Weekly tutorials and news on adapting large language models (LLMs) to your tasks and hardware using the most recent techniques and models. The Kaitchup proposes a collection of 180+ AI notebooks regularly updated.
| Platform |  | Pricing | Freemium | Publishes | Twice weekly |
|---|---|---|---|---|---|
| Issues | 464 | Founded | 3 years ago | Last Issue | 7 days ago |
| Active | |||||
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kaitchup.substack.comLatest Issues
ThinkingCap-Qwen3.6-27B Review: 2x Fewer Tokens, Same Accuracy?
A faster, more stable Qwen3.6 for local AI inference
Qwen3.6 27B is one of the strongest models for local AI, but its extremely long reasoning traces can make inference painfully slow.
DeepSeek-V4-Flash-0731 and Inkling Small: Smaller, but Better?
Hi everyone,
In this edition of The Weekly Kaitchup:
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DeepSeek V4 Flash: Now Better than DeepSeek V4 Pro (Preview)
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Inkling-Small: Better than Inkling (?)
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Escha-W2: Qwen3.6 35B Compressed to 12.3 GB and Faster
DeepS...
Bonsai 27B Review: Can a 3.9 GB 1-Bit Model Match Qwen3.6 27B?
An in-depth look at Bonsai 27B’s accuracy, token efficiency, reasoning stability, and production trade-offs.
Prism ML has released impressive ternary and 1-bit variants of Qwen3.6 27B. The 1-bit version is only 3.9 GB, making it dramatical...
Agentic AI at Two Different Scales: Nanbeige4.2-3B and Laguna S2.1
Hi everyone,
In this edition of The Weekly Kaitchup, I’m looking at two of the week’s most interesting releases for agentic workloads: Nanbeige4.2-3B and Laguna S 2.1.
The Kaitchup – AI on a Budget is a reader-supported publication. To re...
Qwen3.8: What Hardware Will You Need to Run Alibaba’s 2.4T Model?
Estimating the memory, storage, and GPU requirements for BF16, NVFP4, Q4, and TQ1 versions of Qwen3.8.
Alibaba’s Qwen3.8 is a 2.4-trillion-parameter frontier model. The company has indicated that it plans to release the model’s weights, po...
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