Field notes on
building AI in production.
The permanent library — long-form engineering and architecture writing on enterprise AI, MCP & agentic operations, platform engineering, and cloud modernization. LinkedIn is where the discussion happens; this is where the thinking lives.
Inside the Forward Pass — What Actually Happens When an LLM Answers You
The forward pass taken apart stage by stage on a real quantized model — five animated stages, every number verified against the library's own kernels.
Tokenize → embed → attend → vote → loop, each stage re-done with raw matrix ops on Qwen3-0.6B under MLX and checked bit-for-bit: the recomputed attention matched the library kernel with max diff 0.0, the full 28-block pass reproduced model(x) exactly, and the KV cache ran 3× faster than the naive loop — plus the honest footnote where bf16 rounding flipped a greedy argmax. Runs on any Apple Silicon Mac with a 350 MB model.
Read Internals #1 →Agents Fail at the Seams — Field Notes from MCP Dev Summit Mumbai 2026
Two days, 16 sessions, one through-line: multi-agent AI fails between agents, tools, and protocols — and every failure is a missing contract.
A practitioner's synthesis of the summit: the RAM/disk/syscall mental model, the six spines, and a failures→contracts table where every fix is discipline, not a smarter model — security as the floor (confused-deputy authorization), durable context as the ceiling (the empty “event clock”), the tool surface as the product (TDQS), and the unowned MCP↔A2A seam. Plus the gaps worth building and verified ecosystem notes.
Read the field notes →MCP Access to a Database — as the User, Not a Service Account
Principal propagation for AI agents — carry the user's identity to the data source, not a shared service account.
A working demo: an AI agent reaches a Postgres banking database through MCP as the end user — role-based PII masking, per-tool RBAC, and audit attributed to the real human — plus a Postgres-18 path where the database engine itself denies unauthorized columns via SASL OAUTHBEARER. Honest throughout about what's app-layer versus engine-enforced.
Read the case study →How Far Can One Mac Go? Local LLM Limits on an M5 Max
Six models, 3B → 123B, on 128GB of unified memory — measured, charted, reproducible.
A live dashboard of the full ladder through one MLX benchmark harness on an M5 Max: the scaling curve, the memory ceiling that wasn't where Apple said (123B ran in 69GB; the probe hit 128GB with no refusal), and the 14-inch thermal tax — decode holds, prefill pays. Every number reproducible from the open-source repo.
Open the dashboard →SAP NorthStar AI Architecture: Context Moat, HANA's Elevation, and the Gatekeeping Problem
A practitioner's page-by-page read — what it gets right, and which doors stay gatekeeped.
Why the context thesis is the most honest thing an ERP vendor has said about AI, why HANA's move from in-memory database to context engine matters, and where the open-by-design rhetoric collides with data toll roads, ECC reality, and CI/CD.
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More in progress — MCP framework patterns, AIOps agent design, and cloud-modernization field notes. Follow on LinkedIn to catch them first.