Dust for Dust
The Founder's Dilemma
Every founder faces moments of doubt. For me, the persistent challenge has been reconciling the years of effort we've poured into Dust with the creeping suspicion that what we're building might not be truly unique. It's the "if we did it, it must be obvious" trap – the assumption that solutions born from deep focus must already exist elsewhere.
This isn't new. Years ago, before presenting a research paper I'd contributed to but didn't lead, my advisor offered perspective: "You've spent more time on this specific problem than almost anyone. Trust that depth." That advice resonates today as we build Dust – an AI operating system designed to fundamentally reshape how teams work.
Depth Through Obsession
Since late 2022, our team has lived at the intersection of three elements: Large language models' emergent capabilities, enterprise infrastructure (the tangled web of SaaS tools companies rely on), and the daily friction knowledge workers face.
This focus creates compound advantages. While others chase model scale or vertical use cases, we've built infrastructure that lets AI act on organizational context most solutions ignore. The proof? We dogfood relentlessly – every Dust team member interacts with our platform dozens of times daily. Few startups achieve this level of internal reliance.
Dust in Action: An Engineer's Toolkit
To overcome my founder's struggle, let me share the ways I use Dust as an engineer that I could not find anywhere else and would have a hard time giving up. These are workflows that showcase how deep organizational context transforms AI from a novelty into a necessity.
@eng provides instant access to every runbook and design doc while handling first-line triage for production alerts via Slack. When monitors light up, it's my first point of contact, armed with comprehensive knowledge of our systems and procedures.
@***_pr_review transforms our code review process. It performs automated PR analysis against internal coding standards, relevant design documents, and security policies. When our PRs reach human reviewers, they've been so thoroughly vetted that they merge fast. Humans can focus on intent and architecture rather than compliance and style.
@incidentQ becomes mission-critical during outages. It generates user-facing status updates and auto-compiles postmortem drafts with timeline reconstruction. This systematic approach to incident communication ensures consistency and completeness when time pressure is highest.
@Q stands apart as a code agent that delivers zero bullshit. With access to our full codebase, engineering documentation, and PR history, it generates PR drafts that pass 90% of checks before human review. Its responses are grounded in our actual engineering context, not generic coding patterns.
@issue files issue for us following our best practices. We generally call it from Slack threads that discuss a problem or a task to achieve, It’s trivial but it saves real time and gets addictive.
The Context Imperative
These examples share a common thread: they're worthless without deep organizational context. Traditional AI tools operate in silos – they might know your codebase or your Slack history or your monitoring alerts. Dust connects them all, creating what we call "ambient awareness" for AI agents.
Our engineering team currently measures XX% productivity gains combining Dust with tools like Copilot or Cursor. The target is XXX% – not through marginal improvements, but by reimagining workflows that were previously impossible without AI orchestration.
Building the AI Workbench
Dust isn't another chatbot or code generator. We're creating a universal access layer with secure pathways to company data and systems. We're building agent orchestration that lets teams compose AI workers from existing infrastructure. And we're designing interfaces for managing AI "colleagues" at scale.
This approach mirrors how operating systems transformed personal computing. Windows didn't just make DOS prettier – it created universal primitives (clipboard, window management) that let applications interoperate. We're building those primitives for AI-powered organizations.
Join the Build
If reimagining work at this infrastructure level excites you, we're hiring full-stack engineers to shape the agent orchestration layer, front-end specialists designing human-AI collaboration interfaces, and security experts hardening enterprise-grade AI systems.