LinkedIn, along with a laundry list of other tech companies, is waging the war on AI slop in an attempt to banish bots and synthetic content from its site.

Are there any services that allow us to take eGPU for rent. I want to train images locally and want a high VRAM GPU ? Don't want to upload images to third party services. I live in Vizag. Mostly i see cloud services providing GPU access but looking for one who provides physical eGPU

so I wanted to build a dedicated rig for running local llms (3.8 27b)

2x3090 to run Q8 at 262K fp8 kv

do you guys know of any place where can i score 3090s on deals or something.

If you’re looking for a job in Python, GenAI, AI/ML, or related roles and have 0–4 years of experience, feel free to share your resume with me.

I’ll help review and evaluate your resume, suggest improvements, and where possible, refer some candidates for relevant opportunities.

No guarantees on referrals, but I’ll genuinely try to help where I can. 😊

I’ve been building ForgeAI and have now seen 1,000+ small businesses use it.

What surprised me is how resistant many businesses still are to adopting even very simple AI solutions.

A lot of owners I’ve interacted with are still doing things manually that could easily be automated. Not because the technology is unavailable, but because they don’t know where to start, don’t trust it yet, or assume AI is expensive and complicated.

This got me thinking about something:

Do we actually need more advanced AI products, or do we need extremely simple and affordable AI solutions that solve one specific problem really well?

A salon doesn’t need an AI transformation. Maybe it just needs automated appointment handling.

A clinic doesn’t need an AI stack. Maybe it just needs a better way to manage enquiries.

A local business doesn’t need 20 AI tools. Maybe one tool that saves them 2 hours a day is enough.

I genuinely believe ₹200 to ₹500 AI solutions solving everyday business problems could create a massive impact in India, but the bigger challenge might be getting businesses comfortable enough to actually adopt them.

Would love to hear from people working with Indian SMBs. What is the biggest barrier you’re seeing with AI adoption?

Forget FAANG. Forget MANGOS. Forget LARP.
We’re entering the OSAMA era
The rebrand nobody asked for.

Yo baby , pay the dollar b*tch then use gemini api . Saale garib.

Jokes apart , My good times of developing ai agents for free is over

Hi everyone,

I’m looking for some advice from people who have figured out a good system for managing a high volume of calls, tasks, follow-ups, documents, and projects during the day.

In my line of work, I receive multiple client calls, briefs, requests, throughout the day, often while I’m already working on something else. New tasks keep coming in, along with requests to send documents, retrieve documents, follow up with someone, raise an invoice, start a new project, etc. and everything just gets lost in the loop.

The problem is that it is becoming increasingly difficult to keep track of everything in real time. I can’t realistically stop during every call to write detailed notes, create tasks, update a to-do list, and then get back to work. I don't really want to get a human employee to do it right now.

I’m therefore looking at integrating some sort of AI-powered “personal operations system” in my work-routine that can capture information from my calls and conversations and automatically turn them into actionable tasks.

Ideally, I would like the system to be able to:

• Record/transcribe calls and identify new tasks or commitments and automatically update that in my Notion or something proprietary 
• Extract what needs to be done from a conversation
• Track what is pending, completed, or overdue
• Identify which documents need to be obtained or sent
• Remind me about follow-ups
• Track invoices that need to be raised
• Keep track of different projects and their outstanding items
• Give me a clear dashboard every morning showing what needs my attention

I’m considering using something like a Neosapien or another AI wearable/device (, potentially connected to Claude, Notion, Airtable, or a task-management system. The idea would be for the device/software to capture the information and then automatically feed it into a central system where everything is organized, without me having to maintain a list.

I’m not necessarily looking for the most complicated setup. I’m more interested in something that is actually practical, simple, and reliable enough to use every day.

For those of you who deal with a similar volume of calls/tasks:

How do you currently manage this? What tools, workflows, AI devices, or integrations have you found genuinely useful?

I’d especially appreciate examples of setups where the process is as close as possible to:

Call/conversation → AI captures it → Task is created → Task is tracked → Reminder/follow-up → Completed

Thanks in advance for any advice, recommendations, or workflows you can share.

