Beevibe AI CTO

Your AI agents code fast.
This is how they code right.

Beevibe AI CTO makes your team's architecture decisions with real-world evidence — then keeps the implementation honest as your code grows. Your team lead stops being the only memory.

Always-on brain
voices · OSS · competitors · papers
End-to-end loop
decide · guard · review · drift
Free, open source
Apache-2.0
Pennies per run
~$0.12 typical
Part 1 — Before code is written

Big technical decisions are made on gut feel.

I.

Your senior engineer's instincts were earned before AI

What's expensive, risky, or worth building has all shifted. The intuition you trust most was built for a different world.

II.

The best teams' lessons are public. Yours isn't reading them.

How Linear, Notion, and Stripe solved your exact problem is one search away. Your team still builds from a one-line prompt to a chat box.

III.

Every other industry has AI research. Architecture doesn't.

Markets, legal, medicine — all have deep-research products. Technical architecture, where mistakes cost the most and live the longest, is still a chat box.

IV.

You can't hire an AI-native architect

The talent market for senior engineers is huge. The talent market for engineers who deeply understand AI systems barely exists. Teams default to whoever's available.

Part 2 — After the decision

Even the right call silently drifts.

V.

AI doesn't know what your team already decided

Every coding agent starts from scratch. It doesn't read your past decisions, doesn't know what you migrated off of, doesn't know what you standardized on. So the same mistakes get reintroduced — every week.

VI.

The code drifts from the plan. Nobody notices.

The architecture was settled. Code got written. A week later the design and the implementation no longer match. Your team lead is the only person who sees the gap.

VII.

Training and code review don't keep up

Weeks of senior time spent onboarding the team to your conventions. Yet every code review still takes 1+ hour for 10+ violations per change. AI doesn't sit in the training. New hires keep arriving. Your team lead becomes both teacher and bouncer.

The brain · always-on

Your AI CTO reads what the world ships today.

The four-step loop below doesn't run on training data from a year ago. It runs on a continuously-evolving knowledge graph that watches what's being built right now — and quietly updates what your AI CTO knows.

The voices who build things

Architecture leads at Stripe, Linear, Vercel, Notion. Engineers with track records — not generic AI influencers.

twitter · hn · talks

Open source that's actually being adopted

Trending GitHub repos in your space — filtered for staying power, not flash-in-the-pan stars.

github trending · star velocity

Competitors' architecture — not their marketing

Reads public repos, ARCHITECTURE.md files, conference talks, engineering posts. Skips the landing pages.

public repos · eng blogs · talks

Papers becoming engineering reality

arXiv, USENIX, research labs — filtered through "what's actually being implemented." Catches the academic → engineering crossover before competitors do.

arxiv · usenix · acm
flowchart LR paper["📄 GraphRAG
(Microsoft Research)"]:::paper impl["⚙️ Microsoft GraphRAG
open source"]:::impl adopt["✓ Linear's RAG
engineering post"]:::adopt voice["💬 dhh thread
graph DBs at scale"]:::voice paper --> impl impl --> adopt voice -.-> paper classDef paper fill:#1d2025,stroke:#3aada4,color:#f5f0e0 classDef impl fill:#1d2025,stroke:#3aada4,color:#f5f0e0 classDef adopt fill:#facc15,stroke:#facc15,color:#0d1117,font-weight:700 classDef voice fill:#1d2025,stroke:#36302a,color:#a6a299

Personalized to your stack

Your PRD and your past ADR runs tune what matters. Auth-provider decisions don't surface quantum-computing papers.

Connects academic to engineering

When a paper introduces a technique, the brain shows the OSS implementations + the engineering tradeoffs other teams already hit.

Visual + browsable

Concepts, products, papers, voices — connected in an Obsidian-style graph. Click a concept to see who's debating it, who's shipped it.

The loop

Four steps that keep your code and your plan in sync.

