Machine Learning

OpenAI's GPT-5.2 Drops with Math Boosts, Disney Ties, and Leaked Image Tech – Runway Gen-4.5 Steals the Video Show

Dec 12, 2025

Christopher Shaun Godwin

Author

Even as the AI news cycle eases into holiday mode, this week delivered a torrent of updates. OpenAI led the charge with GPT-5.2, a Disney megadeal, potential image model leaks, and a new standards push for AI agents. Runway rolled out Gen-4.5, topping video benchmarks, while Rivian teased ambitious autonomy plans.

GPT-5.2: Sharper Math, Bigger Context, Incremental Gains

OpenAI launched ChatGPT-5.2 after a slight delay, addressing complaints that its predecessor, GPT-5.1, was faltering on accuracy. Early benchmarks spotlight improvements in math, science, and coding, with the model claiming top spots internally against GPT-5.1.

Key specs include a 400,000-token context window (about 300,000 words) and 128,000-token output limit. API pricing sits at $1.75 per million input tokens and $14 per million output tokens, aligning with competitors.

On SWE-bench Pro for software engineering, GPT-5.2 hits 55.6% – up from 50.8% on GPT-5.1, edging Claude Opus 4.5 (52%) and surpassing Gemini 3 Pro (43.3%). Science tasks show dominant gains over GPT-5.1, though external comparisons remain sparse. Hallucinations may be tamed, but real-world tests are pending.

Disney Pumps $1B into OpenAI for IP-Powered Sora Magic

In a surprise move, Disney is reportedly investing $1 billion in OpenAI, granting access to its vast IP library. Expect Disney characters in Sora video generations and native image tools. This could enable personalized Disney+ shorts, like AI-crafted Moana clips, blending generative AI with streaming.

Leaked OpenAI Image Models: Celeb Selfies and Code-Rendering Prowess

Rumors swirled around codenamed “Chestnut” and “Hazelnut,” purportedly GPT-5.2 companions tested on arenas like Design Arena. Leaks reveal strong world knowledge (researching prompts), photoreal celeb selfies rivaling top tools, and crisp text/code rendering – from whiteboard slogans to JSON overlays on PlayStation controllers.

Comparisons to current GPT image gen highlight leaps: fewer proportion errors, better teeth/hair, though subtle AI tells linger in eyes and skin. Celebrity group shots look convincingly real at a glance, signaling relaxed safeguards on real faces.

Agentic AI Foundation: Industry Unites for Interoperable Agents

OpenAI, Anthropic, and Block launched the Agentic AI Foundation under the Linux Foundation, backed by Google, Microsoft, Amazon, Bloomberg, and Cloudflare. The goal: standardize AI agents for seamless cross-app operation, safety, and reliability.

As agents handle emails, bookings, and troubleshooting, fragmented builds risk silos. This neutral body ensures plug-and-play compatibility, akin to universal electrical standards, preventing vendor lock-in.

Runway Gen-4.5: Benchmark King with Physics and Prompt Mastery

Runway began deploying Gen-4.5, hailed for “state-of-the-art” motion, physics, and adherence. It leads global text-to-video charts, simulating weight, fluid dynamics, consistent faces, and nuanced emotions – sans audio.

Hands-on tests impressed:

Glass sphere on marble stairs: Realistic bounces, water splashes, refractions – near-perfect prompt match.
Rainy street walker: Umbrella physics, subtle smile, neon backlighting, handheld jitters nailed.
Anime explorer: Stylized but background wonky; consistency holds for foreground.
Barista latte pour: Swirling milk, steam, blurred patrons, authentic smile – macro details shine.
Neon alley chase: Drone spotlight, sparks, reflections solid; minor physics/camera hiccups in 5-second clip.

Prompt fidelity stands out, though rivals like Veo 3.1 edge on realism and sound integration.

Quick Hits: Models, Integrations, and Controversies

Open Models Surge: Mistral’s open-weight Devstral 2 rivals DeepSeek v3.2 for local coding (72.2% benchmarks). Zhipu AI’s GLM-4.6V (tool-calling vision) and Qwen’s Omni Flash upgrade (human-like voices, personality tweaks) compete fiercely.
OpenAI “Ads” Faux Pas: Shopping suggestions mimicked ads; paused for refinement with user controls.
ChatGPT + Adobe: Free Acrobat, Express, Photoshop edits via connectors – early tests show promise but limitations.
Meta Snaps Limitless Pendant: Always-on audio recorder now under Meta, raising privacy flags.
Alibaba’s Qwen Image2LoRA: One-shot LoRAs from images for style/character replication (e.g., Studio Ghibli vibes).

