Research, Build,
Story

Research, stories, and insights on AI and Data Engineering.

Read the Blog Read a StoryView Research

AI & Machine Learning

Deep dives into neural networks, NLP, and AI systems

Data Engineering

Pipelines, warehouses, GPU-accelerated query engines, and big-data systems

Stories

Illustrated novels that teach the modern AI stack as story, with audio narration in English and Hindi

With gratitude to every unsung hero across AI and Data — the engineers, researchers, and builders whose quiet work makes all of this real.

— P.S.

From The First Mind

All chapters
Chapter 01 10 min

The Hackathon

What is an LLM, intuitively?

Chapter 02 11 min

Words Become Numbers

Tokens & embeddings — text becomes math.

Chapter 03 11 min

The Map of Meaning

Vector spaces, similarity, and the moment they named the model.

Chapter 04 12 min

Friends in High Dimensions

Self-attention, and a stranger at the library.

Chapter 05 13 min

The First Spark

Their toy model says a sentence neither of them was ready for.

Chapter 06 12 min

The Stack of Minds

Layers, residual streams — and the secret hours.

Featured

sqljev: TypeSafe Jev's jev() for SQL Server, Postgres, Snowflake, BigQuery and DuckDB, on Jev or Open-Weight Laya

SQL cannot say 'the customer threatens to cancel'. sqljev adds jev(), jev_prob() and jev_choice() to SQL Server, PostgreSQL, MySQL, Snowflake, Databricks, BigQuery, Redshift and DuckDB, and answers them with Laya, an open-weight decision model that runs on your hardware, returns calibrated probabilities instead of text, and can be fine-tuned on your own tables. 140,000 decisions in 271 seconds on one laptop GPU; re-running all 13 queries, 0.9 seconds. Apache 2.0, version 0.1.0.

Read more

Latest Posts

View all
sqljev: TypeSafe Jev's jev() for SQL Server, Postgres, Snowflake, BigQuery and DuckDB, on Jev or Open-Weight Laya
Data Engineering10 min read

sqljev: TypeSafe Jev's jev() for SQL Server, Postgres, Snowflake, BigQuery and DuckDB, on Jev or Open-Weight Laya

SQL cannot say 'the customer threatens to cancel'. sqljev adds jev(), jev_prob() and jev_choice() to SQL Server, PostgreSQL, MySQL, Snowflake, Databricks, BigQuery, Redshift and DuckDB, and answers them with Laya, an open-weight decision model that runs on your hardware, returns calibrated probabilities instead of text, and can be fine-tuned on your own tables. 140,000 decisions in 271 seconds on one laptop GPU; re-running all 13 queries, 0.9 seconds. Apache 2.0, version 0.1.0.

65 views
Read
pankhllm: The LLM Gateway That Learns to Skip the LLM, Without Replacing the Stack You Already Run
Data Engineering10 min read

pankhllm: The LLM Gateway That Learns to Skip the LLM, Without Replacing the Stack You Already Run

Most of an agent's LLM calls are not writing anything. They are decisions: which tool, which skill, which parameters, made thousands of times a day by a model paid in seconds and tokens. pankhllm sits where your app already calls an LLM, learns those decisions from its own traffic, and starts making them in 0.2 ms on a CPU with a 262 KB model. What it is not sure about still goes to your LLM. On the same 14 questions: a 12B planner 1,743 ms, Laya 49 ms, pankhllm's own model 4 ms, all 14 correct. Here is what it is, what it is not, and where it stops.

76 views
Read
Your Apple Silicon GPU Loses to One CPU Core Until a Million Rows. I Measured 111 Operations, Then Rebuilt ArrowMetal 0.2.0 Around the Answer
Data Engineering19 min read

Your Apple Silicon GPU Loses to One CPU Core Until a Million Rows. I Measured 111 Operations, Then Rebuilt ArrowMetal 0.2.0 Around the Answer

Every GPU data library benchmarks itself at 50 million rows. Your dataframe has 80,000. On an Apple M4 Max, summing 1,000 integers takes the GPU 112 microseconds and Polars less than one: the GPU is more than 100 times behind. I built one of these libraries, so I measured the row count where the GPU overtakes the fastest CPU code for 111 operations: the median needs 10,000,000 rows against a multi-core library, about a million against one core, and sixteen never get there. So ArrowMetal 0.2.0 refuses the GPU below the line, byte-identical, and around that router it grew GPU readers for CSV, JSON, nested Parquet, Delta Lake and Iceberg, a Polars engine and a DuckDB optimizer extension.

77 views
Read
DuckDB on the Apple Silicon GPU: Plain SQL, 19 of 22 TPC-H Queries on the Mac's Own GPU, and a Rule That Says Never Slower
Data Engineering15 min read

DuckDB on the Apple Silicon GPU: Plain SQL, 19 of 22 TPC-H Queries on the Mac's Own GPU, and a Rule That Says Never Slower

DuckDB has no GPU backend of its own, and the GPU engines built for it need an NVIDIA card. gpudb 0.7 is my Apache-2.0 DuckDB extension for the GPU already inside your Mac, and for CUDA too. You write plain DuckDB SQL; the GPU takes a statement only where it has been measured faster than DuckDB on your own machine. On an Apple M4 Max, 19 of 22 TPC-H SF10 queries run on the Metal GPU at 1.06x to 48x with zero rows differing. The one row below parity is printed, not dropped.

92 views
Read
Apple's GPU Has No 64-bit Floats. I Made It Sort 50 Million Doubles Anyway
Data Engineering15 min read

Apple's GPU Has No 64-bit Floats. I Made It Sort 50 Million Doubles Anyway

The Metal Shading Language has no double type, and float64 is the default number in Python, pandas and Apache Arrow. Building ArrowMetal meant getting past three walls: a GPU with no 64-bit floats, a missing 64-bit atomic add, and a Swift compiler bug that reports errors nobody threw. Here is how each one was solved, what it cost, and why a GPU that cannot add two doubles sorts 50,000,000 of them in 32 ms.

139 views
Read
Polars vs DuckDB vs ArrowMetal GPU on Apple Silicon: Sort and Group-By Benchmarks
Data Engineering12 min read

Polars vs DuckDB vs ArrowMetal GPU on Apple Silicon: Sort and Group-By Benchmarks

Polars, DuckDB and ArrowMetal on an Apple M4 Max: sort and group-by benchmarks at 10M and 50M rows, wall time next to CPU time, and the rows where the CPU is still ahead.

171 views
Read

Stay Curious

Exploring the frontiers of AI, data, and technology. New research and insights published regularly.

About the Author