A reliability layer like Linden could become a critical part of a production AI workflow very quickly.
How are you planning to communicate changes to validation rules, SDK behavior, maintenance, or service incidents to developers using it? Are you considering release notes, email updates, or a public changelog and status page?
The interesting decision isn't whether to validate AI outputs—it's where to draw the line between usable and unsafe. I'd keep validating whether developers are buying schema validation or confidence that only conclusions their applications can safely trust ever reach production. That's a much stronger promise.
Great point — I agree that the bigger problem is helping developers trust AI outputs before they reach production.
The current MVP starts with structured output validation because it's one of the most common failure points in AI applications — malformed JSON, missing fields, inconsistent outputs, and business rule violations.
Linden is designed as a reliability layer where applications can define what a trustworthy output looks like and make a decision before the response reaches users: ALLOW, WARN, REGENERATE, or BLOCK.
Over time, the goal is to expand this reliability layer with deeper evaluation and trust signals. Appreciate the feedback — this is exactly the direction we're exploring.
Reading your reply gave me one thought about what changes once applications stop trusting the model and start trusting the reliability layer instead. I'd rather explain it in the context of Linden than try to reduce it to a few comments.
If you're interested, what's the best email to reach you on?
Thanks Aryan, I really appreciate the thoughtful feedback.
I agree that the bigger opportunity is moving beyond just validation and toward helping applications trust AI outputs through a reliability layer.
The current MVP starts with structured output validation and business rules because those are common failure points, but the long-term direction is around defining what makes an AI output trustworthy for a specific application.
Happy to continue the conversation here — I’d love to hear your thoughts on the positioning and category framing.
The reason I'm hesitant is that I don't think the positioning or category framing exists independently of the product. The same principles can lead to very different conclusions depending on what you're optimizing for, which is why I'm reluctant to turn it into a generic framework in a thread.
I'd rather leave it there than oversimplify it publicly.
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Linden helps developers safely use LLMs in production by validating AI outputs and catching malformed JSON, schema violations, missing fields, and logical errors before they reach users.
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A reliability layer like Linden could become a critical part of a production AI workflow very quickly.
How are you planning to communicate changes to validation rules, SDK behavior, maintenance, or service incidents to developers using it? Are you considering release notes, email updates, or a public changelog and status page?
The interesting decision isn't whether to validate AI outputs—it's where to draw the line between usable and unsafe. I'd keep validating whether developers are buying schema validation or confidence that only conclusions their applications can safely trust ever reach production. That's a much stronger promise.
Great point — I agree that the bigger problem is helping developers trust AI outputs before they reach production.
The current MVP starts with structured output validation because it's one of the most common failure points in AI applications — malformed JSON, missing fields, inconsistent outputs, and business rule violations.
Linden is designed as a reliability layer where applications can define what a trustworthy output looks like and make a decision before the response reaches users: ALLOW, WARN, REGENERATE, or BLOCK.
Over time, the goal is to expand this reliability layer with deeper evaluation and trust signals. Appreciate the feedback — this is exactly the direction we're exploring.
I'm glad it resonated.
Reading your reply gave me one thought about what changes once applications stop trusting the model and start trusting the reliability layer instead. I'd rather explain it in the context of Linden than try to reduce it to a few comments.
If you're interested, what's the best email to reach you on?
Thanks Aryan, I really appreciate the thoughtful feedback.
I agree that the bigger opportunity is moving beyond just validation and toward helping applications trust AI outputs through a reliability layer.
The current MVP starts with structured output validation and business rules because those are common failure points, but the long-term direction is around defining what makes an AI output trustworthy for a specific application.
Happy to continue the conversation here — I’d love to hear your thoughts on the positioning and category framing.
I appreciate that.
The reason I'm hesitant is that I don't think the positioning or category framing exists independently of the product. The same principles can lead to very different conclusions depending on what you're optimizing for, which is why I'm reluctant to turn it into a generic framework in a thread.
I'd rather leave it there than oversimplify it publicly.