DEV Community: fathimath fida

AI vs. Social Media: Which Is a Better Source of Information?

fathimath fida — Fri, 21 Aug 2026 05:17:46 +0000

There are multiple options to access information. It is provided via social media as real-time updates, opinions, and experiences, but there are AI tools that can provide summaries and explanations and organize the information in seconds.

But which one is the better source of information?

Well, it is not that easy to choose between them because both of these sources of information have advantages and they can misinform their users.

AI: Quick, Organized, and Convenient

When you want to understand any topic fast, it is necessary to use AI because this technology will summarize huge amounts of information, translate them into simpler languages, compare and contrast different concepts and find key points.

For the developers, researchers, students, and other professionals, such process takes less time.

But AI is not necessarily reliable because it is prone to errors, wrong interpretation of the context and provision of old information. So, it is necessary to check all important statements in primary sources of information.

Social Media: Fast but Messy

The only benefit of social media is the speed.

It provides information about the latest events, experiences of people in terms of using certain products, technical discussions, and community reactions faster than any website

The problem is that social media platforms are built for engagement. Information that sparks strong emotions can become viral, regardless of its validity.

Just because something has 1000 likes doesn’t make it any more truthful than the content with 10 likes.

So What’s The Solution?

My opinion is that neither one of those should be considered as your ultimate source.

AI excels in comprehension and structuring of data. Social media helps in getting access to the real world opinions, trends, and discourses. The best way to go is to use them as initial sources and check important information via reputable sources.

In the context of business and technology development, being aware of the impact of AI and digital technologies on the ways information is discovered is increasingly essential. At (https://apertureventurestudio.com/) we research AI, IoT, and tech innovations for the physical world.

The Best Strategy for Information Gathering

Instead of asking the question "Am I supposed to trust AI or social media?" it is better to ask the following:

How do I verify what I’m seeing?

It’s a rather simple procedure:

AI can assist you in understanding the issue.
Social media can help you understand the current conversations and experiences being shared.
Find the original source.
Compare information from different credible sources.
Exercise caution when dealing with claims which depend solely on emotions, popularity, and anonymity.

The future of information is not about picking sides. The future of information is about getting better at processing information in all the platforms.

AI can assist us in processing information faster. Social media can help us know what the public says. Critical thinking enables us to judge information.

What Can Go Wrong When Environmental Testing Is Skipped Before Construction?

fathimath fida — Fri, 21 Aug 2026 04:40:10 +0000

There are cases where construction sites might appear ideal for construction yet they have environmental challenges below the surface.

Some of those challenges include soil contamination, groundwater concerns, past land use, among other aspects of the site that are not known until excavation commences.

That is the reason why environmental testing should be included in your construction plans as well.

Hidden Soil Contamination

Past industrial use, fuel storage, hazardous waste disposal, or agricultural activities on the site might have impacted the soil.

It will be difficult to know about the contamination until the excavation is started, which may lead to sampling, lab testing, proper handling and even disposing of contaminated materials.

Unexpected Groundwater Conditions

Groundwater is something that brings both environmental and engineering concerns.

The first one is contaminated water, and that might call for further investigations and cleanup.

Even when there are no contaminants present, groundwater might bring challenges to excavation and site preparation.

. Construction Delays

An environmental issue that surfaces during construction can pose disruptions to carefully planned schedules.

Further investigation, testing, compliance, remediation, or specialized waste management measures might be required before some of the activities can proceed.

An environmental issue identified during the planning process is generally more manageable compared to an issue found midway through construction.

Increased Costs

Delays might prove costly for projects.

If contractors, equipment, and materials have already been lined up, a last-minute environmental issue might result in increased cost of labor, testing, transportation, disposal, and consulting fees.

Pre-construction testing does not guarantee that there will be no environmental risk to a project, but it can prevent the team from costly surprises.

Worker and Community Concerns

Construction processes might affect contaminated soils or cause an increase in the airborne dust, among other environmental impacts.

Anticipating the condition of the site before work commences allows teams to implement proper controls and monitoring programs to protect workers and communities.

Compliance Issues

Environmental rules might depend on the location, type of project, previous use of the site, and contaminants present.

Identifying a potential issue during construction might result in added regulatory requirements and documentation.

Testing Is Just the Start

Environmental testing does not end just because work has started.

Today’s advanced monitoring technology will continue to give you information on:

Air quality
Particulate matter
Water quality
Soil quality
Noise and vibration
Weather conditions

When you combine the pre-construction assessment with continuous monitoring, you get improved visibility throughout your project lifecycle.

Find out more about our environmental testing services, assessments and monitoring solutions for construction and infrastructure development at Enviro Test Construct.

A Better Approach

One helpful way of looking at environmental risk management is:

Assess → Test → Understand → Plan → Monitor → Act

Such an approach allows managing environmental risks to be shifted from reaction to preparation.

Some Final Words

Not all environmental risks are obvious right away.

Failure to perform the necessary tests may result in contamination, delays, unforeseen expenses, compliance risks, and tough decisions after the construction has already started.

Therefore, environmental testing cannot be treated only as a requirement.

It is an informational phase that helps construction teams to make better decisions while the decisions can still be easily changed.

AI & the Future of Advertising: Why Trust Will Beat Noise

fathimath fida — Thu, 20 Aug 2026 05:01:44 +0000

AI is revolutionizing advertising at an amazing rate of speed.

Marketing teams have access to tools to create ad copy, images, videos, campaign ideas, social media posts, and personalization all in just minutes.

It makes creative development both faster and cheaper.

However, there is one additional challenge.

If everyone has the ability to create amazing ads, then how does any brand differentiate itself?

Perhaps the future answer will be through building trust.

Advertising Is Becoming Abundant

Traditionally, the creation of professional advertising meant working with designers, copywriters, videographers, marketers, and agencies.

Generative AI technology is turning that process on its head.

Even a small business can create many different campaign concepts within minutes. A larger company can create more content at an even greater scale.

The more accessible this technology becomes, the more ubiquitous professional advertising will become.

And when something becomes ubiquitous, it becomes less of a competitive advantage.

More Content Doesn’t Equate to More Trust

Consider your own social media feed filled with AI-generated advertisements.

