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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Podcasts I listen to
May not be a full list.
Posts
What is the most difficult 3 words to say?
“I don’t know”. It is the acceptance of our limited knowledge.
Winning a game
What does it mean to win a game? Many people try to win every game. But at what cost, what was the point of winning?
Intelligence
Are we intelligent? Will any intelligent person think themself of intelligent?
Entitlement
Are we entitled to have something for sure? Are we even entitled to think about our entitlement?
Non-resistance
The dictionary meaning “the principles or practice of passive submission to constituted authority even when unjust or oppressive” doesn’t capture the eastern...
Leverage
Leverage is something you can use to get maximum advantage of something. It is a tool. For you to use a tool, first you have to understand the fundamental pr...
Person and perspective
Is someone separate from their perspective? How can we disentangle these two?
Evaluated Experience
It is a standard consensus that experience is the best teacher. How does just the experience can be the best teacher?
Catch up with status quo
What if you could get rid of the idea that you always need to catch-up with your peers status wise? What changes will you make in your life? What stops you f...
Conformist
If everything you believe is something that you are supposed to believe, what are the odds that it is really a coincidence?
Learning vs Education
Education is the industrial process of making people compliant. Command and control is the backbone of it. While learning is unleashing of a curious mind ag...
Pleasure vs happiness
The former is about taking, whereas the latter is all about giving. One is short-lived and the other is long-lived.
First post
Hello world, and everyone.
ai_agents
What are AI Agents?
“From Passive Tools to Active Assistants: The Cognitive Revolution in Software.”
LLM Capabilities for Agents
related_dsa_day: 2 related_ml_day: 2 related_speech_day: 2
Prompt Engineering for Agents
related_dsa_day: 3 related_ml_day: 3 related_speech_day: 3
Tool Calling Fundamentals
related_dsa_day: 4 related_ml_day: 4 related_speech_day: 4
Memory Architectures
related_dsa_day: 5 related_ml_day: 5 related_speech_day: 5
Agent Frameworks Landscape
“To Framework or Not to Framework? Navigating the Agent Ecosystem.”
Building Your First Agent
related_dsa_day: 7 related_ml_day: 7 related_speech_day: 7
Agent Workflow Patterns
“Better workflows beat better models.” — Dr. Andrew Ng
Retrieval-Augmented Generation (RAG)
“Giving the Brain a Library: The Foundation of Knowledge-Intensive Agents.”
Document Processing for Agents
“Garbage In, Garbage Out. The Art of Reading Messy Data.”
Vector Search for Agents
“Finding a Needle in a High-Dimensional Haystack: The Mathematics of Recall.”
Context Window Management
“The Finite Canvas of Intelligence: Managing the Agent’s RAM.”
Multi-Step Reasoning
“Thinking Fast and Slow: How to make LLMs stop guessing and start solving.”
The ReAct Pattern Deep Dive
“Reason + Act: The Loop that Changed Everything.”
Planning and Decomposition
“If you fail to plan, you are planning to fail (and burn tokens).”
Real-Time Agent Pipelines
“Speed is not a feature. Speed is the product.”
Voice Agent Architecture
“Talking to machines: The end of the Keyboard.”
Voice Agent Frameworks: LiveKit & Pipecat
“Don’t build the phone network. Just build the app.”
Voice Activity Detection (VAD)
“The art of knowing when to shut up.”
Real-Time Speech-to-Speech Agents
“Removing the Text Bottleneck: The Omni Future.”
Vision Agent Fundamentals
“Giving eyes to the brain: How Agents see the world.”
Multimodal LLMs (GPT-4V, LLaVA)
“One model to rule them all: The Unification of Text and Vision.”
Screen Agents (UI Navigation, OmniParser)
“The ultimate API: The User Interface.”
Object Detection & Segmentation (YOLO, SAM)
“Knowing WHAT is where, and precisely WHERE it acts.”
Video Analysis & Real-World Perception
“Time: The 4th Dimension of Vision.”
dsa
Two Sum
The hash table trick that makes O(n²) become O(n) and why this pattern appears everywhere from feature stores to embedding lookups.
Valid Parentheses
Why a simple stack solves bracket matching, expression parsing, and even neural network depth management in one elegant pattern.
Merge Two Sorted Lists
The pointer manipulation pattern that powers merge sort, data pipeline merging, and multi-source stream processing.
