A Atlas
Viewer Design
knowledge infrastructure · not an LLM wrapper

See what you didn’t think of — before it becomes a bug.

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.

Atlas works not at a single moment but across the whole path — from idea to postmortem. Each stage has its own surface.

  1. 00
    IdeaDesign
    “I want a SaaS for nutrition tracking”

    Before the first line of code, Atlas surfaces the domains involved and the questions worth asking up front.

  2. 01
    First featureDesign
    “I’ll start with email login”

    A blind-spot scanner for one feature → a ready prompt or checklist for your LLM.

  3. 02
    In the codeMCP · soon
    “Adding webhooks — what did I miss?”

    A quick check right in the flow, without leaving the IDE — via a local LLM over MCP.

  4. 03
    Pre-commitMCP · soon
    “A check before the PR”

    A blind-spot checklist for the touched features — the reviewer a solo dev doesn’t have.

  5. 04
    IncidentViewer
    “What should I have known?”

    A postmortem on the map: find the trap that fired and its neighbors — so it doesn’t happen twice.

38
domains
2,183
features
75,215
blind spots
65,934
nodes
903
edges
19/24
lenses

Not an abstract 75 thousand. Here’s one feature everyone builds — email/password login — seen through three of the 24 lenses:

red flagcriticalsecurity

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.

owasp_password_storage · nist_sp_800_63b
red flagcriticalprivacy

Email written to logs on every login

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.

gdpr_art_5 · owasp_logging
red flagcriticalux

Raw DB error surfaced on a failed login

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”.

nielsen_heuristics
Not an LLM wrapper

Works with no AI connected. The value is a structure of attention, not text generation. An LLM amplifies it but is never load-bearing.

Evidence-backed

Every blind spot cites a source — OWASP, NIST, RFC, vendor docs — not “trust me”. Verifiable.

From idea to deploy

38 domains cover the whole product path: access and auth, payments, data, UX, reliability, prod, support.

The base is actively growing

Phase 3 · lens sweep · 19 / 24

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.