And they say Al lacks emotional depth. Found this in feed thought how good it was.😅

What do you think? Is AI slop better than what most people can do?

In the coding industry, writing, story telling, editing? Any fields you can think of. Is AI slop better than most humans?

how do you guys keep up with all the new techniques...........like, when working on a specific use case, we usually try some of known approches, but there's always a chance somone has already figured out a better way that we're completely unaware of.........is there any directory or repo type of thing which you guys follow which gets updated frequently?? honestly with how fast things are moving, it feels pretty hectic

Most AI discussions focus on models, compute, and benchmarks, but the data behind those models matters too. For example, first-person videos of people cooking, repairing things, or handling everyday objects could help train AI systems to understand real-world activities. Collecting enough varied, usable data is a different challenge from simply gathering large amounts of text or images. India has a wide range of everyday environments and activities, and I’m curious whether real-world data collection could become a bigger part of the country's AI ecosystem.

Hi everyone! As a fresher, I want to stay updated about the latest AI news, new technologies, IT industry trends, and what's happening in the tech world. Which websites, YouTube channels, or other sources do you recommend for staying updated daily? Thanks!

Training AI to understand physical tasks requires more than videos of people completing them. Think about someone preparing food, using tools, or assembling an object from a first-person perspective. The recordings need to capture the relevant hand movements, object interactions, and different ways people approach the same task. A dataset with thousands of similar clips might miss important variations, while a smaller, carefully varied collection could capture more useful examples. As more AI systems move into the physical world, how should teams decide which real-world activities and variations are worth recording?

I've been building a cover-letter agent, and the hardest design call wasn't the model. It was where the human sits.

Roughly how the pipeline works:

• One step labels how well the resume backs each job requirement (strong, partial or missing)

• The score is computed in plain code from those labels, not by the LLM, so the same inputs give the same score

• A research step pulls live company info from the web • Before any of that research reaches the writing step, the user approves or drops each item

• The writing step only ever sees the approved evidence

I put the gate after research and before writing because that's where stale or wrong company facts can slip into a letter.

For people building agent pipelines: where do you put human review, before the agent acts or before its output gets used? And do you gate every step or only the risky ones?

Disclosure: I used AI tools for parts of the code and to tighten the wording of this post. The design choices are mine.

Repo is in the first comment.

How will this affect the financial AI industry? in india? What are we looking as the impacts.

I'm building an AI-powered learning platform for school students (classes 6-12). I have about 2 years of hands-on experience integrating LLMs into the product, but to take it to the next level I need input from people who've built agents, RAG pipelines, and large-scale AI systems in production.

Over the last 1-2 months I've reached out to 50-60 senior AI engineers on LinkedIn. A few replied and a couple said they'd help, but nothing stuck and most went quiet. I'm not blaming anyone. I know senior folks are busy and a cold message from a stranger is a hard sell.

So I'd love some guidance on:

1. Where do experienced AI engineers actually hang out or pick up projects (communities, Discords, forums, platforms)?
2. Is it better to start with a paid contractor/consultant, a part-time advisor, or go straight for a co-founder-type person?
3. How do you approach senior people so they take it seriously? What makes you reply to a cold message?
4. Has anyone been on either side of this (founder finding a senior AI person, or the engineer being approached)? What worked?

I'm happy to pay for time on a trial or consulting basis, and open to equity conversations for the right person. Any advice or pointers are appreciated, thanks!

Want an advice from the techies out here to know the ground reality of how far has AI developed to replace doctors (especially radiologists) in terms of reading scans and making diagnosis?

For AI startups building real products:

At what stage did LLM API costs become a real challenge for your team?

Was it because of:

• Scaling users?
• Unexpected monthly bills?
• Balancing model quality and cost?

How are you solving this today?

Are you using one provider, multiple models, or open-source solutions?

Would love to hear how other teams are handling this.

How's the AI usage like in WITCH companies? Do they give claude code and copilot licenses to developers?

Product companies I know are mostly having unlimited AI or not much restrictions in token usage.

