Machine learning
Source reports covering Machine learning. Stories will appear when analysis and community reactions are ready.
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Source reports
Qwen 3.8-Flash-Next releasing tomorrow (125B a6B)
tool
1 min read
ModelScope is a machine-learning model community that aggregates advanced models from multiple fields. The platform provides tools for exploring models, trying inference, training, deployment, and application development in one service. Users can discover, learn about, customize, and share machine-learning models through ModelScope. The service also supports collaborative development of an open-source model community.
Seed: Minimal, self-modifying agent harness
tool
2 min read
Seed is an open-source agent starter that gives a language model a single exec tool for running shell commands. Its system prompt is loaded from self/SELF.md, and the agent can edit the self/ directory to preserve tools, notes, and behavior across otherwise ephemeral sessions. Rather than receiving a prebuilt framework’s memory, skills, tools, and conventions, each agent develops them over time by writing files into that directory. Running uvx --from git+https://github.com/vivekhaldar/seed.git seed in a new directory copies seed.py and run_seed.sh without overwriting existing files, creates self/SELF.md, initializes a Git repository, and opens a REPL. Each directory becomes a distinct agent whose retained files and Git history reflect its own interactions. The local runner can reopen an existing agent and optionally select a model, including gemini-2.5-pro. Seed records verbatim session transcripts in self/sessions/*.json after every turn, but does not automatically load those transcripts at startup. Model access is managed by Simon Willison’s llm library; the documented default is openai-codex/gpt-5.6-sol using a ChatGPT login through the Codex CLI. Supported providers include OpenAI, Anthropic, Gemini, and OpenRouter.
Google buys crashed airline Spirit's data at auction, because AI
news
1 min read
Google bought more than 100 million emails, 30 million recorded phone calls, and data from Microsoft Teams, Oracle, and SAP for $10 million at an auction involving crashed airline Spirit. The purchase includes a large collection of airline-related communications and enterprise-system data. The source text does not specify how Google plans to use the data or identify the auction date.
Nvidia's Risky Business
opinion
19 min read
Nvidia is navigating significant risks as it expands its business in the AI sector. The company's investments and market strategies are critical to its future growth amidst competitive pressures and technological advancements.
DeepMind's WeatherNext model achieves breakthrough forecasting cyclones
tool
6 min read
WeatherNext is an AI model that provides accurate cyclone forecasts, offering an additional day of warning. The model has been open-sourced to enhance forecasting capabilities for tropical cyclones, which cause significant global fatalities and economic losses.
Building an Advanced Agentic Harness
tool
1 min read
An advanced agentic harness is being developed to enhance AI capabilities for various applications. This harness aims to improve decision-making and task execution efficiency in AI systems.
Why Large Language Models Fail at Tabular Prediction
research
2 min read
Large language models (LLMs) struggle with predictive analytics over tabular data, which is a common machine learning workload. This limitation has led to the emergence of tabular foundation models aimed at addressing this gap.
Harness Engineering for Self-Improvement
research
32 min read
Recursive self-improvement (RSI) allows an AI to enhance its own cognitive capabilities, potentially surpassing human intelligence. This concept, first defined by I. J. Good in 1965, involves a feedback loop where an AI uses its intelligence to improve the mechanisms that generate its intelligence.
The Computational Theory of Mind (2015)
research
106 min read
The Computational Theory of Mind (CTM) posits that the mind functions as a computational system capable of reasoning, decision-making, problem-solving, perception, and linguistic comprehension. Advances in computing technology support the idea that machines can emulate these mental processes.
Gemini Robotics 2 brings whole body intelligence to robots
news
9 min read
Gemini Robotics 2 enables robots to achieve whole-body intelligence, allowing for intelligent control, fine dexterity, and teamwork in completing complex tasks. This advancement moves beyond traditional pre-programmed or teleoperated robots, enhancing their ability to learn and adapt in dynamic environments.
Open Weights and American AI Leadership [pdf]
research
1 min read
Open Weights advocates for the importance of open-source AI models to enhance innovation and competitiveness in the U.S. AI landscape. The document outlines strategies for maintaining American leadership in AI through collaboration and accessibility of AI resources.
The Little Book of Reinforcement Learning
tool
1 min read
The Little Book of Reinforcement Learning provides a concise introduction to reinforcement learning concepts and algorithms. The associated GitHub repository includes the book, Pytorch-based implementations of various algorithms in the algos/ folder, and detailed explanations in the supplementary/ folder.
Mathematics of Data Science
research
1 min read
The book "Mathematics of Data Science" covers the mathematical foundations essential for data science applications. Key topics include high-dimensional analysis, singular value decomposition, principal component analysis, linear regression, clustering, and dimension reduction techniques.
Detecting LLM-Generated Texts with “Classical” Machine Learning
research
11 min read
Mainstream LLM-generated text can be distinguished from human-written content using traditional machine learning models due to strong statistical patterns. Many AI plagiarism checkers likely utilize these detection methods.
