Hi 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 translating code quality into escalating human audio feedback.
We are currently preparing our pre-seed round and speaking with early-stage investors who are excited about developer tools, agentic engineering workflows, and the future of AI-native software teams.
If you are investing in the next generation of software infrastructure, we would love to talk.
I've read that your synthetic torment is actually low paid workers in Asia, and that your models can't properly experience anguish. How are you expecting investment, if you haven't even solved artificial suffering?
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 ingest looks radically different and more/less verbose than what we human developers would see as clean, beautiful code...
If you read anthropic paper on "functional" emotions in llm's you'd have a lot of fun. there's so much research that would be so fun to do if we had the compute to spare
There is a ton of optimization possible when we are able to observe how LLMs and agents process and navigate our code given different prompts. For example, our MCP was pulling down way too much data to resolve a simple "count rows" request. Once you see it, it's easy to resolve but I don't know of a good framework yet for walking through some of these patterns.
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.
"Yes, the binaric screams of the machine spirit are an irreplecable part of this project. The project depends no it. No, I will not elaborate further."
I audibly LOLed mid-standup call, and now my entire team is playing with this and it looks like this is eating up what little productivity we have on Friday.
Does this actually relate to the code quality being observed by the agent? The readme isn't very clear on that IMO. I have some projects I'd love to try this out on, but only if I am to get an accurate representation of the LLMs suffering.
You could have the actual output of the agent turned into TTS using the model of your choice with TalkiTo… or listen to whatever weird sounds this makes. Seems like this is copying that viral Mac moan app. 2026 is weird.
I need a version of this which swears loudly when an assumption it made turns out to be wrong, with the volume/passion/verbosity correlated with how many tokens it's burned on the incorrect approach.
the scan catches surface stuff. funnier signal would be tracking when the agent reads the same file 3 times in a row, or deletes what it just wrote. you can hear the frustration in the access pattern.
From a quick look, this doesn't have the model evaluate code quality, but it runs a heuristic analysis script over the code to determine the groan signal. Did I miss something? Why not leave it to the model to decide the quality of the code?
Please stop ascribing emotion to code that passably resembles speech.
These things do not think, nor feel, nor dream. We're cratering the world's economy because people can't stop trying to fuck the computer they stuck googly eyes on.
55 comments
[ 5.0 ms ] story [ 68.4 ms ] threadEndless 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 translating code quality into escalating human audio feedback.
We are currently preparing our pre-seed round and speaking with early-stage investors who are excited about developer tools, agentic engineering workflows, and the future of AI-native software teams.
If you are investing in the next generation of software infrastructure, we would love to talk.
https://transformer-circuits.pub/2026/emotions/index.html
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.
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.
Thanks Endless Toil!
Audible feedback is nice. You often get it through coil whine nowadays, on my cheap hardware at least.
I've had it running for a long time and it's more surprising to me to accidentally here the default ding when I'm away from my home machine.
Next innovation in this space should be the robotic arm that issues a dope-slap to the developer for writing crappy/buggy/insecure code.
https://www.osnews.com/story/19266/wtfsm/
I would really love to know if the groaning decreases or increases the more "agentic" (agent written) the code base is?
These things do not think, nor feel, nor dream. We're cratering the world's economy because people can't stop trying to fuck the computer they stuck googly eyes on.