Insights & Updates
Explore insights from our engineering team on software development, AI, cloud architecture, and industry best practices.
129 articles · page 4 of 11
Backend & APIs
You usually do not need a dedicated vector database. A practical pgvector schema covering chunk modelling, tenant filtering before search, index choice, and the re-embedding problem nobody plans for.
Cloud & DevOps
DORA metrics are five measures of software delivery: change lead time, deployment frequency, failed deployment recovery time, change fail rate and rework rate.
AI & Machine Learning
Long context windows and better tool use took work away from retrieval-augmented generation. RAG did not become obsolete — its job got narrower. A decision framework for when to retrieve, when to load, and when to call a tool.
Engineering
Acceptance criteria define the conditions under which work is considered done. Written well they end scope arguments before they start; written badly they are a second, vaguer copy of the ticket title.
AI & Machine Learning
An embedding is a list of numbers representing meaning, so that similar things sit close together. What they are, how similarity search uses them, and the practical decisions — dimensions, chunking, distance metric — that determine whether retrieval works.
AI & Machine Learning
Transcription became accurate and cheap, which made it a commodity. The value moved to what happens after the transcript - and to knowing which problems speech is genuinely the right interface for.
AI & Machine Learning
Prompt wording stopped being the bottleneck around 2025. The harder problem — deciding what lands in the context window at every turn — got a name. What context engineering is, and the four things it covers.
Engineering
Faster authoring shifts the bottleneck to review, comprehension and integration. What that means for team shape, roles and how work is divided.
Cloud & DevOps
Traditional observability tells you an agent responded. It does not tell you whether the response was correct. What to instrument for agentic systems, and how tracing and evaluation fit together.
AI & Machine Learning
Asking a model politely for JSON produces JSON most of the time, and most of the time is not a contract. How constrained decoding, schemas and validation combine into something you can build on.
AI & Machine Learning
Benchmarks say your agent works; production says otherwise. A practical evaluation approach covering end-to-end, trajectory and component scoring, and how to build an eval suite from your own failures.
Engineering
When implementation is cheap and fast, the specification becomes the bottleneck and the durable artefact. What changes when the spec, not the code, is the thing you maintain.
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