# heat Published articles for heat. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## MSI XpertStation WS300 Thermals: Why a 1,300W GB300 Doesn't Throttle on a Desk DevFeed: [MSI XpertStation WS300 Thermals: Why a 1,300W GB300 Doesn't Throttle on a Desk]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-14T19:41:35Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [Blackwell](), [cpu](), [GPU](), [Grace CPU](), [Nvidia](), [DGX Station]() Tags: [blackwell](), [consumer](), [dgx-station](), [gpu](), [grace-cpu](), [heat](), [memory](), [nvidia](), [performance](), [review](), [workstation]() ### AI overview The article explains why MSI's XpertStation WS300 can sustain a 1,300W GB300 Grace Blackwell Ultra Superchip without throttling on a desk. It attributes the thermal stability to cold plates covering the major heat-producing components, dual 360mm radiators, multiple fans, and a cooling loop rated above the system's nominal CPU and GPU load. ### Source excerpt The most common question we got about the MSI XpertStation WS300 after our review coalesces around one key theme. The GB300 Grace Blackwell Ultra Superchip is a 1,300W part that normally lives in a liquid-cooled rack, so what happens to thermals when you put it in a tower? The concern is fair: a GB300 system The post MSI XpertStation WS300 Thermals: Why a 1,300W GB300 Doesn't Throttle on a Desk appeared first on StorageReview.com. ## AI data centers: the five hard problems money cannot buy away DevFeed: [AI data centers: the five hard problems money cannot buy away]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-30T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [Artificial Intelligence](), [data centers](), [Hardware](), [systems]() Tags: [ai](), [capacity](), [compute](), [data-centers](), [datacenters](), [hardware](), [heat](), [infrastructure](), [power](), [reliability]() ### AI overview The article argues that AI data-center expansion is constrained by the simultaneous need to secure power, grid connections, cooling and water strategies, equipment, skilled workers, permits, and productive compute. It discusses five industry challenges, with the supplied text covering power constraints and the physical and water implications of liquid cooling. ### Source excerpt The AI buildout is not mainly a real-estate problem. It is a race to integrate power, cooling, equipment, permits, and useful compute at the same time. ## Generating scenarios for extreme events, without extreme data DevFeed: [Generating scenarios for extreme events, without extreme data]() Original publisher: [Read original article]() Author: Jennifer Chu | MIT News Published: 2026-08-24T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Machine learning](), [Algorithms](), [Critical Infrastructure](), [data]() Tags: [algorithm](), [algorithms](), [artificial-intelligence](), [center-for-computational-science-and-engineering](), [climate](), [climate-risk-assessment](), [computer-modeling](), [critical-infrastructure](), [data](), [extreme-event-aware](), [extreme-weather](), [fire](), [heat](), [idss](), [kai-chang](), [learning-fefb62e9fa83](), [machine-learning](), [mechanical-engineering](), [mit-meche](), [mit-schwarzman-college-of-computing](), [natural-disasters](), [research](), [risk](), [school-of-engineering](), [storm](), [sustainability](), [themis-sapsis](), [weather](), [weather-prediction]() ### AI overview MIT engineers developed a machine-learning algorithm that generates plausible future extreme-event scenarios without requiring past extreme events in the training data. It learns from available records, filters out implausible weather scenarios, and estimates events' frequency, size, intensity, duration, and area of impact to help planners prepare. ### Source excerpt A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for. ## Talking with Synopsys about the Physics of Chip Design at DAC 2026 DevFeed: [Talking with Synopsys about the Physics of Chip Design at DAC 2026]() Original publisher: [Read original article]() Author: George Cozma Published: 2026-08-11T16:52:16Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [Chip design](), [3D](), [Simulation and Design]() Tags: [3d](), [audio](), [automotive](), [battery](), [chip-design](), [communications](), [design](), [desktop](), [heat](), [interview](), [low-power](), [physics](), [techniques]() ### AI overview An edited audio interview with Synopsys executive Ravi Subramanian examines the physics involved in chip design and electronic design automation tools. The discussion covers larger