Focusing on Post-Training
Why I'd invest in post-training existing open-weight LLMs, with Fireworks' Ember-1 as an example of more token-efficient reasoning.
25 stories
Why I'd invest in post-training existing open-weight LLMs, with Fireworks' Ember-1 as an example of more token-efficient reasoning.
Notes on MiMo-V2.6 Pro's GQA and sliding-window attention, agent training tasks, reward signals, and large RL batches.
A short note on Jev's generalization, possible encoder-style architecture and training, and Choice and Noul API examples.
My take on AI model pacing as a framework for release checks and the competitive pressure around model releases.
A 90-minute LinkedIn Learning course on how reasoning models relate to conventional LLMs and how they are developed.
A short note on OpenAI Astra, recurrent depth, looped transformers, Nanbeige 4.2, and the Mixture-of-Recursions paper.
A short video on how conventional LLMs relate to reasoning models and agents, along with Python and PyTorch setup using uv.
Two free live book club Q&A sessions on September 3 about Build a Reasoning Model From Scratch and related reader questions.
Architecture note on GLM-5.3-Flash, formerly Ox Alpha, covering its KDA and MLA/DSA hybrid attention, sparse MoE backbone, and four-stream mHC residual path.
Short illustration of how Claude's text watermarking is supposed to work based on Anthropic's released materials.
Short note on the Amazon availability of Build a Reasoning Model From Scratch and a warning about counterfeit black-and-white copies sold through Amazon India.
Short architecture note on Meta Muse Glimmer 30B, including gated local and global GQA, KV-cache efficiency, and release-time benchmark comparisons.
Short note celebrating the LLMs-from-scratch repository passing 100,000 GitHub stars and summarizing its learning materials.
Short architecture note on Kimi K3, including LatentMoE, Kimi Delta Attention, Attention Residuals, NoPE, multimodality, and inference-efficiency choices.
Short note on the architectures of six new open-weight models, including Nanbeige 4.2, Laguna S 2.1, Motif-3-Beta, Solar Open 2, Antares 1B, and BTL-3.
Short correction note for the random seed in Listing 6.5 on page 198 of Build a Reasoning Model From Scratch.
Architecture and benchmark notes on Thinking Machines Lab's 975B Inkling MoE, including short convolutions, relative-position bias, training, and effort control.
Short note celebrating Ahead of AI reaching 200,000 subscribers.
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