Flying Blind: Measuring Keyword Coverage on a Cluster Without First-Party Tools
screamingfrog.co.uk·15h
📈Query Optimization
Gabriele Bartolini: CNPG Recipe 22 - Leveraging the New Supply Chain and Image Catalogs
gabrielebartolini.it·17h
🗑️Stack Scanning GC
Superbase on the Commodore 64
stonetools.ghost.io·15h·
Discuss: Hacker News
💾Minimal Databases
How We Found Our Stars
hackernoon.com·8h
🌱Minimal ML
🎲 Collaborative Text Editing from Scratch in Lexical
mortenson.coffee·19h
📝Rope Editors
RediShell: Critical Remote Code Execution
wiz.io·5h·
Discuss: Hacker News
🔗Redis Protocols
Measuring scaleup for Postgres 18.0 with sysbench
smalldatum.blogspot.com·1d·
Discuss: smalldatum.blogspot.com
⚡Performance
AI Vector Database: Transforming Search with Smarter Vector Database Solutions
future.forem.com·15h·
Discuss: DEV
🔍Query Engines
Embracing the parallel coding agent lifestyle
simonw.substack.com·1d·
Discuss: Substack
⚡Live Coding
Sharing is Caring: Arbitrary Code Execution for Breakfast
trebledj.me·4d
🪆Embedded DSLs
Responsible AI: How PowerSchool safeguards millions of students with AI-powered content filtering using Amazon SageMaker AI
aws.amazon.com·9h
🏰Capability Machines
blink-calc - blink source for math expressions 🧮
github.com·17h·
Discuss: r/neovim
🐍lisp
Title: A Comprehensive Guide to Learning Python for Machine Learning
dev.to·2d·
Discuss: DEV
🌱Minimal ML
Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
arxiv.org·1d
⚡Partial Evaluation
DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding
arxiv.org·1d
🚀Tokenizer Performance
The Secret to Efficient RAG: A Step-by-Step Guide to Chunking and Counting Your Vectors
dev.to·4d·
Discuss: DEV
🚀Tokenizer Performance
INDEXING - HASHING - AND -QUERY
dev.to·1d·
Discuss: DEV
🌸Bloom Indexing
Training Dynamics of Parametric and In-Context Knowledge Utilization in Language Models
arxiv.org·1d
🪜Recursive Descent
From Working Code to Efficient Code: Big O in Practice with C#
dev.to·3d·
Discuss: DEV
🚀Code Motion
DRIFT: Learning from Abundant User Dissatisfaction in Real-World Preference Learning
arxiv.org·1d
📈Earley Parsing