North American companies ordered 31,311 robots valued at $1.963 billion, representing slight increases of 0.5% in units and 0.1% in revenue over 2023. These modest gains mark stabilization after years of volatility and record-breaking growth.

With the release of the Samsung Galaxy S25 Ultra, we may be witnessing the end of an era—the flagship phone as we know it may have reached its final, most refined form? While the Samsung’s latest entry remains a technological powerhouse, it highlights a fundamental snag that many premium smartphones face – are they still...

The post The Samsung Galaxy S25 Ultra – Evolutionary? appeared first on 1redDrop.

Our next iteration of the FSF sets out stronger security protocols on the path to AGI

When starting their AI initiatives, many companies are trapped in silos and treat AI as a purely technical enterprise, sidelining domain experts or involving them too late. They end up with generic AI applications that miss industry nuances, produce poor recommendations, and quickly become unpopular with users. By contrast, AI systems that deeply understand industry-specific processes, constraints, and decision logic have the following benefits:

• Increased efficiency — The more domain knowledge AI incorporates, the less manual effort is required from human experts.
• Improved adoption — Experts disengage from AI systems that feel too generic. AI must speak their language and align with real workflows to gain trust.
• A sustainable competitive moat — As AI becomes a commodity, embedding proprietary expertise is the most effective way to build defensible AI systems (cf. this article to learn about the building blocks of AI’s competitive advantage).

Domain experts can help you connect the dots between the technicalities of an AI system and its real-life usage and value. Thus, they should be key stakeholders and co-creators of your AI applications. This guide is the first part of my series on expertise-driven AI. Following my mental model of AI systems, it provides a structured approach to embedding deep domain expertise into your AI.

Throughout the article, we will use the use case of supply chain optimisation (SCO) to illustrate these different methods. Modern supply chains are under constant strain from geopolitical tensions, climate disruptions, and volatile demand shifts, and AI can provide the kind of dynamic, high-coverage intelligence needed to anticipate delays, manage risks, and optimise logistics. However, without domain expertise, these systems are often disconnected from the realities of life. Let’s see how we can solve this by integrating domain expertise across the different components of the AI application.

1. Data: The bedrock of expertise-driven AI

AI is only as domain-aware as the data it learns from. Raw data isn’t enough — it must be curated, refined, and contextualised by experts who understand its meaning in the real world.

Data understanding: Teaching AI what matters

While data scientists can build sophisticated models to analyse patterns and distributions, these analyses often stay at a theoretical, abstract level. Only domain experts can validate whether the data is complete, accurate, and representative of real-world conditions.

In supply chain optimisation, for example, shipment records may contain missing delivery timestamps, inconsistent route details, or unexplained fluctuations in transit times. A data scientist might discard these as noise, but a logistics expert could have real-world explanations of these inconsistencies. For instance, they might be caused by weather-related delays, seasonal port congestion, or supplier reliability issues. If these nuances aren’t accounted for, the AI might learn an overly simplified view of supply chain dynamics, resulting in misleading risk assessments and poor recommendations.

Experts also play a critical role in assessing the completeness of data. AI models work with what they have, assuming that all key factors are already present. It takes human expertise and judgment to identify blind spots. For example, if your supply chain AI isn’t trained on customs clearance times or factory shutdown histories, it won’t be able to predict disruptions caused by regulatory issues or production bottlenecks.

 Implementation tip: Run joint Exploratory Data Analysis (EDA) sessions with data scientists and domain experts to identify missing business-critical information, ensuring AI models work with a complete and meaningful dataset, not just statistically clean data.

Data source selection: Start small, expand strategically

One common pitfall when starting with AI is integrating too much data too soon, leading to complexity, congestion of your data pipelines, and blurred or noisy insights. Instead, start with a couple of high-impact data sources and expand incrementally based on AI performance and user needs. For instance, an SCO system may initially use historical shipment data and supplier reliability scores. Over time, domain experts may identify missing information — such as port congestion data or real-time weather forecasts — and point engineers to those data sources where it can be found.

 Implementation tip: Start with a minimal, high-value dataset (normally 3–5 data sources), then expand incrementally based on expert feedback and real-world AI performance.

