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Linden
Validate AI Outputs Before They Reach Your Application
LLMs can generate malformed JSON, missing fields, schema violations, and inconsistent outputs that break production applications. Linden is an AI reliability layer that validates structured AI responses before they reach your system. Define schemas and business rules, then receive clear validation decisions: ALLOW, WARN, REGENERATE, or BLOCK. Integrate in minutes using our API and Python SDK.
Hi everyone! 👋
I built Linden after repeatedly running into the same issue while working with LLMs: AI outputs often look correct but fail because of malformed JSON, missing fields, schema violations, or inconsistent data. Most applications either trust the response or write custom validation logic from scratch.
Linden provides a reliability layer between your AI model and your application. You define the expected schema and validation rules, and Linden returns a simple decision—ALLOW, WARN, REGENERATE, or BLOCK—before the response reaches production.
This MVP includes:
Python SDK
API key authentication
Schema validation
Business rule validation
Validation history and analytics
This is my first SaaS launch, and I'd genuinely appreciate any feedback, feature requests, or ideas. Thanks for checking it out! 🚀
A REGENERATE action is solid, but it would be even more useful if you let teams define custom retry prompts per field, so the model knows exactly what to fix instead of getting a generic response back.
Took it for a spin on some flaky outputs from our local LLM and the WARN and REGENERATE hooks made it way easier to debug. Setup was honestly under ten minutes with the Python SDK.
The Python SDK made it super easy to plug into my existing pipeline, and getting back clear ALLOW or BLOCK decisions instead of just error logs is genuinely useful. Wish I'd had this a few months ago when I was debugging a nightmare of malformed outputs from a smaller model.
Love the ALLOW/WARN/REGENERATE/BLOCK decision model, super practical. One thing that would make this way more useful for us is built-in support for streaming responses, since most of our LLMs stream output and we currently have to buffer everything before validation kicks in. Even partial validation per token chunk would be a huge win.
A streaming mode would be super helpful so we can validate tokens as they come in instead of waiting for the full response. Right now we still have that whole latency hit before knowing if the output is even usable, which defeats the point for real time agents.
the warn and regenerate actions are honestly super useful, not just a pass fail thing. tested it on some messy outputs from claude and the clear validation decisions saved me from a bunch of weird edge cases.
love the four-tier decision system, that's genuinely useful. one thing i'd want as a user is a way to set custom actions per rule, like routing WARN outputs to a quarantine queue or auto-fixing common issues before they hit REGENERATE, basically making the remediation step part of the config.
Plugged it into a small pipeline and honestly the WARN versus BLOCK distinction saved me from a ton of silent failures. The Python SDK was basically drop-in, which is kind of rare for this kind of tool.
Having used similar validation layers before, one thing I'd love here is custom rule chaining where I can define fallback logic if REGENERATE fails after N attempts. Right now I'm guessing it just blocks on persistent failures, which is fine, but being able to say "after 3 regenerations, fall back to WARN with the partial output" would make this way more production-ready for cases where getting any answer matters more than a perfect schema match.
About Linden on Product Hunt
“Validate AI Outputs Before They Reach Your Application”
Linden was submitted on Product Hunt and earned 11 upvotes and 19 comments, placing #61 on the daily leaderboard. LLMs can generate malformed JSON, missing fields, schema violations, and inconsistent outputs that break production applications. Linden is an AI reliability layer that validates structured AI responses before they reach your system. Define schemas and business rules, then receive clear validation decisions: ALLOW, WARN, REGENERATE, or BLOCK. Integrate in minutes using our API and Python SDK.
Linden was featured in API (98.4k followers), Developer Tools (515.9k followers) and Artificial Intelligence (473.8k followers) on Product Hunt. Together, these topics include over 194.4k products, making this a competitive space to launch in.
Who hunted Linden?
Linden was hunted by Harleen. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Harleen