# Space Published articles for Space. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Sol Protocol Early Access Impressions: A Co-op Action Roguelike with a Promising Foundation DevFeed: [Sol Protocol Early Access Impressions: A Co-op Action Roguelike with a Promising Foundation]() Original publisher: [Read original article]() Author: James Galizio Published: 2026-09-17T20:28:19Z Content type: article Language: en Sources: [UploadVR]() Topics: [multiplayer]() Tags: [map](), [multiplayer](), [space](), [teamwork](), [vr-gaming](), [warp]() ### AI overview Sol Protocol is a co-op action roguelike set in space. Players work together aboard a ship and during zero-gravity exploration to gather resources, upgrade systems, and fight enemies. The game has a promising co-op foundation, though its current gameplay loop is simple and limited in scope. ### Source excerpt Sol Protocol in Early Access on Quest has a good foundation of co-op teamwork and exploration, but the limited scope has a lot of room for growth. ## A Speaker That Clips to City Poles and Leaves No Mark DevFeed: [A Speaker That Clips to City Poles and Leaves No Mark]() Original publisher: [Read original article]() Author: Ida Torres Published: 2026-09-16T22:30:15Z Content type: article Language: en Sources: [Yanko Design]() Topics: [systems]() Tags: [architecture](), [audio](), [audio-technology-concept-designs-public-speaker](), [building](), [concept-designs](), [design](), [public](), [sound](), [space](), [speaker](), [technology]() ### AI overview Mariami Kurtishvili's Orchid Sound System is a graduate thesis proposing stainless steel high-frequency horns that clip onto existing urban infrastructure without screws, drilling, or permanent marks. The project explores temporary communal sound in public spaces. ### Source excerpt A Speaker That Clips to City Poles and Leaves No Mark Walk down almost any street in an American city without your headphones in, and you will notice how little of that walk actually belongs to... ## NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error DevFeed: [NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T16:43:16Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [lunar foundation model](), [foundation-models](), [ibm](), [Open Source](), [hugging face](), [dataset](), [multimodal](), [Benchmark]() Tags: [ai](), [benchmark](), [data](), [enterprise](), [fine-tuning](), [foundation-models](), [hugging-face](), [ibm](), [lunar-foundation-model](), [machine-learning](), [multimodal](), [nasa](), [open-source](), [space]() ### AI overview IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source on Hugging Face, along with its weights, technical report, and training dataset. Built on TerraMind, the model uses multimodal lunar observations for tasks including ice-deposit mapping, volcanic-feature detection, and crater detection. Reported benchmarks show up to 22% lower ice-mapping error than SwinV2-B, while the accompanying dataset contains roughly 2 million image tiles from nine instruments across four lunar missions. ### Source excerpt IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source, one of the first publicly available foundation models built for scientific study of the Moon. The weights, a technical report, and the machine-learning-ready dataset it was trained on are up on Hugging Face under the Prithvi family, which already covers Earth observation, The post NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error appeared first on StorageReview.com. ## Britain's technology brief is now everyone's job and nobody's responsibility DevFeed: [Britain's technology brief is now everyone's job and nobody's responsibility]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-11T13:12:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Machine learning](), [Architecture & Design](), [Security & Privacy](), [Vibe coding](), [NVLink](), [Aeternum]() Tags: [ai](), [cybersecurity](), [government](), [government-of-the-united-kingdom](), [open-source](), [opinion](), [public-sector](), [science](), [space](), [spacex](), [systems](), [technology](), [whitehall]() ### AI overview This technology news roundup examines how Britain's science, AI, and digital-government responsibilities are spread across competing ministerial portfolios. It also covers security incidents, AI companies, semiconductor infrastructure, open-source software, operating systems, and developer tools. ### Source excerpt Whitehall has scattered science, AI, and digital government across a thicket of competing ministerial portfolios ## Introducing IBM and NASA's new foundation model for the Moon DevFeed: [Introducing IBM and NASA's new foundation model for the Moon]() Original publisher: [Read original article]() Author: Kim Martineau Published: 2026-09-10T12:30:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [lunar foundation model](), [ibm](), [foundation-models](), [data](), [Artificial Intelligence](), [AI Architecture]() Tags: [accelerated-discovery](), [ai](), [ai-architecture](), [algorithms](), [ibm](), [lunar-foundation-model](), [mapping](), [model](), [nasa](), [release](), [science](), [space](), [us]() ### AI overview IBM and NASA are open-sourcing the NASA-IBM Lunar Foundation Model, a multimodal AI model that integrates lunar observations from US and Japanese missions across viewing angles, spatial scales, and measurement types. The model is intended to support lunar mapping, volcanic-history research, and searches for polar ice. ### Source excerpt The multi-modal model could help astronauts navigate craters, investigate ancient lava, and search for ice, as the US plans for a long-term lunar presence. ## The /3 Body Problem DevFeed: [The /3 Body Problem]() Original publisher: [Read original article]() Author: Remco van Mook Published: 2026-09-10T09:22:46Z Content type: article Language: en Sources: [RIPE Labs]() Topics: [networking](), [Internet](), [Internet Engineering Task Force (IETF)]() Tags: [article](), [ietf](), [internet](), [ipv6](), [networking](), [policy](), [space]() ### AI overview The article examines how IPv6 addressing and policy mechanisms may need to evolve for space networking. It argues that terrestrial assumptions break down beyond low Earth orbit and calls for addressing policy questions now, including through the proposed EXPANSE working group. ### Source excerpt A large chunk of IPv6 is heading for space, and every policy question about it is currently going to be deferred to the RIR communities. This article outlines what those questions look like, and argues why now is the time to start asking them. ## Rebuilding AUTOMATIC1111 with Gradio Workflow DevFeed: [Rebuilding AUTOMATIC1111 with Gradio Workflow]() Original publisher: [Read original article]() Author: yuvraj sharma; Abubakar Abid Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI Development](), [Graphs](), [stable-diffusion](), [Inference](), [Large Language Model](), [hugging face](), [vlm]() Tags: [automatic1111](), [comfyui](), [diffusion](), [flux](), [gradio](), [hugging-face](), [image-to-image](), [image-to-video](), [inference](), [inference-providers](), [llm](), [models](), [python](), [space](), [spaces](), [text-to-image](), [vlm](), [workflow](), [workflows]() ### AI overview A walkthrough of Workflow1111, a Gradio graph that recreates AUTOMATIC1111-style media pipelines with connected operator nodes for image generation, editing, prompting, and related tasks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Pico Space Pro Appears To Receive FCC Certification Following Delay To Q4 DevFeed: [Pico Space Pro Appears To Receive FCC Certification Following Delay To Q4]() Original publisher: [Read original article]() Author: Luna Published: 2026-09-05T23:39:38Z Content type: news Language: en Sources: [UploadVR]() Topics: [Hardware](), [Software]() Tags: [battery](), [compute](), [fcc](), [headsets-tech](), [product-launch](), [regulatory](), [release](), [release-schedule](), [space]() ### AI overview Pico Space Pro, an upcoming mixed-reality headset, appears to have received FCC certification after its launch was postponed to the fourth quarter of 2026. The article reports that FCC filings reveal a tethered compute and battery puck. ### Source excerpt A new Pico device, highly likely the upcoming Space Pro, received FCC certification following a delay to Q4. Filings reveal a tethered compute and battery puck. ## Mapping global methane emissions from space with deep learning DevFeed: [Mapping global methane emissions from space with deep learning]() Original publisher: [Read original article]() Published: 2026-09-01T18:40:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Deep neural networks](), [Google]() Tags: [climate-sustainability](), [deep-learning](), [earth-ai](), [framework](), [global](), [google](), [machine-intelligence](), [mapping](), [nasa](), [research](), [space]() ### AI overview The article presents MAPL-EMIT, a deep-learning framework for automating global detection, enhancement prediction, and source estimation of methane plumes from EMIT hyperspectral satellite measurements. ### Source excerpt Climate & Sustainability ## Optimizing Redshift Write Patterns: Tackling Tombstones and Ghost Rows DevFeed: [Optimizing Redshift Write Patterns: Tackling Tombstones and Ghost Rows]() Original publisher: [Read original article]() Author: Harichandan Pulagam Published: 2026-08-24T20:18:12Z Content type: article Language: en Sources: [Wealthfront]() Topics: [Amazon Redshift](), [dashboards](), [Data