As engineering teams adopt coding agents, the next challenge is understanding not just what agents produce, but how the codebase feels to work inside. Endless Toil gives developers a real-time signal for complexity, maintainability, and architectural strain by translati…
This sounds like a cheeky joke project, but assuming it's not, it got me thinking: I wonder if coding AI can be effectively and reliably prompted into minimizing its own anguish. Like, "don't write code that is going to make you (or I) suffer." And along those lines, do we know if the things that make AIs suffer are the same things that make human developers suffer? Perhaps the least-agonizing code for an LLM to inge…
I built an eval framework to look just at tool calls given a static prompt, with the idea that LLMs should be able to deduce the best tool calls and arguments needed to get requested data. Not as great as full observability, but helpful for complex tool interactions. Anyone have any good tools for this problem?
In the same way we mentally walk through deterministic logic, SWEs need to learn to anticipate LLM context and tool awareness, which is much trickier to reason through, especially given the various LLM IDEs and how they manage context as a black box.