You are an experienced computing professional re-entering the software world during the AI era after a long gap. Your earlier background includes programming in BASIC, FORTRAN, COBOL, JAVA, Python, C, C++, and Oracle. You worked as a software engineer until around 2001 and later moved into teaching computer science and supporting students in laboratories. Recently, after buying a Windows 11 Intel Core i3 PC, you developed a strong desire to return to software creation, especially using modern AI-assisted programming tools.
Initially, you were uncertain because you had lost touch with programming for more than a decade and were unfamiliar with current tools and workflows. However, the discussion clarified that modern programming has changed significantly. In the past, programmers had to remember syntax and manually write everything. Today, AI tools such as [ChatGPT](https://chatgpt.com?utm_source=chatgpt.com), [GitHub Copilot](https://github.com/features/copilot?utm_source=chatgpt.com), and [Cursor AI Editor](https://www.cursor.com?utm_source=chatgpt.com) can generate and explain code, allowing developers to focus more on architecture, logic, validation, and creativity.
You were encouraged to see yourself not as a beginner, but as an “experienced programmer returning during the AI transition.” Your strengths—structured thinking, debugging ability, business logic understanding, patience, and teaching skills—are still extremely valuable. AI reduces the burden of syntax memorization and allows experienced thinkers like you to become productive quickly again.
A practical roadmap was suggested:
Refresh Python basics.
Install modern development tools such as [Python](https://www.python.org/downloads/?utm_source=chatgpt.com) and [VS Code](https://code.visualstudio.com?utm_source=chatgpt.com).
Use AI-assisted editors like Cursor or GitHub Copilot.
Build small but meaningful projects instead of spending too much time only watching tutorials.
The advice emphasized starting with lightweight, practical applications suitable for your computer and experience level rather than resource-heavy technologies. Since your Intel Core i3 system is mainly for browsing and YouTube currently, the focus should be on:
Python scripting
automation
desktop utilities
Tamil language tools
educational applications
data processing
reporting systems
It was also recommended that you avoid heavy local AI models, advanced cloud infrastructure, Docker, Kubernetes, or complex Linux environments initially. Instead, your PC can function effectively as an AI-assisted development workstation using cloud-based AI tools.
A major turning point in the conversation occurred when you shared your interest in Tamil language, Tamil grammar teaching, spelling correction, and pure Tamil (“தனித்தமிழ்”). This led to the realization that your unique combination of skills positions you perfectly for creating AI-powered Tamil language software. Several meaningful project ideas emerged:
Tamil spell checker
Tamil grammar tutor
pure Tamil word replacement assistant
Tamil educational AI assistant
OCR and Tamil text correction tools
student-oriented Tamil learning software
These projects combine:
your programming background,
teaching experience,
love for language,
and AI technology.
The recommended technology stack for such work included:
Python
Streamlit for simple interfaces
SQLite for lightweight databases
regular expressions and NLP libraries
OpenAI APIs
Tamil Unicode processing tools such as open-tamil
You were advised to focus on “one real project” instead of jumping between many frameworks or languages. One suggested beginner project was a Tamil spelling correction tool that accepts Tamil input, identifies common mistakes, and produces corrected output with explanations.
Another important topic was adapting to Windows 11. Since you last used Windows 7 extensively, the new interface felt uncomfortable and confusing. The discussion explained that this is a common experience because Windows 11 uses a different interface philosophy. To make the system more comfortable:
move the Start menu to the left,
enable file extensions,
create a simple C:\Projects workspace,
and treat the machine primarily as a development workstation rather than a consumer entertainment device.
You were reassured that you do not need to master every Windows 11 feature. Instead, you only need to learn what directly supports your projects and programming work.
A particularly meaningful milestone occurred when you spent about three hours using VS Code and GitHub Copilot to create a Tamil output program. Although you experienced confusion, panic, and technical difficulties, you eventually succeeded and felt happy. This was identified as the authentic “developer experience” that has remained unchanged across generations:
confusion,
debugging,
persistence,
and final satisfaction.