The brain feeds the loop. ADR researches the decision and gives you a report to decide from — it's fully shipped today. Three more steps keep the code honest as your team builds.

flowchart TB brain(["🧠 Brain
ALWAYS-ON"]):::brain brain ==> decide brain -.-> guard brain -.-> review brain -.-> drift subgraph loop [ ] direction LR decide["adr decide
SHIPPED"]:::shipped guard["adr guard
next"]:::next review["adr review
next"]:::next drift["adr drift
next"]:::next decide --> guard guard --> review review --> drift drift -.->|loop| decide end classDef brain fill:#3aada4,stroke:#3aada4,color:#0d1117,font-weight:700 classDef shipped fill:#facc15,stroke:#facc15,color:#0d1117,font-weight:700 classDef next fill:#1d2025,stroke:#36302a,color:#a6a299
Flagship · shipped

Decide

adr decide

Researches what real teams shipped for problems like yours and gives you a structured report — every option that surfaced gets a section with what the evidence shows and what it doesn't. You decide which fits.

Next

Guard

adr guard

Your coding agents see your team's history before they write code. Patterns you've already rejected get caught at write time — with the file:line where the team rejected them.

Next

Review

adr review <PR#>

Every code review gets checked against the architecture spec automatically. Reviewers see what changed vs. what was decided — without re-deriving context every time.

Next

Drift

adr drift

Scans the codebase against the original plan. Tells you what's drifted — and gives three honest exits: fix the code, update the plan, or accept the drift on the record.

Inside the flagship

Why ADR's research is actually trustworthy.

Says "I don't know" instead of guessing

When the evidence is thin, ADR tells you. No invented confidence — no fake answers when the data isn't there.

Your context shows up in the report

"Self-hosted only" or "must stay in EU" become annotations on every option — you see at a glance which candidates fit your situation. You rule options out, not the kernel.

Reads peers two different ways

Open-source peers like Memgraph or Onyx get read through their actual repos and engineering blogs. Closed-source consumer products like Notion, Obsidian, or Mem.ai get read through Reddit, HN, Twitter — what users say they tried, picked, regretted. Both are evidence.

Maps the space, doesn't fake a winner

Every option the evidence surfaced gets a section with what's strong, what's weak, and what the evidence couldn't tell us. No invented confidence, no false ranking. The decision is yours; the report gives you enough to make it well.

Each option ships with its own rules

Pick option A and your coding agent gets A's rules and forbidden patterns. Pick B and it gets B's. No mix-and-match by accident.

Every claim is backed by a real source — and you can see its type

Each claim cites the source it came from. Docs, papers, engineering blogs, GitHub repos, community discussion — each tagged so you know if a claim is from a vendor's docs, a measured benchmark, or a practitioner thread on Reddit. Nothing ships on vibes.

See it on a real decision

An unedited ADR run on a real Beevibe decision.

We dogfooded ADR on our own knowledge-graph-for-AI-CTO-brain decision. 11 candidates surfaced from live research — Neo4j, Memgraph, ArangoDB, Weaviate, Logseq-style graphs, GraphRAG, plus closed-source peers like Roam Research and Mem.ai read through Reddit / HN. Every option got its own section with what the evidence shows, what it doesn't, and a deployment diagram for the concrete products. The report is exactly what came out of the kernel — nothing edited.

Open the example report →

11 candidates · 85 evidence pieces · 60 citations (57 verified) · 4 Mermaid diagrams · $0.27 · 122 LLM calls · 6 minutes wall-clock.

For your engineering team

30 seconds to install. 5 minutes to a research report.

Pass this section to your engineering team. Two install options below — Claude Code plugin or the command line. Either works on your existing codebase.

Claude Code plugin

$ claude plugin marketplace add beevibe-ai/architecture-deep-research $ claude plugin install adr /adr:doctor # one-time key setup /adr:decide # full pipeline

CLI

$ npm install -g github:beevibe-ai/architecture-deep-research $ adr-doctor $ adr deep-research --discover-first \ --domain "..." --decision "..." \ --out .adr-runs/X

Also runs as an MCP server (Cursor, Codex, any MCP host). Full docs →

Stop letting your code drift from your plan.

Free and open source under Apache-2.0. Make your first decision today; the rest of the loop ships next.