Software Localization Strategies

🧑🏽 Designing Better Personalization UX. With guidelines on how to better tailor content and features to user’s needs and interests. ✅ Customization allows users to choose exactly what they want. ✅ Personalization anticipates what they want behind the scenes. ✅ We personalize to match specific needs without user’s effort. ✅ We allow users to customize preferences, filters, layout, data. 🤔 But often only very few people customize their experience. 🚫 Past behavior doesn’t always predict future actions. 🤔 Users often have different needs at different times. ✅ Design a wide range of presets, templates and defaults. ✅ Track frequent actions and errors, and suggest shortcuts. ✅ Always add content, or reshuffle it, rather than removing it. ✅ Expose users to non-matching topics to avoid filter bubbles. 🤔 Often users don’t know what they need, or what they’d like. ✅ Good personalization is deeply embedded in a user journey. ✅ Search for moments when you want to win user’s attention. ✅ Ask users explicitly about their intent to learn their context. ✅ Let users override personalization if it goes against their needs. ✅ When journey breaks, don’t stitch it, but tie a beautiful bow. We can’t personalize without research. Collect reliable data about users first. Then segment users into groups with shared needs. Decide what messages you have for each group. And define a user model, content model and metadata that go along with it. Then decide on individual or role-based personalization. Choose touchpoints where personalized UX will be served. Apply the logic across your channels, but give users full control of their data. In that process, define how the team will test and measure the impact of personalization over time. Such a project might often feel like a huge leap of faith without immediate benefits. But if done well, it can increase customer lifetime value significantly — but you will need short-term victories to get a long-term commitment. So start slowly. Run experiments. Personalize where you can make the highest impact. More often than not, the outcome will be worth the effort — even although most users will never even notice it, they might stay for many years to come. ✤ Useful resources Personalization Pyramid, by Colin A. Eagan M.S., Jeffrey MacIntyre https://lnkd.in/eaztWU8e Definitive Guide To Personalization (free eBook, PDF) https://lnkd.in/eggR4hzB Five Levels Of Recommendations, by Guillaume Galante https://lnkd.in/eKqsZtJ5 Personalization Planning, by Jennifer Leigh Brown https://lnkd.in/e9N48x6F Successful Personalization, by Amy Schade https://lnkd.in/eNSUgQ9B Personalization UX Stats (Medium), by Mallory Kim https://lnkd.in/eRy9pvqt ✤ Books – Hello {first name}, by Rasmus Houlind – The Person in Personalisation, by David Mannheim – The Personalization Paradox, by Val Swisher, Regina Lynn Preciado – Personalization Mechanics, by John Berndt #ux #design

NVIDIA just dropped the LocateAnything VLM model. Here is the direct architectural breakdown: The Problem Most Vision-Language Models (VLMs) are terrible at spitting out coordinates quickly. They formulate visual grounding as a sequential task, predicting bounding boxes token by token. This strictly sequential generation creates a massive inference bottleneck. The Fix: Parallel Box Decoding (PBD) NVIDIA introduces Parallel Box Decoding, which treats a bounding box (or a point) as a single atomic unit. Instead of generating coordinates one by one, the framework predicts the entire coordinate set simultaneously in a single forward pass. The Architecture Vision Encoder: Utilizes a MoonViT encoder to extract visual tokens at their native resolution, which preserves fine-grained spatial details. Language Decoder: Powered by a Qwen2.5 language decoder. The Bridge: An MLP projector connects the two, converting visual tokens directly into block-level predictions. The Results To train this, NVIDIA curated LocateAnything-Data, a massive dataset with 138 million queries and 785 million bounding boxes. The result is a model that preserves high-IoU localization accuracy while delivering up to 2.5× higher throughput compared to older autoregressive methods. Paper: https://lnkd.in/g-s2WmYm Live demo on Huggingface: https://lnkd.in/gf_yp7ke #ComputerVision #EdgeAI #Robotics #MachineLearning #NVIDIA #DeepTech

I built a ROS 2 visual-inertial localization and mapping stack. Stereo cameras, IMU data, AprilTags, optimization with GTSAM, RealSense, offline mapping tools, dense fusion with nvblox, and RViz visualization. The goal wasn’t to make a perfect SLAM system. It was to understand the real architecture: sensor pipelines, frontend/backend separation, logging, optimization, mapping, and the details you only run into when building the system yourself.
 I wrote a post describing the project and results. The GitHub repo also has detailed readmes if you want to dig deeper. https://lnkd.in/gzs7J8UD #ROS2 #ComputerVision #Robotics #SLAM #Odometry #Mapping #OpenSource #Realsense #GTSAM

