import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass
torch.manual_seed(0)
@dataclass
class Cfg:
d_model: int = 192
n_head: int = 6
n_layer: int = 4
ffn_mult: int = 2
n_mod: int = 3
…
Bunkuser
Limitations: Omni offers 10-second video generations currently, with longer durations coming soon. Uploading audio references and scene extension is not yet supported in the Gemini API for this model. Video references up to 3 seconds in duration are accepted by the API schema but are not correctly processed by the model at this time. Character…
# Introduction
You want to add Claude to a Python application. Creating an account and making your first API call is straightforward. The official documentation can get you from zero to a working request in a few minutes. The next questions are usually more practical:
What does the response object contain?
How do…
Today, Google DeepMind and A24 are announcing a first-of-its-kind partnership focused on research. The collaboration pairs a world-leading research lab with the industry’s most filmmaker-forward studio to help artists develop new workflows and techniques. This ensures the tools of the future are shaped by the creators who use them. This partnership creates a deep research…
Traditional robot programming is hard to scale. It requires orchestrating multimodal perception, physical contact dynamics, diverse configurations, and execution failures by hand. Code-as-policy systems let language models compose these into executable robot programs. That makes robot behavior inspectable, editable, and debuggable.
But existing robotic coding agents run in naive execution environments. They receive only coarse,…
In AI agents, a context graph is the part of agent memory that captures decisions. This post explains why AI agents need to capture decisions, how context graphs capture them, how agents use them, and how agents enhance their capabilities and performance with them. An agent failure case It's the last week of the quarter. A renewal agent…
Retrieval is critical in AI agents. To do any task correctly, the agent needs to be able to retrieve all the information that is relevant to the task from its memory. Context graphs are all the rage right now, so I benchmarked them against the alternatives. This post explains how each memory method works, what…
# Fine-Tuning Language Models on Apple Silicon with MLX
Fine-tuning a language model used to mean renting cloud GPUs and watching the meter run. If you own a Mac with an Apple Silicon chip, you can now adapt an open model to your own data locally, at zero cloud cost, using a framework…
def _purge(*prefixes):
for name in [m for m in list(sys.modules)
if any(m == p or m.startswith(p + ".") for p in prefixes)]:
del sys.modules[name]
def _load_ocrmypdf():
_purge("PIL", "ocrmypdf")
import…
Making computer use safe in 3.5 Flash To mitigate some of the prompt injection risks for agents operating in live environments, we use targeted adversarial training for computer use in Gemini 3.5 Flash. We’re also releasing two optional enterprise safeguard systems that enable enterprises to: Require explicit user confirmation for sensitive or irreversible actions. Automatically…