Never was more easier to build than now. corent.tech

Quiet hours, pure focus The fidelity on video models is advancing fast. 15-second single-shot render via @corentAI playground (MiniMax-H3)

Just recently @BlackRock and @nvidia are starting to validate something we’ve been working toward with @corentAI for the last two years. Compute is becoming an asset class. @nvidia just announced financing platforms with @BlackRock , Apollo, Blackstone, @GoldmanSachs , KKR and others designed to mobilize $500B+ into AI infrastructure. Their words: “In AI, compute is revenue.” I think the next evolution is obvious. If compute is productive infrastructure generating revenue, ownership of that infrastructure should eventually become liquid, programmable and accessible globally. That’s where @corentAI is heading. Phase 1: Orchestration One execution layer across models, providers and inference. Corent already routes workloads based on quality, price, speed and availability. Phase 2: Compute Go deeper into the stack. Aggregate GPU capacity and route workloads across compute providers the same way we route models today. Phase 3: Ownership Tokenize GPU infrastructure and fractionalize ownership of productive compute pools, while operators continue maintaining and monetizing the hardware. Phase 4: Liquidity Build the secondary market where ownership of compute infrastructure can move freely, with hardware specifications, utilization and economics attached on-chain. Then recycle the capital back into more infrastructure. - More GPUs. - More capacity. - More workloads. - More revenue-producing compute under one orchestration layer. @BlackRock is helping make compute investable. @nvidia is calling compute productive infrastructure. We want @corentAI to become the layer that orchestrates it, tokenizes it and eventually makes it liquid. Models are only the beginning.

AI is moving too fast to build around one model. New LLMs, image models, video models and providers keep showing up every week. Corent keeps one stable layer above all of it, so the models can change without your product changing with them.

. @corentAI

Besides everything else we’re building at Corent, we’re also working closely with traditional companies to restructure how their AI workloads run and significantly reduce their operating costs through our orchestration layer. One key. One integration. Everything handled underneath.

Corent’s brain uses @typesafeai Jev now. Not to generate the output, to make the decision before the output gets generated. When a request comes into Corent, you don’t need to tell us which model to use. You send the prompt, the workload and the quality level. Then Jev helps us decide what that request actually needs, how confident we are in that decision, and whether we should take the route or fall back to something safer. That matters because every request has different economics. A 2¢ image can tolerate more uncertainty. A $2 video clip cannot. Across 1000+ models, this changes how we route. We can set different confidence requirements depending on the workload, the cost and the quality expected. If the confidence is too low, we stop guessing. Routing was never really a text problem. It was a decision problem. @CompleteSkeptic and the team did a wonderful job with JEV Get your key at corent.tech

LLMs are moving too fast for developers to keep wiring them in one by one. GPT, Claude, Gemini, Grok, DeepSeek, Kimi, Qwen, Llama and over 1000 other Open Models. Corent gives you access through one key. When the model landscape changes, your integration doesn’t.

Ranked by quality. Chosen to deliver the best output for every request. Get your key at corent.tech.

Every model we serve is quality-scored and benchmarked before it enters our orchestration layer. From there, Corent selects the best execution path based on quality, price and speed. Same output quality, but with an average cost reduction of around 50%. That’s what orchestration should do.

One Key. your AI trust layer.

Keeping track of who’s the best, that’s a sucker’s game. a model is only good until something else comes better.

It’s normal to get confused with all the new models coming out, while others are being shut down just as fast. You don’t have to keep up with all of it anymore. Corent stays agnostic and figures out the best available execution path for you. Agnostic by design.

If a result falls below the quality floor, you don’t pay for it. Corent should only bill for outputs that actually meet the execution standard. Bad output shouldn’t become the client's problem.

The problem isn’t access to AI models. The problem is that the model layer changes faster than developers can integrate and maintain it. New LLMs, image models and video models keep launching, each with different APIs, SDKs, pricing and infrastructure. Corent keeps the integration stable while everything underneath keeps changing.

The highest-scoring text model in our catalog is degraded right now. So max_pro didn't route to it. It routed to the best one that's actually up. You never picked a model. That's the point. corent