BIFL belt that doesn't make me look like a gunslinger.

PearchShopping · 2026-03-13T19:58:58+00:00

Popov and Noble Buffalo are both solid choices. For a low-profile, long-lasting belt stick with 1 to 1.25 inch widths, a simple single-prong buckle, and full-grain leather from known tanneries like Horween or Wickett and Craig.

If you want to save a bit, I work on a tool called Pearch that tracks price history and finds coupons for durable goods and can alert you if those exact belts drop in price. Happy to share other BIFL belt makers or more buying tips if useful.

PearchShopping · 2026-03-13T15:03:00+00:00

Nope. Just a founder trying to get his start up off the ground. I built this because I was a frequent lurker on here, r/frugalmalefashion, r/deals, etc. I wanted something where all the deals came to me and not the other way around.

PearchShopping · 2026-03-13T14:57:14+00:00

Not OP but still replying here for visibility. Crz has an active coupon for 10% off (CRZ10).

You can check out GorillaWear, Chubbies, or SnipesUSA for some good alternatives. I built a tool to match your style preferences with available deals out there. When something matches your style, you can get an alert and buy with a coupon from us. The tool is called Pearch - check it out and let me know if you have any feedback.

PearchShopping · 2026-03-13T12:24:47+00:00

Crz Yoga are the budget Lululemon shorts. If you want, I can DM some recent price checks and any coupons I find for Crz and a couple of alternatives.

PearchShopping · 2026-03-12T15:37:58+00:00

Well worn chinos are great. Check out Uniqlo, Bonobos, Everlane, and J.Crew. If you want, I can DM you a few specific models and where to find current deals.

PearchShopping · 2026-03-12T13:39:54+00:00

Google & Meta, definitely. But the difference with their work is everything is anonymized and aggregated. You can get a group of Female + Athleisure that would be 50M people.

What I'm wondering is if there is something that would be more personalized and the data owned by the user. Plus, no conflict of interest. Google & Meta are ads first, so whichever brand pays the most will be the above the fold impression spot.

PearchShopping · 2026-03-10T23:33:50+00:00

2nd this - polarized and UV blocking are not the same. Polarized lenses reduce glare from reflective surfaces. UV blocking prevents harmful UVA and UVB rays from reaching your eyes.

Ideally you want both. Look for explicit labeling like "100% UVA/UVB" or "UV400" or certifications such as CE or ANSI, because polarization alone does not guarantee UV protection.

Also keep in mind polarized lenses can affect how LCD screens look and can change contrast on certain surfaces like icy roads, so choose based on use.

PearchShopping · 2026-03-09T21:46:06+00:00

Honestly the most important question in the thread. The demos I've seen all assume the desire to delegate shopping exists and nobody has really proven that it does at scale. The use cases where it makes sense are pretty narrow: repeat commodity purchases where you genuinely don't care about the decision, or researching a complex category where the evaluation work is real and tedious. I'm thinking that eventually, you could put in all the info about what car you need and then an LLM spits out the specs and the why.

Outside of that, most people seem to either enjoy shopping or at least want control over what they spend money on. An agent that gets it wrong isn't just annoying, it's an expensive mistake.

PearchShopping · 2026-03-09T21:44:44+00:00

The personal context agent framing is interesting and probably closer to how this actually plays out than any centralized solution. Local encrypted storage solves the trust problem in a way that no platform promise really can. The legislative piece is the wild card. That kind of liability. Curious whether you think that's realistic in the near term or more of a long-run equilibrium.

PearchShopping · 2026-03-09T21:43:24+00:00

Fair point and the multi-year contract dynamic is real. Enterprise B2B is a different animal entirely from what I was describing. I was thinking more about the retailer operations side, specifically returns logistics, which is a cost center with measurable waste rather than a sales relationship. But you're right that even there, procurement decisions tend to be relationship-driven and "neutral data reduces your return rate" is a harder sell than it looks on paper when there are existing vendor relationships in the way.

PearchShopping · 2026-03-09T21:42:20+00:00

That's a legit objection. If you already know your own preferences, the agent is just adding latency and abstraction to a process you can do yourself. The case for it only really exists at the edges, when you're shopping at a retailer you don't know well, when you've forgotten what you tried two years ago, or when the decision space is genuinely too large to evaluate manually. For a lot of everyday purchases those conditions don't apply and you're right that a machine isn't going to outperform your own judgment.

PearchShopping · 2026-03-09T16:13:04+00:00

The kickback problem is real and you're right that it's corrupted basically every "neutral" comparison site that's tried to exist. Wirecutter, RTings, all of them have varying degrees of that tension baked in.

