u/jjjardev

I built HoardCore, an agent harness for retrieval and deep research, designed to be driven by an AI agent and tested inside OpenCode. The core idea is to turn the web and your own documents into a permanent local SQLite vault. Your agents can search, recall from, and cite.

The key features are:

Hybrid Retrieval:

SQLite FTS5 keyword search fused with dependency-free hashed vectors via Reciprocal Rank Fusion, no embeddings model and torch.

Resilient fetching:

aiohttp → curl_cffi TLS-impersonation → optional FlareSolverr, so it gets through anti-bot pages.

Universal Parsing:

HTML, PDF with OCR, DOCX, EPUB

Junk Filtering:

Boilerplate, 404s, and captcha pages never pollute your index

And a one-command Research Loop:

DISCOVER → INGEST → RECALL → EMIT.

It ships as a single Python file with `skill.md` written as the agent's operating manual, so the harness literally teaches your agent how to use it, how to map your request to the right action, how deep to go, and how to tag every claim. Deliverables come out with `[V]/[E]/[H]` provenance tags and a Source Links / Citations block, so the agent can't silently invent a number. The vault persists between sessions, later searches are easily fetched.

You can also state how many sources that the research should have.

To show it's capabilities, I pointed it at a real question: can Filipino (Tagalog, Cebuano, Hiligaynon) get real AI support, on-device and offline? (based on my own profile, I just insert my resume for context). The full output it produced is in the comment below.

If you build Filipino NLP, edge AI, or agent tooling, I'd love feedback on both the tool and the output

Link: https://github.com/jjjardev/HoardCore

I built FiliSenti because I noticed a gap. Most sentiment analysis tools are built for English. They don't work well on Tagalog. They definitely don't work on Hiligaynon. And they all require internet, which means your customer data goes to a third party.

That's a problem for Philippine businesses. Customer feedback in local languages is often ignored because there's no easy way to analyze it at scale.

So I built a solution. FiliSenti is a Flutter Android app that analyzes sentiment in Tagalog and Hiligaynon. It runs entirely on-device. The model achieves 89.1% macro F1, which is the highest published score for 3-class Filipino sentiment.

You can deploy this as an API, embed it in your own app, or use it as a customer feedback analysis tool for BPOs, e-commerce platforms, or social listening.

The model is open source.

I built the whole thing on free Google Colab.

I did all the full pipeline end to end: dataset curation, training, quantization, mobile deployment.

If you're building anything that needs to understand Filipino sentiment, this might help you.

Want to use it in your phone? download the onnx version of the model from Huggingface and load it to the app (Android only, for now)

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

Code: https://github.com/jjjardev/filisenti

I built FiliSenti to see if I could make a state-of-the-art AI model run entirely on a phone with no internet. It works. You can type something in Hiligaynon or Tagalog and it will tell you if the sentiment is positive, neutral, or negative. It also highlights each sentence with colors: green for positive, yellow for neutral, red for negative.

The app uses a model I fine-tuned on 56,781 sentences from Hiligaynon and Tagalog. It achieves 89.1% F1, which is the highest published score for 3-class Filipino sentiment I know of. I quantized the model to run on a phone and built the app in Flutter. It works completely offline. No data leaves your phone.

I built this whole thing on free tools. Google Colab for training. Google Drive for storage. Open-source models. It's open source and available for anyone to use or modify. If you're into AI, language tech, or just want to see something built here in Negros Occidental get some recognition, check it out.

If you want to use it, download the onnx version of the model from Huggingface and load it to the app.

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

App: https://github.com/jjjardev/filisenti

I built FiliSenti because I wanted to see if a 355-million-parameter model could actually run on a phone. Turns out, it can. I fine-tuned XLM-RoBERTa-large on 59,023 sentences across Tagalog and Hiligaynon, quantized it to INT8 ONNX, and deployed it as a Flutter Android app. The model achieves 89.1% macro F1, which is the highest published score for 3-class Filipino sentiment that I'm aware of.

The app works offline. You can paste text, upload a file, or type something directly. It highlights each sentence with color coding: green for positive, yellow for neutral, red for negative. It also shows you a summary breakdown with percentages and a majority sentiment. I built the full pipeline myself. Dataset curation. Training. Quantization. Mobile deployment. All on free Google Colab.

Everything is open source. The model is on Hugging Face. The Flutter app code is on GitHub with a release APK you can install right now. If you're into NLP, on-device AI, or just want to see what a solo dev can build with free tools, check it out.

If you want to use it, download the onnx version of the model from Huggingface and load it to the app.

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

App: https://github.com/jjjardev/filisenti

A month ago, I released HiliSenti v1, the first public sentiment analysis dataset for Hiligaynon, 23,337 real‑world sentences labeled as negative, neutral, or positive. Today, I'm releasing the fine‑tuned model itself: an XLM‑RoBERTa‑large (355M parameters) that achieves 93.5% test accuracy and 93.4% macro F1, with per‑class F1 scores of 0.95 (Negative), 0.91 (Neutral), and 0.94 (Positive). The model handles code‑switching (Tagalog/English) and performs well.

Everything was built on zero budget, free Google Colab T4 GPU, free 15GB Google Drive. The model weights are now publicly available on Hugging Face under CC BY‑NC‑SA 4.0 (same as the dataset), and the training code is open‑source on GitHub under MIT. I also secured a DOI for the model (10.57967/hf/9302) so it's permanently citable even without an arXiv paper yet.

If you're into NLP, low‑resource languages, or just want to see a Filipino regional language get some ML love, go check it out. The model is ready for inference via transformers pipeline, just load it and run. I'd love to hear your feedback, especially if you're working on similar projects for other Philippine languages.

Links:

• Model: https://huggingface.co/jjjardev/hilisenti-v1-model
• Dataset: https://huggingface.co/datasets/jjjardev/hilisenti-v1
• Code: https://github.com/jjjardev/hilisenti

You can try the model interactively using the Colab notebook available in this repository:

hilisenti_test.ipynb

Simply open the notebook in Google Colab and run all cells to test the model on your own Hiligaynon sentences.

I want to showcase HiliSenti v1, the first public sentiment analysis dataset for Hiligaynon. It’s a multi‑domain collection of 23,337 real‑world Hiligaynon sentences, many with natural Tagalog/English code‑switching labeled as negative, neutral, or positive. I trained an XLM‑RoBERTa‑large model on it and got 93.5% accuracy (macro F1 of 93.4%), which is far above the 80% target I originally set. This means the model can reliably understand sentiment in actual Hiligaynon text, not just in English or Tagalog, which is a first for our language.

Everything was done on a free Google Colab GPU and the free 15GB Google Drive, without any paid API or cloud credits, just a lot of manual dataset curation and some creative checkpoint pruning when Drive storage kept filling up. The code is open‑source on GitHub, the dataset is on Hugging Face, and I’m working on a paper (aiming to submit to ACL). If you’re into NLP, low‑resource languages, or just want to see a Filipino regional language get some ML love, go take a look.

Dataset: https://huggingface.co/datasets/jjjardev/hilisenti-v1

Code: https://github.com/jjjardev/hilisenti