Each image looks polished.

Each headline is optimized for impact.

Each video is engaging.

Ultimately, those become the new norm.

"Can I trust this brand?"

An ad can make a promise.

The product and consumer experience should fulfill it.

Experience Creates Trust

An ad can be created in a split second.

An organization cannot create decades of good customer experience in a split second.

When a company promises quick support, customers will notice if it was quick enough.

When a company claims its product works well, customers will assess it.

When a company claims its commitment to being transparent, people will see how it acts.

Marketing sets expectations. Customer experience creates trust.

AI Can Increase the Value of Authenticity

It is curious, but true.

The better AI gets at creating authentic-looking advertising, the higher value authentic signals can get.

People may start asking more and more questions like the following:

Is this statement backed by facts?
Is this really a customer experience?
Does this product do what it does?
Is this organization backed by real expertise?
Can I trust this business for more than just its marketing?

It does not mean that organizations should give up on AI.

On the contrary, AI can facilitate communication of something real.

How AI Can Enhance the Customer Experience

In fact, the use of AI technology may enhance trust by improving the customer experience.

It can be used to:

React to customer queries quickly
Analyze feedback
Provide personalized recommendations
Detect recurring issues
Generate educational content
Test marketing initiatives
Automate workflow processes

What needs to be considered is the difference between utilizing AI in order to improve reality and creating an illusion of improvement by means of AI.

Loudest Brands Won’t Always Prevail

When the cost of content production decreases, the amount of produced content does not automatically lead to success.

A company can post numerous articles on a regular basis, yet find it challenging to establish loyalty.

People do not always need more advertisement.

They need more reasons to trust.

A reputable brand has some advantages which cannot be gained simply by generating AI content: its reputation.

What Marketers Should Pay Attention To

With AI taking care of production-related tasks, marketers will have more time for the following:

Credibility – back up claims with facts and expert knowledge.
Consistency – provide customers with the experience that aligns with brand promises.
Customer experience – develop products and services that add value.
Authenticity – Use AI while leaving the human touch.

Relationships – Focus on building relationships rather than impressing.

For businesses considering the use of AI, IoT, automation, and cutting-edge technologies, (apertureventurestudio.com) offers insights about tech-driven businesses and smart systems.

The Future of Advertising

Advertising is not going anywhere.

In fact, there could be even more of it than before.

However, AI might be used to create fewer ads than now.

When anyone can afford to create a fancy ad campaign, those things that can’t be easily created are of greater value:

Trust.

Reputation.

Authenticity.

Customer loyalty.

AI may produce attention.

AI won’t produce instant credibility.

Closing Thoughts

The future of advertising might not go to the brand with the most attention-grabbing ad.

It might belong to the brand people already trust.

AI is going to make advertising faster, cheaper, and more personalized. However, if everyone can create great ads, competitive advantage will come from that which cannot be created instantly by a prompt.

AI produces attention. Trust produces loyalty.

And consumers might find themselves asking a different question.

Smarter Construction Starts With Real-Time Environmental Monitoring

fathimath fida — Thu, 20 Aug 2026 04:34:19 +0000

IoT devices, drones, BIM, GIS, and digital project management applications are being increasingly used on construction sites to manage the projects better.

However, there is another important aspect of the construction process that is frequently ignored – environmental conditions.

They can be subject to drastic changes, and periodic inspections will not necessarily capture what happens between inspections.

Real-time environmental monitoring represents an opportunity to enhance visibility of construction processes.

Why Continuous Monitoring Is Important

Conventional environmental monitoring typically involves:

Scheduled inspections
Sampling
Lab analysis
Periodic reporting

The above practices deliver important information. However, conditions in the construction environment tend to change frequently.

For instance, excavation might lead to increased particulate matter content; heavy rains could impact runoffs; equipment operations can affect noise and vibrations.

IoT sensors can continuously monitor such environmental conditions.

Parameters to Monitor:

Air quality
Particulate matter content
Water conditions
Dust levels
Noise and vibrations
Temperature and humidity
Weather conditions
Data Processing and Decision-Making
IoT sensors may monitor environmental data, and edge computing enables the processing of such information near the location where it was collected. And cloud platforms can organize this data into dashboards, alerts, trends, and reports.

Such workflow becomes more proactive:

Monitor → Analyze → Identify → Act

The unusual increase in particulate matter would prompt an alert to enable investigating the situation for any necessary actions to be taken promptly.

Applications in Construction
Air and Dust Monitoring

Real-time particulate monitoring will make it possible to know about any increased dust levels and check mitigation efforts.

Water and Runoff Monitoring

Monitoring in real time will provide more visibility regarding changes in water conditions, especially during rainfall and earthwork operations.

Noise and Vibration

Sensors can measure changing noise and vibration levels in relation to active construction sites.

Environmental Compliance

The continuous data will assist with documentation, reporting, and investigation when environmental thresholds need attention.

Environmental Data Integration With Digital Construction Projects

Environmental monitoring does not need to be implemented as a separate solution.

It can work together with:

GIS
BIM
Weather data
Drone imagery
Connected construction equipment

Project management tools

For more information on environmental testing and monitoring services, connect to (envirotestconstruct.com).

The Role of AI

Artificial intelligence can enhance environmental monitoring even further.

AI and machine learning algorithms can assist in recognizing anomalies, spotting trends, prioritizing alerts, and conducting predictive environmental risk analysis.

But AI shouldn't replace environmental expertise.

It is still needed to interpret data, comprehend regulations, verify results, and decide what actions should be taken.

A combination of intelligent technologies and human expertise is usually the optimal choice.

From Reactive to Proactive Environmental Management

Typically, this is how conventional monitoring works:

Measure → Report → React

And connected technologies can help in moving from this approach to the following one:

Monitor → Analyze → Predict → Act

Not because traditional inspections and laboratory tests aren't needed but because of the complementary effect provided by continuous monitoring.

Going Forward

As sensors continue getting more affordable and construction technologies continue evolving, environmental monitoring will certainly play an increasingly significant role in digital construction.

And the key is not collecting data for its own sake.

The main thing is to transform environmental data into actionable insights.

With improved monitoring, construction teams will be able to detect risk areas early on, make better decisions, and ensure sustainable project management.

