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Meetup Parameters

This is based on organising GenerativeAI Meetups in Bengaluru, India. This is a living document and will be updated as we learn more.

Venue

1. Date & Time & Duration: Choose suitable timing and duration. Pick something that works for your community. Consider weekday and weekend meetups both.
   • Example: 4:00-5.00pm start on a Saturday works great in BLR! Chennai did a GenAI meetup on Saturday morning -- since that city wakes up early, it worked well for them.
2. Camera: Consider the requirements for A/V if planning to do talks, streaming, or recording? Who is going to record? What kind of camera do we need? GenerativeAI outsources this to Hasgeek. Camera set-up for meetups is reduced to iPhone capture, with a Pivo Pod and tripod.
3. Format: Define the structure of the meetup - is it just for drinks? Will there be talks? Is food provided? Is it open or by invitation only? Are plus ones allowed? Examples: GenerativeAI has never served alcohol, we often have 1-3 talks with snacks and is open by invitation only. We also have a Code of Conduct that we share with attendees.
4. Speakers: If there will be talks, secure 1-3 speakers in advance. It's also fine to have a meetup with no speakers.
5. Security: Keep in mind some venues might require pre-registration for security. Different venues enforce security with varying degrees of strictness. Some venues didn't allow anyone without registration, while the other venues allowed folks we didn't want to attend to enter. Discuss this upfront with your venue's security incharge.

Theme

• Select a theme. This is crucial as it shapes the shared identity of all attendees and influences the discussions they initiate with strangers
• Narrower themes are better than wider ones e.g. "DevTools" is better than "Enterprise tools for Devs"
• Choose a catchy name to attract more attendees -- your naming is the most important branding
• There are icebreaker lists on the Internet which you can use for more intimate meetings
• Name tags: I've tried to enforce these, but failed at the GenerativeAI Meetups -- they're great! I've seen them work well at other meetups.

Photo and Video Documentation

• Encourage attendees to take photos, tag you, or send them to you for sharing on social media
• Consider recording the talks and posting them on YouTube afterwards to provide value to the community and allow great talks to live on. We use Hasgeek for this.
• Note that live-streaming is not a must, but if done, it can add an extra layer of engagement for those who can't attend in person.

Tax Tips for Consultants with Foreign Income

Managing taxation and financial compliance as a consultant in India involves several considerations: GST to the benefits of Section 44ADA and the complexities of foreign entity considerations, consultants must juggle multiple aspects to ensure compliance and tax optimization. I'm sharing what I know in the hopes it's helpful to others.

1. Compliance: GST, LUT and FIRC

• Threshold: Acquiring a GST number becomes almost mandatory once your income surpasses some X=20 lakhs INR in a given financial year. This step legitimizes your income and ensures compliance with Indian taxation laws. The X limits keeps changing and I'd recommend you check the latest one with a CA and NOT rely on Google for this. Some online services like ClearTax offer GST registration services, but I'd recommend you get it done via a CA. It's a one-time process and you don't need to renew it every year.

• Sole Proprietorship: If you are an individual, you get a GST number as a sole proprietor. This is the simplest way to get a GST number and is recommended for most consultants. You can also get a GST number as a private limited company, but that might be overkill for most consultants. My CA also recommended that I get an Udyog Certificate, so I did get one but I'm not sure if it's necessary.

• Quarterly Filing: If you are a sole proprietor or operating as an individual consultant, quarterly GST filing can be more manageable than doing it monthly. This approach helps streamline administrative tasks and keeps compliance straightforward.

• FIRC: Every time that you receive a payment from a foreign entity, you will need to get a Foreign Inward Remittance Certificate (FIRC) from your bank. This document is a proof of payment and is required for filing taxes. It is advisable to get a FIRC for each payment, as it can be difficult to get one later. Salt is a great tool for automating this process.

• Accounting & Compliance: Using a CA to file for managing both your GST and Letter of Undertaking (LUT) is advisable. It serves as an effective tool for official documentation and ensures a smoother filing process.

• Current Account: Having a current account separate from your personal finances helps in meticulous financial management. It allows you to distinctly separate your business income and expenses, making accounting more cleaner. E.g. you can use this current to pay for your AWS bills. This can help you save on GST, as you can claim GST input credit on these expenses.