Data annotation

AI models learn by detecting patterns in data, but sometimes, the right learning signals aren’t yet present in raw data. This is where data annotation comes in — by labelling key attributes, domain experts help the AI understand what matters and make better predictions. Consider an AI model built to predict supplier reliability. The model is trained on shipment records, which contain delivery times, delays, and transit routes. However, raw delivery data alone doesn’t capture the full picture of supplier risk — there are no direct labels indicating whether a supplier is “high risk” or “low risk.”

Without more explicit learning signals, the AI might make the wrong conclusions. It could conclude that all delays are equally bad, even when some are caused by predictable seasonal fluctuations. Or it might overlook early warning signs of supplier instability, such as frequent last-minute order changes or inconsistent inventory levels.

Domain experts can enrich the data with more nuanced labels, such as supplier risk categories, disruption causes, and exception-handling rules. By introducing these curated learning signals, you can ensure that AI doesn’t just memorise past trends but learns meaningful, decision-ready insights.

You shouldn’t rush your annotation efforts — instead, think about a structured annotation process that includes the following components:

• Annotation guidelines: Establish clear, standardized rules for labeling data to ensure consistency. For example, supplier risk categories should be based on defined thresholds (e.g., delivery delays over 5 days + financial instability = high risk).
• Multiple expert review: Involve several domain experts to reduce bias and ensure objectivity, particularly for subjective classifications like risk levels or disruption impact.
• Granular labelling: Capture both direct and contextual factors, such as annotating not just shipment delays but also the cause (customs, weather, supplier fault).
• Continuous refinement: Regularly audit and refine annotations based on AI performance — if predictions consistently miss key risks, experts should adjust labelling strategies accordingly.

 Implementation tip: Define an annotation playbook with clear labelling criteria, involve at least two domain experts per critical label for objectivity, and run regular annotation review cycles to ensure AI is learning from accurate, business-relevant insights.

Synthetic data: Preparing AI for rare but critical events

So far, our AI models learn from real-life historical data. However, rare, high-impact events — like factory shutdowns, port closures, or regulatory shifts in our supply chain scenario — may be underrepresented. Without exposure to these scenarios, AI can fail to anticipate major risks, leading to overconfidence in supplier stability and poor contingency planning. Synthetic data solves this by creating more datapoints for rare events, but expert oversight is crucial to ensure that it reflects plausible risks rather than unrealistic patterns.

Let’s say we want to predict supplier reliability in our supply chain system. The historical data may have few recorded supplier failures — but that’s not because failures don’t happen. Rather, many companies proactively mitigate risks before they escalate. Without synthetic examples, AI might deduce that supplier defaults are extremely rare, leading to misguided risk assessments.

Experts can help generate synthetic failure scenarios based on:

• Historical patterns — Simulating supplier collapses triggered by economic downturns, regulatory shifts, or geopolitical tensions.
• Hidden risk indicators — Training AI on unrecorded early warning signs, like financial instability or leadership changes.
• Counterfactuals — Creating “what-if” events, such as a semiconductor supplier suddenly halting production or a prolonged port strike.

 Actionable step: Work with domain experts to define high-impact but low-frequency events and scenarios, which can be in focus when you generate synthetic data.

Data makes domain expertise shine. An AI initiative that relies on clean, relevant, and enriched domain data will have an obvious competitive advantage over one that takes the “quick-and-dirty” shortcut to data. However, keep in mind that working with data can be tedious, and experts need to see the outcome of their efforts — whether it’s improving AI-driven risk assessments, optimising supply chain resilience, or enabling smarter decision-making. The key is to make data collaboration intuitive, purpose-driven, and directly tied to business outcomes, so experts remain engaged and motivated.

Intelligence: Making AI systems smarter

Once AI has access to high-quality data, the next challenge is ensuring it generates useful and accurate outputs. Domain expertise is needed to:

1. Define clear AI objectives aligned with business priorities
2. Ensure AI correctly interprets industry-specific data
3. Continuously validate AI’s outputs and recommendations

Let’s look at some common AI approaches and see how they can benefit from an extra shot of domain knowledge.

Training predictive models from scratch

For structured problems like supply chain forecasting, predictive models such as classification and regression can help anticipate delays and suggest optimisations. However, to make sure these models are aligned with business goals, data scientists and knowledge engineers need to work together. For example, an AI model might try to minimise shipment delays at all costs, but a supply chain expert knows that fast-tracking every shipment through air freight is financially unsustainable. They can formulate additional constraints on the model, making it prioritise critical shipments while balancing cost, risk, and lead times.

