
Crypto-focused AI infra and tooling Built by @alt_layer.
The LLM Land
Joined March 2023
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AltLLM can bring tokenomics, price, market cap and volume into the analysis.
The result does not predict selling. It shows the size of the unlock, the assumptions behind each scenario and what the market may need to absorb.
Build with AltLLM → altllm.ai
Route the request before generating the final answer.
AltLLM provides crypto data tools and access to multiple models through one OpenAI-compatible API.
The application decides which model to call, what structure to return and which requests need additional checks or approval.
altllm.ai
AltLLM brings current prices, crypto news, gas data, project research and tokenomics into the same conversation.
Start with the headline. Check what is confirmed. Compare the arguments. Understand what could matter next.
altllm.ai
Friday prompt:
What was loud on Crypto X this week, and what actually changed?
For each narrative, separate:
• the headline
• the supporting data
• what changed in practice
• what is still speculation
altllm.ai
AltLLM includes an impermanent-loss calculator alongside crypto prices and project data.
Builders can turn those results into a structured position brief before the user decides what to do.
altllm.ai
What’s worth asking AltLLM this week? 👀
→ Why @ethereum researchers are debating whether AI could threaten wallet cryptography
→ What @SuiNetwork and @alibaba_cloud’s per-call stablecoin plan could mean for AI agents
→ Why EIP-8363 was pulled from Ethereum’s Hegota upgrade
→ What ended $XRP ETFs’ 13-week inflow streak as Bitcoin and Ether ETFs also saw outflows
AltLLM brings current prices, crypto news, gas data, project research and tokenomics into the same conversation.
Catch up on what happened. Compare the arguments. Understand what could matter next.
altllm.ai
Friday builder idea: unit tests for tokenomics.
Turn every claim in the docs into a check:
• Do allocations add up to 100%?
• Do vesting dates match the unlock schedule?
• Do fee splits reconcile?
• Do supply numbers agree across sources?
Run the tests again whenever the data changes.
Tokenomics should fail loudly, like code.








