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Building Trustworthy Applications Through Security by Design Most applications that handle your personal data don’t encrypt it. They store it in plaintext on third-party servers. They send it through public APIs to train machine learning models. They back it up to cloud services owned by companies you’ve never heard of. Then they call it “privacy.” They’ll add a privacy policy. They’ll mention encryption in their marketing. They’ll promise not to sell your data (while still feeding it to […]

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Reinforcement learning in real-world systems is often accompanied by delayed feedback, which breaks the Markov assumption and impedes both learning and control. Canonical state augmentation approaches cause the state-space explosion, which introduces a severe sample-complexity burden. Despite recent progress, the state-of-the-art augmentation-based baselines remain incomplete: they either predominantly reduce the burden on the critic or adopt non-unified treatments for the actor and critic. To provide a structured and sample-efficient solution, we propose delayed homomorphic reinforcement learning (DHRL), a […]

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Federated learning (FL) research increasingly relies on single-node simulations with hundreds or thousands of virtual clients, making both efficiency and reproducibility essential. Yet parallel client training often introduces nondeterminism through shared random state and scheduling variability, forcing researchers to trade throughput for reproducibility or to implement custom control logic within complex frameworks. We present BlazeFL, a lightweight framework for single-node FL simulation that alleviates this trade-off through free-threaded shared-memory execution and deterministic randomness management. BlazeFL uses thread-based parallelism […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Enabling BDX On-Ramp and Off-Ramp to Onboard the Next Billion Crypto Users By @beldexcoin [ 3 Min read ] Beldex users can now buy BDX with fiat using Alchemy Pay’s seamless on-ramp payment methods. Read More. How OP_NET Enables Smart Contracts on Bitcoin Without […]

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The weights in the Proof of Usefulness algorithm were not assigned by committee consensus, philosophical preference, or gut feeling. They reflect the consistent findings of startup failure research — particularly the question of which early signals most reliably predict whether a project creates lasting value. This is what that research shows.

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I think it’s no secret that I was, for the most part, disappointed with the character designs in Mortal Kombat 1. Mortal Kombat 11 is the best-looking game in the franchise, and then to go to MK1 left a lot to be desired. However, I like to give credit where credit is due. MK1 did have some gems, quite a few in fact. So, here are the top 10 best skins in Mortal Kombat 1. P.S. I got […]

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[GitHub] platform activity is surging. There were 1 billion commits in 2025. Now, it’s 275 million per week, on pace for 14 billion this year if growth remains linear (spoiler: it won’t.) GitHub Actions has grown from 500M minutes/week in 2023 to 1B minutes/week in 2025, and now 2.1B minutes so far this week. — Kyle Daigle, COO, GitHub Tags: github, github-actions

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This study surveys the historical development of regularization, tracing its evolution from stepwise regression in the 1960s to recent advancements in formal error control, structured penalties for non-independent features, Bayesian methods, and l0-based regularization (among other techniques). We empirically evaluate the performance of four canonical frameworks — Ridge, Lasso, ElasticNet, and Post-Lasso OLS — across 134,400 simulations spanning a 7-dimensional manifold grounded in eight production-grade machine learning models. Our findings demonstrate that for prediction accuracy when the sample-to-feature […]

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This study surveys the historical development of regularization, tracing its evolution from stepwise regression in the 1960s to recent advancements in formal error control, structured penalties for non-independent features, Bayesian methods, and l0-based regularization (among other techniques). We empirically evaluate the performance of four canonical frameworks — Ridge, Lasso, ElasticNet, and Post-Lasso OLS — across 134,400 simulations spanning a 7-dimensional manifold grounded in eight production-grade machine learning models. Our findings demonstrate that for prediction accuracy when the sample-to-feature […]

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