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I know there are issues with minors traveling unaccompanied, but if a 17 year old (almost 18) is accompanied by their older sibling, are there any issues going from the US to Europe and back?

# New research reveals scars of Gambia’s witch hunts, carried out by former President Yahya Jammeh. Victims were subject to beatings, rape, forcible consumption of a noxious liquid, and forced to strip naked in front of strangers and bathe in a herbal liquid. Most victims were elderly folks and women.

Hey SEO folks,

I’ve been working on SEO for my own small business website (local niche, service-based), and while I’m seeing good growth in impressions and clicks via Google Search Console, the actual leads/conversions are still super low.

Here’s what I’ve done so far:

1. Fixed technical issues (indexing, mobile usability, Core Web Vitals)
2. Built out service pages + blog content targeting long-tail keywords
3. Improved meta titles/descriptions (based on CTR data)
4. Added schema markup (LocalBusiness, FAQ, etc.)
5. Optimized Google Business Profile regularly

Traffic is up. CTR is okay. But leads are trickling in slowly; It not matching the traffic growth.

My Questions:

1. What have you done to bridge the gap between SEO traffic and actual conversions?

2. Do you think I should focus more on UX/CRO at this point?

3. Any advice for someone trying to scale local SEO results into real business impact?

I’d appreciate any honest tips, even harsh truths. Just want to learn and improve. 🙌

Thanks in advance!

Recently I ordered a soft toy from Amazon, its huge in actual size, like 3ft or something but Amazon had vacuum packed it to the size of a laptop. Since then I started wondering if I could use that.

I’m moving across country, normally I’d leave most of my clothes and blankets here because they are bulky and puffy and take up a lot of space. But in reality they are very light. And I always tend to travel light from the 50lb weight limit, it’s just that the clothes take up almost all of the space where I can’t push it any further.

So I was starting to consider if vacuum packing would be a good idea. In my mind if I can use that I can even compress my pillows and take them with me and then leave them in the sun for a couple days to let them stretch out.

Do the magic bags and vacuum bags sold on Amazon and Walmart do job effectively enough if anyone has experience with them.

The difference in my situation is that I do not have to pack the bags on the way back so I was considering purchasing a pump and bags separately, and then I could just leave the pump instead of half my stuff.

If anyone has other suggestions let me know as well.

Thanks a lot

Hey,
I’m building a Uniswap-like platform, but for DeFi vaults only where users will be able to deposit, withdraw, and swap between vaults across multiple chains (Yearn, Beefy, Ribbon)
Would love your feedbacks!
Cheers

I’ve been wondering if it’s worth consistently posting on our Google Business Profile. I know it “keeps it fresh,” but does it actually move the needle for visibility or rankings?

We started posting four times a week about two months ago. Mostly short updates, promos, seasonal tips. Weirdly, we did notice an uptick in views and map actions.

I use a tool that lets me queue up a month’s worth of posts ahead of time, so it’s not a hassle. I’ve definitely noticed better local reach since doing it regularly.

Anyone else seen similar results? Or is this just a coincidence?

A while back, I was working on localization with GPs and had a thought: could we encode vehicle dynamics directly into the GP kernel?

I know GPs are used to model parameters in physical models. But my idea was that a car’s trajectory resembles a smooth GP sample. A faster car takes smoother paths, just like longer length scales produce smoother GPs. Instead of modeling `y(x)` directly, I used cumulative distance `s` as the input, and trained two separate GPs:

* `x(s)`
* `y(s)`

Both use an RBF kernel. So we are basically maximizing the probability function:

https://preview.redd.it/ksoisiw9r9ef1.png?width=430&format=png&auto=webp&s=e01f1827f3c74550f596de2ee02fe4b7d2e93178

Which translates to something like

*“Given a speed, how probable is it that these data points came from this vehicle?”*

**The algorithm goes like this:**

1. Collect data
2. Optimize the kernel
3. Construct the `l(v)` function
4. Optimize the lap

I fitted the kernel’s length scale `l` as a function of speed: `l(v)`. To do this, I recorded driving data in batches at different constant speeds, optimized the GP on each batch, then fit a simple `l(v)` relation, which turned out to be very linear.

With the optimized kernel in hand, you can ask questions like:

*“Given this raceline and a speed, can my car follow it?”*

As the GP is a probabilistic model, it doesn’t give a binary answer that we requested. We could optimize for “the most likely speed” the same way we optimized the length scales. However, this would be more like asking, “What is the most likely speed this raceline can be achieved?”, which is okay for keeping your Tesla on the road, but not optimal for racing. My approach was to define an acceptable tolerance for the deviation from the raceline. With these constraints in hand, I run a heuristic window-based optimization for a given raceline:

**Results?**

Simulator executed lap plan times were close to human-driven laps. The model didn’t account for acceleration limits, so actual performance fell slightly short of the predicted plan, but I think it proved the concept.

There are a lot of things that could be improved in the model. One of the biggest limitations is the independent models for x and y coordinates. Some of the things I also tried:

1. Absolute angle and cumulative distance model – This one considers the dynamics in terms of the absolute heading angle with respect to cumulative distance. This solves the problem of intercorrelation between X and Y coordinates, but introduces two more problems. First, to go back from the angle-domain, you need to integrate. This will lead to drifting errors. And even if you don’t want to go back to trajectory space, you still lose the direct link between the error definition of the two domains. And second, this function is not entirely smooth, so you need a fancier Kernel to capture the features. A Matérn at least.
2. “Unfolding the trajectory” – This was one of my favorites, since it is the closest to the analogy of modeling y relation to x directly, wiggly road style. In the original domain, you would face the multivalued problem, where for a single x-value, there can be multiple y-values. One can “unfold” the lap (loop) by reducing the corner angles until you have unfolded the points to a single-valued function. This, however, also destroys the link to the original domain error values.

Here is the code and the data if you want to make it better:
[https://github.com/Miikkasna/gpdynalgo](https://github.com/Miikkasna/gpdynalgo)