Passwords stored with a fast or unsalted hash
Why it bites: one DB leak becomes instant GPU cracking → mass account takeover, often reused on other sites.
What to do: argon2id with a per-user salt + a server-side pepper outside the DB.
Atlas is a graph of engineering blind spots in product development, from idea to deploy. Not another AI chat or a list of best practices — a structure of attention.
Browse the whole base: domains, features, blind spots and graph edges. Read what can go wrong — and what to do about it. Read-only.
Open Viewer →Describe or pick what you’re building → get a curated list of blind spots for that feature → a ready prompt, checklist or brief for your own LLM. Works with no AI.
Open Design →Atlas works not at a single moment but across the whole path — from idea to postmortem. Each stage has its own surface.
Before the first line of code, Atlas surfaces the domains involved and the questions worth asking up front.
A blind-spot scanner for one feature → a ready prompt or checklist for your LLM.
A quick check right in the flow, without leaving the IDE — via a local LLM over MCP.
A blind-spot checklist for the touched features — the reviewer a solo dev doesn’t have.
A postmortem on the map: find the trap that fired and its neighbors — so it doesn’t happen twice.
Not an abstract 75 thousand. Here’s one feature everyone builds — email/password login — seen through three of the 24 lenses:
Why it bites: one DB leak becomes instant GPU cracking → mass account takeover, often reused on other sites.
What to do: argon2id with a per-user salt + a server-side pepper outside the DB.
Why it bites: the log aggregator (Datadog, Splunk) becomes an undisclosed data processor; email + IP + timestamp is personal data under GDPR.
What to do: hash the email (HMAC-SHA256), set log TTL to ~90 days, document the processors.
Why it bites: the user can’t tell — wrong password, locked account, or a broken server; they retry blindly or leave.
What to do: catch infra errors at the login boundary and return one clear message, distinct from “invalid credentials”.
Works with no AI connected. The value is a structure of attention, not text generation. An LLM amplifies it but is never load-bearing.
Every blind spot cites a source — OWASP, NIST, RFC, vendor docs — not “trust me”. Verifiable.
38 domains cover the whole product path: access and auth, payments, data, UX, reliability, prod, support.
Blind spots are extracted by sweeping 24 analytical lenses (security, privacy, reliability, ux…) across the whole base — one lens at a time. Feature breadth and structural edges are already closed; the last layer of depth is in progress.