u/AcanthisittaHorror86

So I went to Hafeez Center in Lahore to buy 6 laptops for my staff. Budget was around 1 million rupees, so not exactly small money.

I know my stuff (computer science grad, 6 years in the field), so I gave the guy clear specs: SSD 500GB+, RAM must be DDR5, 10th gen or above.

He shows me a few laptops. I like one, ask him "what RAM is this?" He says "DDR5 hi hai bhai." I check task manager myself... it's DDR3.

I tell him, calmly, "bhai this is DDR3, not DDR5."

Instead of just saying "oh my bad, let me show you another one," he says:

Bhai jab aap 2,3 mahiny use karo gy na, ye khud b khud DDR5 ho jaye ga.

I genuinely thought I misheard him. I said that's not how RAM works, that's not a thing. He got annoyed and goes:

Tumhe mujh se ziada pata hai? Hum 10 saal se ye kaam kar rahe hain.

Bro I was standing there ready to hand over a million rupees and getting gaslit about basic hardware specs by someone who apparently thinks RAM levels up like a Pokémon.

The biggest mistake a salesman can make is assuming the customer knows nothing. Some of us actually do our homework before spending that kind of money. A simple "sorry, let me get you the right one" would've closed the deal in 2 minutes. Instead I just walked out.

Anyway, still hunting for those 6 laptops. Any recommendations for actually reliable shops in Hafeez Center or elsewhere in Lahore that don't try to convince you RAM evolves?

I've spent the last several months building an AI tool that speeds up quoting for wholesale distributors, mostly ones running Business Central. Posting because the hard parts turned out to be completely different from what I expected, and I want a sanity check from people who live in this.

The problem, if you've never watched it: a customer emails asking for 14 things. Someone opens the email, finds each item in the catalogue, checks who can supply it and at what price, types the whole lot into a quote, sends it back. On a normal Tuesday that's a day or two of turnaround on an order worth maybe £900. The customer buys from whoever answers first.

So the obvious job is "read the email, match the lines, price it." That part is mostly solved. Item numbers, customer's own part numbers, what this customer bought last time, text search, then a model to pick from a shortlist. Runs in about 70ms against a thousand-product catalogue. Fine.

Here's what I got wrong.

Half the lines in a real email aren't products. "Needed on site Thursday if possible" is not a thing you can buy. Send it to the catalogue and you get back "not stocked", which is a confident, wrong, invisible answer. Worse one: "Sorry, make the pipe insulation 4 boxes not 2." That's a correction to a quantity. If you file it as an unmatched product, the customer's change silently disappears and you ship the wrong amount. I had to write a whole classifier that runs before matching just to keep instructions out of the catalogue.

A parser that half-works is worse than one that fails. Reading customer EDI files, nine lines came back with no quantity. The code helpfully defaulted them to 1. Everything showed green ticks and £729.20 quietly fell off the total. Now a missing quantity comes back as null and the whole document gets held. Both EDI standards also send their own line count, so if what I parsed doesn't equal what they say they sent, that's a stop, not a warning.

The AI is never allowed near a price. Prices come from the catalogue, full stop. Ask a model for a number and you will eventually quote someone a made-up one and honour it.

Where it sits now: 46% of lines still get flagged for a human. To be genuinely useful that needs to be 15-20%. I know why (two of the matching stages aren't switched on yet), but I'm not going to pretend the number is better than it is.

Two things I'd like from anyone doing this for real:

1. What's your current quote turnaround, and what would it need to drop to before it changed anything commercially?

2. Has anyone written quotes back into BC through the API? I'm missing two endpoints that just don't exist in the standard set, and I'd like to know how people work around that before I do something stupid.

Wanted to come back and give this community some credit.

About 18 months ago I was scrolling through ideas here, mostly just looking for interesting problems people wished someone would solve.

One of them stuck with me because I had the same problem.

I have several credit cards, all with different rewards, categories and offers, and I was constantly standing somewhere about to pay thinking, which card am I supposed to use here?

The idea was pretty simple.

What if your phone already knew where you were and could surface the best card you already have?