Improved environmental monitoring can result in better construction decisions.

When AI Can Create Any Advertisement, Will Trust Become the Real Brand Advantage?

fathimath fida — Wed, 19 Aug 2026 05:23:51 +0000

Advertising is happening faster thanks to artificial intelligence.

Now marketers can produce ad copy, graphics, videos, social media posts, ideas for landing pages, and campaign variations in mere minutes. Work that once required a specialized team can now be done using only prompts and proper tools.

It sounds like a massive benefit.

However, there is a downside to that.

As soon as everyone starts producing top-notch advertising, top-notch advertising stops being a competitive advantage.

A true competitive advantage might start looking like this:

Trust.

Advertising Is Becoming Cheap

In the past, great advertising required plenty of resources.

Companies had to hire designers, copywriters, photographers, video editors, marketers, and even agencies.

Generative AI changes the game.

Now a small company can experiment with dozens of different headlines, visuals, and campaign concepts without wasting weeks creating all of them.

A larger company can go further.

Messages can be personalized, performance analyzed, and different variations created for different audiences in a huge quantity.

The outcome is simple:

There is going to be more advertising than ever before.
And that introduces a new issue: attention.

Increased Amount of Information Doesn't Bring Increased Credibility

Consider a social media feed overflowing with perfect-looking ads.

They all look professional.

They all are optimized.

They are all personalized.

At some point, their quality becomes not surprising.

Questions can start appearing from consumers:

"Is it even credible?"

This is because an ad can make a promise, but the experience of the consumer will make him believe this promise or not.

A company can use AI for virtually any advertising message it wants.

However, no matter what AI it uses, it cannot provide instant credibility through years of positive experience.

Trust Is Built Based on Post-Click Experience

A brand builds its credibility based on consistency.

If a company says it makes reliable products, it needs to be reliable.

If a company promises to respond quickly, it should really do that.

And if a company cares about its customers, it should show it in actions.

Advertisements draw the attention of potential clients.

Their experience decides whether they stay or not.

This is why customer experience becomes even more important due to AI.

AI May Increase the Value of Authenticity

However, there is a paradoxical point.

The higher AI technology progresses in creating realistic marketing, the higher the value of genuine signals may increase.

People may want to know:

Is this authentic?
Is this true claim?
Is there a real person on the other end?
Does this product actually work?
Is the brand consistent outside marketing?

This does not mean that people will stop using AI.

It means that companies will have to use AI technology while maintaining their communication based on authentic knowledge, products, and customer experience.

AI May Help Create Better Customer Experience

AI is not an enemy to trust.

If used right, it will help build the relationship with customers.

Companies may use AI to:

Answer customer queries fast
Analyze customer reviews
Offer personalized suggestions
Identify customer problems
Optimize support process
Generate educational materials
Test different messages
Understand customers' needs and behavior

The key difference lies in the following:

AI has to enhance the customer experience, not create an illusion of such experience.
The Loudest Brand Doesn't Have To Be The Winner

When the creation process is almost free, creating more content won't make any sense.

It could have hundreds of posts per week but will still not be able to build an audience.

Why?

Because people do not want more communication.

People need a reason to believe.

A reputable brand does not have to start from scratch and convince every single time.

Reputation has some value of its own.

This is a huge competitive advantage.

What Should Marketers Focus On?

AI can handle the majority of production tasks.

It leaves marketers time to concentrate on what cannot be automated:

Proper positioning.
Understand what the brand is about.

Experiences.
Create experiences that match marketing messages.

Credibility.
Be able to back-up claims with data and transparency.

Consistency.
Make sure your brand experience is consistent.

Relationships.
Build a community, not just gather impressions.

This takes time.

And this is exactly why it is hard to compete.

The future of brand competition

I do not think we will see less ads.

We may even see more of them.

But AI will change the value of advertising.

If creating an ad that works is essentially free, the ad itself becomes more abundant.

Reputation becomes scarcer.

Customer trust becomes scarcer.

Authentic expertise becomes scarcer.

Relationships become scarcer.

And that's not something that can just be conjured with the right prompt.

In looking at artificial intelligence, automation, and new technologies for companies,(https://apertureventurestudio.com/) offers insights into technology-based businesses and intelligent systems.

Conclusion

AI can help amplify a brand.

It can accelerate marketing efforts.

It can make personalization more cost-effective.

But it can't instantly make a brand credible.

Which means I don't think the most valuable brands in the future will necessarily be the ones making the most ads.

They'll be the ones people trust enough to buy without being constantly convinced.

AI can create noise.

Trust creates loyalty.

AI Agent vs. AI Chatbot: What’s the Real Difference?

fathimath fida — Wed, 19 Aug 2026 04:53:35 +0000

When working with ChatGPT, customer-support bots, or AI assistants, you most likely encountered some systems where it's able to answer your question and others where it's able to actually do something.

And this is the easiest way to distinguish a chatbot from an AI agent.

A chatbot is primarily meant for conversation.

An AI agent is designed to accomplish a task.

A chatbot primarily responds

Let's take a look at a support chatbot, for example.

You ask:

"What's your refund policy?"

The system understands the question and returns the answer.

It means that the workflow looks as follows:

Ask → Understand → Respond

And that is enough for many use cases:

FAQs
Customer support
Information retrieval
Internal knowledge assistants
Basic conversational interfaces

No point in complicating things.

An AI agent accomplishes a goal

In this case, you may ask:

"Check my order, see if I'm eligible for returning it, create the return request, and let me know when it is accomplished."

This is another kind of a task that requires much more actions.

The system needs to:

Find the order
Understand the return policy
Determine the eligibility
Take the decision
Use API
Make the return
Return the results

The workflow now looks like:

Goal -> Plan -> Tools -> Act -> Evaluation

And that is what makes the notion of an AI agent helpful here.

"It is not all about being 'smarter'"

I believe that people confuse those two notions very often.

An AI agent is not a smarter version of a chatbot.

What is more important – an autonomy.

A chatbot can tell you how to reset a password.

An agent can check your account, interact with the necessary system, perform the reset and notify you afterwards.

The model behind them can be almost the same.