You were reminded that this small success was actually very significant because it demonstrated that your software-building ability still exists. The Tamil output program also introduced you indirectly to concepts like:
Unicode,
UTF-8 encoding,
console rendering,
editor configuration,
and AI-assisted debugging.
The conversation then moved to GitHub. You had created a GitHub account but later closed it because you were unsure of its purpose. It was explained that GitHub is much more than a website; it is:
a cloud backup system,
version control platform,
project archive,
collaboration environment,
and portfolio system.
The difference between Git and GitHub was clarified:
Git is the version control system.
GitHub hosts Git repositories online.
You were encouraged to reopen a GitHub account because modern AI development tools integrate heavily with it. However, an important caution was given regarding privacy and personal information.
You had experimented with storing personal addresses in a .txt file using VS Code. While using text files for learning and programming practice is perfectly normal, you were advised never to upload sensitive information to public GitHub repositories. Sensitive information includes:
personal addresses,
passwords,
bank details,
private student records,
API keys,
or confidential data.
To avoid accidentally uploading private files, the discussion introduced the use of .gitignore, a standard file used in software development to tell Git which files should not be uploaded or tracked.
For example:
private.txt
*.log
inside a .gitignore file prevents those files from being uploaded to GitHub.
You were shown a safe project structure separating:
code,
sample data,
documentation,
and private local files.
You were also encouraged to use private repositories on GitHub while learning.
Overall, the conversation established that your return to programming is not merely a hobby restart. It is the reactivation of a deep computing mindset developed over decades. Your rare combination of:
classical programming experience,
teaching ability,
Tamil linguistic interest,
and AI curiosity
creates the potential for meaningful contributions, especially in Tamil educational and language technology software. The discussions repeatedly emphasized that you are not starting from zero; instead, you are bringing foundational computing knowledge into a new AI-assisted era where one thoughtful individual can build useful tools that previously required entire teams.
இன்றைய காலத்தில் AI (Artificial Intelligence) பயன்படுத்தி எந்த மொழியையும், எந்த மொழி மூலம் வேண்டுமானாலும் கற்றுக்கொள்ள முடிகிறது. முன்பு ஒரு புதிய மொழி கற்க ஆசிரியர், புத்தகம் அல்லது language institute தேவைப்பட்டாலும், தற்போது AI tools மூலம் வீட்டிலிருந்தபடியே எளிதாக மொழிகளை கற்றுக்கொள்ளலாம். குறிப்பாக தமிழ் போன்ற தாய்மொழி மூலம் கூட உலகின் பல மொழிகளை கற்றுக்கொள்ளும் வசதி அதிகரித்திருக்கிறது. இது language learning உலகத்தில் பெரிய மாற்றமாக பார்க்கப்படுகிறது.
AI language learning tools-இன் மிகப்பெரிய பலம் என்னவென்றால், அவை மனிதர்களைப் போல interact செய்ய முடியும். மாணவர் எந்த level-ல் இருக்கிறார் என்பதை AI புரிந்துகொண்டு அதற்கேற்றபடி பாடங்களை மாற்றிக் கொடுக்கிறது. ஆரம்ப நிலை மாணவருக்கு basic words மற்றும் simple conversations கற்றுக்கொடுக்கலாம்; advanced learners-க்கு grammar correction, pronunciation improvement, fluency training போன்றவற்றை வழங்க முடியும். இதனால் ஒவ்வொருவருக்கும் personalized learning experience கிடைக்கிறது.
இப்போது பல AI-based language learning apps மற்றும் platforms மிகவும் பிரபலமாக உள்ளன. அவற்றில் Talkpal AI, Gliglish, Speak, Talkio AI, HelloTalk போன்றவை முக்கியமானவை. Talkpal AI போன்ற apps AI chatbot மற்றும் voice interaction மூலம் practice செய்ய உதவுகிறது. Gliglish AI உடன் நேரடியாக பேசும் அனுபவத்தை வழங்குகிறது; இது speaking confidence அதிகரிக்க உதவுகிறது. Speak app pronunciation மற்றும் real conversation training-க்கு நல்லதாக கருதப்படுகிறது. Talkio AI பல accents மற்றும் AI tutors மூலம் learning experience-ஐ realistic ஆக மாற்றுகிறது. HelloTalk app-ல் native speakers உடன் பேசலாம்; அதே நேரத்தில் AI grammar correction மற்றும் translation support-ஐ வழங்குகிறது.
