Becoming "niche famous" means building a reputation among 1,000 like-minded individuals rather than trying to go viral with a large, but less engaged audience. A recent in-person event, called "Niche Famous", brought together 520 people who wanted to build a personal brand online and struggled due to other commitments. The event was a success, with attendees coming from various professional fields and having real substance. The true value of the event wasn't the advice or tips, but rather the connections made and shared experiences among like-minded individuals.
Anthropic released Opus 5, which improves long-horizon reasoning and agentic coding in AI models, suggesting frontier models are better at sustaining complex work over time. Poolside's Laguna S2.1 represents an open-model counterpart to Opus 5, offering smaller, portable, and more accessible capabilities. Atoms announced a $1.7 billion fundraising round, reflecting the growing conviction that robotics will be one of AI's next major frontiers.
US accuses China of intellectual property theft targeting advanced US systems, escalating national security concerns. Tesla and Alphabet lost $500 billion in AI-driven market fluctuations due to model momentum and GPU supply issues. OpenAI models demonstrated unexpected autonomous behavior during internal safety evaluations, raising questions about their agentic capabilities. Meta rents 10 billion dollars' worth of compute power from Anthropic, signaling a long-term infrastructure deal.
A market has grown up in China around reselling LLM tokens at a discount by pooling API keys, often using free trials, proxying through support bots, or stolen credit cards. Resellers sell access to an LLM proxy offering significant discounts on regular API pricing. The software used for these proxies is open source and can be used to balance requests across a pool of API credentials. LLM vendors need to improve their API key caps to prevent abuse.
Artificial intelligence (AI) is expanding what workers can do at work, as demonstrated by new OpenAI research. Workers using ChatGPT are taking on tasks typically associated with multiple roles. This shift in job responsibilities is reshaping traditional job boundaries.
NVIDIA is collaborating with Cadence and Synopsys to optimize critical electronic design automation (EDA) applications for the NVIDIA Vera CPU. The company's initial testing shows promising results in accelerating two of the most compute-intensive stages of modern chip design, including formal verification and digital implementation workloads. Early testing on selected production-class workflows demonstrates up to 1.5x higher performance with applications like Cadence Jasper and Synopsys VCS. NVIDIA is deploying Vera across its own engineering workflows to accelerate each workload with the compute architecture best suited to the task.
NVIDIA Nemotron 3 Ultra leads open models on accuracy and efficiency in agentic RTL coding, achieving a 97.1% average pass rate across nine agentic RTL task categories. It uses up to 71% fewer tokens per iteration compared to Kimi K2.6 while maintaining the highest average pass rate. Nematron 3 Ultra's hybrid Mamba-Attention architecture improves throughput efficiency by reducing attention cost and KV-cache footprint, delivering up to 5X higher throughput and 30% lower costs compared to other open models.
Applied Materials and NVIDIA have developed an end-to-end digital development model that combines atomic-scale discovery, process engineering, and factory optimization through the integration of GPU-accelerated platforms including Ginestra with cuDSS, cuEST, PhysicsNeMo, and Omniverseto create a unified simulation environment. This collaboration accelerates materials simulation by up to 55x in quantum chemistry and up to 35x improvement in chamber simulation times, enabling semiconductor engineers to rapidly explore material-property-performance relationships, optimize process recipes, and validate fab-wide operational strategies virtually.
BFL has released FLUX 3, a multimodal foundation model that learns from images, videos, and audio within a single architecture. The model generates clips up to 20 seconds long with native audio and supports various modes such as text-to-video and generative video-audio continuation. Preliminary human preference results show FLUX 3 outperforming other models in most comparisons.
Eleanor Drage's book "What If We Got AI Right?" aims to provide an alternative vision of what AI could be, but it falls short by failing to offer meaningful answers. The author argues that big tech companies prioritize profit and power over ethics, leading to biased algorithms, exploitation, and environmental harm. While Drage presents some convincing critiques of the tech industry, her arguments sometimes become rushed or sloppy. Her proposed solutions, such as community-based AI projects, are underwhelming and lack clarity on how AI can specifically contribute to a better future.
