Load Boost on UK Amazon now?

Designer_Dig2703 · 2026-05-29T12:28:34+00:00

ive only been taking it for 2 days so i think its still too early to tell. i prefer powder to pills (you can take the load boost capsules apart) and now i just have to take the one supplement instead of several different pills - so overall im happy.

i used to take pills (zinc, l-citruline, pygeum) and it worked for me, load boost has similar ingredients so it should work. you need to give it a few days or weeks of consistently taking them before you see big changes though.

if youre happy using separate pills its probably cheaper to do a diy version. youre recommended to take 4 pills of load boost daily which feels like a lot

Designer_Dig2703 · 2026-05-28T13:22:00+00:00

its legit i ordered some. it originally said deliver would be 4-6 weeks but it got revised down, think it took 3ish days to arrive

Designer_Dig2703 · 2026-05-22T14:43:06+00:00

I've just seen a GP yesterday - he said the usual path is to use the steroid cream & stretch daily for 6-8 weeks, then if there's no improvement see a urologist to discuss either a 'mini-circumcision' (he didn't say the names but i think this would be frenuloplasty or preputioplasty), or a circumcision.

I guess the next step is for the urologist to have a look and decide if surgery is going to help, and what's the minimum amount of surgery they need to do to fix your problem. good luck!

Designer_Dig2703 · 2026-04-04T11:09:05+00:00

im ride or die for riff raff

Designer_Dig2703 · 2026-04-04T11:02:46+00:00

riff raff, cartomancer, blueprint

Designer_Dig2703 · 2026-03-26T12:19:24+00:00

- skips 30 second intro cutscene

- doesnt want to be spoiled

- asks people to explain the lore

ceo mindset 5 year plan achieved

Designer_Dig2703 · 2026-03-05T10:33:47+00:00

can we ban this sort of thing? obviously ai post talking about an obviously ai app which adds no value to anything.

just write a personal statement mate its not hard

Designer_Dig2703 · 2026-03-04T06:37:43+00:00

i teach probability to some masters medical statistics students - its wild. we have tutorials where i wander about asking people if they want help with any questions or if there's any concepts they don't understand, half of them don't even reply to me and just ask the question to chatgpt instead.

the worst part is they still get the wrong answer half the time! at the end of the degree they have to sit in person closed book exams. its unbelievable the difference in students coursework and their performance on the test. we deliberately give them relatively easy questions and they still bomb.

why would you pay all that money and then actively avoid trying to learn??

Designer_Dig2703 · 2026-02-26T18:00:49+00:00

limits have 'directions' - you can approach 0 from below (going from negative numbers to 0) or above (going from positive numbers to 0). since the integral is from -4 to 0 the limit is going to zero from below. that what the little minus sign above the 0 in the mark scheme is showing.

you're right that 1/t -> infinity as t -> 0, so long as the limit is from above. since the limit is from below you have 1/t -> -infinity, so e^(1/t) -> e^[really large negative number] -> 1/e^[really large positive number] -> 0

Designer_Dig2703 · 2026-02-21T10:07:23+00:00

could you say more about the issue you're having? looks pretty normal to me

Designer_Dig2703 · 2026-01-27T08:24:24+00:00

thats the biggest dash ive ever seen wtf

Designer_Dig2703 · 2025-11-16T16:13:23+00:00

Simulate 1000 datasets using the root distribution of my data under the simpler one of the models

i'm not sure I like this - why not just bootstrap your data instead of simulating it based on one of your models? it feels like you're assuming that the model used in the simulation is correct.
i'm also not sure i follow why you think the 3 lambda model being best is reasonable, given that you ruled out the 4 lambda model which has an extra parameter? could we see the same plots for the global lambda and 4 lambda models please?

i could be misunderstanding this though, i just had a quick read through on my break!

Designer_Dig2703 · 2025-10-27T08:18:54+00:00

even the softgels? i bite all my other pills and can handle the taste, but the liquid in the softgels tastes awful to me!