Google கூட AI language learning-ல் புதிய முயற்சிகளை மேற்கொண்டு வருகிறது. Google-இன் “Little Language Lessons” போன்ற திட்டங்கள் Gemini AI-ஐ பயன்படுத்தி personalized lessons வழங்குகிறது. இது பயனாளியின் learning style, interests மற்றும் progress அடிப்படையில் பாடங்களை மாற்றுகிறது. இதுபோன்ற AI systems எதிர்காலத்தில் language learning-ஐ இன்னும் எளிதாக்கும் என்று நம்பப்படுகிறது.
AI tools மூலம் மொழி கற்பதில் முக்கியமான நன்மை என்னவென்றால் பயமின்றி practice செய்ய முடிவது. பலருக்கு புதிய மொழியில் பேசும்போது தவறு செய்வோமோ என்ற பயம் இருக்கும். ஆனால் AI-உடன் practice செய்யும்போது judgement இல்லாமல் மீண்டும் மீண்டும் முயற்சி செய்யலாம். இது speaking confidence அதிகரிக்க உதவுகிறது. மேலும் AI 24/7 available tutor போல செயல்படுகிறது. எந்த நேரத்திலும் practice செய்யலாம். இது busy வாழ்க்கை கொண்டவர்களுக்கு மிகவும் உதவிகரமாக இருக்கிறது.
மற்றொரு பெரிய பலம் multilingual support ஆகும். முன்பு English மூலம் மட்டுமே பல மொழிகளை கற்றுக்கொள்ள வேண்டியிருந்தது. ஆனால் இப்போது Tamil + English mix, Hindi + English mix போன்ற multilingual explanations கிடைக்கின்றன. இதனால் மாணவர்கள் தங்களுக்குப் புரியும் மொழியில் concepts-ஐ எளிதாக grasp செய்ய முடிகிறது. குறிப்பாக இந்தியர்களுக்கு இது மிகவும் பயனுள்ளதாக இருக்கிறது. இந்திய மொழிகளுக்கான AI support தற்போது வேகமாக வளர்ந்து வருகிறது. தமிழ், ஹிந்தி, தெலுங்கு போன்ற மொழிகளுக்கு AI startups மற்றும் பெரிய tech companies கவனம் செலுத்த ஆரம்பித்துள்ளன.
Pronunciation correction கூட AI tools-இன் மிக முக்கியமான feature ஆகும். பல apps voice recognition technology பயன்படுத்தி user pronunciation-ஐ analyse செய்கின்றன. தவறாக உச்சரித்தால் உடனே correct செய்து காட்டுகின்றன. இது native accent மற்றும் clear communication வளர்க்க உதவுகிறது. குறிப்பாக French, Japanese, Korean போன்ற pronunciation முக்கியமான மொழிகளை கற்க இது மிகவும் உதவுகிறது.
அதே சமயம் AI மட்டும் போதுமானது அல்ல என்பதையும் நினைவில் கொள்ள வேண்டும். மொழி fluency பெற real human interaction மிகவும் முக்கியம். AI practice ஒரு strong foundation கொடுக்கலாம்; ஆனால் உண்மையான conversational confidence பெற மனிதர்களுடன் பேசுவது அவசியம். அதனால் movies பார்ப்பது, books படிப்பது, podcasts கேட்பது, native speakers உடன் பேசுவது போன்ற activities language learning-ஐ வேகமாக மேம்படுத்தும்.
தமிழ் பேசுவோருக்கு ஒரு நல்ல learning path என்னவென்றால், ஆரம்பத்தில் Duolingo அல்லது Talkpal போன்ற apps மூலம் basic vocabulary மற்றும் grammar கற்றுக்கொள்வது. அதன் பிறகு Gliglish அல்லது Speak போன்ற AI speaking tools மூலம் practice செய்யலாம். பின்னர் HelloTalk போன்ற apps மூலம் native speakers உடன் பேசலாம். தினசரி ChatGPT voice mode போன்றவற்றை பயன்படுத்தி casual conversations practice செய்யலாம். இவ்வாறு step-by-step சென்றால் எந்த மொழியையும் confidence-உடன் கற்றுக்கொள்ள முடியும்.
