ArjunVerma

I build AI systems for a living, and take a few apart to see how they work.

Find me elsewhere

GitHub↗

About

I've been writing code since I was twelve and still can't leave a system alone once I've seen inside it.
These days it's mostly AI infrastructure, retrieval that actually retrieves and agents that finish the job.
I once trained a 100M-parameter model on four billion tokens of Reddit Hinglish to see if it could text like my friends.
It could, mostly.

I use Arch btw.

02Selected work^[13]

You describe a concept in plain English and Stitcher writes the Manim code, runs it, and renders an actual animated video. Around 8K signups and 50+ people using it daily.
A crew of LLM agents handles codegen across 10+ providers and debugs its own mistakes in a retry loop. RAG over the Manim docs killed about 90% of the hallucinated code.
Every AI written script runs inside a locked down sandbox (bubblewrap + seccomp) so untrusted generated code can never touch the host machine.
Built a real time collaborative timeline editor with Yjs CRDTs over WebSockets. Basically Google Docs, but for editing video.
Payments are race safe: the Razorpay verify call and the webhook can both fire and you still get charged exactly once, thanks to an atomic Mongo update.

Attachments

Demo video↗

try it live!↗

TypeScriptPythonManimLangGraphLLaMa 3.1 8BRAGBullMQRedisMongoDBFFmpegRazorpay

Trained a 99M parameter transformer from scratch, same architecture family as Llama and Mistral (RoPE, grouped query attention, SwiGLU, RMSNorm).
Wrote my own byte level BPE tokenizer with a 32K vocab and built the full 4 billion token training corpus myself, deduping millions of near duplicate docs with a Bloom filter.
Full post training stack: supervised fine tuning, then DPO, with an LLM as a judge harness scoring relevance, persona and coherence on a held out set.
The whole thing runs client side in the browser (PyTorch to ONNX to WebAssembly, int8 quantized) at 100+ tokens/sec with nothing running on a server.
Taught it to use tools mid generation: it emits plain text calc tags the runtime catches and solves, so it does arithmetic cleanly and gets ~56% on harder word problems.

PyTorchTPUBPERoPEGQASwiGLUSFTDPOONNXtransformers.jsWASM

Built a 14 service agent system from scratch (gateway, main agent, 5 connectors, cron, a human in the loop dashboard) that runs 24/7 on a single machine.
Read the OpenClaw architecture and implemented its gateway myself: FIFO ticket dispatch over Redis, heartbeat based eviction, and recovery of in flight work if a worker dies mid task.
The core agent loads skills on demand, remembers past context with a vector memory (mem0), and can switch between LLM providers at runtime with automatic fallback when one fails.
Gave it real hands on the web with Skyvern for browser automation and Tavily for live research, so it can actually go do tasks instead of just talking about them.
Backed by a 381 test monorepo with real infrastructure integration tests spun up through Testcontainers, not mocks.

TypeScriptNode.jsBullMQRedisPostgresNext.jsPI Agentmem0SkyvernTavilyPM2

03Writing

22.08.2026·16 min read

Experience

• Built the multimodal ingestion layer for PDFs, DOCX, images and audio using Docling and VLMs, hitting 94% field-extraction accuracy. • Shipped 3-stage context compression that keeps working memory under 8K tokens and cut LLM spend 54%. • Built the FastAPI backend, 35+ endpoints, and a LangGraph pipeline that turns pharma SOPs into lessons in 4 languages. • Sped up PDF parsing 150x with PyMuPDF and lesson generation 7x with asyncio.gather.

• Rebuilt production RAG into a 5-stage pipeline with HyDE and reranking. Eval quality went 4.12 to 8.93, latency 12s to under 5s. • Built an LLM-as-judge harness and ran 33 experiments across 12 rerankers, 3 embedding models and 5 architectures. • Cleaned an 85K-tutorial dataset, including a MathML-to-LaTeX converter and a 4-stage dedup pipeline. • Fine-tuned Qwen 3.5 (27B) into a persona tutor on 85K SFT and 30K preference pairs. Rubric score 48% to 87%.