Quality]() Tags: [amazon-redshift](), [batch](), [dashboards](), [data-quality](), [latency](), [load](), [performance](), [redshift](), [space](), [wealthfront-engineering]() ### AI overview This Wealthfront engineering post examines how Redshift tables grew to nearly 10 times the size of their useful data because deleted rows remained on disk as ghost rows. It describes the resulting read and write latency and the write strategies adopted to control table size. ### Source excerpt Amazon Redshift is a core part of our analytics platform, powering dashboards, data quality checks, ad-hoc analytical workloads, and downstream reporting on a shared cluster. Because everything runs on the same cluster, the size and health of our tables directly affects every workload. At Wealthfront, data drives every decision we make, which means any performance... Read more ## Experiment in reducing target directory size on nightly DevFeed: [Experiment in reducing target directory size on nightly]() Original publisher: [Read original article]() Author: Jakub Beránek Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Inside Rust Blog]() Topics: [Rust](), [Compiler](), [data]() Tags: [compilation](), [compiler](), [disk-space](), [information](), [rust](), [space](), [state]() ### AI overview The Cargo Team is testing -Zembed-metadata=no by enabling it by default on the nightly channel. The experiment aims to reduce Rust target directory size by avoiding duplicated crate metadata in build artifacts, while gathering feedback about viability and user impact. ### Source excerpt TL;DR: The Cargo Team will be rolling out an experiment to identify user impact for a proposed change. Cargo will enable the -Zembed-metadata=no feature on the nightly channel by default, which can help reduce the size of the target directory somewhat. This is an experiment designed to gather feedback about viability of this feature. Users are not expected to migrate to support this feature, but to report any issues and opt-out if needed in the meantime. What is this about? High disk usage of Rust compilation artifacts is frequently cited as one of the biggest annoyances of Rust users. In our 2025 State of Rust survey, it was actually the second most commonly reported problem, right after compilation speed. There are various reasons why the target directory can become quite large, such as: Cargo compiles the whole crate graph from scratch by default, which produces a lot of build artifacts. Debug information takes a lot of disk space. Incremental compilation artifacts take a lot of disk space. While you can disable debug information or incremental compilation to reduce the target directory size, that of course comes with severe trade-offs in compilation speed and debuggability of your program. However, there is one source of data in the target directory that currently takes too much size even though it doesn't really have to. It is the "crate metadata", which can be duplicated across multiple files. We will focus on that in this blog post. What causes duplicated (meta)data For years, Cargo has been using pipelined compilation to speed up building of crate graphs. When compiling a library crate, it tells the compiler to produce an .rmeta file (which contains all the crate metadata required to use this library) as soon as possible, even before having the final executable code available. This enables dependent crates to start compiling sooner. However, once the library does finish compiling, the final produced .rlib file will contain both the executable code and the Ru ## The Database at 550 Kilometers: What Orbital Computing Means for Distributed Databases DevFeed: [The Database at 550 Kilometers: What Orbital Computing Means for Distributed Databases]() Original publisher: [Read original article]() Author: Isaac Wong Published: 2026-08-06T00:00:00Z Content type: opinion Language: en Sources: [Cockroach Labs]() Topics: [data centers](), [Databases](), [Artificial Intelligence](), [Inference](), [GPU]() Tags: [2026](), [ai](), [compute](), [data-center](), [data-centers](), [database](), [distributed](), [energy](), [gpu](), [inference](), [infrastructure](), [nvidia](), [solar](), [space](), [spacex]() ### AI overview The article examines orbital data centers as a possible response to the energy, cooling, and land constraints of terrestrial facilities. It describes proposed satellite-based computing projects, including an orbital Nvidia H100 Gemini inference workload, and outlines the roles of solar power, radiative cooling, and open orbit. ### Source excerpt In Ashburn, Virginia, a row of servers draws 40 megawatts from the grid and exhales it as heat. ## Worth Reading 070126 DevFeed: [Worth Reading 070126]() Original publisher: [Read original