March 2026

Stop the memory rot TL;DR: You can keep your AI sharp by forcing it to summarize and prune what it remembers (a.k.a. compacting). Common Mistake ❌ You keep a single, long conversation open for hours. You feed the AI with every error log and every iteration of your code. Eventually, the AI starts to ignore your early instructions or hallucinate nonexistent functions. Problems Addressed 😔 Context Decay: The AI loses track of your original goals in the middle of […]

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How are you, hacker? 🪐 What’s happening in tech today, March 7, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Bell Receives Patent for Telephone in 1876, and we present you with these top quality stories. From HackerNoon Projects of the Week: Black Market SSP, CutePetPal SudoDocs to Your Life as an RPG: Why Lifespans Feels Uncomfortably True, let’s dive right in. Your Life as an RPG: Why Lifespans Feels Uncomfortably […]

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Patient no-shows disrupt outpatient clinic operations, reduce productivity, and may delay necessary care. Clinics often adopt overbooking or double-booking to mitigate these effects. However, poorly calibrated policies can increase congestion and waiting times. Most existing methods rely on fixed heuristics and fail to adapt to real-time scheduling conditions or patient-specific no-show risk. To address these limitations, we propose an adaptive outpatient double-booking framework that integrates individualized no-show prediction with multi-objective reinforcement learning. The scheduling problem is formulated as […]

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Chaotic time series are notoriously difficult to forecast. Small uncertainties in initial conditions amplify rapidly, while strong nonlinearities and regime dependent variability constrain predictability. Although modern deep learning often delivers strong short horizon accuracy, its black box nature limits scientific insight and practical trust in settings where understanding the underlying dynamics matters. To address this gap, we propose two complementary symbolic forecasters that learn explicit, interpretable algebraic equations from chaotic time series data. Symbolic Neural Forecaster (SyNF) adapts […]

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Packworks is building the operating system for Southeast Asia’s informal retail economy by digitizing hundreds of thousands of sari-sari stores in the Philippines. Through a mobile ERP platform, data analytics, and AI-driven insights, the company helps micro-retailers manage inventory and sales while enabling FMCG brands to reach fragmented last-mile markets. With over 15 million transactions annually and $272M GMV processed in 2025, Packworks is transforming neighborhood stores into a scalable retail data network.

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Movement Network Foundation is building a modular blockchain framework that integrates the security-focused Move language with Ethereum’s liquidity to enable safer smart contracts and scalable decentralized applications. By combining the Move VM, modular rollups, and decentralized governance tools like Snapshot and Tally, the project aims to create a high-throughput infrastructure for Web3 builders while advancing its broader mission of democratizing digital ownership.

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Second primary cancer (SPC), a new cancer in patients different from previously diagnosed, is a growing concern due to improved cancer survival rates. Early prediction of SPC is essential to enable timely clinical interventions. This study focuses on lung cancer survivors treated in Taiwanese hospitals, where the limited size and geographic scope of local datasets restrict the effectiveness and generalizability of traditional machine learning approaches. To address this, we incorporate external data from the publicly available US-based Surveillance, […]

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Accurate and efficient 3D medical image segmentation is essential for clinical AI, where models must remain reliable under stringent memory, latency, and data availability constraints. Transformer-based methods achieve strong accuracy but suffer from excessive parameters, high FLOPs, and limited generalization. We propose LightMedSeg, a modular UNet-style segmentation architecture that integrates anatomical priors with adaptive context modeling. Anchor-conditioned FiLM modulation enables anatomy-aware feature calibration, while a local structural prior module and texture-aware routing dynamically allocate representational capacity to boundary-rich […]

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Auditing AI Systems: A Practical Guide to Testing Models for Bias, Compliance, Security, and Explainability Photo by Stephen Dawson on Unsplash Why accuracy alone is not enough and how organizations can audit AI systems before regulators, attackers, or users expose the failures. Artificial intelligence systems are now embedded in decisions that affect people’s lives credit approvals, fraud detection, hiring, underwriting, and customer support automation. But while AI adoption has accelerated rapidly, governance frameworks have struggled to keep up. Most […]

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