Best Time to Buy and Sell Stock
The single-pass pattern that powers streaming analytics, online algorithms, and real-time decision making in production systems.
Maximum Subarray (Kadane’s Algorithm)
Master the pattern behind online algorithms, streaming analytics, and dynamic programming, a single elegant idea powering countless production systems.
Climbing Stairs
The Fibonacci problem in disguise, teaching the fundamental transition from recursion to dynamic programming to space optimization.
Binary Tree Traversal
Master the fundamental patterns of tree traversal: the gateway to solving hundreds of tree problems in interviews.
Validate Binary Search Tree
Master BST validation to understand data integrity in tree structures, critical for indexing and search systems.
Binary Search
Master binary search to understand logarithmic algorithms and efficient searching, foundational for optimization and search systems.
Reverse Linked List
Master linked list manipulation through reversal - a fundamental pattern for understanding pointer logic and in-place algorithms.
LRU Cache
Master LRU cache design: O(1) get/put with hash map + doubly linked list. Critical for interviews and production caching systems.
Add Two Numbers
Master digit-by-digit addition with linked lists: Handle carry propagation elegantly. Classic problem teaching pointer manipulation and edge cases.
Container With Most Water
Master the two-pointer greedy technique that powers resource optimization in production ML systems.
Generate Parentheses
Master backtracking to generate all valid combinations—the foundation of ensemble model selection and multi-model systems.
Group Anagrams
Master hash-based grouping to solve anagrams—the foundation of clustering systems and speaker diarization in production ML.
Merge Intervals
Master interval processing to handle overlapping ranges—the foundation of event streams and temporal reasoning in production systems.
Add Two Numbers (Linked List)
Simulate arbitrary-precision addition on linked lists—the same sequential pattern used in large-scale distributed training and streaming pipelines.
Rotate Image
Master in-place matrix rotation—the same 2D transformation pattern that powers image and spectrogram augmentations in modern ML systems.
Spiral Matrix
Master systematic matrix traversal—the same pattern used for tracking experiments, processing logs, and managing state in ML systems.
Jump Game
Master greedy decision-making to determine reachability—the same adaptive strategy used in online learning and real-time speech systems.
Unique Paths
Master grid path counting with dynamic programming—the same optimization technique used in neural architecture search and speech model design.
Minimum Path Sum
The classic grid optimization problem that bridges the gap between simple recursion and 2D Dynamic Programming.
Decode Ways
A deceptive counting problem that teaches the fundamentals of state transitions and connects directly to Beam Search.
Word Break
The fundamental string segmentation problem that powers spell checkers, search engines, and tokenizers.
Validate Binary Search Tree
The gatekeeper of data integrity. How do we ensure our sorted structures are actually sorted?
Binary Tree Level Order Traversal
How do you print a corporate hierarchy level by level? CEO first, then VPs, then Managers…
Construct Binary Tree from Preorder and Inorder Traversal
Given two arrays, can you rebuild the original tree? It’s like solving a jigsaw puzzle where the pieces are numbers.
Kth Smallest Element in a BST
Finding the median or the 99th percentile is easy in a sorted array. Can we do it in a tree?
Lowest Common Ancestor of a Binary Tree
“Find the point where two paths in a tree first meet.”
Number of Islands
“Counting connected components in a 2D grid.”
Course Schedule (Topological Sort)
“Can you finish all courses given their prerequisites?”
Word Ladder (BFS)
“Transforming ‘cold’ to ‘warm’ one letter at a time.”
Clone Graph (DFS/BFS)
“Creating a deep copy of a graph structure.”
Evaluate Division (Graph/Union-Find)
“Modeling algebraic equations as graph path problems.”
Surrounded Regions (DFS/BFS)
“Capturing regions by identifying safe boundaries.”
Partition Equal Subset Sum
“Can you split the treasure evenly?”
Longest Increasing Subsequence (LIS)
“Finding the longest upward trend in chaos.”
Coin Change (Unbounded Knapsack)
“Making change with the fewest coins.”
Word Break
“Making sense of a stream of characters.”
Trapping Rain Water
“Calculating capacity in a fragmented landscape.”
Jump Game II
“Finding the optimal path through a sequence of choices.”
Merge K Sorted Lists
“Combining order from chaos, one element at a time.”
