Hey Hackers! Welcome to the second-ever edition of HackerNoon Projects of the Week, where we spotlight standout projects from the Proof of Usefulness Hackathon, HackerNoon’s competition designed to measure what actually matters: real utility over hype. This week, we’re excited to share three amazing projects that have proven their necessity and usefulness: Agent Observatory, Formonger, and Olio AI. Introducing the Projects of the Week! Agent Observatory https://hackernoon.com/agent-observatory-earns-a-56-proof-of-usefulness-score-by-making-ai-agents-observable-without-risk?embedable=true If you’re a backend or platform engineer, then Agent Observatory is the […]

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Lifelong learning is critical for embodied agents in open-world environments, where reinforcement learning fine-tuning has emerged as an important paradigm to enable Vision-Language-Action (VLA) models to master dexterous manipulation through environmental interaction. Thus, Continual Reinforcement Learning (CRL) is a promising pathway for deploying VLA models in lifelong robotic scenarios, yet balancing stability (retaining old skills) and plasticity (learning new ones) remains a formidable challenge for existing methods. We introduce CRL-VLA, a framework for continual post-training of VLA models […]

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arXiv:2602.11169v1 Announce Type: new Abstract: Transformer hidden states encode information as high-dimensional vectors, yet whether direction (orientation in representational space) and magnitude (vector norm) serve distinct functional roles remains unclear. Studying Pythia-family models, we discover a striking cross-over dissociation: angular perturbations cause up to 42.9 more damage to language modeling loss, while magnitude perturbations cause disproportionately more damage to syntactic processing (20.4% vs.1.6% accuracy drop on subject-verb agreement).This finding is enabled by L2-matched perturbation analysis, a methodology ensuring […]

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Hi everyone, I’m working on a large-scale reinforcement learning experiment to compare the convergence behavior of several classical temporal-difference algorithms such as: SARSA Expected SARSA Q-learning Double Q-learning TD(λ) Deep Q-learning Maybe I currently have access to significant compute resources , so I’m planning to run thousands of seeds and millions of episodes to produce statistically strong convergence curves. The goal is to clearly visualize differences in: convergence speed, stability / variance across runs Most toy environments (CliffWalking, […]

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In 2023, AI tools are incredibly versatile, encouraging content creators, designers, programmers, and even business owners to use them to achieve their professional goals. Artificial intelligence tools help businesses and individuals to automate processes, make more informed decisions, and improve efficiency. They can analyze large amounts of data quickly and accurately, identify patterns and insights that may not be immediately apparent to humans, and provide recommendations based on that analysis. In this article, we will share some of […]

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A lot of people want to replicate YC in some other industry or some other place or with some other strategy. In general, people seem to assume that: 1) although there was some degree of mystery or luck about how YC got going, it can’t be that hard, and 2) if you can get it off the ground, the network effects are self-sustaining. More YC-like things are good for the world; I generally try to be helpful. But […]

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Data-efficient neural decoding is a central challenge for speech brain-computer interfaces. We present the first demonstration of transfer learning and cross-task decoding for MEG-based speech models spanning perception and production. We pre-train a Conformer-based model on 50 hours of single-subject listening data and fine-tune on just 5 minutes per subject across 18 participants. Transfer learning yields consistent improvements, with in-task accuracy gains of 1-4% and larger cross-task gains of up to 5-6%. Not only does pre-training improve performance […]

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arXiv:2601.02447v1 Announce Type: new Abstract: Routine clinical imaging of the retina using optical coherence tomography (OCT) is performed with large slice spacing, resulting in highly anisotropic images and a sparsely scanned retina. Most learning-based methods circumvent the problems arising from the anisotropy by using 2D approaches rather than performing volumetric analyses. These approaches inherently bear the risk of generating inconsistent results for neighboring B-scans. For example, 2D retinal layer segmentations can have irregular surfaces in 3D. Furthermore, the […]

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