What if visual reasoning could be 97% more efficient while outperforming top models? Researchers from MBZUAI, Fudan, Renmin, and Harvard introduce Laser. Instead of forcing step-by-step text, it uses “forest-before-trees” reasoning—maintaining a big-picture understanding
MACHINE LEARNING
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Grok now ranks feeds end-to-end using learned signals
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Appreciate the commentary. From my understanding of the new algorithm changes: – Grok runs the entire feed now. No hand-crafted rules, no manually weighted features. The model reads your engagement history and figures out the ranking on its own. – It scores 15 signals per
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AI Agent Development Over Two Months
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I built the agent over two months. And Levangie Labs is better than any other
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Claude Cowork’s Federated MCP Performance Benchmark
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Claude Cowork just got 10x more powerful!
— Sumanth (@Sumanth_077) 15 mai 2026
Glean benchmarked centralized vs federated MCP in Claude Cowork. Same harness, same model, same queries, different context layer.
The federated approach: Each data source (Gmail, Slack, Drive, Salesforce) has its own MCP server. Claude… pic.twitter.com/0lSWyHsKriClaude Cowork just got 10x more powerful! Glean benchmarked centralized vs federated MCP in Claude Cowork. Same harness, same model, same queries, different context layer. The federated approach: Each data source (Gmail, Slack, Drive, Salesforce) has its own MCP server. Claude
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Solving Differential Equations With Neural Networks
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Solving Differential Equations With Neural Networks! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/DE-NNs -
AGI ALPHA: public implementation layer for AI self-improvement
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AI self-improvement is now a multi‑billion‑dollar category. AGI ALPHA is building the public implementation layer: proof‑bound agents, SecureRails, Open RSI Eval, Evidence Dockets & replayable enterprise machine labor. https://
github.com/MontrealAI/agi
alpha-first-real-loop
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Spontaneous symmetry breaking and Goldstone modes in deep information propagation
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Spontaneous symmetry breaking and Goldstone modes for deep information propagation Iqbal et al.: https://
arxiv.org/abs/2605.14685 #ArtificialIntelligence #DeepLearning #AIAgents -
Codex critiques developer-focused interface and bias against non-coders.
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Codex is very good, but it is still a very "developer coded" interface for an everything app. And it continues the somewhat annoying AI perspective that non-coders are just not as competent and need stuff hidden from them, as opposed to requiring a different form of complexity.
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Research on deep information propagation and symmetry breaking in neural networks
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Spontaneous symmetry breaking and Goldstone modes for deep information propagation Iqbal et al.: https://
arxiv.org/abs/2605.14685 #ArtificialIntelligence #DeepLearning #AIAgents -
Sparse Convolutional Autoencoders for Fine-Tuning
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It does work. With stacks of pre-trained sparse convolutional autoencoders, we can achieve a strong starting point for fine-tuning on small labeled datasets (like Caltech 101, which had 30 training samples per category) and reach near-state-of-the-art performance.