u/alizahidrajaa

Author here. This one started as a weird idea and turned into a paper + open-source release, so I'm sharing the story.

Modern agent pipelines have a silent-failure problem. An answer passes through a scraper, an extractor, several models, a synthesizer — and when one link is unreliable, the failure doesn't announce itself. You get a fluent, confident answer that's quietly wrong. Everyone's building to verify the agent — identity, permissions, access. Barely anyone's verifying the claim.

The verification-of-transmitted-knowledge problem was rigorously solved a long time ago. Islamic scholars couldn't trust a statement just because it sounded right, so they built one of history's most demanding systems for it: every claim carries its full chain of transmitters (isnād), every transmitter is graded on integrity and precision (rijāl), the chain is only as strong as its weakest link, independent chains raise confidence, and even a flawless chain doesn't excuse a flawed message.

The rigor belongs to twelve centuries of scholars. The transfer to AI is mine. I call it ISNAD — a claim-level trust layer for multi-agent systems. Everyone verifies the agent; ISNAD verifies the claim.

I also wrote the failures into the paper — what's validated, what isn't yet, in detail. A trust framework that hides its weaknesses is a contradiction in terms.

If it makes you think — agree or disagree — I want to hear it.

Built this myself and just put it on arXiv — sharing here because this sub is exactly the people who'll poke the right holes in it.

Here's the thing about agent chains: one answer moves through a scraper, an extractor, a few models, a synthesizer. Some links are reliable, some aren't. When they fail, they fail silently — a confident, fluent answer that's quietly wrong. We're all racing to verify the agent's identity, permissions, access. Almost no one's verifying the claim: whether what it said is true and independently corroborated.

So I took a ~1,400-year-old methodology built for precisely this. Islamic scholars verifying transmitted statements graded every claim by its chain of transmitters (isnād), scored each transmitter on integrity and precision (rijāl), treated a chain as only as strong as its weakest link, let independent chains raise confidence, and judged the message separately from its chain. I rebuilt it as a claim-level trust layer for multi-agent AI. It's called ISNAD.

The failures are in the paper too — validated mechanisms and not-yet-validated ones, spelled out. Honesty is kind of the whole point of a trust framework.

Would love the disagreement as much as the agreement.

When a multi-agent AI system answers you, that answer has passed through several “hands” - a scraper, an ingestion model, a synthesis model. Each can distort or invent. Current tools log *what* happened, but nothing grades *who* transformed a claim or how much to trust the result.

Classical Islamic hadith scholarship spent \~1,200 years on a structurally identical problem: whether to trust knowledge passed through chains of human narrators. Their solution: grade every transmitter, judge a chain by its weakest link, require independent corroboration, criticize content separately from the chain — maps surprisingly cleanly onto AI pipelines.

So I built it, a framework, a paper (with DOI), and a Python package (pip install isnad). I’m developing it in the open and being honest about what’s validated vs. still experimental, early results show the core grading mechanism works, but full pipeline validation is ongoing.

I’m an independent researcher, so critique is genuinely welcome!

https://doi.org/10.5281/zenodo.21211291

As a Pakistani independent researcher, I published a paper applying 1,200-year-old Islamic hadith methodology to a modern AI problem

Classical Islamic hadith scholarship spent ~1,200 years on a structurally identical problem: whether to trust knowledge passed through chains of human narrators. Their solution was to grade every transmitter, judge a chain by its weakest link, require independent corroboration, criticize content separately from the chain maps surprisingly cleanly onto AI pipelines.

So I built it: a framework, a paper (with DOI), and a Python package (pip install isnad). I’m developing it in the open and being honest about what’s validated vs. still experimental, early results show the core grading mechanism works, but full pipeline validation is ongoing
Happy to answer questions. alizahidraja.com/isnad

Got heavy critique on the Pakistan sub, let’s try it here

When a multi-agent AI system answers you, that answer has passed through several “hands” - a scraper, an ingestion model, a synthesis model. Each can distort or invent. Current tools log what happened, but nothing grades who transformed a claim or how much to trust the result.

