WhatsApp is testing another change to its Android app layout. A new Contacts tab is beginning to appear in the bottom navigation bar for some beta users, taking the place currently used by Communities.

The change may look small, but it could make everyday messaging quicker. Instead of searching through a long chat list to find someone, users may get a dedicated space to see favourite contacts, browse their contact list and check who is online.

What is the new WhatsApp Contacts tab?

The new Contacts tab is being tested with a limited number of WhatsApp beta users on Android. It sits at the bottom of the app and acts like a quick contact hub.

According to beta-tracking site WABetaInfo, the tab has a section for favourite contacts at the top, followed by a wider list of WhatsApp contacts. Users may also be able to sort that list by name, birthday or activity.

The activity option is perhaps the most useful part. It can place contacts who are currently online near the top of the list. For users who have not shared their online status, WhatsApp will continue to respect those privacy settings.

In short, the tab is not designed to change how WhatsApp messages work. It is meant to reduce the steps needed to start a new conversation.

Why is Communities being replaced?

WhatsApp is not removing the Communities feature itself. Communities help schools, housing societies, workplaces and other groups manage several related WhatsApp groups under one larger space.

Instead, WhatsApp has already added a Communities filter to the Chats section for some users. That means people can still find their communities without needing a permanent Communities button in the bottom bar.

With that shortcut available, WhatsApp appears to be testing whether the bottom-bar space can be used for something that more people may use every day – finding individual contacts.

This is still an experiment. WhatsApp is reportedly testing different layouts for different accounts, so some people may see changes involving the Updates or Meta AI sections instead.

What are the benefits for users?

The biggest benefit is speed. Imagine a person who has hundreds of chats, including office groups, family groups, shopping messages and old conversations. Finding one friend who is online may mean using search or scrolling for several seconds.

The Contacts tab could make that task much simpler. Users may be able to open WhatsApp, tap Contacts and start a chat immediately.

The favourites section can also be helpful for people who regularly message the same few contacts. Parents, business owners, students and professionals may find it easier to keep important conversations within quick reach.

WhatsApp is also reportedly including an option to add a new contact from this section. Users can enter a name, username and phone number, with the option to sync phone-number contacts to the device address book.

What it means for Communities users

People who use Communities often should not panic if the bottom-bar button disappears. The feature is expected to remain available through the Chats filter during this test.

However, the change may take some getting used to. Users who are used to opening Communities from the bottom bar will need to look for the filter in their chat screen instead.

This seems to be part of WhatsApp’s larger effort to make the bottom navigation bar more flexible. The company has been testing different positions for Contacts, Communities, Updates and Meta AI, so the final design may not look exactly like the current beta version.

When will it reach all WhatsApp users?

There is no confirmed public release date. The Contacts tab is currently rolling out gradually to some Android beta testers, which means even beta users may not see it immediately.

As with many WhatsApp tests, the feature could change before a wider release, arrive later on iPhone or remain limited to selected users for some time.

Meta has released Muse Glimmer, a new open-weight AI model designed to run on regular laptops and PCs. That one line is enough to make the launch interesting, because most powerful AI tools today still depend on cloud servers, paid APIs, or large data centres.

Muse Glimmer is Meta’s latest move in the open AI race. The company wants developers, students, researchers, and smaller businesses to have more control over how they use AI models. Instead of keeping everything locked behind a private system, Meta is again pushing the idea that more people should be able to download, study, and modify powerful AI tools.

With Muse Glimmer, we need to understand where AI is moving next. If useful AI can run directly on personal devices, laptops may become smarter work machines, not just screens connected to cloud apps.

What is Meta Muse Glimmer

Meta Muse Glimmer is a compact AI model from Meta’s Muse family. It is built to handle tasks such as reasoning, coding, planning, and everyday computer-based work.

In simple words, it is an AI model that can help with thinking and doing. It may help write code, organize files, draft emails, check information, plan workflows, or support small software tasks.

The most important part is that Muse Glimmer is described as an open-weight model that can run on a standard laptop or PC. That makes it different from many large AI systems that need expensive server hardware.

Open-weight does not always mean fully open-source. This is an important difference. In an open-weight model, the model’s trained parameters are shared so developers can download and use them. But the full training data, training method, or internal development process may not be completely public.

So, Muse Glimmer gives more freedom than a closed chatbot, but it may not mean everything behind the model is open.