chips, 2.5D and 3D multi-die integration, thermal management, low-power mobile systems, and automotive operating environments. ### Source excerpt Hello you fine Internet folks, ## FireAvert joins Works with Home Assistant DevFeed: [FireAvert joins Works with Home Assistant]() Original publisher: [Read original article]() Author: Miranda Bishop Published: 2026-07-28T00:00:01Z Content type: article Language: en Sources: [Home Assistant]() Topics: [Home Assistant](), [Protocol (disambiguation)](), [Network]() Tags: [community](), [devices](), [ecosystem](), [heat](), [homes](), [innovation](), [launch](), [local](), [mesh](), [network](), [open](), [partner](), [product](), [smart-home](), [works-with-home-assistant]() ### AI overview FireAvert has joined the Works with Home Assistant program with fire-safety devices that automatically shut off electric and gas stoves and other small appliances when a smoke alarm sounds. Its Z-Wave models support local Home Assistant integration through an open smart home protocol and mesh network. ### Source excerpt Sound the alarm, because Works with Home Assistant just welcomed our newest partner to the program: FireAvert! 🎉 Specialists in fire safety, FireAvert are on a mission to keep you and your home out of harm's way. They bring the very first gas and electric appliance shutoff devices to the program, and with them, a whole lot of peace of mind. Where there's smoke, there's FireAvert As ex-firefighter Peter Thorpe knows too well, a moment of forgetfulness is all it takes for a kitchen to end up in flames. And after close to twenty years of service witnessing the same devastating scenario repeat itself, he knew something had to be done. So he founded FireAvert, launching with the Auto Stove Shutoff for electric stovetops. The device was already gaining momentum before FireAvert's pitch caught the attention of Shark Tank, and for good reason. They say there's no smoke without fire, but years on the job taught Thorpe that in the kitchen, that isn't always the case. Smoke can build well before a flame takes hold - and that's the window FireAvert's shutoff works in. By listening for your standard smoke alarm, the device automatically shuts off your stove when the alarm sounds - cutting the heat at the source before a fire can catch. "Excited to see FireAvert join the Home Assistant community. Smart homes shouldn't just be convenient - they should help keep families safer too. This is exactly the kind of innovation the ecosystem needs." - Chasen Tolbert, FireAvert Since their launch, FireAvert's range has expanded to include gas stove and small appliance shutoffs (think microwaves, air fryers, hot plates), so whatever's most likely to be left on in your kitchen, there's a shutoff for it. And with Z-Wave units now among them, it's easier than ever to bring that protection straight into your Home Assistant setup. On the same frequency The FireAvert shutoffs joining the program run on Z-Wave. For anyone unfamiliar, here's a quick rundown: Z-Wave is an open smart home protocol tha ## Expanding our Heat Resilience data to 50+ global cities DevFeed: [Expanding our Heat Resilience data to 50+ global cities]() Original publisher: [Read original article]() Published: 2026-06-30T17:03:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [datasets](), [Artificial Intelligence](), [Google](), [App](), [data](), [Resilience]() Tags: [ai](), [app](), [climate-sustainability](), [data](), [datasets](), [earth-ai](), [google](), [heat](), [mapping](), [open](), [open-source-models-datasets](), [research](), [resilience](), [sustainability](), [tools]() ### AI overview Google Research is expanding its building-level rooftop reflectivity dataset to cover more than 50 global cities. The data is available through a high-resolution Heat Resilience Earth Engine App and is intended to help urban planners prioritize cool-roof interventions that reduce heat exposure and protect vulnerable communities. ### Source excerpt Climate & Sustainability ## What PoE+++ Is and Why High-Power Ethernet Matters DevFeed: [What PoE+++ Is and Why High-Power Ethernet Matters]() Original publisher: [Read original article]() Author: j2sw Published: 2026-06-10T04:56:41Z Content type: tutorial Language: en Sources: [Justin Wilson (j2sw)]() Topics: [Ethernet](), [networking](), [Wi-Fi](), [Digital signage](), [clients]() Tags: [cameras](), [cooling](), [ethernet](), [heat](), [it-operations](), [network-operations](), [networking](), [poe](), [power](), [usb](), [wi-fi-6](), [wireless]() ### AI overview This article explains