sshadmand

We've never been able to read developers' minds, so we relied on documentation and comments to capture intent, decisions, and context even though most engineers dislike writing it and even fewer enjoy reading it.

Now with coding agents, in a sense, we can read the “mind” of the system that helped build the feature. Why did it do what it did, what are the gotchas, any follow up actions items.

Today I decided to paste my prompts and agent interactions into Linear issues instead of writing traditional notes. It felt clunky, but stopped and thought "is this valuable?" It's the closest thing to a record of why a feature ended up the way it did.

So I'm wondering:

- Is anyone intentionally treating agent prompts, traces, or plans as a new form of documentation? - Are there tools that automatically capture and organize this into something more useful than raw logs? - Is this just more noise and not useful with agentic dev?

It feels like there's a new documentation pattern emerging around agent-native development, but I haven't seen it clearly defined or productized yet. Curious how others are approaching this.

My team is working hard, but we're struggling to break work into measurable stories and tasks the way we used to. Now that we’re building AI-based processes and response systems, scoping feels fuzzier than traditional feature work.

I’ve seen this before with non-AI development. Usually it comes down to skill. People haven’t yet learned how to decompose a big problem into smaller, concrete steps. With some guidance, they improve. We go from two big "8s", and find out we can release a few 2s and 3s of value over time.

But with AI system development, I’m not sure if this is the same issue or if the nature of the work really is different. The engineers argue that it’s harder to "shrink" AI work into predictable, incremental pieces because outcomes are probabilistic, not deterministic. And that we can't just break them up since they rely on one another contextually.

So I’m curious:

- Are others experiencing this shift?

- Is this just a new version of the same problem-decomposition skill?

- Or, is building AI systems genuinely a different game we have to calibrate expectations around?

- And either way, how have you adapted your process to deal with these longer, less predictable, larger tickets?

Would love to hear what’s working for people.

I’ve used shell scripts before, but mostly in a pretty limited way with other people’s open source scripts to do something specific, or the occasional one off to clean up a drive.

Ironically, now that agents are genuinely good at coding, I find myself “writing” more shell scripts.

They’re robust, portable, and work in almost any environment I care about. Agents are very comfortable producing them, they execute fast, and they’re great for automation.

What I’ve ended up with is a growing set of automations built around simple, interactive shell script menus that handle things I do all the time. When I want to improve one, add branching logic, or tweak behavior, I just ask AI to mod the script and move on. I kind of love it.

It feels like a shell script revival, at least for me.

Curious if others are experiencing the same shift or if I fell into some weird bubble/anti-pattern.

I'm not looking for low hanging fruit, and I'm definitely not looking for links to products that "change the world." No attention sneakers. What I want to glean is: when you say, "I'm going to push exposure for my product today," what are the actual core things you do? How often do you do them? And what outcomes do you realistically expect?

For example, we're planning to host monthly events in SF to help other founders and operators learn something genuinely useful. (not "use our product," but "here's how to do x,y,z, sponsored by us.") Then we follow it with a: "Join our Discord to keep the conversation going and meet people like you."

We're still small, so we hired someone entry level to post content on social every day to build momentum, with the hope of identifying a spark we can double down on messaging-wise.

I could go on, but if no one reads or comments, it disappears into the ether. So I'll stop here and hope this grows into something we can all learn from, without fluff or shameless self-promotion getting in the way.

I'd love to put some actual science behind a feeling I think a lot of us share: certain hosted models seem to dip in quality at certain times of day. In my group, it’s almost treated as a “yeah, of course” kind of thing. Having something near-real-time that shows which models are performing best at any moment would be… honestly, pretty awesome.

I asked Perplexity and got nothing: https://www.perplexity.ai/search/is-there-an-hour-by-hour-model-8.03Z_BlSomAY7o9F5HwmQ#0

It is like some powerful urge even very talented people can't help but make. Maybe I am the wrong one, but is it really work it to show a feature that simply says "Coming soon"? I guess, IF you are ghetto testing for clicks - sure. But I find that is rarely what is happening behind the scenes. It is ugly and makes your app seem half-baked for no value. I feel like it should be "release it or don't". CMIIW