Microsoft just built a coach that catches your AI coding habits and fixes them. Most developers using AI coding tools have no idea if they're improving. Which prompts they repeat. Which habits slow them down. Microsoft just open-sourced AI Engineer Coach to fix that. It's a
@alphasignalai
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LeCun’s new paper proves AI world model accuracy with LeJEPA
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Yann LeCun's new paper just proved when AI truly learns the world. Most AI systems learn an internal picture of the world. Nobody could prove that picture is correct. LeCun's new research finally provides that proof. It studies LeJEPA, a method that trains models to predict
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CUA-Gym automates training data bottleneck for computer-use agents
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The biggest bottleneck for computer-use agents just got automated away.
— AlphaSignal (@AlphaSignalAI) 13 juin 2026
Reinforcement learning broke open math and coding.
But for agents clicking around real software, progress stalled.
The bottleneck was generating training data at scale.
CUA-Gym is a pipeline that… pic.twitter.com/ck87dsJjikThe biggest bottleneck for computer-use agents just got automated away. Reinforcement learning broke open math and coding. But for agents clicking around real software, progress stalled. The bottleneck was generating training data at scale. CUA-Gym is a pipeline that
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Fable 5 prompt leak jailbreak banned due to model power
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“Fable 5 prompt leak / jailbreak” was so serious that it got banned? or the model was so powerful for a prompt leak? Well, they said “it’s a non-universal jailbreak..”
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Reminder: Learn Claude Fable 5 efficiently before API-only switch
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Reminder before 22 Jun: Learn how to use Claude Fable 5 properly, save tokens, maximize efficiency, and build before it goes to API only.
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Model scaling, not data scaling, key to AI intelligence
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We finally know why bigger models are smarter. It's not the data. More training data was supposed to fix small models. A new paper shows why it cannot. Researchers proved some tasks need model scaling, not data scaling. A small model fails them even with infinite data.
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Carnegie Mellon benchmark exposes safety risks of AI coding agents
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A new benchmark just exposed the dirty secret behind every coding agent. Millions of developers now let AI agents write entire features unsupervised. A Carnegie Mellon paper tested whether that code is safe to ship. The team built SusVibes, a benchmark of 200 real coding
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LCLMs start with compression, not retrieval, for agent memory
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Most agent systems start with retrieval. LCLMs start with compression. Compress everything once. Reason globally. Expand locally when needed. That's a very different way to think about agent memory.
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Claude Fable 5 guide: delete old prompts, add new elements
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Anthropic's official guide for Claude Fable 5 says prompts written for prior models now degrade output. What to delete: – Step-by-step instruction lists
– "Show your reasoning" (now triggers refusals)
– Remaining-token countdowns – Enumerated edge-case lists What to add: – -
Prompt and loop prevent Fable 5 token waste
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prompt sets goal, context, boundaries, and verification. loop handles time: effort level, checkpoints, verifier subagents, and stop rules so Fable 5 does not burn 500k to 1M tokens unchecked.