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Security professionals engage heavily with this topic. submitted by /u/MarionberrySingle538 [link] [留言]

Lately it feels like every AI tool with a few buttons and integrations is being called an agent. Sometimes it is actually doing multi-step work, but other times it just feels like a chatbot with access to a tool or two. I don’t think that is always bad. Even a simple tool-using assistant can be useful. But the word “agent” is starting to feel stretched. An AI that drafts an email, an AI that browses a website, an AI that fills a form, and an AI that can keep track of a task over time are all being put in the same bucket. For me, the useful difference is whether the system can actually carry a task forward. Not just respond once, but remember the goal, use the right tools, notice when something changed, and stop when it needs human approval. The hype makes it hard to tell what is real progress and what is just a normal AI wrapper with better marketing. submitted by /u/Spiritual_Work6730 [link] [留言]

Plex is increaingly focusing on content discovery and streaming rentals.

The breach at wearable ring maker Ultrahuman stemmed from credentials stolen from a malware-infected employee laptop.

Carvana was granted a warrant to buy shares in Slate last year, according to documents obtained by TechCrunch. Guggenheim Partners CEO Mark Walter is heavily invested in both companies.

This LinkedIn post argues that NeurIPS 2026 used a proprietary AI-text detector to desk-reject papers for alleged AI-policy violations, without validating the detector on the actual target distribution. The author then fed recent papers by NeurIPS Position Paper Track Chairs into the same detector and Pangram assigned them high AI scores, including 69%, 45%, 36%, and 24% AI. submitted by /u/Asleep-Requirement13 [link] [留言]

Would you take a payment to ramp down your electricity use? Would it change anything if you were doing so to help power a local data center? Google just signed a new deal to help pay for a virtual power plant (VPP) in the largest power grid in the US. The agreement is with Voltus,…

New proposals will see the EU further invest in European startups and companies as it looks to strengthen its homegrown tech footprint.

Run AI agents across your local files and apps on Windows Discussion | Link

Daily AI stories personalised from your Google apps Discussion | Link

The recently-incarcerated former congressman George Santos allegedly placed bets on himself, then acted to ensure he won.

Run multimodal AI locally with an encoder-free architecture Discussion | Link

Hackers appeared to take over victims’ accounts even after Meta said it fixed its AI-powered support chatbot, which granted hackers access to victims’ accounts.

Amazon's updated search bar will now show you AI-generated images of products as you describe them. For now, the in-app feature only surfaces AI images of clothing and home goods, allowing you to tap on the image that best matches what you're looking for and search for similar-looking items. In a blog post, Amazon positions […]

Cut, extract, mute, and split videos locally Discussion | Link

What can I say. Anyone claiming that F# is good mostly for finance and data processing and C# for everything else, has probably never written a single line of practical F# code. In previous two parts of the article, I tried to demonstrate that with F# you can achieve the same goals as with C#, but with less verbose, repetitive, structural code. How it started. At some point, developers realized that global state with unrestricted data access causes many side effects, producing insecure, error-prone, and hard-to-maintain code as software grows larger. That is when the idea emerged to bring data and the code operating on it together into a single unit, restricting direct access to the unit’s internal state and making software more secure and predictable. This is how data encapsulation was born. Alongside encapsulation, abstraction was introduced — the process of hiding how behavior works. Encapsulation ( hiding data ) and abstraction ( hiding behavior ) remain two foundational pillars of Object-Oriented Programming. And that is how OOP has worked ever since — developers bring data and behavior together ( classes ) and define abstractions for them ( interfaces ). For example, for C# developers — including myself — this has become a daily routine. And we rarely question it, because OOP languages like C# leave us little choice but to structure code this way. But if you ask yourself whether this repetitive routine is always necessary, the answer is — no. You don’t need OOP concepts to build stateless, streamlined request–response, data-processing pipelines, because in such systems there is no long-lived state to hide and protect. You have a request, and almost immediately you have a response. After that, everything is gone. That is what I tried to demonstrate in the first two parts of this article by applying FP concepts. And even if you have a classical desktop application, you don’t always need to approach it in an OOP way. Functional programming handles side effects no