Median of Two Sorted Arrays
“Finding the middle ground between two ordered worlds.”
Largest Rectangle in Histogram
“Finding the maximum hidden in the valleys and peaks.”
Sliding Window Maximum
“Finding the king of every window.”
ml_system_design
Recommendation System: Candidate Retrieval
How do you narrow down 10 million items to 1000 candidates in under 50ms? The art of fast retrieval at scale.
Classification Pipeline Design
From raw data to production predictions: building a classification pipeline that handles millions of requests with 99.9% uptime.
Data Preprocessing Pipeline Design
How to build production-grade pipelines that clean, transform, and validate billions of data points before training.
A/B Testing Systems for ML
How to design experimentation platforms that enable rapid iteration while maintaining statistical rigor at scale.
Batch vs Real-Time Inference
How to choose between batch and real-time inference, the architectural decision that shapes your entire ML serving infrastructure.
Model Evaluation Metrics
How to measure if your ML model is actually good, choosing the right metrics is as important as building the model itself.
Feature Engineering at Scale
Feature engineering makes or breaks ML models, learn how to build scalable, production-ready feature pipelines that power real-world systems.
Model Serving Architecture
Design production-grade model serving systems that deliver predictions at scale with low latency and high reliability.
Online Learning Systems
Design systems that learn continuously from streaming data, adapting to changing patterns without full retraining.
Caching Strategies for ML Systems
Design efficient caching layers for ML systems to reduce latency, save compute costs, and improve user experience at scale.
Content Delivery Networks (CDN)
Design a global CDN for ML systems: Edge caching reduces latency from 500ms to 50ms. Critical for real-time predictions worldwide.
Distributed ML Systems
Design distributed ML systems that scale to billions of predictions: Master replication, sharding, consensus, and fault tolerance for production ML.
Resource Allocation for ML
Build production ML infrastructure that dynamically allocates resources using greedy optimization to maximize throughput and minimize costs.
Model Ensembling
Build production ensemble systems that combine multiple models using backtracking strategies to explore optimal combinations.
Clustering Systems
Design production clustering systems that group similar items using hash-based and distance-based approaches for recommendations, search, and analytics.
Event Stream Processing
Build production event stream processing systems that handle millions of events per second using windowing and temporal aggregation—applying the same interva...
Distributed Training Architecture
Design distributed training architectures that can efficiently process massive sequential datasets and train billion-parameter models across thousands of GPUs.
Data Augmentation Pipeline
Design a robust data augmentation pipeline that applies rich transformations to large-scale datasets without becoming the training bottleneck.
Experiment Tracking Systems
Design robust experiment tracking systems that enable systematic exploration, reproducibility, and collaboration across large ML teams.
Online Learning Systems
Design online learning systems that adapt models in real-time using greedy updates—the same adaptive decision-making pattern from Jump Game applied to stream...
Neural Architecture Search
Design neural architecture search systems that automatically discover optimal model architectures using dynamic programming and path optimization—the same pr...
Cost Optimization for ML
A comprehensive guide to FinOps for Machine Learning: reducing TCO without compromising accuracy or latency.
Beam Search Decoding
The industry-standard algorithm for converting probabilistic model outputs into coherent text sequences.
Tokenization Systems
The critical preprocessing step that defines the vocabulary and capabilities of Large Language Models.
Model Monitoring Systems
The silent killer of ML models is not a bug in the code, but a change in the world.
Batch Processing Pipelines
Not everything needs to be real-time. Sometimes, “tomorrow morning” is fast enough.
Model Architecture Design
Architecture is destiny. The difference between 50% accuracy and 90% accuracy is often just a skip connection.
Ranking Systems at Scale
How does Google search 50 billion pages in 0.1 seconds? The answer is the “Ranking Funnel”.
Hierarchical Classification Systems
“Organizing the world’s information into a structured hierarchy.”
Graph-based Recommendation Systems
“Leveraging the connection structure to predict what users will love.”
ML Pipeline Dependencies & Orchestration
“Managing complex ML workflows with thousands of interdependent tasks.”
Semantic Search Systems
“Moving beyond keywords to understand the meaning of a query.”
Model Replication Systems
“Ensuring your ML models are available everywhere, all the time.”
Knowledge Graph Systems
“Structuring the world’s information into connected entities and relationships.”