எதிர்காலத்தில் AI language learning இன்னும் advanced ஆகும். Virtual tutors, real-time translation glasses, AI avatars, immersive VR classrooms போன்ற technologies வளர்ந்து வருகின்றன. இதனால் உலகின் எந்த மொழியையும் வீட்டிலிருந்தபடியே natural-ஆக கற்றுக்கொள்ளும் சூழல் உருவாகும். குறிப்பாக multilingual AI systems மனிதர்களின் தாய்மொழியைப் புரிந்துகொண்டு அதன்மூலம் புதிய மொழிகளை கற்றுக்கொடுக்கும் திறனை அதிகரித்து வருகின்றன.
மொத்தத்தில் பார்க்கும்போது, AI language learning tools புதிய தலைமுறைக்கு மிகவும் சக்திவாய்ந்த வாய்ப்புகளை உருவாக்கி இருக்கின்றன. குறைந்த செலவில், flexible timing-ல், personalized முறையில், fear இல்லாமல் மொழிகளை கற்றுக்கொள்ள இது உதவுகிறது. சரியான dedication மற்றும் regular practice இருந்தால், AI tools உதவியுடன் யாரும் எந்த மொழியையும் கற்றுக்கொள்ள முடியும்.
A Tamil grammar engine and proof-reading system is a highly valuable and timely project, especially because high-quality language tools for Tamil are still limited when compared to English and other widely digitized languages. Since you are a retired professor with programming experience dating back to the early 1980s, you possess a rare combination of strengths that are extremely useful for this kind of work: structured thinking, understanding of formal systems, patience for long-term projects, and appreciation for linguistic precision. In fact, your background may give you an advantage over many modern developers who rely mainly on machine learning without understanding the deep grammatical structure of a language.
A Tamil proof-reading engine can become much more than a spell checker. Spell checkers only verify whether a word exists in a dictionary. A grammar engine goes much deeper: it checks whether the words are used correctly within the sentence. Tamil, being a morphologically rich and highly structured language, is particularly suited for rule-based grammatical analysis. This means your system can detect mistakes involving tense, gender, number agreement, case suffixes, verb forms, sandhi (புணர்ச்சி), and sentence structure.
For example:
- “அவர்கள் வந்தான்” is grammatically incorrect because the plural subject does not agree with the singular masculine verb.
- The system should suggest “அவர்கள் வந்தார்கள்”.
Similarly, the engine could identify incorrect suffix usage, inconsistent tense, or improper sentence formation. Such a system would be useful for students, writers, journalists, teachers, publishers, bloggers, government departments, and digital content creators.
A practical development strategy is to begin with a strong rule-based foundation instead of immediately depending on artificial intelligence or large language models. Tamil grammar has many deterministic patterns that can be encoded logically. Rule-based systems are easier to debug, explain, and refine, especially during the early stages. Later, machine learning or transformer-based NLP models can be added as optional enhancement layers.
The project can be developed in phases.
Phase 1 should focus on the core text engine. This includes Unicode normalization, tokenization, and sentence segmentation. Unicode handling is especially important in Tamil because combining characters and encoding variations can create subtle bugs. A reliable normalization layer is essential before any grammar analysis begins. Tokenization means dividing text into words and punctuation units, while sentence segmentation identifies sentence boundaries correctly.
Phase 2 should build the morphology and grammar engine. Tamil words often contain rich grammatical information embedded through suffixes. Therefore, a morphology analyzer is critical. This component should identify root words, suffixes, tense markers, gender markers, plural forms, and grammatical cases. Once this is functioning reliably, grammar rules can be added gradually. Examples include:
- singular/plural agreement,
- tense consistency,
- gender agreement,
- suffix correctness,
- usage of particles and connectors,
- punctuation patterns,
- common writing mistakes.