Why running AI on laptops matters

Most people use AI through cloud-based tools. You type a prompt, the request goes to a server, and the answer comes back. This works well, but it has some limits.

First, it needs internet. Second, it can cost money at scale. Third, users may not want to send sensitive files, personal notes, or business documents to a cloud service.

A laptop-friendly AI model can reduce some of these problems. If the model runs locally, it can work faster for some tasks, give more privacy, and allow developers to build tools without paying for every single request.

For example, a small business owner could use a local AI assistant to sort customer queries, prepare invoices, or draft product descriptions. A student could use it to understand code without depending fully on an online chatbot. A developer could build a private helper for internal documents.

This is why Muse Glimmer is not just another model launch. It points toward a future where AI becomes part of the device itself.

How Muse Glimmer is connected to Muse Spark

According to reports, Muse Glimmer was developed using a method called distillation from Meta’s more powerful Muse Spark model.

Distillation is easier to understand with a classroom example. Imagine a senior teacher has deep knowledge, but it is not practical for every small school to hire that teacher. So the teacher’s knowledge is simplified into a useful guide that junior teachers and students can use more easily.

In AI, distillation means a smaller model learns from a larger model. The aim is to keep much of the usefulness while making the smaller model cheaper and easier to run.

Meta had earlier introduced Muse Spark as part of its Meta Superintelligence Labs work. Muse Spark 1.1 is designed for coding, tool use, computer tasks, and multimodal work, which means it can work with more than just text. Meta has also made Muse Spark available through the Meta Model API for developers in public preview.

Muse Glimmer looks like the lighter, more accessible member of this family.

Why Meta is pushing open-weight AI again

Meta has a long history with open AI models through its Llama series. The company gained strong developer attention by releasing models that people could download and build with.

With Muse Glimmer, Meta seems to be returning strongly to that approach. Mark Zuckerberg has argued that AI should not be controlled only by a few companies or governments. His recent essay, “The Future Is for Everyone,” presents Meta’s view that personal AI tools should be widely available.

This is also a business strategy. If many developers build apps using Meta’s models, Meta can become an important part of the AI ecosystem. It may not need to win every paid chatbot battle directly. It can still benefit if its models become widely used across apps, devices, and developer tools.

Who can use Muse Glimmer

Muse Glimmer may be useful for people who want more control over AI.

Developers can test it for local coding assistants, offline tools, automation, and private apps. Researchers can study how smaller models behave. Startups can build prototypes without depending completely on expensive cloud models. Power users can experiment with personal AI workflows on their own machines.

A practical example would be a freelance developer building a local assistant that reads project files and suggests fixes. Another example could be a small accounting firm using a private AI tool to summarize documents without uploading everything to a third-party server.

Of course, performance will depend on the laptop, memory, setup, and the model’s actual size. A normal office laptop may handle smaller tasks, while heavier use may need stronger hardware.

Competitors and market landscape

Meta is not alone in this race. OpenAI, Anthropic, Google, Mistral, DeepSeek, Alibaba, and several other companies are fighting for developer attention.

OpenAI and Anthropic mainly offer powerful closed models through apps and APIs. Google has Gemini models and strong cloud integration. Mistral has become known for open-weight models in Europe. DeepSeek and Alibaba’s Qwen models have pushed low-cost and open-weight AI from China into global developer discussions.

Meta’s advantage is its large user base, strong AI research teams, and history of developer adoption through Llama. Its challenge is trust. Some developers like open-weight models, but critics worry about safety, misuse, copyright, and the control Meta may still have over the larger ecosystem.

What this means for normal users

For everyday users, Muse Glimmer may not immediately feel like a new app they can download and use in one click. The first wave will likely matter more to developers and technical users.

But over time, laptop-ready AI models can change normal software. Your writing app may get a private assistant. Your coding tool may work better offline. Your personal finance app may analyze spending without sending data outside your device. Your laptop may handle more tasks even when the internet is weak.

That is the real promise of local AI – more useful tools, less waiting, and better control over private data.

Conclusion with key takeaways

Meta Muse Glimmer is important because it brings the AI conversation closer to personal devices. Instead of keeping advanced AI only in the cloud, Meta is showing that smaller, capable models can run on laptops and PCs.

It is still early, and users should wait for more technical details, real-world testing, and independent reviews. But the direction is clear. AI is moving from big servers toward personal machines.