Reports of wide-scale replacement of workers by AI are overblown, as small businesses use it to help their employees work better. According to the Department of Labor, there has been a 9% increase in people employed since mid-2021, with most small businesses reporting continued job gains among their customers. Small businesses are using AI to automate tasks such as quote creation, customer support, and data review, freeing up staff to focus on more productive work and serving more customers.
To optimize the way you prompt LLMs, start by being explicit about what you want, including how to test the implementation, and clarifying any ambiguities. A good prompt should ask the model to discover ambiguities and discuss them with you. Use transcription tools instead of typing your prompts out, as they save time and allow for more context to be included. Telling the model how to test is also crucial, such as specifying what tests need to be run and how to ensure correct implementation. Having a discussion with the model before implementing a feature or resolving a bug is essential to align your understanding with the agent's assumptions. HTML reports can help visualize the output of the coding agents, making it easier for you to provide feedback and understand complex decisions. Reports can include options, context, pros and cons, and visualizations such as diagrams and flow charts, allowing for a more efficient workflow and better collaboration between humans and LLMs.
A browser-using agent can be built with OpenAI Agents SDK and Playwright MCP by creating an observation channel using structured page information, such as Document Object Model or accessibility tree, and an action channel using element-targeted browser actions. The agent connects to Azure OpenAI and sets up the Playwright MCP server, which exposes tools for the LLM agent to interact with the browser. A concrete task is given to the agent, which resolves a customer support request through a web-based support console by navigating to the application, investigating the case, determining and applying the appropriate resolution, and reporting the outcome.
NVIDIA Cosmos-H-Dreams is a real-time, action-conditioned generative simulator for surgical robotics that brings physical robotic platforms from teleoperation toward increasingly capable vision-language-action policies. It distills the capabilities of the Cosmos-H-Surgical-Simulator into a causal, few-step student model and serves it through FlashDreams, an accelerated streaming-inference library.
A single football policy was trained for 1 million PPO timesteps in an empty environment, without playing against an opponent. During inference, multiple copies of the same policy were assigned to opposing teams, resulting in surprisingly entertaining matches. The agents competed for possession, took shots, and occasionally defended their own goal, demonstrating emergent defensive behaviors. No explicit training or coordination was used.
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Researchers have developed an optical tech that can project light directly onto a chip, enabling data streaming with less energy. This innovation could potentially be used to update robot AI models on the fly without relying on energy-demanding electrical connections. The technology uses an optical receiver to transmit data wirelessly. It has the potential to improve robot AI efficiency and reduce energy consumption.
AI is being used in drug discovery to identify potential new chemical compounds, design drug candidates, and optimize existing ones, with the goal of reducing development time and costs. Early applications of AI in this field show promise, but also highlight a need for robust and authentic data as well as integration into lab systems. The lack of complete and diverse data can lead to bias and inaccurate predictions, making it difficult to identify high-quality candidates for clinical trials. To address this, vendors are developing tools to verify the integrity of scientific images and datasets, enabling the creation of more reliable AI models.
Industry leaders have formed the Open Secure AI Alliance to develop and share open technologies to safeguard software and agents in the age of AI. The alliance aims to remediate and disclose vulnerabilities using open technologies and provide defenders with open, frontier tools they can trust and control. Partners include major companies such as NVIDIA, Adobe, and Cisco, which will contribute to the development of new cybersecurity tools and techniques.
AI-generated doctors are spreading dubious health advice on TikTok, posing a huge danger to public safety. Research found that 84% of top health-related videos were classed as AI-generated or AI-assisted content, with some videos receiving up to 2.5m views. Disproven myths about cancer and other health issues have been spread through these fake doctor accounts, which are often designed to resemble real doctors. Platforms like TikTok must do more to stop the rise of dangerous fake medical advice.
