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. ## The Long Now of the Web: Inside the Internet Archive’s Fight Against Forgetting By @zbruceli [ 18 Min read ] A deep dive into the Internet Archive’s custom tech stack. Read More. CodeRabbit vs Code Reviews in Kilo: Which One Is Best For You […]

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Malicious litellm_init.pth in litellm 1.82.8 — credential stealer The LiteLLM v1.82.8 package published to PyPI was compromised with a particularly nasty credential stealer hidden in base64 in a litellm_init.pth file, which means installing the package is enough to trigger it even without running import litellm. (1.82.7 had the exploit as well but it was in the proxy/proxy_server.py file so the package had to be imported for it to take effect.) This issue has a very detailed description of […]

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arXiv:2604.06296v1 Announce Type: new Abstract: AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has primarily focused on emph{server-side} efficiency, proposing methods such as caching, speculative execution, traffic scheduling, and load balancing to reduce the cost of serving agentic workloads. However, as users increasingly construct agents by composing local tools, remote APIs, and diverse models, an equally important optimization problem arises on the client side. Client-side optimization asks […]

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arXiv:2602.00870v1 Announce Type: new Abstract: Neural operators aim to learn mappings between infinite-dimensional function spaces, but their performance often degrades on complex or irregular geometries due to the lack of geometry-aware representations. We propose the Finite Element Eigenfunction Network (FEENet), a hybrid spectral learning framework grounded in the eigenfunction theory of differential operators. For a given domain, FEENet leverages the Finite Element Method (FEM)toperformaone-timecomputationofaneigenfunctionbasisintrinsictothegeometry. PDE solutions are subsequently represented in this geometry-adapted basis, and learning is reduced to […]

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I’ve been thinking a lot about the relationship between reinforcement learning and neuroscience lately, and something about the usual framing doesn’t quite capture it. People often say the two fields developed in parallel. But historically it feels more like a spiral. Ideas move from neuroscience into computational models, then back again. Each turn sharpens the other. I’m considering writing a deep dive series about this, tentatively called “The RL Spiral.” The goal would be to trace how ideas […]

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arXiv:2601.09037v1 Announce Type: new Abstract: Probabilistic computers built from p-bits offer a promising path for combinatorial optimization, but the dense connectivity required by real-world problems scales poorly in hardware. Here, we address this through graph sparsification with auxiliary copy variables and demonstrate a fully on-chip parallel tempering solver on an FPGA. Targeting MIMO detection, a dense, NP-hard problem central to wireless communications, we fit 15 temperature replicas of a 128-node sparsified system (1,920 p-bits) entirely on-chip and achieve […]

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London, UK Busha, a Nigerian SEC-licensed digital asset exchange, expands to the UK to serve families with connections across both regions   Busha selected Uphold as its platform partner thanks to experience in delivering on-chain infrastructure services in the UK   Busha, a crypto platform focused on building the future of money in Africa, has selected Uphold, the modern infrastructure provider for on-chain finance, to support the launch of its services in the UK. Busha serves more than a million […]

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arXiv:2212.14857v4 Announce Type: replace-cross Abstract: Estimators of doubly robust functionals typically rely on estimating two complex nuisance functions, such as the propensity score and conditional outcome mean for the average treatment effect functional. We consider the problem of how to estimate nuisance functions to obtain optimal rates of convergence for a doubly robust nonparametric functional that has witnessed applications across the causal inference and conditional independence testing literature. For several plug-in estimators and a first-order bias-corrected estimator, we […]

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arXiv:2603.12440v1 Announce Type: new Abstract: Optimizing GPU kernels presents a significantly greater challenge for large language models (LLMs) than standard code generation tasks, as it requires understanding hardware architecture, parallel optimization strategies, and performance profiling outputs. Most existing LLM-based approaches to kernel generation rely on simple prompting and feedback loops, incorporating hardware awareness only indirectly through profiling feedback. We introduce KernelFoundry, an evolutionary framework that efficiently explores the GPU kernel design space through three key mechanisms: (1) MAP-Elites […]

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