Connect with us

Business

Mihir Sukthankar’s Life of Finance

mm

Published

on

People enter the trading and investing game for a wide variety of reasons. Primary to these, of course, is to make money. The exact type varies, with people entering the market to make a quick buck, save their money as assets, or “grow” their money with investments as a source of passive income. For some more successful traders, trading can become a solid career that provides various benefits, like flexible working arrangements and potential financial freedom. For those who are even more dedicated, trading can become a lucrative lifestyle that results in riches unachievable through a conventional nine-to-five job.

Though it is the main reason, money is not solely why people start trading. For those with the cash to spare, trading is done as an enjoyable and occasionally profitable hobby. These people see trading as a game, enjoying the gamble of risk and reward the activity provides.

Stocks and options trader Mihir Sukthankar is a little bit of both. Starting on the stock market at just 14 years old, Mihir quickly discovered his interest as well as his aptitude for the endeavor. Like most young traders, Mihir initially saw trading as an easy source of alternative income, as well as an entertaining way to pass the time. It did not take long for Mihir’s spark of interest in finance, however, to turn what was once a hobby into a lifestyle and full-time career.

At just 18 years old, Mihir is now highly successful as a trader, mentor, and entrepreneur, being the owner of three financial companies. His mindset of passion, resilience, and hard work allowed him to acquire the skills and experiences needed to thrive in the highly competitive financial industry.

In contrast to Mihir’s journey, the story of most young investors is vastly different. After being pushed to the market by an ailing economy and a pandemic-borne global financial crisis, impetuous and inexperienced young investors are being eaten up by finance veterans. Compounding the problem is the popularity of various fintech firms that promise quick and easy profits and provide avenues for trading without offering essential guidance to its new investors.  

With his firsthand knowledge of the young investor experience, Mihir saw the situation as a problem that he is in a unique position to solve. As a bonus, his experience in coding and managing teams in his past work with nonprofit organizations helped him establish the financial companies he had in mind.

Mihir’s first company was Traders Circle X, an association of options traders under Mihir’s guidance. It was based on the idea of signals, which are easily comprehensible and navigable instructions that can be followed by traders of any kind. Under the expert analysis of Mihir and his hand-picked partners, TCX has grown to a group of 4,000 traders. As a further sign of the organization’s success, the confidence of its member traders has seen them leaving their jobs for a full-time career in trading despite the difficulties brought about by the pandemic.

Client feedback from TCX inspired Mihir’s second company, BoostedQuant. In contrast to TCX, BoostedQuant is targeted more toward passive traders without the time but with the resources required to engage in trading. BoostedQuant is a machine-learning trading AI that analyzes and learns from past and present market conditions to foresee and recommend financial decisions for the future. As a unique added feature, BoostedQuant also allows its users to modify its algorithm to account for their risk preferences and trading behavior.

Mihir’s latest company is Market Dice, a one-stop hub that condenses relevant market information to a newsletter format to allow clients to make informed decisions. To further this objective, Mihir aims for Market Dice to offer online seminars in the future tackling lessons on stocks, real estate, cryptocurrency, futures trading, and other traditional, new, and emerging forms of financial markets.

To develop his skills for himself and the thousands of traders who follow him, Mihir continues to engage in trading on top of his efforts in maintaining and developing his companies. Mihir aims to become a successful and equally innovative owner of his own hedge fund and prop trading firm in the near future. In parallel, Mihir wants to use his hard-earned knowledge to help others achieve the same level of financial success.

You may follow Mihir on his Instagram, @mihirtrades.

The idea of Bigtime Daily landed this engineer cum journalist from a multi-national company to the digital avenue. Matthew brought life to this idea and rendered all that was necessary to create an interactive and attractive platform for the readers. Apart from managing the platform, he also contributes his expertise in business niche.

Continue Reading
Advertisement
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Business

MetaWorx: Building Full-Stack AI Teams, Not Just Automation

mm

Published

on

Automation still dominates most headlines, yet the returns often fail to meet expectations. A sprawling chatbot rollout might shave a few support tickets, but it rarely shifts the profit-and-loss statement in a lasting way. 