The CBN's latest data localization directive may be about payments today, but every CIO in a critical infrastructure sector should be paying attention. Starting January 1, 2027, banks, fintechs and payment service providers will be required to store payment transaction data generated in Nigeria on local servers. Many people will see this as a banking regulation. I see it as part of a broader trend. Across the world, governments are increasingly treating data as a strategic national asset. The conversation is no longer just about storage costs, cloud adoption, or digital transformation. It is now about: ✅ Data sovereignty ✅ Regulatory oversight ✅ Cybersecurity ✅ National resilience ✅ Critical infrastructure protection. If you lead technology in sectors such as power, telecommunications, healthcare, transportation, or oil and gas, this raises an important question. If regulators believe payment data should reside in Nigeria, what happens when they apply the same logic to other forms of critical national data? For the power sector, that could include: 1. Customer and billing records 2. Smart meter data 3. Network and infrastructure information 4. Operational and outage data 5. Geographic and asset intelligence The smart CIO is not waiting for a regulation before thinking about these issues. Instead, they are asking: 1. Can our systems operate if data localization becomes mandatory? 2. Where are our backups and disaster recovery environments located? 3. How dependent are we on foreign jurisdictions for access to critical data? 4. How quickly can we adapt if regulations change? By the time a regulator issues a directive, the organizations that benefit most are usually the ones that started preparing years earlier. The CBN's announcement is about payments. The strategic signal is much bigger.

We are excited to share our #RAL2025 paper "Drift-free Visual SLAM using Digital Twins"! We remove drift in #VSLAM by aligning the sparse 3D point cloud from VSLAM to a Digital Twin using point-to-plane matching; no visual data association is needed! The result? Accurate, globally consistent localization, even without GPS. Code released! PDF: https://lnkd.in/gRdGpcfX Video: https://lnkd.in/g-m7RMr6 Code: https://lnkd.in/gqjqeT6g Kudos to Roxane MERAT, Giovanni Cioffi, Leonard Bauersfeld! University of Zurich, University of Zurich Faculty of Science, UZH Department of Informatics, European Research Council (ERC), AUTOASSESS, UZH Innovation Hub #SLAM, #Localization, #DigitalTwins

I have been exploring NVIDIA's newly released LocateAnything-3B, a compact 3B parameter vision-language model focused on visual grounding. Most vision models can tell you what is in an image. LocateAnything-3B focuses on a different question: "Where exactly is it?" Give the model an image and a natural language query such as: • Locate the damaged road section • Find the traffic cone • Identify the fire extinguisher • Locate the electrical panel Instead of only describing objects, the model returns their precise locations within the image. Potential applications include: • AI-powered inspections • Smart city operations • Municipal issue detection • Robotics and automation • Visual agents • Document intelligence • Industrial safety monitoring What caught my attention is the balance between capability and size. At 3B parameters, it looks practical for experimentation while addressing a very useful real-world problem. The future of vision AI is not only about understanding what is present in an image, but also understanding exactly where it is. #AI #ComputerVision #VisionLanguageModels #VisualGrounding #NVIDIA #MachineLearning #ArtificialIntelligence #EdgeAI #Robotics #SmartCities #DocumentAI #MLOps

Below is a diagram of our agentic architecture (well, part of it). See the top-right box: "recommender service"? Let’s talk about that. At Aampe, we split copy personalization into two distinct decisions: ➡️ Which item to recommend ➡️ How to compose the message that delivers it Each calls for a different approach. For item recommendations, we use classical recommender systems: collaborative filtering, content-based ranking, etc. These are built to handle high-cardinality action spaces — often tens or hundreds of thousands of items — by leveraging global similarity structures among users and items. For message personalization, we take a different route. Each user has a dedicated semantic-associative agent that composes messages modularly — choosing tone, value proposition, incentive type, product category, and call to action. These decisions use a variant of Thompson sampling, with beta distributions derived from each user’s response history. Why split the system this way? Sometimes you want to send content without recommending an item — having two separate processes makes that easier. But there are deeper reasons why recommender systems suit item selection and reinforcement learning suits copy composition: 1️⃣ Cardinality. The item space is vast — trial-and-error is inefficient. Recommenders generalize across users/items. Copy has a smaller, more personal space where direct exploration works well. 2️⃣ Objectives. Item recommendations aim at discovery — surfacing new or long-tail content. Copy is about resonance — hitting the right tone based on past response. 3️⃣ Decision structure. Item selection is often a single decision. Copy is modular — interdependent parts that must cohere. Perfect for RL over structured actions. 4️⃣ Hidden dimensions. Item preferences stem from stable traits like taste or relevance. Copy preferences shift quickly and depend on context — ideal for RL’s recency-weighted learning. 5️⃣ Reward density. Item responses are sparse. Every content delivery yields feedback — dense enough to train RL agents, if interpreted correctly. In short: recommenders find cross-user/item patterns in large spaces. RL adapts to each user in real time over structured choices. Aampe uses both — each matched to the decision it’s best for.

