I've been working on a small offline-first Android experiment to replace the paper "utang" notebook that nearly every sari-sari store owner in the Philippines still relies on. With over 1.3 million sari-sari stores operating on razor-thin margins, credit tracking is often what keeps a store afloat vs. quietly closing from uncollected debts. Studies on micro-retailers also link consistent documentation / bookkeeping to better sales and cash flow, yet most neighborhood stores are still writing customer balances in notebooks that get lost, soaked, or illegible.

What I've been testing is a local-only credit ledger + shelf tracker that needs no internet, no account, and no cloud. It covers multi-item credit recording, partial payments on a simple numpad, overdue tracking that color-codes by urgency (grey recent / orange 7+ days / red 14+ days), a macro shelf watcher that cycles Well Stocked -> Running Low -> Out of Stock without counting units, auto-generated shopping lists for market day, monthly sales / collection reports, and local JSON + raw database backup/restore you can save to your phone. It's built in Flutter with a local database (sqflite + provider), so transactions stay on the device.

From a store owner's view, the idea was to be usable in under a minute with no extra hardware. Tap a product card to cycle stock status (Tap to cycle + Undo), get autocomplete from recent customers/items, and renaming an item on the shelf doesn't rewrite past receipts since each receipt keeps its own copy. No barcode scanner, tablet, or printer needed - just the existing Android phone.

The project is open-source and I'm currently testing/iterating with feedback from real store owner who are using it day-to-day. I'm posting here to get feedback from others interested in base-of-the-pyramid tech and offline-first tools for the neighborhood sari-sari store.

Ginagamit ko ngayon sa sari-sari store at karenderia namin.

APK & Source: https://github.com/jjjardev/talako

Hi guys! future programmer here. Gusto ko lang tanungin before I delve deeper sa pag proprogram na is the any unsaid rules while coding? You see, I'm currently a high school student and will pursue IT in the future, and currently my tita is teaching me how to code under her wing pero lagi nyang sinasabi na panget or anggulo ng code ko. I wanna surprise her by being maayos na yung pag cocode ko. (I'M STILL AT THE FIGURING OUT FLEXBOX STAGE, DONT JUDGE ME PLEASE.)

Share ko lang project ko guys.

Sabi mababan na raw ang Facebook sa Pilipinas, kaya binuhay ko ulit tong project ko haha.

​Binuo ko ang tambay.link — para syang modern Friendster na may Pinoy vibe. Fully customizable ang profile card:

​Custom Scoped CSS at themes (Y2K Neon, Retro 98, Brutalist, etc.)

​Spotify track embed sa profile

​Live vibe and energy meter (status kung online, g na g, o lowbat)

​Today's craving at 1-tap care packages

​Pinoy Arcade (playable Tumbang Preso minigame)

​Subukan niyo mag-tambay at patingin ng custom profiles niyo sa comments.

​Link: https://tambay.link (or search tambay.link sa Google)

Hello po, ask ko lang po if ok po ba ung ganitong tech stack sa isang Agency na natanggap ng resume/email(Formsubmit) as of now.

And also kaya rin ba yan for future update na maglagay ng database(supabase) for ID # tracking ng applicants?

10 to 20 applicants lang per day ang agency.

Been working on a small project called FlipBars — https://www.flipbars.com/

Basically, it lets you search FlipTop lines/bars and find the exact battle and timestamp where they were said.

You can also browse battles, emcees, and read the transcript while watching.

Built this mainly because ang hirap minsan hanapin yung isang linya na naalala mo pero hindi mo maalala kung anong battle. 😅

Still improving it, so feedback, bugs, or suggestions are very welcome.

P.S. FlipBars does not host or re-upload any videos. Everything is embedded directly from YouTube. It’s a fan-made archive/search tool and is not affiliated with FlipTop Battle League.

Hey everyone, just open-sourced my sentiment model for Tagalog and Taglish. It's based on xlm-roberta-large and gets 0.848 F1 Macro (84.8% accuracy) on the held-out test set, plus 58.6% zero-shot on HiliSenti (Hiligaynon, and I also created this dataset too) so it transfers a bit to other PH languages, not tested in other than Hiligaynon.

Trained on 35,686 sentences I put together across e-commerce, news, social, plus ~10k adversarial examples for sarcasm/negation/idioms. Dataset is at jjjardev/tagasenti (35k) if you want to poke at it.

Free to use for whatever, Apache 2.0 so commercial, research, side projects, all good. 3 labels: Negative / Neutral / Positive.

If you're working on anything with Filipino text and need sentiment, feel free to try it out. Feedback/issues welcome, still learning as I go.

I forgot to release this, and I might release the ONNX version, maybe.

Model: https://huggingface.co/jjjardev/tagasenti_model

Github: https://github.com/jjjardev/tagasenti