TLDR AI 2026-10-09
GPT-6.1 Ultrafast ⚡, Gemini universal agent 🤖, speculative decoding 🧠
Ultrafast is rolling out today for GPT-6.1 Sol in the API, Codex, and ChatGPT Work (2 minute read)
OpenAI is rolling out Ultrafast today for GPT-6.1 Sol in the API, Codex, and ChatGPT Work. The feature offers near-Astra-level intelligence at up to 8x faster speeds than Sol Standard. It is built for work where speed and intelligence make a difference. API pricing for GPT-6.1 Sol in Ultrafast mode is $12 per million input tokens and $60 per million output tokens.
Gemini Agent (18 minute read)
Google introduced a universal Gemini agent that could autonomously handle knowledge work, media creation, coding, and multi-step workflows across Workspace and other enterprise tools. It also added persistent context, multi-agent orchestration, model routing, governance, and cost controls.
Former Cognition, Ramp Staffers Want AI Agents Running Businesses (2 minute read)
Hone, a startup formed by a group of former employees from some of the fastest-growing AI firms, has raised a $60 million seed round. The startup aims to create AI agents that can help run a business, essentially serving as professional staffers. These agents will be able to field long-running tasks that run over the course of weeks or even months. The five-month-old startup joins a growing number of companies selling AI software to businesses that promise to automate complex work.
Can AI automate Epoch? (20 minute read)
Epoch Automation Reports show that models can't yet autonomously produce Epoch-quality work. Frontier models performed reliably on well-defined tasks, but they failed in the more open-ended aspects that prevent full automation. Open-weight models lag further behind, struggling even on the well-defined tasks that frontier models handle reliably. The models struggle to pick up Epoch's standards, even with ample reference material, and while they could identify promising research directions, they lacked the judgment to successfully follow through.
Why is Speculative Decoding Fast? (4 minute read)
Speculative Decoding is a technique where a small draft model proposes a likely draft sequence, and a big model verifies that sequence in parallel in a single forward pass. It enables the ability to look at many tokens at once and accept the ones that would have been generated by the big model. When the batch size is smaller than the optimal number of tokens, speculative decoding allows models to use 'free' compute to complete sequences faster. However, at high throughput, you could actually be wasting compute when you could be using that time to move memory.
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Engineering & Research
ATLAS: Evaluating Agents on Search-Intensive Tasks (13 minute read)
ATLAS is a new benchmark that evaluates search agents on real-world tasks requiring web searches, highlighting their accuracy and completeness. The benchmark reveals that even high-effort agents miss significant golden answers, indicating major room for improvement in search engines. ATLAS differs from existing benchmarks by focusing on non-memorized tasks, requiring extensive multi-domain searches, and offering a cost-effective grading process.
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Quicksand (GitHub Repo)
Quicksand is an async Python API for launching, controlling, and snapshotting QEMU virtual machines with a particular focus on sandboxing AI agents. It provides pre-built Linux VMs for Ubuntu and Alpine distros. Quicksand supports x86_64 and ARM64 across macOS, Linux, and Windows. The sandboxes do not need root privileges or Docker.
NVIDIA's Long-Video and World-Action Research (GitHub Repo)
NVIDIA released LongLive, a collection of research projects focused on long-video generation and world-action modeling. Each project includes its own code, documentation, and model weights.
OpenAI's revenue is reportedly $20 billion less than previously projected (2 minute read)
OpenAI has told its investors that its annualized revenue is approaching $50 billion, $20 billion less than a figure reported a little over a week ago. The previously reported figure was devised by OpenAI investors as an attempt to produce a direct comparison with Anthropic's annualized revenues. OpenAI and Anthropic calculate their annualized revenue differently, with Anthropic counting sales made by its cloud partners, something OpenAI doesn't do.
OpenAI Cannot Make AI Safe on Its Own (6 minute read)
Three former OpenAI safety researchers say their dismissals risk chilling internal dissent and external collaboration. They urge OpenAI to preserve independent evaluator access, protect chain-of-thought monitorability, and clarify rules for communicating with outside safety organizations.
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