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Trump Officials Revive Push to Bar Chinese AI Models After Kimi K3 Alibaba Claims Qwen3.8 Is Second Only to Fable 5 Germany's Soofi S AI Model Tops All Open-Source Rivals on German Benchmarks Moonshot's Kimi K3 Won't Fit on a Single Nvidia DGX B200 Anthropic Resets Claude Limits for Third Week Without Explaining Why Moonshot Launches Kimi K3 With 2.8 Trillion Parameters and 1M Context Trump Officials Revive Push to Bar Chinese AI Models After Kimi K3 Alibaba Claims Qwen3.8 Is Second Only to Fable 5 Germany's Soofi S AI Model Tops All Open-Source Rivals on German Benchmarks Moonshot's Kimi K3 Won't Fit on a Single Nvidia DGX B200 Anthropic Resets Claude Limits for Third Week Without Explaining Why Moonshot Launches Kimi K3 With 2.8 Trillion Parameters and 1M Context
1 Top Signal
AI News

Trump Officials Revive Push to Bar Chinese AI Models After Kimi K3

Trump Officials Revive Push to Bar Chinese AI Models After Kimi K3

Four separate attempts to restrict Chinese AI models reached internal consideration inside the Trump administration last year and were killed before any took effect, and parts of the government are weighing them again after Moonshot released Kimi K3. The measures ran from Entity List designations for Chinese AI labs to a draft executive order making U.S. companies liable for breaches of any model they hosted. Sources point to procurement rules and pressure campaigns as the likelier tool.

By Marcus Schuler · 11 min read ·
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01 Latest Intelligence
5 Tools & Workflows
Repo Radar: 5 GitHub Projects Worth Your Week
Tools & Workflows

Repo Radar: 5 GitHub Projects Worth Your Week

Repo Radar No. 13: graphify turns any folder of code, papers and screenshots into a queryable knowledge graph. Tencent's CubeSandbox boots a hardware-isolated agent sandbox in under 60ms. Together AI's hallmark stops coding agents shipping the same gradient hero. Microsoft's Flint compiles agent chart specs into Vega-Lite, ECharts or Chart.js. PentAGI runs autonomous security tests inside Docker. Five projects that narrow what an agent may see, run, render or spend.

Marcus Schuler · 11 min read ·
Repo Radar: 5 GitHub Projects Worth Your Week
Tools & Workflows

Repo Radar: 5 GitHub Projects Worth Your Week

This week's Repo Radar tracks five GitHub projects where AI agents move from chat into real production work: OpenMontage turns a coding assistant into a video studio, Google Labs' design.md gives agents a design-system spec, Strix runs autonomous penetration tests, Alibaba's page-agent drives live web interfaces in natural language, and MinerU converts messy PDFs into LLM-ready markdown. Difficulty scores, licenses, and push dates for each, plus why OpenMontage is Repo of the Week.

Marcus Schuler · 11 min read ·
Repo Radar: 5 GitHub Projects Worth Your Week
Tools & Workflows

Repo Radar: 5 GitHub Projects Worth Your Week

Repo Radar's eleventh issue tracks five GitHub projects builders attach to AI agents once a demo becomes a workload: Agent-Reach, a CLI giving agents live access to Twitter, Reddit, and YouTube; Flue, the Astro team's sandbox agent harness; cognee, a graph-based memory layer; hunk, a review-first diff viewer for agent-written code; and mistral.rs, a Rust engine for local inference. Each scored on stars, language, license, push date, and setup difficulty.

Marcus Schuler · 11 min read ·
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Claude Fable 5 and GPT-5.6-Sol: How to Orchestrate Claude and Codex to Ship More Reliable Code

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Every Tuesday Morning

When Claude Fable 5 Is Worth Double, and When to Use Opus 4.8

Anthropic reports Claude Fable 5 falls back to Opus 4.8 on a fifth of Terminal-Bench trials, and after the July relaunch one tester measured three of four debugging tasks rerouted. The launch drew a researcher revolt over hidden limits; Andon Labs found an alignment slip. Double the price.

Every Tuesday Morning

Give Your AI a Memory Layer That Survives Sessions

Coding agents, support bots, and assistants all restart cold. A bigger context window does not fix it; a memory layer does. A build-along with Mem0 and Qdrant, the contradiction test most demos skip, and the real token math, costs, and controls a production memory store actually needs.

7 Analysis
Thinking Machines’ Inkling Takes U.S. Open-Model Lead With 41 Score
Analysis 17 min read

Thinking Machines’ Inkling Takes U.S. Open-Model Lead With 41 Score

Thinking Machines’ Inkling leads U.S. open-weight releases with a 41 index score. Independent tests also found high pricing and a 63% hallucination rate, while the full checkpoint needs two terabytes of GPU memory. The open weights leave companies with a harder deployment decision.

Thinking Machines Lab’s first production model has taken the U.S. open-weight lead with a score of 41 on Artificial Analysis’s Intelligence Index. Inkling uses fewer output tokens than several Chinese rivals, yet testing found high prices and a 63% hallucination rate on one knowledge benchmark. Its weights are free, but the full checkpoint needs at least two terabytes of GPU memory. The test begins after the download: which companies can afford to turn open access into a working system?

Marcus Schuler
Marcus Schuler

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