AI and Tech Summaries - AI Weekly Malaysia

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Short AI and tech summaries with source links, signal scores, and why each update matters for builders, founders, and Malaysian tech workers.

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Date	Provider	Score	Summary
06 Nov 2026, 6:35 PM	Latent Space	6.5	[AINews] Inkling 2 Small: 17A-594B trained in 68 hours on 12T tokens, beating Muse Spark Latent Space's AI News brief covers Thinking Machines' Inkling 2 Small, described as a 17B-active/594B-total model pretrained from scratch in under 3 days (68 hours) on under 12T tokens in native NVFP4, followed by a single joint RL run. The post says it is competitive with and often beats same-size Chinese models, that it has a notably strong vision encoder, and that people are calling it the smartest Western open-weight model by a margin. The excerpt also flags a METR study of AI research at Anthropic and quotes John Schulman on the alignment process, but gives no benchmark tables, eval numbers, or pricing. Why: The only concrete, checkable claims here are the training recipe — 68 hours, <12T tokens, native NVFP4 pretraining, one joint RL run — plus an emphasis on intentional customizability and a strong vision encoder. If you are picking an open-weight base model to fine-tune, that recipe is the thing to probe (can you reproduce a short NVFP4 pretrain plus a single RL pass on your own domain data?), not the 'beats Muse Spark' headline, which the text asserts without any eval numbers. Treat this as a lead to verify against the actual model card and benchmarks before swapping anything in production; the source alone does not justify a migration decision. open-weights mixture-of-experts training-efficiency nvfp4 model-release vision

Date

Provider

Score

Summary

06 Nov 2026, 6:35 PM

Latent Space

6.5

[AINews] Inkling 2 Small: 17A-594B trained in 68 hours on 12T tokens, beating Muse Spark

Latent Space's AI News brief covers Thinking Machines' Inkling 2 Small, described as a 17B-active/594B-total model pretrained from scratch in under 3 days (68 hours) on under 12T tokens in native NVFP4, followed by a single joint RL run. The post says it is competitive with and often beats same-size Chinese models, that it has a notably strong vision encoder, and that people are calling it the smartest Western open-weight model by a margin. The excerpt also flags a METR study of AI research at Anthropic and quotes John Schulman on the alignment process, but gives no benchmark tables, eval numbers, or pricing.

Why: The only concrete, checkable claims here are the training recipe — 68 hours, <12T tokens, native NVFP4 pretraining, one joint RL run — plus an emphasis on intentional customizability and a strong vision encoder. If you are picking an open-weight base model to fine-tune, that recipe is the thing to probe (can you reproduce a short NVFP4 pretrain plus a single RL pass on your own domain data?), not the 'beats Muse Spark' headline, which the text asserts without any eval numbers. Treat this as a lead to verify against the actual model card and benchmarks before swapping anything in production; the source alone does not justify a migration decision.