Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio
Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio
Read original on Apple Machine Learning Research ↗
Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents
Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents
Read original on Apple Machine Learning Research ↗
Plan or Retrieve First? A Scale Study of Tool Discovery Architectures
AuthorsMingfei Zhang, Amit Surana, Ziauddin Majid, Chenxi Wang, Michael Xu
Read original on Apple Machine Learning Research ↗
GHARP: Real-Time Gaussian Head Animation from Large-Scale Reconstruction Prior
GHARP: Real-Time Gaussian Head Animation from Large-Scale Reconstruction Prior
Read original on Apple Machine Learning Research ↗
What I Did with Hermes Agent, Ollama & Local Models
Build a local multi-agent travel planner with Hermes Agent and Ollama: a coordinator and three specialist agents that research destinations, suggest stays, and generate real Google Flights search links.
Read original on KDnuggets ↗
Web Scraping 7,000 Pages in 3 Minutes with Python and Olostep
Scrape thousands of pages in a few minutes with the Olostep Batch API, download Markdown, and measure what actually finished.
Read original on KDnuggets ↗
Top 7 Python Libraries for Time Series Analysis and Modeling
Working with time series data? These Python libraries cover everything from classical forecasting to deep learning and anomaly detection.
Read original on KDnuggets ↗
How to Give Your AI Agent Memory
Learn how to give AI agents long-term memory using the OpenAI Agents SDK, Mem0, and MCP to remember preferences, context, and important information across sessions.
Read original on KDnuggets ↗
Purplle SDE Internship Interview Experience 2026 (Off-Campus) | Complete Journey from Purplle Tech…
Everything about the Purplle Tech Challenge and off-campus SDE Intern hiring process .
Read original on Medium - data science interview ↗
Software Engineering Jargon Explained: A Beginner’s Guide (Part 1)
This article is written for anyone who works alongside software engineers or applied scientists but does not come from a computer science…
Read original on Medium - data science interview ↗
Pandas for Data Science Interviews: A medium/hard difficulty series on reusable Pandas Patterns
Part 1: Event Stream Pairing with shift()Continue reading on Medium »
Read original on Medium - data science interview ↗
25 Data Science Interview Questions That Keep Coming Up in Real Interviews
SQL, Python, Statistics, Machine Learning, and Real-World Case Studies
Read original on Medium - data science interview ↗
10 statistics interview questions for data science and ML, and what interviewers actually listen…
MLE, outliers, correlation and bootstrapping: the traps that catch most candidates
Read original on Medium - data science interview ↗
How Nvidia’s New Nemotron Model Works
Some integrations belong to the whole company: a shared Slack bot, a team analytics API key, a server-to-server credential. Asking every user to connect their own account means duplicate tokens, half-finished setups, and a new support ticket every time someone joins.
Read original on ByteByteGo (system design interviews) ↗
How Netflix Uses AI Agents for Performance Engineering
How does full-stack observability impact engineering speed, incident response, and cost control?
Read original on ByteByteGo (system design interviews) ↗
How Anthropic Redesigned Source Control
Your team connected Slack, Jira and GitHub over MCP, and engineers still spend their time steering agents to the right answer. This article looks at why: connectors fetch from one tool at a time, so every agent stitches the pieces together itself, trusts the first doc that looks right, and adds a search to your token bill each time.
Read original on ByteByteGo (system design interviews) ↗
The Hidden Work Behind Every Database Request
Mostly, an application’s storage system has to do work only when users make requests to that application. However, there’s a difference between the work requested and the work that the storage system has to perform. A storage system does extra work as well, which is equally important.
Read original on ByteByteGo (system design interviews) ↗
EP232: 6 Frequently Used Orchestration Frameworks in an AI system
Match or beat harnesses on accuracy with 28% fewer tokens. Strands harness is open source and ships with benchmarked defaults for the system prompt, tools, memory, sessions, and context management, so you can skip wiring up a harness piece by piece.
Read original on ByteByteGo (system design interviews) ↗