How to Train a Gen AI Kick Drum Model on Your Old Linux Desktop with 6GB VRAM
tool
1 min read
Zhinit has developed a kick drum diffusion model that utilizes advanced training techniques to enhance audio generation. The model aims to produce high-quality kick drum sounds for music production.
The LLM Critics Are Right. I Use LLMs Anyway
opinion
18 min read
Many users acknowledge the criticisms of large language models (LLMs) yet continue to utilize them extensively. The Local-First Conf in Berlin showcased discussions on the complexities and dissonance surrounding LLM usage, with notable speakers like Armin Ronacher contributing to the conversation.
Running Gemma 4 26B at 5 tokens/sec on a 13-year-old Xeon with no GPU
tool
10 min read
Gemma 4, a 26-billion-parameter open-weights mixture-of-experts model, runs on a 13-year-old Xeon server without a GPU at a speed of approximately five tokens per second. The server is a repurposed HP StoreVirtual storage box originally designed for disk storage.
What will be left for us to work on?
opinion
38 min read
A keynote titled “What will be left for us to work on?” was delivered at the International Conference on Machine Learning in Seoul, addressing concerns about adapting to increasing AI capabilities. Slides from the presentation, along with an annotated transcript, are available for viewing online.
Is x86 ready to ACE it?
research
17 min read
CPU designs must evolve to accommodate changing workloads, sometimes by extending the instruction set. Intel's AMX extension accelerates matrix multiplication for machine learning by offering 2D tile registers and configuration registers, allowing programmers to configure specialized execution units to target matrix data.
The Economics of Recursive Self-Improvement [pdf]
research
1 min read
Recursive self-improvement in AI refers to systems that enhance their own capabilities through iterative learning and optimization. This concept presents potential economic implications, including shifts in labor markets and changes in productivity dynamics.
The early History of the Singular Value Decomposition (1993) [pdf]
research
1 min read
The Singular Value Decomposition (SVD) is a mathematical technique used in linear algebra for decomposing a matrix into singular values and vectors. It has applications in various fields, including statistics, signal processing, and machine learning.
What to learn to be a graphics programmer
tool
7 min read
To become a hireable graphics programmer, knowledge in real-time rendering techniques, shader programming, and graphics APIs is essential. Current trends suggest a potential shift away from machine learning hype towards other technologies like quantum computing in the coming years.
TabFM: A zero-shot foundation model for tabular data
research
4 min read
TabFM is a new foundation model designed for tabular data, simplifying classification and regression workflows. It utilizes a "zero-shot" logic approach, similar to that of TimesFM, to enhance the handling of enterprise data infrastructure.
Sakana Fugu
research
4 min read
Sakana Fugu is a multi-agent system that autonomously enhances a small GPT's training recipe using AutoResearch, which iteratively edits training code and conducts experiments. The AI agent completed 123 experiments over approximately 14 hours on a single H100 GPU, tracking improvements in validation bits-per-byte (BPB).
Petition against Meta's employee training data collection for ML models
news
1 min read
A petition has been launched against Meta's collection of employee training data for machine learning models. The petition raises concerns about privacy and consent related to the use of personal data in training AI systems.
AI Compute Extensions (ACE) Specification
tool
1 min read
The AI Compute Extensions (ACE) specification introduces x86 extensions designed to accelerate computation tasks, particularly for matrix multiplication kernels and reduced precision data formats relevant to machine learning workloads. ACE defines new matrix multiplication primitives that enhance AVX and scalar code with features like ACE register state, data processing operations utilizing AVX input, and data move operations for managing tile register state.
Notes on DeepSeek
tool
1 min read
Posted by vinhnx. Score: 86 points. Comments: 63.
Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
research
13 min read
Ultrafast inference and online learning can be achieved using hardware architectures designed for Kolmogorov-Arnold Networks (KAN) on FPGAs. The research focuses on optimizing these architectures to enhance machine learning performance.
Do agents.md files help coding agents?
news
1 min read
Sebastian Raschka@rasbthttp://x.com/i/article/2063647807437705216…午前1:47 · 2026年6月8日391239361,38039件の返信を読む
The Smallest Brain You Can Build: A Perceptron in Python
tool
9 min read
A perceptron is the simplest form of a neural network, processing one input to produce a binary output. It can be built from scratch in Python using basic components like a weight, a bias, and a loop, allowing for live learning demonstrations in a browser without complex mathematics or libraries.
Do transformers need three projections? Systematic study of QKV variants
research
2 min read
Transformers utilize a query, key, and value (QKV) attention formulation that is crucial for AI tasks. The study investigates the individual contributions of these three projections and the effects of omitting any of them.
A 10 year old Xeon is all you need
tool
15 min read
Gemma 4’s MTP drafters can be quantized and verified on older hardware, specifically a recycled server with 128 GB of DDR3 RAM and an Intel Xeon E5-2620 v4 CPU from 2016. Despite the server's lower performance compared to modern laptops, it is capable of running complex AI tasks.
