KOOYA / AI DIGEST / MANILA
Philippine languages, model research and the foundations of useful AI.
Browse the library ↓A READING GUIDE / BY KOOYA
A model can sound natural and still misunderstand the task. Philippine-language AI therefore needs more than a familiar greeting or a translated benchmark. The question is whether the system preserves meaning, follows constraints and produces a useful result in the language people actually use.
This collection connects language research with coding, voice interfaces and verifiable tasks. For a buyer, it offers questions to ask about realistic inputs and human handoffs. For a researcher, it highlights the distinction between linguistic fluency, task correctness and the conditions of an experiment.
Pino SI is Kooya’s research effort to adapt an existing open model for the Philippines. SI expresses a long-term superintelligence ambition. Its public research snapshot describes a planned coding comparison and future directions; model-size targets and data counts are not performance results. The linked notes explain both the approach and the evidence still needed.
Mixed-language requests can carry constraints that separate English and Tagalog tests miss. Native-speaker review can examine intended meaning, while task-specific checks determine whether the answer actually works.
Read the language evaluation rationale ↗Pino SI’s planned coding comparison starts two training paths from the same open model. Interpreting any future result requires attention to training effort, attempt budgets, equivalent prompts and English retention.
Examine the planned coding experiment ↗Parameters describe model size; tokens describe data units. A target is a plan, and an inventory is a resource count. Neither establishes that a system succeeds on the language or task a reader cares about.
Interpret the roadmap’s numbers ↗Connect real speech and language needs to a practical acceptance decision.
Start with the mission, then inspect the comparison and what a checkable answer means.
Showing 1–6 of 6 articles
Kooya explains Pino SI’s Philippine AI mission, open-model adaptation and planned first experiment, with a clear distinction between ambition and demonstrated results.
A Kooya research note on Pino SI’s Tagalog, English and Taglish goals, regional-language ambition and the need to test meaning with native speakers.
Kooya explains Pino SI’s planned coding comparison, the language and compute confounds to examine, and how executable tests can support a careful conclusion.
Kooya’s research note separates mathematical answers, scientific reasoning and machine-checked proofs in Pino SI’s planned STEM work.
Understand Pino SI’s public model-size targets, measured and estimated token counts, planned pilots and the distinction between fundraising and Kooya’s own investment.
A field guide to voice AI for Philippine customer operations, multilingual teams, and controlled automation.
