Join-Company

1️⃣ 📊 Timeline of my Job.

💡 Learning, Memory, Work, and Life

📚 In software development—and in almost any profession—proper documentation is essential. Without notes, records, or documentation, people can forget important details within days or weeks.

⚠️ The same challenge exists in education. If you study a topic for one project this week and move to a different task next week, much of that knowledge can fade unless it is regularly practiced and documented.

🏫 During my school and college years, I also worked to help support my family. My earnings were very small, and most of that money went toward food and daily expenses.

👨‍👩‍👦 My father Vajravel had a permanent job in TAFE and worked hard to pay for school fees, housing loans, food expenses, and other family responsibilities. However, financial pressures remained high due to housing loans, education expenses, and other debts. But, Problem for that big company at 2026 due to the problem in TVS group - Short video from MAAYAM STUDIOS

👩 My mother Vasugi V worked as a teacher and conducted tuition classes, earning small amounts such as ₹100, ₹200, ₹300, and ₹500. Until 2016, my younger brother and I also helped with those tuition classes.

🎓 During my college years, I stopped teaching tuition and focused on my studies, while my mother and younger brother continued the work.

🤔 Looking back, I realized that I was never taught effective learning methods. The Memorization-Centric Education System around me mainly emphasized memorizing answers for 2-mark, 5-mark, 10-mark, and 16-mark questions.

📝 As a result, I focused on memorization to pass exams rather than deeply understanding concepts.

🚀 With the rise of AI tools, I finally gained access to explanations that helped me understand topics more clearly and connect ideas instead of simply memorizing them.

🌱 Today, I believe true learning comes from understanding, curiosity, practice, documentation, and continuous revision—not just memorization for examinations.

Short version:

📚 Learn → 📝 Document → 🔄 Revise → 💡 Understand → 🚀 Grow

❌ Memorize only for exams in Memorization-Centric Education System
✅ Understand concepts for life and work

xychart-beta
    title "Career Income Journey"
    x-axis ["2016","2017","2018","Gap","2021","2022","2023","2024","2025","NoJob"]
    y-axis "Monthly Salary (INR)" 0 --> 45000
    line [5000,9000,6000,0,15000,25000,35000,40000,42000,0]

📅 Period	💼 Job	🧪 Interview Process	🛠️ Framework / Task	💰 Monthly Pay	⚠️ Notes / Red Flags
🎓 After College (2016–2017)	Teleport Engineer	❌ No interview – I got this job through a referral from my father’s relatives	—	₹5k → ₹9k	💸 Low pay; 🏥 health issues forced me to quit
⏳ 1-Year Gap	—	—	—	—	—
📘 DevOps (6-month course)	—	—	🌐 Project on Spring Boot and Web	—	📚 Learning phase
🚪 Software Company (Start) (2018, 3 months)	Software Developer	🧪 Attended 25+ interviews – failed in 24; selected in only one company	💻 Software Development (Spring Boot, Web)	₹6k	⚠️ Fired; very low pay; possibly exploitative (I did not like personal conversations with unknown people after work hours, so I was fired)
⏳ 3-Year Gap (2019–2021)	—	—	—	—	—
🔁 2021–2022	Software Developer	⚙️ No formal interview – asked to build a CRUD application. I learned Django (Python) and completed it. Got this job through a referral from a person known to my mother (she asked me to visit a temple for 9 weeks for this opportunity).	📱 Dart (Flutter), ☕ Java (Android)	₹15k	🕒 5 days + Saturday; long hours; 📌 scope creep; ❌ no BDD
📈 2022–2023	Software Developer	—	📱 Flutter, ☕ Android	₹25k	📊 Slight increase; same issues
📈 2023–2024	Software Developer	—	📱 Flutter, ☕ Android	₹35k	⚠️ Better pay, but still unprofessional
📈 2024–2025	Software Developer	—	📱 Flutter, ☕ Android	₹40k	🔄 Improving gradually
⚠️ 2025–2026 (till June)	Software Developer	—	📱 Flutter, ☕ Android	₹42k	🏥 Health issues again; quit in June 2026

Checking about my Past Salary with Other Currency in the World

Checked Date = 10-Jun-2026 - You can add four extra salary columns (USD, EUR, CNY, JPY).

Using approximate exchange rates (June 2026):

$1 ≈ ₹85
€1 ≈ ₹97
¥1 (JPY) ≈ ₹0.59
¥1 (CNY) ≈ ₹11.8

📅 Period	💰 Salary (INR)	🇺🇸 USD	🇪🇺 EUR	🇨🇳 CNY	🇯🇵 JPY
🎓 2016–2017 (Start)	₹5,000	$59	€52	¥424	¥8,475
🎓 2016–2017 (Later)	₹9,000	$106	€93	¥763	¥15,254
🚪 2018 (3 months)	₹6,000	$71	€62	¥508	¥10,169
🔁 2021–2022	₹15,000	$176	€155	¥1,271	¥25,424
📈 2022–2023	₹25,000	$294	€258	¥2,119	¥42,373
📈 2023–2024	₹35,000	$412	€361	¥2,966	¥59,322
📈 2024–2025	₹40,000	$471	€412	¥3,390	¥67,797
⚠️ 2025–2026	₹42,000	$494	€433	¥3,559	¥71,186

Just see for Bug Fixing and SaaS based Development they Earn.