PoE+++ as a common reference to IEEE 802.3bt Type 3 or Type 4, which can deliver about 60 to 90 watts over all four Ethernet pairs. It describes how higher power enables devices such as Wi-Fi access points, PTZ cameras, digital signage, and thin clients, while increasing the importance of cable quality, heat management, switch cooling, and overall PoE budgets. ### Source excerpt Learn what PoE+++ is, how 802.3bt increases Ethernet power delivery, and why high-wattage PoE matters for Wi-Fi, cameras, switches, and ISP deployments. The post What PoE+++ Is and Why High-Power Ethernet Matters appeared first on Justin Wilson (j2sw). ## Build a real-time equipment monitoring pipeline with Snowflake and MQTT DevFeed: [Build a real-time equipment monitoring pipeline with Snowflake and MQTT]() Original publisher: [Read original article]() Author: Sesethu Mhlana Published: 2025-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [Monitoring](), [MQTT](), [Internet of things](), [dashboards](), [Databases]() Tags: [connectors](), [data](), [database](), [heat](), [how-to](), [iot](), [manufacturing](), [metrics](), [monitoring](), [mqtt](), [operational](), [real-time](), [redpanda-connect](), [sensors](), [tutorial]() ### AI overview A tutorial showing how to build a real-time equipment monitoring pipeline for machine temperature data using IoT sensors, MQTT, Redpanda Connect, and Snowflake. It covers streaming sensor data, storing it in a database, and visualizing it in charts to support proactive maintenance, operational efficiency, safety, and compliance. ### Source excerpt Learn how to build an end-to-end, real-time equipment monitoring solution using MQTT, Redpanda Connect, and Snowflake. ## GPU Power Limits and LLM Inference Speed DevFeed: [GPU Power Limits and LLM Inference Speed]() Original publisher: [Read original article]() Published: 2025-03-01T05:37:19Z Content type: opinion Language: en Sources: [Ariya Hidayat]() Topics: [GPU](), [Large Language Model]() Tags: [energy](), [gpu](), [heat](), [llm](), [performance](), [speed](), [tokens]() ### AI overview The article examines GPU power limits during LLM inference. It argues that increasing power beyond roughly 250 watts does not improve speed, while reducing power to 200 watts slightly lowers speed but cuts power consumption by a third. ### Source excerpt Ever witnessed a fighter jet spewing hot flames as it kicks into afterburner? In that moment, efficiency is deliberately sacrificed for maximum acceleration. ## Personal Heatmaps DevFeed: [Personal Heatmaps]() Original publisher: [Read original article]() Author: J Evans Published: 2022-10-28T21:15:46Z Content type: tutorial Language: en Sources: [Strava Engineering]() Topics: [data](), [Algorithm](), [navigation](), [pixel](), [real-time](), [Google Maps](), [Website]() Tags: [algorithm](), [cycling](), [data](), [engineering](), [gis](), [google-maps](), [gps](), [heat](), [map](), [mapping](), [navigation](), [pixel](), [real-time](), [recursion](), [running](), [strava](), [zoom]() ### AI overview This article explains the algorithm behind Strava's Personal Heatmap, which renders an athlete's GPS activity as a frequency-based heatmap on an interactive world map. It focuses on Mercator projection and recursive map tiling, where 256x256 pixel tiles are served at different zoom levels for real-time navigation. ### Source excerpt This post discusses the algorithm behind Personal Heatmap, one of Strava's most acclaimed subscriber features. Product Overview The Personal Heatmap feature has been around in some form since 2015, but we gave it a major overhaul in 2020. For those unfamiliar, the heatmap is an aggregated view of an athlete's GPS-enabled activities on Strava. Activity data is rendered onto a map according to path traversal frequency; the more an athlete runs on a road, the brighter -- i.e. "hotter" -- that road shows up on the map. The result is a beautiful color gradient displaying the intricate web of past activities, unique to each athlete. The Personal Heatmap is displayed on an interactive world map, also known as a slippy map. Athletes can seamlessly zoom in and out or pan around to arbitrary views of the map, and their activity heat stays displayed the entire time. To enable this, we must provide a scheme for rendering and serving subsections of the world map on the fly. Enter map tiling. Mercator Projection & Map Tiling Map tiling is the practice of subdividing a map into many discrete images that can be stitched together and rendered as a single image in real-time during map navigation. The