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

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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.

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From The First Mind

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Chapter 01 10 min

The Hackathon

What is an LLM, intuitively?

Featured

samkhya v1.0: Plug Claude, GPT-4o-mini, or Local Ollama Into Your SQL Query Optimizer

samkhya v1.0 ships an LLM-pluggable corrector backend for embedded analytical engines — DataFusion, DuckDB, Polars, Postgres, Iceberg, gpudb. Plug Claude, GPT-4o-mini, or local Ollama into the cardinality-estimation slot via a simple HTTP wire contract (Python FastAPI and Node TypeScript reference servers ship in the box). Every LLM output is clamped from above by a provable pessimistic ceiling (LpJoinBound — 40.95× tighter than the 2008 AGM bound) so the LLM can never make your plan worse than the engine's native estimate. Transport-floor latency measured at P95 0.07–0.11 ms; live-LLM end-to-end cells honestly marked PROJECTED pending API budget.

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Latest Posts

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samkhya v1.0: Plug Claude, GPT-4o-mini, or Local Ollama Into Your SQL Query Optimizer
Data Engineering16 min read

samkhya v1.0: Plug Claude, GPT-4o-mini, or Local Ollama Into Your SQL Query Optimizer

samkhya v1.0 ships an LLM-pluggable corrector backend for embedded analytical engines — DataFusion, DuckDB, Polars, Postgres, Iceberg, gpudb. Plug Claude, GPT-4o-mini, or local Ollama into the cardinality-estimation slot via a simple HTTP wire contract (Python FastAPI and Node TypeScript reference servers ship in the box). Every LLM output is clamped from above by a provable pessimistic ceiling (LpJoinBound — 40.95× tighter than the 2008 AGM bound) so the LLM can never make your plan worse than the engine's native estimate. Transport-floor latency measured at P95 0.07–0.11 ms; live-LLM end-to-end cells honestly marked PROJECTED pending API budget.

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Why I built a GPU SQL engine in 2026 — when every other one died
Data Engineering27 min read

Why I built a GPU SQL engine in 2026 — when every other one died

Every standalone GPU database built between 2013 and 2024 was acqui-hired or pivoted. So why ship gpudb in 2026? Because nobody had wired Apple Silicon's unified memory into a SQL engine — and DuckDB hands you a hundred-thousand-user distribution channel without writing a database from scratch.

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The Cost of Being Right: AI-Generated Code at Production Scale
Opinion4 min read

The Cost of Being Right: AI-Generated Code at Production Scale

Generating code with AI is cheap. Reviewing, testing, and deploying it isn't. The new bottleneck for engineering teams isn't writing the code — it's trusting it.

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Databricks vs Snowflake vs The New Wave: The Data Engineering Paradigm Shift
Data Engineering5 min read

Databricks vs Snowflake vs The New Wave: The Data Engineering Paradigm Shift

Snowflake just posted $4.68B in FY26 revenue at 29% growth. Databricks crossed $5.4B ARR in February at 65% growth. And neither chart explains why the most interesting data infrastructure being shipped in 2026 is single-process, embeddable, and runs on a laptop.

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Why Small Models Are Eating Their Teachers
AI & Machine Learning4 min read

Why Small Models Are Eating Their Teachers

In 2024, you needed a 70B model to get good answers. In 2026, a 7B model trained on the right data beats it on most real-world tasks. The mechanism isn't a secret — it's distillation done well, and it's reshaping the entire model economy.

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The Vibe Coding Economy: When 'Make This' Is the Spec
Opinion3 min read

The Vibe Coding Economy: When 'Make This' Is the Spec

The phrase 'vibe coding' was a joke in 2024. By 2026, it describes how a non-trivial fraction of new software is actually built. The economics of this shift are stranger and more durable than the meme suggested.

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