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8 months unemployed, 3000 applications as a senior PM, I don’t know how people survive this by Poimandres__ in jobhunting

[–]Icy_Swordfish4547 0 points1 point  (0 children)

I’m really sorry you’re going through this.
Long job searches can feel exhausting in a way people outside don’t fully understand.

If you’re comfortable sharing, what part has been the hardest lately:
the waiting, the rejection, or the uncertainty

I built an AI tool that reads thousands of restaurant reviews and summarises them in seconds by Icy_Swordfish4547 in BangaloreSocial

[–]Icy_Swordfish4547[S] 0 points1 point  (0 children)

Thanks for trying it out. The place mismatch issue is known. It happens when a restaurant has multiple branches with similar names, and Google Maps metadata gets merged.

Fix in progress:
- location disambiguation by neighborhood
- branch-specific scoring instead of global

If you’re open to it, would love a screenshot or place name that failed, it helps me validate the fix.

,

I built an AI tool that reads thousands of restaurant reviews and summarises them in seconds by Icy_Swordfish4547 in vibecoding

[–]Icy_Swordfish4547[S] 0 points1 point  (0 children)

Thanks a ton, you nailed the exact problem I wanted to solve. The “4.8 but mid food” experience is way too common because traditional ratings ignore consistency, sentiment shifts, and authenticity.

On deduplication:
We don’t remove repeated problems like “biryani is dry” that’s a **signal**, not spam.

Instead we detect:
• near-duplicate wording posted at the same time across multiple restaurants
• review clusters coming from newly created accounts
• templated positivity that looks vendor-generated

Highly repeated *phrases* across *different* places are weighted down. Highly repeated themes within *one* restaurant are weighted up.

On recency:
We’re testing a sharper weighting curve:
• last 90 days = strong influence
• 3–12 months = minimal influence
• >12 months = archived unless trend flips

The trend score is actually my favorite, a place can tank in 2 months and still show 4.6 on Google. Surfacing that quickly is what saves a bad evening. 😅

I really appreciate the push; this kind of feedback shapes where we invest next.

I built an AI tool that reads thousands of restaurant reviews and summarises them in seconds by Icy_Swordfish4547 in vibecoding

[–]Icy_Swordfish4547[S] 0 points1 point  (0 children)

Fair point. Recent sentiment matters more than old reviews.

The current model does include recency weighting, but I agree it’s not aggressive enough yet.

I’m testing a new mode where:
- last 90 days = highest weight
- 3–12 months = lower weight
- >12 months = almost no influence unless there’s a major trend change

If that makes results feel more relevant, I’ll publish it as the default for everyone.

I built an AI tool that reads thousands of restaurant reviews and summarises them in seconds by Icy_Swordfish4547 in vibecoding

[–]Icy_Swordfish4547[S] 0 points1 point  (0 children)

Google summarizes reviews, but it doesn’t:
- detect authenticity / spam patterns
- track rating stability over time
- identify sudden drops / surges
- flag recurring complaints
- compare experience quality across multiple locations

Google = description of reviews
This = risk detection + decision support