Classical Islamic hadith scholarship spent ~1,200 years on a structurally identical problem: whether to trust knowledge passed through chains of human narrators. Their solution: grade every transmitter, judge a chain by its weakest link, require independent corroboration, criticize content separately from the chain — maps surprisingly cleanly onto AI pipelines.

So I built it, a framework, a paper (with DOI), and a Python package (pip install isnad). I’m developing it in the open and being honest about what’s validated vs. still experimental, early results show the core grading mechanism works, but full pipeline validation is ongoing.

I’m an independent researcher, so critique is genuinely welcome!

https://doi.org/10.5281/zenodo.21211291

I’ve been working solo, no lab, no university backing, on a problem in AI: when systems built on multiple AI agents give you an answer, that answer passes through many “hands,” and there’s no good way to know how much to trust it.

It turns out our own scholars solved a structurally identical problem 1,200+ years ago with the science of hadith transmission - grading every narrator in a chain, judging a chain by its weakest link, requiring independent corroboration. I adapted that methodology into a framework for modern AI systems.

It’s now a published paper (with a DOI), open-source code, and a Python package anyone can install. I’m building the whole thing in public.

I’m posting mostly to reach the appropriate audience here in Pakistan

Happy to answer questions. alizahidraja.com/isnad

I am currently uploading the preprint to arXiv under cs.AI and am seeking an endorsement from an active researcher in the AI/provenance space. You can review the full paper and technical architecture via this Zenodo link: https://zenodo.org/records/21211291

Wanted to share this here because it sits right at the intersection of our tradition and modern tech, and I think the ummah has an underused edge in this space.

Modern AI systems have a trust problem: when a multi-agent system answers you, that answer has passed through several "hands" — a scraper, an ingestion model, a synthesis model — and each can drop, distort, or invent. The field can log what happened, but has no principled way to grade who transformed a claim and how much to trust the result.

If that sounds familiar, it should. Our scholars faced a structurally identical problem 1,200+ years ago with hadith transmission, and built arguably the most rigorous transmission-criticism methodology in pre-modern history: isnād (complete chain attached to every narration), rijāl (grading every narrator's ʿadālah and ḍabṭ), weakest-link evaluation, corroboration through independent chains, and naqd al-matn (criticizing content independently of the chain).

My paper transfers that methodology into AI system design — a graded registry of "narrators" (models, scrapers, sources), claim-level chains, and a decision matrix for what to serve, review, or quarantine.

No lab, no university, just an independent builder from Pakistan who's spent years standing in both worlds through Islam & AI. Sharing in case it inspires someone here to mine our tradition for ideas the rest of the field hasn't thought of yet.

Paper: https://doi.org/10.5281/zenodo.21211291
Gist: https://gist.github.com/alizahidraja/56beaadf493976182f38aa602b8958e2

JazakAllah khair

Wanted to share this here because it sits right at the intersection of our tradition and modern tech, and I think the ummah has an underused edge in this space.

Modern AI systems have a trust problem: when a multi-agent system answers you, that answer has passed through several "hands" — a scraper, an ingestion model, a synthesis model — and each can drop, distort, or invent. The field can log what happened, but has no principled way to grade who transformed a claim and how much to trust the result.

If that sounds familiar, it should. Our scholars faced a structurally identical problem 1,200+ years ago with hadith transmission, and built arguably the most rigorous transmission-criticism methodology in pre-modern history: isnād (complete chain attached to every narration), rijāl (grading every narrator's ʿadālah and ḍabṭ), weakest-link evaluation, corroboration through independent chains, and naqd al-matn (criticizing content independently of the chain).

My paper transfers that methodology into AI system design — a graded registry of "narrators" (models, scrapers, sources), claim-level chains, and a decision matrix for what to serve, review, or quarantine.

No lab, no university, just an independent builder from Pakistan who's spent years standing in both worlds through Islam & AI. Sharing in case it inspires someone here to mine our tradition for ideas the rest of the field hasn't thought of yet.

