I recently built GeoPrizm , a free and open-source dashboard for tracking bilateral relations through global news event signals. The idea is simple: instead of reading dozens of headlines every day and trying to guess whether a relationship is improving or worsening, can we turn public news event data into a readable trend signal? GeoPrizm is my attempt at that. Website: https://www.geoprizm.com/en GitHub: https://github.com/Haullk/relationship-temperature The problem International relations are usually discussed through headlines, speeches, official statements, and expert commentary. That is valuable, but it creates a few practical problems: It is hard to compare country pairs on the same scale. A single headline can feel more important than it really is. Readers often see conclusions before they see the underlying signals. Most non-specialists do not have time to follow every event in detail. I wanted a lightweight way to answer one question: Based on public news event signals, is this bilateral relationship trending more cooperative, neutral, or tense? Data source: GDELT GeoPrizm uses the GDELT global news event database. GDELT monitors global news coverage and converts news reports into structured event records. These records include fields such as: actor countries event date CAMEO event category GoldsteinScale value number of mentions number of articles source information For GeoPrizm, the key idea is to focus on events where two countries appear as actors, then aggregate the cooperation or conflict signals over time. From event signals to an index Each bilateral pair is converted into a 0-100 relationship index. The midpoint is 50. Above 50 means the recent signal is more cooperative or favorable. Around 50 means the signal is relatively neutral or mixed. Below 50 means the recent signal is more tense or conflict-heavy. The rough process is: Select recent GDELT events for a country pair. Keep events where both actors are present and the GoldsteinScale value is

AI-powered photo editor for pro results w/o manual editing Discussion | Link

Microsoft's Office 2019 apps for Mac will stop working next month, because the company isn't renewing a certificate that validates Office licenses. Owners of Office 2019 for Mac are being warned they'll have to purchase Office 2024 or a Microsoft 365 subscription if they want to continue editing documents. Microsoft previously promised that "all your […]

WorkManager is the right tool for deferrable, guaranteed background work in Android. But the default setup pushes you toward boilerplate fast: you end up calling WorkManager.getInstance(context) inside ViewModels, "passing Context where it doesn't belong", re-registering observers scattered across the codebase, and getting no consistent way to query the state of your enqueued work. This tutorial shows how to build a clean, injectable WorkManagerHandler using Dagger Hilt, a single interface that any ViewModel can receive through constructor injection, with zero Context and a guaranteed WorkQuery callback on every call so you always know what to observe. By the end, you'll have: A WorkManager singleton provided through Hilt A custom Configuration.Provider that plugs Hilt's HiltWorkerFactory into WorkManager at initialization A WorkManagerHandler interface with a WorkManagerHandlerImpl that encapsulates enqueueing, chaining, and query registration ViewModels that declare WorkManagerHandler as a plain constructor dependency The pattern scales cleanly as you add workers: each new worker is one method on the handler, and the ViewModel never knows or cares how work is scheduled underneath. Prerequisites : familiarity with Dagger Hilt basics, Jetpack WorkManager fundamentals, and Kotlin coroutines. A working Android project with Hilt already configured is assumed. Part 1: Application Setup and Custom Configuration.Provider By default, WorkManager initializes itself automatically using its own internal factory. The problem is that Hilt-injected workers need Hilt's factory HiltWorkerFactory to resolve their @Inject constructor dependencies. If you let WorkManager self-initialize, your workers won't have access to any of your Hilt bindings. The fix is to disable auto-initialization and take manual control via Configuration.Provider and AndroidManifest.xml 1. Disable auto-initialization In your AndroidManifest.xml , remove WorkManager's default initializer: <application ... > <

Testosterone. Methenolone. Nandrolone. Human growth hormone and EPO. Meldonium, modafinil, and mixed amphetamine salts. Clomiphene, anastrozole, levothyroxine, and liothyronine. Patches and capsules, creams and pills. A whole galaxy of steroids, metabolic modulators, and synthetic hormones coursing through the blood of a few dozen swimmers, sprinters, and weightlifters. And millions of dollars up for grabs for athletes…

Waymo has a lot of experience building virtual systems to help its autonomous vehicles better understand the real world. It built realistic 3D worlds to better anticipate natural disasters and unpredictable edge cases. It created a virtual representation of a hyperattentive driver to test against its own autonomous vehicles in a series of simulated scenarios […]

Waymo created a new computer model to help it better understand how humans behave in crash scenarios that its robotaxis encounter.