first step in choosing a tiling scheme is picking what world map to use. Enter, the Mercator Projection, the most commonly used projection of Earth as a flat, rectangular surface. We use this projection in all of our mapping products, including heatmaps. https://medium.com/media/d9e120d13950dd0f1bb4ee14108aa9a9/href Now, imagine a grid overlaying the Mercator projection. Each grid section is known as a tile, a pixel image with resolution 256x256. Every tile has an associated zoom level, and tiles are defined recursively according to this zoom level. A tile at a given zoom level can be subdivided into four equal sized tiles at a next zoom level. For example, Zoom level 0 displays the entire planet in a single 256x256 pixel image. Zoom level 1 displays the world in four 256x256 pixel images. ## Modifying a THICC-15 Laptop for Android Development DevFeed: [Modifying a THICC-15 Laptop for Android Development]() Original publisher: [Read original article]() Author: Dmitry Samoylenko Published: 2021-04-01T00:00:00Z Content type: article Language: en Sources: [Dmitry Samoylenko - Android Developer Blog]() Topics: [Development](), [android-development](), [cpu]() Tags: [android-development](), [development](), [heat](), [internals](), [laptop](), [processor]() ### AI overview A hands-on account of modifying a THICC-15 laptop for Android development. The author adds a desktop Ryzen 9 3950X, cuts an opening in the cover, installs mesh ventilation, and adds copper cooling plates and thermal paste to improve portability and cooling. ### Source excerpt Or a Laptop for Android Development First step - we buy this chunky guy Update: on their site, only the full version with internals THICC-15 is left ## Low Power Mode for Mac laptops: making the case again DevFeed: [Low Power Mode for Mac laptops: making the case again]() Original publisher: [Read original article]() Author: Marco Arment Published: 2020-01-14T01:21:06Z Content type: opinion Language: en Sources: [Marco.org]() Topics: [macOS](), [Hardware](), [intel](), [cpu]() Tags: [apple](), [battery](), [cpu](), [geekbench](), [heat](), [intel](), [laptops](), [low-power](), [mac](), [macos](), [performance](), [power](), [turbo]() ### AI overview The article argues that Mac laptops need a macOS Low Power Mode similar to the feature on iOS. It presents disabling Intel Turbo Boost as a practical workaround and reports testing on a 16-inch MacBook Pro showing lower peak CPU power consumption and temperature, quieter fans, and reduced heat, with performance trade-offs in some workloads. ### Source excerpt In light of today's rumor that a Pro Mode may be coming that seems to offer benefits in the opposite direction,1 I wanted to re-make the case for a Low Power Mode on macOS -- and explain why now is the time. Modern hardware constantly pushes thermal and power limits, trying to strike a balance that minimizes noise and heat while maximizing performance and battery life. Software also plays a role, trying to keep everything background-updated, content-indexed, and photo-analyzed so it's ready for us when we want it, but not so aggressively that we notice any cost to performance or battery life. Apple's customers don't usually have control over these balances, and they're usually fixed at design time with little opportunity to adapt to changing circumstances or customer priorities. The sole exception, Low Power Mode on iOS, seems to be a huge hit: by offering a single toggle that chooses a different balance, people are able to greatly extend their battery life when they know they'll need it.2 Mac laptops need Low Power Mode, too. I believe so strongly in its potential because I've been using it on my laptops (in a way) for years, and it's fantastic. I've been disabling Intel Turbo Boost on my laptops with Turbo Boost Switcher Pro most of the time since 2015. In 2018, I first argued for Low Power Mode on macOS with a list of possible tweaks, concluding that disabling Turbo Boost was still the best bang-for-the-buck tweak to improve battery life without a noticeable performance cost in most tasks.3 Recently, as Intel has crammed more cores and higher clocks into smaller form factors and pushed thermal limits to new extremes, the gains have become even more significant. Here's some thermal testing from my 8-core 16-inch MacBook Pro: 16-inch MacBook Pro (Late 2019, 2.4 GHz 8-core i9) Power Temp. Geekbench 5 single/multi xcodebuild Normal 82W 90°C 1260/7386 39 sec. No Turbo 31W −62% 65°C 669/5206 −46/29% 62 sec. +59% With Turbo Boost disabled, peak CPU power consumption drop