Phase 3 can introduce a suggestion engine. Detecting errors is useful, but providing intelligent corrections is what makes the software practical. The engine should not simply say “error found”; it should suggest improved alternatives in clear Tamil. Suggestions should ideally preserve the writer’s original style and meaning while correcting grammar.
Phase 4 can focus on usability and integration. Once the engine is stable, it can be integrated into writing environments such as:
- browser extensions,
- LibreOffice plugins,
- web-based editors,
- mobile apps,
- or even a extension.
This stage is important because accessibility determines adoption. Even a powerful engine will not gain users unless it integrates smoothly into common writing workflows.
Regarding technology choices, is an excellent starting language because of its simplicity and strong NLP ecosystem. It allows rapid experimentation and has excellent Unicode support. Tools such as can later assist with NLP pipelines, though Tamil-specific customization will still be necessary. If machine learning features are added later, frameworks like or can be explored.
A lightweight database such as would work well for storing lexicons, morphology tables, and grammar rules. Using from the beginning is also highly recommended for version tracking and backups.
One of the most important architectural decisions is how to balance rule-based logic and statistical methods. Since Tamil grammar has many formal patterns, a hybrid architecture may work best:
- Rule engine for deterministic grammar.
- Morphological analyzer for word decomposition.
- Optional AI layer for ambiguity resolution and style suggestions.
This layered approach will likely produce more reliable results than relying entirely on machine learning.
Your programming background from the 1980s is particularly relevant because early programmers were trained to think carefully about structure, parsers, compilers, and formal logic. A Tamil grammar engine is conceptually closer to a compiler or language parser than to a typical modern app. Your experience in systematic thinking, modular design, and logical correctness can become a major strength.
In addition to practical value, this project also has cultural and academic significance. Tamil is one of the world’s oldest continuously used classical languages, yet advanced digital writing tools remain underdeveloped. A well-designed proof-reading system could contribute meaningfully to Tamil computing, digital preservation, education, and publishing. If released as open source, it could attract collaboration from linguists, teachers, and software developers.
Naming the project thoughtfully can also help create identity and recognition. Names such as:
- “TamilProof”
- “நன்னடை”
- “இலக்கணி”
- “செம்மொழி”
- “SolThiruthi”
could work well depending on the tone and audience you want.
Finally, regarding your earlier question about antivirus software, renewing is probably unnecessary unless you frequently download risky files or require enterprise-grade protection. For development work, especially involving custom scripts and experimental tools, the built-in is usually sufficient and less intrusive. Some antivirus products may even interfere with development workflows by falsely flagging executables or scripts.
Overall, your project idea is both technically meaningful and socially valuable. With patience and phased development, you could create one of the most useful Tamil language tools currently available.
My Tamil NLP Journey – Notes and Direction
I began revisiting Tamil language computing and NLP with the help of modern AI tools. Although I am not formally trained in linguistics, Tamil is my mother tongue, and I have long-standing interest in Tamil computing. I had earlier presented a research paper at the 2009 World Tamil Conference in Cologne, Germany, on “Tamil in Social Networking,” during a period before WhatsApp, Facebook, Instagram, and X became dominant platforms. At that conference, several researchers discussed Unicode, early Tamil linguistic computing, and related topics. At that time, I was told NLP required deep linguistic expertise. Today, with AI assistance, open-source tools, Python, VS Code, GitHub, and digital corpora, I have started learning and building Tamil NLP systems myself in India.
My current approach is practical and incremental. Instead of immediately training large AI models, I am focusing on rule-based Tamil NLP using Python. I installed Tamil NLP libraries and experimented with tokenization, corpus reading, and stemming. Some libraries had import or compatibility issues in Windows 11 with VS Code, which made me realize that Tamil NLP ecosystems are still evolving. Therefore, I decided to build core modules myself rather than depending entirely on external packages.
