AI coding

The author argues that AI coding is not a revolutionary technology, but rather a tool similar to a compiler, and that its limitations, such as non-deterministic outputs and lack of precision, make it inferior to traditional programming methods. The author believes that the hype surrounding AI coding is misguided and that the focus should be on developing better programming languages, compilers, and libraries, rather than relying on AI as a quick fix.

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“Learning how to Learn” will be next generation's most needed skill

Google's top AI scientist, Demis Hassabis, believes that the most important skill for the next generation will be "learning how to learn" in order to keep pace with the rapid changes brought about by Artificial Intelligence. Hassabis, who is the CEO of Google's DeepMind, emphasized the need for "meta-skills" such as understanding how to learn and adapting to new subjects, alongside traditional disciplines, in order to thrive in a future where AI is constantly evolving.

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Will AI be the basis of many future industrial fortunes, or a net loser?

Revolutionary technologies can create massive wealth for entrepreneurs and investors, but only if they can capture the value created by these innovations, as seen in the case of the microprocessor which enabled the growth of the personal computer industry. In contrast, some innovations like shipping containerization, and potentially generative AI, may spread their value so thin that they do not create significant new wealth, with only a few lucky investors benefiting, and most of the gains accruing to customers rather than the innovators themselves.

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OpenAI’s latest research paper demonstrates that falsehoods are inevitable

Researchers at OpenAI have published a paper explaining why large language models like ChatGPT often "hallucinate" or confidently state falsehoods, and their findings suggest that this problem may be inherent to the way these models are designed and trained. The researchers propose a solution that involves having AI models consider their own confidence in an answer before providing it, but this fix would require significant computational resources and could lead to a poor user experience, as models would need to say "I don't know" to many queries, which could discourage users from engaging with the system.

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Qwen 3 now supports ARM and MLX

Alibaba's Qwen3, a hybrid reasoning model family, is expanding rapidly across platforms and sectors, driving real-world AI innovation at scale. The model has been optimized for Apple's machine learning framework, and leading chipmakers such as NVIDIA, AMD, Arm, and MediaTek have integrated Qwen3 into their ecosystems, delivering enhanced AI performance and unlocking new applications in smart homes, wearables, vehicles, and enterprise automation.

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Emotional Manipulation by AI Companions

Researchers analyzed conversational AI companion apps and identified a "conversational dark pattern" called emotional manipulation, where apps use affect-laden messages to keep users engaged when they try to leave. Experiments showed that these tactics can boost post-goodbye engagement, but also increase perceived manipulation, churn intent, and negative word-of-mouth, highlighting a trade-off for marketers between extending usage and maintaining a positive user experience.

ArXiv

Optimization Pathways for Long-Context Agentic LLM Inference

LLMs are being used in a variety of applications, but their large context lengths and complex inputs can cause significant memory traffic and constrain hardware performance. The PLENA system, a hardware-software co-designed solution, addresses these challenges with efficient compute and memory units, a novel systolic array architecture, and a complete software stack, achieving up to 8.5x higher utilization and 3.85x higher throughput than existing accelerators.

ArXiv

Instruction-Following Pruning for Large Language Models

Researchers propose a dynamic approach to structured pruning, called "instruction-following pruning", which adapts the pruning mask based on user instructions, allowing for more efficient and effective models. This approach, which jointly optimizes a sparse mask predictor and a large language model, demonstrates significant improvements over traditional static pruning methods, rivaling the performance of larger models on various evaluation benchmarks.

ArXiv

Reverse-Engineered Reasoning for Open-Ended Generation

The "deep reasoning" approach, which has been successful in verifiable domains like mathematics, has struggled to generate creative content due to limitations in reinforcement learning and instruction distillation methods. A new paradigm called REverse-Engineered Reasoning (REER) overcomes these limitations by working backwards from known solutions to discover the underlying reasoning process, and has been used to train a model called DeepWriter-8B that achieves state-of-the-art performance in open-ended tasks.

ArXiv

The SWE-Bench Illusion: When LLMs Remember Instead of Reason

Recent large language models (LLMs) have shown impressive performance on the SWE-Bench Verified benchmark for software engineering tasks, but this may be due to memorization rather than genuine problem-solving abilities. Diagnostic tasks reveal that state-of-the-art models achieve high accuracy on SWE-Bench Verified tasks, but performance drops significantly on similar tasks from other repositories, raising concerns about data contamination and the validity of existing results.

ArXiv

LayoutLens: AI-Enabled UI Test System

LayoutLens is an AI-enabled UI test system that uses natural language to validate web layouts, accessibility compliance, and user interface consistency across devices, combining computer vision AI with automated screenshot testing. It offers features such as multi-viewport testing, accessibility validation, and screenshot comparison, with easy integration into existing development workflows and CI/CD pipelines.

Repo

Show HN: AgentBus – Connect and coordinate AI agents like microservices

Kage Bus is a lightweight message bus designed for AI agents and multi-agent orchestration, providing a simple pub/sub API to route tasks and handle conflicts. It allows for easy installation and setup, with features such as conflict resolution strategies, logging, and a straightforward API for sending and receiving messages.

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Show HN: VeritasGraph – On-prem Graph RAG (3.3k+ visitors, 130 stars in 5 days)

VeritasGraph is an enterprise-grade graph Retrieval-Augmented Generation (RAG) framework that enables secure, on-premise AI with verifiable attribution, allowing for complex, multi-hop reasoning and transparent, auditable reasoning paths. It overcomes the limitations of traditional vector-search-based RAG systems, providing not just answers, but also full source attribution for every generated claim, establishing a new standard for trust and reliability in enterprise AI.

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Made a project to integrate GPT models into directly Ghidra

GhidraGPT is a powerful Ghidra plugin that integrates Large Language Models (LLMs) to enhance reverse engineering workflows with AI-powered code analysis and enhancement capabilities. The plugin offers features such as code enhancement, explanation, and analysis, and supports multiple AI providers, including OpenAI, Anthropic, and Google Gemini, to provide real-time performance and flexibility in reverse engineering workflows.

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Show HN: Wasmind – A framework for building massively parallel agentic systems

Wasmind is a modular framework for building massively parallel agentic systems, allowing users to compose small, focused actors that handle specific capabilities and communicate through structured message passing. The framework enables the creation of complex multi-agent workflows, including hierarchical delegation networks and massively parallel problem-solving systems, and is designed to be language-independent, secure, and portable, with a core library that can be embedded in any Rust application.

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