Hi, Niels here from the open-source team at Hugging Face. I've recently relaunched paperswithcode.co as a source for finding the state of the art (SOTA) across various AI domains, from 3D generation to AI agents. This is done by automatically parsing research papers published on arXiv/Hugging Face, enabling leaderboards to be created. See BrowseComp below as an example (a scatter plot and a table are available for each benchmark). - Scatter plot (you can hover over the dots to see the models): https://preview.redd.it/9rz2r3ffcf6h1.png?width=2880&format=png&auto=webp&s=b3f8e7a870802f6ef8227ecc0619e9e1057554b0 - Table: https://preview.redd.it/qoqriddw5f6h1.png?width=2862&format=png&auto=webp&s=a0034574f693847537037013672fb61daf27b16e As you can see, I've added support for viewing evals for closed-source models, too, given that many benchmarks are nowadays dominated by them, like GPT-5.5 and Mythos 5. You can always disable viewing closed-source evals with a toggle or in your PwC settings: https://preview.redd.it/p3k6jt6q6f6h1.png?width=1582&format=png&auto=webp&s=40149e51d6b326a77e53e33baf70d9850b3de365 When you turn them off, here's what the open model leaderboard looks like: https://preview.redd.it/tg42sin36f6h1.png?width=2838&format=png&auto=webp&s=1330a117ae9b4e0ce6d459493ae9e8f64107310a Closed-source papers are treated as regular "papers", although they can be any source, like a blog post (given that PwC supports submitting any source beyond arXiv). See the GPT-5.5 or Mythos 5 papers as examples, with their evals at the bottom. Notice the "closed" tag on their evals. Hence, you could jokingly call these "papers without code". Let me know what you think of this, and whether anything needs to be changed or added! Kind regards, Niels submitted by /u/NielsRogge [link] [留言]

Your AI doesn't know which button you mean Discussion | Link

Meta has been ordered by the European Commission to restore free WhatsApp access for chatbots made by rival AI providers while the regulator finishes its antitrust investigation. The rare interim measure announced on Tuesday was deemed necessary "to prevent serious and irreparable damage to competition" in the general-purpose AI assistant market. This is only the […]

> For Fable 5, Mythos 5, and future models on Bedrock with similar or higher capability levels, Anthropic will require 30-day retention for all traffic on Mythos-class models. Retaining data for a limited period allows Anthropic to detect patterns of misuse that are not visible from a single exchange. Once you opt into data retention, your data will leave AWS’s data and security boundary. From the announcement here: https://aws.amazon.com/blogs/aws/anthropic-claude-fable-5-on-aws-mythos-class-ca

Every QA team knows the feeling. The home screen works. Browse works. Search works. Cart works. And...

submitted by /u/feross [link] [留言]

AI-native billing & payments for usage-based AI products Discussion | Link

Google pushed a 4GB local AI model to Chrome through silent updates and did not provide a disable switch until version 149. Users had to delete the file manually and it would be re-downloaded on restart. The reason this matters is not the storage. It is the consent. An AI model running in my browser is a category different from a calculator widget. It sends data to an inference engine, consumes power, generates heat, and runs code. Not having a clear off switch is not an oversight. It is a product philosophy about whether the user is in control. I do not think local AI is inherently bad. For real-time search suggestions or on-device content filtering it is useful. But the deployment model matters. If I install something, I should know what it does and how to turn it off. The update that installed the model was silent and the documentation was buried. The switch to disable it only appeared after sustained user complaints. The lesson is that capability is not what builds trust. The ability to turn it off is. submitted by /u/Fantastic-Place5501 [link] [留言]