From 8 items, 7 important content pieces were selected
Other Updates
Show HN: Llama 3.1 70B on a single RTX 3090 via NVMe-to-GPU bypassing the CPU âď¸ 8.0/10
A developer released ântransformerâ, a system that successfully runs the 70-billion-parameter Llama 3.1 model on a single consumer-grade RTX 3090 GPU by implementing a direct data path from NVMe storage to the GPU, bypassing the CPU and system RAM entirely. This demonstrates a novel, cost-effective architecture for running very large language models on consumer hardware, which could significantly reduce the barrier to entry for local batch processing and experimentation with state-of-the-art models without relying on expensive cloud APIs or server-grade hardware. The system uses a 3-tier adaptive caching strategy (VRAM > pinned RAM > NVMe/mmap) and achieves a speed of approximately 0.2-0.3 tokens per second for the 70B model quantized to Q4_K_M, with the PCIe Gen3 x8 interface at ~6.5 GB/s identified as the primary performance bottleneck.
Source: hackernews/xaskasdf
Background: Running large language models typically requires loading the entire model into GPU memory (VRAM), which is limited on consumer cards. When a model is too large, parts of it must be swapped in and out from system RAM or storage via the CPU, creating a performance bottleneck. Technologies like NVIDIAâs GPUDirect Storage aim to create a direct memory access (DMA) path between storage and GPU memory to avoid this CPU overhead, but this project implements a similar concept through custom software for a specific inference task.
Discussion: The community praised the project as impressive systems engineering, with discussion focusing on its practical value for non-interactive, batch processing workloads despite its slow token generation speed. Commenters analyzed the technical trade-offs, noting the PCIe bottleneck and debating whether the architectureâs innovation outweighs the performance limitations compared to running smaller, fully resident models.
Tags: #llm-inference, #gpu-optimization, #systems-engineering, #model-serving, #hardware-hacking
(D) Is this what ML research is? âď¸ 7.0/10
A researcher submitted a novel multimodal learning paper to CVPR 2026, which was initially scored 5/3/3 by reviewers, but was ultimately rejected after the rebuttal phase when two reviewers lowered their scores. The rejection centered on reviewer demands for comparisons to vastly larger models and evaluations on datasets the author deemed unsuitable for their methodâs focus on high-resolution, fine-detail settings. This incident highlights systemic issues in machine learning research culture, where a resource gap in academia can stifle innovation by favoring large-scale, compute-heavy projects over novel, efficient approaches. It sparks a critical discussion about the peer review process in top-tier conferences like CVPR, questioning whether it effectively evaluates scientific merit or simply rewards engineering scale. The researcherâs method was tested on a relatively small model with 500 million parameters, and they pursued âhorizontal scalingâ through extensive evaluation and analysis instead of âvertical scalingâ with larger models and more data. A key point of contention was that reviewers requested comparisons to models that were 14x larger, had 4x higher resolution, and required 5-10x more inference time, which the author argued placed the methods in completely different computational resource brackets.
Source: reddit/r/MachineLearning/AdministrativeRub484
Background: CVPR (Conference on Computer Vision and Pattern Recognition) is a premier, highly competitive academic conference where the peer review process involves authors submitting papers, receiving scores and comments from anonymous reviewers, and then writing a ârebuttalâ to address those comments. Multimodal learning is a subfield of machine learning that builds models that can process and relate information from multiple types of data, such as vision and text. In this context, âscaling verticallyâ refers to increasing a modelâs size (parameters) and training data, while âscaling horizontallyâ refers to running more evaluations or analyses on a model of a fixed size.
Discussion: The community discussion reveals a mix of sympathy and practical advice, with a consensus that the authorâs rebuttal tone may have been a critical factor in the score drop. Several commenters suggested that the author should have diplomatically addressed reviewer requests by contextualizing comparisons to show efficiency trade-offs, rather than refusing them outright. Others noted that modern ML research often resembles an engineering competition, but that navigating reviewer dynamics with politeness is a necessary skill for publication success.