Key takeaways –

• Meta Muse Glimmer is an open-weight AI model built for laptops and PCs.
• It is designed for reasoning, coding, planning, and computer-based tasks.
• Open-weight means developers can access model weights, but it is not always the same as fully open-source.
• Muse Glimmer is reportedly distilled from Meta’s stronger Muse Spark model.
• The model could help developers build more private, local AI tools.
• Meta is competing with OpenAI, Anthropic, Google, Mistral, DeepSeek, Alibaba, and others in the AI model race.

Facts Input- BI, AP News, The Guardian

Meta is trying to make a louder entry into the serious AI model race with Muse Spark 1.1, its updated agentic AI model built for coding, tool use and multimodal work.

The timing is interesting. OpenAI, Anthropic and Google have already built strong developer ecosystems around their models. Meta, known for Llama and consumer AI features inside Instagram, WhatsApp and Facebook, now wants developers to take its own in-house model more seriously.

Muse Spark 1.1 is not just another chatbot update. It is being positioned as a model that can help with longer tasks, coding problems, multi-step workflows and inputs like images, videos and documents.

What is Meta Muse Spark 1.1 Agentic Model

Meta Muse Spark 1.1 is the newer version of Meta’s Muse Spark AI model. It is designed for more “agentic” work, which means it can do more than answer a question and stop.

An agentic model can plan steps, use tools, check information, work across apps and continue toward a goal. For example, instead of only explaining a bug in code, it may help identify the problem, suggest a fix and support a larger coding workflow.

Meta has made Muse Spark 1.1 available in Thinking mode through the Meta AI app and website. It is also being opened to developers through the Meta Model API, which is in public preview for developers in the US.

Why this launch matters

Meta has millions of users across WhatsApp, Instagram, Facebook and its smart glasses ecosystem. If it can place a stronger AI model inside those products, AI may become part of daily social, shopping, messaging and work habits.

For developers, the bigger news is API access. Meta is no longer only using its AI inside its own products. It wants outside developers to build apps and tools using Muse Spark 1.1.

That puts Meta in direct competition with OpenAI, Anthropic, Google and xAI, especially in coding and agent workflows.

The pricing angle also matters. Reports say Meta is using aggressive pricing to attract developers. That could put pressure on rivals if the model performs well enough for real production use.

Main capabilities of Muse Spark 1.1

Muse Spark 1.1 is focused on three useful areas – coding, agentic workflows and multimodal understanding.

• In coding, Meta says the model can help detect and fix complex bugs. That is useful because developers do not only need code suggestions. They need help understanding why something broke.
• In agentic work, the model is expected to support end-to-end workflows across apps, including multi-agent systems. Multi-agent systems are setups where more than one AI agent works on different parts of a task.
• In multimodal work, the model can understand images, videos and documents. This can help with tasks like reading a screenshot, checking a file, reviewing a product image or understanding a video-based input.

Coding use cases –

The clearest use case for Muse Spark 1.1 is software development.

A developer could use it to inspect code, explain errors, suggest patches or help with repetitive coding tasks. A startup could connect it to an internal coding tool and use it for bug triage, test writing or documentation.

For example, if a team has a payment bug, the model may help compare logs, read the code path and suggest where the problem could be. A human developer still has to review the final fix, but the model can reduce the time spent searching.

That is the appeal. Good AI coding tools do not replace engineers overnight. They remove some of the slower parts of engineering work.

Multimodal use cases –

Muse Spark 1.1’s ability to understand different input types could make it useful beyond coding.

A product manager may upload a screenshot and ask what is confusing in the user interface. A marketing team may ask it to analyse a video script or a campaign document. A support team may use it to read customer-uploaded images and draft a response.

If Meta connects these abilities deeply with Instagram, WhatsApp and its smart glasses, the model could become more personal and practical. A user wearing smart glasses may ask about something they are seeing. A seller on Instagram may ask AI to create better product replies. A creator may ask for content ideas based on a video.

This is where Meta has an advantage. It already owns the platforms where people communicate, shop, post and create.

Meta Model API and developer push

The Meta Model API is important because it gives developers a way to build with Muse Spark 1.1 outside Meta’s own apps.

Reports say new Meta Model API accounts are getting $20 in free credits. That is a small but useful push to make developers test the model.

