Human-in-the-loop AI agents scale when oversight is matched to the risk of each action, approvals are cheap and unambiguous, and you measure whether reviewers are still reviewing.
Human in the Loop AI Agents: Approval Patterns That Scale
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AI Engineering
AI Proof of Concept to Production: Why Most Pilots Stall
Most AI pilots stall because the proof of concept tested whether a model could do the task, not whether the business could run it every day, so here is how to design a pilot that is built to reach production.
Dataflow Security in AI Pipelines: Map, Restrict, Audit
Most AI data leaks happen in the plumbing around the model, so dataflow security means mapping every hop, restricting each one and auditing until the map stays true.
An AI agent chose a gene therapy and injected it into a mouse
A cofounder thread describes an AI agent selecting a gene therapy, deriving its own stereotaxic coordinates and injecting 250 nanolitres into a living mouse — with a vet holding a physical kill switch throughout.
RAG vs Fine-Tuning: Which Does Your Use Case Actually Need?
For most businesses the answer is retrieval first: RAG fixes what a model doesn’t know, fine-tuning changes how it behaves, and plenty of use cases need neither.
Axiom’s AI agents formalise the finite simple groups proof in 24 days
Axiom Math says its AxiomProver agents formalised the Classification of Finite Simple Groups in Lean in 24 days and flagged 34 deviations from the published record, but nobody outside the company has rebuilt it yet.
LangGraph vs CrewAI vs Custom: Choosing an Agent Framework
Choose LangGraph for explicit state and control, CrewAI for fast role-based prototypes, and custom code when your agent is one model calling a few tools, but draw the workflow before you pick any of them.
Black October: PHP2Shell, LiteLLM 1.107.0 and what to patch now
Two actions matter after Black October: get PHP onto the 21 October fixes, and treat any machine that installed LiteLLM 1.107.0 as compromised.
LLM Integration: A Complete Guide for Production Systems
LLM integration is mostly about the layers around the model: a gateway, context assembly, validation, evaluation, observability and guardrails. This guide covers how to build each one for production.
How to Reduce Technical Debt Faster with Automated Systems
Automation pays down technical debt fastest when it handles the mechanical work, such as upgrades, codemods, tests and guardrails, so engineers can spend their judgement on design.
GPT-6 reaches all ChatGPT tiers with Intelligent UI: what changes
GPT-6 is now rolling out to every ChatGPT tier, and its Intelligent UI turns answers into streamed, interactive interfaces, which is a pattern every team shipping AI output should study.
What We Do
Product
Consumer and enterprise software, built with taste and shipped fast.
Infrastructure
AI agents, automation, and orchestration - production systems, not demos.
Advisory
Hands-on engineering and strategy for teams shipping AI into production.