Anthropic just reset everyone’s 5-hour AND weekly rate limits. Either xAI’s Colossus compute is turning into a big win for all of us, or OpenAI and Codex competition is forcing Anthropic to give users more. Either way: big W for us.
LLMS
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Codex AI tool for code development and file management explained
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Then open the web browser pointing to https://t.co/G5VusyT6qP, log in, open a new file, and you're done.
— Pietro Schirano (@skirano) 15 mai 2026
We built the skill so Codex is aware of your open project (you can also ask Codex to work on any file you want, or start a new one). pic.twitter.com/m0FPX0PdhaThen open the web browser pointing to http://
MagicPath.ai, log in, open a new file, and you're done. We built the skill so Codex is aware of your open project (you can also ask Codex to work on any file you want, or start a new one). -
MagicPath Native Canvas in Codex for Designing Functional Apps
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You can now run MagicPath as a native canvas inside Codex to design and build functional apps.
— Pietro Schirano (@skirano) 15 mai 2026
It's pretty incredible.
Here's how to do it 👇 pic.twitter.com/3dVKxq7pGeYou can now run MagicPath as a native canvas inside Codex to design and build functional apps. It's pretty incredible. Here's how to do it
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Weekly AI news roundup: New models, agents, and enterprise tools
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Here's all the AI news from the past week: – @thinkymachines unveiled “Interaction Models”
– @OpenAI launched Codex From Anywhere – @AnthropicAI launched Agent View in Claude Code and increased usage limits
– Anthropic also launched Claude for Legal and Claude for Small -
Codex generalizes problems, limiting non-coding work unnecessarily
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Another aspect of this is that Codex, like a good programmer, wants to generalize problems. It has a tendency to write a repeatable code base that generates the required output. But for a lot of non-coding work, this is unnecessary and often limiting, since it anchors the work.
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LLM Failure Modes in Learning Negations During Fine-tuning
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“Negation Neglect: When models fail to learn negations in training” LLMs can understand a disclaimer in-context, but often fail to learn it during finetuning. So when training on documents saying a claim is false can still implant the claim as true. Qwen3.5 belief in
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Hermes Orchestrator and Codex Builder Vision
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100% this is the way. /goal with Hermes as orchestrator and Codex/Claude Code as builder that could all be tracked on a single Kanban makes it really 2028.
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Controlling Algorithms: Why I Built My Own
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The one who controls the algorithm controls you. Why I built my own. 🙂
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AI coding assistant automates workflows and learns commit patterns
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loads – it's super helpful, it has learnt my commit patterns, testing nuances and surprisingly also what order of things do I ask and automatically run workflows that I'd run over and over!
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AI Model Efficiency: Building a Snake Game with Three-Tier Memory
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Nostalgia hit for the millennials: let's build a Snake Game 🐍
— SambaNova (@SambaNovaAI) 15 mai 2026
Watch what happens when you ask an AI model to build something. Three-tier memory moves models and activations across DDR, HBM, and on-chip SRAM to keep inference fast & efficient. https://t.co/F3zKGeG829 pic.twitter.com/sQfQuJC8G7Nostalgia hit for the millennials: let's build a Snake Game Watch what happens when you ask an AI model to build something. Three-tier memory moves models and activations across DDR, HBM, and on-chip SRAM to keep inference fast & efficient. https://
sambanova.ai/blog/why-dataf
low-matters-more-than-ever?utm_source=x&utm_medium=organic&utm_content=blog-announcement
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