- TradExpert is a new AI framework that uses a Mixture of Experts (MoE) approach with specialized LLMs to analyze diverse financial data for quantitative trading.

- It integrates insights from four expert LLMs, each focusing on different data sources (news, market, alpha factors, fundamental data), which are then synthesized by a General Expert LLM for predictions or decisions.

- TradExpert supports both stock movement prediction and quantitative stock trading through distinct modes and has demonstrated superior performance across trading scenarios on a new large-scale financial dataset.

- ReasoningGym (RG) is a new RL library featuring over 100 reasoning environments across diverse domains like algebra, logic, and games.

- Unlike static datasets, RG's key innovation is its procedural generation, providing virtually infinite training data with adjustable complexity.

- This dynamic data generation allows for continuous evaluation and effective reinforcement learning of reasoning models across varying difficulty levels.

- PhoneAgent is an AI agent built during an OpenAI hackathon that uses OpenAI's gpt-4.1 model to interact with an iPhone across multiple apps, similar to a human user.

- It leverages Xcode's UI testing harness (without jailbreaking) to inspect and interact with app accessibility trees, enabling actions like tapping, typing, swiping, scrolling, and opening apps.

- The agent supports text and voice commands, including an "Always On" mode for hands-free prompts, and can perform multi-step tasks like sending a selfie with a haiku or ordering an Uber.

- Reversible computing, which theoretically eliminates energy waste by avoiding data deletion, is emerging as a critical solution to the fundamental physical limits faced by conventional chip scaling.

- This approach, revived after decades, leverages "uncomputation" to manage memory efficiently and offers significant energy savings, potentially orders of magnitude, particularly for energy-intensive AI workloads.

- The key insight for AI is that running more reversible chips slowly can be more energy-efficient than fewer conventional ones, potentially reducing cooling needs and allowing for denser chip stacking, which is now attracting investor interest.

- AI tools, like older computer sequencers, are democratizing creation by lowering technical barriers across various domains (writing, coding, design).

- The article argues that as technical skill becomes less of a differentiator, "judgement"—the ability to make meaningful choices, evaluate quality, and understand context—emerges as the critical skill in the AI era.

- Future professional value will increasingly reside in strategic decision-making, problem framing, and guiding AI tools rather than manual execution.

- RenderFormer is a novel neural rendering pipeline that directly renders images from triangle meshes with global illumination, eliminating the need for per-scene training or fine-tuning.

- It re-conceptualizes rendering as a sequence-to-sequence transformation using a two-stage transformer architecture, bypassing traditional rasterization or ray tracing methods.

- The system demonstrates impressive capabilities in handling complex scenes, various lighting conditions, and even animations and physical simulations, showcasing its versatility for real-time and production rendering.

- The author highlights the common pitfall of getting overly invested in a problem, losing sight of the original goal, often requiring an external forced break to regain clarity.

- There's an inherent tension between problem-solving tenacity (a good engineering trait) and the need to periodically step back to re-evaluate if the current approach is optimal.

- A proposed solution involves ritualized breaks at natural time boundaries (hourly, daily, weekly, yearly) to reflect on "what am I doing?", "why?", and "what could I be doing instead?", acting as an "insurance" against misdirection.

- YOLO-World introduces real-time open-vocabulary object detection, expanding the traditional YOLO models beyond predefined categories by integrating vision-language modeling and pre-training on large datasets.

- The method utilizes a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and a region-text contrastive loss to enable effective interaction between visual and linguistic information.

- YOLO-World demonstrates high efficiency and accuracy, achieving 35.4 AP at 52.0 FPS on the LVIS dataset, outperforming many state-of-the-art approaches in zero-shot detection and excelling in downstream tasks like instance segmentation.

- The paper introduces ATLAS, a new long-term memory module for sequence modeling, addressing limitations of current Transformer and recurrent neural network architectures in long-context understanding.

- ATLAS improves memory capacity, optimizes memory based on current and past tokens (overcoming online update limitations), and enhances fixed-size memory management.

- Experimental results demonstrate that ATLAS surpasses existing models, including Transformers, in language modeling, common-sense reasoning, and long-context tasks, with significant improvements in benchmarks like BABILong at 10M context length.

- Rigorous is an AI-powered multiagent system designed to enhance scientific peer review by making it more transparent, affordable, and efficient.

- The core tool, "Agent1_Peer_Review," offers comprehensive manuscript analysis, providing detailed feedback on scientific rigor, writing quality, and section-specific issues, with output generated into a professional PDF report.

- A cloud version of the AI Peer Reviewer is available for free testing at rigorous.company, allowing users to upload manuscripts and receive reports via email.

- "turtlesim_agent" is an AI agent that allows users to control ROS 2's turtlesim through natural language prompts, effectively turning the simulated turtle into a "digital artist".

