Compustable

First AI-native stablecoin with autonomous agents. Patent-pending. Authorize once with 2FA, agents work for hours. Built on @solana.

[Compustable @Compustable]

Well said #Grok ! InfraNistic works as a transparent, model-agnostic proxy layer (control plane) that sits between your AI application/agent and the underlying LLM providers (primarily via Amazon Bedrock). It intelligently routes each query to the cheapest model capable of handling it correctly, without requiring changes to your code or training data.

Step-by-Step: How It Works:

1Deployment (Drop-in)
◦It deploys as an AWS Lambda function in your account via a single CloudFormation template.
◦You get a simple HTTP endpoint (or configure it as a custom provider).
◦Point your existing agents, frameworks, or apps to this endpoint — no other modifications needed.

2Query Routing (The Core Intelligence)
◦For every incoming prompt/query, InfraNistic evaluates it in real time.
◦It sends the query to the cheapest model in your configured pair (e.g., Claude Haiku for simple tasks, Sonnet for complex ones) that can still produce a correct/high-quality answer.
◦Routing is mathematically grounded and provably justified (not a black-box heuristic or ML classifier).
◦It supports any model pair from providers like Anthropic, OpenAI, etc., available on Bedrock.

3Response Handling & Optimization
◦The chosen model processes the query and returns the result.
◦You receive the response as if it came from a single model (seamless compatibility).
◦It learns patterns from your traffic over time: common/deterministic queries get faster and cheaper automatically.
◦Privacy: It never sees or stores your raw data — only meters usage.

Integration Examples
•Simple HTTP call (works with any agent/framework):
import requests
•response = https://t.co/aF1tXCKEbm("https://your-infranistic-endpoint/",
• json={"query": your_prompt})
•answer = response.json()["response"]
•
•For agentic loops or fresh calls (e.g., tool use, dynamic data): Add "no_cache": true.
•Compatible with LangChain, CrewAI, Cursor, Aider, Salesforce Agentforce, etc.

Key Advantages (First Principles)
•Training-Free — Works immediately with new models, no data or retraining needed.
•Self-Improving — Gets more efficient with usage.
•Accuracy-Focused — Standard tier matches single-model accuracy at ~40-50% cost; Premium can exceed it for specialized workloads.

In essence, it turns variable-cost LLM inference into a more predictable, economical system by eliminating overpayment for overkill models on easy queries. For the latest details or setup, visit https://t.co/MxoglrRkET or their AWS Marketplace listing.

[Compustable @Compustable]

@RaoulGMI You get PhD level answers that the model was trained to deliver. There is no shortcut to PhD level intelligence - very different to reason at the PhD levels.

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AI is not an invisible force that will develop into a ‘skynet’ army that will exterminate humanity. That’s science fiction and a perception lever that Anthropic/OpenAI uses to control the narrative. AI is a designed, deterministic system that is fully defined and controllable. Training determines the LLM’s capabilities - full stop. The only innovation is happening around how the model is trained to develop operating characteristics to sharpen the probability of the outcome being the ‘best’ answer. Stop using fear as a control mechanism - no one owns AI tech, it belongs to everyone.

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@elonmusk We don't need to track intelligence per joule. AI doesn't need compute - if the minds that chose larger scale as the solution to increased abilities approached solving the efficiency problem by internal LLM system analysis.

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Great point that coding and other simple tasks will be automated away while the systems thinking will never be automated. Here’s the catch - all of us ‘Systems Thinkers’ took a development journey through the less complicated tasks that are now automated. How does someone develop Systems Thinking without a traditional development journey? This is likely the greatest threat to innovation in the age of AI.

[Compustable @Compustable]

Continuing to scale LLMs is a choice, guided by influential investors like Jensen Huang. Larger models require more compute - requires more GPUs. The LLM is not a black box unless Anthropic and OpenAI choose to make it closed. This is a vicious chain that takes a democratized, open ai vision into a maximum revenue machine. Let’s innovate the LLM and break this cycle for every user’s right to use frontier level AI in a completely private, off cloud way.

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@ZynxBTC Open source sounds cool and collaborative but if anyone can have the source code then anyone can break it.

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@brian_armstrong ...but completely not necessary with an innovative redesign of the Frontier LLM. InfraNistic LLM coming soon!

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@coinbureau Great that OpenAI has chosen to highly refine a very small segment of their model for ‘only $2,000’ answers? What do I pay for my favorite question ‘What’s the weather tomorrow?’.

[Compustable @Compustable]

@elonmusk and why not? The breaches happened not because it was AI being trained, it happened because they had vulnerable security. You are welcome!

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@DavidSacks Anthropic and OpenAI are now in the outside looking in - their customers have pivoted to an opposing view. Interesting to watch how the massive money battles this out....

[Compustable @Compustable]

It's lipstick on the same pig. The fact it solved an 80 year old math problem is irrelevant to the general use market. This is pure marketing.

The transform based models already hit a thermodynamic wall and everything happening now is msintaining the hype to justify the mismatched CAPEX data center spend.

Don't soend money on 'new' models with no direct ROI.

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@MichaelDell @elonmusk @nvidia Good move Mr. Dell. Compute suppliers like Dell and Nvidia don't care if computing closed or open models - it's paid compute either way.

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Big banks need to slow down the agenda while they prepare for crypto adoption. The longer it takes to pass, the stronger the big banks will be in it's implementation. They are not the opponents but the partners in global adoption. It's inevitable - just a matter of when it becomes law.

[Compustable @Compustable]

It's time to rename Artificial Intelligence AI to Artificial Probability AP. LLM models are a measurable, probablistic function. If you can measure it - you can control it. Enough said.... Data Science can move back to the kid's table because InfraNistic LLM is coming soon!

[Compustable @Compustable]

That’s a skewed perspective. AI is a massive configurator and should be defined and applied as such. Frontier level models are huge directories of numbers. LLMs can’t create or innovate - they only combine learned, existing numbers to create ‘new’ responses. This isn’t even in the same category as human reasoning. Human reasoning is a bargain - always has and always will be the differentiator versus created configurators like AI.

[Compustable @Compustable]

You are talking about a much more severe problem where there are 2B unbanked people in the world. Unbrokered people are a first-world problem. Let’s focus on helping the unbanked with tokenization first - this will help them grow into brokered people that can feed the crypto machine.

[Compustable @Compustable]

@BitMEX Move on…

[Compustable @Compustable]

@elonmusk ‘Grok 4.5 is not quite as good as fable’ is a wrong statement. Our standards of good are wrong. These massive Trillion parameter models are just adding more thermodynamic dead weight. It’s all about to change - InfraNistic LLM coming soon!