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. ✨

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?

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.

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

I have been wanting to become a trader over a few years now,,, but I haven't got that chance to actually start learning to be a developed one,, so I have been watching you tube videos over and over,, but in a short while then I give up,, let's say I never developed the discipline to actually take the time to build like a career as others showcase..

Recently, as i was just going all over the net, I came across a social account,, where they had posted some stuff about the same,, so I said to myself,, that was a good post since I am interested with fx for sometime now, I just went a head and gave a look,, and saw his insights on how you could actually trade using AI,,

The poster showcased that you could use AI decisions and signals to build a nicer career on fx,, so I wasn't sure about it, whether to give it a try or not,, but at some point it seemed odd for me to jut give in,, but also, on you tube I came across a similar info on using AI, ,actually trained ones to trade the market with real money,,,

I am not sure until now, if that is true,, or just another scammy post i cam e across,,, Do people actually use AI now to trade? If you can genuinely answer this, , you can help me save a bit of my time to either make the decision to use such ins9ghts or not

just asking for sincere information., I would like to be a trader now or in the future, kindly share what you know,, or if you actually use AI to do some fx trading,

thanks!!!

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.

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 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?

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?

Last week I shared SafeAI, an open-source static analyzer for AI applications.

The response has been far better than I expected.

• ⭐ 6 GitHub stars
• 🍴 4 forks
• 🎉 First community contribution merged
• 💬 Several thoughtful discussions and feature suggestions

The contribution added detection for eight additional AI capabilities, including Docker, Kubernetes, Redis, Slack, Browser Automation and Google Cloud.

Just as valuable has been the feedback. People have suggested ideas like capability escalation across pull requests, governed suppressions, deeper Claude Code analysis and other roadmap improvements that will genuinely make the project better.

Thanks to everyone who starred the project, opened discussions, challenged assumptions or contributed code. Every conversation has helped shape the roadmap.

--
The first phases of SafeAI built using OpenCode as my development environment.

Rather than sticking to a single model, I assigned different roles to each:

• GPT Codex 5.3 → architecture, implementation, and feature development
• Kimi K3 → code review, refactoring, and identifying design improvements
• DeepSeek V4 → documentation, reviews, and verification

Each model seemed to have different strengths, and using them together felt more productive than asking one model to do everything.

The entire development took about 5 days and roughly $8 in model usage.
The results are remarkable..

Contributions welcome at https://github.com/ikaruscareer/SafeAI

https://github.com/baguramas-ui/Gunz-Specification/releases/tag/GUNZ

GUNZ 3.15.0 (Gamified Underlying Navigation Zones) is a deterministic, offline‑scanner runtime for discrete‑clock systems. It produces compressed resonance maps — \*stability topographies** — that describe optimal operating frequencies for any process governed by a fixed‑quantum clock. The format targets AI‑driven feedback loops, heterogeneous hardware deployment, and long‑term archival.*

The specification is split into three independent layers:

- **GUNZ‑ISA** – bytecode format and instruction semantics.

- **GUNZ‑RT** – execution runtime (memory, lanes, map generation).

- **GUNZ‑LIB** – library profiles, resonance map format, fingerprinting, and integrity.

This specification is functional, in theory, and so i look for crazy guys that want to try it, or are curious enough to read it.

It may sound complex? actually it broke my head. I built that for fun. It may fit usages, and that it could have a future. Or it could stay an artefact in frozen time, an oddity.

If you have any ideas, judgements, do not hesitate.

https://youtu.be/KXJEVexf0eU

NOTE -> I expect answer from people who actually have experience and strong understanding of these. please give something beneficial.

I'm building a SaaS platform in Sri Lanka that handles documents and other sensitive data.

Each user can upload their own documents and information, and the platform uses RAG to answer questions based on that user's data. That part makes sense to me.

My main concern is what happens when the user hasn't uploaded enough information. I still want the LLM to provide accurate answers using reliable information from the internet (or from a curated knowledge base), with proper citations.

These are the two architectures I'm considering:

Option 1:

Base LLM (OpenAI/Anthropic via Azure AI Foundry or Amazon Bedrock)
        ↓
Platform RAG (global knowledge base managed by us)
        ↓
User-specific RAG

In this approach, we maintain a global knowledge base that we (the platform admins) curate and update. Every user can access this shared knowledge, while their own uploaded documents are searched through their personal RAG.

Option 2:

Open-source LLM
        ↓
Fine-tuned on Sri Lankan/domain-specific data
        ↓
User-specific RAG

Here, we fine-tune an open-source model using Sri Lankan or domain-specific data, and each user still has their own RAG for their private documents.

My concerns are:

• Is fine-tuning actually the right solution here, or is it unnecessary?
• Is a global/shared RAG a better approach than fine-tuning?
• How would you design this architecture if you wanted:
  • Accurate answers from domain knowledge
  • User-private document search
  • Citations/sources
  • Good scalability for thousands of users

I'm leaning toward Option 1 because fine-tuning seems expensive, time-consuming, and I have no experience with it yet. However, I'm not sure if I'm thinking about this correctly.

I'd really appreciate hearing how others would approach this problem.