Boundary Detection in ML
“Defining where one object ends and another begins.”
Resource Partitioning in ML Clusters
“How to share a supercomputer without stepping on each other’s toes.”
Sequence Modeling in ML
“Predicting the next word, the next stock price, the next frame.”
Hyperparameter Optimization
“Finding the perfect knobs to turn.”
Model Interpretability and Explainability (XAI)
“Trust, but verify. Why did the model say No?”
Distributed Training Patterns
“Scaling from one GPU to thousands.”
Model Compression Techniques
“Fitting billion-parameter models into megabytes.”
Feature Stores
“The centralized truth for machine learning features.”
Vector Databases
“The infrastructure for semantic search and AI-native applications.”
LLM Serving Infrastructure
“Serving models that think at human scale.”
RAG Systems
“Grounding LLMs in facts, not hallucinations.”
publications
Blizzard Challenge 2015 Submission by DONLab, IIT Madras
[Challenge] Blizzard Challenge 2015
Resources for Indian languages
[Conference] Community-based Building of Language Resources(CBBLR), Brno, Czech Republic, September 2016
A unified parser for developing Indian language text to speech synthesizers
[Conference] International Conference on Text, Speech, and Dialogue(TSD), Brno, Czech Republic, September 2016
TBT Toolkit to Build TTS A High Performance Framework to build Multiple Language HTS Voice
[Conference] INTERSPEECH 2017 (Show and Tell), Stockholm, Sweden, August 2017
Deep Learning Techniques in Tandem with Signal Processing Cues for Phonetic Segmentation for Text to Speech Synthesis in Indian Languages
[Conference] INTERSPEECH 2017, Stockholm, Sweden, August 2017
Speech Synthesis in Indian Languages and Future Perspectives
[Conference] Global Conference on Cyberspace (GCCS), New Delhi, India, November 2017
A Hybrid approach to neural networks based speech segmentation
[Conference] Frontiers of Research in Speech and Music (FRSM), Rourkela, India, December 2017
Signal Processing Cues to Improve Automatic Speech Recognition for Low Resource Indian Languages
[Conference] The 6th Intl Workshop on Spoken Language Technologies for Under Resourced Languages, Gurugram, India, August 2018
Code-switching in Indic Speech Synthesisers
[Conference] INTERSPEECH 2018, Hyderabad, India, September 2018
A Unified Approach to Speech Synthesis in Indian Languages
[MS Thesis] IIT Madras: February 2019; Supervised by Prof. Hema A Murthy
An ASR Guided Speech Intelligibility Measure for TTS Model Selection
[arXiv] arXiv, May 2020
Significance of spectral cues in automatic speech segmentation for Indian language speech synthesizers
[Journal] Speech Communication: Volume 123, October 2020, Pages 10-25
Non-native English lexicon creation for bilingual speech synthesis
[Conference] Speech Synthesis Workshop (SSW), Hungary, Aug 2021
Robust Speech Recognition Using Meta-Learning for Low-Resource Accents
[Conference] National Conference on Communications (NCC 2024), February 2024
Robust Speaker Personalisation Using Generalized Low-Rank Adaptation for Automatic Speech Recognition
[Conference] International Conference on Acoustics, Speech, and Signal Processing ( ICASSP), April 2024
speech_tech
Streaming ASR Architecture
Why batch ASR won’t work for voice assistants, and how streaming models transcribe speech as you speak in under 200ms.
Speech Command Classification
How voice assistants recognize “turn on the lights” from raw audio in under 100ms without full ASR transcription.
Audio Feature Extraction for Speech ML
How to transform raw audio waveforms into ML-ready features that capture speech characteristics for robust model training.
Voice Activity Detection (VAD)
How voice assistants and video conferencing apps detect when you’re speaking vs silence, the critical first step in every speech pipeline.
Speaker Recognition & Verification
How voice assistants recognize who’s speaking, the biometric authentication powering “Hey Alexa” and personalized experiences.
Text-to-Speech (TTS) System Fundamentals
From text to natural speech: understanding modern neural TTS architectures that power Alexa, Google Assistant, and Siri.
Audio Preprocessing & Signal Processing
Clean audio is the foundation of robust speech systems, master preprocessing pipelines that handle real-world noise and variability.
Streaming Speech Processing Pipeline
Build real-time speech processing pipelines that handle audio streams with minimal latency for live transcription and voice interfaces.
