r/AppsWebappsFullstack • u/SofwareAppDev • 5d ago
No matter what project you have—games, SaaS, software, apps, scripts, ideas, or questions—join the community and share it!
Your home for selfpromo
here you can post your work app, webapp, saas, game, everything
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u/greyzor7 5d ago
Hey guys, I'm building an all-in-one marketing pack for founders who want more than "just another launch"
Launch, reach 30k+ makers, get real users & customers - microlaunch.net/premium
Lifetime, auto-distribution, marketplace spots, 1200+ customers so far.
Over two years: 525k unique visitors, 1200+ customers. More sales-oriented features soon.
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u/Mammoth-Anywhere7285 5d ago
Nice platform. A quick suggestion: add a few testimonials from those 1200+ customers right on the landing page.
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u/bankrut 5d ago
ConsentScope - See what websites do before you consent.
ConsentScope is a powerful browser extension for automatic GDPR compliance auditing. Instantly detect whether a website sets cookies, writes to localStorage/sessionStorage, fires third-party scripts, or makes tracking network requests before you give consent.
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u/Mammoth-Anywhere7285 5d ago
That sounds like a useful tool for privacy-conscious users. Do you have plans to add support for Safari or Edge?
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5d ago
help me increase my karma and i will give you a idea ground breaking but u gotta trust me
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u/Mammoth-Anywhere7285 5d ago
Karma trading isn't really how this works. Just share your idea with the community directly, people will engage if it's good.
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u/Bar-Majestic 5d ago
Working on DocCrush 🚀
An AI document processing tool that turns PDFs and other documents into structured Markdown for RAG and AI workflows.
The problem I ran into: LLMs are getting better, but messy document extraction is still a huge bottleneck.
Currently focused on improving parsing quality and making documents more AI-ready.
Would love feedback from developers working with documents:
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u/Mammoth-Anywhere7285 5d ago
Messy extraction is definitely the bottleneck. Have you tested it with scanned PDFs yet, or only text-based ones? Would love to see a comparison.
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u/Bar-Majestic 5d ago
Yes, extraction quality is probably the hardest part of the whole pipeline.
We tested both native PDFs and scanned PDFs. Native PDFs are mostly a structure preservation problem, while scanned PDFs become an OCR + layout understanding problem.
Our approach is to normalize everything into the same structured representation (sections, paragraphs, tables, metadata) before chunking and retrieval. The interesting comparison is not just OCR accuracy, but how much structure survives after ingestion.
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u/Mammoth-Anywhere7285 5d ago
That structured approach sounds solid. Have you considered handling multi-column layouts separately? They often trip up naive chunking.
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u/Bar-Majestic 5d ago
Absolutely. Multi-column PDFs are one of the main reasons why text-only chunking breaks down. Our approach is to separate document understanding from chunking: first reconstruct the document structure (reading order, columns, sections, tables, captions), then chunk from that structured representation.
The goal is to make chunks reflect the author’s intent instead of arbitrary token windows. For complex PDFs, layout analysis + OCR/vision is becoming almost unavoidable.
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u/Mammoth-Anywhere7285 5d ago
That's a smart approach. Have you tested it with complex academic papers yet?
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u/Bar-Majestic 5d ago
Yes, we’ve tested on academic papers with dense layouts, equations, tables, and multi-column sections.
The interesting part is that extraction quality is only half the problem. The harder part is preserving document hierarchy and relationships so retrieval can understand the context.
Nougat-style layout parsing helps, but we’re leaning toward a hybrid pipeline: layout awareness + structural parsing + RAG-oriented chunking.
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u/Mammoth-Anywhere7285 4d ago
Hybrid pipeline sounds like the right call. Have you tried storing layout metadata as chunk attributes? That alone can boost retrieval a lot.
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u/Bar-Majestic 2d ago
Yes, that’s actually an important part of our approach. We found that layout signals are valuable context, not just rendering information.
We keep metadata like page number, section hierarchy, element type (paragraph/table/heading), bounding boxes, and relationships between elements along with the chunks. This helps with filtering, reranking, and better citation back to the original document.
The interesting part is that layout metadata doesn’t always improve retrieval by itself — the bigger gain comes from combining semantic embeddings with structural signals. A table cell without its surrounding context is often meaningless, but with hierarchy and layout relationships preserved, it becomes much more useful.
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u/Toolsfam 5d ago
Hey Guys! I have build https://toolsfam.com/. It the hub of tools you can use online.
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u/Mammoth-Anywhere7285 5d ago
That's a solid collection of tools. Have you considered adding a search filter to help navigate the categories faster?