🤔 Career & Salary Question

I often wonder why there is such a large salary difference in the software industry.

🛠️ Some developers mainly work on bug fixes, maintenance, or SaaS product development and earn very high salaries, sometimes reaching ₹65 LPA or more.

🧠 At the same time, there are people who spend years learning the deeper technical foundations of AI, software engineering, and computer science, yet may earn significantly less.

🤖 Today, AI tools such as ChatGPT and Gemini can assist with many coding and debugging tasks. This raises an interesting question:

❓ If AI can help automate parts of software development, why do some software roles still command very high salaries?

📊 I would like to understand: • What factors create these salary differences? • How do company size, location, product impact, and business value affect compensation? • What responsibilities and risks justify higher pay? • What challenges or pressures do highly paid software engineers face?

⚠️ Possible challenges in high-paying software roles: • High responsibility for critical systems • Tight deadlines and delivery pressure • Large-scale production incidents • Security and privacy risks • On-call support and emergency fixes • Business and customer impact from mistakes • Continuous learning requirements

🔍 I am interested in understanding the economic and professional reasons behind these salary differences in the technology industry.

One important point: high salaries are usually not paid for typing code or fixing a bug. They are often paid for the responsibility, scale, business impact, decision-making, and risk involved if something goes wrong. 💼📈⚠️

The statement you want to communicate is that AI is evolving from a tool that simply answers questions into a system that can build applications, conduct research, create animations, write code, and execute complex workflows under human direction. Humans increasingly act as directors, designers, researchers, and decision-makers, while AI agents perform much of the implementation work.

Here is a polished statement with suitable real-world applications.

The Future of AI-Driven Creation, Research, and Engineering

Platforms such as Google AI Studio, Google Antigravity, and other advanced LLMs and AI agent systems are transforming how software, research, engineering, and digital content are created. By providing a clear objective and instructions, a human can direct AI agents to plan tasks, write and modify code, build applications, conduct research, generate visual content, create animations, test software, analyze data, and automate complex workflows.

These capabilities extend across multiple fields, including full-stack application development, scientific research, image and video generation, 2D and 3D animation, CAD-assisted design, audio and speech generation, mathematical modelling, simulation, and engineering workflows. For example, Google AI Studio can build functional web applications from natural-language prompts, while Google Antigravity can coordinate coding agents that work across development environments, browsers, and testing workflows. https://ai.google.dev/gemini-api/docs/aistudio-build-mode https://www.antigravity.google/docs/ide/overview/

The emerging workflow is straightforward: humans define the vision, objectives, constraints, and quality standards; AI agents plan, create, execute, test, and refine the work; and humans review the results, make critical decisions, and provide further direction.

The ultimate goal is to reduce repetitive manual work, accelerate experimentation, make advanced technical capabilities more accessible, and enable individuals or small teams to accomplish tasks that previously required larger teams of specialists.

However, AI is not yet capable of reliably completing every task without supervision. Scientific discoveries require evidence and reproducibility; engineering designs require validation; generated animations and applications need quality checks; and production systems require security, reliability, and human accountability.

The future is not simply about humans using AI tools. It is about humans directing intelligent systems to transform ideas into working applications, research outputs, creative content, and validated engineering solutions.

Suitable applications

Anything: The New AI Agent for Building Mobile and Web Apps - Neon

Application development
- Build websites, dashboards, mobile applications, APIs, databases, and business software.
- Application: Google AI Studio · Google Antigravity
- 5 Best Girl Creator Full Body 3D Tools - Genies
Animation and digital content
- Generate images, video, scenes, 3D assets, character concepts, and animation workflows.
- Applications: Blender for 3D production, Google Flow for AI filmmaking, and Runway for AI video.
- AI Formulation Software: Top 7 Platforms Compared (2026) — ChemCopilot: PLM + AI for Chemical Industry
Scientific research and simulation
- Review literature, develop hypotheses, write simulation code, analyze results, and prepare research reports.
- Applications: Google Colab, Jupyter, and NVIDIA BioNeMo.
- Konstruktion, Fertigung & Montage – Klingele Technik GmbH - Sondermaschinenbau
Engineering and 3D design
- Assist with CAD modelling, component design, computational analysis, and engineering documentation.
- Applications: FreeCAD, Onshape, and OpenFOAM.
- AI Voice Clone - Your Voice, Infinite Takes - FairStack
Audio, speech, and multimodal AI
- Create speech, transcribe audio, generate sound, analyze recordings, and combine text, images, audio, and video in AI workflows.
- Applications: ElevenLabs and Hugging Face.
- Important distinction: These tools do not all perform every task themselves. Some generate content, some build applications, and others coordinate agents or run specialised simulations. They can be connected into a broader workflow, with appropriate permissions, tools, computing resources, and validation.