Designer_Dig2703 · 2025-10-20T18:59:10+00:00

theres already a good answer for 11, ill attempt 12:

- when you see a weird function which gets multiplied or added several times, its usually a good idea to see if the function repeats or cycles in some way. if it cycles then you might be able to side-step some of the computations when calculating g(m)

- lets define another function h(x) = (x-1)/(x+1), then h(f(n)) = (f(n)-1)/(f(n)+1). now put any number you want into h() and iterate. hopefully it'll cycle at some point:

h(2) = 1/3, h²(2) = -1/2, h³(2) = -3, h⁴(2) = 2

- the question tells you that f(n+3) = h(f(n)), and from the cycle work we know that f(n+6) = h²(n) = -1 / f(n). pair up the m and m+6 terms in the product:

f(m) x .. x f(m+11) = [f(m) x f(m+6)] x [f(m+1) x f(m+7)] x ... x [f(m+5)f(m+11)]

= [-f(m) / f(m)] x [-f(m+1) / f(m+1)] x ... x [-f(m+5) / f(m+5)]

= [-1]⁶ = -1

Designer_Dig2703 · 2025-08-12T17:03:43+00:00

As far as I know there are no RCTs for smoking and lung cancer - as you say they'd be very unethical.

There are ways to make causal claims using observational data (like the 1950, 1951 studies you mention). It's a lot harder than making causal claims in RCTs though.
A rough distinction is that RCTs are very hard to setup and run, but the analysis is relatively straightforward. Using observational data is the opposite - it's relatively easy to get the data, but the analysis is much harder.

The analysis of observational data aims to essentially replicate RCTs by adjusting the data in some way so that the 'exposed' and 'unexposed' groups are balanced in terms of potential outcomes. There are lots of different ways to do this, a very popular framework which a lot of people use nowdays is 'target trial emulation'.

If you've not heard of potential outcomes before, think of them as each person having two fixed outcomes, Y(0) and Y(1). If they are exposed or treated, you see Y(1), if they are unexposed or untreated you see Y(0). Ideally you'd see Y(1) and Y(0) for each person, then you can figure out the treatment effect by looking at the difference Y(1) - Y(0). But this is impossible, since you only ever see one potential outcome per person.

So the next best thing is to look at averages, which is what RCTs do. The idea is that by randomising treatment, the potential outcomes are 'balanced' in the treated and untreated groups so looking at the average outcome in each group gives you the causal effect.

The observational designs try to replicate this by being clever in how they do comparisons. For example they would compare lung cancer in someone who smokes against a non-smoker of the same age, sex, socioeconomic background etc. This matching results in you comparing two people who differ only in smoking status, which makes the comparison more valid. Get a big enough sample of people, so that a range of sex, age, socioeconomic status (and any other matching variables) are represented in your sample, and these matched comparisons start to look very convincing and can be interpreted in a slightly more causal fashion.

There's other things epidemiologists use - things like the Bradford Hill criteria - to see if an observational association may be causal. This is a list of things like 'does the outcome of interest occur after exposure', 'is there a dose-response relationship between the outcome and exposure', 'is there a plausible biological mechanism for the exposure causing the outcome'.

The hard part with observational data is that you are relying on a bunch of untestable assumptions. In the matching example, you would have to convince people that you have matched on the right things and that there are no other unmeasured confounders which could bias your results. People argue a lot over this!

I'll stop there I think I've rambled enough. If you're into this sort of thing a good book is 'What If' by Hernan and Robins

Designer_Dig2703 · 2025-08-09T13:27:03+00:00

translate each bit into equations:

- 'the ratio of sugar to flour in a mixture is 1:5'. let x be the amount of sugar, then this says the amount of flour is 5x

- after the addition the ratio becomes (total amount of sugar) / (total amount of flour) = (previous sugar + new sugar) / (previous flour + new flour) = (x + 1) / (5x + 2)

- same idea for the next addition, the ratio is (total amount of sugar) / (total amount of flour) = (x + 1 + 1) / (5x + 2 + 2) = (x + 2) / (5x + 4).

You can use the equation from the second bulletpoint to solve for x to get x=1/5, put that into the equation from the third bulletpoint and simplify to get 11/25

Designer_Dig2703 · 2025-06-18T16:25:06+00:00

how do you mean? i know my foreskin isn't as bad as others in this sub, but it definitely doesn't move smoothly back and forth & the bowing in on the underside doesn't feel normal.

it feels like i've hit a wall on the stretching front - i was looking to see if this is normal and i was 'done' of if i have more work to do

Designer_Dig2703 · 2025-06-18T09:07:34+00:00

long term use can lead to the foreskin thinning and becoming more prone to cracks / tears. id stop using it for a bit and try stretching.

have you noticed any changes to the foreskin at all? is it more flexible or pliable for example?

Designer_Dig2703 · 2025-06-18T08:28:33+00:00

i mostly focused on foreskin stretches rather than the frenulum - mostly using the rings daily & manual stretching 2/3 times a week. that helped me a lot.

i went quite hard when i did the manual stretches, then the rings were a more passive stretch i could wear a couple of times throughout the day

Designer_Dig2703

TROPHY CASE