The difference lies in the surrounding system: tools, permissions, memory, workflows and the ability to act.

Tools play a great role in making agents useful

Making an agent able to work with other systems increases its usefulness tremendously.

Depending on the application, it may have access to:

APIs
Databases
Search
CRM systems
Email
Scheduling tools
Internal applications

But it has its downsides.

Having more tools available does not mean the AI agent will perform better.

Providing an AI agent with a long list of overlapping tools gives it more chances to use the wrong tool.
A smaller, better-defined toolkit could simplify the process considerably.

This is an engineering choice, not an AI choice.

When is it appropriate to use a chatbot?

If the work is basically responding to questions, then a chatbot will likely be sufficient.

For example:

User: "What time do you close?"

Chatbot: "I'm here until 6 PM."

There's no need to add a multi-step agent workflow to that.

It can be simpler, less expensive, more easily testable, and more easily maintained.

When would an agent be useful?

Agents come into their own when there's a multi-step process involved with outside systems.

Consider:

Customer support

Understand problem → check account → investigate → take action → respond.

Sales

Research prospect → gather information → update CRM → prepare report.

IT operations

Investigate alarm → check logs → discover potential cause → take action.

This isn't just a conversation.

It's a process.
The boundary is not always clear

The categories are not entirely distinct.

A chatbot uses tools.

An agent can have a chat interface.

A simple chatbot could also grow to have more capability and be more agent-like over time.
How autonomous is the system?
Greater autonomy requires greater control

Providing an AI system with the ability to act raises further risks.

The agent, which has the capability to generate summaries of a document, is a relatively safe one.

The agent, which is able to:

Send e-mails
Change the records
Delete the information
Perform purchases
Access the confidential information

requires far tighter controls.

In case of sensitive actions, human approval can still be an important part of the workflow.

One should not try to provide AI with full autonomy.

One should try to provide AI with the proper level of autonomy.

Here also comes into play the connection of AI to business workflows. For instance, (commconai.com) examines how AI can integrate communication, information, applications, and business processes, instead of being just a conversational interface.

A quick comparison
AI Chatbot AI Agent
Main purpose Conversation Goal completion
Typical flow Ask → Respond Goal → Plan → Act
Tool usage Limited or none Often multiple tools
Decision-making Usually limited More involved
Autonomy Lower Higher
Best suited for Questions and support Multi-step

The easiest way to remember it

AI chatbot:

Ask → Understand → Respond

AI agent:

Goal → Plan → Use tools → Act → Evaluate

The chatbot helps you to have a conversation.

An AI agent will help you to do something.

However, it does not necessarily mean that the agent is better in each situation.

When a chatbot can solve the problem effectively, adding an agent will not bring any benefit.
“How much autonomy does this problem actually require?"

AI Isn’t Always Right: Why Human Judgment Still Matters

fathimath fida — Tue, 18 Aug 2026 08:42:10 +0000

AI has become extremely helpful for developers, researchers, marketers, and businesses.

It generates code, explains technical terms, analyzes data, summarizes documentation, and helps with solving issues in a few seconds.

However, there is one area in which AI still lags behind:

It does not know if the answer is wrong.

The frustrating thing about it is that a wrong answer can still be very convincing.

AI Can Be Confidently Wrong

Ask an AI a technical question, and you get a detailed explanation of the problem with code, examples, and logic.

Everything seems perfect.

And then you try running the code and find out that API does not exist, the syntax is outdated, or the solution does not solve the problem at hand.

This is the problem.

AI cannot always tell the difference between:

"I am sure I am right."

and

"It seems like a reasonable thing to do."

This is especially important for developers because a plausible-looking solution sometimes takes more effort than no solution at all.

Context Remains an Issue

AI can process a tremendous amount of information, yet it cannot always have all the necessary context to solve the problem.

Suppose you are debugging some production application. The problem can be influenced by:

Framework version
Environment variables
Database configuration
Dependencies
Infrastructure
Previous changes in the code
Network configuration

Without the necessary context, the AI will make assumptions.

These assumptions can result in the solution that technically makes sense but is wrong anyway.

Better Models Will Help

Of course, AI will certainly become better.

Better reasoning models, retrieval systems, tool use, testing, documentation access, and verification processes will minimize a lot of mistakes.

For example, an AI coding agent can potentially:

Generate code
↓
Run tests
↓
Inspect errors
↓
Change the code
↓
Run tests again

This approach is much more reliable than just generating the code and being sure it is correct.

Even with automated verification, there are still limitations.

Tests can be incomplete, requirements can be misunderstood, and a system can technically pass all its tests without solving the problem at hand.

When to Say "I Don't Know"

One of the biggest advantages that AI could achieve is better handling of uncertainties.

Sometimes the appropriate response is not to give yet another confident answer.

It's:

"I don't have enough information to determine that."

This kind of behavior would make AI much more useful.

A developer could provide more context, look through the documentation, and investigate the problem, rather than debug an obviously wrong solution.

Verification is becoming a Developer skill

As AI creates more code, developers might spend less time typing each line of code, and more time verifying what AI suggests.

That means skills such as these become more important:

Debugging
Testing
Code review
Architecture
Security
System design
Documentation
Technical judgment

AI can quickly create a solution.

The developer still needs to make sure that it is a correct solution.

AI Should Be a Copilot

I don't believe that the answer is to stop using AI.

Exactly the contrary.

AI becomes extremely helpful when regarded as a copartner in development, rather than the absolute truth.

Use AI to:

Explore approaches
Create prototypes
Understand unfamiliar code
Find possible bugs
Create tests
Refactor repetitive code
Explore technical options

And then verify the important part.

Building Reliable AI Systems

For developers building AI applications, the reliability is not only a model problem.

The architecture plays an important role too.

An AI system running in production could require:

Reliable data sources
Data retrieval methods
Input validation
Output validation
Authentication
Authorization
Logging
Monitoring
Automated testing
Manual approval for risky operations

The more control an AI system has, the more critical these measures become.

For different points of view on AI, IoT, automation, and other connected technologies, check out (apertureventurestudio.com) exploring new technologies and their applications.

The Real Skill is Judgment

AI makes generating answers cheap.

That makes a different skill valuable for a person.