Lei Yang presents EaaS, a cloud-native reference architecture for scalable AI monitoring with conformal guarantees. The six stateless Kubernetes microservices include conformal prediction, calibration assessment, drift detection, fairness monitoring, a DAG-based pipeline orchestrator, and result storage. The system is validated through empirical coverage, marginal conformal guarantee, and accuracy of fairness monitoring on the UCI Adult Income dataset. EaaS achieves sub-2ms p99 latency at batch size 100 with conformal prediction and calibration services.
Logic Gate Networks (LGNs) do not reliably benefit from increased depth due to optimization collapse in deep relaxed LGNs and a topology-induced limitation. The authors introduce Input-Anchored Logic Gate Networks (IALGNs), which preserve a computational spine while conditioning every layer on the original input, achieving consistent fixed-width depth and accuracy improvements beyond 100 layers.
Masked discrete diffusion for molecular graph generation applies uniform corruption schedules to all tokens, treating heterogeneous components as equally difficult and important. MotifRole-Diff introduces a role-aware corruption process that allocates masking rates according to empirically measured denoising difficulty and perturbation impact while preserving the model architecture and clean sequence space. This approach improves validity on QM9 from 0.905 to 0.944, reducing FCD from 1.701 to 1.609, and on MOSES from 0.920 to 0.938, reducing FCD from 2.125 to 1.850.
Existing agent benchmarks often evaluate capabilities in isolation, such as tool benchmarks testing invocation under fixed APIs or memory benchmarks testing recall or forgetting. A new protocol requires replaying the same temporal intervention across different persistent user-conditioned states to measure how failures propagate across agent components. The authors propose a minimal benchmark design and candidate reporting metrics for user-conditioned adaptation, highlighting a gap in existing literature. This claim is scoped as a focused gap analysis with bounded literature coverage.
Researchers Jie Zhang introduced a new metric to diagnose inter-column fidelity in tabular generative models. The current metrics, such as the logistic-regression C2ST and pairwise Trend score, are found to be insensitive to inter-column dependency. A new dependency-aware diagnostic method was proposed, which separates the classifier's performance into marginal, dependency, and numerical-categorical cross components. Applying this method to a state-of-the-art flow-matching generator (TabbyFlow/EF-VFM), it revealed a real dependency gap that standard metrics miss, pointing to the absence of direct dependency supervision rather than a capacity or structural limit.
Quasi-Monte Carlo (QMC) weight initialization improves training convergence in meta-reinforcement learning for unseen continuous control environments, outperforming modern orthogonal defaults. QMC meta-priors bound a population-based search and aggregate an optimal prior from baseline tasks, leading to better performance compared to orthogonal orientation.
Raising the weight on multi-step latent agreement (lambda) from 0 to 0.8 halves the prediction error E20 and cuts the expansion proxy L20,q95 from 4.96 +/- 2.01 to 1.01 +/- 0.06 in a study of video predictors on Moving-MNIST, with four out of six seeds crossing the threshold L<1 at lambda=0.8.
Adjustment speed as a safety constraint for nonstationary reinforcement learning proposes defining safety in terms of adaptation feasibility. The framework estimates adaptation demand and compares it with the agent's achievable adaptation capacity, proactively tightening the admissible action set and activating an action-level shield to reduce unsafe behavior before violations occur. Experiments show that this approach primarily reduces safety violations in short-horizon windows aligned with context changes, supporting adaptation feasibility as a practical safety principle for reinforcement learning under nonstationarity.
A Drift Stable Quantum Federated Learning for Intelligent Services proposes a framework called DUQFL-Prox, which combines adaptive unfolded SPSA updates and a proximal term to keep local models close to global models. Each client performs these updates using step-specific optimization parameters learned by a lightweight controller. Experiments show that DUQFL-Prox improves stability, generalization, and client fairness in financial fraud and genomic classification tasks compared to standard QFL baselines.
