1046 posts · Page 28 of 88. ← Blog
When REST is the wrong shape for the problem, GraphQL and gRPC each fix a different pain — and each buys that fix with a new cost you have to design around.
When REST isn't the right shape: GraphQL (client-selected fields, and its N+1 / caching / complexity costs) and gRPC (Protobuf, HTTP/2, streaming, codegen) — plus a decision framework for REST vs GraphQL vs gRPC.
How queues, pub/sub, and log-based streaming let systems stay responsive under load — the delivery semantics, ordering rules, backpressure, and outbox patterns that decide whether async saves you or sinks you.
Decoupling with queues and events: message queues vs pub/sub vs log-based streaming, delivery semantics (why exactly-once delivery is a myth — do idempotent processing), ordering, backpressure, the transactional outbox, and dead-letter queues.
How an engineer should read AI regulation without a law degree — what the EU AI Act, ISO/IEC 42001, the NIST AI RMF, and sector rules actually ask for, and how each obligation maps to a control or artifact your pipeline can already produce.
Orient in AI regulation and translate it into controls (not legal advice): the EU AI Act's risk tiers and obligations, ISO/IEC 42001 as a certifiable AI management system, NIST AI RMF, and a crosswalk mapping each obligation to the artifact that satisfies it.
The defensive layer that screens what goes into a model and what comes out — input rails, output rails, topical rails, and groundedness checks — plus the real tooling ecosystem and a vendor-neutral Python pipeline that wraps a model call and knows how to refuse.
The defensive layer that screens inputs and outputs: input/output/topical/groundedness rails, the real ecosystem (Llama Guard, Granite Guardian, NeMo Guardrails, Guardrails AI, Presidio, hosted moderation), and a vendor-neutral guardrail pipeline — with honest false-positive/negative trade-offs.
Why enterprises pick watsonx for its governance story, what watsonx.governance actually gives a Python team, and how to wire monitoring, factsheets, and generative-quality metrics into an LLM feature — honestly, as the platform-heavy work it is.
Governance is why many enterprises pick watsonx: model inventory, automated factsheets, monitoring (quality, drift, bias, and generative metrics like faithfulness), explainability, and EU AI Act-aligned risk workflows — with the ibm-watsonx-gov evaluation SDK and what to log.
When to graduate from the hosted API Catalog to inference you run yourself — deploying a NIM container, reaching for Triton and its Python client, the TensorRT-LLM levers that raise throughput and cut latency, and the one base_url change that carries your Python client across unchanged.
Move from the hosted API Catalog to self-hosted inference: run a NIM container (your Python client works unchanged by swapping base_url), use Triton directly via tritonclient, and understand the TensorRT-LLM levers — quantization, in-flight batching, paged KV cache, tensor parallelism.
Taking an Amazon Bedrock Go service from a working prototype to something you can run on-call — least-privilege IAM, credentials without static keys, tuning the SDK's built-in retryer, tracking token cost, and wiring up logging and metrics with aws-sdk-go-v2.
Taking a Bedrock Go service to production: least-privilege IAM and role-based credentials, tuning the SDK's built-in retryer for throttling, token-based cost tracking, and observability via model-invocation logging, structured metrics, and request IDs.
Streaming is gRPC's most powerful feature and its most misused. Sending a stream of messages sounds simple until one side produces faster than the other can consume — then, without flow control, you get unbounded memory growth and crashes. gRPC inherits HTTP/2's flow control to prevent exactly this. Understanding backpressure is the difference between streaming that scales and streaming that falls over under load.
Streaming is gRPC's most powerful feature and its most misused. Sending a stream sounds simple until one side produces faster than the other consumes — then, without flow control, you get unbounded memory growth and crashes. gRPC inherits HTTP/2's flow control to prevent exactly this. Backpressure is the difference between streaming that scales and streaming that falls over.
Most engineers reach for cryptography wanting "make this secure," but crypto doesn't provide "secure" — it provides four specific, separable guarantees, and using the wrong one (encrypting when you needed to authenticate, hashing when you needed to encrypt) is how most real-world crypto failures happen. This series is about using cryptography correctly as an engineer who builds on top of it, not about inventing it.
Most engineers reach for cryptography wanting 'make this secure' — but crypto doesn't provide 'secure.' It provides four specific, separable guarantees: confidentiality, integrity, authenticity, and non-repudiation. Using the wrong one is how most real-world crypto failures happen.
Governance is the one phase whose ordering is non-negotiable: every major framework treats it as a lifecycle function established up front, and retrofitting it after an incident is how you end up with unexplainable models and regulatory exposure.
Governance is the one phase whose ordering is non-negotiable — established before real users, not retrofitted after an incident. Phase 1: NIST AI RMF, EU AI Act risk tiers, ISO 42001, and the artifacts to produce now.
A model is only as useful as the context and tools it can reach, and MCP is the open standard that lets any AI app plug into any tool through one interface instead of a hundred bespoke integrations.
MCP turns the M×N mess of wiring every AI app to every tool into M+N: wrap each system as a server once, make each app a client once, and any app can use any system.
Most agents are frozen the moment they ship, repeating the same mistakes forever, and self-evolving agents are the attempt to break that ceiling by letting the system improve itself as it runs.
Most agents are frozen at deployment and repeat their mistakes forever. Self-evolving agents route their own experience back into their own behavior — here are the axes of change and the loop underneath them.