AI & ML interests

Open language models for South-East Asia

Recent Activity

Abhaykoul 
posted an update 13 days ago
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Shipped v0.1.2 of vtx — a minimalist coding agent for the terminal.

Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.

Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.

What ships out of the box:

→ Textual TUI + headless CLI (vtx -p "fix the failing test")
→ 49 LLM provider gateways, all declared in a single provider.yaml
→ 5 core tools (read / edit / write / bash / find) plus web search and fetch
→ Session tree with compaction, handoff, and resume
→ AGENTS.md / CLAUDE.md auto-discovery
→ Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands
→ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
→ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist

This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.

Apache 2.0. uv tool install vtx-coding-agent and you're running.

GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent

Built in the open. Feedback, extensions, and PRs welcome.
jojo-ai-mst 
posted an update 2 months ago
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🇲🇲 Releasing the Myanmar Tuberculosis Instruction Dataset — a Myanmar–English parallel corpus for medical NLP in one of the lowest-resourced language settings in Southeast Asia.

Most TB datasets are either structured clinical data or English-only research corpora. This one fills a different gap: instructional, guideline-based content in Burmese, formatted for instruction tuning and medical QA.

### What's inside

- 2,043 instruction–response pairs
- Myanmar–English parallel
- 7 TB domains: treatment, diagnostics, drug management, MDR-TB, infection control, patient education, healthcare worker training
- Sourced from WHO guidelines, Myanmar NTP protocols, and standard medical references
- MIT licensed

Useful for

- Fine-tuning Myanmar-language medical LLMs
- TB question answering
- Translation evaluation in a medical domain
- General low-resource medical NLP



from datasets import load_dataset
ds = load_dataset("jojo-ai-mst/Myanmar-Tuberculosis-Guidelines-Instructions")


👉 jojo-ai-mst/Myanmar-Tuberculosis-Guidelines-Instructions
Built by Min Si Thu and Khin Myat Noe. Feedback welcome — especially from anyone working on SEA medical AI or Burmese NLP.

#MedicalAI #LowResourceNLP #Myanmar #Burmese #Tuberculosis #InstructionTuning