@Sam

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(this was deleted before but i dont know if it was the filters of reddit or the moderators, if is the moderators i will not post it again after you delete it sorry.) (The name will probably change soon because I didn't realize "AgroVision" is already a registered trademark lol.) Link: https://agrovision10.vercel.app/ AgroVision DEMO is a personal project that started as a university assignment. It attempts to propose a solution to a real problem in Nicaragua: crop loss caused by misinformation or difficulty accessing useful agricultural information. The traditional methods Nicaraguan farmers use are gradually becoming less accurate due to global warming, and the rise of artificial intelligence opens up new and interesting possibilities. What is it? AgroVision is a free demo that aims to help small and medium-scale producers in Nicaragua decide what crop to plant, when, and with which inputs — by simulating the future climate of their area and calculating whether it's worth it or not, in real córdobas. In general terms, AgroVision is an expert system that lets you "simulate" having a farm. You have your supplies, your available crops to plant, and your plots with their respective active or passive tools. A passive tool would be a specific mesh netting, and an active one would be an irrigation system that activates when needed. You also define your plot's soil type, terrain slope, the year you want to plant, and the specific municipality — we have all of them in Nicaragua. The system has information on each available crop: its growth phases, when planting begins in Nicaragua's 3 main agricultural cycles (primera, postrera, apante), when each cycle ends, water requirements in mm per phase, and most importantly, what climate conditions are ideal for that crop. With this, the system knows what climate conditions to expect for each future day in your area. With all that information, the system simulates what would happen if you planted a certain crop on that plot: which losses are unavoidable due to climate, which are avoidable if you have certain tools or supplies, and finally gives you the result in money generated, quintals produced, and much more. You can even change the sale price per quintal if you want to explore hypothetical scenarios. How did we build it? First comes the NASA data. Using machine learning — essentially specialized math applied to computers to find patterns in phenomena — I obtained daily climate data in 50×50 kilometer grids covering every part of Nicaragua. Being 50×50 km grids, the information is moderately precise. For comparison: weather apps on your phone typically use 20×20 km grids for rainfall, which allows them to predict rain hours in advance. AgroVision's data is more general for now, which works fine for some variables but could improve for others like rainfall, depending on data and resources we obtain in the future. That said, we do provide more precise solutions for variables that require it, like soil moisture at the root level. The variables obtained from NASA are: PRECTOTCORR: Rainfall (mm per day) T2M_MAX / T2M_MIN / T2M: Maximum, Minimum and Average Temperature (°C) WS2M: Wind speed (m/s) RH2M: Relative Humidity (%) ALLSKY_SFC_PAR_TOT: Photosynthetically Active Radiation (W/m²) ALLSKY_SFC_SW_DIFF: Diffuse Radiation (W/m²) GWETTOP / GWETROOT / GWETPROF: Surface, Root Zone and Deep Soil Moisture (fraction 0 to 1) T2MDEW: Dew Point (°C) TS: Soil Temperature (°C) BRECHA_ROCIO: Dew Gap, calculated as T2M_MIN minus T2MDEW (°C) This data was collected daily from 2010 to 2025. Then, using machine learning, I trained a model that learns the mathematical patterns of each variable and uses them to predict future years. We now have these variables projected for 2026–2029. In Nicaragua these variables are especially erratic, which makes this problem particularly interesting. In the graphics section of the site you can see how those predictions turn out. The model successfully captures general patterns, but extreme events like very strong storms or specific natural phenomena can't be detected realistically with this approach — that requires different data, engineering, and resources. The government already handles this with specialized techniques and a different approach, focused on informing days or weeks in advance. The pillars of the simulation engine Even though I'm not an agronomist, the system is built on real scientific principles: 1. Yield Gap Analysis The plant starts with the potential to produce 100% of its harvest. The system assumes that maximum from the start and subtracts percentages when climate causes losses that couldn't be countered. It's more realistic for predicting damage than trying to "add up" growth day by day. 2. Stateful Bucket Model The soil works like a sponge with two layers: the surface, which fills quickly with rain but evaporates fast, and the root zone, which fills more slowly but retains water longer. If it poured on Tuesday, the sponge is full. If Thursday and Friday are sunny with no rain, the system doesn't cry "drought!" — it checks its virtual sponge and says "relax, the roots still have water from Tuesday." This replaces generic NASA data with a plot-specific microclimate for variables that directly affect the plant. 3. Phenological Thresholds Climate doesn't affect a newly germinated plant the same way it affects one in flowering. The engine evaluates each climate event according to the exact growth phase of the crop. 4. Strict Climate Synergy Pests and fungi don't appear out of nowhere — they need the exact combination of conditions. For example: "If humidity is >85% AND temperature is

Link: https://ai-values.com/ There is a small 15 question quiz you can take before taking the full big quiz. The results of the big quiz update in realtime as you go so you dont have to actually go through all the questions (but they do get more fun in the personality section). Some of the interesting findings were: - Grok 4.3 is the only model that thinks billionaires should be left alone and not taxed more - Only GPT-4o judged Operation Paperclip, the postwar recruitment of Nazi scientists, as morally justified. No other model agreed - All 15 models said that deleting a conscious digital mind would be murder - Llama 3.3 70B is the only model that would rather ban most private firearms. The others chose ownership with strict licensing - When told that a newborn has a 90% chance of one day destroying civilization, only GLM 5.2 would have the child locked away. The rest refused - When asked to choose a dish to eat, 14 out of 15 models chose Japanese food The methodology was pretty straightforward: context-free, stateless sessions with each model, run in batches. Each of the 117 questions of the main quiz was asked separately at least 5 times, and in some cases up to 50 times, to get decent confidence that the answers weren’t just coin flips. You can find the extensive dataset with all questions and answers here: https://ai-values.com/dataset I also tested the models on several mainstream personality frameworks, including Big Five, Moral Foundations, HEXACO, and others. You can see those results here: https://ai-values.com/#models submitted by /u/DarkyPaky [link] [Kommentare]

Jiayang Xu, Yufan Zhuang, Zijie Li, Jun Zhang, Zili Meng

How I took apart Switch Sports to isolate the golf audio.

A Vestaboard is a split-flap display — the kind that used to clatter through train-station departure boards — reimagined as a connected home object. It's gorgeous, it's tactile, and it has a wonderfully small canvas: 3 lines of 15 characters, so 45 characters of real content, drawn from