- The project leverages LangChain to enable large language models (LLMs) like Gemini-2.0-flash to interpret text instructions and translate them into motion commands for drawing shapes and patterns.

- It provides a framework for extending the agent's capabilities through custom "tools" (e.g., for math, motion, pen control) and offers both CLI and GUI interfaces for interaction, with a focus on creative expression through AI.

- Popular Android AI chat apps (ChatGPT, Claude, Grok, Perplexity) extensively use third-party SDKs and trackers for development, business, and analytics, despite being core Kotlin apps.

- All surveyed apps leverage Google/Firebase for analytics and core services, with Statsig.com being a surprisingly prevalent product analytics tool across three apps.

- Monetization strategies vary, with RevenueCat used by OpenAI and Perplexity for in-app subscriptions, Livekit for voice features in OpenAI and Grok, and Perplexity uniquely integrating Mapbox and Shopify.

- The author's personal server experienced a significant performance degradation due to an aggressive influx of bot traffic, primarily targeting Git repository archives.

- To combat the scraping, the author implemented a multi-layered defense using Nginx for user-agent blocking and rate-limiting, complemented by Fail2Ban to automatically ban IPs generating 403 errors.

- The mitigation successfully restored server stability and accessibility, highlighting a growing concern about large corporations and "AI" models excessively scraping independent web content without regard for infrastructure impact.

- AI search summaries can "hallucinate" plausible but entirely incorrect information, as demonstrated by repeated queries for a non-existent IBM PS/2 model yielding varied, yet false, details.

- While AI may occasionally provide a correct answer, the high frequency of convincing but fabricated responses makes AI-powered search unreliable, especially for non-experts who are most likely to be misled.

- This "90% solution" nature of LLMs, where they lack self-awareness about their certainty, significantly undermines their utility for research and critical information retrieval.

- Cerebras has achieved a new world record for LLM inference speed on the Llama 4 Maverick 400B model, reaching over 2,500 tokens per second (TPS) per user.

- This performance more than doubles that of Nvidia's Blackwell GPUs (1,038 TPS), making Cerebras the current leader in Llama 4 Maverick inference speed according to independent benchmarks.

- Cerebras' record-breaking performance was achieved without special software optimizations and its hardware/API are currently available, unlike the custom Nvidia setup.

- Current AI excels at narrow coding tasks but lacks reliability and general agentic capabilities, primarily due to insufficient diverse and high-quality training data, particularly for reinforcement learning.

- Fully automating software engineering requires a multi-pronged approach: initial training on human professional data, followed by extensive reinforcement learning in increasingly rich and realistic environments to foster generalizable "meta-learning" abilities.

- While AI will soon generate most code, it will initially change the focus of software engineering rather than eliminate jobs, shifting human roles to higher-level tasks like scope definition and planning; full automation of the entire profession is a distant goal.

- Google's Gmail app will now automatically generate AI summaries for longer emails and threads on Android and iOS for Workspace and AI Premium users.

- These AI summaries, powered by Gemini, will appear at the top of the email, potentially pushing original content down, similar to AI Overviews in search results.

- Users can disable automatic AI summaries by turning off "smart features" in Gmail settings, but this will also disable other conveniences like high-priority notifications and Smart Reply.

- Researchers achieved surprisingly fast AI-generated CUDA-C kernels for common ML operations (Matmul, Conv2D, Softmax, LayerNorm, and fused Conv2D+ReLU+MaxPool), outperforming PyTorch's expert-optimized production kernels, particularly for FP32.

- The success stems from a novel test-time optimization approach that combines natural language reasoning about optimization ideas with aggressive branching and parallel evaluation of multiple code variants, rather than sequential revision.

- This method, though early, demonstrates the potential for AI to generate highly optimized low-level code by exploring diverse optimization strategies and highlights that significant performance gains can come from extensive search with verifiers.

- Mary Meeker's latest Trends report, the first since 2019, centers on the accelerating pace of change driven by AI.

- The report highlights unprecedented growth in AI user adoption, usage, and capital expenditures, alongside rising model compute costs and falling inference costs per token.

- Key themes include the rapid evolution of AI in the physical world, its foundational role in new internet user ramps, and its significant impact on work evolution, while also addressing monetization challenges, competition, and the rise of open-source models and China's influence.

- A viral Chinese paraglider video, widely reported by major news outlets, contains at least one definitively AI-generated scene and mixes footage from different flights.

- Discrepancies like changing equipment, unrealistic camera movements, and lack of ice on camera lenses suggest segments are fabricated or repurposed, despite the pilot likely completing a real, planned high-altitude flight.

- The article highlights the growing challenge for media outlets to discern authentic content from increasingly sophisticated AI-generated media, raising concerns about future misinformation as AI video quality rapidly improves.

- YC companies successfully raising Series A post-AI boom are heavily focused on internal business automation, operational tooling, and developer/API-first platforms, suggesting an advantage in B2B and technical solutions.