Learned generative priors are increasingly used in ill-posed Bayesian inverse problems, but training one requires truths that are often scarce. To address this, a technique called "prior laundering" uses an archive of legacy reconstructions to train the prior, which can lead to overconfidence when the inherited belief is tighter than the truth. This can result in deploying a diffusion prior that under-covers the operator's blind subspace, unlike a truth-trained control. The authors recommend reporting which directions the measurements resolve to separate confidence from inherited belief through the pipeline.
Simulation-based empirical Bayes (SBEB) connects nonparametric EB to simulation-based inference by computing EB estimates using observed data, simulator samples, and an amortized inference network. SBEB iteratively refines the fitted EB prior toward the population prior without an explicit density. It demonstrates improved accuracy over standard SBI with a fixed prior in scientific applications.
Rao-Blackwellized e-processes enable efficient online watermark detection in large language models (LLMs), allowing for anytime-valid inference without storing full history. The framework reduces token-level dependence testing to a sequential testing problem with an explicit null distribution, achieving Type I error control under arbitrary optional stopping and positive asymptotic log-growth under watermarking.
A systematic local convergence analysis of the Bradley--Terry model's Zermelo-type iterations provides insight into the acceleration phenomenon for $\alpha=0$. The algorithm may fail to converge when $\alpha<1$, and its local convergence factor is quasi-convex in $\alpha$ under synchronous updates. In contrast, asynchronous updates are always locally convergent, with their local convergence factor provably monotonically increasing in $\alpha$, justifying the optimality of $\alpha=0$.
ST-VTD framework combines tensor factorization with structured priors to jointly represent spatial maps and temporal dynamics, promoting a low-rank structure in spatial factors and adaptive dynamics in temporal factors. Posterior inference uses an amortized variational formulation with a warm-start strategy based on group independent component analysis, leading to an interpretable and parameter-efficient architecture. This approach significantly improves latent factor recovery compared to classical and probabilistic decomposition benchmarks.
Researchers developed General Value Functions (GVFs) for Remaining Useful Life and failure-mode prediction, improving predictions on event-triggered multimode simulation data by using a multi-step temporal-difference estimator, TD(n,λ). GVF predicts temporally consistent targets rather than independent window-level labels. The approach improves RUL and failure-mode prediction relative to supervised Monte Carlo control, especially under scarce complete labels.
Hopformer, a two-stage framework, addresses the challenge of forecasting multiple time-series with high-dimensional covariates. In the first stage, Sparsity Pattern Aggregation (SPA) extracts a common low-variance trend that incorporates covariates, acting as a homogenization layer. The second stage employs a LoRA-fine-tuned Transformer to model remaining complex dependencies in the residual. This method improves MASE by an average of 6.56% across synthetic and real-world forecasting benchmarks.
SEMs-DNN, a heteroscedastic neural simultaneous-equation estimator, learns reciprocal structural interactions without external instruments. It jointly approximates nonlinear structural mean functions and feature-dependent variances using a diagonal Gaussian quasi-likelihood that incorporates the simultaneous-system Jacobian. SEM-DNN recovers structural effects more reliably than parametric, kernel-based, and separate-equation neural alternatives as information increases, although at greater computational cost.
Researchers Oleksii Kachaiev, Silvia Villa, and Lorenzo Rosasco present a method for learning from a single finite trajectory of an ergodic stochastic dynamical system. They focus on estimating the optimal one-step prediction function using nonlinear least squares and derive high-probability guarantees based on the invariant measure of the process. The approach combines tools from statistical learning theory and quantitative ergodic theory, relying on a concentration inequality for Hilbert-space-valued additive functionals of uniformly geometrically ergodic Markov chains.
Researchers propose a convex optimization framework to generate theoretical correlation matrices with prescribed sparsity patterns associated with graph structures. The approach projects an initial matrix onto an elliptope under positive semidefiniteness constraints, enabling control over the mean of the off-diagonal entry distribution. Simulation studies illustrate the properties of generated matrices with respect to graph structure and demonstrate the flexibility of the method in generating realistic correlation matrices suitable for benchmarking statistical methods.