article]() Author: Russ Published: 2026-07-01T12:59:45Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [recommendation systems](), [Cybersecurity](), [systems](), [Rocket]() Tags: [analysis](), [article](), [china](), [cybersecurity](), [space](), [spacex](), [systems](), [worth-reading]() ### AI overview A roundup of developer and technology reading covering recommender-system task analysis, SpaceX and orbital debris, semiconductor cooling reproducibility, and the evolution of cybersecurity risks and defenses. ### Source excerpt A better, more nuanced understanding of tasks in recommender systems can help minimize user costs across the entire recommendation process. The current estimate of the world's population is 8.264 billion people, so the share price of SpaceX is currently at a phenomenal USD $261 per head. The analysis from space monitoring firm LeoLabs, provided to Breaking Defense, found that from January 2021 to January 2025 China has abandoned 51 spent rocket bodies in LEO above 650 kilometers (about 404 miles) in altitude, more than doubling the number for the previous five years to bring the total to 96. A big problem, Pauzauskie said, revolves around reproducibility. So far, labs haven't been able to show that they can consistently cool semiconductors. This article recounts the evolution of modern computing systems to provide an analysis of security risks and their evolution, with a past and present look at key cyber-defense innovations as well as a perspective on future cybersecurity hard problems. ## Reclaim Ceph Capacity Through CephFS Transcoding DevFeed: [Reclaim Ceph Capacity Through CephFS Transcoding]() Original publisher: [Read original article]() Author: Anthony D'Atri Published: 2026-07-01T00:00:00Z Content type: article Language: en Sources: [Ceph Blog]() Topics: [Transcodings](), [Software](), [data]() Tags: [2026](), [4k](), [article](), [blog-post](), [ceph](), [cephfs](), [dram](), [efficiency](), [en-article](), [en-blog-post](), [enterprise](), [enterprise-storage](), [filesystem](), [fujitsu](), [git](), [hardware](), [performance](), [software](), [space](), [storage](), [tentacle]() ### AI overview This article addresses rising storage demands and hardware costs by discussing CephFS capacity efficiency. It describes replicated pools, Erasure Coding, and Fast EC in Ceph Tentacle, while the title identifies CephFS transcoding as the article's focus. ### Source excerpt Data expands to fill available storage (and beyond)! ¶ It used to be that enterprise storage meant 6RU rackmount Fujitsu 2351 Eagles, each holding a mind-boggling 380 MiB of data: enough for a whole company! Today that 380 MiB can't even hold a 4k pickleball video. Enterprises, educational instutitions, and really just about anyone these days demand storage capacities that start on the order of hundreds of tebibytes and rapidly grow to pebibytes. As this article is written in the spring of 2026, the memory market, which includes DRAM, SSDs, and legacy HDDs, has experienced a dramatic escalation of pricing. It is not uncommon to be quoted a price four times what the same hardware cost a year ago, and there are signs that it is going to get worse before it gets better. What's a poor ammonite to do?? Cephers find themselves between the Charybdis of quotes approaching Disaster Area's hypermathematics and the Scylla of hungry users armed with torches and git forks. Git forks, pitchforks. Get it? Sigh. Tough room. Anyway... Short of nuking the site from orbit, how do we make everyone happy, or at worst mildly discontented? Efficiency! CephFS ¶ CephFS is a popular, highly available and scalable software-defined POSIX-style distributed filesystem that can easily store tens of pebibytes of precious data. Or, alternately, cat videos. Ceph deployments often begin small, with replicated pools for perceived performance needs. As the cluster grows to more nodes and more data, it may become feasible and desirable to switch to Erasure Coding (EC) to make more efficient use of raw capacity. An EC pool thus can require substantially less raw storage for a given amount of user data, or store gobs more user data on a given amount of raw capacity This EC overhead table presents efficiency (space amplification) factors for a spectrum of EC profiles. Replicated pools usually maintain three copies of data, so for comparison they manifest an overhead factor of 3.0. EC 4+2 or 6+3 presents a ## How an astrophysicist uses Codex to help simulate black holes DevFeed: [How an astrophysicist uses Codex to help simulate black holes]() Original publisher: [Read original article]() Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [codex](), [Algorithms](), [data-processing](), [Programming](), [Simulation and Design]() Tags: [algorithms](), [applied-ai](), [codex](), [data](), [data-processing](), [event](), [images](), [model](), [relativity](), [scale](), [space](), [time](), [tools](), [workflows]() ### AI overview Astrophysicist Chi-kwan Chan uses Codex to refine and test algorithms for simulating electrons and ions around black holes. The article explains how these simulations, data processing, and large-scale computing workflows help interpret Event Horizon Telescope observations and study extreme physics and general relativity. ### Source excerpt Discover how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein's theory of general relativity. ## A Problem Framing Kernel DevFeed: [A Problem Framing Kernel]() Original publisher: [Read original article]() Author: Morteza Pourmohamadi Published: 2026-06-04T02:51:27Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [User experience (UX)](), [Artificial Intelligence]() Tags: [concepts](), [design-thinking](), [requirements](), [space](), [ux]() ### AI overview This commentary examines the essential structure of problem framing in design. It argues that effective framing begins by collecting relevant concepts, needs, requirements, stakeholders, constraints, assumptions, and unknowns, then connecting those elements to generate insight; it identifies the kernel as "collect, connect, commit." ### Source excerpt What you need before you have a problem worth solving A few weeks ago, I caught up with my good friend Lucas Mara, a thoughtful design leader. We were deep in a conversation about the value of design in the age of AI when he mentioned Richard Rumelt's classic, Good Strategy Bad Strategy. It had The post A Problem Framing Kernel appeared first on UX Magazine. ## 5 countries, 12 days, one ClickHouse: inside Alexey's APJ AI Tour DevFeed: [5 countries, 12 days, one ClickHouse: inside Alexey's APJ AI Tour]() Original publisher: [Read original article]() Author: Siddhant Agarwal Published: 2026-05-27T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [Streaming](), [data](), [Flight](), [fly]() Tags: [australia](), [clickhouse](), [customer](), [ecosystem](), [executive](), [real-time](), [scale](), [space](), [streaming](), [tech](), [time]() ### AI overview ClickHouse's CTO and creator, Alexey Milovidov, undertakes a 12-day tour across Mumbai, Bengaluru, Singapore, Jakarta, Seoul, and Tokyo, combining customer meetings, keynotes, roundtables, press interviews, technical sessions, and meetups. The article highlights customer demand for real-time analytics at scale and ClickHouse's presence in the APJ developer and technology ecosystem. ### Source excerpt 5 Countries, 12 Days, One ClickHouse: Inside Alexey's APJ AI tour ## Shipping a Trillion Parameters With a Hub Bucket: Delta Weight Sync in TRL DevFeed: [Shipping a Trillion Parameters With a Hub Bucket: Delta Weight Sync in TRL]() Original publisher: [Read original article]() Author: Amine Dirhoussi; Quentin Gallouédec; Kashif Rasul; Lewis Tunstall; Edward Beeching; Albert Villanova del Moral; Leandro von Werra; Sergio Paniego Published: 2026-05-27T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [NCCL]() Tags: [artificial-intelligence](), [compute](), [hub](), [hugging-face](), [inference](), [llm](), [model](), [models](), [nccl](), [open-source](), [payload](), [policy](), [rl](), [space](), [storage](), [sync](), [train](), [training](), [trl](), [update](), [vllm]() ### AI overview The article describes delta weight synchronization for asynchronous reinforcement-learning training. A TRL change stores only modified model weights in sparse safetensors files and lets vLLM fetch them from a Hugging Face bucket, reducing transfer payloads and enabling disaggregated training without a shared cluster. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Dual-Stack SR-MPLS DevFeed: [Dual-Stack SR-MPLS]() Original publisher: [Read original article]() Published: 2026-05-21T06:16:00Z Content type: tutorial Language: en Sources: [ipSpace.net blog]() Topics: [SR-MPLS](), [networking](), [IS-IS](), [Routing (disambiguation)](), [Demo](), [GitHub]() Tags: [github](), [implement](), [ipv4](), [ipv6](), [is-is](), [links](), [mpls](), [netlab](), [network](), [router](), [routing](), [space](), [sr-mpls]() ### AI overview This tutorial demonstrates a dual-stack SR-MPLS lab built with netlab. It explains IPv4 and IPv6 SID addressing, loopback and core-link address pools, and the resulting IS-IS and MPLS forwarding-table entries. ### Source excerpt After the introduction to SR-MPLS demo I did during the Segment Routing workshop @ ITNOG10, we moved to dual-stack SR-MPLS - can we assign node segment identifiers (SIDs) to IPv4 and IPv6 prefixes? The demo used the same three-router network as the previous one, with IPv4 SIDs starting at