Yupa watches public knowledge — research papers, open-source releases, engineering conversations — and generates feature specs tailored to your repos. Your stack learns what's out there and tells you how to get better.
"The ecosystem moves every day. Your codebase only knows what it was built with."
Research papers, library releases, engineering blog posts, open-source conversations. Relevant improvements to your stack ship constantly. You catch a fraction of them.
Finding something useful is step one. Figuring out if it applies to your repo, which files it touches, and what the migration looks like — that's where the real time goes.
Without someone doing this work continuously, your stack falls behind. Yupa does it for you — watches public knowledge, scores it against your repos, and writes grounded specs you approve before anything ships.
Yupa runs this cycle daily, without you lifting a finger.
Curated sources scraped daily — research blogs, trending repos, engineering posts, open-source conversations. Each article is reduced to a structured concept signature: what the technology does, what stacks it fits, and what problem it solves.
Your repos are fingerprinted by dependency graph, framework, and architecture. Each concept is scored 0-100 against that fingerprint. Language and framework mismatches are caught before the LLM runs, so you're not paying for obvious misses.
High-scoring matches get a full PRD: affected files, dependency changes, integration steps, risks, and further reading. Every claim cites the source article directly. If the source doesn't cover something, the spec says so.
You approve every spec before anything happens. On approval, Yupa creates a feature branch and opens a PR. You merge when ready.
Yupa runs overnight. By morning, your dashboard has specs waiting — each one tied to a real change in the ecosystem that applies to your repo. You didn't search for it. You didn't read the article. You didn't check compatibility or write the migration plan. That work is done.
When you approve a spec, Yupa searches GitHub for open-source implementations that match. It pulls real code from real repos — not generated from scratch — and uses those blueprints alongside your repo's structure to write the actual changes. It creates a branch, commits the files, and opens a PR. You review it like any other pull request.
The loop resets every day. New articles come in, new concepts get extracted, new matches surface. Your codebase stays aware of what's happening around it without anyone on your team spending time on research, triage, or spec writing. The effort shifts from "find and plan" to "review and merge."
Every source is verified for signal quality. Free tier includes a selection from each family — full access unlocks the complete list.
Research blogs and announcements from the labs building frontier models. First to publish new capabilities your stack can adopt.
Daily papers, trending repositories, and pre-print feeds filtered for applied AI and ML engineering.
Launch announcements, investment theses, and market signal from accelerators and funds tracking AI adoption.
Independent writers covering the practitioner side — benchmarks, integration guides, architecture decisions, and field reports.
No surprise charges. You see what every action costs before it runs.
Per concept scored against your repo via Claude Haiku. Pre-filtered by language first.
Per spec generated via Claude Sonnet with grounded citations. One auto-spec per repo per run.
Run specs locally via Ollama + Nemotron. No API costs. Slightly lower quality, full privacy.
Get notified when Yupa launches. Early users get free access to all source families.