Real-time Keyword Spotting
Build lightweight models that detect specific keywords in audio streams with minimal latency and power consumption for voice interfaces.
Voice Enhancement & Noise Reduction
Build systems that enhance voice quality by removing noise, improving intelligibility, and optimizing audio for speech applications.
Speech Separation
Separate overlapping speakers with 99%+ accuracy: Deep learning solves the cocktail party problem for meeting transcription and voice assistants.
Multi-Speaker ASR
Build production multi-speaker ASR systems: Combine speech recognition, speaker diarization, and overlap handling for real-world conversations.
Compute Allocation for Speech Models
Optimize speech pipeline throughput by allocating compute to bottleneck stages using greedy resource management.
Multi-model Speech Ensemble
Build production speech systems that combine multiple ASR/TTS models using backtracking-based selection strategies to achieve state-of-the-art accuracy.
Speaker Clustering (Diarization)
Build production speaker diarization systems that cluster audio segments by speaker using embedding-based similarity and hash-based grouping.
Real-time Audio Segmentation
Build production audio segmentation systems that detect boundaries in real-time using interval merging and temporal processing—the same principles from merge...
Distributed Speech Training
Design distributed training pipelines for large-scale speech models that efficiently handle hundreds of thousands of hours of sequential audio data.
Audio Augmentation Techniques
Use audio augmentation techniques to make speech models robust to noise, accents, channels, and real-world conditions—built on the same matrix/tensor transfo...
Speech Experiment Management
Design experiment management systems tailored for speech research—tracking audio data, models, metrics, and multi-dimensional experiments at scale.
Adaptive Speech Models
Design adaptive speech models that adjust in real-time to speakers, accents, noise, and domains—using the same greedy adaptation strategy as Jump Game and on...
Speech Architecture Search
Design neural architecture search systems for speech models that automatically discover optimal ASR/TTS architectures—using dynamic programming and path opti...
Cost-efficient Speech Systems
Strategies for building profitable speech recognition systems by optimizing the entire pipeline from signal processing to hardware.
ASR Beam Search Implementation
Implementing the core decoding logic of modern Speech Recognition systems, handling alignment, blanks, and language models.
Speech Tokenization
The breakthrough that allows us to treat audio like text, enabling GPT-style models for speech.
Speech Quality Monitoring
How do we know if the audio sounds “good” without asking a human?
Batch Speech Processing
Real-time ASR is hard. Offline ASR is big.
End-to-End Speech Model Design
Goodbye HMMs. Goodbye Phonemes. Goodbye Lexicons. We are teaching the machine to Listen, Attend, and Spell.
Voice Search Ranking
“Play Call Me Maybe”. Did you mean the song, the video, or the contact named ‘Maybe’?
Hierarchical Speech Classification
“From broad categories to fine-grained speech understanding.”
Social Voice Networks
“Building recommendation and moderation systems for voice-based social platforms.”
Speech Pipeline Orchestration
“Orchestrating complex speech processing pipelines from audio ingestion to final output.”
Phonetic Search in Speech
“Finding ‘Jon’ when the user types ‘John’, or ‘Symphony’ when they say ‘Simfoni’.”
Multi-region Speech Deployment
“Deploying speech models close to users for low-latency voice experiences.”
Dialog State Tracking (DST)
“The brain of a task-oriented dialogue system: remembering what the user wants.”
Speech Boundary Detection
“Knowing when to listen and when to stop.”
Multi-task Speech Learning
“One model to rule them all: ASR, Translation, and Understanding.”
Sequence-to-Sequence Speech Models
“From waveforms to words, and back again.”
Speech Hyperparameter Tuning
“Tuning speech models for peak performance.”
End-to-End Text-to-Speech (TTS)
“Giving machines a voice.”
Wake Word Detection
“Hey Siri, Alexa, OK Google: The gateway to voice AI.”
Speaker Diarization
“Who spoke when? The art of untangling voices.”
Automatic Speech Recognition (ASR) Decoding
“Turning acoustic probabilities into coherent text.”
Speech Enhancement
“Extracting clear speech from the noise of the real world.”
Voice Conversion
“Speaking with someone else’s voice.”
Speech Emotion Recognition
“Teaching machines to hear feelings.”
statuses
November 2020
While it’s not a principle, I often think of the parable of the Taoist farmer. The Taoist farmer has one horse, and the horse runs off. The villagers lame...
