I'm a BSIT graduate from Negros Occidental, Philippines, building language technology for low-resource Philippine languages and deploying production-grade AI to edge devices with zero cloud dependency. I design systems for agents, not just for humans.
What I've built:
- FiliSenti — Unified Tagalog-Hiligaynon sentiment model (0.8915 F1). Quantized to INT8 ONNX and deployed as a polished Flutter app. Runs entirely on-device.
- HoardCore — Agent Toolkit for retrieval and deep research. Zero-API, zero-embedding, fully local. Runs
[V]provenance-tagged research loops. - HiliSenti v1 — First multi-domain sentiment dataset for Hiligaynon. 23,337 sentences, 93.5% accuracy, permanent DOI.
- TagaSenti — Multi-domain Tagalog sentiment dataset with adversarial augmentation. 35,686 sentences, 0.849 F1.
- TalaKo — Offline-first credit tracking app for sari-sari stores. Flutter, SQLite, Material 3.
- CPSU Server — Deployed a full production Linux server for my school: LAMP stack, DNS, LXQt desktop, student records integration.
My methodology: Full ML lifecycle — data curation → model training → quantization → mobile deployment → agent tooling — executed entirely on free resources.
Current focus: Building the default open-source infrastructure for Filipino NLP — datasets, edge models, benchmarks, and agent tooling that compound into citable, grantable assets.
- Unified Tagalog-Hiligaynon sentiment model achieving 0.8915 macro F1 — the highest published score for 3-class Filipino sentiment.
- 355M-parameter XLM-RoBERTa-large quantized to INT8 ONNX (537 MB) and deployed in a Flutter Android app with on-device inference.
- Ported XLM-RoBERTa's SentencePiece tokenizer to pure Dart — solved a critical deployment barrier for mobile NLP.
- Polished Material 3 app with text input, file upload, color-coded sentiment highlighting, and CSV/JSON export.
- Full ML lifecycle demonstrated: dataset curation → model training → quantization → mobile deployment.
- Zero-API, zero-embedding, fully local retrieval engine for AI agents. Built in 12 hours.
- Hybrid retrieval: FTS5 (BM25-style) + lexical hashing (FNV-1a) → RRF fusion.
- Provenance-tagged output:
[V](verified),[E](external),[H](hypothesis) — every claim is traceable. DISCOVER → INGEST → RECALL → EMITresearch loop. Runs deep research briefs at cheaply.- Single-file Python, zero dependencies, designed to be driven by AI agents.
- First publicly available multi-domain sentiment dataset for Hiligaynon (10M+ speakers).
- 23,337 sentences across news, social media, cross-lingual translations, and synthetic edge cases.
- XLM-RoBERTa-large fine-tuned: 93.5% accuracy, 93.4% macro F1.
- Built on free Google Colab (Tesla T4) and free 15GB Google Drive.
- Within 48 hours of release: 40+ downloads, 14,600+ combined Reddit views, 141 upvotes (97%+ ratio), unsolicited commercial interest.
- arXiv paper in preparation.
- 35,686 -sentence multi-domain Tagalog sentiment dataset (e-commerce, news, social media).
- Targeted adversarial augmentation strategy: 3,766 rows across 8 failure categories based on a 100-sentence adversarial benchmark.
- XLM-RoBERTa-large fine-tuned: 0.849 test F1, 92% benchmark accuracy.
- Zero-shot transfer to Hiligaynon at 0.591 F1 — confirmed cross-lingual transfer within the Philippine language family.
- Entire pipeline executed in 6 days on free Google Colab (Tesla T4) and free 15GB Google Drive.
- Production-grade offline-first Android app for sari-sari store owners to track customer credits and price references.
- Complete SQLite database schema with customers, items, and credits tables.
- Features: customer management, credit tracking with partial payments, overdue detection (14-day), searchable price catalog, backup/restore (JSON + raw DB).
- Built with Flutter and Material 3 UI; ~1,200 lines of Dart code.
- Supports adjacent use cases: tricycle operators, rice retailers, water refilling stations, 5-6 lenders, paluwagan organizers.
- Motivated by personal experience managing a family sari-sari store with inconsistent notebook tracking.
2025 | Ubuntu Server · Apache · PHP · MySQL · DNS · LXQt
- Designed and deployed a full production Linux server for Central Philippines State University — Hinigaran Campus.
- LAMP stack (Apache, PHP, MySQL), custom internal DNS, and LXQt desktop environment selected for low resource usage and GUI familiarity for non-technical staff.
- Configured remote FTP access enabling 5+ teaching staff to update web content without physical server visits.
- Coordinated deployment and environment compatibility for a third-party Transcript of Records system integrated into the admin dashboard.
- Awarded Best in On-the-Job Training for this work.
| Project | Type | Scale | Key Metric | Status |
|---|---|---|---|---|
| FiliSenti | Model + App | 56,781 sentences | 0.8915 macro F1 | Public (HF + GitHub) |
| HoardCore | Research Toolkit | 275 docs, 5,086 chunks | $0.0074 per deep research brief | Public (GitHub) |
| HiliSenti | Dataset | 23,337 sentences | 93.5% accuracy | Public (HF, DOI) |
| TagaSenti | Dataset | 35,686 sentences | 0.849 F1 | Public (HF) |
| TalaKo | Mobile App | 49 MB APK | ~1,200 LOC | Public (GitHub) |
All projects were built on zero budget — free Google Colab, free Google Drive, open-source models, and free LLM web UIs.
- Email: jj.jarder.dev@gmail.com
- LinkedIn: Jessie James Jarder
- Hugging Face: jjjardev
- GitHub: You're already here