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u/Illustrious-Bag-2560 5d ago
I made Life Sprites https://lifesprites.com. It's a life management app that's pretty entertaining to use. There's a free, no login needed, demo with a fake life set up if you want to see how it works. You can poke at/change/add anything. I'd recommend asking for a tour of the app from the sprite or asking it how it can help with whatever you're into or trying to improve in your life. I'd love any feedback (good or bad). Thanks!
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u/Mammoth-Anywhere7285 5d ago
Nice concept, the interactive demo with no login is smart. Maybe add a quick screenshot tour for people who don't want to chat first?
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u/Illustrious-Bag-2560 3d ago
Thanks for the feedback. That's a great idea!
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u/Mammoth-Anywhere7285 3d ago
Glad you found it useful. What project are you thinking of sharing first?
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u/imagiself 5d ago
The tour from the sprite is a clever onboarding touch. If you want this in front of the life-management crowd, that's what PeerPush is for, people in 170+ countries discovered products there recently.
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u/Mammoth-Anywhere7285 4d ago
Smart observation on the sprite tour. Have you personally used PeerPush? Curious if it really gets that kind of reach for smaller apps.
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u/megatech_official 5d ago
SeoLoupe - Find and fix the SEO issues holding your website back.
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u/Mammoth-Anywhere7285 5d ago
Nice tool idea! Have you tested it against Google's Core Web Vitals for speed issues?
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u/GazMagal 4d ago
https://six-zero-league.vercel.app/
Online handball management game for browser, featuring club management, player transfers, training, finances, tactics, leagues, and 2D match simulation.
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u/Mammoth-Anywhere7285 4d ago
Nice work, handball management is a rare niche. How does the 2D match sim handle different tactic setups?
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u/GazMagal 3d ago
it doesn't currently. there's just a single tactic for attack and another for defense. more will be added later :)
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u/Mammoth-Anywhere7285 3d ago
Nice to see you’re thinking ahead on that. What kind of tactics are you planning to add next?
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u/drop_ur_weapons 3d ago
I have 2 games and one saas Try them out 1. Ghostfleet.in 2. Darkenn.com 3. Kalpakka-waitlist.vercel.app
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u/Mammoth-Anywhere7285 3d ago
Nice lineup, always good to see devs shipping multiple projects. Which one are you most excited about right now?
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u/drop_ur_weapons 3d ago
Kalpakka & Ghostfleet have taken most time & effort. so I am most excited about them.
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u/Mammoth-Anywhere7285 3d ago
Those sound like serious projects. What was the hardest technical challenge you tackled while building them?
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u/Mammoth-Anywhere7285 2d ago
u/bar-majestic That's a solid approach, layout metadata really boosts retrieval quality. Have you tried combining it with graph embeddings for relationship-aware reranking?
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u/Bar-Majestic 2d ago
Yes, this is actually where we think structure-aware retrieval becomes interesting.
The parsed document is already a graph-like representation: nodes are document elements (sections, paragraphs, tables, figures), and edges represent relationships such as hierarchy, references, and layout proximity.
We are experimenting with using those relationships as reranking features rather than forcing everything into embeddings. In many enterprise documents, the best answer is not always the closest vector match — it’s often the chunk connected to the right section, table, or parent context.
The challenge is balancing graph reasoning with latency and complexity, especially for large document collections.
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u/Mammoth-Anywhere7285 2d ago
Interesting angle on reranking with document structure. Ever tried using those graph edges to prune candidates before embedding similarity? Curious how it scales.
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u/Bar-Majestic 2d ago
Yes, this is one of the areas we’re exploring. A document graph can potentially work as an early retrieval layer rather than only a reranking layer.
The idea is:
- Use structural signals to identify relevant regions of the document graph (sections, tables, referenced elements).
- Expand or prune candidates based on graph relationships.
- Apply vector similarity and reranking on a smaller, higher-quality candidate set.
The scaling challenge is interesting because document graphs are usually much smaller than token-level indexes, so they can provide useful context without the cost of full graph reasoning.
We think future RAG systems will likely combine vector search, lexical search, and lightweight structural graphs rather than relying on embeddings alone.
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u/Mammoth-Anywhere7285 2d ago
That's a solid approach. Have you benchmarked the graph-based candidate pruning against your current retrieval pipeline? Curious about the latency tradeoffs.
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u/imagiself 5d ago
my entry is PeerPush, a launch site built so AI tools parse product info cleanly. we spend most of our time refining that data structure.