Inference Labs (@inference

Inference Labs

1,499 posts

Inference Labs

@inference_labs

Autonomy unbridled. Governed by math, not blind faith.

Hamilton, Ontario

Joined March 2023

Pinned
Inference Labs
@inference_labs
May 12
1/ Most AI systems can tell you what a model predicted. Very few can show exactly how it arrived there. This is a look inside Sertn’s Proof Inspector: a 3D layer for verifiable inference. From the original frame → activations → proof artifacts → final detections.
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14K
Inference Labs
@inference_labs
22h
Anthropic's IPO filing is another sign that AI is becoming infrastructure, not experimentation. As the industry matures, governance, auditability, and verification matter more. The bigger AI gets, the harder "trust us" becomes as a strategy.
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Inference Labs
@inference_labs
Jun 9
Article
The Computer Vision Trust Problem No One's Talking About
Enterprise AI systems are increasingly being deployed into environments where model outputs influence operational decisions with financial, regulatory, or safety implications. Computer vision models...
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Inference Labs
@inference_labs
Jun 8
SpaceX, Anthropic, and OpenAI are all being discussed as potential IPO candidates. That says something important about where the market is. Investors are no longer valuing AI purely on research breakthroughs. They want durable businesses, revenue, and production adoption.
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Inference Labs
@inference_labs
Jun 7
Replying to @inference_labs
2/ But we also focus on something that's often missing from AI deployments: verification. Instead of relying solely on logs, Sertn can attach a cryptographic proof to an inference, helping create a verifiable record of what happened. As AI moves into operational environments,
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Inference Labs
@inference_labs
Jun 7
1/ What is Sertn? At its core, Sertn.ai is a computer vision platform built for real-world deployment. Teams can annotate data, train models, deploy computer vision workflows, and monitor results from a single environment.
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Inference Labs
@inference_labs
Jun 6
Major publishers are continuing to challenge how AI models are trained. Whether the courts ultimately side with AI developers or rights holders, one thing is becoming clear: Provenance, transparency, and verification are moving from nice-to-have features to business
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Inference Labs
@inference_labs
Jun 5
Everyone is talking about AI agents. Far fewer people are talking about verification. As AI systems move from copilots to autonomous workflows, the question shifts from "Can it do this?" to "Can we trust what it did?" That's where the next wave of AI infrastructure will be
Inference Network | Auditable Autonomy
From inferencelabs.com
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Inference Labs reposted
HudsonGraeme
@hudsongrae_me
Jun 3
Loss curves provide a really low bandwidth picture of what's really going on within models. With this visualizer, it becomes easy to understand what specifically needs to be done to improve the model.
Inference Labs
@inference_labs
Jun 3
1/ Training a model isn't just about accuracy. It's about understanding 'what the model is actually learning' and where it is struggling. Sertn's Model Art visualizes class performance in real time, helping teams identify underrepresented classes, missing samples, and training
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2K
Inference Labs
@inference_labs
Jun 4
Computer vision is becoming easier to deploy. Trusting the output is still hard. At Sertn, we're building a workflow that brings annotation, training, deployment, and verifiable inference together in one platform, helping teams move from experimentation to production with
Sertn: Verifiable Computer Vision
From sertn.ai
2.2K
Inference Labs
@inference_labs
Jun 3
Replying to @inference_labs
2/ Instead of digging through logs, users can explore how individual classes evolve throughout training. Rich class diagnostics make those insights accessible even to non-computer vision users, helping teams quickly understand model behavior and potential areas for improvement.
817
Inference Labs
@inference_labs
Jun 3
1/ Training a model isn't just about accuracy. It's about understanding 'what the model is actually learning' and where it is struggling. Sertn's Model Art visualizes class performance in real time, helping teams identify underrepresented classes, missing samples, and training
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4.3K
Inference Labs
@inference_labs
Jun 2
For years the industry focused on model training. Now we're watching billions of dollars flow into: * data infrastructure * deployment * inference * governance * verification
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