• Letter of Undertaking (LUT): For consultants involved in international transactions, an LUT is crucial. This legal document clarifies that all relevant taxes will be paid exclusively in India, thereby simplifying compliance requirements for cross-border business. It is advisable to get an LUT even if you are not sure if you will be involved in international transactions. You need to mention the LUT in your invoices to foreign entities.

• GST on Foreign Transactions: GST is not applicable on foreign transactions. However, you will need to mention the LUT in your invoices to foreign entities. This is to ensure that the GST authorities are aware that you are not liable to pay GST on these transactions.

2. Taxation

2.1 Personal Income Tax: Section 44ADA

• 50% Rule: Section 44ADA offers a benefit by allowing you to assume 50% of your income upto 75 lakhs as an expense for tax filing. This helps significantly in reducing your taxable income, which can be advantageous for many consultants.

Example Math:

If you make 100 lakhs INR in a year, you can assume 37.5 lakhs INR (half of 75 lakhs) as your expenses and pay taxes on the remaining 100-37.5=62.5 lakhs INR. At 30% income tax rate, you'd pay 19.3 lakhs INR in taxes. Effectively, you'd be paying 19% of your income in taxes.

💡 You do NOT need to maintain books of expenses for Section 44ADA. This is a huge benefit as it simplifies the process of filing taxes!

There is another catch on 100 lakhs INR: 15% surcharge on income tax. Source: ClearTax -- I'm unclear if Section 44ADA applies to this surcharge.

• >50% Expenses: If your actual expenses amount to more than half of your income, then Section 44ADA may not offer you the best tax advantage. This is because you can only claim 50% of your income as an expense, even if your actual expenses are higher. You will have to get audited by a CA and file your taxes later (usually in September) if you want to claim more than 50% of your income as expenses.

2.2 Foreign LLC or C-Corp

• LLC Abroad: If you are working with a foreign entity, it is sometimes useful to have a Limited Liability Company (LLC) in the country of the entity. E.g. you can open a Delaware LLC via Stripe Atlas and use that to invoice your clients. You will then pay taxes via this entity in the source country. It's only for the amount that you move to India, that you will pay taxes in India.

If I remember correctly, you will be paying 8.7% corporate tax in Delaware, and similarly 8-9% in Dubai. With all of these, you pay additional 30% personal income tax in India. So if you make 100 lakhs INR in a year, you will pay 8.7 lakhs INR in the US. And then you pay personal income tax in India on your living expenses: So say, you move 18 lakhs INR to India, you will pay 0.3*18 = 5.4 lakhs INR in taxes in India. Effectively, you'd be paying 14.1% of your income in taxes. If you are making more than 100 lakhs INR in a year, this might be a good option for you.

This can help simplify the process of receiving payments. However, it is important to note that this option is not always the most tax-efficient. It is advisable to consult a CA to understand the implications of this option for your specific amount, transaction frequency and expected personal expenses.

RemoteIndian has a similar Tax Guide that you might find useful. I'm not a CA, so please consult a CA before making any decisions. I'm sharing what I know in the hopes it's helpful to others. If you have any questions, please feel free to reach out to me on Twitter.

Why I Quit Data Science

Question from a friend: I am interested in knowing how did you come to this decision of moving to SWE from DS/MLE. Since I've been asked a variant of this question quite a few times, I thought it would be good to share my answer.

What kind of research did you do to get to this decision?

I spoke to a lot of people who were both big companies and startups. I also spoke to folks across multiple markets: Singapore, India, US & Europe. I primarily spoke to people with more than 10-12 years of experience. This is a big difference in my perspective.

What were your considerations while making this decision?

Skills

This is how I understand the world today: There are 3 primary functions around data: data engineering, modeling (e.g. predictive) and data analytics.

I could keep going deeper into modeling e.g. learning more about CNNs and Transformers. Between writing the NLP Book and professional Machine Learning, I'd guess that I'm in top 20-30% of the world doing this. The journey to get from here to being the best is hard and I'm not sure if I'm going to be able to do it.

The field also suffers a bit from the Red Queen effect on the applied side of things. I'm not sure if I want to keep doing it 5, 10, or 20 years from now. I started doing Machine Learning because I was interested in the field and I was curious about how it would work.

