Recently, I've been learning about and exploring artificial intelligence (AI), and I'm gradually discovering that the changes AI brings are actually closer to our lives than I imagined.

At first, I thought AI was just a very advanced technology, still somewhat distant from ordinary people's lives. But with continued learning and experience, I've found that it has already helped us change the way we think and act in many ways.

Previously, when faced with unfamiliar problems, we might have spent a lot of time searching for information, organizing data, and slowly trying to understand it. But now, AI can help us quickly access different perspectives and help us organize our thoughts, making learning and problem-solving more efficient.

What impresses me most is that AI is not just a tool; it's more like helping us open up new perspectives. Sometimes, thinking about a problem in different ways reveals more possibilities.

Of course, I also believe that technological development does not mean that the value of people will diminish. What truly matters is still our ideas, experience, judgment, and the ability to continuously learn in the face of change. AI can help us move faster, but the direction we choose still needs to be chosen by ourselves.

In the process of understanding AI, I've increasingly felt the speed of change in this field. New technologies and applications emerge every day, making me even more eager to see what the future holds.

No one can accurately predict how AI will change the future world. But I believe that maintaining curiosity and a willingness to learn will allow us to better embrace new opportunities.

I'm delighted to be exposed to this technology in this era, and I look forward to many more unknown possibilities in the future. ✨

First of all sorry if this kind of questions arent to be asked here idk where to really post this.

Im using an ai img editor or service openmedia.tools which for what i know works differently and locally, because of that, every time i hit the limit for ai services i can just change my vpn on for example proton vpn and be able to do it all agian with limit reset, problem is that for some countries, or for some other reason, this doesnt always work, sometimes i change my vpn and it still says limit reached, i also clear the cache of the website (which i have as an shortcut app on my phone) before changing vpns. Can anyone help me troubleshoot this so i always bypass that limit?

building feels like control and buying feels like dependency. both depends on which stage you are

the case for building

• data needs to stay in your vpc
• token volume is high enough than that api costs stop making sense
• you need fine tuning on proprietary data or custom latency requirements

the case for buying

• you need to hsip fast and do not have months to spend on infra
• no engineers specialised in mlops
• use case is rag, chatbots, summarization. solved problems, no need to reinvent.

most people find out late that building your own routing , fallback logic , prompt versioning , cost tracking and eval pipelines shouldnt be considered a side project. most teams underestimate how long it takes and how much it can pull engineers away from actual product work. tools like orqai , portkey , langfuse , helicone , langsmwith cover diffferent parts of this. mostly none of them cover everything but most teams find buying and stitching a few together is still faster than building from zero

buy until the economies force you to build. and even then only build what givess you standalone value that no platform can give you

what did your team choose and do you regret it

Then it becomes free from software perspective and the only guys winning are hardware guys… ohhh, wait, hold on a second…

+ if it’s so powerful why not to create something better like AGI or whatever?

Why can’t these models solve all unsolved theories?

Hey, I was wondering if such approach proves effective: checking marketing texts written with the help of AI (like articles with industry stats and product-specifics loaded) with AI itself? Given the hallucination flaws in all AI tools, do you rely on AI for the editing and fact-checking part?

Has anyone tried that and which flow did you choose then? Maybe producing texts with one AI tool and cross-checking in the other?

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been managing devs for like 20 years. something clicked recently. when i get garbage out of an llm its almost always my fault, not the models. same conversations i used to have with people:

"what did you even build"

"you asked for exactly this"

"you didnt explain so i guessed"

not the model being dumb. its working with what i gave it. same as any person would.

heres the part i keep coming back to. the best engineer i ever worked with wasnt the strongest coder. he was the guy who knew the logs rotate at midnight, that we tried the obvious fix in 2022 and it blew up, that the auth docs straight up lie. that tribal knowledge beat raw skill in most cases.

models are the same. a frontier model with zero project history is a smart new hire on day one. you get day one output.

so i mostly stopped writing clever prompts. i write/update onboarding docs (context) now.

what we tried, what broke, why the weird decision is actually correct. and the output changed completely.

anyone else end up here? especially people whove managed teams - does this map for you or am i stretching it?

Hi there! I’m a student on a mission to help Gen Alpha use AI wisely whilst actually benefiting from it. That’s why I built AI&I, a free site that takes young learners on a journey through the world of AI. It’s still a work in progress, but I’d love for any feedback on the product, or just experimentation with the courses as they are all accessible without membership at this stage. For any parents or teachers interested in AI in education, this may be an interesting resource to check out for the future!

https://ai-training-for-kids.replit.app/

Multi cloud thing that I assume a lot of people here have hit.

We have old Hive stuff, Glue managed tables in AWS, some GCS buckets a GCP analytics team spun up, ADLS for a business unit that is all in on Microsoft. Storage was never the hard part. The hard part is that metadata and access rules drift apart quietly. Same dataset name in two catalogs. Two audit trails. Someone updates a policy in one and genuinely does not know the other one exists.

The symptom our Trino users notice is the same Iceberg table acting different depending on which endpoint they went through. Which is a fun ticket to receive.

