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MeetUp on Building Apps Using Meteor.JS at Mantra Labs

On July 24, 2016, Mantra Labs organized a MeetUp at their Head-office in Bangalore. Mr. Atul Yadav was the lead speaker at the conference. He kicked off the MeetUp with the keynote Meteor and addressed why Meteor is becoming a mainstream framework? And why developers are going Meteor way. The MeetUp was attended by Web and Mobile development experts, who were eager to know why Meteor?

In his power point presentation he highlighted some of the major points that support developers for choosing Meteor.

He said, “Every developer is looking for a common framework that can be used for the web and mobile – to save time and effort. Meteor is one such framework that solves this problem for the developer community”. “At the same time, this also speeds up the development process for the client”, he added.

Why Meteor?

Meteor is the simplest possible app framework, yet fully-powered “gateway drug” into modern JavaScript development. Even if you don’t end up sticking with Meteor, your mind will be opened to new possibilities after spending some time with it.

Meteor has been built on concepts from other frameworks and libraries in a way that makes it easy to prototype applications. Even Angular and React are not as accessible to a wide range of developers as Meteor is, because of a steeper learning curve, and a bit more abstraction that requires more programming skills to use. Meteor on the other hand is easy to learn and quick to build with, as it is flexible and requires less code, which means less bugs and typically a higher quality and more stable end result.

This framework from JavaScript can help you to get a MVP built quickly, and the framework has the ambition to allow developers to scale their apps well beyond MVP-stage. It is establishing itself as a mainstream development technology on the same level as Rails or even vanilla Node.js.

The reasons why Meteor is hottest frameworks for development in today’s time. The 11 major point on Meteor were:

1. Real Time Web Development:
Meteor is a development framework that has got the distinctive feature of real time development.

2. Develop with a Single Language:
With Meteor, the development process is highly simplified with frontend, backend and database all rolled into one language – Javascript. Another benefit of this feature is that it works equally well for the client side as well as the server side.

3. Avail Smart Packages:
Meteor helps you to create users through and accounts system that is highly simplified. The system makes the process highly simplified. You can also use the smart package to do other things like: Writing CoffeeScript apps etc.

4. Large and Helpful Community:
Meteor has a large and helpful community for you to get on with the basics really fast. There is lots of proper documentation of the framework that makes it really useful.

5. Simplified For Developers:
Javascript is devoid of CSS, HTML and Javascript which makes the development process really simple in Meteor.

6. Easy To Learn:
There is enough community support and by just knowing a single development language, one can learn Meteor with ease.

7. Meteor Is The Framework Of The Future:
With features like real time development and ease of use for developers and users, Meteor is certainly the development framework of the future.

8. Meteor Is Easy To Set Up:
One can easily start creating projects in Meteor as soon as it is installed. This makes the process much simpler and faster.

9. Faster Development and Testing of Lean Products:
Start-ups are mostly looking to develop lean products which are quick to develop and can be test marketed equally quickly. Meteor provides suited solution for lean start-ups. They can create smaller product and test market it, in a short span of time.

10. Meteor for Native Mobile Apps:
A developer can build faster native mobile apps with Cordova integration using meteor.

11. Project Scalability:
Scalability is the prime concern of large projects run by enterprises. Meteor is a highly scalable framework and that is what makes it so highly preferred for large scale projects. In addition to that, meter is soon coming up with a hosting service which shall definitely be an add-on for businesses.

Mr Atul wrapped-up the MeetUp with these highlighted points. Over all the MeetUp was successful.
If any queries on Meteor MeetUp, feel free to approach us on hello@mantralabsglobal.com.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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