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This week, Liquid AI released two new retrieval models. They are LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M . Both hold 350M parameters. Both are the first bidirectional members of the LFM family. They build on LFM2.5-350M-Base , released in March. The pair targets fast multilingual and cross-lingual search across 11 languages. Their footprint is small enough to run almost anywhere. Both are available now on Hugging Face under the LFM Open License v1.0. LFM2.5 Retrievers The two models share one backbone but represent text differently. LFM2.5-Embedding-350M is a dense bi-encoder. It turns each document into a single vector. Pick it when you want the fastest search and the smallest, cheapest index. LFM2.5-ColBERT-350M is a late-interaction model. It converts each token into a vector rather than one vector per document. This lets it match queries word-by-word for higher accuracy and better generalization. The trade-off is a larger index. Pick it when accuracy matters m...

In this tutorial, we implement an end-to-end workflow for Salesforce CodeGen . We load a CodeGen model from Hugging Face, prepare it for code generation, and use it to generate Python functions from natural-language prompts. We then move beyond basic inference by adding function extraction, syntax checking, static safety checks, unit-test-based validation, best-of-N candidate reranking, multi-step program synthesis, prompt-style experimentation, benchmark visualization, and artifact export. Through this workflow, we learn how CodeGen can be used not only as a code completion model but also as part of a structured code-generation pipeline that evaluates, filters, and organizes generated solutions. Loading the Salesforce CodeGen Model from Hugging Face Copy Code Copied Use a different Browser import os, sys, subprocess, textwrap, json, re, time, math, ast, tempfile, multiprocessing as mp from pathlib import Path def sh(cmd): print(f"\n$ {cmd}") subprocess.run(cm...

Most AI memory remembers the user. It stores your preferences, your tastes, and your role. Perplexity is taking a different path. Today, Perplexity launched Brain , a self-improving memory system for its agent product, Computer . Brain does not focus on remembering you. It remembers what the agent did. That reframes what memory in AI is for. What is Perplexity ‘s Brain Brain is a self-improving memory system. It builds a context graph of the work Computer performs. At set intervals, such as overnight, Brain reviews that graph. It then teaches itself how to do the work better. The idea is straightforward. The more work you do, the more efficient Brain makes your Computer. Brain is rolling out today to Perplexity Max and Enterprise Max subscribers in Research Preview. Two Axes of AI Memory Perplexity frames memory along two axes . The first is what the memory is about. The second is what the memory is for. Traditionally, AI memory has been about the user. It s...