guuuy

[Avenro @avenrotech]

Avenro is an AI integration gateway that lets you access multiple frontier models (like OpenAI, Anthropic, Google, xAI, and DeepSeek) through a single unified endpoint.

Instead of dealing with separate API keys, different client libraries, and individual billing accounts for every AI provider, you only change two things in your setup:

The Base URL: Point your existing AI client

The API Key: Drop in your Avenro key.

Because it uses OpenAI's standard Chat Completions format, any code you've already written for OpenAI works natively without rewrites. You just fund your balance using USDC or USDG on Robinhood Chain, and you're good to go across multiple models from a single dashboard.
https://t.co/CiNV7wTGyV

[guuuy @guuuyrt]

@avenrotech Will there be a dev and team reveal in the future?

[Crypto94 - Owner of Infinity Gainz @CryptoExpert101]

Even @claudeai said $ANR gives more discounts flat 40% everytime on any price whereas $Orbio gives on varied price which could be higher for any AI model.

There’s no supplier risk for $ANR whereas $Orbio has.

Easy millions.

0x1d08d2e96e7712b754048dd1c2e7a6e878ed4913

@AlphaSeeker21 @BitmanTW @PainCrypt0 @CookerFlips @rasmr_eth @frankdegods @bigwarzeth @DegenCapitalLLC @Bullrun_Gravano

[Avenro @avenrotech]

$ANR utility is now implemented within Avenro.

The integration brings token-based access into the product through an on-chain balance verification and entitlement framework, giving $ANR a defined role in how users access platform capabilities.

The utility framework includes three pillars:

→ ANR Plus — Extended request history, advanced usage analytics, cost insights, and CSV exports.

→ ANR Pro — Expanded developer capabilities, advanced routing where supported, team workspaces, and shared usage reporting.

→ Platform Payments — Optional $ANR payments for selected services and credits, planned as a later phase.

Access is designed around verified token balances, without requiring users to stake or transfer tokens to qualify for features.

Avenro’s core API remains accessible without $ANR, preserving the existing access and billing model.

$ANR now has a defined role within the Avenro product ecosystem connecting token ownership to platform utility.

Implemented. Integrated. Built for real use

https://t.co/dNNDfelFsJ

[guuuy @guuuyrt]

@RInuhood Why are you holding so much supply 50%? This is really scary

[guuuy @guuuyrt]

@RInuhood Why are you holding so much supply? This is really scary

[guuuy @guuuyrt]

@avenrotech Any buyback and burn dev?

[guuuy @guuuyrt]

@avenrotech Any buyback and burn?

[Avenro @avenrotech]

The demo walkthrough is live , showing the end-to-end integration workflow:

• Base URL configuration (⁠[https://t.co/hLbxTJYq9B](https://t.co/hLbxTJYq9B)⁠)

• Multi-model access across Anthropic, OpenAI, Google, xAI, and DeepSeek

• Per-key spending limits and zero-storage memory execution

[Avenro @avenrotech]

The Avenro roadmap is live on our website.

Today, we shipped Request-Level Observability another step forward for $ANR

More updates are planned and you can explore our Under Consideration section to see what’s being considered for future releases.

We’ll keep building and shipping according to the roadmap, with updates as each release goes live.

Check it out
https://t.co/bQZy9EDg11

[Avenro @avenrotech]

Request-level observability is now live.

Every inference call leaves behind useful operational information. The challenge is making that information accessible without compromising the data being processed.

Avenro now provides request-level metadata that helps developers connect API activity with actual usage and execution.

With request identifiers, token accounting, cost reporting, and execution-path details, developers have a clearer basis for investigating unexpected behavior and reconciling inference spend.

The implementation keeps observability focused on operational metadata rather than making prompts and generated responses part of persistent logs.

As AI workloads move into production, understanding how requests execute and what they cost becomes just as important as receiving the response.

That visibility is now part of Avenro.

Built for inference you can inspect and account for.

https://t.co/CiNV7wTGyV

[guuuy @guuuyrt]

@avenrotech When is the next update ready?

[Avenro @avenrotech]

The cost of inference is not just a model-pricing problem.

It is a workload allocation problem.

A production application rarely sends every request through the same reasoning path. Some tasks require advanced reasoning. Others involve extraction, classification, summarization, or routine generation.

Using the same model for every task can introduce unnecessary cost and complexity.

A more efficient architecture considers the requirements of each workload, the capabilities of the available models, and the economics of executing the request.

This is where model choice and inference infrastructure intersect.

Avenro is built around that intersection: unified access to multiple model providers, with a focus on reducing the cost and operational overhead of inference.

The objective is not to use the largest model for everything.

It is to make model access more efficient at the application level.

[guuuy @guuuyrt]

@avenrotech Lets gooo

[Avenro @avenrotech]

GM everyone.

Next update: Request level observability.

Understanding an AI request shouldn’t end when the response arrives.

We’re extending Avenro’s API observability with request level execution metadata, giving developers a clearer view of how inference requests are processed.

The focus is on four things:

— Request identification
— Token usage
— Exact request cost
— Execution path

The objective is straightforward: make inference easier to inspect, debug, and account for without exposing prompts or generated outputs through observability data.

Better visibility into every request. Without adding complexity to the integration.

[guuuy @guuuyrt]

@PR0H0S I'm in. I need this job capt

[Rovyn @rovyndotfi]

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Watch till the end

[guuuy @guuuyrt]

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[guuuy @guuuyrt]

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[guuuy @guuuyrt]

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