As a programmer, I spend most of my time in a terminal application like Kitty. I use Neovim as my code editor. I use CLI based AI agents. But the biggest pain, even in 2026, is that there is no terminal that can render complex scripts like Indic languages or Arabic. This is a significant limitation for me, as most of my work involves language processing. In this article, I will give a brief overview of why this issue remains unsolved—covering the character-cell grid model, width measurement, and the distinction between text shaping and rendering—along with ongoing efforts and a small tool I built recently that illustrates a solution path.
Transformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is a Bayesian network. We establish this in five ways. First, we prove that every sigmoid transformer with any weights implements weighted loopy belief propagation on its implicit factor graph. One layer is one round of BP. This holds for any weights -- trained, random, or constructed. Formally verified against standard mathematical axioms. Second, we give a constructive proof that a transformer can implement exact belief propagation on any declared knowledge base. On knowledge bases without circular dependencies this yields provably correct probability estimates at every node. Formally verified against standard mathematical axioms. Third, we prove uniqueness: a sigmoid transformer that produces exact posteriors necessarily has BP weights. There is no other path through the sigmoid architecture to exact posteriors. Formally verified against standard mathematical axioms. Fourth, we delineate the AND/OR boolean structure of the transformer layer: attention is AND, the FFN is OR, and their strict alternation is Pearl's gather/update algorithm exactly. Fifth, we confirm all formal results experimentally, corroborating the Bayesian network characterization in practice. We also establish the practical viability of loopy belief propagation despite the current lack of a theoretical convergence guarantee. We further prove that verifiable inference requires a finite concept space. Any finite verification procedure can distinguish at most finitely many concepts. Without grounding, correctness is not defined. Hallucination is not a bug that scaling can fix. It is the structural consequence of operating without concepts. Formally verified against standard mathematical axioms.
An illustrated children's book for the decline of large technology platforms. Perfect for digital marketers, tech professionals, savvy toddlers, and tired adults alike—a brief reprieve before your next doomscrolling
Ubuntu systems support secure boot using grub. grub contains a lot of parsers for file systems and other things which are a constant source of security issues. In 26.10, we’d like to propose removing the following features from signed GRUB builds: Filesystems Remove btrfs, hfsplus, xfs, zfs Retain ext4, fat, iso9660 (and squashfs for snaps) Image formats: Remove jpeg, png Retain none We do not use images, but using that in your grub.cfg locally is a massive security risk (if even still al...
AI automation platform built for modern operators and teams. Instead of mapping out complex triggers and actions, you simply describe your desired workflow in plain English. Our AI agents securely connect to 900+ apps, execute long-running tasks