$ whoami --deep
What Pulls Me
Not a résumé — that's the uptime --career line. This is the map of what I'm drawn to.
ishan@prod:~$ cat ~/curiosity
# retrieval
Tree-structured & agentic RAG (PageIndex) — moving past vector-only. Reads more like how you'd actually search a codebase or a textbook. In practice: FTS5 and local embeddings fused by Reciprocal Rank Fusion, because lexical and semantic recall fail on different queries.
# agent-tooling
Running agentic coding tools (Conductor, Superset) against real PRs to see which loop converges — Cursor stays the daily driver. The 'agent reads a repo and writes its scaffolding' idea started early.
# the-model-layer
Karpathy's nanochat lineage — single-GPU, a reference for what 'tiny but real' looks like down at the model layer.
# agent-boxes
Hermes & OpenClaw on a Hostinger VPS, kept alive to watch where production-grade loops degrade: tool-loop convergence, memory bleed, behaviour under sustained load.
ishan@prod:~$ ls -t ~/obsessions
# agent-memory
Procedural memory (AST-indexed) vs episodic (vector-backed) — the two halves coding agents need. Retrieval precision beats recall when a human gates the library; and a memory without provenance is a rumour, so every episode gets stamped with who, where and when.
# harness-engineering
Wiring evals & guardrails into the LLM workflow itself, not bolted on after. Open question: pre-tool, post-tool, or a separate critic agent? The part I keep landing on: the boundary that stops a plausible skill from activating itself.
# multi-agent-orchestration
Agent boundaries that follow output artifacts, not 'roles' — most of the work is contracts between agents and retry semantics.
# production-under-limits
The agentic frontier has to sit on real free-tier limits — I'm drawn to designs where the limit becomes an architecture input, not a wall.
ishan@prod:~$ git log --oneline ~/convictions
8007e90 rate-limits-are-architecture
Rate limits are an architecture input, not an ops nuisance — the cheapest request is the one you never make. Reduce calls; don't just pace them.
9db45f8 concurrency-is-layered
Three overlapping concurrency controls aren't paranoia — each fails differently (lock TTL expiry vs txn serialization vs lost update). Audit trails are cheap early, impossible to retrofit honestly.
1320d5d agents-follow-artifacts
Agent boundaries should follow output artifacts (report sections), not 'roles'. BYOK shapes architecture — no server-side key custody simplifies everything.
782e90f constraint-is-style
No build step forces the discipline frameworks let you skip — separation of content from layout, by hand.
$ cat ~/certifications
Certifications
- Google · Cloud Skills Boost ─ badge #24918574
Google · Cloud Skills Boost · apr 2026
Gen AI Agents: Transform Your Organization
Building gen AI agents — models, reasoning loops and tools — and leading organizational transformation with gen AI. The capstone of the Gen AI Leader learning path.
- agent components
- reasoning loops
- Google Cloud gen-AI
- measuring impact
- adoption
- Google · Cloud Skills Boost ─ badge #23424455
Google · Cloud Skills Boost · apr 2026
Orchestrate Complex Multi-Agent Workflows
Orchestrating multi-agent workflows: unifying data across first- and third-party sources, automating actions across systems, and balancing machine speed with human oversight.
- multi-agent orchestration
- agentic assistants
- human-in-the-loop
- cross-system automation
- Databricks Academy ─ accreditation
Databricks Academy · apr 2026
AI Agent Fundamentals
Foundations of AI agents on Databricks — Mosaic AI and Agent Bricks; agentic workflows and multi-agent systems; building, deploying and evaluating enterprise-ready agents.
- Mosaic AI
- Agent Bricks
- agentic workflows
- multi-agent systems
- enterprise agents