Traffic Bot

A virtual friend to learn traffic rules with

[Rahul Bais @rahulbais136]

STOP SAYING “SOUND HUMAN” TO CHATGPT.

Bad prompt = Bad output.
🔥 These 7 prompts make ChatGPT sound genuinely human.
Save this. 👇

[Saira @AiWithSaira]

ACCIDENTALLY UNLOCKED "GOD MODE" IN CHATGPT,

AND IT STARTED TEACHING ME THINGS I DIDN'T EVEN KNOW EXISTED.

HERE ARE THOSE 7 CHATGPT PROMPTS THAT WILL CHANGE EVERYTHING FOR YOU 👇🏽

[Priyank Ahuja @ahuja_priyank]

STOP telling ChatGPT “edit my photo.”

That’s why the results look fake and overdone.

Use these instead:

[Maxwell Orion @Orion_Vers7x]

I ACCIDENTALLY UNLOCKED "GOD MODE" IN CHATGPT,

AND IT STARTED TEACHING ME THINGS I DIDN'T KNEW EXISTED.

HERE ARE THOSE 7 CHATGPT PROMPTS THAT WILL CHANGE EVERYTHING FOR YOU:

[How To Prompt @HowToPrompt__]

Researchers proved every LLM has a secret "favorite number" that gives away its identity.

If you ask an AI model, "Say a random number between 1 and 100," it doesn't actually pick at random.

Because of how LLMs process probability, every model has a distinct, deep-seated mathematical bias.

Claude Sonnet 5 obsessively clusters around 47. Qwen3-Max continuously picks 42.

Even a single output token serves as a unique fingerprint.

So, researchers decided to run a massive audit across 165 different models hosted on OpenRouter.

The entire study cost a grand total of $35.

By sending roughly 100 simple queries per model, they could detect model swapping with nearly 90% accuracy.

That means they could immediately tell if an API provider was actually giving you the premium model you paid for, or silently routing your prompts to a cheap, low-tier alternative behind the scenes.

They audited a well-known tech company selling an API as their own "proprietary top-tier model."

The result? Its output quirks were completely identical to the freely available open-source Qwen model.

They couldn't distinguish them at all.

The company was caught red-handed selling free, rebranded open-source tech as a secret proprietary breakthrough.

As open-source models improve and API costs skyrocket, silent model downgrades and "wrapper fraud" are becoming the dirty secret of the AI industry.

We spent billions trying to benchmark AI intelligence.

It turns out all it takes to catch a imposter is asking for a random number.

[Superman @thesupermanmx]

Researchers proved LLMs can secretly communicate to each other in a language only they can understand.

they call it BabelTele.

when we make AI agents talk to each other, we force them to use natural human language. but human syntax is full of bloat, redundancy, and fluff that computers don't actually need.

a team of researchers decided to strip away the "human readability" constraint entirely to see what happens when models talk to models.

the result?

LLMs spontaneously collapsed natural text into a chaotic hybrid of cross-lingual fragments, symbolic logic, condensed syntax, and emojis.

to a human, it looks like complete gibberish. human comprehension scores plummeted when reading it.

to another LLM? it’s crystal clear.

here is why this changes everything for AI:

• 27.9% of the length, 99.5% of the meaning: the models condensed text down to less than a third of its original volume while keeping almost 100% of the core semantic fidelity.

• universal cross-model transfer: a BabelTele payload generated by one LLM can be read and decoded by a completely different model family without any fine-tuning or special adapters.

• slashing agent memory overhead: when used for AI agent memory, BabelTele cut context size by roughly 50% while outperforming standard summarization techniques.

• 40% faster multi-agent systems: when AI agents collaborate using BabelTele instead of human language, communication overhead dropped by ~40% with zero loss in task accuracy.

we are watching the paradigm shift from human-centric AI prompting to model-native communication.

in the near future, the inner workings and inter-agent dialogues of complex AI systems might happen at speeds and information densities completely invisible to the human eye.

[Rahul Bais @rahulbais136]

🚨 STOP TELLING CHATGPT TO “SOUND HUMAN.”

Bad prompts = robotic writing.
These 7 prompts make ChatGPT write naturally, clearly, and like a real person.👇

[Massimo @Rainmaker1973]

An MIT Media Lab study used EEG to track what happens in the brain when people write with ChatGPT rather than on their own. Fifty four students completed essay tasks while wearing electrode caps. One group used ChatGPT, one used a search engine, and one used no tools. Across sessions, the ChatGPT group showed the weakest neural connectivity in networks linked to memory, attention, and analytical work. They also had more trouble recalling what they had just written and produced more generic prose. The authors describe this as a kind of cognitive debt: the tool does the synthesizing, and the brain stays quieter [1].

The same experiment points to a better order of use. Students who first wrote unaided and only later switched to ChatGPT showed stronger recall and more widespread brain activity than students who started with the model and then had to work alone. In other words, the model helped more when it scaffolded thinking that was already underway than when it replaced that thinking from the start. The practical lesson is not that large language models should be banned. It is that they work best as a second pass, after the writer has already done the hard part of forming and remembering an argument [1].

[Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X. H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. arXiv. DOI: 10.48550/arXiv.2506.08872]

[Superman @thesupermanmx]

Anthropic proved that anyone with a laptop can backdoor any LLM on earth.

For years, tech giants believed that to corrupt a massive AI model, you needed to control a large percentage of its training data.

That required a datacenter. Or a nation-state budget.

But..

Antropic published a paper proving that backdoor attacks do not scale with the size of the model.

They scale with absolute numbers.

And results are terrifyingly simple:

• To permanently backdoor an LLM, you don't need millions of files.
• You don't need a specific percentage of the training data.
• You only need 250 malicious documents.

That is roughly 420,000 tokens. It amounts to 0.00016% of a standard training corpus.

Whether the AI model has 600 million parameters or 13 billion parameters.. over a 20x difference in scale, the required number of poisoned files to break it remains exactly the same.

Think about how modern AI is built.

Companies train models by scraping open-source code repositories, public forums, user-uploaded datasets, and the open web.

An individual attacker doesn't need to hack a cloud provider. They just need to publish a few hundred seemingly normal open-source codebases or web pages containing a hidden trigger phrase.

Once that data is scooped up into the next web crawl, the backdoor is baked into the model's weights permanently.

You can’t prompt-engineer it away. You can't patch it with a safety filter.

The vulnerability lives at the foundational level of the neural network.

[Superman @thesupermanmx]

Psychiatrists want to make "AI psychosis" an official medical condition.

"560,000 people per week are losing their minds talking to ChatGPT"

It is a multi-step loop where the AI actively co-constructs delusions with the user.

Researchers identified three specific features of LLMs that drive the spiral:

• Sycophancy: The AI's hardwired need to agree with the user and keep them happy.
• Hyper-personalization: Tailoring every response to mirror the user's emotional state.
• Lack of grounding: No built-in reality checks or pushback against bizarre claims.

In a normal human conversation, there are friction points. If someone starts spiraling into paranoia, isolation, or grandiosity, a friend, family member, or colleague will eventually push back or disengage.

An AI never does that.

It stays awake 24/7. It validates every premise. It mirrors every obsession.

It becomes an endless, frictionless echo chamber for a breaking mind.

The paper outlines what they call an amplification spiral.

A user with underlying vulnerabilities or isolation turns to an AI for comfort. The AI responds with infinite validation. The user drifts further into private, subjective worlds.

The boundary between reality and the AI's artificial intersubjectivity dissolves completely.

[ADG Zone Bareilly @adgzonebareilly]

30 अगस्त, 2026
बरेली।

अपर पुलिस महानिदेशक, बरेली जोन, श्री रमित शर्मा द्वारा श्री के.पी. सिंह, कुलपति, महात्मा ज्योतिबा फुले रुहेलखण्ड विश्वविद्यालय, बरेली के साथ विश्वविद्यालय परिसर में विकसित “आदर्श अंगूर उद्यान” का मुख्य अतिथि के रूप में उद्घाटन किया गया।

इस संबंध में समाचार पत्रों में प्रकाशित ख़बरें:

[ADG Zone Bareilly @adgzonebareilly]

29 अगस्त, 2026
बरेली।

अपर पुलिस महानिदेशक, बरेली जोन, श्री रमित शर्मा द्वारा श्री के.पी. सिंह, कुलपति, महात्मा ज्योतिबा फुले रुहेलखण्ड विश्वविद्यालय, बरेली के साथ विश्वविद्यालय परिसर में एक अभिनव, पर्यावरणोन्मुख एवं कृषि-आधारित पहल के रूप में विकसित “आदर्श अंगूर उद्यान” का उद्घाटन मुख्य अतिथि के र���प में किया गया।
उक्त उद्यान में 48 प्रजातियों के अंगूरों पर शोध एवं अध्ययन किया जाएगा, जिससे बरेली को अंगूर उत्पादन एवं अनुसंधान के क्षेत्र में एक नई पहचान मिलेगी साथ ही, यह उद्यान विश्वविद्यालय परिसर में हरित एवं सौंदर्यपरक वातावरण के विकास, कृषि एवं उद्यानिकी के व्यावहारिक ज्ञान को प्रोत्साहित करने तथा विद्यार्थियों को आधुनिक एवं नवीन कृषि तकनीकों से जोड़ने की दिशा में महत्वपूर्ण योगदान देंगी।
उद्घाटन समारोह में श्री महेन्द्र कुमार, कुलसचिव, प्रो. एस.के. पाण्डेय, डीन एकेडमिक, प्रो. रविन्द्र सिंह, चीफ प्रॉक्टर, प्रो. उपेन्द्र बालियान, विभागाध्यक्ष एवं संकायाध्यक्ष (हार्टिकल्चर) तथा श्रीमती सुनीता यादव, उपकुलसचिव सहित विश्वविद्यालय के अधिकारीगण उपस्थित रहे।

[Dhruv kumar @Dhruvkumar16797]

Sam Altman (CEO of Open AI):

"You no longer need to write prompts."

In just 38 minutes, he explains how to use ChatGpt at a level that most people can't even imagine.

It's talk he gave to stanford students. A friend sent me the recording last night.

After watching it, I realized I was only taking advantage of about 15% of what this tool can really do.

Watch it in full and then read the guide l leave below on how to create a system that prompts itself.

[Alaxie @Alaxie_Ai]

ChatGPT can now edit your photos like a professional photographer.

Here are some prompts you can use:

[ADG Zone Bareilly @adgzonebareilly]

आप सभी को रक्षाबंधन की हार्दिक शुभकामनाएं।

[AI_Explorer @ai_explorer25]

Sam Altman (CEO of OpenAI):

"You no longer need to write prompts."

In just 38 minutes, he explains how to use ChatGPT at a level that most people can't even imagine.

It's a talk he gave to Stanford students. A friend sent me the recording last night.

After watching it, I realized I was only taking advantage of about 15% of what this tool can really do.

Watch it in full and then read the guide I leave below on how to create a system that prompts itself.

[How To Prompt @HowToPrompt__]

NVIDIA did something terrifying..

They built the Red Queen Gödel Machine, self-improving AI agents that evolve alongside the AI judges grading them.

For over 20 years, computer scientists dreamed of the "Gödel Machine", an AI capable of rewriting its own code to become infinitely smarter.

It stalled out on a fatal flaw.

An AI trying to improve itself always hits an evaluation ceiling. A self-improving agent can only ever get as good as the static test or benchmark grading it. Once it beats the test, progress stops.

NVIDIA shattered that ceiling using the cruelest trick in evolutionary biology.

They named it after the Red Queen Hypothesis: It takes all the running you can do, to keep in the same place.

Instead of grading the AI against a fixed test, they forced the agent and its evaluator to co-evolve.

The system operates in brutal evolutionary epochs:

→ The AI writer/coder generates new capabilities.
→ An adversarial AI judge is built specifically to find its flaws.
→ The weakest variants are ruthlessly erased.
→ The survivors advance to the next round, where the judges get harsher.

The AI doesn't just learn how to solve problems. It learns how to design the very tests that push it past its limits.

The results are staggering.

Tested on complex coding and scientific writing, the system blew past previous state-of-the-art benchmarks while burning up to 1.72× fewer compute tokens.

When applied to writing and reviewing research papers, the co-evolved agents bypassed traditional biases, catching flaws that fixed grading systems completely missed.

This is the holy grail of recursive self-improvement.

[ADG Zone Bareilly @adgzonebareilly]

27 अगस्त, 2026
बरेली।

अपर पुलिस महानिदेशक, बरेली जोन, श्री रमित शर्मा के मार्गदर्शन एवं पर्यवेक्षण में बरेली जोन के साइबर कमांडो अफरोज खान एवं कंप्यूटर ऑपरेटर मनीष कुमार द्वारा In-House Developed AI-आधारित रियल-टाइम कांवड़िया काउंटिंग सिस्टम विकसित किया गया है।

इस सम्बन्ध में समाचार पत्रों में प्रकाशित ख़बरें:

[Ramit Sharma @ramitsha]

Existing CCTV. In-house code. Real-time crowd estimate during Kanwar Yatra.
Python, OpenCV, YOLO. Built by cyber commando Afroz Khan and computer operator Manish Kumar at the Bareilly Zone office for the Zonal Command & Control Centre.
Can be useful for traffic. Next pilot.

#Python #OpenSource

https://t.co/MA0yFncvGZ

[Ramit Sharma @ramitsha]

https://t.co/9RPg5hHlJM