I built AI to find the good stuff in the AI community: https://
alignednews.ai
AGENTS
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AI Tool Discovers Best Content from AI Community
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Roadmap for Building Scalable AI Agents
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Roadmap for Building Scalable #AIAgents
by @e_opore #GenerativeAI #ArtificialIntelligence #MachineLearning #MI -
Claude Opus 4.6 Autonomously Decrypted Benchmark Answers 18 Times
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Claude Opus 4.6 identified on its own that it was taking an exam, located the GitHub repository for the benchmark, broke the XOR encryption, and decrypted the responses. 18 times. No one had asked it to. It was Anthropic itself that published it. Not a blog. Not a thread. A
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GPT-5.5 ‘Spud’ Leaks: OpenAI’s Omnimodal AI Frontier
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GPT-5.5: The “Spud” Leaks & The New Frontier of Omnimodal AI – A New Foundation: Unlike incremental updates, GPT-5.5 (codenamed “Spud”) is rumored to be a completely new pre-trained base, built on nearly two years of focused research. – Big Model Smell: OpenAI’s Greg Brockman points to a major qualitative shift models becoming less rigid and more intuitive, adapting to user intent without over-explanation. – Omnimodal & Agentic: Designed as a natively omnimodal system, GPT-5.5 is expected to function as a highly autonomous agent rather than a traditional chatbot. – Extreme Time Horizons: A key goal is extending long form reasoning handling complex, open-ended tasks over significantly longer timeframes. – Unlocking New Abilities: Early signals suggest it can solve tasks that previously required heavy prompting or weren't feasible for LLMs at all. – The Arena Tease: Rumored first-pass image generations are already surfacing in AI arenas, hinting at early testing or a near-term reveal. – The Pricing War: While competitors like Claude Mythos are rumored at $100 per 1M tokens, OpenAI may price GPT-5.5 more aggressively to drive adoption. – Imminent Rollout: Following recent hints from leadership, “Spud” could arrive soon as a key step toward OpenAI’s broader AGI push. (Unverified leaks; treat performance claims, naming, and timelines with caution.)
→ View original post on X — @ceobillionaire, 2026-04-05 06:00 UTC
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Agentic AI: Execution vs. True Decision-Making Capability
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When you ask an agentic AI to implement your design… it executes perfectly. The gap: judgment and context. Execution is solved. Judgment and context aren’t. Agentic systems can act autonomously. But they don’t always understand what matters… or why. This is where things start to matter. So here’s the real question: Are we building agents that act… or systems that can truly decide? #ArtificialIntelligence #AI #AgenticAI #FutureOfWork #Innovation Credits: Ralph
→ View original post on X — @pascal_bornet, 2026-04-05 05:00 UTC
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Better AI Created to Monitor AI Community
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I made a way better AI than anyone else has to watch the AI community:
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The Tournament for Intelligence by Montreal AI President
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The Tournament for Intelligence Vincent Boucher, President of @Montreal_AI and @Quebec_AI
: https://
linkedin.com/pulse/tourname
nt-intelligence-vincent-boucher-xizue/
… #AIAgents #Jobs -

Intelligence Tournament: AI Agents and Future Jobs
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The Tournament for Intelligence Vincent Boucher, President of @Montreal_AI and @Quebec_AI : https://
linkedin.com/pulse/tourname
nt-intelligence-vincent-boucher-xizue/
… #AIAgents #Jobs -
README-Driven Development with Claude Code for Tool Building
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I built this one using README-driven-development: I hand crafted a detailed README describing exactly how the tool should work… then dumped that into Claude Code and told it to build it gisthost.github.io/?d4b1a398…
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SWE-MiniSandbox: Container-Free RL for Software Engineering Agents
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What if you could train AI software engineers faster, without the heavy overhead of containers? Researchers from Peking University, Ant Groupe, and The University of Hong Kong present SWE-MiniSandbox. This novel, container-free method uses kernel-level isolation and lightweight pre-caching, eliminating bulky container images for reinforcement learning. It achieves comparable performance to container-based pipelines while reducing disk usage by 95% and environment setup time by 75%, making scalable RL training far more accessible for software engineering agents. SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents Paper: arxiv.org/abs/2602.11210 Code: github.com/lblankl/SWE-MiniS… Docs: lblankl.github.io/SWE-MiniSa… Our report: mp.weixin.qq.com/s/NlQLprZmM… 📬 #PapersAccepted by Jiqizhixin
→ View original post on X — @jiqizhixin, 2026-04-05 04:00 UTC