McKinsey’s 2025 workplace survey pegs AI’s long-term productivity upside at $4.4 trillion, but only one percent of enterprises say they’ve reached true “AI maturity.” MetaWorx, a Dallas, Texas-based AI employee agency founded by Rachel Kite, argues that the shortfall has nothing to do with models and everything to do with people. 

“Treat AI like a point solution and you’ll get point-solution results,” shares Kite. “You need a roster that can carry the ball from raw data to governance, or the whole thing stalls at the proof-of-concept phase.”

The pod blueprint

When a plug-and-play automation script collapsed under real-world data drift, costing Kite a lucrative contract, she sketched the six-person “pod” that now anchors every MetaWorx engagement:

  1. An infrastructure architect to tame compute costs.
  2. A data engineer to secure and shape pipelines. 
  3. An applied scientist to prototype models against live feedback loops. 
  4. An MLOps engineer to automate rollback and retraining. 
  5. A domain product lead translates forecasts into features users actually notice. 
  6. Ethics and compliance analysts to stress test outputs for bias and keep the audit. 

The team’s first sprint still delivers a quick-win bot — “small enough to calm the CFO,” jokes Kite — but the roadmap quickly pivots to reliability, explainability, and eventually optimization. By tying every algorithmic decision to a quantifiable business metric, the pods turn AI from a science project into a growth lever. 

Recruiting for curiosity, not credentials

With Bain & Company predicting a global AI-skills crunch through 2027, MetaWorx has stopped chasing unicorn résumés. Instead, it hires “adjacent athletes”: a computer-vision PhD who hops from medical imaging to warehouse surveillance, or a former journalist who recasts her nose for story into prompt-engineering finesse.

“Domain expertise expires fast,” Kite says. “What doesn’t expire is the instinct to ask better questions.” The result is a lattice of overlapping skills that stays flexible when models wander into the long tail of edge-case data.

A culture of rapid experiments

Inside MetaWorx, every idea faces the same litmus test: ship something — anything — into a user’s hands within 21 days. The “three-week rule” forces prototypes into the wild early, where failure is cheap and feedback is swift. Post-mortems, including cost overruns, are circulated company-wide, erasing any stigma associated with missteps.

That laboratory mindset powers velocity. “Our first model is almost always wrong,” Kite admits, “but version 1.0 is the tuition we pay for version 2.0.” The philosophy echoes her TEDx talk on resilience: progress is iterative, not heroic.

How leaders can steal the playbook

Executives itching to replicate MetaWorx’s results don’t need a blank check. Kite offers a five-step sequence:

  • Inventory pain points, not tools: Walk the P&L line by line and tag the friction you can measure.
  • Map the stack to the problem: A recommendation engine, for instance, requires behavior data, retraining triggers, and feedback capture — automation alone won’t suffice.
  • Stand up a pod: Reassign existing talent into a cross-functional tiger team before hiring externally; the chemistry test is free.
  • Measure the story, not just the statistic: Pair model accuracy with human-scale metrics like ticket backlog or employee churn.
  • Budget for the boring: Reserve at least 30 percent of spend for MLOps and governance; Stanford’s HAI review links most AI failures to neglected upkeep.

Taken together, those steps shift AI from a pilot novelty to an operational habit that compounds value rather than topping out after an initial PR splash.

Character still scales faster than code

MetaWorx plans to double its headcount this year, yet Kite insists the secret isn’t a proprietary framework or a monster war chest. It’s credibility. Clients see a founder who has wrestled with the same outages and surprise bills they face. That authenticity converts skeptics faster than any algorithmic novelty.

“Tools level out,” Kite says. “Culture compounds.”

The insight lands in a marketplace still dazzled by generative fireworks. Yes, MetaWorx ships models and dashboards, but its true product is a mindset: resilience over rigidity, questions over credentials, experiments over edicts. In Kite’s world, automation is merely the appetizer. The main course is a full-stack team that knows why the model matters to the business and who owns its success after launch day.

And that, Kite argues, is how AI finally graduates from cost-cutter to growth engine, one curious pod at a time.

Continue Reading

Trending