Hey guys, I created a virtual fortune reading website via AI lovable and would like to get some feedback to enhance the site. Do try out the 1 free full reading and compatibility reading per month using the link provided. Share with me your feedback so that I can make improvement to the site. Thanks in advance.
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.
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.
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 🙏🏻
Please give me your opinions , of what model to use???
Can I use it in my 8 GB ram Laptop?
Hey everyone
I’m looking to get a Claude Pro subscription at a discounted price.
If anyone has a legitimate discount, student/annual offer, unused subscription, or knows of a way to get Claude Pro cheaper, please let me know.
I’m not looking for cracked/shared accounts
DM me with the price/details if you have any leads
Thanks!
Got a call from bankbazaar AI voice agent and we used Chat Gpt voice against it. This is a snippet from the call.
Qwen 3.8 27B just scored higher than: GPT 5.6 Terra, GLM 5.2 DeepSeek, V4 Pro, Claude Opus 4.8 on the Artificial Analysis Agentic Index. And you can run this on a single RTX 3090/4090. Go show your GPU some respect.
I've been looking at the shift from cloud-hosted AI to local, private and sovereign deployments, and one distinction keeps coming up: local AI and sovereign AI aren't necessarily the same thing.
A paper I found particularly useful for thinking about this is:
AI Compute Sovereignty: Infrastructure Control Across Territories, Cloud Providers, and Accelerators
Hawkins, Lehdonvirta & Wu — Oxford / Aalto
What I liked about the paper is that it doesn't treat sovereignty as binary. It breaks it into three layers:
The authors' census of nine major public-cloud providers found 225 cloud regions across 43 countries, with 132 accelerator-enabled regions across 33 countries. Only 24 countries had training-relevant compute in the dataset.
India is an interesting example of why these layers matter.
In the paper's November 2024 snapshot, India had 5 accelerator-enabled regions, including 3 with training-relevant compute. The provider breakdown was 4 US-provider regions and 1 Chinese-provider region, which the authors describe as a form of “hedging” rather than dependence on a single foreign power.
But that snapshot is already dated.
By 2026, official Indian figures show 38,231 GPUs onboarded from 14 providers under the IndiaAI Compute framework, alongside 1,050 TPUs. India has also been setting up a 3,000-GPU secure national cluster for sovereign and strategic AI workloads, while another 20,000 GPUs were announced for addition beyond the existing capacity.
So, India's compute capacity has changed dramatically since the paper's dataset.
But the paper's deeper question is still relevant.
At the accelerator layer, it found that 95.5% of accelerator-enabled regions in its census were powered by US-owned accelerators.
That means: more compute in India doesn't automatically mean more sovereignty.
And I don't think the paper's argument is that India should try to build every component domestically either. More domestic compute can mean greater control and supply security, but data centres also bring significant demands on energy, water and land. The paper explicitly treats sovereignty as a trade-off rather than an objective that is automatically good.
That seems to be where the industry is heading as well.
NVIDIA and HPE are pushing sovereign AI heavily from the infrastructure/compute side, while Microsoft and IBM are building increasingly explicit AI control-plane capabilities around deployment, governance and operations. Lyzr is another interesting example at that layer, taking a more framework-agnostic approach to governing agents across different stacks and environments.
Which brings me to the part I find most interesting:
Maybe sovereign AI isn't ultimately about owning every component. Maybe it's about controlling the layers that actually matter for a particular threat model — compute, data, models, identity, deployment, governance, or the control plane itself.
For a local-LLM user, that might simply mean local models + local inference + local data. For a government or enterprise, the definition could be much broader.
So for India: what should “sovereign AI” actually mean?
Is owning the GPUs enough?
Is domestic cloud infrastructure enough?
Do we need domestic models and chips?
Or is strategic autonomy through managed interdependence the more realistic goal?
I genuinely need help from people who are already into this field, doing jobs, building projects and also if you can able to provide me the resource that could be really helpful
I believe this is the common pattern in almost all startups and product-based tech companies. Most of the engineers, directly or indirectly, use AI to increase productivity. Eventually, we will reach that level where AI itself can do 100% of the work. Maybe a couple of engineers will be there to guide and review the AI code. I would like to know others thoughts, about the future of the career in tech roles.
Hi guys, I wanted to know what you are using AI for in your freelance work or company.
Let me start.
We have 40 subscriptions for premium seats of Claude Code, and everyone is using it. Developers for writing code, QA for writing test cases, and marketing for content and research.