Restaurant. Costco. Hotel. Gas station. Wherever.

At first we were thinking mostly about physical cards. Then pretty quickly it became obvious that mobile wallets are where in person payments are heading, so we shifted the whole idea around that.

Instead of opening an app, searching a merchant, checking reward categories, then going back to pay, the goal became much simpler:

Surface the right card from your mobile wallet when you’re ready to pay. That eventually became Payelle.

18 months later, we’ve built the technology, filed the IP, launched the product, and are now in conversations with some very large companies and financial institutions in the space.

Still a ton to figure out, especially distribution, and I definitely don’t know where this ends up.

But I remembered where the idea started and wanted to come back here.

There are thousands of “somebody should build this” ideas posted on Reddit.

Every once in a while somebody actually does.

So seriously, thank you to this community. A lot of the comments and ideas here pushed me further down the rabbit hole than I probably expected.

If anyone remembers the old discussions around automatically figuring out the best credit card to use at checkout, there’s a decent chance I read what you wrote.

Still building. Still figuring it out.

Cheers.

Hey, i am sure not everyone is comfortable in discussing their salaries face to face. But i want to know what's the norm.

Would you be able to answer these questions, i believe it'll be helpful to everyone to understand if they are being paid fairly or are they underpaid.

City

Company mnc/pakistan based

Years of experience

Gender

Field and designation

Working hours

Salary range

How did you get that job

I’m 26 years old, and I’ve never celebrated my birthday. Not even once.

When I was a kid, I didn’t even really know what my birthday was. It just wasn’t something we paid attention to or celebrated in my family.

As I got older, I obviously learned my date of birth. I know when my birthday is now, but somehow I’ve still never celebrated it.

No birthday cake.
No party.
No candles.
No birthday wishes from a bunch of people.
Nothing.

And honestly, I don’t even know how I feel about it anymore.

Sometimes I think it would be nice to experience what it feels like to actually celebrate your own birthday. Other times, I wonder if I’ve simply become so used to it that I don’t really care.

I’m curious though: is anyone else here like this?

Have you never celebrated your birthday, or did you grow up without birthdays being a big thing?

I'm curious what people think about this.

Let's say you're building an AI application that connects to an ERP to solve one very specific problem and make the ERP more efficient.

Most ERPs have interfaces that feel... well, let's just say they weren't designed yesterday.

So should your AI app match the ERP's UI for consistency?

Or should you take a completely modern approach?

My instinct is to go modern.

But there's a potential problem:

If the UI is too different from the ERP, could it create confusion for users?

Would you prioritize:

1. Visual consistency with the ERP
2. Modern UX even if it looks completely different
3. A hybrid where the AI app has its own modern UI but uses familiar ERP terminology and workflows

If you've worked with ERP implementations, I'd especially like to hear your take.

I'm curious what people think about this.

Let's say you're building an AI application that connects to an ERP to solve one very specific problem and make the ERP more efficient.

Most ERPs have interfaces that feel... well, let's just say they weren't designed yesterday.

So should your AI app match the ERP's UI for consistency?

Or should you take a completely modern approach?

My instinct is to go modern.

But there's a potential problem:

If the UI is too different from the ERP, could it create confusion for users?

Would you prioritize:

1. Visual consistency with the ERP
2. Modern UX even if it looks completely different
3. A hybrid where the AI app has its own modern UI but uses familiar ERP terminology and workflows

If you've worked with ERP implementations, I'd especially like to hear your take.

I've been at my current job for a year now. When I started, they told me that after three months they'd bump my pay to RS 70-80k. It's been a full year and my salary is still RS 55,000. When I bring it up, they tell me I don't have direction and I'm not providing value.

Here's the thing ,how can I provide value when they won't let me stay in one role long enough to actually do anything?

Timeline of what's happened over the past year:

• Started as an AI content creator
• Company stopped doing content, so they put me on Upwork for about a week
• Moved me to social media for a month
• Switched me to email campaigns for a month
• Put me on lead generation for a bit
• Brought me back to website blogs for a bit
• Then back to social media again

There are three different decision-makers above me, and they don't seem to coordinate. One tells me to do X, another says do Y, and then a few days later someone else moves me to something completely different.

