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CodeMorph 2.0 — Now with Vertex AI tooling

Pseudocode in. Production TypeScript out.

CodeMorph 2.0 is the only translation engine that learns from every line you write. A self-improving AI memory core, fed by Vertex AI tooling, turns your team's pseudocode into typed, tested, ship-ready TypeScript — and gets sharper with every translation.

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Anthropic Vertex AI pgvector Voyage AI Next.js 14
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Paste any pseudocode. Watch CodeMorph turn it into production-ready TypeScript in seconds. Real engine, real output.

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Pseudocode Input 0 chars
TypeScript Output
// Your TypeScript will appear here
app.codemorph.com / codemorph / workspace
CodeMorph 2.0 WORKSPACE 📂 ContentOS / scheduler.pseudo 📄 petal-billing.pseudo 📄 axiom-memory.pseudo 📄 throughline-agent.pseudo 📁 Archive (47) AI MEMORY ▣ 12,847 patterns ▣ 31 conventions learned ▣ 4 stack profiles LEARNING +218 patterns today Accuracy: 97.4% ↑ 2.1% scheduler.pseudo ● live translate ⚡ Run ↗ Ship PSEUDOCODE TYPESCRIPT — auto-typed, tested 1 function scheduleNextPost (posts, channel): 2 filter posts where status = "queued" 3 for each platform in channel.platforms: 4 pick earliest matching post 5 if rate-limited, defer 15 min 6 send to platform queue 7 log dispatch + update status 8 return dispatched count 1 export async function scheduleNextPost ( 2 posts: Post[], 3 channel: Channel 4 ): Promise<DispatchResult> { 5 const queued = posts.filter(p => p.status === 'queued' ); 6 let dispatched = 0; 7 for ( const platform of channel.platforms) { 8 const next = pickEarliestFor(queued, platform); 9 if ( await isRateLimited(platform)) { 10 await defer(next, '15m' ); continue ; 11 } 12 await dispatch(next, platform); 13 dispatched++; 14 } 15 return { dispatched, channelId: channel.id }; 16 } AI SUGGESTION · matched 4 prior translations In your codebase, "send to platform queue" maps to await dispatch(item, platform) — applied automatically ENGINE Vertex AI · Anthropic Claude · Voyage embeddings Cycle 14,892 · 1.4s · type-checked ✓ · tests generated ✓ CONFIDENCE 97% Calibrated against your last 12,847 translations SHIP TO GitHub Vercel VS Code
More to explore

A workspace that earns its place on your dock.

output / scheduler.ts
Generated TypeScript type-safe · tested · idiomatic to your codebase 1 interface Post { 2 id: string 3 status: 'queued' | 'sent' | 'failed' 4 scheduledFor: Date 5 } 7 export type DispatchResult = { 8 dispatched: number 9 channelId: string 10 } 12 export async function scheduleNextPost 13 (posts: Post[], channel: Channel) INFERRED FROM MEMORY DispatchResult shape matched 4 prior outputs ✓ TYPE-CHECK PASSED tsc strict · 0 errors ✓ TESTS GENERATED vitest · 7 cases · all pass ⚡ 1.4s · 97% confidence cycle 14,892 · 8 pattern matches
team / analytics
Team velocity Hours saved · per engineer · this month Markus 42h Priya 35h Daniel 28h Yara 22h TEAM TOTAL · NOVEMBER 127 hours saved vs hand-written translation · ~$19,050 in eng time PATTERN GROWTH +1,847 patterns this month
The CodeMorph Engine

The first translator that gets smarter every time you use it.

Most code translators treat every input like the first. CodeMorph 2.0 doesn't. Every translation you accept, edit, or reject becomes training signal for a private AI memory tuned to your codebase. Vertex AI's tooling layer un-restricts the engine — letting it call validators, run type-checks, scan your monorepo, and re-query its own memory mid-generation. The result: an engine that writes the way your team writes, and writes it better tomorrow than today.

+4.2%
accuracy gain per 1k translations
97.4%
type-check pass rate
1.4s
median translation latency
CodeMorph Engine 2.0 12,847 patterns 01 INPUT Pseudocode user writes 02 EMBED Voyage AI vectorize 03 RETRIEVE pgvector k-nearest 04 TRANSLATE Claude + Vertex tools 05 VALIDATE tsc + tests type-check 06 LEARN memory write-back re-index ↻ self-improve
editor / billing.pseudo
billing.pseudo ● learning 1 function handleSubscriptionRenewal (customer): 2 // fetch current plan from Stripe 3 if customer is on trial: 4 convert to paid + charge prorated 5 if payment fails: 6 retry with backup card 7 if still fails: pause access + notify 8 log event + emit "renewal" webhook AI SUGGEST · from your codebase retry with backup card on file ● 7 matches use Stripe SetupIntent for retry apply 3-day grace before pause emit subscription.payment_failed ↑↓ to navigate · Tab to accept · Esc to dismiss ⚡ 4 patterns matched · Stripe billing module · 12 prior translations ⚡ Translate ⌘↵
▸ Live AI suggestions

As you type...