It's no longer about the thrill of solving a puzzle/problem anymore. The roles I've access to, have the drudgery of making the same pipelines work in similar ways and then applying them to different problems.

I'd much rather add another skill and get to the top 25% in it -- and then quickly rise to the top on it's intersection. This will also be easier as I've tons of novelty and new ideas to learn.

Since analytics roles are neither well respected nor well paid, the process of elimination works. I'd rather be a platform enginer than a data/product analyst.

Competition

Within this, let's talk separately about pre-series D startups and big companies (e.g. FAANG/MAGA) for modeling roles. Startups are usually open to hiring folks without a MS/PhD degree, while big companies are more open to hiring folks with a MS/PhD.

For modeling roles at big companies you will be competing with folks with a PhD. For startups, I often see they end up hiring better trained folks as they scale up and relegate older, less 'specialised' folk to roles closer to engineering (e.g. API Design, uptime) and away from modeling.

It's also much harder for me, personally to find truly exceptional Machine Learning mentors, but relatively easier to find proven, battle-tested quite senior engineers. And as much as you might underestimate the role of coaching, I believe that in our craft - it can save you 4-5 years of learning time.

Title/Impact

I've the least confidence on this being true over a longer duration. But I'm mentioning it here since a lot of senior people do think about this.

Growing within modeling-related roles is hard and you hit the ceiling as Head of Machine Learning. Notice that in most of the cases, you are not even Head of Data, you're Head of Research or ML or some function within Data. The Head of Data in turns reports to the senior most Engineering Leader e.g. the CTO.

This means your influence over things which shape your day: tooling, infrastructure, product direction, org structure, promotions etc. is limited. You can't even learn these things.

I'd like to keep my options of becoming a Engineering Manager/VP Engineering/CTO in a few years. I'd much prefer that to Head of Data Science or Analytics. This option is so much more valuable to me that I'm happy to pay a price to "buy" it.

What was/is the goal of this particular switch? What were you trying to optimize?

I'm optimizing for being great (but not best) at the intersection of 3 things instead of 1 narrowly, clearly defined role. I'm trying to get to the top of the intersection.

I was also bored by the mundanity of problems you encounter in typical early-stage startups. The need to trade off personal-notion-of-quality for speed is sometimes a bit of a problem, but I usually enjoyed the challenge.

Why not Machine Learning Engineering at a Big Company?

I fear that this role combines the worst of two worlds. You've the skills of a backend engineer: you can design microservices, implement them, scale them, deploy them, and manage the infrastructure. But you also have the skills of a Data Scientist: you can build models, train them, deploy them, and manage the experimentation infrastructure.

You don't get paid or recognized for either of them. The backend developer thinks of you as a "ML guy" and the Data Scientist thinks of you as a "Backend guy". This is made worse at a Big Company because they tend to reward specialists via promotions. You're going to get underpaid for both roles.

Not to mention that a large fraction of your knowledge is getting outdated faster than I can learn. Of course, you might be 10x faster, better learner than me - in which case this blog post is not meant for you.

Why not take a 1 year Research focussed Sabbatical?

Well, because companies which ask for skills acquired via a MS/PhD are often not willing to pay for a 1 year research year. It'd not be that much better than being endorsed by Dr. Andrew Ng, writing a book, mentoring folks for ACL papers and speaking at PyCon India.

What information did you find for and against this switch?

Stepping away from Machine Learning Lead roles can be a massive cash and title/designation downgrade. It definitely turned out to be true for me.

My alternate job offer was a Series B/C Machine Learning Lead, instead of a Platform Engineer. I would not be happy with the role, but I'd be very happy with the salary. It'd be 3-4x in cash, and 4-5x in total compensation terms. Another way to look at this, I took at 75% cut on my cash compensation.

I'm betting that I'll have a lot more fun doing this, but I'm also betting that I'll be a little more successful - which will compensate for this over a 4-10 year chapter of the career.

In addition to the cash and title hit, it's a bit of social shaming: People might be inclined to assume that you were not quite good as a Data Scientist and that is why you moved to SWE. I don't care enough about that to influence my decision but I do care about it and hence worth mentioning.

My Machine Learning skills will also atrophy with time. I'll be able to get to similar productivity faster in a few years because the half-life of knowledge in the field is really short and tooling improvements make it easier to ship well.