I show up on time every day, I never take leave, I do whatever they ask, and I hand over the work what they do with it after that is on them. I work 10-hour days. And still I get told I'm replaceable and that I'm not delivering results.

If a company won't let you stay in a role for more than a few weeks, how are you supposed to build anything measurable? I'm seriously considering leaving. Has anyone else dealt with this kind of constant role-switching without a real say in it? How did you handle it?

A space where guys can actually talk about the things that come up in life.

Grooming, fitness, style, health, relationships, work, money, gear — the stuff you Google at 1am because you don't know who else to ask.

What this place is for:

• Honest questions, honest answers
• Real experiences, not influencer takes
• Men helping men figure things out

What this place isn't:

• An echo chamber
• A place to perform masculinity

No dumb rules. Just don't be a d*ck.

Jump in, ask something, answer something.

What's one thing you genuinely wish more men talked about openly?

This one doesn't get talked about enough in legal tech circles.

I keep having the same conversation with in house legal teams. They've seen the demos. The tool looks good. The accuracy is reasonable. The workflow makes sense. And then procurement stalls for six months and eventually quietly dies.

When you dig into why the answer is almost never about the AI itself. It's about where the data goes.

Contracts are not like other business documents. An MSA with a Fortune 500 customer contains your pricing, your liability exposure, your IP terms, your indemnification limits. An NDA contains the identities of who you're talking to and what you're exploring together. A supply agreement contains your supplier relationships and your cost structure. Taken together your contract portfolio is basically a map of your entire business.

And legal teams know this better than anyone because they're the ones who negotiated those terms. They're not being paranoid. They're being exactly as careful as their job requires them to be.

The standard response from vendors is we're SOC 2 compliant, we don't train on your data, here's our DPA. And that's fine as far as it goes. But it doesn't answer the actual question which is what happens if there's a breach. What happens if your vendor gets acquired and the new parent company has different data practices. What happens if a subpoena lands on your vendor's servers and your contracts are sitting there.

Legal teams have seen enough to know that the risk is not theoretical.

The model that actually addresses this isn't better SaaS security. It's deploying the AI inside the customer's own cloud environment entirely. No data leaves. No third party servers. No vendor to worry about. The contracts stay in your environment the same way they always have, the AI just runs there too. You even bring your own LLM, whether that's Azure OpenAI or AWS Bedrock. The vendor never touches your data at any point.

The tradeoff is it's not a plug and play SaaS signup. It requires implementation. But for enterprise legal teams handling sensitive commercial contracts that's not a bug, that's the whole point.

Curious whether others have run into this. Is data sovereignty a real blocker in your experience or do legal teams eventually get comfortable once the security docs check out?

Genuinely curious because I keep seeing a gap between what vendors show in demos and what actually gets used day to day.

I work on the tooling side so I get to talk to a lot of legal teams and what I hear repeatedly is something like yeah we tried it, it was impressive in the pitch, then it sat unused after three months. And when I ask why the answers are usually the same few things.

It required lawyers to work outside of Word. Nobody wanted to do that.

Or the AI flagged everything as risky so reviewers stopped trusting it.

Or the suggestions it gave were generic and didn't match how their company actually thinks about risk.

So I'm curious what the actual picture looks like from people inside legal teams.

Are you using AI for contract review in any meaningful way right now? Not a pilot, not something being evaluated, something that's actually in the workflow.

If yes what does that look like practically. Which contract types. How much of the review does the AI handle vs a human. Do lawyers trust the output or do they just redo the work anyway.

If no what killed it. Was it adoption, accuracy, workflow friction, IT security concerns, something else.

I'm also curious about the data privacy angle because I hear this come up a lot especially in regulated industries. A lot of teams are uncomfortable sending contracts to a third party SaaS even with NDAs in place. Is that a real blocker for your team or is it more of a theoretical concern that doesn't actually stop procurement.