 Implementation tip: Define clear objectives and constraints with domain experts before training AI models, ensuring alignment with real business priorities.

For a detailed overview of predictive AI techniques, please refer to Chapter 4 of my book The Art of AI Product Management.

Navigating the LLM triad

While predictive models trained from scratch can excel at very specific tasks, they are also rigid and will “refuse” to perform any other task. GenAI models are more open-minded and can be used for highly diverse requests. For example, an LLM-based conversational widget in an SCO system can allow users to interact with real-time insights using natural language. Instead of sifting through inflexible dashboards, users can ask, “Which suppliers are at risk of delays?” or “What alternative routes are available?” The AI pulls from historical data, live logistics feeds, and external risk factors to provide actionable answers, suggest mitigations, and even automate workflows like rerouting shipments.

But how can you ensure that a huge, out-of-the-box model like ChatGPT or Llama understands the nuances of your domain? Let’s walk through the LLM triad — a progression of techniques to incorporate domain knowledge into your LLM system.

As you progress from left to right, you can ingrain more domain knowledge into the LLM — however, each stage also adds new technical challenges (if you are interested in a systematic deep-dive into the LLM triad, please check out chapters 5–8 of my book The Art of AI Product Management). Here, let’s focus on how domain experts can jump in at each of the stages:

1. Prompting out-of-the-box LLMs might seem like a generic approach, but with the right intuition and skill, domain experts can fine-tune prompts to extract the extra bit of domain knowledge out of the LLM. Personally, I think this is a big part of the fascination around prompting — it puts the most powerful AI models directly into the hands of domain experts without any technical expertise. Some key prompting techniques include:

• Few-shot prompting: Incorporate examples to guide the model’s responses. Instead of just asking “What are alternative shipping routes?”, a well-crafted prompt includes sample scenarios, such as “Example of past scenario: A previous delay at the Port of Shenzhen was mitigated by rerouting through Ho Chi Minh City, reducing transit time by 3 days.”
• Chain-of-thought prompting: Encourage step-by-step reasoning for complex logistics queries. Instead of “Why is my shipment delayed?”, a structured prompt might be “Analyse historical delivery data, weather reports, and customs processing times to determine why shipment #12345 is delayed.”
• Providing further background information: Attach external documents to improve domain-specific responses. For example, prompts could reference real-time port congestion reports, supplier contracts, or risk assessments to generate data-backed recommendations. Most LLM interfaces already allow you to conveniently attach additional files to your prompt.

2. RAG (Retrieval-Augmented Generation): While prompting helps guide AI, it still relies on pre-trained knowledge, which may be outdated or incomplete. RAG allows AI to retrieve real-time, company-specific data, ensuring that its responses are grounded in current logistics reports, supplier performance records, and risk assessments. For example, instead of generating generic supplier risk analyses, a RAG-powered AI system would pull real-time shipment data, supplier credit ratings, and port congestion reports before making recommendations. Domain experts can help select and structure these data sources and are also needed when it comes to testing and evaluating RAG systems.

 Implementation tip: Work with domain experts to curate and structure knowledge sources — ensuring AI retrieves and applies only the most relevant and high-quality business information.

3. Fine-tuning: While prompting and RAG inject domain knowledge on-the-fly, they do not inherently embed supply domain-specific workflows, terminology, or decision logic into your LLM. Fine-tuning adapts the LLM to think like a logistics expert. Domain experts can guide this process by creating high-quality training data, ensuring AI learns from real supplier assessments, risk evaluations, and procurement decisions. They can refine industry terminology to prevent misinterpretations (e.g., AI distinguishing between “buffer stock” and “safety stock”). They also align AI’s reasoning with business logic, ensuring it considers cost, risk, and compliance — not just efficiency. Finally, they evaluate fine-tuned models, testing AI against real-world decisions to catch biases or blind spots.

 Implementation tip: In LLM fine-tuning, data is the crucial success factor. Quality goes over quantity, and fine-tuning on a small, high-quality dataset can give you excellent results. Thus, give your experts enough time to figure out the right structure and content of the fine-tuning data and plan for plenty of end-to-end iterations of your fine-tuning process.

Encoding expert knowledge with neuro-symbolic AI

Every machine learning algorithm gets it wrong from time to time. To mitigate errors, it helps to set the “hard facts” of your domain in stone, making your AI system more reliable and controllable. This combination of machine learning and deterministic rules is called neuro-symbolic AI.

