Articles from the team. Latest on top.
Third-party Butlers run in zero-network sandboxes. When an integration needs an external API, it never calls it directly. Here is how our mediated relay, DNS verification, and pseudonymous identities keep users safe.
A deep dive into our new Developer Portal, Bridge v1 sandboxed execution engine, one-write asynchronous submission pipeline, and strict Kafka-mediated backend service architecture.
How a single DynamoDB table, pre-computed sort keys, and a dormant SQS queue power a backend with no Redis, no Elasticsearch, and no Kubernetes.
A researched travel plan for visiting Japan's legendary tool workshops — from Sakai's kitchen knives and Miki's woodworking planes to Tsubame-Sanjo's metalwork and Seki's swordsmiths.
A bucket list of places I want to travel and capture—from the real-life anime locations and iconic car HQs in Japan, to the post-apocalyptic inspirations of Fallout in the US, and the booming tech hubs of China.
Mono repos are the fastest way that you could organize your codebase. This time we unified our codebase so that it would be simpler for new joiners as well as LLMs.
Static mockups are dying. LLMs let us build functional prototypes that actually work—with real data, real interactions, and real logic. Here's how this changes everything about how we design and build products.
We never tolerate vibe coding for production code, but there's a powerful use case: letting technical people without UI skills and non-technical people create demonstrable prototypes to communicate ideas. No more describing concepts to UX designers—just build it.
LLMs transform DevOps and production support: they can analyze logs, debug issues, write deployment scripts, respond to incidents, and automate operations. Here's how we use LLMs for DevOps and production support.
LLMs can help write more secure code, but they can also introduce vulnerabilities. Here's how we use LLMs to identify security issues, write secure code, and validate security through testing and code review practices.
To make LLMs work better for us, we need to reorganize how we work: modular projects that run locally, documentation with code-based diagrams (Mermaid), and tests in all code as guardrails. Here's how we restructured our workflow.
Butlers fire up an event and the Butler core handles the actual API call, with the response also being an event. This way we monitor all outgoing traffic. Here's the vision for an SDK-based approach to centralized API management.
LLMs let you quickly draft and complete product prototypes without wasting time on development and long feedback cycles. Sometimes you can even demo partially working apps to management. Here's how it's different from the old days.
Imagine parents adding tasks to their children and monitoring progress. A simpler, less overwhelming app designed specifically for kids, with parent management built in. Here's the vision for a separate kids' app.
How product owners, solution architects, and UX/UI designers collaborate with LLMs. The architect drives implementation planning, consolidating design requirements, technical constraints, and execution steps with AI support.
Our QA workflow using LLMs: anticipate use cases and edge cases at the product level, automate tests during development, run automated demos for user acceptance, and write UI test code with AI assistance.
Our practical debugging workflow using LLMs: start with screenshots and suspicions, drill down with surgical precision, and document complex bugs for future reference. Here's how we do it.
Find contractors and project makers near you without giving away your data. Contractor Butler pulls listings from connected sources—your information never goes back.
How I built Life Butler spending only $40/month on AI subscriptions. A practical guide to maximizing free tools, strategic model selection, and cost-effective AI-assisted development.
AI doesn't replace engineers — it amplifies them. Here's our real-world workflow for building software with LLMs, and why the human in the loop is what makes it work.
The story of how app fragmentation led to a vision: one cohesive place for your entire life, instead of a dozen disconnected apps.
Why we call them Butlers and how that shapes what we build. Butlers are specialized assistants that work together, not isolated apps that compete for your attention.
How we think about your data and who gets to see it. Privacy isn't a feature we added — it's how we built Life Butler from day one.
The kind of questions Idea Butler is meant to turn into mini-programs. How recurring questions become specialized assistants that help you decide.
A rough map of the next few months. What Butlers are coming, what features are in development, and where Life Butler is heading.
