▲ ASHUTOSH TRIPATHI · SOFTWARE ENGINEER

I build software people can reason about.

Developer tools and AI-native systems. I design the infrastructure that lets agents — human and automated — operate without friction. A decade of product engineering.

What I'm working on

I build systems where the contract between components is precise enough that both engineers and AI agents can reason about what's happening — typed pipelines, clear queue handoffs, error messages that tell you exactly what broke and why. The same principle applies whether you're debugging a deliverability issue or reasoning about an AI agent's state.

Smriti comes from the same instinct applied to the development layer. Every AI session starts fresh — decisions lost, context duplicated, zero continuity across teammates. Smriti captures Claude Code, Cursor, and Codex sessions locally, indexes them with hybrid search, and synthesises context on demand using a local LLM. Team knowledge syncs through git. Nothing touches the cloud.

Before that, a decade across frontend engineering, video streaming infrastructure, and developer experience. I also consult on developer tools, API design, and the systems that surround AI-assisted engineering workflows.

Work

  • Smriti ↗ Bun · TypeScript · SQLite · BM25 · node-llama-cpp · Ollama
    Shared memory for AI-powered engineering teams. Captures and indexes Claude Code, Cursor, and Codex sessions locally, then shares team knowledge through git. No cloud required.
  • Avkash ↗ v2.0 Bun · Hono · Drizzle · PostgreSQL · OpenFGA · BullMQ · TypeScript
    Open-core HR API for teams that need real control — leave, attendance, shifts, people, and fine-grained field-level access. Self-hostable.
  • tabstack-cli ↗ Bun · TypeScript · SSE · NDJSON
    A Bun/TypeScript CLI for the Tabstack AI API — built live on stream, in a single sitting. Extract any page as structured JSON, run multi-source research, drive browser automation, and pipe everything through standard Unix tools. Includes a self-installing agent skill and a local credit tracking system.

what i work on

Where I'm useful

Six areas, and the problems each one actually solves. If yours is on this list, an email gets a real answer rather than a discovery call.

Agent system design

Designing typed agent pipelines: stage contracts, queue handoffs, worker boundaries, retry and failure semantics.

Best for: A multi-step LLM pipeline that fails in ways nobody can reproduce · Moving an agent prototype onto infrastructure that can be operated · Deciding where determinism belongs and where the model belongs.

AI memory and context engineering

Retrieval, indexing, and on-demand synthesis so agents stop starting from zero every session.

Best for: Engineering teams losing decisions between AI coding sessions · Hybrid search over a corpus that keyword search alone cannot serve · Local-first context capture with no cloud dependency.

DX and API design

Naming, resource shape, error messages, and documentation — treated as product decisions rather than cleanup.

Best for: An API that is powerful but nobody can learn · Preparing a product surface for both human developers and agent callers · OpenAPI descriptions that are accurate enough to drive function calling.

Developer tooling

CLIs, SDKs, and the feedback loop around them.

Best for: A CLI that needs to be scriptable by both people and agents · Shortening the edit-run-observe loop on a slow codebase.

Frontend performance

Perceived speed, interaction responsiveness, and server-rendered output that crawlers and agents can read.

Best for: A React or Svelte app that benchmarks fine and still feels slow · A client-rendered site invisible to AI crawlers.

Video and streaming infrastructure

HLS, adaptive bitrate, player internals, and delivery for live and on-demand video.

Best for: Startup time and rebuffering problems · Player and ABR ladder decisions.

Writing

all writing →

contact

Want to work together?

Available for contracts and consulting in developer tools, AI systems, and API design.