March 2026

This paper presents a lightweight K-Means anomaly detection model and a distributed model-sharing workflow designed for resource-constrained microcontrollers (MCUs). Using real power measurements from a mini-fridge appliance, the system performs on-device feature extraction, clustering, and threshold estimation to identify abnormal appliance behavior. To avoid retraining models on every device, we introduce the Distributed Internet of Learning (DIoL), which enables a model trained on one MCU to be exported as a portable, text-based representation and reused directly on other […]

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Table of Links Abstract and Introduction Definitions Literature Review Argument Development The AI Model’s Potential for Feeling During Inference Conclusion and References Abstract This paper explores the hypothesis that the OpenAI-o1 model–a transformer-based AI trained with reinforcement learning from human feedback (RLHF)–displays characteristics of consciousness during its training and inference phases. Adopting functionalism, which argues that mental states are defined by their functional roles, we assess the possibility of AI consciousness. Drawing on theories from neuroscience, philosophy of […]

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Reinforcement learning algorithms assume that observations satisfy the Markov property, yet real-world sensors frequently violate this assumption through correlated noise, latency, or partial observability. Standard performance metrics conflate Markov breakdowns with other sources of suboptimality, leaving practitioners without diagnostic tools for such violations. This paper introduces a prediction-based scoring method that quantifies non-Markovian structure in observation trajectories. A random forest first removes nonlinear Markov-compliant dynamics; ridge regression then tests whether historical observations reduce prediction error on the residuals […]

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Active learning (AL) reduces labeling cost by querying informative samples, but in tabular settings its cold-start gains are often limited because uncertainty estimates are unreliable when models are trained on very few labels. Tabular foundation models such as TabPFN provide calibrated probabilistic predictions via in-context learning (ICL), i.e., without task-specific weight updates, enabling an AL regime in which the labeled context – rather than parameters – is iteratively optimized. We formalize Tabular Active In-Context Learning (Tab-AICL) and instantiate […]

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I’ve been trying to get to know more about self-improving agents and I’m specifically interested in systems where the agent detects it has failed, for instance with things like wrong tool calls, bad retrieval, hallucination, couldn’t find the right answer, and then automatically adjusts its own prompts or strategy so the same class of failure doesn’t happen again. I don’t talk about weight updates, but the prompt/instructions/orchestration logic evolving based on observed errors. I’m aware of work in […]

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I’ve been trying to get to know more about self-improving agents and I’m specifically interested in systems where the agent detects it has failed, for instance with things like wrong tool calls, bad retrieval, hallucination, couldn’t find the right answer, and then automatically adjusts its own prompts or strategy so the same class of failure doesn’t happen again. I don’t talk about weight updates, but the prompt/instructions/orchestration logic evolving based on observed errors. I’m aware of work in […]

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How are you, hacker? 🪐 What’s happening in tech today, March 28, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Kodak Releases DC40 in 1995, and we present you with these top quality stories. From Real-Time Agentic RAG: Eradicating Context Rot With Spark Iceberg to Reading Without End: The Crisis of Linear Knowledge, let’s dive right in. Data, Surveillance, and the Law: What Is Policing? By @id [ 4 Min read […]

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This story was originally published in February 2024. The minimum requirements for Tier 1 toolchains targeting Windows will increase with the 1.78 release (scheduled for May 02, 2024). Windows 10 will now be the minimum supported version for the *-pc-windows-* targets. These requirements apply both to the Rust toolchain itself and to binaries produced by Rust. Two new targets have been added with Windows 7 as their baseline: x86_64-win7-windows-msvc and i686-win7-windows-msvc. They are starting as Tier 3 targets, […]

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Go 1.21 includes new features to improve compatibility. Before you stop reading, I know that sounds boring. But boring can be good. Back in the early days of Go 1, Go was exciting and full of surprises. Each week we cut a new snapshot release and everyone got to roll the dice to see what we’d changed and how their programs would break. We released Go 1 and its compatibility promise to remove the excitement, so that new […]

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