Gemma 3n leverages a Google DeepMind innovation called Per-Layer Embeddings (PLE) that delivers a significant reduction in RAM usage. While the raw parameter count is 5B and 8B, this innovation allows you to run larger models on mobile devices or live-stream from the cloud, with a memory overhead comparable to a 2B and 4B model, meaning the models can operate with a dynamic memory footprint of just 2GB and 3GB. Learn more in documentation.

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a man doing stand up comedy in a small venue tells a joke (include the joke in the dialogue) https://t.co/LrCiVAp1Bl

A medium shot frames an old sailor, his knitted blue sailor hat casting a shadow over his eyes, a thick grey beard o…

This editor pays homage to the classic MS-DOS Editor, but with a modern interface and input controls similar to VS Code. The goal is to provide an accessible editor that even users largely unfamiliar with terminals can easily use.

import pandas as pd
import numpy as np

pd.isna(np.NAN) # True
pd.isna(pd.NA) # True

Experimental: the behaviour of NA can still change without warning.

Starting from pandas 1.0, an experimental NA value (singleton) is available to represent scalar missing values. The goal of NA is provide a “missing” indicator that can be used consistently across data types (instead of np.nan, None or pd.NaT depending on the data type).

Cetus, a Sui-based decentralized exchange, is suspected of suffering a major exploit potentially draining over $200 million, with users reporting sharp value drops across listed assets, according to report.

A flaw was found in the Linux kernel's VMware network driver, where an improperly timed socket unbinding could result in a use-after-free issue. This flaw allows an attacker who can create and destroy arbitrary connections on virtual connections to read or modify system memory, potentially leading to an escalation of privileges or the compromise of sensitive data.