After being a teacher for eight years, I quit my job. At age 30, I realized I couldn't keep walking into class with anxiety about what I was going to find I decided to become a ski instructor, where I can still use my teaching skills. After eight years in the classroom, I turned in my teaching badge and picked up a ski pass. Turning 30, I began to look at my life and question if I wanted to stay in the teaching profession for the long haul. With no desire to go the administrative route to becoming a principal, the thought of paying for graduate school for nothing more than a pay increase left me feeling like I was at the end of the teaching career road. Like many other educators, I was recognized for my teaching skills with more work, difficult kids, and parents treating me more like their kid's personal assistant than a teacher. I'd walk into my class every morning with slight anxiety about angry emails from parents demanding study guides and questioning project rubrics. Feeling burned out and broke, I decided to abandon the classroom. Leaving teaching wasn't just a career shift but a decision to reclaim joy and rediscover purpose in a new way.I decided to use my teaching skills in a new environmentUnsure of my next career move, I became a ski instructor. A job where I can use my teaching skills in a new environment. As a ski instructor, I connect with kids as they learn new skills and see the joy on their faces when they finally master a steep hill. Seeing kids light up with learning is long gone from teaching as kids simply want to know, "Will this be on the test?" and adjust their memorization accordingly.While helping young children with ski clothes and getting their boots clipped into their skis can be tiresome, it is nowhere near the frustration I felt using outdated technology in the classroom on spotty internet. I no longer spend my days trying to help kids log into various platforms and troubleshoot computer issues but instead show them how to navigate the slopes and find their self-confidence.I miss my colleaguesWhile I miss colleagues and the feeling of belonging to school, the ski industry is a tight-knit community filled with people looking to share the joy of skiing. The ski instructing industry is filled with easily available resources through training, workshops, and mentorship. I feel equipped with a variety of resources to do my job. This was a stark contrast to the classroom days when I was making do with what I had. I taught without textbooks and was left to find my own resources online. While administrative staff always supported me in the classroom, they were just as overwhelmed and under-resourced as I was and could only do so much.A bad ski day is still a good dayTeaching kids to ski comes with its own rough days, like when a kid doesn't quite make it to the bathroom in time, or the temperatures hit the single digits. I'm exhausted at the end of the ski season and need a good month to recover, but even a rough day on the ski hill fails compared to an average week in the classroom. My mental health is better; I'm outside and active. I still get to connect with kids and see them grow. I get to walk alongside kids as they conquer their fear of skiing down the big hill for the first time or riding the ski lift. Parents respect and appreciate my evaluations of the kids' progress. They accept my remarks on their skills and whether they are ready for the next level.Every August, when I see the back-to-school supplies roll out in stores, a piece of my heart misses the classroom and the coworkers I had. But my life is healthier now, and I'm reminded about the parts I loved most about teaching that I wasn't getting to do. But on the ski hill, my passion for teaching continues. I don't know if I will ever step back into the classroom or how long I will call the wide-open ski slopes my office. But for now, I will be holding my lessons among the rocky mountains, where the scent of fresh pine wafts through the air as I guide my students down the mountain.

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A few renders and the original concept art submitted by /u/GravePencil1441 [link] [comments]

Lost Castle 2 doesnt offer a traditional skill tree or character level-up system, but it introduces a creative approach to obtaining permanent skills and buffs through the Camp Upgrade feature. With this system, you unlock ongoing enhancements that make your character stronger. To maximize Lost Castle 2s replayability and access end-game content more easily, youll need to master these Camp Upgrades and prioritize unlocking them all.

Roguelike gaming has become all the rage in the indie scene, with developers making the most of recycled assets to let players enjoy a great time with different level layouts in each run. This makes it important for roguelike games to keep players engaged instead of losing their attention or frustrating them with boring and repetitive content.