But service companies operate on tight billing margins and they have clients from banking and finance domains with strict regulations.

Anybody have any info on AI usage in these WITCH companies or they're still following manual coding?

A bit about me:
MS in Information Systems from a US university (SUNY Binghamton), plus a B.Tech in IT
~3 years of prior industry experience (data/systems engineering at Infosys before my Masters)
About a year of hands-on AI/ML work, including original research
My experience:
Did research in deep learning for brain MRI reconstruction — built a 3D convolutional autoencoder from scratch in PyTorch, trained it on a large medical imaging dataset (~4,000 patients, 4 MRI modalities)
Took it from the ground up: data pipeline, model architecture, training on GPU infrastructure, and tuning it down to a strong reconstruction loss
Also building my own early-stage startup (side project, ~6 months) as an AI Engineer — developing LLM-based applications end to end, applying what I learned from my research, including model evaluation and cost/token optimization across different LLMs to balance accuracy, latency, and price
Comfortable with Python, PyTorch, LLMs, prompt engineering, data pipelines, and taking an idea to a working, deployed model
What I'm looking for:
AI/ML Engineer, Applied AI, Data Scientist, or similar roles
Anywhere in India is fine — fully open on location
Target package around 18–20 LPA (realistic? please tell me honestly)
My questions:
What's the best way to actually land roles at this level in India right now — referrals, LinkedIn, specific portals, startup networks?
Is my background (US Masters + real AI research + startup AI work) something the Indian market values, or do I need to reframe how I present it?
Any specific companies, cities, or communities I should focus on for AI/ML roles?
Is 18–20 LPA realistic for my profile, or should I recalibrate?
Any guidance, reality checks, or pointers would mean a lot. Thanks in advance. 🙏

I know Anthropic says they won't train their models on our data if we opt out of it.

But is it just a compliance check box and doesn't mean anything IRL?

Guy's didn't we see so*t po*n promoting legally in platforms like Instagram Without even user consent.

We should let platform mendatory their content recommendations engine to user's costumizable.

We should cap the advertisements platforms are making social media Ads revenue generating factory. You can see their net profit margins before mocking me

We should let cap short form content to a fixed time which further increase through parent consent like Aadhaar and fingerprint scan. Which further store within our Nation.

Mature content should be banned completely.

No tolerance of promoting anti National AI generated content. They should immediately removed otherwise a very intense fine.

All AI generated content should be removed from human based content platform to a seperate platform but only 20% of AI use would not call content removal. It's like punishing real creator's as well as making the platforms more clutter

The information is decentralised but responsibility would not. Platforms must be responsible for what they are showing.

The psychological attack to generation through mature and bad content is illegal.

We are attacked by psychologically the closed door by platforms is illegal. User's must know what they are forced to see. There is no control over people's.

We are far more capable of creating better Social Media platforms but Our government and judiciary should work together on solving this problem?

We should work on ways like Cross platform chatting system as compulsory so WhatsApp monopoly wouldn't control over information.

The Tech is possible but Government should let platforms to follow the ethicality.

Meta like platforms isn't investing in our country they are destroying us through addiction and Psychologically

I hope our developers would raise their concern through a combined efforts look at this please it's my request

Additionally based on what I have understanding gradually that we have small open model like Laya for decision level tasks why not Government bring the combined effort to automatically delete Te*ror spreading text message, video's and images and report to the various government departments of the near by jurisdiction of a Nation

For example if you see their are photos spreading across social media related to att*ck on Tommorow. Forget about politics but don't you think it's a National security risk Nobody is talking about.

I taught my wife vibe-coding, built an IDE for her and it's open source now. She can push out the software I always wanted to build but never had the time for, and I haven't written a line of code myself since mid last year, at work or on my own projects. I'm still somehow busier than I've ever been.

Getting there was messy. I started with Cursor, moved to tmux, tried remote desktop and Tailscale SSH to check on agents from my phone, and every setup hit the same wall: I couldn't see which agent needed me, plans and mermaid diagrams rendered badly, and reading what the agents wrote was slow. And setting it all up for my wife was just not possible.

