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Exploit brokers pay $500k for WordPress RCEs. I found one with GPT5.6 and $25
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The Hidden Reason Self-Taught Data Scientists Are Failing Technical Interviews in 2026
The chasm between viewing Python tutorials and creating production-level artificial intelligence models continues to grow. Here s what it Continue reading on Medium »
Your AI Agent Has a Job Description. It Doesn’t Have Rules of Conduct.
Created a repo to solve your biggest pain pointContinue reading on Everyday AI »
AI confidence just dropped 17 points in six months. That’s actually great news.
Presented by JumpCloudThe organizations losing confidence in AI are the ones most likely to get it right.Six months ago, 40% of IT leaders described their organizations as mature in AI deployment. Today that number is 23%. Before you read that as a setback, consider what it actually reflects.We recently surveyed 800 IT leaders across the U.S. and U.K. for our Q3 2026 trends report, and the data tells a consistent story: the organizations revising their self-assessment downward are overwhelmingly the ones that have moved AI agents from pilots into production. They’re not losing faith in AI. They’re running into the problems that only show up when agents are doing real work in real systems, and they’re being honest about what they found.That kind of honesty is harder to come by than it sounds, and it matters more than the confidence number itself.Deployment was the easy part84% of organizations plan to expand AI use in IT operations over the next 6 to 24 months, so the drop in confidence
Complete Guide to Thinking Machines Inkling
Thinking Machines Lab has unveiled Inkling, its first general-purpose open-weights foundation model. It is a multimodal MoE model with 975B parameters, 41B active parameters, and a 1M-token context window. Rather than chasing benchmark supremacy, Inkling is designed as a customizable foundation for multimodal reasoning, agentic AI, coding, tool use, audio and vision tasks, and domain-specific [ ] The post Complete Guide to Thinking Machines Inkling appeared first on Analytics Vidhya.
Agentic Engineering Patterns: From Code as Expensive to Code as Infrastructure
The Paradigm Shift Nobody is Talking AboutContinue reading on Medium »
Lekh AI Refund Policy Explained: How to Get a Refund on Lekh AI Pro
Thinking about buying Lekh AI Pro? Here s exactly how the Lekh AI refund policy works, what the 7-day window covers, and how to request Continue reading on Medium »
LoRA Speedrun – a public wall-clock leaderboard for fine-tuning techniques
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Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
arXiv:2607.15281v1 Announce Type: new Abstract: Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding underlying causal mechanisms. However, existing LLM-based methods often rely on implicit language-level reasoning, resulting in opaque causal assumptions, unverifiable reasoning paths, and fragile predictions under complex interventions, particularly in context-free settings. In this paper, we propose an explicit and auditable causal reasoning framework for context-free intervention-based question answering. Our method formulates causal inference as structured reasoning over an explicit causal graph through four modular stages, rather than implicit end-to-end prediction. A key innovation is a target-aware causal graph construction strategy that treats the target variable as a core constraint during graph expansion, effectively suppressing irrelevant variables, spurio
Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
arXiv:2607.15330v1 Announce Type: new Abstract: We present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a wide range of mobile manipulation tasks in unseen environments out-of-the-box, and (2) efficiently adapting to novel downstream tasks with minimal fine-tuning data. We propose a two-stage training recipe consisting of pre-training and post-training. During pre-training, we imbue the model with broad and generalizable action-generation capabilities by training on over 100k hours of real-world manipulation trajectories collected via UMI devices. Crucially, we develop a scalable auto-labeling pipeline that annotates trajectory clips with natural languages describing scene state transitions, providing rich and precise conditioning for action learning. During post-training, we aim to align these capabilities with robot embodiments and imperative instructions that humans naturally use to prompt robo
Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling
arXiv:2607.15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities. This position paper argues that applying this probabilistic paradigm to generic quantum circuit synthesis is a directional error. Unlike natural languages, quantum circuits require strict adherence to mathematical constraints that manifest a significant syntax-semantics gap. Training on unverified quantum programs means that models learn syntax but fail to capture the physical semantics of the Hilbert space. Since the valid subset of circuit designs decays exponentially with the number of qubits, post-hoc filtering is mathematically intractable. We propose a pivot from human-centric copilots to verifier-centric agents. We integrate hierarchical constraints, topological masks, and symbolic proxies directly into generation. Our analysis suggests that scale alone cannot bridge the validity gap. Verification-aware architectures offer a viab
NeuroCommitSSM: Decision-Centric Shared Autonomy for Safe Assistive Manipulation via EEG-EMG-ET Commit Readiness
arXiv:2607.15395v1 Announce Type: new Abstract: We present NeuroCommitSSM, a decision-centric framework that models when to execute, not just what to do, for safe commit-to-execute control in assistive robotic manipulation. NeuroCommitSSM predicts a continuous commit-readiness score c_t in [0,1] from synchronized electroencephalography (EEG), electromyography (EMG), and eye-tracking (ET), and converts it into discrete commit events through dwell and hysteresis filtering. A three-state finite-state supervisor, HOLD-ASSIST-COMMIT (HAC), gates execution by requiring both a sustained commit-readiness signal from the neural model and real-time perception and robot-state feasibility, including target visibility, inverse kinematics solvability, and collision-free planning, before initiating motion. We evaluate the framework on N=32 subjects performing five activities of daily living (ADL) tasks aligned with the International Classification of Functioning, Disability and Health (ICF), using le