Rivian Accelerates AI Autonomy

At Rivian’s AI & Autonomy Day, highlights included custom silicon (Nvidia-hybrid), phased self-driving (hands-free to unsupervised Level 4 by 2027-28), integrated LiDAR, and a voice assistant syncing calendar/texts/car controls (“Warm the seats, skip passenger”).

Test drives showed reliable city navigation, though interventions needed.

McDonald’s AI Ad Backlash: Fatigue Hits Peak

A fully AI-generated McDonald’s spot – grumpy holiday mishaps – drew ire for “slop” from a deep-pocketed giant. Amid social media AI overload, viewers crave human craft over cheap gen-AI, urging hybrids: real talent augmented sparingly.

This week’s releases underscore AI’s maturation: specialized leaps, ethical guardrails, and ecosystem bridges. Stay tuned – the firehose persists.

Google's Coral Edge TPU: Turning a Humble Raspberry Pi into an AI Powerhouse

Dec 11, 2025

Christopher Shaun Godwin

Author

Imagine taking the pocket-sized Raspberry Pi—a board beloved by hobbyists for its affordability and versatility—and transforming it into a beast capable of real-time video object recognition, one of the most demanding tasks in computer science. That’s exactly what Google’s latest Coral AI Edge TPU promises, and recent hands-on tests confirm it’s no hype.

The Hardware That Changes Everything

At the heart of this upgrade is the Coral AI Edge TPU, a compact accelerator designed exclusively for machine learning inference. It’s not about raw CPU power; this USB stick-sized device offloads neural network computations from the Pi’s general-purpose processor, delivering speeds that make high-end GPUs blush on low-power setups. Priced accessibly and built for edge devices, it bridges the gap between cloud AI and on-device processing, enabling applications from smart cameras to autonomous drones without internet dependency.

Quick Setup: From Box to Brainiac

Getting started is deceptively simple. Attach a compatible camera module to your Raspberry Pi, plug the Edge TPU into a USB port, and power up. Head to coral.ai for the essential packages—PyCoral libraries and model zoos—which install via a few terminal commands. No PhD required; even if the code looks like ancient runes at first glance, it’s plug-and-play for most.

Pre-built models are ready to roll. Point the setup at a snapshot of a bird, and in a blink—faster than you can say “neural net”—it classifies the feathered friend with pinpoint accuracy. The TPU’s magic shines here: inference times plummet from seconds on the Pi alone to mere milliseconds.

Real-Time Video: Where the Rubber Meets the Road

Static images are child’s play. The real test? Live video detection. Fire up the video object detection script from Coral’s repo, and you’re off to the races. In a demo, the rig effortlessly tracked a person striding into frame, guitar in hand, tagging it with a staggering 91% confidence score. No lag, no dropped frames—just smooth, responsive AI on hardware that costs less than a decent dinner out.

This isn’t throttled lab performance; it’s sustained operation on a device sipping power like a miser. The Pi’s CPU idles while the TPU crunches tensors, freeing resources for other tasks.

Why This Matters for Makers and Beyond

For tinkerers, it’s a game-changer: home security cams that spot intruders, wildlife monitors identifying species, or robotic arms sorting recyclables—all running locally with privacy intact. Developers gain a scalable path to production edge AI, unburdened by cloud costs or latency.

Google’s Coral ecosystem keeps expanding, with dev boards, PCIe cards, and more models incoming. Pair this with the Pi’s GPIO pins, and the possibilities explode—IoT gateways, portable analyzers, you name it.

The verdict? Yes, the Raspberry Pi can handle “supercomputer” workloads for AI inference. Grab a Coral Edge TPU, and watch your projects soar from toy to titan.

Hot Stuff: Managing Thermals

A word of caution for the eager maker: “Supercomputer” power generates supercomputer heat. The Coral USB Accelerator can get very hot—often exceeding 60°C (140°F) under load. If it overheats, it throttles performance to protect itself, killing that “real-time” responsiveness. Don’t just plug it in and bury it in an enclosure. Use a USB extension cable to keep it away from the Pi’s own heat, and consider a small heatsink or fan if you’re planning 24/7 inference. It sips power, but it spits fire—plan accordingly.