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In this tutorial, we build an advanced Django-Unfold admin dashboard. We start by installing Django, Django-Unfold, and the required dependencies, then we create a fresh Django project with a shop application. We configure Unfold with a modern admin theme, custom sidebar navigation, dashboard callbacks, product badges, tabs, filters, actions, and a custom admin homepage. We also define realistic e-commerce models such as categories, products, customers, orders, and order items, seed the database with sample data, and launch the Django server through Colab’s proxy so we can access the admin panel from the browser. Copy Code Copied Use a different Browser import os, sys, shutil, subprocess, time, signal, urllib.request, urllib.error from pathlib import Path print(" Installing django + django-unfold ...") subprocess.run([sys.executable, "-m", "pip", "install", "-q", "django>=5.0,<5.2", "django-u...

Poetiq has just published some very interesting results showing its Meta-System reached a new state-of-the-art on LiveCodeBench Pro (LCB Pro), a competitive coding benchmark, by automatically building and optimizing its own inference harness — without fine-tuning any underlying model or accessing model internals. The result: GPT 5.5 High with Poetiq’s harness scores 93.9% on LCB Pro (25Q2), up from its baseline of 89.6%. Gemini 3.1 Pro, the model the harness was specifically optimized on, jumps from 78.6% to 90.9% — surpassing Google’s own Gemini 3 Deep Think (88.8%), a model that isn’t even accessible via API for external verification. https://ift.tt/CZ318Jk What is LiveCodeBench Pro? Before getting into the mechanics, it helps to understand why the benchmark matters. LiveCodeBench Pro (LCB) is designed to test AI coding ability in a way that resists two common failure modes in benchmarks: data contamination and overfitting. LCB Pro pulls problems...

In this tutorial, we delve into CuPy as a powerful GPU-accelerated alternative to NumPy for high-performance numerical computing in Python. We start by inspecting the available CUDA device, checking the CuPy version, runtime details, GPU memory, and compute capability so that we understand the hardware environment before running heavy computations. Then, we compare NumPy and CuPy on large matrix multiplication and FFT workloads to see how GPU acceleration changes execution speed. Also, we work with memory pools, custom elementwise kernels, reduction kernels, raw CUDA kernels, CUDA streams, sparse matrices, dense linear solvers, GPU image processing, DLPack interoperability, event-based profiling, cupyx.jit, and kernel fusion. Through these examples, we build a practical understanding of how CuPy lets us write familiar Python code while still accessing advanced CUDA-level performance features. Copy Code Copied Use a different Browser import sys, time, subprocess try: import cu...

Cline became ‘agentic’ before it was cool, but building on the bleeding edge usually leads to some structural debt. Over time, the agent loop and the VS Code extension became a package deal—making it a headache to maintain or move to new environments. Its tough to just keep layering features on a rigid core. Cline, the open-source AI coding agent used by millions of developers, shipped a significant architectural change this week: it extracted its internal agent harness into a standalone, open-source TypeScript SDK called @cline/sdk , and is rebuilding all of its own products on top of it. Rather than adding another layer on top of that architecture, the Cline team chose to rebuild the foundation. What the SDK is, and How It is Structured The core agent harness has been abstracted into a pluggable agent SDK ( @cline/sdk ). It now powers Cline across VS Code, JetBrains, and the CLI, and is open-sourced so any team can build on it. A key behavioral benefit of this...