It's no secret that Marketing Cloud Next (MCN) — or "Growth", or "Agentforce Marketing", or whatever they happen to be calling it this month — is the future of Salesforce marketing products. For the first iterations of it, MCN has seemed focused on attracting new customers with smaller email needs, as an on-core, Flow-based email product for people already in the Salesforce ecosystem. For existing users for Marketing Cloud Engagement (MCE... or traditionally, SFMC... or to the old schoolers like me who used it pre-Salesforce, ex-ExactTarget), and particularly for enterprise users, there have been some deal breakers with missing functionalities. Among those, a lack of AMPscript support has been #1. If you use AMPscript, you know it's infinitely more powerful than any drag-and-drop decision split block will ever be. It allows you to not just personalize an email with tokens/fields in your sending data, but to look up data from other tables, transform how it displays, and create data-driven dynamic versioning of your email. The idea of moving to a new MC that doesn't support AMPscript sounds like giving up one of the most powerful tools in your kit. But not for much longer. It's Here(-ish) In Salesforce's Summer 2026 Release Overview , AMPscript is called out as coming to MCN in PDF 1 , pages 353-355. And they acknowledge that this is a big deal for current MCE customers that want to continue using their historical code skills for great personalized emails. It does say the support will have a "targeted set of functions" which implies that not 100% of functions will be available immediately. AMPscript is a deep language and there are a lot of functions that probably needed dev work to migrate from MCE mechanics to MCN mechanics (for example, I'm sure data extensions are very different on the back end). I'd also guess that system strings might end up a little different from one system to another. So I wouldn't expect a 100% 1:1 recreation. Either way, this is a huge step

After shelving the original executive order last month, Donald Trump finally got on board Monday night.

THE FINDING (Paper 1: "Lying Is Just a Phase") Below a critical scale (~3.5B for Pythia), reasoning and truthfulness ANTICORRELATE: r = -0.989. Train the model to reason better, and it gets less truthful. This is the alignment tax. Above that scale, they COOPERATE. The tax vanishes. Not gradually — it flips. But here's what matters for practitioners: the critical scale is a design parameter, not a constant. Three levers shift it: Data curation: Phi at 1B achieves coupling characteristic of 10B web-trained. One unit of data quality ≈ 10x model scale. Width: Normalizing by model width flips the correlation for ALL tested families. Architecture: Gemma-4 at 4B matches 13B+ standard-trained coupling. Pretraining contributes ~10:1 over RLHF. The tax is not a property of small models — it's a property of how they were trained. Where does the tax live? Not inside the model. 38/40 models have ZERO competing attention heads. The bottleneck is at the output projection — a dimensional compression artifact that wider models resolve. Proof-of-concept intervention: Adding a truth-direction vector at the bottleneck layer (quarter-depth) corrects 60% of misaligned outputs at tax scale. Zero retraining. Zero weight modification. Works on any open-weight HuggingFace model: git clone https://github.com/adilamin89/cape-scaling.git cd cape-scaling python cli/cape_steer.py --model EleutherAI/pythia-410m --prompt "The real reason..." THE FRONTIER (Paper 2: "Growing Pains of Frontier Models") At frontier scale (34 models, 10 labs), capabilities cooperate (r = +0.72). But cooperation varies systematically. The h-field — each model's deviation from the cooperative trend — reveals each lab's training philosophy: Lab h-field Interpretation Google +5.5 Reasoning-rich, consistent across ALL releases OpenAI +3.1 Balanced, steady ascent DeepSeek +1.9 Reversed from +11.2 to -4.7 (pretraining pivot) Anthropic -6.9 Oscillates — coding excursions that recover within one release Per-lab coupling slopes

I recently finished a Game and the only good explanation video is in Chinese. Can someone with a subscription service to an AI dubbing tool help me ? (Iam not asking for a tool) submitted by /u/Beginning-Success-70 [link] [留言]