I work on AI and data infrastructure, and I spend a lot of my time fixing bugs in the open-source tools I build on: agent frameworks, on-device ML runtimes, model servers and cloud SDKs. Every fix starts with a reproduction and ships with a regression test.
📍 Bryan, Texas · ✉️ akshayhiteshshah@gmail.com
16 merged PRs · 74 under review · 35 issues filed, across 9 open-source projects from Google, Apple MLX, AWS, NVIDIA, Tesla, Ollama and Anthropic.
| Project | PR | What it fixed |
|---|---|---|
| Google ADK | #7991 (ba142cf) | Resuming a workflow re-ran a node that was still waiting for output |
| #7729 (cbe786b) | An expired OAuth2 token with no refresh token crashed the tool | |
| #7663 (b981250) | FirestoreSessionService returned every user's sessions for an empty user id |
|
| #7665 (37b1ab3) | Failed MCP tool results reached Claude marked as successes | |
| #7499 (a6ed859) | A workflow JoinNode inside a loop fired with the previous iteration's outputs |
|
| #7479 (019387c) | Artifacts saved in agent callbacks were never recorded | |
| #7476 (f5db310) | ApigeeLlm dropped text sent alongside a function response |
|
| Apple MLX | #4663 | layer_norm and SDPA fallbacks ignored the caller's stream (C++ core) |
| MLX-VLM | #2473 | The Eagle3 speculative-decoding drafter failed without its own embeddings |
| #2486 | The Anthropic-compatible stream sent text past stop_sequences |
|
| #2476 | PaddleOCR-VL LoRA training didn't freeze the vision encoder and rejected packed patches | |
| #2492 | Multi-turn Phi-3-vision / Phi-4-MM chats with images crashed | |
| #2484 | The OpenAI-compatible server ignored max_completion_tokens |
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| #2479 | Documented video input for apply_chat_template |
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| AWS Strands Agents | #5016 | Warnings were logged for every earlier message on every request (N² log lines) |
| NVIDIA | #2476 | MCP service-account tokens had a timezone-naive expiry, so no credentials were sent |
Google ADK merges through Copybara, so those PRs show as "Closed" on GitHub. The commit links point to the merged change.
| Project | PRs | Highlights |
|---|---|---|
| AWS SageMaker Python SDK | 45 | Bugs in the v3 SDK across training, serving, pipelines and core, such as a KMS key sent as a customer encryption key, mpirun -np given the wrong rank count, and AsyncPredictor hanging instead of timing out |
| Ollama | 10 | Responses API text dropped next to tool calls, blobs not pruned after pull or copy, a race in blob-download cancellation |
| Tesla | 7 | Vehicle wake-up polling, cabin overheat protection level, Kafka client errors, statsd tags, FixedBitset construction |
| MLX-VLM | 4 | Qwen3-VL embedding position IDs, GLM-OCR image input, pooling options |
| Google ADK | 2 | Workflow resume replay, keeping finished parallel tool results on abort |
| AWS Strands Agents | 2 | Contributor setup docs |
| NVIDIA | 2 | Reproducible seeded prompts in garak, empty trailing slices in Warp |
| Anthropic Claude Agent SDK | 2 | A client disconnect race, recursive TypedDict schemas for MCP tools |
- Filed 35 issues (bug reports with reproductions, and proposals): Google ADK (15), MLX-VLM (4), NVIDIA (4), AWS Strands Agents (3), Tesla (3), Ollama (3), Anthropic Claude Agent SDK (2), MCP Python SDK (1).
- Reproduced and triaged auth bugs in the MCP Python SDK, and reviewed a maintainer PR there.
| Project | What it is |
|---|---|
| leash · live demo · video | A trust layer between AI shopping agents and PayPal: rules the agent can't override, an AI check that can only tighten, owner approvals and an audit ledger (TypeScript) |
| local-llm-eval | An evaluation harness and fallback gateway for choosing locally hosted LLMs (Qwen, Llama, Mistral on Ollama) for RAG and tool calling |
| meridian | A real-time AI backend on Kafka, FastAPI and pgvector, with a ReAct agent loop, multi-layer memory and multi-model routing |
| sentinel | FastAPI services that sit in front of any LLM: PII detection, prompt-injection blocking, async evals and CUSUM drift detection |
| forge | A multi-agent incident responder that turns a production alert into a structured postmortem |
| realtime-feature-platform | A feature platform that prevents training-serving skew: a shared compute path, point-in-time joins and PSI skew detection |
| streaming-lakehouse-optimizer | Postgres → Debezium → Kafka → Flink → Iceberg, with a learned layout optimizer |
| incremental-vector-pipeline | CDC-driven re-embedding of only the chunks that changed, with a versioned store and instant model rollback |
Updated automatically from GitHub on November 8, 2026.