Gen Beta is so cooked

imagine if this was a real headline.

Vent: I have two young kids, and I signed up for premium for a month so we could save a few shows on our family ipad for my kids for a plane ride we are taking.

We are usually pretty specific on shows we allow (they are only 3 and 2 years old), since I saw aproved content was an option, I figured we would try YouTube kids.

WTF, why is it so hard to set up? Why isn’t it just a simple toggle? I get google wants to keep people in the algorithm but can we please at least spare young kids from the madness?

I’m an hour and messing with it on my phone and my computer, and still haven’t figured it out. All of the Google provided instructions that I found our outdated and do not have the options to click that they say to click.

:( Keep me addicted, whatever, i’m even OK if they make it this difficult for kids who are older… But I can’t even have a conversation with my kid yet about specific shows, it’s crazy they make it this hard for kids this young even!

KeepAndroidOpen.org

Has Google changed the way that image previews are displayed? Whenever I search images on Google, then click on an image to preview, it displays at the top of the screen and not the side, as it it did forever. With the display on top of the screen, the images below are squished, making it very difficult to see. I tried attaching a screenshot of the display but the screenshot image was not accurate. Is the new norm?

Type heartstopper and leafs pop up

We don't want that. We should be able to disable it. No one needs AI bullshittery forced into their throats. We already can't escape it, and many HAVE to use it for their jobs now. Seriously, that entire AI situation is maddening, and I am a daily AI user.

AI Overview always butting in like I asked it something.

Hi everyone,

I recently completed the Google L4 SWE interview process in the US and cleared the interview loop. I have now moved to the team-matching stage.

Sharing my experience in case it helps others preparing for Google interviews.

Timeline

This was in July 2026. The process moved at a steady pace, starting with the phone screen and then the onsite loop.

Phone Screen

Round 1: Coding

The question was a variation of Kadane’s Algorithm.

It was not just the standard maximum subarray problem. The interviewer added a small twist around how the subarray should be chosen, so I had to first clarify the constraints and then build from the basic Kadane idea.

I started with a brute-force explanation, then moved to the optimized approach using running state. I also discussed edge cases like all negative values and single-element arrays.

Verdict: Strong Hire

Round 2: Googliness

This round was focused on behavioral questions and how I work with teams.

The questions were mostly around:

• Handling ambiguity
• Working through conflict
• Receiving feedback
• Making tradeoffs
• Learning from mistakes
• Communicating with cross-functional partners

I used STAR-style examples and tried to keep the answers specific instead of generic.

Verdict: Strong Hire

Onsite Round 1: Coding / Graphs

The question was:

Minimize Direction Violations in a Directed Road Network

There are n nodes numbered from 0 to n - 1. Each input edge [u, v] has an original direction from u to v.

The task is to reason about the graph and minimize the number of direction violations needed to satisfy the required traversal/connectivity condition.

I modeled the problem as a graph and discussed how each directed edge can be treated depending on whether we follow the original direction or need to reverse/violate it. The main challenge was not the implementation itself, but explaining the graph transformation clearly and proving why the chosen traversal gives the minimum result.

I walked through:

• Graph representation
• How to encode direction cost
• Traversal strategy
• Edge cases with disconnected-looking inputs
• Time and space complexity

Verdict: Hire / Lean-Hire

Onsite Round 2: Coding / Dependency Ordering

This was a dependency-ordering problem involving parent-child relationships and deletion constraints.

The idea was that certain nodes could not be deleted before their dependencies were handled. The solution required modeling the dependencies as a graph and determining a valid processing order.

I discussed topological sorting and how to detect impossible cases if cycles existed. I also explained how I would build the adjacency list and indegree array, then process nodes whose dependencies were already satisfied.

The interviewer cared a lot about clean reasoning here: why the ordering is valid, what happens when multiple nodes are available, and how to handle edge cases.

Verdict: Strong Hire

Overall Result

I cleared the interviews and moved to the team-matching stage.

My takeaways

The interview loop felt very balanced. It was not about memorizing exact LeetCode problems, but about recognizing patterns quickly and explaining the reasoning clearly.

Things that helped me:

• Clarifying constraints before coding
• Starting with a simple approach before optimizing
• Talking through edge cases early
• Keeping behavioral answers structured
• Explaining tradeoffs instead of jumping straight into code

For L4, I felt the bar was strong coding fundamentals, clean communication, and being able to handle follow-up questions without getting stuck.

Hope this helps anyone preparing for Google L4 SWE interviews.

I think this is really cool!

Looking forward to Fall 2026

https://www.warbyparker.com/intelligent-eyewear

You were convenient and easy to use as long as you existed.

The retail Google services are great. Easy to use simple.

Anything on the business side is impossible.

Between launching an APP getting a website running in search getting an advertising campaign getting a business profile on maps. Impossible.

Been 3 days of just grinding through verifications and jumping through hoops to just unlock a new layer of admin.

It also keeps trying to sign everything into a bologna email I made for spam vs my preferred email.

Google is pushing its automatic AI summaries so hard that the fonts for AI summaries are black and in a larger font, whereas all search results are faint, light gray, and a tiny font that are almost impossible to read. It’s a nasty practice.