December 2020
I am so firmly determined, however, to test the constancy of your mind that, drawing from the teachings of great men, I shall give you also a lesson: Set ...
January 2021
“A fit body, a calm mind, a house full of love. These things cannot be bought—they must be earned.”
February 2021
“If you ever want to have peace in your life, you have to move beyond good and evil.” “Nature has no concept of happiness or unhappiness. Nature follow...
March 2021
“Reading is to the mind what exercise is to the body, ”- Richard Steele.
April 2021
Happiness is not a consumable product. It is not something you find by searching for it. It is a naturally arising byproduct of a fulfilling, well-lived l...
May 2021
When you care more about getting things right than being right, you get better outcomes and you save time and energy.
June 2021
The ceramics teacher announced on opening day that he was dividing the class into two groups. All those on the left side of the studio, he said, would be ...
July 2021
The best way to improve your ability to think is to spend time thinking. Most of us are too busy to think. We have too many meetings. Too many calls. Too ...
August 2021
We rarely do or say something intentionally that surprises us. That’s because we are in intimate contact with the noise in our heads–we spend our days loo...
September 2021
Nothing will change your future trajectory like your habits. While goals rely on extrinsic motivation, habits, once formed, are automatic. They literally ...
October 2021
“How we spend our time is how we spend our days. How we spend our days is how our life goes. How our life goes determines whether we thought it was worth ...
November 2021
While we tell ourselves that the next level is enough, it never is. The next zero in your bank account won’t satisfy you any more than you are now. The ne...
December 2021
“Expectation is the grandfather of disappointment. The world can never own a man who wants nothing.” — Aphorisms for Thirsty Fish
January 2022
One simple way to unlock your best self is to shape your environment so that your desired behavior is the path of least resistance.
February 2022
“The nature of illusion is that it’s designed to make you feel good. About yourself, about your country, about where you’re going – in that sense it funct...
March 2022
People are much more honest with their actions than their words.
April 2022
In turning education into a system of mass production we created a superbly democratic system that made the majority of people, and the world as a whole, ...
May 2022
“He who knows only his own side of the case, knows little of that.” — John Stuart Mill
June 2022
Say no (a lot).
July 2022
To improve your outcomes in life, respond to the world as it is, not as you wish it would be.
August 2022
Sturgeon’s law states that 90% of everything is crap. If you dislike poetry, or fine art, or anything, it’s possible you’ve only ever seen the crap. Go lo...
September 2022
“It’s time you realized that you have something in you more powerful and miraculous than the things that affect you and make you dance like a puppet.” — M...
October 2022
The person who is consistent outperforms the person who is intermittent every time. While inconsistent effort works for some things, for the things that r...
November 2022
“One day, you will wake up and there won’t be any more time to do the things you’ve always wanted. Do it now.” - Paulo Coelho
January 2023
New year, new me? Nah, I’m just going to keep on being fabulous and making mistakes like I always do 😜 Happy New Year everyone!
February 2023
Most people spend the first half of their lives collecting and the second half choosing what to keep. Which lessons learned and pieces of advice do you...
March 2023
Don’t believe everything you think.
April 2023
A simple and easy approach to decision-making that prevents us from manipulating ourselves. First, understand the forces at play. Then, understand how you...
May 2023
Productivity is often a distraction. Don’t aim for better ways to get through your tasks as quickly as possib`le. Instead aim for better tasks that you ne...
June 2023
Are those things that keep you busy truly important in your life and career?
July 2023
Don’t define your identity by your beliefs. Define your identity by your willingness to learn.
August 2023
No one is thinking about you very much. So don’t worry about looking stupid or embarrassing yourself or whatever. No one cares.
September 2023
Worrying is praying for what you dont want.
January 2024
We are who we are when nobody else is watching.
February 2024
Those who cannot live in harmony with the world are fools though they may be highly educated.
March 2024
The work you do while you procrastinate is probably the work you should be doing for the rest of your life.
April 2024
“To travel means, ultimately, nothing more than coming back home a different person from the one who left.” — PICO IYER
May 2024
Try to define yourself by what you love and embrace, rather than what you hate and refuse.
July 2024
The price you pay for doing what everyone else does is getting what everyone else gets.