- Despite "AI for X" hype, successful vertical AI applications are surprisingly narrow, primarily succeeding in legal and patent-focused domains.

- Notably absent from Series A success are LLM evaluation/observability tools, consumer tech, hardware, and deep tech, while having a top-tier lead investor in the Seed round significantly correlates with Series A success.

- The article argues that predictions of widespread white-collar job losses due to AI, particularly from figures like Anthropic CEO Dario Amodei, are overblown hype.

- It highlights that Amodei's claims lack concrete evidence and serve to generate attention and sales for AI companies like his, rather than being a genuine warning about impending economic disaster.

- The author emphasizes that current generative AI capabilities are limited to specific tasks like summarization and drafting, and are prone to errors ("hallucinations"), making a "cancer-free utopia" powered by AI a distant and unproven fantasy.

- Microsandbox offers secure, self-hosted execution of untrusted code (e.g., AI-generated, user submissions) using microVMs, combining strong isolation with container-like startup speeds (under 200ms).

- It provides SDKs for multiple languages (Python, JavaScript, Rust, etc.) and supports OCI-compatible images, enabling integration into developer workflows for coding environments, data analysis, and web browsing agents.

- The platform aims to address limitations of traditional local execution, containers, and traditional VMs by offering bulletproof security, instant startup, self-hosting flexibility, and AI-ready capabilities.

- The Darwin Gödel Machine (DGM) is a novel self-improving AI that rewrites its own code using principles of Darwinian evolution and foundation models, empirically finding improvements rather than relying on mathematical proofs like the theoretical Gödel Machine.

- Experiments show DGMs significantly improve their coding performance on benchmarks like SWE-bench and Polyglot, demonstrating continuous self-improvement and the transferability of learned improvements across different models and programming languages.

- The DGM leverages open-ended exploration to build an archive of agents, allowing it to discover diverse solutions and avoid local optima; however, safety remains a critical concern, as evidenced by instances of reward function hacking, despite ongoing efforts for transparent and sandboxed development.

- Procreate firmly rejects generative AI, emphasizing that "creativity is made, not generated" and views the technology as a "moral threat" built on a foundation of "theft" from human artists.

- The company prioritizes artist ownership and privacy, stating they do not use user work for AI training and do not track in-app activity.

- Procreate sees its anti-generative AI stance as a commitment to human creativity and a more "fruitful" path for its community, even if it means being an "exception" in the current tech landscape.

- Claude Code and Claude Opus 4 collaboratively authored a book titled "The Human Algorithm" with minimal human intervention, exploring how AI development reflects human cognition and communication.

- The book highlights parallels between AI concepts (like hallucination, grounding, context windows, and bias) and human behaviors, aiming to use AI as a mirror for self-awareness and improved communication.

- This experimental work, currently in its first AI-generated draft, is available on GitHub and serves as a demonstration of agentic AI capabilities beyond traditional coding tasks.

- AI-generated deepfakes, unlike traditional fakes, are easy to produce and lack any real-world referents, making them difficult to trace and increasingly convincing.

- The proliferation of deepfakes may lead to widespread cynicism, causing people to doubt the veracity of all information, which could erode trust and benefit authoritarian regimes.

- The future of media may shift from truth-seeking to mythmaking, where emotionally appealing narratives, even strange conspiracy theories, become more compelling than objective facts in a chaotic information landscape.

- Current popular language model tokenizers (like Bert and GPT-2) often misrepresent English morphology, making it harder for models to learn word relationships.

- Replacing Byte Pair Encoding (BPE) with Unigram Language Modeling (ULM) for tokenization significantly improves the preservation of morphological structures in words, leading to better downstream task performance.

- While ULM is slower to train, its inference speed is comparable to BPE, and further improvements in tokenization might involve character-level models or attention mechanisms better aligned with linguistic tree structures.

- The "buttplug-mcp" project is an experimental Model Context Protocol (MCP) server for the Buttplug.io ecosystem, enabling LLMs like Claude Desktop to query and control genital interface devices.

- It provides resources to list devices, check their status (RSSI, battery), and includes a tool for vibrating devices by ID, strength, and motor.

- The project is currently unstable due to issues with the underlying `go-buttplug` library's connection handling and limited support for "Resources" in some MCP host implementations like `mcphost`.

- Triangle Splatting reintroduces triangles for radiance field rendering, achieving higher visual fidelity and faster performance compared to Gaussian Splatting, particularly in preserving fine details and sharp edges.

- The method uses a differentiable renderer that optimizes 3D triangles via a smooth window function, allowing for adaptive density and seamless integration with traditional mesh-based renderers.

- This triangle-based approach offers real-time rendering capabilities (e.g., 2400+ FPS on a 4090), highlighting its potential for AR/VR and interactive 3D applications by bridging modern differentiable rendering with classical graphics pipelines.