Ad Astra per Aspera! Through Hardships to the Stars

Engineering AI to Push the Boundaries of Human Intelligence, Creating Disruptive Technology to Reach the Stars.


Vector Interactions - Bi-Encoder, Cross-Encoder, Late Interaction, and Everything Between

Every retrieval system answers one question, namely where in the computation graph query information first meets document information. This post traces that single design axis from BM25 through DSSM, kernel pooling, monoBERT, DPR, ColBERT, SPLADE, PreTTR, and generative retrieval, then closes it into a collectively exhaustive taxonomy with a rank-bound argument, a FLOP model, open problems, and portfolio projects.

66 min read   ·   2026

Axioms in Building Startup - Fifteen Ground Truths from Inside the Arena

Fifteen axioms we keep returning to while building a company, statements we treat as ground truth because every hard decision eventually derives from them. Covers survival, revenue as the only metric, failing fast, co-founder trust, timing, team composition, curiosity, excellence, and the three-month feedback loop.

22 min read   ·   2026

Recommender Systems - From Item Neighbors to Generative Sequence Models

A research-grounded story of how recommender systems went from item-item neighbors at Amazon to generative sequence models at Meta. Each wave shipped real business wins, hit a structural ceiling, and seeded the next. Curated through cornerstone papers (Linden 2003, Koren 2009, YouTube DNN 2016, DIN 2018, PinSage 2018, EBR 2020, TIGER 2023, HSTU 2024), industry deployments, and the failure modes that overturned consensus.

24 min read   ·   2026

Why Innovation Does Not Live in Indonesia

A tech founder's economic argument for why Southeast Asia's largest economy cannot make its innovation flywheel spin. From rent-seeking capital allocation and a colonial trust deficit to missing exit markets, every stage of the cycle is broken.

31 min read   ·   2026

Encoders - Squash Reality into a Vector Space

From SIFT histograms to CLIP's billion-parameter shared space, this post traces how vision and text communities independently discovered the same compression trick and then converged into one. Covers handcrafted features, shallow embeddings, deep CNNs, BERT, ViT, self-supervised pre-training, and multimodal alignment; with open problems and portfolio projects.

47 min read   ·   2026

World Models - From Dyna to Foundation Simulators

A decade of teaching machines to imagine. From Sutton's 1990 Dyna planning loop through Ha & Schmidhuber's dreaming agents, the RSSM latent-dynamics lineage, foundation-scale video simulators, and LeCun's JEPA thesis. Includes open problems and a portfolio project guide.

32 min read   ·   2026

Representation Analysis, A Reading Path

Eleven years of geometric thinking about embedding spaces, distilled into eight papers. From Mikolov's linear analogies (2013) to Bricken et al.'s sparse autoencoders (2023), discover, fix, diagnose, exploit. The throughline is one conjecture refined over a decade, high-level concepts are encoded as directions in representation space.

24 min read   ·   2026

Model2Vec, A Static-Embedding Distillation Algorithm, Explained

How to compress a sentence-transformer into a static lookup table that runs on CPU in three lines of NumPy. A re-implementation guide covering the full pipeline, vocab prep, per-token forward, PCA whitening, SIF/Zipf weighting, optional vocab quantization, and dtype quantization, with a ~80-line reference implementation.

15 min read   ·   2026

Agentic AI - From Symbolic Planning to Autonomous Software Engineers

Three decades of teaching machines to act. From STRIPS and BDI, through ReAct and Toolformer, to GRPO-trained agents solving real GitHub issues at superhuman rates. Covers the full lineage of agentic harnesses, tool-use training, end-to-end RL, and the MCP era. Includes open problems and portfolio projects.

34 min read   ·   2026

Personal Identity Problem and Mutability Immutability - Engineers Are Philosophers in Reverse

The personal identity problem asks whether something remains the same after it changes. Philosophers have long debated this through famous examples like the Ship of Theseus. In programming, mutability and immutability echo the same question - when something changes, is it still the same object? Software engineers unknowingly reinvent the personal identity problem each time they design systems.

4 min read   ·   2025

Why Python is Irreplaceable for Data Science, AI, and Automation - a Design Perspective

Python is currently the most popular programming language for machine learning and data science. In this post, I will share my subjective experience on why I've stuck with Python for the last 3 years, and why I think Python's design is the future (for general purposes, not only AI, Data Science, and Automation).

10 min read   ·   2025

Collection of Foundational Math Problems

Foundational math problems to keep me on track after a long time of not doing math.

4 min read   ·   2025

Reinforcement Learning Notes: Theory and Algorithms

Taken on MRL course @ Universitas Indonesia [On progress]

52 min read   ·   2025

Deep Learning Notes from Zero to Hero

Comprehensive notes from the Machine Learning Research Lab course @ Universitas Indonesia [In Progress]

37 min read   ·   2025

Paradigm of Making Machine Learn; Supervised, Unsupervised, and Reinforcement

Here we explain the standard notion of machine learning, a lot of example you see in the internet mostly a practical scenario. In this notes, we tries to explicitly define why! [On progress]

15 min read   ·   2025