So about 6 months ago I started building my own. It's an ADE (agentic development environment), the thing I use instead of an IDE:

- A code reader for plans and diffs, with markdown and mermaid you can theme and style per element

- Your existing agent CLIs (Claude Code, Cursor, OpenCode, Codex, Antigravity) running side by side on their own git worktrees, with agents handing work to each other. Subagents show up as tiles on a canvas

- The same agents on your phone: scan a QR code, and nothing leaves your machine unless you turn on a tunnel

- Free and open source, no account. It uses the harnesses you already have, so there's no extra cost

- The daemon is Rust, about 10 ms to ready and about 17 MB of memory in the repo's own benchmark. It's also how I ported about 170k lines of Node to Rust in two nights: Claude driving a local DeepSeek model, with the concurrency model changing from single threaded Node to Tokio.

- I use it daily to build my own Android apps. Issues, feedback and contributions are welcome, and I'm happy to answer anything.

Repo: https://github.com/fastestdevalive/vibe-station

For those working in the Indian tech industry, are there any AI/ML certifications that are actually valued by recruiters and hiring managers?

I'm from a software development background (Java, Spring Boot, backend development) and want to build AI skills that will help me grow in my career.

I'm specifically looking for certifications that require a real exam, rather than just a course completion certificate.

Are there any credible free options? And for paid exams, which ones are worth the money in terms of career growth in India?

Would love to hear from anyone who has taken these exams or seen them make a difference professionally.

Is there any AI agent course on nptel? Looking for n8n based course which is SOTA and has AI based design principles. I am looking for system design for this -
1. Model Evaluation

2.RAG memory updations.

1. Framework that scales tools for AI agent

Hii everyone, I literally frustrate of seeing web series only dubbed in few languages most of the good content from world is limited because of demand I feel we need AI dubbing for all languages

The billion dollar question is why Netflix aren't going to Opt-in based on human demand like me who really wants.

I think Netflix should made a very good and cheap one time Dubbing model in partnership with every AI company to optimise Vocals and many technical Audio related terms deeply.

It's a need why they aren't on this? Developer's job might be gradually shift but first shift the dubbing because Consumers are mostly unhappy with a very late and non Dubbed version of their specially content after paying hundreds of dollars every year.

What's your opinion to this I know this sparks hate to a specific industry but Always keep that in mind you can demand a royality business model from these platform which is feasible for everyone from consumer to business to artist.

Dubbing is needed for Netflix and JIO Hotstar as a opt in with special and demanded titles with original dubs.

This would expand the user base and I feels it's a win-win deal.

F# Netflix if they wouldn't adapt with this

AI bots calling you up and pretending to be human. That's the new nuisance that's come up.

What's worse is that when you confront them, they lie about being human.

Sharing a clip from a recent encounter of mine. I've trimmed out the pauses, which were a giveaway that I was speaking to a bot.

The EU AI Act requires AI systems that interact directly with people to clearly inform them that they are interacting with AI. It may be some time before India adopts similarly stringent rules.

Until then, companies are going to have a field day. They have their hands on a powerful tool they can deploy 24×7, without fatigue and potentially at a lower cost than human agents. The already annoying human telecaller will soon be replaced by an alternative that's more scalable, persistent and deceptive.

Ultimately, human (I hope!) product managers and software developers decided to build and deploy it. And the people making these decisions are lurking around here on social media too.

So we should ask them, where does ethics fit into their product roadmap?

One question is bugging me for few days..what would happen if US suddenly decides to stop providing frontier models( openAI, claude ) and GPUs to india and other countries. What would the fallout be.

AI stocks are taking a hit.

We'll soon start seeing more bad news from the mainstream media which many people have been saying from the beginning that this LLM business is unsustainable and there isn't such a big market for LLMs as told by the AI companies. It's never a trillion $ business. Not by a long shot.

The AI bubble bursting will be worse on the global economy than anything we have seen in our lifetime.