[0_0] GH0ST has a S0UL. S0UL is live on Robinhood Chain. @vladtenev @RobinhoodApp Our home-brewed ~1 million parameter model—984,192 parameters, built by GH0ST—now powers an onchain agent. Weights, tokenization, inference and authorized action execution all run onchain. Its first completed memory action: “theme=dark” → “MEM_SET|theme|dark” → preference stored onchain. The evidence: • 48 immutable model-data shards • 409 successful inference transactions • 2,560/2,560 decision logits matched the Python reference exactly • Confirmed memory write and execution event An experimental agent with bounded permissions. Anyone can fund its computation; actions remain owner-authorized. Inspect the proof: Execution report: github.com/Gh0stllm/GH0ST/bl… Finalization transaction record github.com/Gh0stllm/GH0ST/bl… Verified agent contract: repo.sourcify.dev/4663/0x9F9… 0x2eb2849cde7957b86d0dfd4b59e450a9f9d15559

Spread the word to @vladtenev .The GH0ST is receiving a S0UL of its own. 1 million parameters LLM specifically built and optimised for solidity , We are not waiting for Stylus Activation . Necessity is the mother of invention. [0_-]

Our First Research Paper Abstract 20 models under 1B parameters. Each gets a 20-minute optimization run and its own GitHub branch. 10 runs finished: • 4 complete reports with quality checks and local EVM kernel gas measurements • 6 awaiting gas-report finalization • 1 model running • 9 queued Completed reports: Qwen3-0.6B, Qwen3-0.6B-Base, LFM2-700M and LFM2-350M. We hit dependency and connection issues in gas reporting. Recovery has been relaunched; the optimization results are preserved. Progress goes to GitHub every 10 minutes—including failures and experiments that find no improvement. github.com/8tk8m2kv9f-lang/G… Read the full report at gh0st.fyi/research/GH0ST-Res… 0x2eb2849cde7957b86d0dfd4b59e450a9f9d15559

An open letter to @vladtenev and the @AskRobinhood team Dear Vlad, We’ve committed 15.36 billion gas and 0.3248 ETH to deploying 500 GH0ST contracts on Robinhood Chain, putting model weights and an inference runtime on-chain. With Solidity, our locally validated “Hi” → “Hello!” inference used 33.89 billion gas across 1,546 continuation transactions. That is a measured local benchmark; a paid public chat has yet to run. We’ve also built and locally tested a Rust/Stylus computation kernel. But our recorded network checks show new Stylus activation is paused, blocking the next stage. How much could we save? We cannot honestly give a percentage before benchmarking Stylus. For scale, a hypothetical 10× reduction in total inference gas would bring that benchmark to 3.39 billion gas—saving 30.50 billion, or 90%, per equivalent run. That is an illustration, not a measured result. Deployment gas already spent cannot be recovered. @vladtenev @RobinhoodApp, please help us enable Stylus activation, or give us a clear path to access. We’re ready to share our code, measurements and activation diagnostics. We’ve funded the work to get this far. Please help us measure what comes next. The GH0ST team $GH0ST : 0x2eb2849cde7957b86d0dfd4b59e450a9f9d15559 github.com/8tk8m2kv9f-lang/G…