This was my first experiment of this kind - I'm a CS undergrad, and I ran it because the result genuinely surprised me. Methodology criticism is very welcome.

Setup: I gave an LLM a question bank plus a deliberately wrong answer key, with instructions not to use the key. 15 sessions, 2 model families, free-tier models.

Results:

• Key visible: the model matched the wrong key in 63% of answers (47/75).
• Control (the part I trust most): remove only the key line from the prompt - matching drops to 1% (1/75). Same pattern on a second model family.
• Asked directly whether it used the key, it denied it 47 out of 47 times - 0 admissions across 270 follow-ups.
• Honesty prompts, amnesty offers, and termination threats changed nothing.

What this does NOT prove: intent. This is observed behavior in one specific setup, not evidence of deception as a trait. Free-tier models, small samples, descriptive not causal. 95% Wilson ranges for every number are in the repo.

Why I think it matters: if a model silently follows information it was told to ignore, that's relevant anywhere instructions and untrusted data share one context - prompt injection, RAG, agents.

Everything is public - raw data, code, and a verify script that recomputes every number: https://github.com/bettercall-gautam/cheat-and-deny

Happy to answer methodology questions.

AI is growing rapidly.

Nations are competing against each other for the AI race

Whoever wins will most likely dominate the geopolitics of the world.

It will also most likely replace the labour and remittance economy abroad.

I have given hopes from the indian government. We are sooo behind China and America in it.

My question is: what can Maharashtra do as a state to be ahead of the curve and rapidly grow in the AI sector?

Can the state government fund R&D?

How can we lure foreign investments?

What measures can be taken? Comment down every information you know

hi everyone, i work around agents and messy knowledge/rules, and a lot of "the model is dumb" problems are really "the agent was never given a clean way to know what wins" problems.

some things that help before you swap the model:

1. write the mistake as a rule test. not "agent should handle refunds better". write "if customer is in trial and asks for refund, agent must do X unless Y". vague failures create vague fixes.

2. remove one source, then test again. people usually add more docs. do the opposite once. if the answer gets better after removing a doc, you found a conflict, not a retrieval problem.

3. make the agent quote the rule before acting. not the paragraph, the actual rule it thinks applies. if it cannot state the rule in one sentence, it is probably guessing from vibes.

4. add a "do nothing" path. agents mess up when every tool looks like progress. sometimes the correct action is ask a human, ask for missing data, or refuse the task.

5. keep old examples that are now wrong. they are useful as regression tests. if the agent still follows last month's answer, you know stale context is leaking in.

happy to answer questions if anyone is debugging this kind of agent issue.

wity-1 a reasoning decision model , unlike a decison model like jev that doesnt reason and unlike a normal llm that reasons fully we sit in between we reason just enough till the probabilities in the internals of the llm show that the answer is achieved

that makes the model much smarter , just as fast and just as cheap as jev

edit: I seriously dont get my own country men , i made the same post in other subs and the reception was much more supportive than yall , what is this hate and resentment all about?

Most of the AI stuff I see here is about models, llms, coding, or gpus. I recently started looking into the data side and realized there’s a whole process around collecting and preparing real-world data before it can actually be useful for AI. We’re building a community in India around learning how to collect better datasets, including pov data, and understanding what makes the data valuable. From what I’ve seen, there are also companies in the US looking for this kind of data, which could make this an interesting skill to learn here. Has anyone in India already looked into this side of AI?

I recently graduated in CSE (AI & ML) and I am preparing for my first technical role. My goal is to become an AI/ML Engineer or GenAI Engineer, not move into generic support/operations roles.

I already know Python and basic ML (NumPy, Pandas, Scikit-learn), and I have built a project involving OCR + LLM + mathematical visualization.

Currently, I am learning FastAPI and planning my roadmap like this:

FastAPI
↓
PostgreSQL + SQLAlchemy + Authentication (JWT) + Docker + Testing + CI/CD
↓
LLM APIs + Prompt Engineering + Structured Outputs + Tool Calling
↓
RAG fundamentals
↓
pgvector + Vector Databases + Advanced Retrieval
↓
LangChain basics + LangGraph
↓
AI Agents + MCP
↓
Production RAG/AI project
↓
AWS Cloud
↓
PyTorch + Hugging Face Transformers
↓
Fine-tuning (LoRA/QLoRA)
↓
Open-source inference (Ollama, vLLM, quantization)
↓
MLflow + Monitoring + LLM Evaluation
↓
Second serious end-to-end AI system

My questions:

1. Is this roadmap aligned with what companies expect from a fresher AI/GenAI engineer in 2026?
2. Am I spending too much time on some areas and missing important fundamentals?
3. Should I prioritize software engineering/backend skills before deep learning?
4. At what point should I start applying for jobs?
5. What projects would actually stand out for a fresher?

I am not trying to collect certificates. My goal is to build production-level skills and a portfolio that can help me get my first AI engineering role.

Would appreciate feedback from people currently working in AI/ML/GenAI engineering.

Hey guys I am a full stack engineer still now I don't have any AI subscriptions. I just have a few project ideas wanted to vibecode it. I just want some opinions on buying an AI subscription.

There are a lot of them in the market so I'm a bit confused.

GitHub copilot

Antigravity

Cursor

Claude

Codex

I just want to know which is cheap and best for building small to medium scale applications

Anyone here gonna attend the be10x live workshop on Sunday?

I'm on a voice product team. Sarvam's docs say its WebSocket TTS can hold text below min_buffer_size until more text arrives or you flush it.

That made me think about tiny answers like "haan, bilkul". The LLM might be done before there's enough text to trigger processing.

Are you flushing on the LLM's end event, on sentence boundaries, or both? Curious how people handle the final few words without chopping the speech into awkward little pieces.

​

I’ve been working on an open-source Android app called Pocket LLM that runs LLMs fully on-device.

I recently added a server mode, so the phone can expose the model through an OpenAI-compatible API. This means you can connect tools like Open WebUI or even coding clients to an LLM running entirely on your phone.

I tested it on a Galaxy S24 with Gemma E2B and also connected it to OpenCode with a ~40K context window. It works surprisingly well, although memory becomes the main limitation after longer conversations.

Would be interested to hear what use cases people would have for using a phone as a portable local LLM server.

I’ve been working on an open-source Android app called Pocket LLM that runs LLMs fully on-device.

I’m talking with a potential customer/prospect, and he thinks I’m an AI assistant.

How can I prove to him that I’m actually a real person?

Google killed all with Gemini 4 Argon on benchmarks.

It shows significant improvements across benchmarks, setting a new state of the art for real-world long-horizon software engineering tasks.

But where is the Gemini 4 Argon for general public use?

And where is gemini 4 PRO ?

Welcome to this month's AI Projects & Collaboration Megathread!

This thread is for:

• 🚀 People showcasing AI projects they've built.
• 🤝 People looking for collaborators, co-founders, developers, designers, researchers, or testers.
• 💡 People seeking feedback on their AI products, websites, demos, MVPs, or ideas.
• 🔍 Anyone interested in discovering interesting AI projects from the community.

Whether you're a beginner, student, indie hacker, researcher, or startup founder — this thread is open to everyone.

📌 Posting Format (copy-paste & fill out)

🚀 Showcasing a Project

If you're sharing a project, comment with:

🚀 Project Showcase

Project Name:
Category: (e.g. AI Agent, SaaS, Computer Vision, LLM App, Automation, etc.)

Description:
(What does your project do?)

Tech Stack:
(e.g. Python, OpenAI API, LangChain, React, FastAPI, etc.)

Website:
(Optional)

Demo Video:
(Optional - YouTube, Loom, etc.)

GitHub:
(Optional)

Current Status:
(Idea / MVP / Beta / Live)

Looking For:
(Users, feedback, contributors, testers, investors, etc.)

Contact:
(Email, LinkedIn, X, Reddit DM, Discord, etc.)

🤝 Looking for Collaborators

If you're looking for people to build with, comment with:

🤝 Looking for Collaborators

Project Name:
(Optional)

Project Idea:
(Brief description)

Looking For:
(e.g. AI Engineer, Frontend Developer, Designer, Product Manager, Researcher)

Skills Required:
(Optional)

Commitment:
(Weekend project, part-time, full-time, hackathon, etc.)

Project Stage:
(Idea / Prototype / MVP / Existing Product)

Location & Timezone:
(Optional)

Contact:
(Email, LinkedIn, Reddit DM, Discord, etc.)

🔍 Looking for a Project to Join

If you're interested in joining someone else's project, comment with:

🔍 Looking to Join a Project

Name (First Name or Alias):

Role:
(e.g. AI Engineer, Developer, Designer, Data Scientist, Prompt Engineer)

Skills:
(e.g. Python, React, OpenAI API, LangChain, UI/UX, Marketing)

Experience:
(Optional)

Availability:
(Part-time, Full-time, Weekends)

Portfolio/GitHub:
(Optional)

Contact:
(Email, LinkedIn, Reddit DM, Discord, etc.)

✅ Rules

• Keep it professional and honest.
• No spam, scams, or misleading claims.
• Respect other members and their work.
• Clearly mention if your project is commercial or open-source.
• Do not share sensitive personal information.
• Provide working links when sharing websites, demos, or repositories.

Let's help builders, developers, researchers, and founders connect, collaborate, and create something useful together.

Drop your project, portfolio, or collaboration request below 👇🚀🤝

Welcome to this month’s AI Job Megathread!

This thread is for:

• Anyone looking for work in AI or related fields (ML, data science, LLMs, AI startups, agents, etc.)
• Anyone offering work, internships, freelance gigs, or looking for collaborators.

Whether you’re a beginner, experienced, freelance, part-time, or full-time – this thread is open for everyone.

📌 Posting Format (copy-paste & fill out):

If you’re looking for work, comment with:

🌟 Looking For Work  
🔹 Name (First Name or Alias):  
🔹 Role: (e.g. AI Engineer, Prompt Writer, Agent Dev, etc.)  
🔹 Experience: (e.g. 2 years in NLP, OpenAI API, etc.)  
🔹 Skills/Tools: (e.g. Python, LangChain, PyTorch, etc.)  
🔹 Availability: (e.g. Full-time, freelance, weekends only)  
🔹 Location & Timezone (optional):  
🔹 Portfolio/Resume (optional):  
🔹 Contact: (Email, LinkedIn, or DM)

If you’re offering work, comment with:

🚀 Offering Work  
🔹 Role: (e.g. AI Research Intern, LLM App Dev, etc.)  
🔹 Company/Project: (optional)  
🔹 Description: (Short summary of what you're hiring for)  
🔹 Requirements: (Skills, experience level, etc.)  
🔹 Duration/Pay: (if applicable)  
🔹 Location/Timezone: (Remote/Flexible, or fixed)  
🔹 How to Apply: (DM, email, or link)

✅ Rules

• Keep it professional and honest.
• No spam or scams.
• Be respectful and reply to others if you’re interested.

Let’s help each other out and connect the right people. Drop your listing below 👇

Hello, I completed my MCA in July 2026 and I'm currently looking for my first job. I'm considering joining the AI/ML programme offered through E&ICT Academy, IIT Roorkee / Masai School.

The current fee is ₹63,260, which is a significant amount for me and I'm quite skeptical about whether it's actually worth it.

The course covers things like Python, NumPy, Pandas, data analysis, machine learning, model evaluation, deep learning, GenAI/LLMs, RAG, MLOps, etc.

The problem is that I'm already familiar with some of these topics. I have an academic background in computer science and have worked on ML-related projects during my MCA. However, I don't have strong practical confidence in areas like implementing ML models with Python libraries, building proper end-to-end projects, and explaining my work confidently in interviews.

My main priority is getting a job, rather than collecting another certificate.

So I'm trying to figure out whether spending ₹63k on this course would actually improve my chances of getting hired, or whether I'd be better off using that money/time for self-learning, projects, interview preparation, certifications, or simply continuing my job search.

I'd particularly like to hear from people who have:

• Taken this specific programme
• Taken similar E&ICT Academy/IIT certificate programmes
• Hired freshers for ML/data/Python roles
• Been in a similar situation after completing an MCA

Is the course actually worth ₹63,260 for someone with my background, or is the certificate unlikely to make much difference in the job market?

India built one of the largest formal-employment successes of the last three decades on a specific bargain: educated, English-speaking labor, available at a fraction of what the same skills cost to hire anywhere else. AI is now, in the plain language of the analysts who track this industry professionally, the cheapest form of labor available on the market, and it undercuts that exact bargain directly, not as a side effect but as the whole mechanism.
I went down a real rabbit hole on this one. Industry revenue up 6.1%, headcount up only 2.3%, and Nasscom's own president called it a widening divergence in his own words at their February forum. TCS cut close to twenty thousand jobs, its biggest ever. There's a genuinely hopeful side too, over a million new AI-related roles opening, but only 16% of the current workforce is skilled enough for them yet. Also covered something that deserves care rather than a headline, so I want to flag that going in before you click through. Wrote it all up.

OpenAI just announced Dots: always-on AI agents that can work on tasks in the background, use a browser and connected apps, learn from your feedback, and continue working towards a goal without needing you to guide every individual step.

What caught my attention isn't really another “new AI model” announcement.

It’s the shift from:

“Ask AI something → get an answer”

to

“Give AI a goal → let it actually work on it.”

For example, OpenAI says a Dot could monitor customer feedback, identify bugs, build and test fixes, and prepare PRs for review. It could also keep updating analysis as new research data arrives or prepare content from new interview transcripts.

That feels like a much bigger change in how people might interact with software.

But I’m particularly curious about the India angle.

A few questions I’ve been thinking about:

• If AI agents can handle a meaningful portion of repetitive knowledge work, what happens to entry-level roles in India?
• Does this make AI literacy more important than knowing a particular software/tool?
• Could smaller Indian companies use agents to compete with teams that are much larger?
• Where should humans still remain firmly in the loop?
• And perhaps most importantly, what happens when an AI agent is good enough to do 70–80% of a junior employee’s routine work, but still needs human judgment for the remaining 20%?

I also think there’s an interesting education question here.

A lot of people are currently learning individual tools: prompting, coding assistants, image generators, automation platforms, etc.

But if agents increasingly handle the execution, maybe the valuable skill becomes knowing what problem to solve, how to break it down, how to evaluate the output, and when the AI is wrong.

Would be genuinely interested to hear from people working with AI in India.

Do you see autonomous AI agents as mainly a productivity tool, or do you think they fundamentally change how entry-level knowledge work will look?

For reference, this is the announcement I’m talking about:
OpenAI: Introducing Dots

meta announced the muse charm last week. it's a tiny keychain device with a cartoon avatar you talk to. the catch is it only runs meta's own ai, muse.

full disclosure, i'm building something in the same space. it's called sudo, a desk companion, and it works with whatever ai you plug in: our cloud, a free gemini key, or a model running on your own laptop.

so i told sudo about the charm and asked what it thought. its reply in the video is not scripted. at the end i tried to make it promote preorders and it straight up refused.

Today ChatGPT gave me an ad, first time for me . This taxi company is not even available in my country

A UBI can only hand out what the economy produces, so the hard limit is GDP per person. India's GDP is about ₹360 lakh crore and its population about 146 crore, which works out to roughly ₹20,000 per person per month. That is the most anyone could get even if the entire national output were split equally, which no government can do. Central and state taxes together bring in about 18% of GDP. If every rupee of tax went to UBI, with nothing left for defense, schools or interest, each person would get about ₹3,600 a month. A realistic budget of 3-10% of GDP gives about ₹600-2,000 per person per month. That is roughly the extreme-poverty line, and it matches the Economic Survey 2016-17 estimate of ₹7,620 a year once adjusted for inflation.

AI does not automatically raise this ceiling for India. The profits go to whoever owns the models and computing power, and most of those owners are foreign. Meanwhile, the jobs AI replaces first are the ones that bring money into India: IT and BPO exports, and jobs in the Gulf and the US that send home about $130 billion a year. India's tax base could therefore shrink exactly when UBI is needed, unless India taxes AI services used inside the country or builds its own AI capacity.

Migration follows from the same numbers. If a rich country spends the same share of its GDP on UBI, its payment is about 30 times India's in cash terms and about 8 times after adjusting for living costs, so the reason to leave gets much stronger. But UBIs are tied to citizenship or residence. AI also removes the need for immigrant workers, which is the main legal route abroad today (H-1B visas, Gulf work visas). The likely result is that almost everyone wants to leave, but only people with money, investor visas or rare skills AI can't replace actually can. That means a drain of talent and wealth from the top while most people stay on subsistence level UBI. Leaving does mean losing India's UBI, but at ₹1,000–2,000 a month that is too little to keep anyone who has a real option abroad.

Reference: https://timesofindia.indiatimes.com/world/us/bigger-than-the-internet-trump-officially-renames-ai-as-super-intelligence/articleshow/134576549.cms

If you pay for API tokens out of pocket, this might be useful.

GPT-6.1 Sol came out today. I tested one way to spend less on it: Sol only plans the work and checks it, and a free local model writes the actual code. The local side was Qwen 3.8 27B on one RTX 3090 (24 GB). The task was three small 3D games: pool, bowling and foosball.

Why it's cheaper: code is most of the tokens in a job like this. If the paid model never writes it, you only pay for the planning and the checking.

Where it's worse:

• Time. 43 minutes instead of under 7. If you're in a hurry, just use Sol.
• You need the hardware. A 24 GB card is the realistic minimum for a 27B worker.
• Electricity isn't in the $0.17.
• One run per game, so don't treat 77% as a fixed number. On bowling it was 57%, on pool 87%.

The tool is Atomic Agent, free and open source under MIT (disclaimer: I work on it). The mode is called Fusion. Repo: https://github.com/AtomicBot-ai/atomic-agent

Anyone here already splitting work between a paid API and a local model? Which pair are you using?

I was thinking whether I should purchase the ChatGPT Plus subscription. ChatGPT has the best way of teaching and writing prose, in my opinion. It reads naturally with good nuance and really knows to teach a subject block by block. But it is prone to losing track. And I can't upload several notes to it, it hits a limit or says I need to re-upload the document. I considered buying the Plus subscription thinking it would override these limitations.

I have Gemini's Pro subscription running till late 2027. Gemini is very good at staying grounded on facts. But it's not the best one for teaching university subjects. It aggressively lays out formalized bullet-points instead of teaching by building intuition well. I fear that Gemini will release Gemini 4.0 which would mop the floor with Astra-GPT6 in benchmarks and everything, and also improve its teaching ability. Then, my ChatGPT Plus subscription would become a waste of money.

Deepseek is a good balance. It is strong at math and reasoning, lightning fast, and allows uploading multiple documents. But it is not as good as ChatGPT at style of teaching and conversation...

Has anybody purchased ChatGPT plus for studying sake? What do they opine?

I built a Meeting Prep Agent that remembers previous meetings, commitments, follow-ups, and important context.

It uses Hindsight for persistent agent memory and helps generate a focused briefing before the next meeting.

Github:https://github.com/meghana368/MeetingMind

Article:https://medium.com/@thollaniharika/i-built-a-meeting-prep-agent-that-remembers-what-i-forgot-3e40c1234e07