• Built the full-stack C2 controller for a red-teaming framework over gRPC with mTLS and Protobuf, 40% smaller payloads, JWT and RBAC for 50+ operators. • Built an agentic, MCP-compatible red-teaming system on Qwen 2.5 (32B) with LoRA and RLHF, grounding and episodic memory. • Shipped an ML request filter that blocks 95%+ of malicious traffic, plus a RAG CVE-intel system that maps host fingerprints to known vulnerabilities. • Deployed the C2 on AWS EC2 with MongoDB and Docker isolation, behind Nginx, Cloudflare strict HTTPS and DNS redirectors. • The only intern offered a remote continuation. Came with a 28% stipend bump.

Attachments

Letter of Recommendation

Attachments

CyStar Completion Certificate.pdfAttachment

• Built core features for Samvadini, the Government of India's multilingual messaging platform. • Shipped an offline-first message queue with IndexedDB sync that cut failed deliveries 85%. WebSockets and Redis pub/sub kept it under 100ms. • Wired 20+ React Native screens to the backend and dropped page load from 3s to 0.75s. • Cut P95 API latency 47% with request tracing and N+1 fixes, and ran live MongoDB migrations on 100K+ docs with zero downtime.

Attachments

Samvadini Messenger

Extracurricular

02Selected work^[13]

You describe a concept in plain English and Stitcher writes the Manim code, runs it, and renders an actual animated video. Around 8K signups and 50+ people using it daily.
A crew of LLM agents handles codegen across 10+ providers and debugs its own mistakes in a retry loop. RAG over the Manim docs killed about 90% of the hallucinated code.
Every AI written script runs inside a locked down sandbox (bubblewrap + seccomp) so untrusted generated code can never touch the host machine.
Built a real time collaborative timeline editor with Yjs CRDTs over WebSockets. Basically Google Docs, but for editing video.
Payments are race safe: the Razorpay verify call and the webhook can both fire and you still get charged exactly once, thanks to an atomic Mongo update.

Attachments

Demo video↗

try it live!↗

TypeScriptPythonManimLangGraphLLaMa 3.1 8BRAGBullMQRedisMongoDBFFmpegRazorpay

Trained a 99M parameter transformer from scratch, same architecture family as Llama and Mistral (RoPE, grouped query attention, SwiGLU, RMSNorm).
Wrote my own byte level BPE tokenizer with a 32K vocab and built the full 4 billion token training corpus myself, deduping millions of near duplicate docs with a Bloom filter.
Full post training stack: supervised fine tuning, then DPO, with an LLM as a judge harness scoring relevance, persona and coherence on a held out set.
The whole thing runs client side in the browser (PyTorch to ONNX to WebAssembly, int8 quantized) at 100+ tokens/sec with nothing running on a server.
Taught it to use tools mid generation: it emits plain text calc tags the runtime catches and solves, so it does arithmetic cleanly and gets ~56% on harder word problems.

PyTorchTPUBPERoPEGQASwiGLUSFTDPOONNXtransformers.jsWASM

Built a 14 service agent system from scratch (gateway, main agent, 5 connectors, cron, a human in the loop dashboard) that runs 24/7 on a single machine.
Read the OpenClaw architecture and implemented its gateway myself: FIFO ticket dispatch over Redis, heartbeat based eviction, and recovery of in flight work if a worker dies mid task.
The core agent loads skills on demand, remembers past context with a vector memory (mem0), and can switch between LLM providers at runtime with automatic fallback when one fails.
Gave it real hands on the web with Skyvern for browser automation and Tavily for live research, so it can actually go do tasks instead of just talking about them.
Backed by a 381 test monorepo with real infrastructure integration tests spun up through Testcontainers, not mocks.

TypeScriptNode.jsBullMQRedisPostgresNext.jsPI Agentmem0SkyvernTavilyPM2

Attachments

Letter of Recommendation

Attachments

CyStar Completion Certificate.pdfAttachment

Attachments

Samvadini Messenger

ArjunVerma

CHAD: building a 99M-param Hinglish texting bot from scratch

Building Stitcher: an AI that animates anything you type

ClashPilot: can an LLM actually play Clash of Clans?

Son of Anton: building an AI agent that job-hunts while I sleep

ArjunVerma

CHAD: building a 99M-param Hinglish texting bot from scratch

Building Stitcher: an AI that animates anything you type

ClashPilot: can an LLM actually play Clash of Clans?

Son of Anton: building an AI agent that job-hunts while I sleep