*Heads-up that this thread will feature some extreme examples of homophobia and transphobia.*
Hey folks, have you ever wondered why trans people face constant accusations of fetishism, sexual predation and child abuse/grooming? Well, today let's talk about stochastic terrorism.
It is to inspire people to take action.
Anti-trans ideologues have been making accusations about trans people being one giant international pedophile grooming ring, who are promoting the use of hormone blockers to keep victims in a perpetual state of adolescence. Yeah.
Even children get painted as groomers.
And we don't have to wait to see the results, it's happening now:
And this is stochastic terrorism.
Hey folks, I want you all to pay very close attention to the "rapid onset gender dysphoria" hoax being perpetuated by TERFs and their evangelical handlers, because it is a roadmap to enacting Section 28 or Russia-style laws against the "promotion" of LGBT lifestyles to children.— Coffee Sloth \u2615 (@CaseyExplosion) October 23, 2018
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2/ This phenomenon—I’m calling it a Sex Recession—really surprised me. It seemed improbable in the age of Tinder, digital porn, and attitudes that are generally permissive and sex-positive.
3/ What’s happening isn’t exclusively American: Similar trends are being observed in other countries, including Japan, Australia, the U.K., Finland, and the Netherlands.
4/ One cause is obvious: Adults under 35 are less likely to be living with a partner than in recent decades, and more likely to be living with their parents—which, it’s safe to say, isn’t great for one’s sex life.
5/ But I also found other explanations, each with profound implications. The first, unsurprisingly, has to do with internet enticements. Netflix and other online entertainment may be substituting for sex.
- respirator (dubbed "pig snouts" in Cantonese)
- eye mask
- heat-proof gloves
- water bottle
- cling wrap
- traffic cones
- pots and pans
Demonstrators find creative methods to battle police tear gas
AFP graphic charting Hong Kong's main socio-economic indicators and opinion polls on press freedom and government performance
AFP graphic showing the main equipment used by hardcore pro-democracy protesters in Hong Kong to battle police tear gas, pepper spray and rubber bullets
Frontline first aid.
Nurses, doctors, medical students and ordinary citizens with first aid training have clamoured to join a small volunteer corps helping treat people involved in the Hong Kong protests
@AFP's Yan Zhao reports: https://t.co/uDfYkMeZJf
📸 Anthony Wallace
Pro-democracy activists kick off three days of rallies at Hong Kong airport.
Protesters hope to win international support from arriving passengers. The last demonstration at the airport on July 26 passed off peacefully without causing flight disruptions
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2/ Sales is often viewed as either a saving grace or proof that the product isn’t good enough (because it should sell itself). Neither are ever true. Some common mistakes that result in...
3/ Mistake 1: Hire a sales rep before reaching product/market fit to get your initial batch of customers. This is a mistake because founders need to work through their MVP with early adopters to truly understand what it is they’re selling.
4/ Mistake 2: Reach product/market fit, need to scale, and rely entirely on self-serve. For enterprise products that require big commitments and internal shifts, almost no product is self-explanatory enough to sell itself.
5/ Mistake 3: Make a first sales hire who isn’t scrappy enough to help mold the sales process from scratch. Some salespeople are amazing at their jobs, but not cut out to establish the processes that others end up following. This skillset is what @rdedatta calls a “sales ninja”.
- Have a lot of emails in the waiting list
- Become #1 on PH
- Become #1 of HN or Reddit
- Have people that saing "I'll pay"
- Have a lot of free users
The best way to know if they're actually pay for it.
The only thing that matters is your product providing a value, and you can't know this until people will pay money for it.
We can have a first signal of a product/market fit after the launch by check how many sales we've got in the first 24h. Some scale:
200+ This has a high potential.
100+: This has a potential.
50+: Some people need it.
10+: People almost don't need it.
0+: People don't need it.
This is for a single time payment product. For a subscription probably it should be less because people hate subscription.
And obviously, you should have a lot of traffic like 3-5k+.
Here is a stat for my products (macOS apps) for the first 24h after the launch:
How did you get your first customers for WIP? How did you grow BetaList’s traffic? Etc.
Makers are looking to reverse-engineer success. I see it everywhere.
I don’t think it works that way and the answers to those questions are mostly useless. 💥
I have built dozens of different products over the last couple of years. The vast majority failed. 😭
Surely if I know the answers to these questions, but still fail over and over again, these answers aren’t that useful. 🤷♀️
So what’s a better question to ask? 🤔
99.9% of the questions I receive are about the products that did well. In a way that makes sense, because we quickly forget about those that didn’t succeed.
🧠 This is known as survivorship bias.
Focusing on what survived, while ignoring what made it survive in the first place.
The real question, what you really want to know, is this:
What makes @WIP, @BetaList, and to some extent @AllStartupJobs succeed where my countless other attempts failed?
What separates a failed product 👎 from a successful product 👍?
Honestly, I don’t know. I wish I did.
It’s like Steve Jobs said “I’ll know it when I see it.” 👀
Same is true when we make products. We don’t know upfront what will work. But once we see an inkling of a product that does have potential, it’s not that hard to spot.
The PageRank algorithm gives each page a rating of its
importance, which is a recursively defined measure of importance, based on if important pages link to it.
It's recursive because the importance of a page refers back to the importance of other pages that link to it
Here's how it works in practice:
1⃣ We start with some pages and crawl them for links
2⃣ Each page has 1/N points (where N as the total number of pages)
3⃣ Add points to each page for the amount of links to it, divided by the number
of links emanating from the sources of these links