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How are you, hacker? 🪐 What’s happening in tech today, April 18, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Osborne Computer introduced in 1983, and we present you with these top quality stories. From What Ghost Murmur Technology Actually Is (Its Not a Heartbeat Detector) to The Proof of Usefulness Algorithm: Is It Good? Do People Use It?, let’s dive right in. What Ghost Murmur Technology Actually Is (Its Not […]

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We present E2E-WAVE, the first end-to-end learned waveform generation system for underwater video multicasting. Acoustic channels exhibit 20–46% bit error rates where forward error correction becomes counterproductive — LDPC increases rather than decreases errors beyond its decoding threshold. E2E-WAVE addresses this by embedding semantic similarity directly into physical layer waveforms: when decoding errors are unavoidable, the system preferentially selects semantically similar tokens rather than arbitrary corruption. Combining VideoGPT tokenization (1024x compression) with a trainable waveform bank and fully […]

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The CIA claims a classified tool called “Ghost Murmur” — built by Lockheed Martin’s Skunk Works — used quantum magnetometry + AI to detect a downed pilot’s heartbeat from 40 miles away in Iran. The rescue was real. The physics story? Multiple physicists say it violates the inverse cube law so severely it’d require detecting a signal 2 quintillion times weaker than what current lab sensors can barely pick up at 1 centimeter. The real story is probably […]

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Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models by leveraging rule-based reward signals. However, RL training typically tends to improve single-sample success rates (i.e., Pass@1) while offering limited exploration of diverse reasoning trajectories, which is crucial for multi-sample performance (i.e., Pass@k). Our preliminary analysis reveals that this limitation stems from a fundamental squeezing effect, whereby probability mass is excessively concentrated on a narrow subset of high-reward trajectories, restricting genuine exploration and constraining attainable […]

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Non-stationary sequences arise naturally in control, forecasting, and decision-making. The data-generating process shifts at unknown times, and models must detect the change, discard or downweight obsolete evidence, and adapt to new dynamics on the fly. Transformer-based foundation models increasingly rely on in-context learning for time series forecasting, tabular prediction, and continuous control. As these models are deployed in non-stationary environments, understanding their ability to detect and adapt to regime shifts is important. We formalize this as an in-context […]

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Research: Claude system prompts as a git timeline Anthropic publish the system prompts for Claude chat and make that page available as Markdown. I had Claude Code turn that page into separate files for each model and model family with fake git commit dates to enable browsing the changes via the GitHub commit view. I used this to write my own detailed notes on the changes between Opus 4.6 and 4.7. Tags: system-prompts, anthropic, claude, generative-ai, ai, llms

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