An Mozilla engineer has shared survey data and calculations suggesting that up to 15% of Firefox crashes are due to a bit flip. These bit flips can be caused by electrical issues, thermal effects, manufacturing defects, aging, crosstalk, or even ionizing cosmic rays. Mozilla received nearly half a million auto-submitted crash reports last week and determined that around 15% of crashes were due to bit flips, with half of those caused by genuine hardware issues. The engineer notes that the memory test used only checks up to 1 GiB of memory for 3 seconds, so the actual number could be higher. Every device with memory is susceptible to bit flips, not just PCs.
This article details how to set up a local AI assistant within a Linux terminal using Ollama and Llama 3.2. It explains the installation process, necessary shell configurations, and practical applications for troubleshooting and understanding system logs and processes. The author demonstrates how to use the AI to explain command outputs, interpret journal logs, and gain insights into disk usage and running processes, improving efficiency and understanding for both beginners and advanced Linux users. It also discusses the benefits and limitations of this approach.
discrawl mirrors Discord guild data into a local SQLite database, allowing you to search, inspect, and query server history independently of Discord. It’s a bot-token crawler – no user-token hacks – and keeps your data local. It discovers accessible guilds, syncs channels, threads, members, and message history, maintains FTS5 search indexes for fast text search (including small attachments), records mentions, and tails Gateway events for live updates with repair syncs. It provides read-only SQL access for analysis and supports multi-guild schemas with a simple single-guild default. Search defaults to all guilds, while sync and tail default to a configured default guild or fan out to all discovered guilds if none is set.
A new ETH Zurich study challenges the common practice of using `AGENTS.md` files with AI coding agents. LLM-generated context files decrease performance (3% lower success rate, +20% steps/costs).Human-written files offer small gains (4% success rate) but also increase costs. Researchers recommend omitting context files unless manually written with non-inferable details (tooling, build commands).They tested this using a new dataset, AGENTbench, with four agents.
RAG combines language models with external knowledge. This article explores context & retrieval in RAG, covering search methods (keywords, TF-IDF, embeddings/FAISS/Chroma), context length challenges (compression, re-ranking), and contextual retrieval (query & conversation history).
Timer-S1 is a scalable Mixture-of-Experts time series model with 8.3B parameters that uses serial scaling and novel TimeMoE blocks to improve long-term forecasting accuracy.
We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of 11.5K. To overcome the scalability bottleneck in existing pre-trained time series foundation models, we perform Serial Scaling in three dimensions: model architecture, dataset, and training pipeline. Timer-S1 integrates sparse TimeMoE blocks and generic TimeSTP blocks for Serial-Token Prediction (STP), a generic training objective that adheres to the serial nature of forecasting. The proposed paradigm introduces serial computations to improve long-term predictions while avoiding costly rolling-style inference and pronounced error accumulation in the standard next-token prediction. Pursuing a high-quality and unbiased training dataset, we curate TimeBench, a corpus with one trillion time points, and apply meticulous data augmentation to mitigate predictive bias. We further pioneer a post-training stage, including continued pre-training and long-context extension, to enhance short-term and long-context performance. Evaluated on the large-scale GIFT-Eval leaderboard, Timer-S1 achieves state-of-the-art forecasting performance, attaining the best MASE and CRPS scores as a pre-trained model. Timer-S1 will be released to facilitate further research.
We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of 11.5K. To overcome the scalability bottleneck in existing pre-trained time series foundation models, we perform Serial Scaling in three dimensions: model architecture, dataset, and training pipeline. Timer-S1 integrates sparse TimeMoE blocks and generic TimeSTP blocks for Serial-Token Prediction (STP), a generic training objective that adheres to the serial nature of forecasting. The proposed paradigm introduces serial computations to improve long-term predictions while avoiding costly rolling-style inference and pronounced error accumulation in the standard next-token prediction. Pursuing a high-quality and unbiased training dataset, we curate TimeBench, a corpus with one trillion time points, and apply meticulous data augmentation to mitigate predictive bias. We further pioneer a post-training stage, including continued pre-training and long-context extension, to enhance short-term and long-context performance. Evaluated on the large-scale GIFT-Eval leaderboard, Timer-S1 achieves state-of-the-art forecasting performance, attaining the best MASE and CRPS scores as a pre-trained model. Timer-S1 will be released to facilitate further research.
Google has removed the "design for accessibility" section from within the Understand the JavaScript SEO basics documentation.
Google said this was removed because the information was "out of date and not as helpful as it used to be."
The old text advised that using JavaScript for page content "may be hard for Google to see," but Google now states this hasn't been true for many years.
While Google Search can handle JavaScript well, it's still important to double-check what Google Search sees using the URL inspection tool in Google Search Console.
Google said this was removed because the information was "out of date and not as helpful as it used to be."
The old text advised that using JavaScript for page content "may be hard for Google to see," but Google now states this hasn't been true for many years.
While Google Search can handle JavaScript well, it's still important to double-check what Google Search sees using the URL inspection tool in Google Search Console.
We designed and built a 12 degree-of-freedom (3 servos per leg × 4 legs) quadruped robot controlled by a Raspberry Pi Pico W, featuring integrated environmental sensing and a wireless WiFi controller. Starting from a custom CAD body and 3D-printed frame, the robot combines mechanical engineering and embedded electrical engineering to create a platform capable of coordinated four-legged locomotion, heading determination, environmental mapping, and target detection. In order to do so, our system leverages several sensors including an IMU, a solid state LiDAR sensor, and a contact-less infrared sensor.
DFRobot has launched the Fermion: BMV080, a low-cost air quality sensor module based on the Bosch BMV080. It provides fanless PM1, PM2.5, and PM10 sensing capabilities for $29.90. The sensor uses laser-based light-scattering technology and has a service life of up to 10 years. It measures particulate concentrations in a 0–1000 μg/m³ range with 1 μg/m³ resolution and supports I2C and SPI interfaces. It consumes about 70 mA in continuous measurement mode and 6 μA in sleep mode. The module also features a 35 cm obstruction zone for accurate readings. Schematics, component location diagrams, and 3D STEP files are available.
This article discusses how to effectively utilize Large Language Models (LLMs) by acknowledging their superior processing capabilities and adapting prompting techniques. It emphasizes the importance of brevity, directness, and providing relevant context (through RAG and MCP servers) to maximize LLM performance. The article also highlights the need to treat LLM responses as drafts and use Socratic prompting for refinement, while acknowledging their potential for "hallucinations." It suggests formatting output expectations (JSON, Markdown) and utilizing role-playing to guide the LLM towards desired results. Ultimately, the author argues that LLMs, while not inherently "smarter" in a human sense, possess vast knowledge and can be incredibly powerful tools when approached strategically.
