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 […]

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

Hey devs! 👋 I'm the creator of this project, and I'm looking for help with an open issue: https://github.com/PedroVitor-Dev/Ped-Os/issues/1 If you're interested in contributing, reviewing possible solutions, or simply sharing ideas, I'd love to hear your thoughts. Contributions of all sizes are welcome, especially from developers looking to get involved in open source. Thanks in advance! 🚀💚 submitted by /u/Opposite-Stranger- [link] [留言]

It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/b

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

Amazon will use visual search and AI to show AI generated product images that match your search queries. The retailer says it will help guide users to products.

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]

At Red Hat Summit 2026, SWIFT shared the approach they’re rolling out — including the pilot results that informed it, and the scale they’re targeting next. Imagine running automation that touches roughly one third of global GDP every day. Tens of thousands of VMs, network devices in production, elevated privileges across production systems — and every playbook you run is, effectively, a software supply chain. That is the everyday reality at SWIFT, the secure financial messaging backbone connecting 11,000+ financial institutions across more than 200 countries. At Red Hat Summit 2026, Suvasish Ghosh , Product Owner for CI/CD Engineering and DevOps Engineering Services at SWIFT, joined Gregor Berginc , CEO of XLAB Steampunk, on stage to talk about how SWIFT is using Steampunk Spotter to govern Ansible automation at this scale. Why automation at SWIFT scale needs governance by design For SWIFT, security, availability and auditability are not features added on top — they are baseline engineering requirements. Regulatory frameworks (including DORA) codify the expectations, but as Suvasish made clear on stage, governance is by design at SWIFT, not driven solely by regulation. That stance reflects a simple truth that more and more platform teams are arriving at: automation is production infrastructure, and it must be governed as such. When you run an Ansible playbook, you are executing a software supply chain — collections, modules, roles, Python packages, system packages, the execution environment, the operating system underneath. The playbook itself is just the tip of the iceberg. Errors propagate fast. The blast radius is large. And yet, until recently, most of the security and compliance attention in IT organizations went to the applications shipping to production. The automation that built and configured everything around them often slipped through. Suvasish put it directly during the session: “We spent a lot of time being compliant and secure in our application, but w

1. Introduction to Docker One of the biggest historical challenges in software engineering has been environment inconsistency , the frustrating situation where an application works perfectly on one machine but unexpectedly fails elsewhere. And this is precisely the problem Docker was designed to solve. Docker is a containerization platform that packages applications and their dependencies into isolated environments called containers . Its goal is simple: Run applications consistently everywhere. Instead of configuring every machine manually, Docker packages everything an application needs to run. 1.1 What is Docker? Docker is a platform used to build, package, and run applications inside containers. A container includes: Application code Dependencies Runtime Libraries Configuration Simple mental model: Code + Dependencies + Runtime = Container This ensures applications behave the same way across development, testing, and production environments. 1.2 VMs vs Containers VMs (Virtual Machines) package: Application Dependencies Full Operating System Containers package only: Application Dependencies Runtime Containers share the host operating system kernel, making them much lighter and faster. Feature Virtual Machine Docker Container Includes OS Yes No Startup Speed Slow Fast Resource Usage Heavy Lightweight Size Large Small Simple analogy: Virtual Machine = Full House Container = Apartment in a Building 1.3 Docker Ecosystem Overview Docker includes multiple tools: Docker Engine The core service that runs containers. Docker Desktop A local GUI and development environment. Docker Hub A cloud registry for storing and sharing Docker images. Docker Compose A tool for running multiple containers together. Example: docker compose up Can start an entire application stack: Backend API Database Redis Frontend with a single command. 2. Docker Architecture & Fundamentals To use Docker effectively, it is important to understand how its core components work together. Docker follows a

Syndicated from the FavCRM blog . The old quote was two weeks. With an agent on the UI and a headless backend, it's an afternoon. A client needs a booking site. The old quote was two weeks: a calendar, a database, an availability engine, payments, a customer table. With an AI agent building the frontend and FavCRM as the headless backend , the real work is an afternoon. Here is the whole job, start to finish. The scenario A small clinic. Three services, one practitioner, online booking with deposit. You are the agency; you have an AI agent in your editor and a terminal. The plan: Register the clinic's FavCRM workspace Configure services and availability Wire one server route that talks to FavCRM Let the agent build the booking UI against that route Test a real booking end to end Step 1 — Register the workspace (~5 min) The favcrm CLI registers a workspace and issues an API key. No dashboard. favcrm signup request --email clinic@example.com \ --organisation-name "Bright Smile Clinic" favcrm signup verify --request-id < id > --code <6-digit-code> The verify step prints a fav_mcp_* key. Put it where your build can read it — never in the repo: export FAVCRM_API_KEY = fav_mcp_... Step 2 — Configure services and availability (~30 min) Hand the brief to your agent and let it call the tools. Inspect a schema first: favcrm tool describe create_service Then create each service: favcrm tool call create_service '{ "name": "New Patient Exam", "durationMinutes": 45, "price": "80.00" }' favcrm tool call create_service '{ "name": "Cleaning", "durationMinutes": 30, "price": "60.00" }' Set when the practitioner works, so availability is real: favcrm tool call set_staff_availability '{ "weekday": "mon", "start": "09:00", "end": "17:00" }' Repeat per weekday. At this point the backend is done — services, hours, an availability engine that knows about clashes. You wrote no schema. Step 3 — One server route (~30 min) The browser must never hold the API key. Put it in one server route tha

Syndicated from the FavCRM blog . The number that predicts whether an agent is safe to let loose isn't the tool count. When people compare agentic CRMs, they count tools. The number that actually predicts whether an agent is safe to let loose is a different one: annotation coverage . An MCP tool annotation tells the agent what a tool does to the world — whether it reads or mutates, whether it's safe to retry, whether it reaches an external service. Without annotations, the agent is guessing. This is what they are, and why a catalog's annotation coverage matters more than its tool count. What an MCP annotation is Every MCP tool can carry hints alongside its input and output schemas: readOnlyHint — the tool only reads; it changes nothing. Safe to call freely. destructiveHint — the tool mutates or deletes. The agent should confirm before calling. idempotentHint — calling it twice with the same input has the same effect as once. Safe to retry on a timeout. openWorldHint — the tool reaches an external service (sends an email, charges a card), so its effects leave the system. These are not documentation for humans. They are machine-readable signals the agent reasons over before it acts. Why they prevent the worst failures The dangerous class of agent failure is not "the agent couldn't do something." It's "the agent did the wrong destructive thing because it misread an ambiguous instruction." Delete the customer instead of the tag. Refund the wrong invoice. Cancel every booking instead of one. Annotations let the agent self-gate. A well-annotated catalog means the agent calls list_members without ceremony but pauses to confirm before cancel_booking , because one is marked read-only and the other destructive. Pre-MCP function-calling had no equivalent — every tool looked the same to the model, so safety lived entirely in the prompt. Why coverage matters more than count A 190+ tool catalog with 100% annotation coverage is safer than a 30-tool catalog with none. A tool that l