Tags: #machine-learning-research, #academic-publishing, #peer-review, #multimodal-learning, #research-culture
Wave Field LLM â O(n log n) attention via wave equation dynamics âď¸ 7.0/10
A researcher introduced Wave Field LLM, a novel language model architecture that replaces the standard O(n²) self-attention mechanism with a system based on damped wave equations, achieving O(n log n) computational complexity. In small-scale tests on WikiText-2 with 6 million parameters, it achieved a perplexity of 6.2 and 50.5% accuracy, which is comparable to a standard transformerâs 5.9 perplexity and 51.0% accuracy. This matters because it presents a fundamentally different, physics-inspired approach to building language models that could dramatically reduce the computational cost of processing long sequences, with projected savings of 31x at 2K tokens and 367x at 32K tokens. If it scales successfully, it could enable more efficient and affordable training and inference for large language models, challenging the current dominance of the transformer architecture. A key technical detail is that the model treats tokens as a continuous 1D field where information propagates via a learnable damped wave kernel, k(t) = exp(-ι¡t)¡cos(Ď¡t + Ď), with each attention head having only three physics parameters. A known limitation is a significant performance gap when using a standard BPE tokenizer instead of a character tokenizer at small scale, which the creator attributes to a model capacity issue they are testing by scaling to 100 million parameters.
Source: reddit/r/LocalLLaMA/Murky-Sign37
Background: Standard transformer models use a self-attention mechanism that has a computational complexity of O(n²), meaning the time and memory required grow quadratically with the sequence length, which is a major bottleneck for processing long documents. The Fast Fourier Transform (FFT) is an algorithm that can compute convolutions in O(n log n) time, which is significantly faster for long sequences, and it is used in other efficient alternatives to self-attention. Damped wave equations describe oscillating systems where the amplitude of the waves decreases over time, and in this model, they are used to govern how information (like linguistic features) propagates through the sequence.
Discussion: The community reaction is a mix of cautious interest and constructive skepticism, with experienced researchers noting that testing at 6 million parameters is too small to judge viability, suggesting the need to scale to at least 100M-1B parameters to see if it outperforms attention. Several commenters questioned technical specifics, such as whether it is truly an âattentionâ mechanism without pairwise token interactions, its compatibility with existing transformer weights, and what exactly the reported âsavingsâ refer to (time or memory). Overall, there is genuine curiosity about the novel physics-based approach, with requests for checkpoints and encouragement to continue the research.
Tags: #attention-mechanisms, #transformer-alternatives, #efficient-ai, #language-modeling, #computational-complexity
How I use Claude Code: Separation of planning and execution âď¸ 6.0/10
A developer published a blog post detailing a specific workflow for using the Claude Code AI assistant that explicitly separates the coding process into distinct planning and execution phases. The author claims this approach, which involves creating detailed documentation and specifications before any code generation, is âradically differentâ from typical usage patterns. This matters because it addresses a common frustration with AI coding assistantsâtheir tendency to produce incorrect or structurally flawed code when given vague instructions. By formalizing a two-phase workflow, the post offers a structured method to improve output quality and reliability, which is a key challenge for developers integrating these tools into their daily work. A key technical detail is the authorâs emphasis on using specific language like âdeeplyâ and âin great detailâ in prompts to force the LLM to perform a more thorough analysis of existing code. However, a notable caveat from the community discussion is that this approach can be brittle, as an imperfect initial plan may require starting the entire process over if execution fails.
Source: hackernews/vinhnx
Background: Claude Code is an AI-powered coding assistant developed by Anthropic that helps developers with tasks like code analysis, editing, and generation. AI-assisted software development uses large language models (LLMs) to augment the software development lifecycle, but these models can be unreliable and produce errors if not guided carefully. The concept of separating planning from execution is an emerging pattern in AI coding tools, with other platforms like Cursor and Windsurf also introducing similar âplanning modesâ to improve results.
Discussion: The community reaction was mixed, with some commenters agreeing they had evolved similar workflows naturally, while others critiqued the approach. Key viewpoints included that the method is not revolutionary but rather a common adaptation to treating LLMs like âunreliable interns,â skepticism about the authorâs understanding of LLM mechanics regarding prompt wording, and suggestions for more iterative, layered modifications to the planning process.
Tags: #ai-coding, #workflow, #claude-code, #developer-tools
they have Karpathy, we are doomed ;) âď¸ 6.0/10
A Reddit discussion with high engagement (1298 score, 96% upvote ratio) critically examined the practical utility and hype surrounding the OpenClaw AI assistant project, with users sharing their experiences setting it up and questioning its real-world applications. The conversation also noted the unexpected participation of prominent AI researcher Andrej Karpathy in the community thread. This discussion matters because it represents a community-driven reality check for a highly-promoted AI project, highlighting the gap between technological hype and practical, everyday utility for end-users. The scrutiny helps potential adopters understand the current limitations of agentic AI assistants and sets expectations for what such systems can actually accomplish outside of controlled demos. A key technical detail noted is that OpenClawâs primary innovation is its integration with existing messaging platforms like Telegram and iMessage, allowing users to communicate with their AI assistant through familiar apps. However, commenters also describe the project as a âcomplete messâ in other aspects and raise specific, unresolved questions about its requirement for a Mac mini and the status of a promised Rust-based version.
