Tag: openai

IBM details training of Granite 4.2 reasoning models

Written by Felipe Santos on October 24, 2026. Posted in AI.

Authors: Granite Team, IBM Overview Granite 4.2 is IBM's first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B. These models are post-trained from Granite-4.1 base models. The Granite-4.1 base models were pre-trained from scratch on roughly 15T tokens with a five-phase strategy that extends the context window to 512K tokens. They were then supervised fine-tuned on chain-of-thought, reasoning, and agentic-trajectory data, and post-trained with a multi-stage reinforcement learning pipeline. That pipeline includes agentic RL, where the 8B and 30B models...

Cloudflare shielded customers 9 hours before PHP disclosure

Written by Felipe Santos on October 23, 2026. Posted in Technology.

Calif.io, Wiz, OpenAI, and Quarkslab disclosed two PHP vulnerabilities, CVE-2026-109618 and CVE-2026-109619, known as php2shell. They affect all web servers running PHP 5.4 or later and may allow denial of service, remote code execution, and data exposure when an application processes specially crafted HTTP traffic. Cloudflare deployed web application firewall (WAF) protections in early October 2026, about 9 hours before public disclosure. Starting October 21 at 3:02 am UTC, customers using the free or paid WAF were protected by default. Cloudflare is part of a coordinated effort with the...

Google Cloud adds three OpenAI GPT-6 models

Written by Felipe Santos on October 23, 2026. Posted in AI.

OpenAI's GPT-6 Astra, GPT-6.1 Sol, and GPT-6 Luna are joining Google Cloud's model garden, making Google Cloud the only hyperscale cloud provider with access to all three leading closed-source LLMs. The models sit alongside more than 200 first-party, third-party, and open models on a unified enterprise AI platform. Google Cloud frames the move as an extension of its long-standing emphasis on choice, enterprise-grade governance, and price-performance, and as a way for customers to run agentic workloads across the models and infrastructure that best balance cost, capability, and...

Notion’s Auto router cuts AI costs about 90%

Written by Caio Campos on October 21, 2026. Posted in AI.

Notion is overhauling how its AI spends credits. Its new model router, Auto, delivered results similar to Opus 5.5 and Astra across more than 20 common use cases at about 90% lower average cost, so credits go about 10× further. The update also includes a rebuilt Notion Agent harness, a new credit system, and new ways to use AI subscriptions you already pay for. Notion says AI is moving fast. In the past month it added seven new models with partners Anthropic, OpenAI, and open-weight providers. Notion Agents can now work on a single task for more than 24 hours, up from five minutes. Agents...

Shape your slides and videos with Gemini Notebook

Written by Caio Campos on October 21, 2026. Posted in AI.

Based on user feedback, Gemini Notebook now gives you more creative control from start to finish on two of its popular outputs: Slide Decks and Video Overviews. Instead of jumping from an initial prompt straight to a final output, Notebook can walk you through the creation flow in a quick process based directly on your sources. ‹ CoCreationLaunch_Slides_BLOG_2784x1566.mp4 1 / 2 › (function () { var fig = document.currentScript.previousElementSibling; var player = fig.querySelector('.abn-videos-player'); var nome...

n8n brings Gemini 4 Argon into your workflows

Written by Felipe Santos on October 14, 2026. Posted in Software.

n8n is one of the first companies to use and integrate Google's Gemini 4 Argon model. You can use it with your existing n8n workflows or build new ones. The article covers three ways to do this: through MCP, through your own API key with the n8n Assistant, and through an Agent inside a workflow. Connect through MCP Model Context Protocol (MCP) is an open standard governed by the Agentic AI Foundation (AAIF). It allows your AI tools to connect to external applications and data sources. n8n has a built-in MCP server that works with any MCP client, so you can build your own workflows from your...

Black Unity and Grok logos side by side on a white background, separated by a vertical line.

Meeting You Where You Work: Unity Plugin for Grok Build

Written by Felipe Santos on October 4, 2026. Posted in AI.

Over the last few weeks, Unity announced the launch of the Unity CLI followed by the official Unity Plugin, supporting the idea that Unity should meet developers in the tools they've already chosen and in the places where they work. With 3 billion Unity-powered games downloaded monthly, the CLI and plugins now sit inside the agents where a growing number of developers start projects. Game development is increasingly beginning in a terminal with an agent already open rather than in a browser on a download page. Unity's goal is to make the creation experience better and easier. The company...

Bold yellow and black text reads "Transformers × GGUF" with navigation labels above and a dark bar below listing "ggml kernels" and model components.

Hugging Face Adds GGUF Model Support to Transformers

Written by Felipe Santos on September 24, 2026. Posted in AI.

Hugging Face is adding support for running GGUF models efficiently in transformers, allowing users to load checkpoints sized for their laptop's memory through the familiar transformers APIs. Pick a GGUF from the Hub, load it with from_pretrained, and start generating on your own machine. Running AI models on your laptop has become much easier, and llama.cpp has been a big part of that. Its inference engine powers local AI tools such as Ollama, LM Studio, and Jan. Alongside projects like MLX, it has helped make local inference a practical option for everyday use. GGUF, developed by the...

Two professionals examine performance graphs on a monitor in a data center with NVIDIA servers.

Benchmarking LLM Performance at Scale with NVIDIA AIPerf

Written by Felipe Santos on September 21, 2026. Posted in AI.

You're deploying a model on a system. It starts up, prompts are getting responses. Now the hard question: Is this fast? Your instincts might lead you to send curl commands, hand-roll an asyncio script, or write yet another one-off load generator. All these approaches share the same problems: single-process performance limits, Python's GIL capping concurrency, or numbers measured against a reference you built yourself. Either way, you end up with results you can't fully trust, attached to tooling you'll have to rewrite the moment requirements change. What you need is a load client that can...

Tag: openai

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