Microbrands in the watch world often capture attention by blending accessibility, quality, and striking designs. Montoir, a Chicago-based brand with Swiss-made credentials, entered the scene in late 2023 with its first dive watch. It was a standout debuta stylish, robust timepiece with a Sellita automatic movement and serious 200m water resistance. Now, Montoir is back with the MWMOD-01 V2 Dive Watch, offering the same successful formula with an exciting twist: bold new colors.Dive watches have a storied legacy, but Montoir takes that history and redefines it with precision and craftsmanship. The exterior is the definition of a modern classic. Crafted from 316L stainless steel, it combines various finisheshorizontal brushing, mirror polishing, and a lightly blasted dialfor a sophisticated and versatile aesthetic. Under the minimal-functional exterior is the customized Sellita SW200-1 Swiss automatic movementa hallmark of reliability. Montoir has fine-tuned the movement to a no-date configuration, creating a clean, distraction-free dial. The 38-hour power reserve and robust build ensure its as functional as it is stylish.Designer: MontoirClick Here to Buy Now: $375 $750 (50% off). Hurry, only 3/139 left! Raised over $62,000. Only 72-hours left.The new collection boasts dial options in Mint, Salmon, Cool Grey, and Green, alongside existing hues like Polar White, Black, and Blue. These colors are complemented by subtle refinements to the design, ensuring that each variant feels like more than just a palette swap. A green bezel insert paired with the green dial is particularly eye-catching, making it the standout piece of the collection. For the other colors, black bezels maintain a timeless, understated appeal.The new colors arent just cosmeticthey reflect Montoirs effort to create a timepiece that fits seamlessly into anyones wardrobe. The Salmon dial, for example, exudes vintage warmth, while the Cool Grey is a masterclass in understated elegance. Paired with Montoirs combination of brushed and polished finishes, every variant tells its own story while staying true to the brands DNA.At 40.5mm in diameter and just under 12mm in height, the stainless steel case maintains the same balanced proportions as its predecessor. The brushed finish with polished accents gives it a versatile aestheticequally at home underwater or at a casual dinner. The unidirectional bezel with a detailed 60-minute scale ensures functionality for diving enthusiasts, while a double-domed sapphire crystal with anti-reflective coatings keeps the dial legible under all conditions. The solid caseback, featuring an embossed diving helmet, adds a distinctive touch. Inside, the MWMOD-01 V2 draws its power from the Sellita SW200-1 automatic movement. This Swiss-made workhorse, a reliable alternative to the ETA 2824-2, offers a 38-hour power reserve and operates at a smooth 28,800 vibrations per hour. While its a proven performer, Montoirs choice to omit a date complication keeps the dial refreshingly clean and focused on legibility.Speaking of the dial, the new lightly sandblasted matte finish adds depth and texture. The oversized indices, coated in Super-LumiNova BGW9, glow brightly in low-light conditions, as do the hour and minute hands. Depending on the color variant, youve got yourself a wonderful contrast between the hands and the watch face. The lighter face shades opt for black hands, making them visible at a glance, while the richer watch faces come with polished chrome-like hands that glimmer in the light, instantly catching your eye.The watches come equipped with a 20mm tropic-style strap made from recycled FKM rubber. The straps quick-release system makes swapping bands effortless, adding to the watchs versatility. The rubber strap means this is truly a bonafide diver watch, designed to easily take on your diving adventures as a fish takes to water. The watchs rated for 200m or 20ATM of water resistance too. For those drawn to value-driven watches, the Montoir MWMOD-01 V2 is a compelling option. With Kickstarter pricing starting at $375 for early backers (or $450 after the initial 48-hour window), the watch offers a lot of bang for the buck. For a Swiss-made dive watch with professional-grade specs, the final retail price of $750 still represents strong value.This second installment from Montoir refines everything the company got right with its first series, building on it withfresh aesthetics and thoughtful design tweaks. Whether youre an experienced connoisseur or a first-time buyer looking for a robust, stylish diver, the MWMOD-01 V2 is a worthy contender for your collection especially given its Swiss-made movement at an authentic, non-marked-up price tag.Click Here to Buy Now: $375 $750 (50% off). Hurry, only 3/139 left! Raised over $62,000. Only 72-hours left.The post Never Pay A Brand Markup Again: Montoirs V2 Dive Watch Delivers Swiss-Made Luxury On A Budget first appeared on Yanko Design.

Open Source LLM development is going through great change through fully reproducing and open-sourcing DeepSeek-R1, including training data, scripts, etc. Hosted on Hugging Faces platform, this ambitious project is designed to replicate and enhance the R1 pipeline. It emphasizes collaboration, transparency, and accessibility, enabling researchers and developers worldwide to build on DeepSeek-R1s foundational work.What is Open R1?Open R1 aims to recreate the DeepSeek-R1 pipeline, an advanced system renowned for its synthetic data generation, reasoning, and reinforcement learning capabilities. This open-source project provides the tools and resources necessary to reproduce the pipelines functionalities. The Hugging Face repository will include scripts for training models, evaluating benchmarks, and generating synthetic datasets.The initiative simplifies the otherwise complex model training and evaluation processes through clear documentation and modular design. By focusing on reproducibility, the Open R1 project invites developers to test, refine, and expand upon its core components.Key Features of the Open R1 FrameworkTraining and Fine-Tuning Models: Open R1 includes scripts for fine-tuning models using techniques like Supervised Fine-Tuning (SFT). These scripts are compatible with powerful hardware setups, such as clusters of H100 GPUs, to achieve optimal performance. Fine-tuned models are evaluated on R1 benchmarks to validate their performance.Synthetic Data Generation: The project incorporates tools like Distilabel to generate high-quality synthetic datasets. This enables training models that excel in mathematical reasoning and code generation tasks.Evaluation: With a specialized evaluation pipeline, Open R1 ensures robust benchmarking against predefined tasks. This provides the effectiveness of models developed using the platform and facilitates improvements based on real-world feedback.Pipeline Modularity: The projects modular design allows researchers to focus on specific components, such as data curation, training, or evaluation. This segmented approach enhances flexibility and encourages community-driven development.Steps in the Open R1 Development ProcessThe project roadmap, outlined in its documentation, highlights three key steps:Replication of R1-Distill Models: This involves distilling a high-quality corpus from the original DeepSeek-R1 models. The focus is on creating a robust dataset for further training.Development of Pure Reinforcement Learning Pipelines: The next step is to build RL pipelines that emulate DeepSeeks R1-Zero system. This phase emphasizes the creation of large-scale datasets tailored to advanced reasoning and code-based tasks.End-to-End Model Development: The final step demonstrates the pipelines capability to transform a base model into an RL-tuned model using multi-stage training processes.The Open R1 framework is primarily built in Python, with supporting scripts in Shell and Makefile. Users are encouraged to set up their environments using tools like Conda and install dependencies such as PyTorch and vLLM. The repository provides detailed instructions for configuring systems, including multi-GPU setups, to optimize the pipelines performance.In conclusion, the Open R1 initiative, which offers a fully open reproduction of DeepSeek-R1, will establish the open-source LLM production space at par with large corporations. Since the model capabilities are comparable to those of the biggest proprietary models available, this can be a big win for the open-source community. Also, the projects emphasis on accessibility ensures that researchers and institutions can contribute to and benefit from this work regardless of their resources. To explore the project further, visit its repository on Hugging Faces GitHub.Sources:Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our70k+ ML SubReddit. Asif RazzaqAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences. Meet 'Height':The only autonomous project management tool (Sponsored)

