Sarthak Agrawal
Sarthak Agrawal is an AI infrastructure and product engineer who builds dependable AI products, backend systems, developer tools, and research software. His public work emphasizes local-first, inspectable systems, execution evidence, and making infrastructure reliable, measurable, and affordable to run.
At a Glance
- Software engineers and teams using coding agents
- Developers who need private, local, inspectable code verification
- AI/ML engineers and researchers working on small or specialist language models
- Developers learning transformer implementation and GPU training
- +1 more
AI Tools by Sarthak Agrawal
(1)
CodeVetter
Local Verification for Coding Agents
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Latest News
PostTrainLLM published fresh paired evidence for a Qwen3-4B ReST-fused routed specialist: file-operations depth improved from 9/12 to 12/12, with breadth regression disclosed and routed-only use retained.
CodeVetter published its execution-backed verification product site and public benchmark/evidence workflow for coding-agent changes.
CodeVetter documented its native macOS app, Rust verification core, CLI, local MCP server, portable evidence bundles, and local-first architecture.
Published 'How I run 23 products on Cloudflare without Argo,' describing a performance program using static Astro overlays, self-hosted fonts, caching, and p75 measurement; five sites reached under 500 ms desktop LCP p75 in the first push.
Products & Services
Local-first execution-backed verification and evaluation for coding-agent changes. It binds a requested task to an exact code revision, runs repository-owned tests, builds, browser/API checks or other executable checks, preserves evidence and limitations, and emits pass, fail, or unverified verdicts. It is available as a native macOS app, CLI, and local MCP server.
A single-developer, Mac-first research lab and local LLM factory for training, post-training, distillation, evaluation, packaging, serving, and interpretability. It also includes a browser playground with hand-written WebGPU kernels and public evidence artifacts.
A public directory and shared workshop for working experiments, launch destinations, reference projects, and reusable tooling across Sarthak's product fleet.
A project-aware GitHub repository discovery and tool-intelligence experiment that helps users find similar projects and inspect the evidence behind tool suggestions.
Market Position
CodeVetter positions itself against hosted AI code reviewers such as CodeRabbit and Greptile, merge-gate tools such as Verdict, PatchDrill, and ProofGate, and LLM-as-judge review. Its stated differentiation is that repository-owned execution evidence—not a second model's opinion, diff reading alone, or a confidence score—sets the verdict boundary, while code and evidence remain local. PostTrainLLM positions itself as a bounded, evidence-first, one-Mac specialist-model lab rather than a frontier-scale or general-assistant product.
Leadership
Executive Team
Sarthak Agrawal
Independent creator; AI Infrastructure & Product Engineer
Software engineer based in India. He holds a B.Tech in Computer Science and Engineering from Manipal Institute of Technology, worked at Front.Page (YC S'21) from 2022 to 2025, and has worked at Vault Wealth since February 2025. His independent projects include CodeVetter, PostTrainLLM, SaaS Maker, and other open-source experiments.
Founding Story
Sarthak's public work grew from a personal interest in the hard, invisible parts of production systems—timeouts, retries, backpressure, data pipelines, and reliable execution—and from a desire to understand AI systems below the API level. He publishes and builds in public, including a from-scratch transformer/browser lab and local-first developer tools that make AI-generated software and model behavior more inspectable.
Business Model
Revenue Model
The public materials describe an independent, open-source and research-oriented project fleet rather than a disclosed commercial revenue model. CodeVetter is distributed as local software and PostTrainLLM is a local research lab; no subscription, API-usage, or license-fee model is stated.
Target Markets
- Software engineers and teams using coding agents
- Developers who need private, local, inspectable code verification
- AI/ML engineers and researchers working on small or specialist language models
- Developers learning transformer implementation and GPU training
- Builders operating independent product fleets and Cloudflare-hosted sites
- Verifying coding-agent-generated changes before merge or handoff
- Finding regressions and bugs with repository-owned tests and runtime evidence
- Local debugging, replay, code review, synthetic-user QA, and evidence-backed codebase analysis
- Training and evaluating specialist language models on one Apple Silicon Mac
- Running private local model inference and tool-calling workflows
- Learning transformer internals through an inspectable browser implementation