Google says criminal hackers used AI to find a major software flaw
news
1 min read
Criminal hackers utilized artificial intelligence to identify a previously unknown software flaw, marking the first instance of AI being used in this manner. Google reported that this attempted cyberattack indicates potential future threats in cybersecurity.
Learning the Integral of a Diffusion Model
research
83 min read
Sampling from a diffusion model involves an iterative process where a denoiser estimates the tangent direction to a path through input space. Neural networks can be trained to directly predict the integral that transforms samples from a simple noise distribution into samples from a target distribution.
Refusal in Language Models Is Mediated by a Single Direction
research
2 min read
Conversational large language models are fine-tuned for instruction-following and safety, allowing them to comply with benign requests while refusing harmful ones. Research indicates that the refusal behavior in these models is mediated by a single directional mechanism.
There Will Be a Scientific Theory of Deep Learning
research
2 min read
A scientific theory of deep learning is emerging that characterizes key properties and statistics related to the training process, hidden representations, final weights, and performance of neural networks. The research consolidates various ongoing studies in deep learning theory.
ML supports existence of unrecognized transient astronomical phenomena
research
2 min read
Machine learning techniques have identified previously unrecognized transient astronomical phenomena in historical observatory images. These phenomena consist of transient, star-like point sources that appeared and disappeared over short timescales before the launch of Sputnik.
TorchTPU: Running PyTorch Natively on TPUs at Google Scale
tool
7 min read
TorchTPU enables running PyTorch natively on Google’s Tensor Processing Units (TPUs), enhancing performance and hardware portability for large-scale machine learning models. It addresses the challenges of distributed systems by supporting clusters of up to 100,000 chips.
The eighth-generation TPU: An architecture deep dive
tool
1 min read
TPU 8t is designed for frontier-model training, while TPU 8i focuses on large-scale inference and reinforcement learning. Both are engineered with system-level co-design to enhance the AI lifecycle.
The future of everything is lies, I guess: Where do we go from here?
opinion
6 min read
The discussion centers on the implications of machine learning and its applications, such as code generation by large language models (LLMs) and melody transformation by Suno. The content emphasizes that the focus is not on the speed or convenience of these technologies, likening it to the common understanding of cars.
The future of everything is lies, I guess: Work
opinion
12 min read
Software development may increasingly resemble witchcraft rather than traditional engineering. The rise of AI coworkers raises concerns about the robustness of systems, as automation can complicate new domains.
The Future of Everything Is Lies, I Guess: Safety
opinion
20 min read
New machine learning systems pose risks to psychological and physical safety. The belief that ML companies will align AI with human interests is considered naïve, as the creation of "friendly" models has facilitated the development of potentially harmful ones.
Sam Altman's Coworkers Say He Can Barely Code and Misunderstands Basic Concepts
news
2 min read
Sam Altman, CEO of OpenAI, is reported by coworkers to have limited coding skills and a misunderstanding of basic machine learning concepts. Despite his public persona as a leading figure in AI, internal insights suggest a more complex and less competent technical background.
The case for zero-error horizons in trustworthy LLMs
research
2 min read
Zero-Error Horizon (ZEH) is proposed as a metric for evaluating the maximum range of error-free performance in large language models (LLMs). An evaluation of GPT-5.2's ZEH reveals significant insights into its limitations, including its inability to accurately count to five.
The ladder is missing rungs – Engineering Progression When AI Ate the Middle
opinion
29 min read
AI is achieving high levels of proficiency in tasks traditionally performed by early-career engineers, which were essential for developing judgment and intuition. The absence of these foundational experiences may hinder the growth of future engineers who would build and supervise advanced AI systems.
There is no spoon – A software engineers primer for demystified ML
tool
4 min read
A machine learning primer called "thereisnospoon" is available on GitHub, designed for engineers to understand ML systems using principles similar to software engineering. It focuses on helping engineers develop an intuitive grasp of machine learning concepts and trade-offs.
Attention Residuals
tool
3 min read
Attention Residuals (AttnRes) serves as a drop-in replacement for standard residual connections in Transformers, allowing each layer to selectively aggregate earlier representations. It includes two variants: Full AttnRes, where each layer attends over all previous outputs, and Block AttnRes, which groups layers into blocks to reduce memory usage from O(Ld) to O(Nd).
A Visual Introduction to Machine Learning (2015)
tool
7 min read
Machine learning employs statistical techniques to automatically identify patterns in data, enabling accurate predictions. A model can be created using a dataset about homes to differentiate between homes in New York and those in San Francisco.
Billion-Parameter Theories
research
10 min read
Billion-parameter theories aim to explain complex phenomena in the universe using concise mathematical formulations. Historical explanations of natural events transitioned from mystical interpretations to scientific inquiry with succinct equations like F=ma and E=mc².
We might all be AI engineers now
opinion
4 min read
Writing agents and tools for AI systems enables enhanced problem-solving and architecture decisions. The integration of AI allows for more efficient workflows, with AI handling heavy lifting while the user focuses on strategic thinking.