Social Media Ideas for Embedding Models

Finding related posts with embeddings

I added a new feature to my blog: a list of related posts at the bottom of each post. I implemented it using embeddings, and this note documents how. I looked at how other content...

The Embedding Model You Choose Matters More Than Your LLM

The Uncomfortable Truth You’ve spent days prompt-engineering your LLM. You’ve benchmarked Claude against GPT. You’ve debated whether to use Mixtral. But your RAG pipeline is still...

Embeddings

jina-embeddings-v4 as an OpenAI-Compatible Embeddings Server

jina-embeddings-v4 is a self-hosted server for the jina-embeddings-v4 embedding model with an OpenAI-compatible /v1/embeddings endpoint. It runs on a single NVIDIA GPU. An applicat...

Why NVIDIA’s New Embedding Models Are a Bigger Deal Than They Look

<p>NVIDIA's Nemotron 3 Embed tops the toughest retrieval benchmark there is. Here's why that number is a cost problem, not</p>

What Are Embeddings and Why They Power Modern Search

What are embeddings? Embeddings are learned numerical representations—dense arrays of floating-point numbers known as vectors—that transform text, images, products, and user querie...

Building Multimodal Workflows with a Local LLM

Image inputs and structured outputs with Gemma 4 and Ollama The post Building Multimodal Workflows with a Local LLM appeared first on Towards Data Science.

How to Generate and Compare Text Embeddings With Ollama

An embedding is a list of numbers where similar meaning gives similar numbers. Run one locally with Ollama, compare two, and semantic search stops being a buzzword and becomes arit...

Embeddings, Cosine Similarity, and Chunking Explained Simply

How Embeddings WorkContinue reading on Medium »

Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet

Tabular foundation models predict the missing column of any spreadsheet zero-shot, the way an LLM completes text — and on the TabArena benchmark they now sit above fully tuned grad...

Mixture-of-Experts (MoE) LLMs

Understanding models like DeepSeek, Grok, and Mixtral from the ground up…Continue reading on Medium »

Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed

Retrieval quality in an AI search product is bounded by two things: how good the embedding model is, and how cheaply you can run it across an index. This week, Perplexity Engineeri...

Beyond the Model: Building Real-World Machine Learning

In the latest Developer Impact Series, Dave Neary of Ampere® Computing talks with Dr. R.J. Nowling from the Milwaukee School of Engineering to discuss how the school is bridging th...

Small Language Models with Hugging Face transformers Library + smolLM3

Running a 70B model in production is expensive, and for many tasks, unnecessary. If you're building a focused pipeline, a well-trained 3B model will match or beat the 70B on your s...

Generate Embeddings in SQL with Aurora and Bedrock

Most embedding pipelines on AWS have the same shape: a job reads rows out of the database, calls Amazon Bedrock, and writes the vectors back. That is a second... The post Generate...

Tabular Foundation Models: A First Look with TabICL

Tabular foundation models are a new category of machine learning model that can perform zero-shot (i.e., without any gradient updates) prediction on tabular datasets. They are pret...

LLM Model Selection Matrix: Pick the Cheapest Reliable Model for Each Feature

Most AI product teams do not have a model problem. They have a matching problem. A chat rewrite, a support answer, a SQL assistant, and an autonomous workflow should not all use t...

Integrating Local LLMs with Spring AI Using LM Studio

Learn how to use a locally hosted chat model and an embedding model with Spring AI in LM Studio. The post Integrating Local LLMs with Spring AI Using LM Studio first appeared on B...

Combining LLM Embeddings with Tabular Features in a Unified Scikit-learn Pipeline

In this article, you will learn how to build a unified scikit-learn pipeline that combines text embeddings generated by a lightweight open-source language model with...

When Models Learn

Explains test-time training through the analogy of a GPS learning a persistent shortcut around daily traffic rather than a one-time reroute: the model takes a gradient step on the...

When Models Learn

Explains test-time training through the analogy of a GPS learning a persistent shortcut around daily traffic rather than a one-time reroute: the model takes a gradient step on the...

Building LLM, Part 8 — DPO, and the Whole Post-Training Landscape

Abstract. We’ve now built four ways to shape a base model — SFT, DPO, PPO, GRPO — and proven each on real numbers. This finale ties them…Continue reading on Medium »

Cutting LLM Costs Without Cutting Quality [Testμ 2026]

Viktoria Semaan of Databricks on running 16 models through one eval set, why Gemma 12B matched Sonnet at a fraction of the cost, and when fine-tuning pays.

Build Your Own Private Chat App With a Model Dropdown

Build a Streamlit multi model chatbot with a dropdown of every model and switch models mid conversation without losing history, in 20 minutes.