⚡ X-Fast Tries - hello · Scour

A Quest to Find the Fastest Search Stack

dev.to·18h·

Discuss: DEV

🗄️Database Internals

xBound: Join Size Lower Bounds

arxiv.org·1d

🐸Leapfrog Triejoin

Postmortem on TreeTracker Join: Simple, Optimal, Fast

zhu45.org·2d·

Discuss: Hacker News

🔗Join Algorithms

Experiments on Reward Hacking Monitorability in Language Models

lesswrong.com·3h

🔓Binary Exploitation

Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors

towardsdatascience.com·18h

How to find things (an intro to binary search)

drmaciver.substack.com·2d·

Discuss: Substack

⚖️Y-Fast Tries

X's algorithm source code drops: what it reveals about the platform's feed mechanics

ppc.land·20h

📈Differential Dataflow

understanding LSM trees via read, write, and space amplification

bitsxpages.com·9h

From HNSW to Information-Theoretic Binarization: Rethinking the Architecture of Scalable Vector Search

arxiv.org·1d

Frédéric Yhuel: The strange case of the underestimated Merge Join node

yhuelf.github.io·3d

🗺️PostgreSQL Planner

Retrieve and Rerank: Personalized Search Without Leaving Postgres

paradedb.com·1d

📊Vectorized Query Execution

Arctic Wolf’s Liquid Clustering Architecture Tuned for Petabyte Scale

databricks.com·14h

🎚️Tiered Storage

Amazon shopping automation without vision: verification gate+local model (3B)

news.ycombinator.com·17h·

Discuss: Hacker News

TigerBeetle vs PostgreSQL Performance: Benchmark Setup, Local Tests

softwaremill.com·1d

🐅TigerBeetle Protocol

The Silent AI Breach: How Data Escapes in Fragments

hackernoon.com·12h

🔓Binary Exploitation

Complexity of ArenaAllocator

ziggit.dev·1d

🏛️Region-Based Memory

Simple Sudoku Solvers SII, EI: Dyalog APL

blog.veitheller.de·2d

🧩Constraint Programming

How I Rebuilt a RAG System that Actually Works

pub.towardsai.net·1d

One part of me wonders whether this would’ve been caught earlier if I spent some more time analizing the possible approaches for this refactor. Honestl...

lmika.org·3h

🦀Rust Macros

Why it’s critical to move beyond overly aggregated machine-learning metrics

news.mit.edu·1d

🧠Machine Learning

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