If developers like the quality and price, Meta could build a new business line around AI model access. That would be different from its usual ad-heavy business model.

But developer trust takes time. OpenAI, Anthropic and Google already have strong API users. Meta will need reliable performance, clear documentation, stable pricing and good support if it wants developers to stay.

How it compares with rivals

Muse Spark 1.1 will be compared with OpenAI’s GPT models, Anthropic’s Claude models, Google’s Gemini models and xAI’s Grok models.

OpenAI is strong in developer tools and general-purpose AI. Anthropic is known for long-context work, coding and enterprise use. Google has Gemini and deep cloud integration. Meta’s edge may come from lower pricing, social product reach and its ability to place AI inside apps people already use every day.

Still, Meta has to prove the model is not only cheap, but dependable. In business use, a weak answer can cost more than a higher API bill.

Limitations and concerns

There are still open questions.

1. First, availability is limited because the API public preview is for US developers.
2. Second, detailed official documentation and independent testing will matter before companies trust it for critical work.
3. Third, Meta’s AI work often raises privacy questions because of the company’s large social data ecosystem. Users and developers will want clarity on what data is used, how prompts are handled and what control they have.

There is also the safety angle. Meta’s earlier Muse Spark safety report discussed safeguards around high-risk areas such as cybersecurity and chemical or biological misuse. As models become more capable, these controls become more important.

Who should try Muse Spark 1.1

Developers working on coding tools, AI agents, automation products and multimodal apps may find Muse Spark 1.1 worth testing.

Startups with tight budgets may also be interested if Meta’s pricing remains competitive. A cheaper model that performs well can make AI features more affordable at scale.

For normal users, the model may show up through Meta AI, WhatsApp, Instagram or smart glasses features over time. They may not care about the model name, but they will notice if the assistant becomes better at planning, explaining, seeing and helping.

Conclusion – Key takeaways

Meta Muse Spark 1.1 Agentic Model is Meta’s attempt to move from consumer AI features into a more serious developer and enterprise AI race.

Its main strengths are coding support, agentic workflows, multimodal understanding and lower-cost API access. The model could help developers build AI tools, support app-based automation and improve Meta AI across consumer platforms.

The real test will be trust. If Muse Spark 1.1 is fast, affordable and reliable, Meta can become a stronger challenger to OpenAI, Anthropic and Google. If it is only cheap, developers may test it once and move back to models they already trust.

Facts Input- Meta, EvaluationReport

Meta and Reliance Industries are working together on a large AI-enabled data center in Jamnagar, Gujarat. Meta will lease 168 MW of capacity at Reliance’s hyperscale campus, making it Meta’s first major built-to-suit AI-ready data center capacity in India. This is a big development because artificial intelligence needs huge computing power. Every AI chatbot, recommendation engine, image tool, language model and business automation system runs on servers inside data centers. As AI usage grows in India, the need for local infrastructure is also rising.

For users, this may not look exciting immediately. But over time, such infrastructure can improve digital services, support AI tools in Indian languages, create jobs, and help India become a stronger base for global technology companies.

What is an AI-enabled data center

A data center is a large facility that stores servers, networking equipment, cooling systems, power systems and security infrastructure. It is where apps, websites, cloud services and AI platforms run in the background. An AI-enabled data center is built for much heavier computing work. AI systems need powerful chips, high-speed networking, strong cooling and stable electricity. Normal data centers can handle websites and apps, but AI workloads need more advanced infrastructure.

For example, when a user asks an AI assistant to write, translate, generate an image or analyze data, that request is processed in a data center. The faster and closer the infrastructure is, the better the experience can become.

Why Jamnagar matters

Jamnagar is already one of Reliance’s most important industrial locations. Reliance has large energy and infrastructure assets there, which makes it a natural place for a power-heavy data center campus. Meta-Reliance facility will have 168 MW capacity, with room to scale in the future. In data center language, 168 MW is a large capacity because it refers to the power needed to run and cool massive server infrastructure.

The project is expected to be completed within about two years, according to media reports. If executed well, Jamnagar could become an important AI infrastructure hub for India.

How Reliance benefits

Reliance is trying to position itself as a major provider of digital and AI infrastructure. The company already has strong telecom reach through Jio, large energy capabilities and deep industrial experience. By partnering with Meta, Reliance can strengthen its role in hyperscale data centers. Hyperscale means very large infrastructure built for global technology companies, cloud providers and AI platforms.