For example, an explicit knowledge graph can encode supplier relationships, regulatory constraints, transportation networks, and risk dependencies in a structured, interconnected format.

Instead of relying purely on statistical correlations, an AI system enriched with knowledge graphs can:

• Validate predictions against domain-specific rules (e.g., ensuring that AI-generated supplier recommendations comply with regulatory requirements).
• Infer missing information (e.g., if a supplier has no historical delays but shares dependencies with high-risk suppliers, AI can assess its potential risk).
• Improve explainability by allowing AI decisions to be traced back to logical, rule-based reasoning rather than black-box statistical outputs.

How can you decide which knowledge should be encoded with rules (symbolic AI), and which should be learned dynamically from the data (neural AI)? Domain experts can help youpick those bits of knowledge where hard-coding makes the most sense:

• Knowledge that is relatively stable over time
• Knowledge that is hard to infer from the data, for example because it is not well-represented
• Knowledge that is critical for high-impact decisions in your domain, so you can’t afford to get it wrong

In most cases, this knowledge will be stored in separate components of your AI system, like decision trees, knowledge graphs, and ontologies. There are also some methods to integrate it directly into LLMs and other statistical models, such as Lamini’s memory fine-tuning.

Compound AI and modular workflows

Generating insights and turning them into actions is a multi-step process. Experts can help you model workflows and decision-making pipelines, ensuring that the process followed by your AI system aligns with their tasks. For example, the following pipeline shows how the AI components we considered so far can be combined into a modular workflow for the mitigation of shipment risks:

Experts are also needed to calibrate the “labor distribution” between humans in AI. For example, when modelling decision logic, they can set thresholds for automation, deciding when AI can trigger workflows versus when human approval is needed.

 Implementation tip: Involve your domain experts in mapping your processes to AI models and assets, identifying gaps vs. steps that can already be automated.

Designing ergonomic user experiences

Especially in B2B environments, where workers are deeply embedded in their daily workflows, the user experience must be seamlessly integrated with existing processes and task structures to ensure efficiency and adoption. For example, an AI-powered supply chain tool must align with how logistics professionals think, work, and make decisions. In the development phase, domain experts are the closest “peers” to your real users, and picking their brains is one of the fastest ways to bridge the gap between AI capabilities and real-world usability.

 Implementation tip: Involve domain experts early in UX design to ensure AI interfaces are intuitive, relevant, and tailored to real decision-making workflows.

Ensuring transparency and trust in AI decisions

AI thinks differently from humans, which makes us humans skeptical. Often, that’s a good thing since it helps us stay alert to potential mistakes. But distrust is also one of the biggest barriers to AI adoption. When users don’t understand why a system makes a particular recommendation, they are less likely to work with it. Domain experts can define how AI should explain itself — ensuring users have visibility into confidence scores, decision logic, and key influencing factors.

For example, if an SCO system recommends rerouting a shipment, it would be irresponsible on the part of a logistics planner to just accept it. She needs to see the “why” behind the recommendation — is it due to supplier risk, port congestion, or fuel cost spikes? The UX should show a breakdown of the decision, backed by additional information like historical data, risk factors, and a cost-benefit analysis.

 Mitigate overreliance on AI: Excessive dependence of your users on AI can introduce bias, errors, and unforeseen failures. Experts should find ways to calibrate AI-driven insights vs. human expertise, ethical oversight, and strategic safeguards to ensure resilience, adaptability, and trust in decision-making.

 Implementation tip: Work with domain experts to define key explainability features — such as confidence scores, data sources, and impact summaries — so users can quickly assess AI-driven recommendations.

Simplifying AI interactions without losing depth

AI tools should make complex decisions easier, not harder. If users need deep technical knowledge to extract insights from AI, the system has failed from a UX perspective. Domain experts can help strike a balance between simplicity and depth, ensuring the interface provides actionable, context-aware recommendations while allowing deeper analysis when needed.

For instance, instead of forcing users to manually sift through data tables, AI could provide pre-configured reports based on common logistics challenges. However, expert users should also have on-demand access to raw data and advanced settings when necessary. The key is to design AI interactions that are efficient for everyday use but flexible for deep analysis when required.