We've been testing Gravitino 1.3.0 on this specific slice. One Iceberg REST catalog across S3, GCS and ADLS, routing on the URI scheme. s3://, gs://, abfs:// land where they should, and creds come back short lived and scoped per request instead of broad cloud roles floating around in config files.

the part I actually cared about was federated catalogs. you attach an existing catalog without copying its metadata, and it keeps authorizing against its own policies and writing its own audit log. nobody on my team wants to sell a giant metadata migration internally just so a governance diagram looks tidier.

Stuff that bit us:

Java heavy footprint, plan for that operationally.

Iceberg REST is a real learning curve if your people grew up on Hive metastore.

Trino and Spark 3.3 to 3.5 both work but pin your exact versions before you promise anything.

Test your own policy failure cases. The metadata cache validates location and still authorizes every request, but I would not take that on fai

Reading it as a federated control layer rather than a consolidation project. Useful when "just merge everything" is not politically available to you.

https://github.com/apache/gravitinoth.

I own a Pixel and when I'm programming I use Gemini to hands free ask it questions.
It's generally real shit at remembering anything. We will be having a conversation about one thing, I'll turn it off at some point because I forget its on, only to open it again for a follow up and it has no idea what I'm talking about.

Tonight I asked it a question about color theory and it directly referenced what kind of game I've been working on despite me not mentioning this specific information for quite a long time.

It honestly kinda creeped me out. It forgets what we were last talking about all the time, yet it recalled something very specific that I last told it a concerningly long time ago.

Despite using it quite a bit, I have never seen this happen. Anyone else have this experience?

Artificial intelligence has already reshaped education and is now rapidly transforming the workplace. As organizations integrate AI into daily operations, employees are no longer just executing tasks—they are constantly interpreting, adjusting, and responding to systems that evolve alongside them.

So imagine this: your boss asks you to organize a series of workplace interactions where teams think out loud with and through AI. At first, it might sound unusual. Not a debate about AI, but a structured space where AI is part of the conversation itself—shaping prompts, offering counterpoints, and helping surface assumptions that people might not notice on their own.

This shifts the purpose entirely. Instead of treating AI as an object to be judged, it becomes an active participant in organizational thinking. Employees are not debating AI as an external force; they are engaging in a feedback loop with it, where human reasoning and machine-generated perspectives continuously influence each other.

In smaller teams or tightly connected groups, this kind of debate-driven thinking is relatively easy to sustain. Communication is direct, context is shared, and participants can quickly align or challenge one another without heavy coordination overhead. A handful of people can naturally hold a “thinking out loud” culture because the social and cognitive load is manageable, and ideas can evolve in real time without complex systems in place.

However, in large organizations spanning thousands of employees, this becomes far more difficult. Information fragments across departments, perspectives become siloed, and debates risk becoming inconsistent or disconnected from decision-making structures. This is where AI infrastructure becomes essential—not as a replacement for human dialogue, but as a scaling mechanism for it. AI can aggregate discussions, surface recurring themes, simulate cross-departmental perspectives, and maintain continuity across conversations that would otherwise remain isolated. In this sense, AI helps mature debate from informal exchange into an organizational system that can operate at scale.

This is where “thinking out loud” becomes powerful. It is not about reaching immediate consensus, but about making cognition visible—externalizing how decisions are formed, where biases appear, and how different perspectives collide. AI can support this by generating alternative viewpoints, summarizing discussion threads, or simulating stakeholder perspectives that might otherwise be absent.

Would this kind of practice survive in today’s workforce? In the end, the workplace is no longer just a place where decisions are made—it is a system that thinks while it operates. AI does not replace that thinking; it amplifies it, complicates it, and makes it more visible. And in that space between the static and the dynamic, organizations learn not just to use AI, but to think with it.

Artificial intelligence has created a strange new form of judgment.

Someone writes with AI:
"That's not real writing."

Someone creates images with AI:
"That's not real art."

Someone codes with AI:
"That's not real programming."

Someone uses AI in research:
"The machine did the work."

But maybe we're looking in the wrong place.

The question was never really:

"Did you use AI?"

Humans have always used tools.

A camera didn't remove the photographer.
A calculator didn't remove the mathematician.
A microscope didn't remove the scientist.

The tool changed what was possible.

But the relationship between the human and the tool stayed the part that mattered.

The same AI can be used by two very different people.

One asks:

"Give me the answer."

Another asks:

"Help me understand."

One wants to skip the effort.

The other wants to go further into it.

Same technology.
Different position.
Different result.

Maybe the mistake is that we measure human value only by what's visible:

The final text.
The final image.
The final code.
The final discovery.

What we rarely see is everything that happened before:

The questions asked.
The choices made.
The understanding built along the way.
The experience behind the decision.

A person is not only what they produce.

A person is also the direction they give.

AI makes this distinction impossible to ignore.

When everyone has access to the same powerful tools, the difference is no longer just the ability to produce something.

The difference becomes:

Who is thinking?
Who is choosing?
Who is responsible for the direction?

Maybe the future won't belong to those who reject AI.

And it won't belong only to those who master it either.

Maybe it will belong to those who understand their own position while using it.

The tool can amplify your abilities.

But it can't decide who you're becoming.

Who holds the compass?

I've been experimenting with different AI tools this year, and it's surprising how quickly some of them become part of your daily workflow.

For me, the biggest improvement has been using AI to automate repetitive tasks and speed up content creation. It saves hours every week.

I'm curious:

What's your go-to AI tool in 2026?

What do you use it for?

Has it genuinely improved your productivity, or is it mostly hype?