I believe the intersection of AI × Crypto is where the hardest, most meaningful problems live.

I build because I want devs and small teams to own their models, their data, and their infra — not rent them behind black-box APIs.
I care about making AI local, auditable, and fast — and crypto the backbone of trust and verifiability.

So I’m shipping:

• Yudai v3 — a programmable codex that turns product context into testable GitHub issues and PRs — crypto-native and local-first.
• solo-server — run open LLMs like Qwen and DeepSeek on your own machine, no middlemen.
• DeepSeek-R1 Distillation — proving reasoning doesn’t need massive clouds — just smart research + open hardware.

I’m here to help builders stay independent — and to push AI and crypto to serve people, not gatekeepers.

Practical AI for US/EU/Canada SMBs: deploy a private copilot, a do-the-task agent, or a tuned model — usually in weeks.

• AI Deployment (RAG & Small Models)
  Private, brand-tuned assistants grounded in your docs, tickets, and data. Options: local/VPC or managed.
• Agentic Workflows
  Bounded agents that actually finish work (refunds within policy, scheduling, ticket triage, lead outreach) with audit logs + guardrails.
• Fine-Tuning & Evaluation
  Domain-tuned SLMs/adapters that beat generic LLMs on your tasks (lower latency, lower cost). Clear win-loss vs baseline.
• Classical ML & Data Science
  Forecasting, lead scoring, segmentation, anomaly detection — notebooks → services with monitoring.

Operations: document AI for invoices/POs/contracts, inventory signals, back-office runner.
Sales & Marketing: lead-gen copilot, brand-tuned content + ad draft + A/B loop, site FAQ/RAG that converts.
Customer Service: Tier-0 deflection bot, after-hours agent (status/refunds), voice inbox summarization.
Software & IT: repo-aware dev copilot, issue-to-PR pipeline, internal code/docs search with pgvector.

1. Discovery (free 30 min): pick 1–2 high-ROI workflows.
2. Design Sprint (1 week): model/tool choices, evals, guardrails.
3. Pilot (2–4 weeks): dockerized delivery, metrics, “go/no-go”.
4. Scale: monitoring, retraining cadence, playbook handover.
   _{Promise: If we don’t see a credible ROI signal by week 2, you pause — no hard feelings.}

• Llama Impact Grant Winner — recognized for pushing open-source AI tooling (announcement)
• solo-server OSS Maintainer — powers 300+ indie dev deployments for local LLMs
• Yudai v3 — cloud-native + local codex chaining PM → Architect → Coder agents to ship test-first PRs
• Kernel KB8 Founder & Community Mentor — Gitcoin’s top 50 global founder cohort driving AI × Web3 innovation
• Web3 Infra Contributor — protocol tools for Mode, FortyTwo Money, EigenLayer, MegaETH testnet
• Finalist, MEGAZU Pop-up City — prototyping cutting-edge Web3 infra
• National-Level Hackathon Mentor — 50+ teams; winners at Smart India Hackathon & Prayatna 2.0 (AITR)
• Petabyte-Scale ETL @ CoinSwitch — Spark & Airflow for ML + risk pipelines
• Vgyaan (pre-GPT) — BERT-powered edtech that resolved 120k+ student questions/night
• I ship → learn → repeat 👷‍♂️ → 🚀

Core papers & concepts shaping Yudai v3 and my agentic stack:

• DeepSeek-R1 — RL + “bag-of-narratives” distillation for efficient reasoning
• Reinforcement Learning with Verifiable Rewards — provable reward guarantees for reasoning agents
• Absolute Zero Reasoner — pushing zero-shot/zero-reasoning capability in LMs
• SWE-smith & debug-gym — scaling bug synthesis + tool-augmented SWE agents
• LLM → SLM Agent Conversion — turning generalist LLMs into specialist SLMs for codex workflows
• DSPy — compositional orchestration for multi-agent pipelines
• NoFeeSwap Yellow Paper — zero-spread AMMs & liquidity design (I’m prototyping this in Solidity)

• Yudai v3 — invite-only pilot cohort rolling out now
• solo-server upgrades — smaller, faster, edge-ready models
• New Codex Agents — task-specialized SLMs, verifiable rewards, test-first PR workflows