STUDIO / SOLO 12.97°N · 77.59°E
OPEN FOR WORK EST. 2025

Software studio · Bala / Shakthi B

I design and ship software that thinks for itself.

build with bala is a one-person studio shipping AI-grounded products — logistics intelligence, on-device computer vision, RAG tooling and evaluation research. Real systems, end to end: architecture, backend, models and interface.

6
shipped builds
0→1
idea to product
AI-native
grounded, not hyped

I’m Bala — I build the kind of software I wished existed, then sharpen it until it ships.

build with bala is my studio. It’s where ideas become working systems: a logistics cockpit that reasons about disruption in real time, an Android coach that reads your form entirely on-device, a service that turns saved reels into a searchable second brain, a benchmark that measures whether models know their own limits.

I work across the whole stack — Django and FastAPI on the backend, Kotlin and Compose on mobile, React and Svelte on the front, and a deliberate, cost-aware approach to anything touching an LLM. The thread through all of it: software that is intelligent without being reckless.

  • 01

    Grounded over hyped

    AI is a tool, not a tagline. Every model call earns its place with budgets, fallbacks and honest evaluation — including knowing when a system should say "I don't know."

  • 02

    End to end, solo

    Architecture, backend, models and interface from one head. No hand-offs lost in translation — the person who designs it is the person who ships it.

  • 03

    Built to run offline

    On-device inference, local-first data, graceful degradation. Software that keeps working when the network and the cloud do not.

Six builds, one through-line:
intelligence that ships.

From real-time logistics to on-device vision and benchmark research — each project is a complete system, designed and built end to end.

01 In development

SupplyLens

2026

Logistics intelligence cockpit

An AI-powered platform that folds real-time fleet tracking, warehouse ops, freight-market intelligence and disruption detection into a single situational-awareness cockpit. A modular Django core streams live data through Redis pub/sub and SSE into a React frontend, with a Gemini-backed AI gateway synthesising narratives and alerts under strict budget controls.

  • Module-agnostic real-time pipeline over Redis pub/sub + Django Channels SSE
  • Pluggable app modules wired through a central bus — no cross-module imports
  • Cost-aware AI gateway with structured Gemini output and budget gating
  • Multi-tenant isolation and HMAC-signed webhooks from day one
  • Django 5
  • React 18
  • Celery
  • Redis
  • PostgreSQL
  • Gemini
Architecture, backend, AI gateway Logistics / B2B SaaS
02 Shipping

ExerciseChecker

2026

On-device form coach, gamified

A fully offline Android app that reads your exercise form in real time using on-device ML Kit pose detection — no internet permission, no cloud uploads. Per-exercise state machines count reps and flag form across squats, push-ups, lunges, planks and jumping jacks, all wrapped in a Solo-Leveling-style "System" of XP ranks, daily quests and achievements.

  • On-device pose detection with zero network permission
  • Debounced form rules + rep-counting state machines per exercise
  • XP ranks (E→S), quests, achievements and a muscle-balance radar
  • Schema-versioned JSON/CSV backups with no storage permission
  • Kotlin
  • Jetpack Compose
  • ML Kit
  • CameraX
  • Hilt
  • Room
Full build — Kotlin / Compose Mobile / Computer vision
03 Working build

Insta Reels → Obsidian

2025

Reels into a searchable second brain

A FastAPI service that captures Instagram reels shared to a burner account and turns them into a searchable Obsidian vault. It fetches metadata, auto-tags with an LLM, optionally transcribes audio, and embeds everything into a local vector store — then answers natural-language questions across your entire reel library. Every stage is provider-swappable and degrades gracefully when a model is offline.

  • Hands-off reel ingestion via private-API polling
  • Composable RAG: transcription, vision, embedding all provider-swappable
  • LLM auto-tagging into Obsidian markdown with YAML frontmatter
  • Graceful degradation and automatic retry for failed fetches

⚑ Uses a burner account; Instagram ToS risk is acknowledged in the project.

  • FastAPI
  • SQLite
  • yt-dlp
  • Whisper
  • Ollama
  • Chrome MV3
Full build — Python / RAG Knowledge tooling / RAG
04 Working build

SlideAlchemy

2026

Raw resources to finished decks

A desktop app that ingests PDFs, DOCX, slides, images, YouTube transcripts and web pages, then generates polished presentation decks through Google NotebookLM. A twelve-question interview refines audience and visual style; a seven-section prompt engine drives generation; and a feedback loop banks high-rated prompt fragments so future decks get sharper over time.

  • Multi-format ingestion with automatic research gap-filling
  • Interactive 12-question interview with learned defaults
  • Seven-section prompt engine with visual directives and quality rules
  • Reinforcement loop seeds a success-pattern DB from rated decks
  • Svelte 5
  • Tauri 2
  • Rust
  • FastAPI
  • SQLite
  • NotebookLM
Full build — Tauri / Svelte / FastAPI Productivity / GenAI
05 Phase 1 — QA

ShutterDock

2026

Your phone as a camera FTP dock

An Android app that turns your phone into an FTP server for a Canon EOS R6 (or any FTP-capable camera). Shoot over Wi-Fi, USB-C or a hotspot and watch frames land live in a gallery grid. Swipe to star, reject or skip, then auto-upload keepers to Google Drive, Dropbox or Lightroom — all driven by an embedded Apache MINA server and a WorkManager upload queue.

  • Embedded MINA FTP server in a session-aware foreground service
  • Live gallery grid with swipe review (star / reject / skip)
  • Multi-cloud auto-upload: Drive, Dropbox, Lightroom
  • Wi-Fi, LocalOnlyHotspot and USB-RNDIS network detection
  • Kotlin 2.0
  • Jetpack Compose
  • Apache MINA
  • WorkManager
  • Room
Full build — Kotlin / Compose Mobile / Photography
06 Open source

GlassBench

2026

Benchmarking when models should say "I don't know"

An open, frozen-design benchmark for LLM memory systems that measures calibration, not just accuracy. Where other benchmarks ask "did it retrieve the right fact?", GlassBench asks "when it is wrong, does it admit it?" — introducing the Confidently-Wrong Rate and a composite Glass Score across answerable, stale, contradiction and false-premise splits drawn from real multi-session conversations.

  • Frozen pre-registration committed before any system is scored
  • Confidently-Wrong Rate isolates the failures that hurt in production
  • Four task splits across 96 real multi-session conversations
  • Deterministic open scorer that defeats degenerate strategies
  • Python
  • NumPy
  • pytest
  • HF Datasets
  • LongMemEval
Research, design, scorer AI research / Evaluation

⚑ Project copy is drawn from each repository’s own documentation. Links, live demos and screenshots are intentionally omitted pending public release.

Have something that
should exist?

I take on a small number of builds at a time — products, prototypes and the occasional thorny research problem. If it’s ambitious and AI-shaped, it’s probably a fit.

Write to the studio →