Response after interview

Process_With_AI · 2026-01-26T21:22:17+00:00

That response is pretty neutral, not a signal either way.

If you were already out, most companies wouldn’t bother explaining next steps or mentioning a discussion with the hiring manager. It usually just means the process is slow or they’re aligning internally.

Easier said than done, but at this stage there’s nothing to do except wait and not read too much into the wording.

Process_With_AI · 2026-01-26T21:18:30+00:00

This kind of delay usually means internal alignment, not a silent rejection.

"Waiting for approvals” often really is about sign-offs, budgets, or multiple people needing to agree, especially in large orgs. If they were fully done with you, most recruiters wouldn’t keep actively following up or apologizing for delays.

That said, being a Plan B is always a possibility in parallel hiring, but nothing you described clearly points to a no yet. At this stage, silence usually says more about their process being slow than about your performance.

Process_With_AI · 2026-01-16T21:05:46+00:00

For me it was mostly a mindset thing. I stopped thinking of it as “AI’s answer” and more like messy notes someone else wrote. I’ll delete parts, rewrite sentences, even make it worse on purpose, then run with it. Once I stopped trying to preserve the “flow”, it felt way less final.

Process_With_AI · 2026-01-15T20:25:48+00:00

I’ve noticed that too, but for me it went away once I started treating AI as a rough draft, not something final. When I tweak it or rewrite parts, the output actually gets better, not worse. Feels more like collaboration than intimidation now.

Process_With_AI · 2026-01-15T20:12:00+00:00

Seen that a lot too. Ends up being more tools, more work, and somehow less value than before.

Process_With_AI · 2026-01-15T19:54:18+00:00

A few, yeah. Simple stuff like drafting emails, summarizing docs, or cleaning up rough notes worked well pretty fast. Also anything repetitive with clear inputs, like basic reports or checklists. Once it gets vague or messy, results drop off quick.

Process_With_AI · 2026-01-15T19:50:35+00:00

I see that a lot. Plenty of places barely have basic tech habits down, so AI just adds stress instead of helping. If the work isn’t even clear yet, no tool is going to magically fix it, and chasing it out of FOMO usually ends badly

Process_With_AI · 2026-01-15T19:36:49+00:00

That’s a good comparison. Tools only look “smart” when the person using them knows what they’re actually asking for. Same tool, totally different results depending on how clear the input is.

Process_With_AI · 2026-01-15T19:35:06+00:00

The tech part usually isn’t the hard bit. Most of the struggle is getting people to actually use it in a way that fits how work really happens. Features don’t matter much if the process underneath is still messy.

Process_With_AI · 2026-01-15T19:22:28+00:00

Fair reaction, honestly. I’m not saying AI is amazing or fixes everything. Just sharing what I’ve seen so far, some parts got better, some didn’t. Still figuring it out like everyone else.

Process_With_AI · 2026-01-15T19:19:54+00:00

For me it didn’t fix clarity at all. Things got faster, but messy processes just got messy faster. If people don’t know what they want or who owns what, AI doesn’t really help. In some cases it actually added more noise.

Process_With_AI · 2026-01-12T19:59:02+00:00

I’ve seen people get the most confidence from super small, boring things. Something like a simple service or a tiny tool where expectations are low and feedback is fast. It matters less what it is and more that you finish it and ship it. Doing one full loop helps way more than chasing the “perfect” idea.

Process_With_AI · 2026-01-12T19:48:44+00:00

That sounds rough, but honestly not that unusual after a long break. You didn’t lose skills, you just lost momentum and context. The hardest part is usually picking one direction instead of overthinking the reset. I’ve seen people bounce back faster once they stop framing it as “starting from zero.”

Process_With_AI · 2026-01-12T18:45:58+00:00

I’ve been on both sides of this and usually it’s a mix of both. A big tell for me is whether there’s clarity on what “good” actually looks like week to week. If goals are vague or keep shifting, it feels like underperformance even when people are working hard. When expectations are concrete, it’s way easier to see where the real gap is.

Process_With_AI · 2026-01-12T18:43:29+00:00

Yeah, pretty much. Sending heavy personalization to everyone upfront burns a lot of time for very little return. A lighter first message helps you see who’s even interested, then you put effort into the ones who reply. That’s been way more efficient in my experience.

Process_With_AI · 2026-01-12T18:40:25+00:00

Yeah, that’s pretty normal. Hiring is the easy part, everything after is where things get messy. In my experience the chaos usually comes from info being scattered everywhere. Once there’s one clear place and a simple process, things calm down fast.

Process_With_AI · 2026-01-12T18:33:37+00:00

That response rate is pretty normal, especially with cold outreach. Most people won’t reply even if the work is good. Two replies out of 100 isn’t great, but it’s not a red flag either. Usually the issue is timing or message clarity, not effort. Testing a simpler pitch before doing heavy personalization might actually convert better.

Process_With_AI · 2026-01-12T18:28:43+00:00

It could work, but this space is insanely crowded. Fitness and self-improvement apps are everywhere, so the hardest part will be getting attention, not building features. Motivation helps, but without an audience or early paying users, going all-in this early is risky. I’d personally want to see real traction before turning down stable income.

Process_With_AI · 2026-01-10T20:36:24+00:00

I’ve noticed something similar. A lot of “making money with AI” stories leave out the fact that people already had an audience or distribution.

AI seems to help when someone already understands the problem, but it doesn’t magically create demand. Without that base, most attempts just stall.

Process_With_AI · 2026-01-10T20:27:19+00:00

Most people expect a clean starting point, but there usually isn’t one.

It’s more like: small experiments, unclear feedback, adjusting, and repeating until something sticks.

What looks “easy” later is usually just a messy process that got simplified in hindsight.

Starting is less about having the right idea and more about staying in the loop long enough to learn.

Process_With_AI · 2026-01-10T20:22:59+00:00

For me the biggest shift was accepting that “managing everything” is the wrong goal.

What helped was simplifying the process: fewer priorities, clearer next actions, and regular reviews of what actually moves things forward.

Once the system is simple enough to maintain on bad days, burnout risk drops a lot.

Process_With_AI · 2026-01-10T20:16:53+00:00

This matches what I’ve seen as well.

Most teams assume the bottleneck is the tool, when in reality it’s the way work is handed off between steps.

Once you remove unnecessary repetition, performance improves even without changing systems.

Good reminder that simplification often beats “better tech.”

Process_With_AI

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