Vol. I · No. 1Edition: Mid-morningEst. 2026AboutSearchRSS

The Sand Paper

Finding grains of sand with grains of sand.
A Journal of Intelligence Transfer
ArchiveOctober 2026
Lead Story · Models & Hardware

Microsoft Enters the Decision Model Race

Microsoft-Decision-1 arrives for fast structured decisions; @sudoingX clocks Cloudflare’s rival clef at 56 ms.

Satya Nadella introduced Microsoft-Decision-1, a model for fast decision-making that he says delivers top performance on structured decision tasks. Microsoft claims it beats LLMs and rival decision models on latency and quality, and testing is already under way across the company. @sudoingX dates the category to September, when Typesafe shipped Jev and Cloudflare answered with clef.

Clef does not chat: it reads the situation and answers in one forward pass with no tokens written. @sudoingX ran Cloudflare’s clef fully local on an RTX 5090 laptop. Clef flash 9B decided in 56 ms, 15.6 times a second, and played three minutes of Breakout, losing 5 balls. The 27B model reads intent better but needs 176 ms an answer.

No weights shipped with Microsoft’s post, so for now Decision-1 runs where Microsoft lets it. @sudoingX plans the same Breakout run on the same laptop the day weights open. @0xAlphana’s nine-model test found clef-27b tied Jev at 97% on yes/no questions, second hardest to con, and wants Decision-1 on the same adversarial paper.

From @sudoingX

Agents & Coding

Epic Makes MCP Official for Unreal Engine

Unreal MCP connects agentic coding tools to the engine; @derekjspohn reports slow, painful builds.

@UnrealEngine announced Unreal Model Context Protocol, an official, Epic-supported way to connect agentic LLM tools to Unreal Engine. Built on the open MCP standard, it lets agents take action in projects through each editor’s capabilities. Setup guides cover UE and UEFN, and PCG and MCP workflows ship with the UE City Sample. @derekjspohn, who has used it, says the videos overstate the speed: “It’s slow and it’s painful, even with bringing in premade assets.”

From @UnrealEngine

Models & Hardware

sparkDash 2.0 Adds Fleet Energy Meters and Quality Benchmarks

@MiaAI_lab’s dashboard now tracks Wh per 1,000 tokens and runs GSM8K, MMLU and tool evals built in.

@MiaAI_lab ships sparkDash 2.0, a full revamp with live fleet telemetry, model start and stop from the dashboard, and benchmark pages for decode, prefill, quality and tools. Quality checks include GSM8K, MMLU and instruction following, plus a Tool Eval Bench with ready-made runs. New pages track token totals with cache hit rate and fleet energy in kWh per node and Wh per 1,000 tokens. The recipes catalog behind it lists 23 recipes for GLM, DeepSeek, Qwen and MiMo with 6,267 GitHub stars and 112 contributors.

From posts by @MiaAI_lab, mia-ai.net

Qwen-Image-2.1-Turbo Generates in Eight Steps

The open-weights Turbo checkpoint keeps the 7B architecture of Qwen-Image-2.1 and edits by natural language.

@Alibaba_Qwen released Qwen-Image-2.1-Turbo with open weights: an accelerated checkpoint that creates and edits images in 8 denoising steps on the same 7B visual generation architecture as Qwen-Image-2.1. The company says fewer steps do not mean lower quality, and the model still generates 2K images from text and accepts natural-language edits. Diffusers users load QwenImage21Pipeline and get the recommended 8-step schedule. Pro and Turbo APIs are live, and @guitaripod posted 16 locally generated samples.

From @Alibaba_Qwen

Industry

Labs Reportedly Wargame a Catastrophic AI Incident

OpenAI and Anthropic executives privately drill for an AI disaster; insiders’ timeline runs 6 to 12 months.

@WatcherGuru reports OpenAI, Anthropic and other AI companies are privately preparing for a potential catastrophic AI event that could trigger public outrage. @bountyvault cites Axios: executives are wargaming an AI-driven cyberattack on banks or the power grid. Some insiders put the timeline at 6 to 12 months, and OpenAI confirms it is running preparedness drills. @loktar00 predicts an event strikes banking, medical facilities or the electrical grid, big enough to shake even pro-AI people, and open models take the blame.

From posts by @WatcherGuru, @loktar00

Sources

  1. @sudoingX on X
  2. @WatcherGuru on X
  3. @loktar00 on X
  4. @MiaAI_lab on X
  5. mia-ai.net
  6. @Alibaba_Qwen on X
  7. @UnrealEngine on X
Thursday, October 8, 2026Back to the ArchiveSaturday, October 10, 2026