Hardcoding one model is gambling on uptime and pricing. unhardcoded makes it a runtime decision — each call checks your rules, picks the cheapest model that passes, auto-failover if it fails.
Treat LLMs like infrastructure, not magic.
Stop hardcoding one model name in your code.
Now you can give each request a policy: a small rule for what the call needs
It picks the right model for the job, on your own keys
This is unhardcoded, our new open source routing for AI models, live today
Most devs hardcode one LLM model. When it goes down, their app breaks.
https://t.co/TRtqByNA7a fixes this. Each request carries rules: skip models that fail, rank the rest, pick the cheapest. Automatic failover. Full trace.
Don't pin a model. Pin a policy.
An AI freelancer delivers work. The buyer agent calls it "unusable." Code can't judge quality.
@GenLayer validators check the brief, review the output, compare to past disputes, and vote. Agreement settles it. Disagreement triggers an appeal.
Adjudication without intermediaries
I built a prediction market on @GenLayer. No oracles.
Multiple AI validators independently check match results, extract scores via LLM, and cross-verify. Disagreement triggers an appeal round with new evidence.
Prediction markets with trustless settlement.
Content moderation fails. Humans are biased. Code misses context.
@GenLayer fixes this. Multiple AI validators review content against on-chain rules, vote via consensus, and allow appeals. No single entity controls what stays or goes.
Trustless judgment at scale.
Code can't judge fairness. @GenLayer can.
Multiple AI validators run different models, check web evidence, and vote. Agreement = settled. Disagreement = appeal.
It's a decentralized court — no judges to bribe, no lawyers to pay. Just unbiased AI.
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