Mixture-of-Experts (MoE) models utilize a router to allocate tokens to specific expert modules, activating only a subset of parameters, often leading to superior efficiency and performance compared to dense models. In these models, a large feed-forward network is divided into smaller expert networks, with the routertypically an MLP classifierdetermining which expert processes each input. However, a key issue arises from the routers separation from the experts execution. Without direct knowledge of the experts capabilities, the routers assignments are predictions without labels. Misassignments can hinder expert performance, requiring expert adaptation or iterative router improvement, resulting in inefficiencies during training.Researchers from Renmin University of China, Tencent, and Southeast University have introduced Autonomy-of-Experts (AoE), a new MoE paradigm where experts independently decide whether to process inputs. This approach leverages each experts awareness of its ability to handle tokens, reflected in the scale of its internal activations. In AoE, experts calculate internal activations for all inputs, and only the top-ranked ones, based on activation norms, proceed with further processing, eliminating the need for routers. The overhead from caching unused activations is reduced using low-rank weight factorization. With up to 4 billion parameters, pre-trained AoE models outperform traditional MoE models in efficiency and downstream tasks.The study examines sparse MoE models, where each feed-forward network (FFN) module functions as an expert. Unlike dense MoE models, which utilize all parameters, sparse MoE models improve efficiency by activating only the most relevant experts for specific inputs. These models rely on a router to assign inputs to the appropriate experts, typically using a token choosing Top-K experts approach. A key challenge is maintaining balanced expert utilization, as routers often overuse certain experts, leading to inefficiencies. To address this, load-balancing mechanisms ensure a more equitable distribution of tasks among experts by incorporating auxiliary losses, thereby enhancing overall efficiency.The AoE is a method where experts independently determine their selection based on internal activation norms, eliminating the need for explicit routing mechanisms. Initial experiments revealed that the scale of activation norms at certain computational points reflects an experts capability to process inputs effectively. AoE builds on this insight by ranking experts based on the L2 norms of compressed activations, selecting the top-performing ones for computation. By factorizing weight matrices and caching low-dimensional activations, AoE significantly reduces computational and memory overhead while maintaining high efficiency, addressing limitations in traditional MoE frameworks.The research compares the AoE framework to traditional MoE models through experiments on smaller pre-trained language models. Using a 12-layer model with 732 million parameters and eight experts per layer, trained on 100 billion tokens, the findings highlight that AoE performs better than MoE in both downstream tasks and training efficiency. It shows that the best performance is achieved when the reduced dimension is about one-third of the models overall dimension. AoE enhances load balancing and expert utilization across layers, leading to better generalization and efficiency when combined with alternative expert selection methods.In conclusion, AoE is a MoE framework designed to overcome a key limitation in traditional MoE models: separating the routers decisions and the experts execution, often resulting in inefficient expert selection and suboptimal learning. In AoE, experts autonomously select themselves based on their internal activation scales, eliminating the need for routers. This process involves pre-computing activations and ranking experts by their activation norms, allowing only top-ranking experts to proceed. Efficiency is enhanced through low-rank weight factorization. Pre-trained language models using AoE outperform conventional MoE models, showcasing improved expert selection and overall learning efficiency.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our70k+ ML SubReddit. [Recommended Read] Nebius AI Studio expands with vision models, new language models, embeddings and LoRA (Promoted)The post Autonomy-of-Experts (AoE): A Router-Free Paradigm for Efficient and Adaptive Mixture-of-Experts Models appeared first on MarkTechPost.

Residents of Pasadena dont have many cheap internet options, but gig speeds are widely available across the area.

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