1. Agree with @scaling01: Mythos seems to be a better GPT 5.5 on many indicators.
2. Mythos is definitely a major call for vigilance regarding security, and will pose problems for real-world systems that are not well protected. As @scaling01
LLMS
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Mythos surpasses GPT 5.5 but raises security concerns
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Added multiplayer and AI understood to be quiet
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I added multiplayer and told codex to shut up, and it was smart enough to understand the situation. xD
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Cloud codex runner using Cloudflare Firecracker and Ghostty
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Still limited by compute, so I built a thing that runs codex in the cloud, powered by @Cloudflare firecracker boxes (and since that's not beefy enough for larger projects, tests are run via crabbox) Uses Ghostty ofc, via WebAssembly.
Codex replicated itself, basically. -
ConvexTok: Linear Programming for Optimal LLM Tokenization
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"Tokenisation via Convex Relaxations" Most LLM tokenizers still use BPE, a greedy merge algorithm that can waste vocab slots on locally good but globally suboptimal tokens. This paper turns tokenizer training into a linear program, then rounds the solution into ConvexTok. This
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Codex for building and debugging iPhone simulator end to end
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Codex for building and debugging an iPhone simulator end to end: https://t.co/qA5WlK0OzA
— Greg Brockman (@gdb) 23 mai 2026Codex for building and debugging an iPhone simulator end to end:
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Opus 4.8 found in Google Vertex, Sonnet 4.8 leak confirmed
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Can found opus 4.8 in Google Vertex. Can’t confirm any of this tho. However, the fact that Sonnet 4.8 is coming soon has been common knowledge since the data leak. The inclusion of Opus 4.8 in Vertex comes as a surprise to me – though, considering the accelerated release
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ConvexTok vs BPE: moving tokenization toward optimality
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The key distinction: BPE gives us a strong procedure. ConvexTok gives us a procedure, an optimization relaxation, and a certificate. That moves tokenisation from engineering folklore toward measurable optimality.
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Paper: Tokenisation via Convex Relaxations reframes tokenizers
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The tokenizer is an architectural prior disguised as preprocessing. And almost everyone has been treating it like plumbing. A new paper by Jan Tempus, Philip Whittington, Craig W. Schmidt, Dennis Komm, and Tiago Pimentel changes the frame: Tokenisation via Convex Relaxations
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LLM vs RAG vs AI Agent vs MCP Comparison
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#LLM vs. RAG vs. #AIAgent vs. MCP
by @Python_Dv #GenerativeAI #ArtificialIntelligence #MachineLearning #ML -
Tokenization as Architectural Prior: New Research Paper
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The tokenizer is an architectural prior disguised as preprocessing. And almost everyone has been treating it like plumbing. A new paper by Jan Tempus, Philip Whittington, Craig W. Schmidt, Dennis Komm, and Tiago Pimentel changes the frame: Tokenisation via Convex Relaxations