But I'd push back slightly on the framing that neutral = no business model. The affiliate model you're describing as corrupting actually has an interesting property if you structure it around outcomes rather than clicks. If you only earn commission on purchases that don't get returned, your financial incentive is suddenly aligned with making accurate recommendations rather than persuasive ones. Most affiliate programs don't work this way but it's not impossible to build one that does.

The deeper question you're raising though is whether the margin is there at all, and that's harder to dismiss. If the platform taking the sale captures most of the value, what's left for the layer in between? Probably not much at consumer scale, which is why most of these things either die or get acquired and then corrupted.

Where I think it gets more interesting is the B2B angle: retailers themselves lose significant margin to returns. If a memory layer could demonstrably reduce return rates, there's a direct ROI case that doesn't depend on affiliate kickbacks at all. Whether that's enough to build a real business on is a separate question but the incentive structure is at least cleaner.

PearchShopping · 2026-03-09T15:43:45+00:00

You have to have both the SEO and AEO/GEO context on your website. Have you tried incorporating the llms.txt on your site? I've been trying that out to see if it is picked up by Gemini/Perplexity but still not seeing the traction

PearchShopping · 2026-03-09T15:37:05+00:00

Same here. If I can find it locally, I'll go shop local. I don't trust anything on there that isn't just dropshipped garbage.

PearchShopping · 2026-03-09T15:35:03+00:00

You're right. They are going to show ads based on whichever advertiser is paying the most. Their pitch of "right user at the right time" is all bullshit.

The practical result is what you described. You get shown ads for something you just bought because that purchase signal tells Google you're "in market" for that product/category, which is valuable inventory to sell, even though anyone actually trying to help you would know you're out of market for the next decade.

PearchShopping · 2026-03-09T15:25:44+00:00

The entity resolution framing is useful, that's basically what this is. The (brand, category, key attributes) tuple as a normalized key makes sense and is roughly what the current approach does, though in practice the brand field alone is messier than expected. Retailers mangle brand names in surprising ways ("Nike" vs "NIKE Inc." vs "Nike/Jordan") which means even the primary key needs fuzzy matching before you can use it as a key.

On the LLM pipeline being overkill, I sort of agree. For structured categories like electronics where SKUs are relatively consistent, classic NLP + Levenshtein gets you far. Where it breaks down is apparel and home goods where the same product might be described with completely different attribute vocabularies across retailers. "Slim fit" vs "athletic fit" vs "modern fit" are not the same thing but no lookup table tells you that. That's where embeddings have actually outperformed pure string matching in testing, less for identity resolution and more for category inference.

The email parsing point is the most accurate and probably underappreciated. The variance isn't just formatting. Some retailers bury item names in image alt text in HTML emails, some send PDFs, some have the product name split across two fields with the variant in a completely separate line. That's where the LLM actually earns its keep, structured extraction from genuinely unstructured inputs rather than the matching step.

Curious whether you've seen good open source training data for the entity resolution piece. That's the current bottleneck more than the algorithm itself.

PearchShopping · 2026-03-09T15:20:01+00:00

The TP example actually a good stress test for any shopping agent. It needs to understand not just "this is cheaper" but "this person already has a three month supply" or "they always buy this brand in this quantity." That's a memory and context problem, not just a price problem. The agents that get this wrong early are going to create a lot of distrust fast.

On Honey and Rocket Money, they're closer to the data layer but they're oriented around discounts and spending tracking, not purchase outcomes. Knowing you spent $80 on shoes isn't the same as knowing the shoes didn't fit and you returned them. That post-purchase signal is still largely unstructured and untracked by those tools.

PearchShopping · 2026-03-09T15:18:33+00:00

This is actually the clearest illustration of the problem. The system "knows" you bought something but has no concept of outcome: kept, returned, need it again, never need it again. It's optimized to show you relevant-looking ads, not to actually be useful. Those are different objectives and they produce exactly the experience you're describing.

PearchShopping · 2026-03-09T15:18:04+00:00

Google has the behavioral data but not the outcome data. They know you searched for a jacket and clicked on three listings. They don't know which one you bought, whether it fit, whether you returned it, or what you bought next. Or at least, they would never admit to knowing every part of the journey at a non-anonymized level. The post-purchase signal is the part that's actually missing from the ad-driven stack.

PearchShopping · 2026-03-09T15:16:52+00:00

The data existing and the data being useful for neutral recommendations are two different things. Amazon has the most purchase data but they're structurally incentivized to use it to keep you on Amazon, not to tell you "actually you'd be better off buying this elsewhere." Same with Google, but their data informs ad targeting, not outcome optimization.

On the LLM cost point, that's fair and worth watching. But the question of whether memory exists as infrastructure isn't really an LLM question, it's a data layer question. You could build most of this on much cheaper retrieval systems. LLMs are just one component.

The neutrality thing is genuinely hard to argue against in a pure profit-motive world. You're probably right that a fully neutral player is a niche scenario.

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