No agenda here, not selling anything, just trying to understand what's real versus what's still aspirational in this space. The vendor perspective is pretty noisy right now and it's hard to get a straight answer from people actually doing the work.

Bit of a long one but hopefully useful for anyone building in this space or evaluating tools.

Background: I've been working on AI assisted contract due diligence for about a year now. Not as a lawyer, more on the engineering and product side, working closely with in house legal teams. What I'm sharing here isn't theory, it's stuff we got wrong first and fixed later.

Why the obvious approach breaks down

The instinct when you first start is to give the model a checklist. Flag unlimited liability. Flag warranty terms over 3 years. Flag missing governing law. Prompt engineer your way to a solution.

Works okay on demo contracts. Falls apart on real ones.

The problem is the model doesn't know why something is a risk. So when it hits an edge case, a clause that's technically fine in isolation but problematic given the rest of the contract, it either misses it or flags it without useful context. A lawyer reading flagged: warranty clause can't do anything with that. They need to know whether this specific clause in this specific deal is actually a problem for their business.

Generic AI treats all contracts the same. Real contracts are not the same.

The shift that actually helped: teaching the WHY

We restructured how we encoded legal knowledge. Instead of a flat list of rules, every rule now has three components.

Definition which is the precise linguistic pattern that triggers a flag. Not long warranty but warranty duration stated or implied to exceed 36 months. Specific enough that the model can pattern match reliably.

Rationale which is the business logic behind the rule. Why does warranty duration matter past 36 months? Because it creates open ended exposure for latent defects that surface after the normal product lifecycle. This isn't for the lawyer, it's for the model. When the agent understands why a rule exists, its output rationale actually tracks back to real business risk instead of generic legal commentary.

Examples which are 1 to 2 actual sentence excerpts from real contracts that represent the pattern. This was the biggest unlock for us. Abstract rule definitions are hard for models to apply consistently. Concrete linguistic examples are much easier to match against.

When you structure it this way the model stops pattern matching and starts reasoning. Small distinction in theory, massive difference in output quality.

Ditch binary classification

Good clause bad clause is not how legal risk actually works.

We moved to three tiers. Green which favors your business and is low risk so you move on. Orange which is acceptable under certain conditions and needs a human decision. Red which is non negotiable so you push back, redline, or walk.

The middle tier is where most of the interesting work lives. A 2x liability cap might be fine on a $500K contract and completely unacceptable on a $10M one. The rule has to encode that conditionality or the model can't make a meaningful call. It'll just flag everything orange and create more work than it saves.

The multi agent thing nobody talks about

Single model reviewing an entire contract hits a ceiling pretty fast. The context window problem is obvious but the less obvious problem is specialization.

One model can't be an expert in warranty law, IP indemnification, data privacy obligations, force majeure, and governing law simultaneously, at least not with the precision you need for real due diligence. It knows a bit about everything. You need something that knows a lot about one thing.

We ended up splitting into domain specific agents. Each one only analyzes clauses in its category. Warranty agent looks at warranty clauses. IP agent looks at IP clauses. And so on.

Then you need an orchestration layer. Because a single paragraph in a contract can trigger three different agents at once and you can't flag everything at the same severity. The orchestrator compares what each agent found, looks at their confidence levels, and decides what to surface. That conflict resolution step is unglamorous and took longer to get right than anything else in the system.

What's still genuinely hard

Confidence calibration. Getting an agent to say I'm 70% confident this is high risk in a way that's consistent and meaningful across different contract types is still not solved cleanly. A model's internal uncertainty and real world legal risk don't map to each other neatly.

Rulebook maintenance. As business conditions change, as case law shifts, the knowledge base needs updating. This is a human process. Anyone claiming their system keeps itself current automatically is exaggerating.

Getting lawyers to trust it. This is maybe the hardest one. The output has to be explainable, not just this clause is risky but here's why, here's the specific language, here's what we'd suggest instead. Without that adoption stalls regardless of how accurate the model is.

Curious whether anyone here has experimented with graph based knowledge representations instead of flat vector search for legal reasoning. Been thinking about whether relationship modeling between clauses changes anything meaningfully.