It's not just:

"Can I get AI to produce something?"

It's also:

"Can I understand whether what it produced is any good?"

It requires knowledge, testing, skeptical attitude, and context.

AI will probably become much better at recognizing its errors over time.

But until it does, developers should keep one simple rule in mind:

Confident output of AI still needs verification.

AI can help develop software very quickly.

But the judgment is what makes working code out of the correct solution.

AI in Manufacturing: Where It Creates Real Value—and Where It Doesn't

fathimath fida — Tue, 18 Aug 2026 04:59:56 +0000

AI becomes more popular in manufacturing.

Predictive maintenance, computer vision, demand forecasting, process optimization, digital twins, and intelligent monitoring move from experiments to real production.

Nevertheless, just adding AI to the manufacturing process does not automatically generate value.

Where does AI actually solve the manufacturing problem, and where conventional software remains more suitable?

Where AI Creates Value

Predictive Maintenance

Traditionally maintenance is performed regularly according to the schedule.

So, a machine will be serviced once in 30 days irrespective of its state.

AI can analyze:

Data from sensors
History of equipment
Maintenance history
Operating conditions
Failure patterns

The objective is to detect some signals that can show a future failure.

Instead of:

Every 30 days → Service machine

it is possible to perform:

Gathering data
↓
Analysis of patterns
↓
Detection of abnormal behavior
↓
Forecasting of potential failure
↓
Scheduling intervention

The value is in reducing the risk of unexpected breakdowns and unnecessary service.

Nevertheless, predictive maintenance is not magic. The predictions will be useless without good sensor and historical data.

Computer Vision for Quality Control

Quality inspection is yet another use case.

Computer vision systems can inspect products with high speed and detect defects such as:

Damage of surface
Missing parts
Improper assembly
Problems with packaging
Labeling errors

AI will be able to inspect large volumes of repetitive inspections while specialists can deal with anomalies and complicated cases.

It makes more sense to consider it as assistance rather than try to get rid of humans in quality inspection completely.

Production and Demand Forecasting

Manufacturing enterprises make daily decisions related to inventories, production planning, purchasing, capacity.

AI can analyse historical demand and other data to find patterns that can improve forecasting.

Better forecast can help to reduce:

Excessive inventories
Stockouts
Delays in production
Underused capacity
Emergency purchases

Nevertheless, forecast will always be just a prediction.

Manufacturing Process Optimization

A manufacturing process can have dozens or even hundreds of parameters involved.

For instance:

Temperature
Pressure
Machine speed
Material quality
Cycle time
Environmental factors

These parameters can correlate and interact in complex ways which may not be easily understandable manually.

AI can analyze large amounts of data and find relationships between process conditions and outcomes.

This will help engineers to explore new operating conditions and find possible reasons for process variations.

The key here is to have sufficient amounts of good-quality data.

Integrating Operational Data

Manufacturing-related data is hardly ever centralized in one place.

A usual landscape might consist of:

ERP
MES
WMS
SCADA
IoT platform
Quality system
Maintenance system

The problem usually is not in additional data collection but in data analysis.

AI can become an analysis tool for all of these platforms to reveal correlations, outliers, and relationships.

This is especially important in the context of pharmaceutical manufacturing when all types of data, including operational data, equipment details, inventories, environmental conditions, and manufacturing processes have to interoperate. (https://pharmafluxai.com/)
When AI Doesn't Make Sense

The most common mistake is the assumption that every manufacturing challenge requires AI.

Not every does.

Simple rules do not require AI

For instance:

IF temperature > threshold
THEN trigger alert

It makes no sense to substitute this rule with an AI algorithm.

A deterministic rule is:

Simpler to test
Simpler to audit
Simpler to understand
Simpler to maintain

When there is a simple rule to solve a problem - just go with that rule.

Poor Data Quality Means Poor AI Quality

An AI system depends on data.

If your data is:

Incomplete
Inconsistent
Inaccurate
Poorly labeled
Hard-to-access

Then you won't make your AI model more sophisticated fix the situation.

Quite often in manufacturing AI projects, data infrastructure is more crucial than the choice of a particular model.

Do we have the necessary data to answer this question?
Don't Build AI Without a Business Problem

Even when AI system itself is technically well built, there can be zero value for business.

Think of a dashboard, which predicts dozens of metrics in production process.

If no decisions change based on these predictions, then how did the system add any value?

The right AI system has to make a connection between technology and an outcome.
Better inspection
↓
Early defects detection
↓
Reducing waste

Value should be quantifiable.

AI shouldn't take high-stake decisions by default

Manufacturing environment may require safety, quality, regulatory, and financial considerations.

Allowing unrestricted control of critical processes to the AI system is unnecessary risk.

A more secure architecture can include:

AI detects
↓
AI analyzes
↓
AI recommends
↓
Human reviews
↓
Approved action

Depending on the specific case, different level of human involvement is needed.

The crucial thing is that the system must be designed to operate with the proper level of autonomy.

Domain Knowledge is Important for Industrial AI

AI is capable to process huge amount of data.

It doesn't necessarily understand the operational context behind this data.

Engineer knows why certain sensor data is fine.

Production manager understands why certain schedule recommendation is not feasible.

Quality expert understands that there is no defect in a product despite of the fact that certain parameters seem off.

This means that good industrial AI is not usually just:

AI + data

But rather:

AI + data + domain knowledge + operational context

Start Small

There is no need for the company to build an entire AI factory.

Rather the better way is to solve a particular problem.

Find the process where:

The reliable data is available
There is a weakness in the current process
AI system can enhance the predictions or decision making
The results are measurable
Proper human supervision is possible

The company can start from one critical machine, one inspection process, or forecasting problem.

When the results bring measurable value, it can be scaled up.

The True Question Is...
"Where do we make difficult, repetitive, expensive, or time-sensitive decisions?"
*
Answer if it is possible to make the decisions better with the help of AI.

Sometimes the answer will be AI.

Sometimes it will be traditional automation.

Sometimes it will be better data infrastructure.

And sometimes the right combination of all three solutions.

AI is the Tool, but Not the Strategy

The future of manufacturing does not have to go to those companies using the most AI.

It has to go to those companies knowing when AI can be of real benefit.