Source: reddit/r/LocalLLaMA/jacek2023
Background: OpenClaw is a personal AI assistant designed to run locally on a userâs own hardware, which is part of the broader LocalLLM (Local Large Language Model) movement. LocalLLMs operate on-device to ensure data privacy, eliminate cloud costs, and give users full control, contrasting with cloud-based AI services. The âhype cycleâ is a concept, popularized by Gartner, that describes the typical pattern of excitement, disillusionment, and eventual productivity that new technologies often follow.
Discussion: The community sentiment is mixed, ranging from skepticism about OpenClawâs overhyped and âastroturfedâ nature to cautious optimism recognizing it as a pioneering, if rough-edged, project. Key viewpoints include confusion over its practical use cases, debates about its security and privacy model, and specific technical critiques about its implementation and hardware requirements. Some users also expressed amusement and surprise at Andrej Karpathyâs apparent involvement in the discussion.
Tags: #AI-Assistants, #OpenClaw, #Community-Discussion, #LocalLLM, #Hype-Cycle
CXMT has been offering DDR4 chips at about half the prevailing market rate âď¸ 6.0/10
Chinese semiconductor manufacturer Changxin Memory Technologies (CXMT) is currently selling DDR4 memory chips at approximately half the prevailing market rate, according to a report from The Korea Herald. This aggressive pricing could significantly reduce hardware costs for systems that still utilize DDR4, including some AI/ML training and inference platforms, and represents a notable market intervention by a major Chinese memory producer. A key caveat is that CXMT announced in 2024 it was shifting entirely to DDR5 production, suggesting this discounted DDR4 may be surplus stock, with the company expected to shut down DDR4 production entirely by 2026.
Source: reddit/r/LocalLLaMA/johnnyApplePRNG
Background: DDR4 is a generation of dynamic random-access memory (DRAM) that has been the industry standard for computers and servers, succeeding DDR3. CXMT is a key Chinese company in the global semiconductor memory market, part of Chinaâs strategy to achieve self-sufficiency in chip production. AI and machine learning systems have substantial memory requirements, and while newer DDR5 and upcoming DDR6 offer advantages, many existing systems are built around DDR4.
Discussion: Community comments note that CXMT is likely selling surplus DDR4 stock as it shifts to DDR5, with some users speculating this could temporarily benefit older hardware platforms. Other comments humorously reference a perceived memory shortage highlighted by Sam Altman and question the practical availability of these chips for purchase outside of China.
Tags: #hardware, #memory, #semiconductors, #AI-hardware, #supply-chain
Open Source
PSA: The software âShadeâ is a fraudulent, plagiarized copy of Heretic âď¸ 6.0/10
A GitHub repository named âShadeâ (https://github.com/assemsabry/shade) was published and aggressively promoted as original software, but it was exposed as a near-exact, plagiarized copy of the open-source project Heretic (https://github.com/p-e-w/heretic) with only the project name and copyright notice changed. The author of Shade deleted critical issues and attempted to obscure the plagiarism by adding minor features with an AI agent, while erasing Hereticâs commit history and contributor credits. This incident is significant because it represents a direct violation of open-source licensing and ethics, attempting to steal credit for another developerâs work and potentially misleading users who might download a malicious copy. It also highlights a recurring risk in the open-source ecosystem where popular projects become targets for bad actors seeking to profit from their popularity, either for resume padding or to distribute malware. A source code comparison shows the Shade repository files are approximately 95% identical to Hereticâs, with the primary alteration being the replacement of the copyright notice to claim original authorship. The repository ownerâs actions, including deleting issues and the lack of an âInitial Commitâ in the history, are technical red flags that corroborate the plagiarism claims.
Source: reddit/r/LocalLLaMA/-p-e-w-
Background: Heretic is an open-source Python tool designed to automatically remove âsafety alignmentâ or censorship from transformer-based language models, using techniques like directional ablation and parameter optimization. The project recently gained significant popularity, reaching the #1 spot on GitHubâs trending chart, which often attracts attention from both legitimate users and malicious actors. Open-source projects are typically licensed (like MIT) to allow use and modification, but they require proper attribution to the original authors, which Shade failed to provide.
Discussion: The community strongly condemned the plagiarism, with users sharing evidence like screenshot comparisons and encouraging others to report the Shade repository to GitHub for violating the original license. Sentiment ranged from viewing it as a case of âfake it until you make itâ intellectual property theft for resume building to serious concern that the software could contain backdoors or malware, with many expressing outrage over the audacious attempt to claim full credit.
Tags: #open-source, #plagiarism, #software-ethics, #github, #community-moderation