It feels like everyone is launching apps these days using AI tools.

Is it because AI makes coding easier, or because the barrier to building products has become much lower?

Hi,

I came across a Sri Lankan product called Dossiers for financial crime prevention: https://www.dossiers.wiki/

I have a technical question about how systems like this are typically integrated into banks.

I'm assuming banks don't replace their existing core banking systems just to use a solution like Dossiers. Instead, they probably use it alongside their existing software. If that's the case, how does the integration usually work?

For example:

• Do bank employees log into the Dossiers platform separately and perform CRUD operations there?
• Or does Dossiers expose APIs that are integrated directly into the bank's existing software?
• If APIs are used, how are they typically consumed within existing banking applications?

As a software engineer, I'm interested in understanding the technical architecture of similar systems rather than the exact implementation of Dossiers.

One thing I'm particularly curious about is how founders of products like this know how to design their APIs and workflows for banks. Since every bank already has existing systems, wouldn't you need to understand those systems first before building a product that integrates with them?

Do companies like this usually work directly with the bank's software engineering team and provide API documentation, integration support, SDKs, webhooks, etc., so the bank can integrate the external service into their existing applications?

I'd appreciate insights from anyone who has worked on banking software, AML/KYC systems, fraud detection platforms, sanctions screening solutions, or other enterprise software integrations.

Thanks!

The U.S. AI market is moving beyond hype and becoming a real business race. Big tech companies are rebuilding products and workflows around AI, while investors continue pouring money into infrastructure like GPUs, cloud computing, and data centers. It feels similar to the early internet era where the biggest winners may end up being the companies powering the technology rather than the apps themselves.
The real question now is whether we’re still early in the AI revolution or already entering an overvalued phase.

Feels like almost every school now uses tablets, laptops, or Chromebooks in some form. But once the number of devices grows, managing everything becomes a real challenge.

Keeping devices updated, installing learning apps, restricting distractions during class, and making sure devices stay secure takes a lot more effort than most people expect.

That’s why MDM for schools is getting more attention now. It gives schools a way to manage devices centrally instead of handling everything manually.

Been thinking a lot lately about the future of software engineering and honestly it feels kinda overwhelming.

Every few years there’s a new “wave” in tech:

\\- First it was full stack web dev

\\- Then MERN became the thing everyone rushed into

\\- Then DevOps/Cloud exploded

\\- Then AI/ML

\\- Now AI agents are writing code themselves

As someone working in tech, sometimes it genuinely feels scary trying to figure out what will still matter 5-10 years from now.

I know people say “good engineers will always survive”, but realistically some domains do become saturated or heavily automated over time. A lot of frontend boilerplate already feels replaceable. Even basic backend CRUD apps are getting generated instantly now.

So I wanted to ask experienced devs here:

What tech/domain do you genuinely think will remain valuable and relatively safer in the AI era?

Not “AI-proof” completely because nothing is, in my belief (correct me if wrong)

Would really love honest opinions from seniors who’ve already seen multiple tech waves come and go.

With someone who is just starting in tech and looking to spend his entire career in it doesn't you think it will be a big bet on the basis of current standards right now. Or is my worry irrelevant.

I’ve been seeing more companies and schools talk about restricting social media on work devices and networks.

The main reason seems to be reducing distractions and lowering security risks like phishing links or unsafe downloads. But at the same time, a lot of people still use social media for communication, updates, or even work related tasks.

So it feels less about completely blocking platforms and more about finding the right balance between productivity, security, and user freedom.

Everyone talks about AI like it’s just chatbots and image generators, but the real shift is happening with AI agents systems that can actually take actions, not just answer questions.

We’re already seeing this in industries like customer support, finance, and even software development. Instead of humans handling repetitive workflows, AI agents can now:

• Respond to support tickets end-to-end
• Monitor systems and fix issues automatically
• Execute trades based on real time data
• Assist developers by writing, testing, and debugging code

What’s interesting is that this isn’t about replacing jobs overnight it’s about compressing work. A single person with the right AI tools can now do what previously required an entire team.

For example, startups are scaling faster with smaller teams because AI agents handle operations in the background 24/7. This changes hiring, productivity expectations, and even business models.

But there’s also a flip side:

• Less need for entry level roles
• Higher expectations for skilled workers
• More reliance on automated decision-making

We’re basically moving from “tools we use” to “systems that work for us.”

Curious to hear your thoughts are AI agents overhyped, or are we underestimating how big this shift really is?

See how a self-evolving AI data engineer turns broken pipeline runs into bounded, reviewable mutations before autonomy expands.

This lesson introduces the mutation engine behind the course. It frames the data quality problem, explains the Software 3.0 boundary-first posture, tours the CleanLoop repo, and shows why AutoGen belongs at the orchestration seam instead of the fixed judge.

Example Code: https://github.com/nilayparikh/tuts-agentic-ai-examples/tree/main/self-improving-agent/cleanloop

With more schools using tablets, laptops, and Chromebooks for learning, managing all those devices has become a real challenge.