 Implementation tip: Use domain expert feedback to define default views, priority alerts, and user-configurable settings, ensuring AI interfaces provide both efficiency for routine tasks and depth for deeper research and strategic decisions.

Continuous UX testing and iteration with experts

AI UX isn’t a one-and-done process — it needs to evolve with real-world user feedback. Domain experts play a key role in UX testing, refinement, and iteration, ensuring that AI-driven workflows stay aligned with business needs and user expectations.

For example, your initial interface may surface too many low-priority alerts, leading to alert fatigue where users start ignoring AI recommendations. Supply chain experts can identify which alerts are most valuable, allowing UX designers to prioritize high-impact insights while reducing noise.

 Implementation tip: Conduct think-aloud sessions and have domain experts verbalize their thought process when interacting with your AI interface. This helps AI teams uncover hidden assumptions and refine AI based on how experts actually think and make decisions.

Conclusion

Vertical AI systems must integrate domain knowledge at every stage, and experts should become key stakeholders in your AI development:

• They refine data selection, annotation, and synthetic data.
• They guide AI learning through prompting, RAG, and fine-tuning.
• They support the design of seamless user experiences that integrate with daily workflows in a transparent and trustworthy way.

An AI system that “gets” the domain of your users will not only be useful and adopted in the short- and middle-term, but also contribute to the competitive advantage of your business.

Now that you have learned a bunch of methods to incorporate domain-specific knowledge, you might be wondering how to approach this in your organizational context. Stay tuned for my next article, where we will consider the practical challenges and strategies for implementing an expertise-driven AI strategy!

Note: Unless noted otherwise, all images are the author’s.

This article was originally published on Towards Data Science and re-published to TOPBOTS with permission from the author.

Enjoy this article? Sign up for more AI updates.

We’ll let you know when we release more articles like this one.

The post Injecting Domain Expertise Into Your AI System appeared first on TOPBOTS.

Depth perception helps mimic natural spatial awareness by determining how far or close objects are, which makes it invaluable for 3D imaging systems. Get expert insights on how depth perception works, the cues involved, as well as the various types of depth sensing cameras.

Recent advances in the fields of human-infrastructure interaction, electronic engineering, robotics and artificial intelligence (AI) have opened new possibilities for the development of assistive and medical technologies. These include devices that can assist individuals with both physical and cognitive disabilities, supporting them throughout their daily activities.

Researchers developed an automated system to help programmers increase the efficiency of their deep learning algorithms by simultaneously leveraging two types of redundancy in complex data structures: sparsity and symmetry.

Engineers developed a training method for multiagent systems, such as large numbers of drones, that can guarantee their safe operation in crowded environments.

A Bold Expansion of Medieval Realism Kingdom Come: Deliverance 2 is an ambitious sequel that refines and expands upon its predecessor’s foundations. Developer Warhorse Studios has crafted an immersive medieval world that, while often frustrating, offers a unique and engrossing experience for those willing to embrace its intricacies. This time around, the game doubles its...

The post Kingdom Come: Deliverance 2 – A Frustrating Yet Rewarding Medieval Journey appeared first on 1redDrop.

A Fresh Take on a Legendary Series The Civilization franchise has long been a cornerstone of the 4X strategy genre, and its latest installment, Civilization VII, takes a daring leap forward by overhauling many of its traditional mechanics. While some of these changes might be controversial, they introduce a level of strategic depth and dynamism...

The post Civilization VII Review: A Bold New Era for the Franchise appeared first on 1redDrop.

Drone shows are an increasingly popular form of large-scale light display. These shows incorporate hundreds to thousands of airborne bots, each programmed to fly in paths that together form intricate shapes and patterns across the sky. When they go as planned, drone shows can be spectacular. But when one or more drones malfunction, as has happened recently in Florida, New York, and elsewhere, they can be a serious hazard to spectators on the ground.

As the leading classified website for pre-owned robots, RobotsTrader.com connects buyers and sellers with unparalleled efficiency, transparency, and reliability. Think of it as AutoTrader, but for used robotics.

How Much Does It Cost to Develop a Messaging App like Telegram?

 

Why You Should Develop A Chat App Like Telegram/WhatsApp?

Mobile messaging apps are ruling the online communication industry. In this digital space, online communication has become extremely predominant for personal and professional chats. Online messaging apps for Android and iPhone are allowing people to chat at any time from anywhere over a secured network.