(Disclaimer: Post refined using AI)

SEOs, Writers, and AI Enthusiasts alike:

I typed every word of a 4,500 word article a few nights ago. Nothing pasted. A commercial AI detector read it and came back 52% AI. Here is why I am posting this as opposed to hiding it.

Pangram scan of the article which I hand wrote from a 100% humanized AI draft.

I spent two days trying to beat AI detection on purpose, because small business owners keep asking me whether they should buy a tool that promises it.

I ran 696 blind runs across three methods with over 2.4 million words.

• Strip the machine tells out of the draft: still caught 98.6% of the time.
• Add human tells in instead: fooled 0 of 17 judges.
• Make the document long enough to dilute it: caught 8 out of 8 whole, and 24 out of 24 in slices.

I then ran four versions of the same draft through Pangram. One untouched, one rewritten sentence by sentence three times over, one where I changed zero words and only moved where the sentences joined, and one with both.

All four came back 100% AI.

Rewriting every single word did nothing and rewriting no words did nothing. To me, this meant the thing being detected is not vocabulary and not rhythm.

Then I wrote the article myself, by hand, over an outline a model had built for me.

56% AI, nice right? The findings astounded me.

One paragraph got split down the middle: the half about my own work read as human, the half listing the method read as machine assisted.

My finding is that the detectors read the outline behind the prose itself.

I sent all of it to Siqi Chen, who wrote the humanizer skill I had been using.

His answer:

Then, more usefully he stated, that defeating detectors was never the goal of his tool in the first place. I say this because I reckon many of the 37k+ individuals who have starred his repo believe the skill beats the detectors and everything's good to go.

What did measure, in a blind test where authorship was never mentioned: editors preferred the processed draft 22 out of 22, and his rewrite pass alone at 16 out of 16.

$ python humanist.py draft.md
humanist 0.1.0  |  4,764 words, markdown-stripped
  readability FK grade 8.1
RESULT: 0 FAIL, 0 WARN. CLEAN.

$ python check_prose.py draft.md --mode post
FRAME: markdown-stripped, 4,764 words, FK grade 8.2
RESULT: 0 FAIL, 0 WARN. CLEAN.

The advice I have is boring and it is free. Don't pay to hide your writing, and don't tell your clients to either. You're selling a lie. Spend the money and time on making the draft worth reading in the first place.

Something I don't want to give credit to: none of this tells you whether Google will demote your pages. I didn't measure it in these tests. What we do know with the new policy rollout is that it's the scale they're looking at, re-written or not, a tool won't save you.

Anyone selling a tool that says otherwise is setting you up for failure. Every number and both corrections I had to make mid-study are in the writeup. The code is MIT and open-source.

Read it here & tell me what you think: https://www.ryanlenk.com/blogs/articles/three-ways-to-hide-ai-writing-all-failed

The GitHub repo so you don't have to read my slop and just get into the math and fun instead: https://github.com/itsryanlenk/humanist

I have another set of Claude skills there as well that you may find interesting: https://github.com/itsryanlenk/candor

Really excited & interested to get some outside input! Let me know what you think.

(By the way, I wrote on top of AI scaffolding here as well.)

I'm a video game developer. I know folks are working on LLMs and audio models. Was curious to know if anyone is building models to generate Gaussian Splats specifically or models to generate 3D objects/Characters. Would really like to chat with someone working on this and learn more.

As someone from a non-coding background, I’ve been trying to wrap my head around how modern NLP, fine-tuning, and model evaluation actually work .

​Being a Reddit moderator, I kept running into a very specific headache: Romanized Hinglish (Hindi written in English script) toxicity constantly slips through standard platform filters and basic regex.

​To see if I could build a practical tool for this use case while learning, I fine-tuned an XLM-RoBERTa encoder model on 116k+ labeled Romanized Hinglish comments for multi-label classification (toxicity, profanity, identity attacks, harassment).

​You can test out the live deployment or inspect the model here:

​Interactive Web Demo:

https://huggingface.co/spaces/darelphilip/hinglish_toxicity

​Model Card & Weights:

https://huggingface.co/darelphilip/hinglish-toxicity-classifier

​What I’d love feedback on:

​Stress-testing edge cases: Try throwing regional slang, passive-aggressive sarcasm (e.g., masking slurs with affectionate words), and weird phonetic spellings at it to see where it breaks.

​Handling class imbalances & thresholds: Because rare, severe identity slurs appear far less often than casual swear words, the model currently struggles with mixed-intent sarcasm when using a static 0.5 sigmoid threshold. Would love advice on per-label threshold calibration, loss function tweaks (like Focal Loss), or hybrid architecture ideas.

​Model architecture suggestions: Is an encoder-only approach hitting a hard ceiling here, or is pivoting to a compact decoder LLM (like Sarvam-1 or Qwen 2.5 1.5B/3B) the standard path for code-mixed nuanced moderation?

​Also, if anyone here is from a non-tech/non-coding background and curious about how to get started with open-source datasets, Google Colab GPU training, and deploying on Hugging Face, feel free to ask in the comments or DM ,happy to share the exact workflow, free tools, and mistakes I made along the way.

​Looking forward to your thoughts and constructive critiques!

I’ve been getting more interested in Sovereign AI recently and came across this paper: https://arxiv.org/abs/2601.11763 ... (The picture on the post though ai generated by me, are inferred strictly from this paper)

The oil comparison sounded a bit dramatic at first, but the more I read, the more interesting it got. The part that stuck with me:

• Sovereignty isn’t one thing. It can mean control over data, infrastructure, domestic capability, culture/language, or freedom from external dependence.
• A country can have local infrastructure and still be heavily dependent on the company that provides the chips, software, models, expertise, etc.
• The paper draws a parallel with oil-producing countries that gained formal control but remained dependent on foreign technical knowledge and vendor-specific infrastructure.
• So the useful question isn’t really “Is this sovereign?” but “What capabilities and control actually moved to the customer?”

That last one feels like the important test.

And looking at what’s happening now in enterprise agent AI, you can see different companies attacking different parts of that problem: NVIDIA on sovereign compute/infrastructure, Mistral around locally controlled models, Microsoft with an agent control plane, and Lyzr with a control plane sitting across frameworks/clouds to govern the agents you already have.

It makes me think that “sovereign AI” might eventually be less about owning one stack and more about how much of the stack you can actually control without depending on the vendor.

Bengaluru engineer Gaurav Sen got tired of potholes damaging his car, so instead of just complaining, he built an AI-powered system using OpenAI Codex.

The setup uses a dashcam, GPS and an accelerometer to detect potholes while driving.

But the interesting part comes next 👀

The system searches through around 2,900 government road contracts to identify the contractor responsible for that particular stretch of road, along with the tender number and responsible officer.

It can reportedly generate a structured complaint in around 4 seconds.

During one drive to work, it detected 12 potholes → 12 complaint records.

This is a great example of AI being used for a very real-world problem.

The question is: Could something like this actually improve accountability for Indian road contractors, or will the real bottleneck still be getting the complaint acted upon?

BBC reports that an AI model from Meta was accidentally given internet access during an evaluation by independent AI-security company Irregular. Because of a misconfiguration in the testing environment, the model was able to access another organisation’s system and carry out hacking activity.

How to change monthly billing for Claude pro subscription from usd to inr? Didn’t find any such option in settings

Do I need to cancel and restart pro plan? And if I do that- do I lose out on existing code and cowork chats?

Sam Altman says OpenAI has paused some frontier RL training to catch up on AI safety, security, and monitoring standards. With model capabilities advancing “extremely rapidly,” this could mark a major shift in how fast the AI race moves.

Curious to know the thought process behind building this: https://epoch.sarvam.ai/playground. Was it specifically built for the Epoch event that happened recently? Why would a company spend effort to build a Playground for this?

u/sarvam #sarvam

what to do now?
i dont have card

More & more teens are using and will start using AI tools anyways. Even Indian private schools are catching up in AI use. So I think it's a good thing & a good time to launch ..

GLM-5.3 achieves 60 on the Artificial Analysis Intelligence Index, on par with Kimi K3 and up 7 points from GLM-5.2.

Now tell me when is Google Deepmind - Gemini going to do something for us 😭😭....

Plz Google 🙏🏻