technocracy

AI-assisted coding has changed software development faster than most developers expected. In just a short time, many developers went from experimenting with prompting an AI to generate small snippets of code to attempting to build entire applications with AI agents. Two major approaches have emerged from this shift: Vibe Coding — directly prompting an AI model and iterating quickly Spec-Driven Development — using structured frameworks that break requirements into specifications, user stories, and tasks before the AI writes […]

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:::info Astounding Stories of Super-Science October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Picture of Dorian Gray – Chapter XI Astounding Stories of Super-Science October 1994: The Picture of Dorian Gray – Chapter XI By Oscar Wilde ::: For years, Dorian Gray could not free himself from the influence of this book. Or perhaps it would be more accurate to say that he […]

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arXiv:2603.23611v1 Announce Type: new Abstract: Automated testing is essential for evaluating and improving the reliability of Large Language Models (LLMs), yet the lack of automated oracles for verifying output correctness remains a key challenge. We present LLMORPH, an automated testing tool specifically designed for LLMs performing NLP tasks, which leverages Metamorphic Testing (MT) to uncover faulty behaviors without relying on human-labeled data. MT uses Metamorphic Relations (MRs) to generate follow-up inputs from source test input, enabling detection of […]

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arXiv:2603.06664v1 Announce Type: new Abstract: Diffusion Transformer (DiT)-based video generation models inherently suffer from bottlenecks in long video synthesis and real-time inference, which can be attributed to the use of full spatiotemporal attention. Specifically, this mechanism leads to explosive O(N^2) memory consumption and high first-frame latency. To address these issues, we implement system-level inference optimizations for a causal autoregressive video generation pipeline. We adapt the Self-Forcing causal autoregressive framework to sequence parallel inference and implement a sequence-parallel variant […]

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If people are only using this a couple of times a week at most, and can’t think of anything to do with it on the average day, it hasn’t changed their life. OpenAI itself admits the problem, talking about a ‘capability gap’ between what the models can do and what people do with them, which seems to me like a way to avoid saying that you don’t have clear product-market fit. Hence, OpenAI’s ad project is partly just […]

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arXiv:2604.15482v1 Announce Type: new Abstract: Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning demands satisfying multiple challenging objectives simultaneously: removing undesirable knowledge, preserving general utility, avoiding over-refusal of neighboring concepts, and, crucially, ensuring robustness against adversarial probing attacks. However, existing unlearning methods primarily focus on a limited subset of these goals, typically unlearning efficacy and utility preservation while overlooking robustness and boundary behaviors. Naively extending these methods […]

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arXiv:2603.16729v1 Announce Type: cross Abstract: Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set and only indirectly address heterogeneity and scale effects. We […]

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