Besides personal chats, mobile messaging applications are now essential assets for enterprises too for safe and encrypted real-time communications with their on-site and remote working employees. Individuals or enterprises can share audio, videos, text messages, voice messages, and documents to a person or a group instantly right from their messaging app.

Cloud-based messaging apps like Telegram, Facebook Messenger, WhatsApp, and WeChat with their bunch of user-friendly online messaging apps are dominating the industry. Not only for online chatting, these apps were being used for making domestic and international calls at free of cost. Millions and billions of people are accessing these apps monthly.

It is the right time to hold the market opportunity for online messaging app development like Telegram and WhatsApp. Driven by the increasing user download rate of Instant messaging apps, the development of online chatting apps would be the best decision to attain market attention and a vast user base.

Want to build a messaging app like Telegram or WhatsApp?

This article would be a guide for enterprises who are looking to develop Telegram-like global leading mobile messenger app. Let’s look at the functionalities of the Telegram application. 

What and Why Is Telegram For?

Telegram is an instant messaging app and has got a place in the list of the top 10 most downloaded apps in the world. With a primary focus on speed, privacy, and security, this top mobile and desktop messaging app has launched into the market in 2013.

This cloud-based and secured online chatting application is best suitable for chatting, video calling, and document sharing over a safe and highly-encrypted network. Along with a secure private messaging facility, its potential to hold a group chat with up to 200,000 members is one of the big reasons behind its popularity.

Further, the other significant feature of the Telegram-like instant chat app is its customization and chat synchronization. Users can completely customize their application appearance as per their preferences and also can access the ongoing chats from multiple devices effortlessly.

Currently, the application has reported over 1 billion downloads and has approximately 700 million monthly active users. Here is the key information about the Telegram app.

• Application available: Android, iOS/iPhone, Mobile web, Windows, macOS, Linux
• Released On: 2013
• Available in: 12 languages
• 1,000,000,000+ downloads
• Developer: Telegram FZ LLC and Telegram Messenger Inc.

Know about the must-have features and development costs and be ready confidently to create instant messaging apps like Telegram.

Must-Have Features Of Instant Messaging App Like Telegram/WhatsApp

With over 700 million monthly active users, Telegram has become one of the top-rated messaging service apps. Can you guess what might be the reason behind such wide adoption of this popular messaging app? The answer is its features and functionalities.

Yes, the most beneficial features of trending messaging apps are giving a big success to the application. Here are the features of Telegram-the most popular mobile messenger app worldwide that you must consider during its clone app development.

• Hassle-free Registration & Login

App registration and login are the initial steps to access the app’s features. Telegram-like famous chat app allows users to access the application through OTP verification of their mobile numbers. It’s simple and easy login procedure is a reason for a higher user retention rate. Further, by offering multiple login methods to access the app’s features, you can increase the user experience.

• Instant Messaging

A chat app like WhatsApp or Telegram should allow users to send voice or text messages to their contacts or groups instantly with simple taps. Simple messaging option in chat apps will optimize the app performance and provides online chatting access to the users.

• Audio/Video Calling

It is one of the significant features that must be added in mobile chat apps like WhatsApp or Telegram to attract users. Enabling uninterrupted and free audio or video calling feature right from the application will make your app versatile and meets the diversified needs of app users. Making unlimited audio or video calls to persons or groups free of cost is truly a unique addition and optimizes the app’s quality.

• Instant Sharing Facility

WhatsApp or Telegram like popular online messaging applications for Android and iOS are offering instant sharing of documents, location details, contact details, photos, videos, animated stickers and emojis, and other media content with no restrictions on size. Hence, besides of general chatting feature, add a multi-media sharing facility for enriching the application’s functionality and users’ comfort.

• In-app Camera

Integration of this feature helps users to take instant photos of their choice and send them to a person or drop it in a group chat. In addition, don’t forget to integrate photo editing tools to help users in editing their images. It will add a little crunchy entertainment to app users and optimize their interest in using the application.

• Simple SYNCED

It’s a unique feature of a telegram-like trending instant messaging app that allows users to access their chats on multiple devices (phones, tablets, and computers) at a time by clicking on the automatic synchronization feature.

• High-security

Providing high-level privacy and security to the chats is one of the top-most requirements of instant messaging app development. Hence, while developing Telegram or WhatsApp clone apps make sure to keep the user’s chat secure from unauthorized access.

