Shipping an AI agent is only the beginning. Once it's being used by real users, monitoring becomes just as important as development.

Traditional software monitoring focuses on uptime, latency, and errors, but AI agents introduce additional questions. Are they making good decisions? Are tool calls succeeding? Are they completing tasks efficiently? Are they getting stuck or behaving inconsistently?

I keep coming back to the same realization when I talk to founders running more than one business or location. Nothing dramatic causes the miss. A lead sits three days longer than it should. A vendor renewal gets noticed the day it charges, not the month before. A client goes quiet and nobody flags it until the account cancels.

None of these look like a failure at the time. They just sit in the gap between two people, or between a system and a person who was too busy that week to check it.

Over the past few months, the concept of vibecoding building products almost purely by guiding AI agents, prompt-driven iterations, and high-level system architecture has picked up massive momentum.

For those of you actively shipping side projects or production tools using Cursor, Replit Agent, or Claude CLI, how has this shifted your actual workflow? Are you finding that you spend more time on system architecture, or are you hitting walls with code maintenance down the line?

Would love to hear real experiences, tech stacks, and takeaways from builders here!

I read three unrelated launches today. A menu bar monitor for coding agents, a desktop widget that runs scripts, and a mail client. Different teams, different categories, nobody talking to each other. Same bug in all three. The monitor falls back to a cached default when it cannot read the real context window, so it shows eighty percent when you are actually at sixteen. The widget keeps displaying the last value a script returned, so a job that died at 3am still looks healthy at noon. The mail client drew an empty window while it waited on its first query, so "still loading" rendered identically to "no mail". None of these are crashes. Every one is a system with no way to say "I do not know yet", so it asserts something instead, and the thing it asserts is always the confident-looking one. It keeps happening because unknown is expensive to represent. You need a third state in the data model, a third rendering in the UI, and a decision about what someone should do when they see it. Falling back to a default is one line. But the cost does not disappear, it just moves to the person reading the screen. Absent data is visible. Wrong data is invisible and confident, and people act on it. So the test I would run on anything with a display: is there a value your UI can show that means "I could not find out"? If there is not, then somewhere in your product missing already renders identically to real, and you will not find out where until somebody acts on it. Where does absent data currently render as an answer in yours?

I recently launched a movies APK and I m trying to improve its overall performance. Some users have mentioned that certain pages and downloads take longer than expected, so I m looking for practical suggestions.

Would optimizing the app size, improving server response time, using a CDN, or changing the way files are delivered make the biggest difference? I d appreciate advice from anyone who has worked on improving Android app performance.

Hey Makers!

Handing over repo access to autonomous coding agents is awesome, but honestly, it's also a great way to break things if you aren't careful.

My team recently set up a strict "Zero Trust" baseline to keep things under control. Here are the 5 rules we actually enforce right now:

1. Bound the blast radius before the first run

2. Assume it reads everything it can reach

3. Treat everything the agent reads as a potential instruction

4. Verify like it's a stranger's pull request

5. Be ready to do forensics

One question I've been thinking about is when an AI agent is actually "ready" for production.

Traditional software often has clear testing processes, but AI agents introduce a different level of uncertainty. They rely on reasoning, external tools, changing context, and non-deterministic outputs, making it difficult to define a clear launch criterion.

Folks, please. This isn t LinkedIn (YET).
Write your own maker comments.
Sheesh.
https://www.reddit.com/r/OpenAI/...

I ve been cutting things from APEX lately instead of adding more.

Some ideas looked good on paper, but once they were in the product they just added more noise.

It s a weird feeling because as a founder you usually want to keep building and adding.

But sometimes the product gets better when you stop trying to make it look more advanced .

Almost every agent memory system I see is built to remember more and find it faster. Nothing in any of them knows how to stop believing something. A note that says "we tried X, it did not work" is written once and then never expires. Nothing in the system changes on the day it stops being true. It just sits there, gets retrieved, and reads as more authoritative every time it survives another run. A system that only accumulates gets more confident and less correct at the same time, which is a genuinely nasty combination because the confidence is the part people can see. There is a smaller version of this that shows up constantly. When an agent runs out of context mid task, everyone reaches for a handoff note, and handoff notes work strangely well. Worth asking why, because the agent had far more information available than the note contains. They work because they are lossy in the right direction. A note keeps the decision and throws away the reasoning that produced it. The raw context does the opposite: it keeps everything, including the three wrong turns that got corrected later, and restoring it restores the confusion along with the conclusion. So decisions and context are two different objects and most memory work treats them as one. A decision is "we went with X over Z because Y". Context is the forty thousand tokens of finding that out. Only one of those is worth carrying forward, and it is not the big one. What I actually want is a memory entry that carries the conditions under which it was true, so it can be invalidated by the world changing rather than by someone remembering to go and delete it. Nobody is asking a knowledge base to forget. Curious whether anyone here has shipped expiry properly, and what triggered it. Time, contradiction, or someone noticing by hand?