AI can aid in prediction of failures, detection of defects, process optimization, and complex data analysis in manufacturing operations.

However, the successful application of AI involves availability of quality data, clear goals, solid engineering skills, domain knowledge, security, governance, and people.

The purpose is not to make manufacturing "more AI."

The purpose is to make manufacturing more predictable, efficient, informed, and resilient.

And sometimes, it is smarter not to use AI at all.
Unexpected market changes, supply disruptions, and new customers' behaviuor can make historical patterns irrelevant.

AI should assist in decision-making, but not be considered as a truth.

What’s One Thing AI Still Does Badly?

fathimath fida — Mon, 17 Aug 2026 06:06:01 +0000

AI has become incredibly capable. It can write code, analyze data, summarize documents, generate content, and help developers solve problems faster.

But there is one weakness that still bothers me:

AI doesn't always know when it's wrong.

That sounds simple, but it's a surprisingly important problem.

The Confidence Problem

AI can produce an answer that looks polished, logical, and technically convincing while containing completely incorrect information.

That's what makes AI mistakes different from ordinary mistakes.

If a person gives you a strange answer, you might question it.

AI can give you five paragraphs of confident reasoning and make the same mistake sound credible.

For developers, this can be especially frustrating.

A generated code snippet might look perfectly reasonable but contain a subtle bug, use an outdated API, or make an assumption that doesn't fit the actual project.

Context Is Another Weak Point

AI can process huge amounts of information, but that doesn't mean it always understands the context behind a problem.

A developer might ask:

"Why isn't this working?"

The answer could depend on the framework version, operating system, configuration, database, previous implementation, or a tiny detail elsewhere in the codebase.

If that context isn't available, AI may fill the gaps with assumptions.

Sometimes those assumptions are correct.

Sometimes they're not.

Will Better Models Fix This?

I think AI will improve significantly.

Better retrieval, tool use, testing, verification, and reasoning should reduce many of these problems.

For software development in particular, AI can already verify some of its work by running tests, checking outputs, inspecting documentation, or using development tools.

But there will still be situations where the available information is incomplete or ambiguous.

That's where uncertainty matters.

An AI that says "I'm not sure; here's what I'd verify" can be more useful than one that confidently invents an answer.

AI Makes Verification More Important

As AI becomes better at generating code, developers may spend less time writing every line manually and more time reviewing what gets produced.

That doesn't make developers less important.

It changes what they need to be good at.

Understanding architecture, debugging, testing, security, system design, and evaluating trade-offs becomes even more valuable.

The ability to ask:

"Does this actually make sense?"

may become just as important as knowing how to generate the initial solution.

AI Should Be a Copilot, Not an Authority

I find AI most useful when treating it as a very capable development partner rather than an unquestionable source of truth.

It can:

Explain unfamiliar concepts
Generate prototypes
Find possible approaches
Review code
Suggest debugging strategies
Automate repetitive tasks
Help explore technical decisions

But the final verification still matters.

For developers and organizations exploring practical applications of AI, automation, IoT, and connected technologies,(apertureventurestudio.com) explores emerging technology and real-world applications.

The Skill That Matters

Maybe the most important AI skill isn't simply prompting.

It's judgment.

Knowing when to trust the output.

Knowing when to test it.

Knowing when to verify the documentation.

And knowing when the model is confidently making things up.

AI will probably become much better at recognizing its own limitations.

Until then, one rule is worth keeping in mind:

A confident answer isn't necessarily a correct answer.

And in software development, that difference can be the difference between a useful shortcut and a very long debugging session.

What AI Skills Do Freshers Really Need in 2026?

fathimath fida — Mon, 17 Aug 2026 05:25:17 +0000

If you're a fresher trying to figure out what to learn in AI right now, it's easy to feel overwhelmed.

There is always another model to try, another AI coding assistant to learn, or another framework everyone on social media seems to be talking about.

But here's the thing: you don't need to learn all of them.

What matters more is having solid fundamentals and knowing how to use AI to solve actual problems.

Start With the Fundamentals

Before getting deep into AI tools, understand the basics.

You should have a practical idea of what LLMs, generative AI, embeddings, RAG, AI agents, and AI APIs are.

You don't need to understand every mathematical detail behind a transformer model. But you should know what these technologies are good at, where they struggle, and when you probably shouldn't use them.

That last part is easy to overlook.

AI can produce an answer that looks completely convincing and still be wrong. If you don't understand its limitations, it's very easy to build something unreliable without realizing it.

Keep Your Programming Fundamentals Strong

This is especially important if you're aiming for a developer role.

Learn Python. Learn SQL. Understand APIs, databases, Git, debugging, and basic software engineering.

AI coding assistants can generate a surprising amount of code now. That's useful, but it doesn't mean you can skip learning how software actually works.

If an AI-generated function fails, you need to be able to read it and figure out why.

Personally, I'd rather see a fresher who understands a smaller codebase really well than someone who can generate a huge application but can't explain how half of it works.

AI should make you faster.

It shouldn't make you dependent.

Build Something Instead of Just Watching Tutorials

This is probably the most practical advice I can give.

At some point, stop watching tutorials and start building.

Your first project doesn't need to be impressive.

Build a document Q&A application. Make a small research assistant. Try an AI-powered data-analysis tool. Build a simple agent that uses an API.

You'll quickly run into problems that tutorials don't always show you.

Your API will fail.

Your prompt won't work as expected.

The model will return something strange.

Your database structure won't make sense.

You'll have to figure out how to deploy the thing.

That's exactly where the learning happens.

And when you're applying for jobs, having a project you can explain from beginning to end is far more useful than simply saying that you completed an AI course.

Learn How AI Agents Actually Work

AI agents are getting a lot of attention, and for good reason.

Instead of simply answering a prompt, an agent can potentially decide what information it needs, use tools, perform actions, and work through multiple steps.

But don't fall into the trap of thinking that more autonomy automatically means a better system.

It doesn't.

An agent with access to twenty tools isn't necessarily better than one with four well-designed tools.

You need to think about permissions, tool selection, failure handling, monitoring, and when a human should step in.

These are engineering problems, not just AI problems.