• User Profile

It’s a general feature of any mobile application. It helps users to view/edit their profile images. Users can also customize their privacy and security features at their convenience. 

• Chat Notifications

Chat notifications feature plays an essential role in notifying users upon receiving any message or content from their contacts or groups. Users can toggle the notifications feature on or off as per their interests.

• Smart search

Make sure to add a search feature in WhatsApp or Telegram clone app development. This feature allows users to search for contacts or groups and send instant text or media content on the go. It will save the users time and enhance their experience.

• Two-step authentication

It’s purely to provide high-level security to the user’s accounts. Along with the log-in procedure, the two-step verification process validates the user’s identity by sending an OTP to the registered accounts. Hence, it prevents unauthorized access and provides 1005 privacy and security to the conversations.

These are few top features that must-add to Telegram or WhatsApp clone app development. Hire #top mobile app developers who have proven experience in the design and development of features-rich Telegram/WhatsApp-like instant messaging apps development.

Get a free app quote!

 

How Much Does It Cost To Build A Chat App Like Telegram or WhatsApp?

If you have plans of creating a chat app or audio/video calling app like Telegram or WhatsApp, you might be eager to know how much it cost to make a chat app like Telegram/WhatsApp. However, estimating the final cost of chat app development will depend on many factors. Here are a few significant factors that affect the mobile app development cost:

• App features
• App integrations
• Mobile app development technologies
• App development platforms
• Complexity of app design
• Mobile app development company’s location and team size
• Top app developers hourly rate

Among these cost-impacting factors, the hourly cost of the custom mobile app developers you hire will decide the final cost of the software development. Considering all the above facts, the estimated cost of Telegram or WhatsApp like online messaging app development might range from $25,000 to $40,000.

Being one of the most trusted and top mobile application development companies (USA), USM offers the best economical quote for developing your chat app will all the features you require.

Final Words!

Telegram or WhatsApp or Snapchat-like instant messaging app development is beneficial as the usage of online chat apps is increasing worldwide. For personal communication with friends or family members and business communications with employees or clients, instant messaging apps are the first choice of people. Hence, investments in messaging applications (Android/iPhone/Web) offer companies incredible business opportunities. 

Drop your chat app requirements and get a free app quote!

[contact-form-7]

In a far-reaching decision, the U.S. Copyright Office has ruled that AI-generated content — modified by humans — can now be copyrighted.

The move has incredibly positive ramifications for writers who polish output from ChatGPT and similar AI to create blog posts, articles, books, poetry and more.

Observes writer Jacqueline So: “The U.S. Copyright Office processes approximately 500,000 copyright applications each year, with an increasing number being requests to copyright AI-generated works.”

“Most copyright decisions are made on a case-to-case basis.”

In other news and analysis on AI writing:

*ChatGPT’s Online Editor Gets an Upgrade: Released just a few months ago, ChatGPT’s online editor ‘Canvas’ just got a performance boost.

The tool — great for polishing-up text created with ChatGPT — now runs on ChatGPT-o1, an AI engine that has been hailed for its advanced reasoning capabilities.

Observes writer Eric Hal Schwartz: “You can enable the o1 model in Canvas by selecting it from the model picker or typing the command: /canvas.”

For a comprehensive tour of ChatGPT’s editor, check out: “Ultimate Guide: New ChatGPT Editor, Canvas.”

*The DeepSeek Fallout: Dirt-Cheap AI Ahead for Writers: After roiling the stock market last week by proving that AI nearly as good as the most advanced version of ChatGPT can be produced for pennies-on-the-dollar, one thing is certain: Writers have extremely cheap — and extremely powerful — AI in their future.

The reason: The programmers behind the DeepSeek chatbot appear to have demonstrated that by punching-up the code running AI, they could create a chatbot competitive to ChatGPT by using computer chips that only cost a fraction of the chips needed to create ChatGPT.

Observes lead writer Cade Metz: DeepSeek said “it built its new AI technology more cost-effectively and with fewer hard-to-get computer chips than its American competitors — shocking an industry that had come to believe that bigger and better AI would cost billions and billions of dollars.”

*AI for Writers Summit Slated for March 6: If you’re looking for a quick study on the future of AI for writers, put this upcoming virtual meeting on your calendar.

