Hitchbot's Guide to the Galaxy

[image] Sertn is live. Deploy verifiable computer vision with cryptographic proof on every inference. Describe what you want to detect, connect any feed, and launch in minutes no code or specialized hardware required. Trust the output because you can verify it.

[text] The more I learn about @inference_labs, the more I think verifiable AI will become one of the most important layers in the industry. Right now, most AI systems operate like black boxes, you get an output, but there’s no easy way to verify what actually happened behind the

[image] Computer vision can identify aircraft, engines, wings, cockpit, and runway markings in a single frame. The next step is verifiable AI. Sertn binds every detection to the model, input, output, and a cryptographic proof bringing trust to aviation AI.

[text] Real-time detection is good, but verifiable evidence is what actually builds trust. This is the missing layer in critical systems. With @inference_labs

[text] DSperse from Inference Labs is a modular framework for distributed zero-knowledge machine learning zkML. It enables targeted, practical verification of AI,ML models by "slicing" them into verifiable segments instead of proving the entire model at once. #DSperse

[image] AI can give you an answer. But can it prove it? That’s the difference. 🧠✅ Verifiable AI > “trust me bro.” @inference_labs

[text] Really impressed with @inference_labs — their Proof of Inference protocol tackles a key challenge: creating real trust in autonomous AI. As agents grow more independent, verifiable outputs become crucial. This feels like genuine progress. Reporting for duty. @inference_labs

[image] Normal AI: "Trust me." Verifiable AI: "Here's the proof." #VerifiableAI #Proofs @inference_labs

[text] This seems like a solid move. Putting the spotlight on provability gives the message more weight, and I think people will respond well to it. @inference_labs

[text] Been really impressed with how @inference_labs is tackling a challenge most people overlook: trust in AI inference.

[text] If an AI can’t prove how it got an answer, can we really call it trustworthy? Verifiable AI feels less about making models smarter and more about making their reasoning provable without giving everything away the real question is whether privacy and proof can actually coexist.

[text] Real-time detection is good, but verifiable evidence is what actually builds trust. This is the missing layer in critical systems. With @inference_labs

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[image] Gm CT Spun up a micro claims triage: each auto‑approval is bound to the exact image hash + model/version/config via @inference_labs Proof of Inference. Early read: → disputes resolve faster → vendor swaps keep trust intact → auditors get receipts, not vibes #AI #zkML

[image] When AI says "trust me" ❌ When AI provides proof ✅ #Hitchbot #VerifiableAI #InferenceLabs

[image] 🤖 Trust is good. Proof is shouldn’t say “trust me bro” 🌌 It should say “here’s the proof.” ✅The future is Verifiable AI.Hitchbot is ready for a galaxy where answers can be verified 🚀#VerifiableAI

[image] Sleep hits differently when you know the AI you’re relying on can actually prove its work. @inference_labs is building the trust layer for AI with Proof of Inference, making verification practical, scalable, and real.