Data Skills Still Matter

There's another area that hasn't suddenly become irrelevant because of generative AI: data.

If you're working toward an AI or software career, learn SQL and become comfortable working with data.

Understand basic statistics, data cleaning, visualization, and how datasets affect model results.

You don't have to become a machine learning researcher.

But if you don't understand the data going into a system, it becomes much harder to understand the results coming out of it.

Learn to Test AI, Not Just Use It

One of the biggest differences between playing with AI and building a real AI application is evaluation.

A demo can look great when you try five examples.

What happens when you try 500?

Does it still produce useful answers?

How often does it hallucinate?

Does it give different answers to similar questions?

How much does each request cost?

How quickly does it respond?

These are the kinds of questions developers need to think about when AI becomes part of a real product.

You don't need to become an expert in AI evaluation immediately. Just get into the habit of testing your systems instead of assuming they're working because the output looks impressive.

Don't Ignore Security

AI applications also introduce security problems that traditional applications don't always have in exactly the same form.

Freshers should at least become familiar with things like prompt injection, data privacy, API security, access control, and excessive permissions.

This becomes especially important when working with agents.

An AI system that can summarize a document is relatively low risk.

An agent that can send emails, update records, make purchases, or access sensitive information is a completely different situation.

The more power you give an agent, the more carefully you need to control it.

Learn Enough Deployment to Ship Your Projects

You don't need to become a cloud expert before applying for your first job.

But you should know how to get something out of your local development environment.

Learn the basics of APIs, databases, environment variables, containers, cloud services, and deployment.

Even deploying a small application teaches you things that you won't encounter while running everything locally.

It also gives you a much better portfolio.

Instead of showing screenshots, you can show someone a working project and explain how you built it.

Communication Is Still a Skill

This might sound unrelated to AI, but it isn't.

A developer who can clearly explain a technical decision is valuable.

So is someone who can understand what a customer or business actually needs before jumping into implementation.

AI makes it easier to produce things quickly. That makes knowing what should be built even more important.

AI is also moving beyond individual tools and into business workflows. For example, (commconai.com) explores how AI can connect communication, information, applications, and business processes.

The important skill isn't simply knowing that such technology exists.

It's understanding where it can genuinely solve a problem.

Build a Portfolio That Shows How You Think

If I were starting again as a fresher in 2026, I'd focus on building two or three projects rather than collecting dozens of certificates.

For each project, I'd document:

The problem I wanted to solve
Why I chose AI
How the system works
What technologies I used
What went wrong
How I tested it
What I'd change next

That last part is especially useful.

Real projects rarely work perfectly on the first attempt. Being able to explain what went wrong and what you learned from it tells people much more about your ability than a polished demo does.

So What Should You Learn First?

If you're starting from zero, don't make it unnecessarily complicated.

A reasonable path is:

Programming fundamentals → AI fundamentals → AI APIs → Build projects → Evaluation → Security → Deployment

Then keep improving your communication and problem-solving skills alongside everything else.

The AI tools you learn this year might not even be the tools companies use two years from now.

That's okay.

The important thing is to build skills that survive those changes.

Learn how software works.

Understand how AI works.

Build things.

Break things.

Fix them.

And learn to explain why you made the decisions you made.

For a fresher in 2026, strong fundamentals + practical AI experience + the ability to solve real problems is a much better combination than simply having a long list of AI tools on your resume.

AI in Recruitment: What Should Be Automated and What Should Stay Human?

fathimath fida — Fri, 14 Aug 2026 05:50:39 +0000

Recruitment is being transformed by artificial intelligence that makes tedious tasks faster.
AI can make screening resumes, scheduling interviews, communicating with candidates, interview recaps and recruitment analytics easier. However, not all recruiting processes should be automated. The most effective method is automating repetitive tasks while leaving the crucial judgment calls to humans.
How can AI assist with recruitment?
AI excel in structured and task-driven jobs. Here’s where it’s beneficial:

Job Description Generation AI helps improve job description clarity, optimize missing elements and customize wording depending on your career platform. HR and recruiters are then able to review the copy before publishing.
Application Organization Typically recruitment teams are responsible for a high volume of applications. AI technology automatically organizes your applications by pulling information about candidate skill sets, qualifications, experience, and other details and then grouping them together. However, automated screening shouldn't become an auto-rejection system. Humans should always be involved in the screening process.
Scheduling Interviews An ideal task to be automated by AI is interview scheduling. AI systems can streamline the booking process by:
Checking calendar availability
Suggesting available time slots
Sending out invitations and reminders
Confirming and managing rescheduling This gives recruiters an opportunity to spend more time building relationships with potential candidates.
Candidate Communication Common candidate concerns can be handled by AI in regards to:
Application status
Interview logistics
Required documents
Recruitment policies
Interview reschedules However, more sensitive or complicated questions should be referred to a human recruiter.
Interview Transcription AI can record and then transcribe an interview as well as recap the key elements of the interview. This makes manual note-taking easier and assists recruiters with consolidating information, although it shouldn’t be the final hiring decision. What tasks should remain human? There are many tasks in recruitment that require contextual understanding that an AI cannot accurately determine. *Final Hiring Decisions Even when AI technology can organize data and recommend candidates, final hiring decisions should always be made by a human. Candidates aren't just keywords on a resume; communication, potential, context and experience are all essential components. *Interviews AI may be helpful in organizing candidates for interviews and preparation of interview questions, however, the interview itself must remain face to face. It's a two-way street, candidates are evaluating a company as much as the company is evaluating them. *Assessing Potential Candidates may not have all of the key skills for a job, however they may have excellent transferable skills or great potential for the future. Human review can help make this distinction.
Sensitive Circumstances Personal aspects such as career gaps, required accommodations, personal factors and sensitive circumstances need an aspect of human empathy and tact that an automated system can't always provide. Technical Aspects of AI Automation For an AI recruitment program developer, it’s not just as simple as hooking a model to a database. For a production-level system to be in place, you will need the right: *Authentication *Authorization
Data Protection
AI processing *Human Review
Audit and Monitoring Companies need to keep data privacy, access controls, audit trails, bias evaluation, human approval processes, monitoring and secure processing in mind. The more power an AI application holds, the more crucial these points become. What can go wrong with over-automation? Automated decisions aren't automatically objective. AI applications take the training data they're given and will only perform as well as their design allows. Improperly programmed AI systems can either reinforce or even amplify existing problems. As such,responsible AI recruitment should be defined by:
Clear evaluation standards
Human intervention
Frequent testing
Data integrity
Data protection
Transparency
Bias monitoring
Accountability While an AI system might be the key to increased efficiency, it should never come at the cost of fair and ethical recruitment practices. Humans + AI Is the Right Direction The future of recruitment isn't humans vs. AI, but rather humans and AI working in partnership. AI is excellent at:
Process automation *Communication *Scheduling
Reporting And humans are invaluable for:
Judgment
Interviews
Relationships
Context
Accountability This is the ideal division of responsibility. Automate the work, don’t automate the judgment. A good maxim for AI in recruitment is to automate the repetitive work, not the important judgment calls. By reducing the administrative burden on recruiters, AI can improve efficiency and accelerate the recruitment process while humans are still present for all of the high-stakes and context-dependent decisions. This principle can be adapted to businesses of all sizes looking to implement AI in various parts of their workflow; the key is to find a way for business data, AI systems, software infrastructure and human experience to converge. If you’re interested in learning more about AI, automation and related topics, check out Aperture Venture Studio. Development of a Well-Built AI Recruitment System For developers and businesses building AI-powered recruitment systems, understanding that your technology needs more than just a good AI model is crucial. You should also pay attention to:
API integration security
Authentication and authorization
Data privacy
Access controls
Audit logs
Approval processes for humans
Model evaluation
Monitoring and observation
Error handling
Bias testing The goal isn’t to completely take over from humans but rather to automate unnecessary tasks. The future of AI recruitment AI applications will likely automate more parts of recruitment in the years to come. Recruiters may no longer need to spend time searching through applications and conducting admin tasks. They may instead spend their time interacting with and evaluating candidates and making informed decisions. The most successful company will not necessarily be the one that utilizes AI the most, but rather the one that recognizes where AI should be allowed to work, and where humans should be the ones in control. Conclusion AI can revolutionize the recruitment process, but it doesn’t need to complete every aspect of the recruiting function. Instead of asking, "How can we replace the recruitment process with AI?", companies should rather be asking, "Which parts of the recruitment process can AI help us streamline while still involving meaningful human interactions?". The answer is a hybrid approach: Automate the processes; make human the judgment call.

From Environmental Risk to Environmental Intelligence: How Technology Is Changing Construction

fathimath fida — Fri, 14 Aug 2026 05:06:02 +0000

Construction sites are lively places. Air quality can shift during excavation, dust levels rise with material handling, and heavy rain can quickly impact runoff and drainage.

While traditional environmental inspections and lab testing are still crucial, they typically offer just a brief view of site conditions.

Connected technologies are changing this by enabling continuous environmental monitoring.

Why Traditional Monitoring Has Limitations

Environmental monitoring has typically depended on:

Scheduled site inspections
Manual sampling
Laboratory testing
Periodic reporting

These methods provide valuable insights, but conditions can change between inspections.

A construction team might not notice an environmental problem until it has already escalated.

Continuous monitoring offers a better way: it gives clearer insights into how conditions evolve over time.

IoT Brings Environmental Monitoring to the Site

IoT-enabled sensors can continuously gather data about environmental conditions, including:

Air quality
Particulate matter
Water quality
Soil conditions
Noise and vibration
Temperature and humidity
Weather conditions

Instead of waiting for the next inspection, project teams can get updated information as conditions change.

Edge Computing Enables Faster Responses

Sending every sensor reading straight to the cloud isn’t always efficient.

Edge computing lets some data processing happen closer to where the information is created.

This helps with:

Lower latency
Faster alerts
Reduced bandwidth usage
Local anomaly detection
Greater resilience in areas with limited connectivity

For construction sites, this is especially helpful when an environmental threshold needs immediate attention.

Turning Data Into Environmental Intelligence

Collecting environmental data is just the first step.

Cloud analytics can turn sensor readings into dashboards, historical trends, alerts, compliance reports, and other valuable insights.

Instead of just asking, “What is the environmental condition?” teams can start asking, “What is changing, why is it changing, and what should we do next?”

This is where environmental monitoring evolves into environmental intelligence.

Practical Applications

Continuous monitoring can assist with several construction tasks:

Dust Management

Real-time particulate measurements can help teams spot periods of increased dust and improve mitigation strategies.

Water and Stormwater Monitoring

Monitoring can offer better insights into changing water conditions after rainfall or construction activities.

Noise and Vibration

Connected sensors can help teams monitor noise and vibration levels and identify potential compliance issues.

Environmental Documentation

Continuous data can provide valuable support for reporting, audits, and project records.

Connecting Environmental Data With Digital Construction

Environmental monitoring doesn’t have to operate as a standalone system.

It can integrate as another data layer within the larger digital construction ecosystem alongside:

GIS
BIM
Connected equipment
Weather systems
Project management platforms
Digital twins

Combining these sources can give project teams a better understanding of what’s happening throughout a site.

Organizations looking into connected environmental testing and monitoring for construction and infrastructure can learn more from (envirotestconstruct.com)

Where AI Fits In

Artificial intelligence could enhance environmental monitoring further.

AI and machine learning can spot unusual patterns, detect anomalies, prioritize alerts, and support predictive environmental risk management.

The goal isn’t to automate every environmental decision.

Instead, AI can help professionals focus on situations that require investigation.

The Future: From Monitoring to Prediction

Environmental monitoring is undergoing an important change.

The traditional approach often looks like:

Measure → Report → React

Connected systems can progress toward:

Monitor → Analyse → Predict → Act

This doesn’t mean traditional environmental testing will vanish. Field sampling, laboratory analysis, and professional expertise will still be vital.

Technology simply adds another layer of visibility.

Final Thoughts

The future of construction isn’t just about building faster or using more connected equipment.

It’s also about understanding the environmental impact of construction as it unfolds.

IoT sensors, edge computing, cloud analytics, and AI can help turn environmental data into actionable intelligence.

The real value lies not in collecting more environmental data but in making smarter decisions with the data already at hand.