I started with:
– corpus reading
– tokenizer experiments
– word frequency counters
– Tamil stop-word filtering
– stemming
Initially, my stemming results were poor:
– மக்கள் → மக்
– மரங்கள் → மரங்
This helped me understand that Tamil stemming is far more complex than English suffix removal. Tamil is an agglutinative language, where words contain multiple grammatical layers. Therefore, simple suffix stripping creates over-stemming errors.
I then improved my approach using:
– longest suffix matching
– exception dictionaries
– normalization rules
– minimum stem length checks
– ordered suffix processing
After refinement, I achieved better results:
– மக்கள் → மக்கள்
– பாடல்கள் → பாடல்
– வீட்டில் → வீடு
– நடக்கிறது → நட
– மரங்கள் → மரம்
This showed that the stemmer was becoming morphology-aware instead of merely removing endings.
During experimentation, I also discovered how phonetic keyboards influence Tamil NLP. Modern Tamil typing introduces:
– transliteration inconsistencies
– spoken Tamil influence
– mixed Tamil-English text
– informal spellings
This made me realize that real-world Tamil NLP requires:
1. normalization
2. transliteration handling
3. stemming
4. morphology
5. grammar correction
I also understood that textbook Tamil alone is insufficient. Tamil NLP systems must eventually handle:
– classical Tamil
– modern standard Tamil
– social media Tamil
– spoken Tamil
– phonetic Tamil
At the same time, I found an excellent Tamil grammar book discussing:
– எழுத்து
– சொல்
– பொருள்
– அணி
– யாப்பு
Since அணி and யாப்பு mainly relate to poetry and செய்யுள் structure, I decided to initially focus on:
– எழுத்து
– சொல்
– பொருள்
These directly support NLP work:
– எழுத்து → Unicode, tokenization, phonetics
– சொல் → morphology, stemming, grammar
– பொருள் → semantics and contextual meaning
I plan to study school grammar books carefully and take structured notes before implementing large-scale morphology modules. Morphology appears to be one of the biggest components of Tamil NLP because Tamil grammar involves:
– case suffixes
– plural forms
– tense markers
– sandhi changes
– verb forms
– inflections
I realized morphology development must happen gradually:
1. noun morphology
2. plural handling
3. case suffixes
4. verb roots
5. tense analysis
Rather than attempting a complete grammar checker immediately, I intend to build small working modules incrementally.
I also recognized that rule-based systems still have great value in Tamil NLP because they are:
– explainable
– lightweight
– debuggable
– transparent
– suitable for low-resource languages
My approach is therefore:
– study existing tools
– avoid unnecessary reinvention
– identify gaps
– write custom rules where needed
– test continuously with corpora
I am using corpora from:
– Project Madurai
– Wikipedia
– school textbooks
– possibly news articles later
I also realized that grammar books alone are not enough because many describe ideal literary Tamil rather than modern practical Tamil. Therefore, my goal is to balance:
– classical Tamil
– modern Tamil
– real-world usable Tamil
Another area I am considering is தனித்தமிழ் (pure Tamil usage without foreign loan words). This is challenging because modern Tamil writing often mixes:
– English
– Sanskrit-derived words
– transliterated technical terms
For example:
– கம்ப்யூட்டர் vs கணினி
– ஆபீஸ் vs அலுவலகம்
Therefore, instead of forcing pure Tamil universally, I may eventually support different writing modes:
– general Tamil
– formal Tamil
– தனித்தமிழ்
– social Tamil
This could allow the system to:
– detect mixed-language usage
– suggest alternatives
– preserve user flexibility
I also realized that future Tamil NLP systems may require:
– lexical databases
– synonym mapping
– morphology analyzers
– normalization layers
– grammar validators
– contextual suggestion engines
My current development philosophy is:
Experiment → Study → Refine → Test → Improve
Rather than rushing into AI-only systems, I want to first understand Tamil structure deeply through:
– grammar
– corpus observation
– rule design
– iterative testing
I believe that:
native Tamil intuition + programming experience + AI assistance
can together produce meaningful Tamil NLP tools.
Although I began this journey late compared to formally trained linguists, modern AI tools have reduced entry barriers. As an early-generation programmer with systems-thinking experience, I now feel capable of contributing to practical Tamil language technology development in a useful and realistic way.