But everything we are using is for the work we need to get done, with no automation.
I would like to know if you guys are using any practices to make your work at least 60-70 percent automated.
We’re a tiny team, and this is the first product we’ve taken seriously to launch.
For the last few months, most of our days have looked pretty boring:
Code.
Debug.
Test.
Break something.
Fix it.
Repeat.
Then today we put Meridian on Product Hunt.
A few hours later, we were sitting at #1.
The weirdest part wasn’t seeing the ranking.
It was seeing people we’d never met actually use something we’d spent months arguing about internally.
Some people immediately understood the problem.
Some completely disagreed with our approach.
Some found edge cases we’d never thought about.
And honestly, that feedback has been more valuable than the ranking itself.
One thing I’m taking away from today:
You can spend months building something in your own bubble. The moment you put it in front of strangers, you find out what you actually built.
We’re going to spend the next few weeks talking to developers, fixing what we got wrong, and figuring out where Meridian actually fits.
Checkout on product hunt we are still #1: https://www.producthunt.com/products/meridian-16
If you’ve launched something on Product Hunt before, what did you learn that you wish you’d known before launch?
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I am a 4th semester CSE student at BMSIT Bangalore, entering 5th sem. Honestly, I feel like I’ve explored too many things without actually getting good at any of them.
I’ve done a little DSA, very little web development (i.e HTML/CSS), some ML/AI, and I’m currently learning FastAPI. I’m interested in AI and I also have a pretty strong mathematical background/intuition, but right now everything feels scattered and incomplete. I don’t have any serious projects or a strong portfolio either.
I want to seriously fix this now. I’m willing to put in 200% over the next 12–18 months. My immediate goal is to get a good internship by the end of 5th sem and eventually become an AI Engineer with a good salary.
I also have an education loan and very little financial support, so I can’t really afford to spend years randomly trying different technologies.
If someone experienced in AI/ML hiring or someone who has gone through a similar journey could guide me, I’d really appreciate it.
If you were in my exact position, what would you do from Day 1 of 5th sem? What would you prioritize between DSA, backend/SWE, ML, deep learning, GenAI, projects and deployment? What would you completely ignore?
And most importantly, what actually gets a Tier-3 student an AI/ML internship?
I’m looking for brutally practical advice. If you had 6 months to make yourself genuinely employable from my position, how would you spend those 6 months?
Saw this ad today. It seems like someone came up with the brilliant idea of paying you $2/hr for recording your entire screen.
This includes recording everything you do including your keystroke data. Why? for AI training.
What dystopia have we found ourselves in?
I keep bouncing between ChatGPT and Gemini—switching based on who had better explanations, details, or speed. And now I'm back to preferring ChatGPT after comparing their responses to the same question.
Now I'm planning to use both at same time, so I'm wondering if any of you could tell me which work one does better than other? like who does Research better, who recommends shopping better, who gives more detailed explanations, etc. [Again, on Free Plan]
i personally have observed, ChatGPT's answers are smarter while Gemini gives easier explanations for any day to day or academic questions. (and this is reason i kept bouncing between two).
Thanks!
Anthropic's latest risk report has been getting attention because it discusses an unreleased internal model called Model 2.
According to the report, Model 2 is slightly more capable overall than Mythos 5 and is being used internally for coding, research, data generation and agentic work. Anthropic currently doesn't plan to release it externally.
But I think there's a more interesting part of the report.
Anthropic says some of its most concrete task-based evaluations have saturated and are no longer adequately capturing increases in model capabilities.
That's a pretty fundamental problem.
A benchmark is useful when the score changes meaningfully as capability changes.
But imagine a benchmark where:
Model A = 80
Model B = 84
You conclude B is only slightly better.
Now imagine B has developed capabilities the benchmark wasn't designed to detect.
The 4-point difference might dramatically understate the actual difference.
Anthropic also says it has become less confident in some of its risk assessments for this reason, while seeing early signs of accelerated AI R&D.
So I'm wondering whether the next major bottleneck in frontier AI is actually evaluation.
Not:
“how do we build a smarter model?”
but:
“how do we discover capabilities that our existing tests weren't designed to see?”
I'd be interested in hearing from people working with evaluations, agents or red-teaming.
What kind of evaluation would you trust more than a static benchmark once models start saturating the benchmark itself?
Platforms are finally recognizing that people don’t want to consume AI slop. A growing number of sites and apps now have tools and policies to flag, label, and ban AI-generated content.