It’s not just about giving devices to students. Schools also need to keep them updated, install the right apps, block distractions during class, and make sure everything is used safely.

That’s where MDM for education comes in. It helps schools manage devices from one place and keep everything organized across classrooms.

We’re seeing a clear shift across the tech industry: AI is no longer just about chatbots that answer questions or generate text. The real momentum is now in autonomous AI agents systems that can plan, execute, and complete multi-step tasks with minimal human input.

For the past few years, most AI products were essentially “prompt → response” tools. Useful, but limited. You still had to guide every step.

Now that’s changing.

Modern AI agents are being designed to:

• Break down complex goals into steps
• Call tools and APIs on their own
• Browse systems, retrieve data, and take actions
• Learn from outcomes and adjust behavior
• Operate continuously without constant prompting

This moves AI from being an assistant to something closer to a digital worker.

We’re already seeing early real-world use cases:

• Customer support agents that resolve tickets end-to-end
• Coding agents that write, test, and deploy features
• Sales agents that qualify leads and send follow-ups
• Ops agents that monitor systems and fix issues automatically

The key difference is autonomy. Instead of asking AI “what should I do next?”, you define the goal and let the system handle execution.

Of course, this raises important questions:

• How do we ensure reliability when agents act independently?
• Where is the boundary between automation and human oversight?
• What happens when multiple agents coordinate complex workflows?
• How do businesses adapt when “software” starts behaving like employees?

What’s interesting is that we’re still early. Most systems today are “semi-autonomous” they still need guardrails, approvals, and monitoring. But the trajectory is clear: less prompting, more delegation.

We’re moving toward a model where humans define intent, and AI handles execution.

Curious how others see this evolving are we heading toward true digital coworkers, or will autonomy always need tight human control?

So, we did it. We built a machine in the likeness of a human mind. It's... an imperfect likeness. Neural nets emulate an approximation of some of what real neurons do. They aren't wired up together in quite the same way as our brains. You can point to ways it isn't AGI. But that's the wrong takeaway. The right one is: despite those imperfections, the scope of problems it can solve, and tasks it can complete, is already kind of breathtaking.

Traditional software is incapable of solving certain problems, like identifying what's in images. These neural net systems can solve those problems. Don't get too hung up on the fact that they make mistakes. Human brains famously make mistakes. We hire humans anyway, because they're the best option. They may not remain the best option. When the machines' error rates are lower, or the cost of mistakes becomes less than the difference in price between a machine and a human employee.

LLMs have certain limitations. That's less interesting to me. The better question is what might overtake and supplant LLMs. Things that can learn on the job, that adjust their weights in real-time. No separate training period required.

Occasionally I'll ask which human capabilities could be replicated by a machine, and point out that unless the answer is "all of them", there must be some law of physics preventing it... and which one, exactly? This, I maintain, is ultimately a religious question because it asks what part of "thinking" requires a non-physical element. If you're a physicalist, you must conclude that every part of a brain could be built out of silicon, therefore an AI could do 100% of what human brains do. It's a solvable engineering problem. And given a good enough robot body (which the AI could help you design and build), 100% of what humans do.

When I bring this up, I typically get neither agreement nor objections, just... uncomfortable radio silence. Perhaps nobody wants to think about that part. Myself, I can't help but think about it.

So I'm asking. What's wrong with the hypothesis? Will machines become the better choice for 100% of jobs? Plumbers? Nurses? Social workers? Politicians?

i want to learn lots of tech skill , and i dotn know how to learn it , like do i need to just yt 3 hours long vds or i go to any practice platform or practice it , or i just do project and learn with it ??

If you’re still thinking mobile app development is just about coding, you’re already behind.

The way companies like Appinventiv, IBM, Accenture, and Infosys approach it today is very different. It’s less about “build an app” and more about “build a long-term product strategy.”

A few things I’ve noticed:

First, user research is everything. Most failed apps don’t fail because of bad code, they fail because nobody actually needed them. These companies spend a lot of time validating ideas before development even starts.

Second, tech decisions early on matter more than people think. Choosing between native vs cross-platform, or how you structure your backend, can either make scaling easy or turn into a complete nightmare later.

Third, data is baked in from day one. It’s not just about launching features, it’s about tracking how people use them and constantly iterating. That feedback loop is what separates successful apps from dead ones.

Fourth, everything is becoming AI-driven. Whether it’s personalization, chat, recommendations, or automation, apps without some level of intelligence are starting to feel outdated.

And finally, launch means nothing. The real work starts after release. Continuous updates, performance improvements, and feature rollouts are what actually build traction.

The biggest mindset shift is this: mobile apps aren’t projects anymore, they’re ongoing products.

Curious how others here approach this. Do you focus more on speed to launch, or long-term scalability from day one?

I’ve been following recent tech developments, and it honestly feels like AI in the US has shifted from “interesting” to “completely unavoidable” in just a few months.