This also fits Reliance’s wider digital strategy. The company has been active in telecom, cloud, retail, payments, media and AI-linked services. A large data center partnership with Meta gives it another strong pillar in India’s digital economy.

How Meta benefits

Meta owns Facebook, Instagram, WhatsApp, Threads and several AI products. India is one of Meta’s biggest user markets, so local infrastructure is important. A data center in India can help Meta support AI features, content delivery, recommendation systems, business tools and future services for Indian users and enterprises.

Meta is also investing heavily in AI globally. Its Llama models, AI assistants, ad tools and creator products all need large computing capacity. Leasing capacity from Reliance gives Meta a local infrastructure base without building everything alone from scratch.

How it can benefit users

The biggest user benefit could be better digital performance. Local data center capacity can reduce the distance data travels, which may improve speed and reliability for some services.

For example, AI features inside WhatsApp, Instagram or business tools may become faster over time if more processing happens closer to Indian users. Businesses using Meta’s advertising and AI tools may also get better support for local campaigns and customer engagement.

Another benefit is AI in Indian languages. India has a huge population using Hindi, Tamil, Telugu, Bengali, Marathi, Gujarati, Kannada, Malayalam and many other languages. Local AI infrastructure can support more India-specific AI development and testing.

It may also help small businesses. A shop owner using WhatsApp Business, Instagram ads or AI-powered customer support tools could benefit if Meta improves its AI services for India.

Jobs and local economy impact

Large data centers create jobs in construction, electrical systems, cooling, security, maintenance, networking, operations and facility management. They also create indirect opportunities for contractors, vendors and service providers. The long-term job impact may be stronger in technical areas. India will need more cloud engineers, data center technicians, AI infrastructure specialists, cybersecurity professionals and energy management experts.

However, data centers do not create jobs in the same way as large manufacturing plants. They are capital-intensive and technology-heavy. So, the real benefit is not only direct hiring, but also the ecosystem around digital infrastructure.

Clean energy angle

Meta has also expanded its partnership with CleanMax to support more than 900 MW of renewable energy capacity in India, according to reports. This matters because data centers consume a lot of electricity. AI data centers need reliable power, and their energy demand is rising globally. If companies use renewable power, the environmental impact can be managed better.

Still, clean energy claims should be watched carefully. Data centers need continuous power, cooling and water management. The project’s real sustainability impact will depend on how energy, emissions, land and cooling needs are handled.

Competitors and market check

India’s data center market is becoming highly competitive. Major players include Yotta, CtrlS, Sify Technologies, Nxtra by Airtel, NTT Global Data Centers, STT GDC India, AdaniConneX and global cloud companies such as Amazon Web Services, Microsoft Azure and Google Cloud.

Reliance entering deeper into AI data centers with Meta can increase competition. This may attract more investment and push other companies to build larger AI-ready campuses in India.

For India, that is useful. AI infrastructure is becoming as important as roads, ports and telecom towers were in earlier growth phases.

Challenges ahead

The project has strong potential, but there are challenges too. AI data centers need huge capital, stable power, advanced cooling and skilled teams. If energy costs rise or cooling systems are not efficient, operating costs can become high. There are also policy and privacy questions. Local infrastructure does not automatically mean better privacy. Data protection depends on company policies, Indian laws, user consent and security practices.

Another concern is environmental pressure. Large data centers can put stress on power and water resources if not planned carefully. Transparent sustainability reporting will be important.

Conclusion with key takeaways

Meta and Reliance’s AI-enabled data center plan in Jamnagar is a major signal for India’s digital future. It shows that global technology companies now see India not only as a user market, but also as an infrastructure base for AI.

Key takeaways

• Meta and Reliance are partnering on a 168 MW AI-ready data center capacity in Jamnagar, Gujarat.
• The facility will support Meta’s growing AI and digital infrastructure needs in India.
• Users may benefit through faster digital services, better AI tools and more India-focused features.
• The project can support jobs, technical skills and local digital infrastructure growth.

Energy use, sustainability, privacy and execution quality will be important factors to watch.

Facts Input- 1, 2, 3

Disclaimer

This article is for informational purposes only. It is not investment advice, stock advice or a recommendation to buy, sell or hold shares of Reliance Industries, Meta or any related company.