It feels like the whole junior hiring playbook is broken. Standard take-home assignments don't work anymore because candidates can just generate an entire app in minutes and companies reacting by raising the bar to ridiculous mid level expectations are shutting out great talent.

We shifted our focus away from raw code generation during interviews and started looking closely at code review and debugging skills instead.

How has your team changed its hiring process to actually spot good potential in junior devs when AI can fake the basics?

This is the toughest part of building engineering teams right now. When I started, you learned by grinding through hundreds of basic CRUD endpoints and simple UI bug fixes.

Now, an LLM handles all of that in a few seconds, which has completely wiped out the traditional stepping stones for fresh grads. Companies expecting mid-level skills for entry-level pay are missing the point entirely.

If you're hiring or mentoring juniors today, what kind of actual entry-level tasks are you giving them to help them grow without burning them out?

Not looking for the usual "DM people on LinkedIn" or "personalized cold emails"

I want the weird stuff you tried that had insane results.

I'll start: We sent 50 pizzas with custom packaging to super specific people in our target persona.

Result: 95% called us back and hopped on a call.

Something I've run into repeatedly and I can't tell whether it's the platform, my settings, or just me.

I found a comment on my own intro thread that had been sitting there 20 days. It was the most useful piece of feedback anyone has given me on this platform, someone had taken the time to pull apart my positioning properly, and I only saw it because I happened to be scrolling my own activity. No notification appeared. Same thing again this week with two people who tagged me on launch day. So I was three weeks late to both.

We used to celebrate hitting high weekly signup numbers, but looking at month-two retention always brought us right back down to earth. A huge chunk of those users were bouncing immediately because our messaging attracted people who wanted a completely different tool.

We were wasting time building features based on feedback from the wrong crowd. Once we explicitly rewrote our landing page to say what the product doesn't do our signups dropped by half, but retention doubled.

Have you ever deliberately slowed down your top of funnel growth to filter out bad-fit users?

There is a super fine line between a helpful reminder and mental noise. Early on we configured notifications for every tiny event inside the dashboard assuming it built a daily usage habit. Turns out people just turned off app permissions or deleted their accounts to clean up their phones and inboxes.

Now we're trying to figure out smart defaults like only pinging users when an action is strictly required from them.

What rules do you follow to make sure your alerts bring value instead of fatigue?

Working on different products and brands gave me some insights into what worked best.

For example, when we promoted minimalist phone (digital detox app):

for paid ads, worked:

We made the classic mistake early on of emailing every canceled user a discount code assuming they left because of the price. Turns out, 80% of them never even configured their account settings or imported their data.

They didn't leave because it was too expensive; they left because the setup process felt like homework. Once we killed three mandatory onboarding steps and let them test the core feature instantly our early retention completely flipped.

If someone abandons your product on day one how do you figure out if it's a pricing issue or just a confusing onboarding flow?

I record a lot of lectures and meetings now, and the recording itself is the easy part. Weeks later, finding the one moment where something was explained is where it falls apart. Scrubbing through an hour of audio never works for me, and a wall of transcript text isn't much better.

I've tried a few things: naming every file by course and topic, writing a timestamp note while recording, searching the transcript for a keyword. Each helps a bit.

Which tools or workflows have actually worked for you? Do you re-listen, skim a summary, or ask questions and jump straight to the answer? I'd love recommendations from other people who deal with long recordings.

I've been using Claude Code heavily for the past few months while building my startup (12k+ line Python codebase), and it's genuinely changed the way I develop. It navigates large codebases with context that I haven't seen from any other tool: understanding file relationships, catching upstream/downstream effects of changes, and reasoning about architecture-level decisions, not just autocompleting lines.

But what really caught my attention is the business side. Anthropic is forecasting cash-flow positive by 2027, while OpenAI is projected to burn through $115B+ through 2029. Claude Code alone reportedly crossed $1B in annualized revenue. Their API revenue is estimated at double OpenAI's. And they're doing all of this with significantly lower burn.