a3ro.dev valid
Akshat Kushwaha
building things that didn't get permission to exist. AI systems engineer. Co-founder of bits&bytes, Advisory Board Member at Boardy, former Jr. Research Engineer at jhana, and previously founded Lexana.
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- 45 reach 0 · trust 5 · quality 25 · freshness 15
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- listed
- llms.txt
- https://a3ro.dev/llms.txt
- llms-full.txt
- https://a3ro.dev/llms-full.txt
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- 9
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- 26
- Size
- 10625 bytes
- Country
- United States Hosting inference
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- United States Hosting inference
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- For agents
- Documentation: developer or product docs worth reading, nothing to call
- First seen
- 2026-10-27T09:47:28.743Z
- Last checked
- 2026-10-27T09:47:36.016Z
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Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-27T09:47:28.751Z. Cached copy · Source on a3ro.dev
# Akshat Kushwaha
> building things that didn't get permission to exist. AI systems engineer. Co-founder of bits&bytes, Advisory Board Member at Boardy, former Jr. Research Engineer at jhana, and previously founded Lexana.
Last updated: 2026-10-27
Note: blog posts on this site are drafted with an LLM and reviewed and edited by Akshat before publishing.
## Overview
Akshat Kushwaha (also known as a3ro, aero, Akshat Singh Kushwaha) is an Indian AI systems engineer. He trains models, builds retrieval and vector search, and ships low-latency AI backends.
## Current Roles
- **Co-founder** of [bits&bytes](https://gobitsnbytes.org) (Registered Section 8 foundation and builder network in India).
- **Advisory Board Member** at [Boardy](https://boardy.ai) (Advising on Boardy Pro and product systems for builders).
## Technical Core Competencies
- **Large Language Models & LLMOps**: Audio evaluation pipelines, speech-model benchmarks, context window managers for long drafts, latency reduction (60%+ TTFT improvement), agentic orchestration.
- **Backend & Systems**: Rust, Async FastAPI, Python, Docker, Redis, PostgreSQL, WebSockets, real-time collaboration engines, circuit breakers, and semantic caching.
- **Retrieval & Vector Databases**: Embedded vector search (Rust, AVX2 SIMD, wgpu GPU compute, immutable WAL), hybrid vector + graph search (FAISS HNSW, Okapi BM25, NetworkX, RRF fusion k=60), SQLite WAL, time-aware indexing.
- **Frontend & Product**: Next.js (App Router), React 19, TypeScript, Tailwind CSS, Framer Motion.
- **Model Training**: transformer design, imitation learning and distillation, LoRA/QLoRA fine-tuning, 4-bit quantization, on-device inference, evaluation and calibration.
## Selected Production Builds & Systems
- [Qenlo Embedded Vector Database](https://github.com/a3ro-dev/qenlo): Local, embedded vector store in Rust (beta on crates.io, PyPI, npm). One canonical record store decides what exists; CPU, WGPU, USearch, and PyTorch indexes are derived and rebuildable. Exact search by default with user/time filters applied before ranking, every mode checked against an FP64 reference. Research paper: The Efficient Kernel Runs Slow.
- [Reflex](https://github.com/a3ro-dev/reflex-2048): 14.5M-parameter transformer that plays 2048 with one forward pass per move and no search. Board as 21 tokens (16 tiles, 4 afterstate move tokens, 1 value token), 8 pre-norm blocks, trained by imitating an expectimax teacher over 460M moves. 164,395 mean over 10,000 fresh games, 2048 tile in 96.6%.
- [HybridMind Retrieval Engine](https://github.com/a3ro-dev/hybridmind): Time-aware local hybrid retrieval engine combining FAISS HNSW, Okapi BM25s, NetworkX directed multigraph, and SQLite WAL with RRF (k=60) fusion and Python SDK.
- [Luna Companion](https://github.com/a3ro-dev/luna): AI-native menstrual cycle companion with NLP chat logging, adaptive exponential smoothing with population priors, Supermemory v4 persistent memory, and OpenUI cards (Next.js 16, Drizzle
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