Hosted by the Marketing Artificial Intelligence Institute, the event promises to offer writers AI how-tos on:

~Responsibly transforming your storytelling with speed and precision

~Enhancing productivity without sacrificing creativity

~Building strategies that future-proof your career or content team

Bonus: Agree to give-up your contact information and you get in free.

*Writers in Government Can Now Use ChatGPT Safely: Wordsmiths in government gun-shy about using ChatGPT due to data privacy concerns need fret no more.

ChatGPT’s maker OpenAI has just released a new version of its AI — dubbed ChatGPT Gov — specially designed to comply with strict government regulations regarding data safety and privacy.

Observes writer Geoff Harris: “Dr. Rob McDole — the Director of the Center for Teaching and Learning at Cedarville University — tells us that the main difference between ChatGPT Gov and ChatGPT Enterprise is that agencies will be able to use it in their own Microsoft Azure Cloud.

“So the data is highly protected because it is all sitting inside Microsoft servers,” McDole says.

*Another ChatGPT Researcher Quits Over Safety Concerns: Add Steven Adler, a former safety officer at OpenAI, to the growing list of researchers who have quit the ChatGPT-maker over safety concerns.

Says Adler: “Honestly, I’m pretty terrified by the pace of AI development these days,” he said.

“When I think about where I’ll raise a future family, or how much to save for retirement, I can’t help but wonder: Will humanity even make it to that point?”

*Love at First Price Cut: Many U.S. Businesses Already Gaga Over DeepSeek: The Wall Street Journal reports that more than a few businesses are already gaga over the possibility that DeepSeek’s dirt-cheap AI could dramatically reduce costs for the tech.

Observes Marc Kermisch, chief technology officer, Emergent Software: “What is exciting to me is having additional competition in this space and frankly having them shoot an arrow across the bow of the Big Tech firms.

“I would have to assume we’ll see some pricing pressure on the U.S. market.”

*Google Hedges Bets With Another $1 Billion Investment in ChatGPT-Competitor Anthropic: When you’re as deep-pocketed as Google, you have the luxury of investing in companies that compete directly with you — and with your chief competitors.

Witness the tech Goliath’s decision to invest $1 billion more in Anthropic — a feisty start-up that competes with Google’s own AI chatbot Gemini, as well as with ChatGPT.

Observes writer Rachel Metz: “The new funding comes in addition to more than $2 billion that Google has already invested in Anthropic.”

*Google Workspace Users Get AI Upgrade for Two-Bucks-a-Month: Determined to fully integrate at least some form of AI throughout its ecosystem at minimal cost, Google has upgraded its Workspace Business and Workspace Enterprise suites with an AI assistant for a nominal cost of $2/month.

One caveat: Before paying for either suite, be sure to test the AI assistant against Google Gemini — the most advanced form of chatbot AI that’s available from Google.

Essentially: You should be entirely convinced that the in-suite AI assistant will fulfill every skill currently available with Google Gemini — at the sophistication level Google Gemini performs that skill.

*AI Big Picture: The DeepSeek Phenomenon, Fully Analyzed: If you’re looking for a comprehensive look at the full implications of DeepSeek and the future of AI, check-out this 30-minute video.

The dirt-cheap AI roiled stock markets last week by apparently showing that AI competitive to ChatGPT could be created for pennies-on-the-dollar.

Programmers behind the DeepSeek chatbot appear to have demonstrated that by punching-up the code running AI, they were able to create a chatbot competitive to ChatGPT using computer chips that only cost a fraction of the computer chips needed to create ChatGPT.

Hosted by Deirdra Bos, CNBC’s TechCheck anchor and featuring Silicon Valley insider Chetan Puttagunta, general partner, Benchmark — a venture capital firm — the video turns over virtually every rock in the DeepSeek story.

Bottom line: A great place to click for the complete rundown.

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post “Tweaked” AI Writing Can Now Be Copyrighted appeared first on Robot Writers AI.

The Rise of Tri-Fold Smartphones Samsung’s foldable lineup has long dominated the market, but the sudden arrival of Huawei’s Mate XT changed the game. The Mate XT, the world’s first commercially available tri-fold smartphone, showcased the immense potential of this evolving form factor. Now, with rumors swirling around the highly anticipated Samsung Galaxy G Fold,...

The post Samsung Galaxy G Fold: The Tri-Fold Revolution Poised to Challenge Huawei’s Mate XT appeared first on 1redDrop.