Paper: https://doi.org/10.5281/zenodo.21211291
Gist: https://gist.github.com/alizahidraja/56beaadf493976182f38aa602b8958e2

JazakAllah khair

Hi everyone, I’m looking for advice on a fraud case in Rawalpindi (Saddar area)

A person took around PKR 300k from me over multiple transactions for mobile phones but never delivered/refunded. I have full proof including bank transfer receipts, WhatsApp chats, voice notes where he keeps delaying and making excuses

I have already filed a complaint with the police (but they did not register an FIR) Current issue is he is avoiding me, blocking numbers, and not responding at all.

I want to ask

1. What is the most effective way to force FIR registration in Pakistan?
2. Has anyone successfully recovered money in similar cases?
3. Any legal or practical steps that actually work in situations like this?
4. If anyone has dealt with fraud cases in Rawalpindi/Islamabad, I’d really appreciate guidance or contacts

If anyone has advice or has gone through something similar please share thanks 🫰🏻

Will send you guys updates, seems like it can replace a lot of AI bills

I recently found out that a person in family has stage4 cancer, what do I do/say to the person’s kids .-.

I’ve asked people around me and they said to say nothing, and I mean, I want to say I’m here if you guys need any help. But I want to do something more. Helppp

Also, pray for her & family

Nai aya Eid Mubarak ka text :”)

Hello!

I have been working in the tech space for a couple of years, and I wanted to share a few pointers that’ll help y’all get remote jobs

It’s not a shortcut, there is none tbh, you’ll have to put in weeks of efforts and face loads of rejection before you get a win, but hey one win is all you need

To start off, open Claude/chatgpt, give it your background experience resume and ask to optimise it, make a online portfolio (free tools) and a posting structure which you can follow for the next couple of weeks (I’ll add the prompt at the end)

The above sets your inbound pipeline, it’ll take a couple of weeks to have this optimised and rank on Google & AIs, the goal here is to have everything you can do present online in 1-2 clicks, your name should be Google-able and your skills should be very visible along with your work and reviews by the people you have worked for/with

The second main thing is the outbound pipeline, go crazy here, there is no number which is going to be big here, aim for 20-50 job applications per day, you can get those from multiple platforms, one of the very main is jack&jill, it’ll help a lot, you should also use Workatastartup, Wellfound, workable, LinkedIn, Reddit, go everywhere where your user can be, even the CEOs of big companies scroll insta/tiktok, make sure you outreach everywhere and leave no stone unturned

For the people who already have a job, please please PLEASE use your time to optimise your inbound pipeline so that it’s easy for recruiters to find you for better opportunities. Whatever you’re doing just keep posting about it or updating your portfolio/linkedin/website on the work you’re doing

It’s hard to find jobs, it’s also hard for recruiters to find good candidates, I’ve been on both ends and you’ll be doing yourself a huge favour if you don’t take any job for granted and keep on working on yourself, your online presence and keeping up with the latest technology

It’s a brutal market out there, but there are still jobs, opportunities, people ready to pay if you can give them ease, find them and let yourself be findable

Best of luck ❤️

Prompt;

Act as a brutally honest career strategist.
Your job is to figure me out before giving advice.
Start by asking me targeted, one-by-one questions about: my background, real work, skills I can actually use, what I enjoy/ dislike, my current situation, and what I've already tried.
Challenge vague answers and ask follow-ups until you have clarity.
Once you understand me, give:
- the most realistic roles I should target now
- my strongest advantages and biggest gaps
- what I'm likely doing wrong or overestimating
- the fastest path for me to start earning
- a clear, practical 30-day plan
Be direct, specific, and honest. No generic advice.
Assume I'm slightly confused and might be wrong about myself - correct me if needed

So guys I have an idea and before I get into it I thought I’ll ask you guys

Imma get some cool stuff from thrift shops and make a online bidding store and put stuff there

The stuff their is really random, the ranges are old cardboard games, golf club set, vintage shot glasses, electronics, all loads of stuff which comes in containers

Anyway, what do you guys think, fun thing to try?
Also if I do, fun thing to post about on social media?