43 articles on engineering from the Alongside engineering team.
Opus 5.5 for long agentic coding, Grok 4.7 for low token cost, GPT-6 Astra for computer use and the largest context. And why Astra 6.1 never shipped.
Codex CLI is our default coding agent harness for most teams in October 2026. Claude Code ties it on Terminal-Bench 4.0, OpenCode wins on model choice. Here is how they compare on sandboxing, extensibility and cost.
Grok Bot and Meta's Muse both give an agent a persistent cloud computer. The real difference is where the trust boundary sits: around a shared per-user machine, or beneath the agent itself.
On 30 September 2026, Railway returned 404 for every new connection for five minutes. A deploy beat its schema migration, and a failed lookup was reported as a missing route.
NeMo Guardrails is usually the better choice than Guardrails AI when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Langfuse is usually the better choice than OpenLIT when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Modal is usually the better choice than Runpod when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Unstructured is usually the better choice than LlamaParse when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
pgvector is usually the better choice than Pinecone when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Qdrant is usually the better choice than Chroma when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Portkey is usually the better choice than LiteLLM when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
LlamaIndex is usually the better choice than LangChain when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Triton is usually the better choice than vLLM when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Direct provider integrations is usually the better choice than OpenRouter when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
LangSmith is usually the better choice than Opik when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Cohere Rerank is usually the better choice than Voyage AI when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
PostgreSQL is usually the better choice than Redis when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Qdrant is usually the better choice than Milvus when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Temporal is usually the better choice than LangGraph when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
FastAPI is usually the better choice than BentoML when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Qdrant is usually the better choice than Weaviate when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
Haystack is usually the better choice than LlamaIndex when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
LangGraph is usually the better choice than OpenAI Agents SDK when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
PydanticAI is usually the better choice than DSPy when a team needs a more defensible production path, stronger control, and clearer operational trade-offs.
vLLM is usually a better choice than Hugging Face TGI when a team needs high-throughput self-hosted inference with stronger efficiency, simpler scaling decisions, and better cost control.
LangGraph is usually a better choice than CrewAI when a team needs stateful, production-safe agent workflows with traceability, retries, and tighter operational control.
Distributed teams do not slow down because they are remote. They slow down because important trade-offs live in calls, DMs, and personal memory instead of shared records.
Most modernization programs fail when they aim for architectural perfection before operational control. Incremental modernization usually wins on risk and learning.
Kubernetes creates leverage when teams have the operating discipline to support it. Without that, the platform can become an expensive complexity multiplier.
Observability gets expensive when each team instruments differently. OpenTelemetry gives growing organisations a better standard for telemetry consistency and analysis.
Retrieval-augmented generation systems fail in production when teams optimise retrieval quality but ignore access control, content freshness, and response accountability.
Threat modeling works best before implementation choices harden. It helps product and engineering teams catch risky assumptions while change is still cheap.
A secure SDLC is not a claim. It is a set of visible practices around requirements, implementation, verification, and response that buyers should be able to inspect.
Product engineering services should not stop at shipping features. They should improve product direction, technical quality, delivery confidence, and long-term maintainability.
Getting an AI feature to work once is not the hard part. Building a production AI system means dealing with reliability, monitoring, governance, cost, and user trust at the same time.
Treating QA as a late-stage gate creates expensive surprises. Treating it as a design function builds clarity, faster learning, and more reliable releases.
Remote onboarding succeeds when external engineers get production context, decision rules, and fast feedback in days—not when they collect links in a wiki.
A convincing AI prototype can be built in a week. A production system takes much longer because the hard part is reliability, control, and operational fit.
Distributed teams do not fail because they are remote. They fail when architecture decisions live in private calls, loose opinions, and undocumented trade-offs.
AI agents are everywhere right now. But honestly? Most explanations are either so vague they're useless or so drowning in jargon you'll give up after page two. So let's fix that. Here's how to build one. No fluff. Just steps that actually work.
Anthropic just shipped something that sounds boring but isn't. They added a dial to Claude. You can now tell it how hard to think. Turn it down: fast, cheap, simple answers. Turn it up: slow, expensive, deep thinking. You pick. Every time.