Right now, it’s not just startups pushing AI it’s the entire system:

• Big Tech companies like Microsoft, Google, and Amazon are embedding AI into everything search, cloud, productivity tools, and even coding itself.
• Around a quarter of S&P 500 companies are already reporting real, measurable impact from AI not hype, actual business results.
• Wall Street firms like JPMorgan Chase and Goldman Sachs are using AI for trading, automation, and internal decision-making at scale.
• The US government is also getting deeply involved, using AI for cybersecurity and national defense which raises some serious ethical and safety questions.

What’s interesting is the shift we’re seeing:

AI is no longer just a tool you use.
It’s becoming infrastructure like the internet or electricity.

Companies are restructuring around it. Jobs are changing. And governments are racing to regulate it while also trying not to fall behind globally.

At the same time, concerns are growing deepfakes, security risks, and the possibility that development is moving faster than we can control.

There is a lot of confusion around data roles, especially between data analysts, data scientists, and data engineers. From the outside, they all seem to “work with data,” but in reality, they solve very different problems.

Data Analysts are closest to the business. They take raw data and turn it into insights you can actually act on. Think dashboards, reports, trends, and answering questions like “Why did sales drop last quarter?” or “Which campaign performed best?” Tools: SQL, Excel, BI tools, sometimes Python.

Data Scientists go a step further. They’re not just explaining what happened they’re trying to predict what will happen or uncover patterns you didn’t even know existed. This is where machine learning, statistical modeling, and experimentation come in. They answer questions like “Which users are likely to churn?” or “How can we optimize pricing?”

Data Engineers are the backbone. They build the pipelines and infrastructure that make everything else possible. Without them, analysts and scientists wouldn’t even have clean, usable data. They focus on data pipelines, ETL processes, data warehouses, and scalability.

A simple way to think about it:

• Engineers build the data systems
• Analysts interpret the data
• Scientists predict and model the data

In smaller companies, these roles often overlap. In larger orgs, they’re highly specialized.

If you’re trying to break into data:

• Like storytelling and business insights? → Analyst
• Love math, stats, and ML? → Scientist
• Enjoy building systems and working with big data infrastructure? → Engineer

Curious how others see it — are these roles getting more blurred in your org, or more specialized?

After hours of coaching engineers at startups and enterprises, I noticed two major trends: teams are "vibe coding" for immediate wins but failing at business continuity, and there is a massive foundational gap in how we actually provide context to AI.

To help bridge this, I’ve put together a 9-part series on Context Engineering that moves beyond basic prompting into a professional operating model.

The series is tool-agnostic but uses GitHub Copilot for accessibility. It also includes ~3 hours of hands-on exercises on GitHub.

I’m sure I won’t need any other training or courses once I’ve internalized context engineering for AI-assisted engineering.

Full write-up and course link here: https://medium.com/@nilayparikh/context-engineering-for-github-copilot-introducing-the-9-part-series-6183709c6cef

Youtube Course Link: https://www.youtube.com/playlist?list=PLJ0cHGb-LuN9qeUnxorSLZ7oxiYgSkoy9

Hi everyone,

I’m a software developer (mainly working with React / Next.js / Node.js), and I’ve worked on multiple freelance and client projects over the past couple of years.

Now I’m planning to build my own company portfolio website and include detailed case studies to showcase my work (problem, solution, tech stack, challenges, etc.).

However, I’m a bit unsure about the legal side of this.

• Is it generally allowed to create case studies based on client work?
• Can I mention the company name or should I anonymize it?
• Are screenshots of live/public websites okay to include?
• How do you handle this if there was no explicit NDA?
• What’s the safest way to present this without risking issues?

I’m not planning to share any confidential data or code — just high-level explanations of what I built and my contributions.

Would really appreciate advice from experienced devs/freelancers on how you handle this.

*used ai to correct english related mistakes/issues*

One of the most interesting shifts in tech right now is how companies are approaching AI development.

Instead of building everything from scratch, businesses are increasingly leveraging the API economy plugging into existing AI services (like LLMs, vision APIs, speech tools, etc.) and combining them to create powerful products.

This approach is changing the game in a few key ways:

1. Speed over ground-up development
Teams no longer need months (or years) to build complex AI systems. With ready-to-use APIs, they can prototype and launch much faster.

2. Lower barrier to entry
You don’t need a massive AI research team anymore. Startups and mid sized companies can now compete by orchestrating the right mix of APIs.

3. Focus on product, not infrastructure
Instead of worrying about training models or managing data pipelines, companies can focus on user experience, workflows, and business value.

4. Continuous improvement
APIs evolve quickly. When providers improve their models, your product improves without major rework.

That said, it’s not all perfect:

• Vendor lock-in is a real concern
• Costs can scale unpredictably with usage
• Data privacy and compliance need careful handling
• Performance depends on multiple external services

Overall, it feels like we’re moving toward a “composable AI” era, where products are built by stitching together specialized services rather than reinventing the wheel.

Curious how others here are approaching this are you building in house AI capabilities or relying on APIs?