#CDSL beautiful company with nice uptrending price chart.....45%ROI in 54 trading days since first post ....#TrendFollowing #Pyramiding #AveragingUp ... https://t.co/25hh9QKamu

The best way to know if people like your product is NOT:

- 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:

(A THREAD)

18 Things I Would Do At 18:

I was a dumb 18-year-old. I thought I had the world figured out. I didn't.

I stayed up till the wee hours. Drank every liquor invented.

Partied non-stop. Woke up after noon.

Surfed the web aimlessly.

Read BS self-help.

Repeat.

If I could do it all over again, here's 18 things I would do...

PHASE ONE: GET MY MIND RIGHT

ACTION #1: Break the chains - I wish I made the decision earlier. I didn't. I was scared. Anxious. Nervous. I knew once I made the decision, there was no going back.

Don't be like me.

Make the decision at 18. The decision to pave your own path in life. To go against the herd. To choose your own adventure. To commit to whatever sets your heart on fire.

ACTION #2: Cut off every negative person in your life.

Scroll through the contacts in your phone...

Notice how the names make you feel.

Delete any and all names that fill you with doubt and negativity.

ACTION #3: Condition your mind to embrace the producer mindset instead of the consumer mindset.

Consume 20% of the time. Produce 80% of the time.

Case closed.

ACTION #4: Set an intention to make $10k a month and write it out every MORNING and NIGHT as if you already achieved it (in the past tense).

Visualize yourself already having the money as you write.

Cheesy?

Yes.

Does it work?

Yes.

PHASE TWO: EDUCATION

#Thread
Benefits of writing SRI RAMA Jayam!!!

Writing Sri Rama Jayam is known as Likitha Japam- Writing Meditation. This gives one a complete sense of surrender to an inner conscience and peace while writing or chanting the mantras.

#Sri_Rama_Jayam #RamaNavami

Cont 👇

You can write this in any language of your choice. It is the connecting chords with the divine and your inner self.

Below are some points that we came across on the benefits :

Cont 👇

1. Vedas tell that as the sun dispels the darkness, the chanting of Rama Nama dispels all evil and obstacles of life. It is a way of liberation and salvation of human suffering.

2. When you think, that all roads are blocked to walk away from day to day problems..

Cont 👇

writing ‘Sri Ram Ram Ram’ gives you the most needed clarity of thoughts to find away out of odd situations.

3. It was told that a calmness engulfs as one indulges in writing the Sri Rama Jayam bringing in more clarity of mind, tolerance & strength to withstand obstacles in life.

4. Devotion of service of life and its varied forms is devotion to God. So there is no right or wrong way of writing this. The very thought and process to write is a connection with god and finds a inner meaning.

Cont 👇

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.