ROS 2 Monocular Visual SLAM [1]. This ROS 2 package (slam_ros2) implements a feature-based monocular Visual SLAM (Simultaneous Localization and Mapping) system. The project processes a simulated camera feed from a video file, reconstructs the camera's trajectory, and builds a 3D map of the environment. The core SLAM logic is self-contained and communicates with the ROS 2 ecosystem for data input and visualization. Key Features:  - Video Publisher: A node to publish video frames and camera info, simulating a live camera feed.   - Core SLAM Node: The main node that orchestrates the SLAM process.   - Feature Extraction & Matching: Uses ORB features and a Brute-Force matcher with ratio and symmetry tests.  - Pose Estimation & Tracking: Initializes with recoverPose and tracks frame-to-frame.  - 3D Landmark Triangulation: Creates new 3D map points from 2D feature matches.  - Backend Optimization: Implements bundle adjustment using g2o to refine camera poses and the 3D map.  - Map Maintenance: Culls unstable or poorly observed landmarks.  - Visualization: Publishes the camera's trajectory, current pose, and the 3D point cloud map for viewing in RViz.  - Launch File: Provides a simple way to start the entire system with a single command. #ros #ros2 #opensource #robot #robotics #navigation #slam #localization #mapping [1] https://lnkd.in/dTctWhPN

The Data Residency Regulations in the UAE and Middle East: Security Implications and Operational Challenges Amid Crises- For years, I have emphasized that data residency requirements—mandating local storage of sensitive data—are not inherently security controls. In fact, they can create concentrated attack surfaces, making systems more vulnerable to targeted cyberattacks by limiting dispersal and creating predictable points of failure. The UAE and broader Middle East have rapidly adopted stringent regulations in recent years. The UAE's Federal Decree-Law No. 45 of 2021 (Personal Data Protection Law, or PDPL) provides a comprehensive framework for personal data privacy, while sector-specific rules apply to finance (e.g., UAE Central Bank mandates for local storage of customer/transaction data) and healthcare (Federal Law No. 2 of 2019 on ICT in Health Fields, restricting health data storage/transfer outside the UAE without approval). Similar privacy-driven laws have emerged in Saudi Arabia (PDPL) and other GCC countries, emphasizing data localization for sensitive financial, healthcare, and personal information. Data localisation push is being seen globally. Recent geopolitical crises and wars in the region have highlighted a critical limitation: compliance with data residency ensures data is "present," but it does not guarantee business continuity or operational resilience. Conflicts can disrupt infrastructure, power, connectivity, or access within a country or region, rendering localized data inaccessible despite being compliant. Relying solely on on-premises data centers[or not using resilient architectures in cloud] exacerbates risks, as physical or cyber disruptions halt operations entirely. Even multi-region setups within the same affected area may fail to mitigate issues. Cloud providers often offer advantages, enabling swift data relocation to unaffected countries or maintaining operational control planes outside impacted zones—provided contractual and regulatory flexibility exists. Probably it is time when Industrywide regulators should introduce greater fluidity, allowing temporary or emergency data movement outside impacted jurisdictions during crises, balanced with safeguards. This would enhance true resilience without undermining privacy goals, ensuring organizations can maintain operations amid uncertainty. [views are entirely personal] #cloud #dataresidency , #Crisis

Most content creators do not have a content problem. They have a repurposing problem. They spend hours coming up with a single idea, write one post about it, and then start the whole exhausting process again the next day. Meanwhile that same idea had at least ten more pieces of content inside it that they never touched. Claude can pull all of them out. Here are 5 prompts to turn one idea into 10 content pieces in minutes: 1. The content exploder Every strong idea has layers. A story, a lesson, a stat, a debate, a how-to, a hot take. Most people only ever use one layer and move on. This prompt finds the rest. Prompt: "Here is my core idea: [describe it]. Explode this into 10 different content angles. For each one, tell me the format that works best, the hook I should open with, and who it will resonate with most." 2. The platform adaptor The same idea hits differently depending on where it lives. A LinkedIn post is not a tweet is not a newsletter. Most people either write for one platform or water everything down trying to write for all of them. Prompt: "Here is a piece of content I have written: [paste it]. Adapt this for LinkedIn, Twitter, and a newsletter. Each version should feel native to that platform, not like a copy and paste job with the word count changed." 3. The format shifter A written post can become a carousel. A carousel can become a video script. A video script can become a podcast talking point. The idea never changes. The container does. Prompt: "Here is my core idea: [describe it]. Turn this into five different formats. A short form post, a carousel outline, a video script hook, a newsletter intro, and a thread. Keep the core message identical but make each feel built for its format." 4. The angle finder The same idea can be framed as a story, a lesson, a warning, a case study, or a contrarian take. Each framing attracts a different reader. Most people only ever use one. Prompt: "Here is my idea: [describe it]. Give me 10 different ways to frame this as a post. Include a story angle, a contrarian take, a data-led angle, and one that opens with a question. Write the opening line for each." 5. The series builder One idea does not have to be one post. It can be a five-part series that keeps your audience coming back all week. Most people think in single posts. Start thinking in series. Prompt: "Here is an idea I want to explore: [describe it]. Turn this into a five-part content series. Give each part a clear focus, a hook, and a reason why someone who read the previous part would need to read this one." --------- The creators who show up consistently are not coming up with more ideas than everyone else. They are getting more out of the ideas they already have. Save this and use it on your next idea before you move on to the next one. P.S. ~ For more updates like this: 1. Scroll to the top 2. Click "View my newsletter" 3. Subscribe, and you'll never miss a thing in the world of AI ever again.