I tried running multiple terminal sessions with different coding agents to speed up feature delivery but I immediately ran into a massive review back log. The AI built things in minutes but context switching between three huge diffs burned through my focus before lunch.

Generating code quickly means nothing if the human review process takes three times longer to spot bugs or duplicate functions.

Do you cap the number of parallel agents you run or do you have a specific system to review their PRs fast?

Building tools quickly with AI agents works great until something randomly breaks and you have no idea which change caused it.

Tracing errors back through AI-generated code can take hours when there are no proper tests set up. I've heard people talk about using evaluation agents or automated checks but setting those up properly isn't always clear when you just want to ship fast.

Not which model benchmarks best.

Which one is actually open on your machine right now? And what did it replace?

The October 31st Leaderboard email had a neat trick that allows you to have four coding agents at once and chat with the one that requires your attention. I see the logic here. I found myself wasting time waiting for an answer from one particular agent when I did nothing else but waiting.

However, when I tried my own version of it last weekend three tabs of the terminal with three different tasks in the budgeting application I'm working on the speed slowed down somewhere else. Agents were done faster than me. It took me much more time to read three diffs than one; and my concentration is low after some point. I noticed only one duplicate helper function thanks to luck.

I tried switching to voice dictation for writing prompts last week because typing out long logic explanations gets exhausting. The funny thing is, when I speak out loud I end up giving the AI way more context about edge cases than I ever would when typing in a hurry.

The only catch is trying not to look completely insane while talking to your laptop in a quiet room. Has anyone made voice workflows a regular part of their day or is it just a fun gimmick?

The weekday kept coming out wrong in my agent. Reading the time from the system was never the issue. It would take that time, compute the day on its own, and get it wrong. So now a hook feeds it the current time along with a two week calendar on every message I send. If a reply contains a date that doesn't line up, the agent has to rewrite it.

From then on, I stopped letting it generate anything that a program would read. Frontmatter, JSON, config values, file paths, URLs, dates. A script handles all of those, and it also decides pass or fail. The model's only job is writing the plain text.

When I genuinely can't avoid handing a decision to a model, I don't trust a single one. Gemini will review whatever Claude came up with. For the larger tasks, Codex and Grok get pulled in too.

Are you still letting your agent pick dates or decide pass or fail anywhere?

With modern AI tools making it way too fast to spin up fresh components in an afternoon I ve caught myself adding random features just because it's fun while completely ignoring marketing.

Having a working app with zero actual distribution is a trap many of us fall into.

When do you finally force yourself to close the code editor and focus entirely on talking to real people?

It is dangerously easy to spend weeks building cool secondary features because AI lets you ship code ten times faster than before.

But a bigger feature set doesn't fix a lack of users or traction. I'm trying to impose a strict ban on new code until our current sign up numbers improve.

How do you personally draw the line between building products and actually doing distribution?

I normally keep 8 or more sessions of Claude Code open simultaneously. At some point, I discovered each new session was launching a complete copy of the MCP servers I've set up so far (Gmail, Calendar, Drive, Zoom, LINE and one more). Each one required approximately 560 megabytes, so my computer was carrying multiple copies of these processes at all times, all through the day, even when nothing was calling them.

Thus, I decided to get rid of them from the always-on list and start them only when necessary. Now I only need to execute a small command (mcp-call) to get a particular tool running. For instance, running a search on my emails is simply mcp-call gmail-local with the search tool and a query, and the process finishes right after that. The agent can still find the tools since a short skill lists them, and the hooks that used to check the old MCP calls (the HTML format of emails, asking before anything gets sent or deleted) now run on the new command too. I actually thought it was likely that at least one of these would break.

AI can get me to a working app in a day, but "it works on my machine" and "it's safe for real users" are very different things.

The part that worries me is the boring stuff that's easy to miss when the code looks clean and everything runs: API keys sitting in the frontend, auth checks that exist on the page but not on the endpoint, database rules left wide open from the prototype stage, no rate limits on anything. The AI rarely warns you about these unless you ask, and by the time you think to ask, you've already moved on to the next feature.

Right now my pre-launch process is mostly manual and a bit random: I skim through the auth flow, search the repo for hardcoded secrets, and try to break things as a logged-out user. It catches some things, but I never feel fully confident.

I'd love to hear how others handle it: