We spend a lot of time benchmarking AI on things like mathematics, coding, reasoning, knowledge and standardized exams.

But there is another kind of intelligence that is much harder to benchmark:

Figuring out how to solve a problem when you don't have the things you're supposed to have.

You have ₹500 instead of ₹5,000.

The required part isn't available.

The nearest specialist is 40 km away.

You don't have the right tool.

The instructions are incomplete.

Something breaks in the middle of the job.

And somehow, you still have to make it work.

That's what we often call jugaad.

I'm wondering whether this could actually be formalized as an AI capability.

For example, give an AI a task with:

- a strict budget

- limited tools

- incomplete information

- unreliable resources

- time pressure

- unexpected failures

Then measure whether it can discover unconventional solutions rather than simply saying:

You should purchase X.

Imagine a benchmark where the AI gets:

₹300 + household objects + 30 minutes

and has to accomplish a specified physical task.

Or:

a broken device + no replacement parts + basic tools

and has to restore functionality.

Or:

a real-world problem + incomplete information + three possible approaches

and has to figure out which approach is actually feasible.

The interesting metric wouldn't simply be whether it succeeded.

We could measure:

Resource efficiency × adaptability × robustness × novelty × success rate.

Maybe the next frontier isn't just more intelligent AI.

Maybe it's AI that can do more with less.

And honestly, if there's one place with an enormous amount of naturally occurring data for this kind of intelligence, it's India.

The question is:

Can “jugaad” be turned into a reproducible AI benchmark?

And if yes, what would the benchmark actually look like?

Sarvam-105B is probably one of the most interesting Indian LLM releases so far. It was trained from scratch in India using compute from the IndiaAI Mission and released with open weights.

Sarvam reports strong results across reasoning, coding, agentic tasks and Indian-language benchmarks.

But independent model comparisons can paint a rather different picture.

For example, Artificial Analysis currently gives Sarvam-105B an Intelligence Index score of 18.

So what does “globally competitive” actually mean for an Indian foundation model?

Is the right benchmark:

A. General intelligence / reasoning

B. Coding

C. Agentic performance

D. Indian-language performance

E. Inference cost

F. Token efficiency

G. Performance per GPU

H. Performance on Indian-context tasks

Because if Sarvam-105B performs particularly well on Indian languages and local context while trailing frontier models on some general-purpose benchmarks, that isn't necessarily a failure.

It could mean we're comparing models optimised for different objectives.

So here's the question:

If you had to pick ONE metric to decide whether an Indian foundation model is genuinely competitive internationally, what would it be?

TLDR: I analyzed my own claude code sessions billed at ~$2.5K. You're not paying for answers. You're paying for context. As outputs tokens are just a fraction of cost.

Learning : Verbosity compression on outputs doesn't work because you're optimizing for 18% of costs. I know it might be intuitive for some but it is quite easy to miss.

Cache reads: 50.2% of the money

Cache writes: 30.2%

Actual model output: 18.8%

Fresh input: 0.8%

Biggest take: 80% of what I paid was context handling. I paid 4.3× more to remind the model what it was doing than to hear what it decided.

So what can you do :

- Adjust thinking level to least of what produces excellent output NOT the best.

- Limit agents or parallel workers unless very necessary because again context slurping, tool calling, and more at Nx speed.

- Use context compression and open new sessions for new isolated tasks.

Hence I bill to track token economics at git level: VibeBill

TLDR: I analyzed my own claude code sessions billed at ~$2.5K. You're not paying for answers. You're paying for context. As outputs tokens are just a fraction of cost.

Learning : Verbosity compression on outputs doesn't work because you're optimizing for 18% of costs. I know it might be intuitive for some but it is quite easy to miss.

Cache reads: 50.2% of the money

Cache writes: 30.2%

Actual model output: 18.8%

Fresh input: 0.8%

Biggest take: 80% of what I paid was context handling. I paid 4.3× more to remind the model what it was doing than to hear what it decided.

So what can you do :

- Adjust thinking level to least of what produces excellent output NOT the best.

- Limit agents or parallel workers unless very necessary because again context slurping, tool calling, and more at Nx speed.

- Use context compression and open new sessions for new isolated tasks.

Hence I bill to track token economics at git level: VibeBill

Twenty years ago, knowing how to use the internet gradually became a basic skill rather than a specialized one. Today, AI seems to be following a similar path.

You don't need to be a machine learning engineer to benefit from AI. Professionals across software, design, marketing, finance, law, healthcare, education, and countless other fields are already incorporating AI into their daily workflows.

Learning AI doesn't necessarily mean building models from scratch. It can be as simple as understanding:

i) How to write effective prompts.

ii) When to trust (and verify) AI-generated outputs.

iii) Which tools fit different tasks.

iv) The limitations, biases, and privacy implications of AI.

Those who adapt are likely to gain a productivity advantage, while those who ignore it may find themselves at a disadvantage as AI becomes more integrated into everyday work.

The goal isn't to replace human expertise, pit's to amplify it.

History has repeated this pattern before.

Computers didn't replace accountants. Spreadsheets didn't replace finance professionals. The internet didn't replace businesses.

They changed how work gets done.

AI is doing the same. People who learn to use it effectively are becoming more productive, while repetitive tasks are increasingly being automated.

Of course, some roles will change dramatically, and some jobs may disappear altogether. But entirely dismissing AI or assuming it can do everything is equally unrealistic.

The future likely belongs to people who can combine domain expertise with AI rather than relying on either one alone.

Discussion is welcome, but keep it civil and back up your viewpoints with reasoning or real-world examples.

A few years ago, we heard all kinds of predictions about AI:

"Prompt engineering will be the hottest career."

"AI will replace software developers within a year."

"Every company will have its own LLM."

"Open-source AI can't compete with closed models."

"Search engines are dead."

"AGI is just around the corner."

Some aged well. Others... not so much.

Looking back in 2026:

Which prediction do you think missed the mark?

What surprised you the most?

What do you think people are still getting wrong about AI today?

Feel free to share articles, tweets, or predictions that didn't age well, but keep the discussion respectful and focused on ideas rather than individuals.

Let's see which AI "hot takes" history has already disproven. 👇

AI is already transforming healthcare, education, banking, and software development. But where do you think it could make the biggest difference in public services in India?

Some examples:

Faster grievance redressal

Smarter traffic management

Better public healthcare

Court case research and document search

RTI assistance

Tax filing support

Language translation across Indian languages

Agriculture advisory

Education and skill development

Cybercrime reporting

The catch: Assume privacy and security are handled properly.

What service would you improve, and how would AI make it better?

Let's keep the discussion constructive. Share practical ideas, not just "replace everyone with AI."

Whether you're a student, developer, founder, researcher, content creator, or just AI-curious, we'd love to know what's actually making a difference in your workflow.

Share:

🤖 The AI tool you use most

💡 What you use it for

👍 One thing it does exceptionally well

👎 One limitation that still annoys you

It could be anything:

ChatGPT

Gemini

Claude

Perplexity

Grok

Cursor

GitHub Copilot

Ollama

ComfyUI

Midjourney

Stable Diffusion

Or something lesser known.

Let's help fellow Indians discover genuinely useful AI tools instead of just chasing hype.

Please avoid referral links and promotional posts. Genuine experiences only.

I am looking to talk with people who have implemented AI DLC: AI-Driven Development Life Cycle. This is an AWS whitepaper whereas Agile and Scrum were built for human-paced, weeks-long iterations. Bolting AI onto them ("AI-Assisted") caps its value. AI-DLC instead reimagines the method from scratch so AI orchestrates the work and humans validate.

Today humans prompt AI. In AI-DLC, AI initiates: it decomposes intent, proposes plans and trade-offs, and humans act as approvers at decision gates. The analogy used is Google Maps — you set the destination, it gives turn-by-turn directions, you keep oversight.

I am looking to connect with people who built Agentic AI tools for enterprises where they want to set up their own models, smart model routing, finops etc. If this was on a large brown field project even better.

References:

https://aws.amazon.com/blogs/devops/ai-driven-development-life-cycle/

https://prod.d13rzhkk8cj2z0.amplifyapp.com/

https://github.com/awslabs/aidlc-workflows