one and IPv6 SIDs starting at 101: Read more ... ## How NASA and Chainguard used AI to support secure, compliant Artemis mission readiness DevFeed: [How NASA and Chainguard used AI to support secure, compliant Artemis mission readiness]() Original publisher: [Read original article]() Author: Dimension Published: 2026-04-17T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [chainguard](), [data](), [Retrieval-Augmented Generation](), [Model Context Protocol (MCP)](), [datasets](), [cloud-infrastructure](), [User Experience](), [Scalability](), [Grafana]() Tags: [ai](), [ai-in-space](), [artemis-2](), [artemis-ii](), [automated](), [chainguard](), [chainguard-containers](), [compliance](), [data](), [embeddings](), [fedramp](), [mcp](), [pipelines](), [retrieval](), [safety](), [scalability](), [space](), [user-experience]() ### AI overview The article describes how NASA and MRI Technologies partnered with Chainguard to build a secure, continuously compliant software foundation for Artemis and Habitable Worlds Observatory missions. It discusses attempts to use retrieval-augmented generation and MCP with mixed-format safety data, while highlighting unresolved data-fidelity and traceability challenges. ### Source excerpt How NASA Artemis used AI and Chainguard to enable secure, compliant software and faster mission readiness in high-stakes environments. ## Meet FlexBox: The Powerful New Layout System for Compose DevFeed: [Meet FlexBox: The Powerful New Layout System for Compose]() Original publisher: [Read original article]() Author: Nav Singh Published: 2026-03-27T18:22:23Z Content type: article Language: en Sources: [Stories by Nav Singh 🇨🇦 on Medium]() Topics: [Jetpack Compose](), [Compose](), [ui](), [CSS]() Tags: [3](), [adaptive](), [alignment](), [android](), [android-app-development](), [androiddev](), [api](), [code](), [compose](), [configuration](), [container](), [css](), [flexbox](), [introduction](), [jetpack-compose](), [layout](), [space](), [ui]() ### AI overview This tutorial introduces FlexBox, a new Jetpack Compose layout system inspired by CSS Flexbox. It explains how FlexBox arranges children dynamically and covers FlexBoxConfig parameters for direction, wrapping, alignment, spacing, and item distribution, along with Modifier.flex controls and code samples. ### Source excerpt Header image Jetpack Compose continues to evolve, and with the introduction of the new FlexBox layout, we finally have a powerful, flexible way to design adaptive UIs -- inspired by the CSS Flexbox model. https://developer.android.com/develop/ui/compose/layouts/adaptive/flexbox If you've ever used Row, Column, or FlowRowYou'll feel right at home. FlexBox acts as a superset, combining their capabilities while providing granular control over alignment, wrapping, and item distribution. Until now, we had to choose between rigid layouts (Row/Column) or more dynamic ones (FlowRow/FlowColumn). FlexBox merges the best of both worlds -- flexibility and simplicity. What Is FlexBox? Composable brings the concept of flexible layouts from the web into Jetpack Compose. Arranges its children dynamically, allowing them to grow, shrink, or wrap based on available space and configuration. Preview FlexBoxFlexBox API🏗 Building blocks and their roles.FlexBoxThe main composable responsible for arranging children.FlexBox( config = { direction(FlexDirection.Row) wrap(FlexWrap.Wrap) justifyContent(FlexJustifyContent.SpaceBetween) alignItems(FlexAlignItems.Center) gap(8.dp) }, modifier = Modifier .border(1.dp, Color.Black) ) { Text( "Item 1", Modifier .flex { grow(1f) } .background(color = randomColor()) .border(1.dp, Color.Black) ) Text( "Item 2", Modifier .flex { basis(80.dp) } .background(color = randomColor())) }Screenshot FlexBox SampleFlexBoxConfigConfigures the container's layout behavior -- direction, wrapping, justification, alignment, and spacing. Key parameters: direction: Controls the main axis (Row, Column). wrap: Determines if items flow onto new lines. Here we have 3 options: Wrap, NoWrap, WrapReverse justifyContent: Distributes space along the main axis(start, end, etc). alignItems: Defines how items align on the cross-axis(start, end, etc). alignContent: Controls how multiple lines are distributed along the cross-axis. This applies only when the wrap is FlexWrap.Wrap or FlexWr ## RocksDB Compression in Ceph: Space Savings with No Performance Cost DevFeed: [RocksDB Compression in Ceph: Space Savings with No Performance Cost]() Original publisher: [Read original article]() Author: Daniel Alexander Parkes, Anthony D'Atri Published: 2025-12-17T00:00:00Z Content type: article Language: en Sources: [Ceph Blog]() Topics: [Compression](), [Database](), [Hardware](), [ibm]() Tags: [blog-post](), [ceph](), [compression](), [configuration](), [cost](), [devices](), [en-article](), [en-blog-post](), [ibm](), [mon](), [nvme](), [osd](), [performance](), [rados](), [reef](), [rocksdb](), [space](), [storage]() ### AI overview This article reports Ceph performance tests showing that enabling RocksDB compression can substantially reduce metadata database space, especially for smaller objects, without harming throughput or resource consumption. The tests used IBM Storage Ceph 7.1 with BlueStore OSDs, HDD object storage, and NVMe devices for the RocksDB WAL and database. ### Source excerpt Introduction ¶ In the world of data storage, engineers and architects constantly face a fundamental dilemma: the trade-off between performance and efficiency. It's a balancing act. When you want to save space, you typically enable features like compression, but the common assumption is that this will cost you performance, a CPU cycle tax that slows throughput. But what if you could significantly reduce your metadata storage footprint without slowing things down? This search for an answer to this question started with research work from Mark Nelson, who published a blog post on ceph.io that covers RocksDB tuning in depth, exploring RocksDB compression with positive results. These promising results sparked a conversation on the upstream GitHub about enabling compression by default; a link to the PR is available here. To build on the previous investigation, the Ceph performance team ran tests on a robust hardware configuration running IBM Storage Ceph 7.1 (Reef). The cluster used the BlueStore OSDs for an erasure-coded (EC 4+2) pool, with a hybrid OSD storage setup: HDDs for object data and fast NVMe drives for the BlueStore WAL+DB. To understand the test, it's helpful to know what the WAL+DB is. In modern Ceph, the BlueStore storage engine manages all data on the OSDs (physical devices). To do this, it must maintain a vast catalog of internal metadata: think of it as a high-speed index that quickly locates every piece of data. RocksDB, a high-performance key-value database, manages this critical index. In our hybrid cluster, the RocksDB database runs on the fast NVMe deviceses, while the actual object data resides on the slower HDDs. Because this metadata can grow very large, RocksDB's efficiency, how much space it consumes on those expensive NVMe drives, is a critical factor in the cluster's overall cost and performance. Our test, therefore, focuses on a simple, high-stakes question: Can we compress this metadata to save space without paying a performance penalty? Ex ## TU Delft lecture: Security of Science DevFeed: [TU Delft lecture: Security of Science]() Original publisher: [Read original article]() Published: 2025-12-01T10:35:00Z Content type: article Language: en Sources: [Bert Hubert's writings]() Topics: [national security](), [Security](), [data]() Tags: [national-security](), [research](), [security](), [space](), [technologies](), [university]() ### AI overview A transcript of a TU Delft lecture examining the relationship between universities, science, and national security. It argues that universities are structured to disseminate knowledge rather than keep military secrets, and suggests that defense innovation is better developed in associated but separate laboratories. ### Source excerpt This is a mostly verbatim transcript of my lecture at the TU Delft VvTP Physics symposium "Security of Science" held on the 20th of November. Audio version (scroll along the page to see the associated slides): Thank you so much for being here tonight. It's a great honor. I used to study here. I'm a dropout. I never finished my studies, so I feel like I graduate tonight. This is a somewhat special presentation, it has footnotes and references, which you can browse later if you like what you saw. ## Exploring a space-based, scalable AI infrastructure system design DevFeed: [Exploring a space-based, scalable AI infrastructure system design]() Original publisher: [Read original article]() Published: 2025-11-04T16:58:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI Infrastructure](), [Google](), [AI Platform](), [AI Research]() Tags: [2025](), [ai](), [ai-infrastructure](), [compute](), [engineering](), [general-science](), [google](), [machine-learning](), [modular](), [orbit](), [paper](), [research](), [responsible-ai](), [science](), [solar](), [space]() ### AI overview Google describes Project Suncatcher, a research moonshot exploring solar-powered satellite constellations equipped with TPUs and connected by free-space optical links. The proposed system aims to scale machine learning compute in space while addressing communication, orbital dynamics, radiation, and modular satellite design challenges. ### Source excerpt General Science [Next page]()