Autocomplete that knows your codebase.

As you write pseudocode, CodeMorph 2.0 surfaces patterns from your own translation history. You wrote a billing retry once — you'll write the next one in three keystrokes. The engine completes your conventions, not GitHub's average.

  • Inline suggestions ranked by pattern similarity
  • Tab-to-accept · ⌘↵ to translate the whole file
  • Per-suggestion provenance: see which file taught the engine
▸ Integrations

Drop in anywhere...

Lives where your code already lives.

Translate from VS Code, JetBrains, the web app, or the API. Push translated TypeScript straight to a GitHub PR, a Vercel preview, or a Supabase migration. CodeMorph 2.0 is a connector, not another silo.

  • VS Code & JetBrains extensions
  • GitHub PR bot — translates pseudocode in commit messages
  • Vercel preview integration · Supabase RLS validator
  • REST + streaming SSE API for custom workflows
integrations
CodeMorph 2.0 VS Code extension GitHub PR bot Vercel preview ship Supabase RLS validator JetBrains plugin Slack team alerts REST API + SSE stream CLI npx codemorph 8 connectors. Translation lives where you live.
What CodeMorph 2.0 does

A whole pipeline, replacing a whole afternoon.

From whiteboard pseudocode to deployed TypeScript — typed, tested, idiomatic to your codebase, and traceable to the patterns that produced it.

▸ Vertex AI tooling

Wait, it gets sharper...

Tooling that un-restricts the engine.

Standard LLMs are stuck in a sandbox. CodeMorph 2.0 isn't. Vertex AI's tooling layer lets the engine call type-checkers, run unit tests, query your monorepo, and re-search its own memory — during generation, not after. When confidence drops below 92%, it doesn't guess. It checks.

  • tsc & eslint as live tools — caught before you see them
  • monorepo-aware imports — no more invented module paths
  • recursive memory re-query — the engine asks itself again
  • test scaffolding — Vitest cases generated alongside the code
engine / pipeline
Translation pipeline CYCLE 14,892 · 1.4s 01 Parse AST + intent 02 Embed Voyage AI 03 Retrieve pgvector k=8 04 Generate Claude + tools 05 Validate tsc + tests Tools available to the engine mid-generation When confidence dips, the engine calls these instead of guessing. ⚙ tsc.check() 📦 monorepo.import() 🔍 memory.requery() 🧪 vitest.scaffold() 📐 lint.run() 📚 docs.lookup() ● 7 tool calls this cycle · 0 hallucinated imports · type-check ✓
workspace / learning
Learning over time Translation accuracy, calibrated against type-check + human review Patterns indexed 12,847 ↑ 218 Accuracy 97.4% ↑ 2.1% Translations 14,892 all-time Confidence 94% avg Accuracy curve · last 90 days Started 89.3% → today 97.4%. Each accepted translation refines the next. 100 95 90 85 TODAY 97.4% · best ever
▸ Self-learning AI

Make it truly yours...

Memory that's private, plural, and yours.

Every accepted translation, every edit, every rejected suggestion — they all go into a private vector store that only your team queries. The engine learns your naming conventions, your error-handling style, your preferred test framework, the way your senior engineer would have written it. The longer you use it, the less it sounds like a robot, and the more it sounds like you.

  • Per-project + per-repo memory isolation
  • Pattern provenance — see which prior translation taught the engine
  • One-click "unlearn" if a bad pattern slips in
  • Zero training on cross-customer data, ever
▸ Project workspace

Now ship it...

Every translation, traceable. Every revert, one click.

A workspace built for engineers who answer to a code review. See every translation, every confidence score, every pattern that produced it. Branch, fork, diff, and revert — your translation history is a first-class artifact, not a chat scrollback.

  • Side-by-side diffs with confidence overlays
  • "Why this output?" panel showing the 8 patterns that contributed
  • Branch translations like git — try alternatives without losing the original
  • Export as PR-ready commits, straight to GitHub
workspace / projects
Projects 📁 ContentOS 📁 Petal 📁 Throughline 📁 Axiom Memory 📁 GENESIS scaffold RECENT scheduler.pseudo billing.pseudo memory.pseudo ContentOS · 47 translations Last updated 2m ago · 12 contributors · main branch ✓ scheduler.pseudo → scheduler.ts 98% · 8 pattern matches · type-check ✓ · 2m ago View diff ↻ x-poster.pseudo → x-poster.ts 87% · branched · 2 alt translations · 14m ago Compare ✓ cron-loop.pseudo → cron-loop.ts 96% · 11 pattern matches · shipped to Vercel · 1h ago View PR ○ webhook-handler.pseudo in progress · validating types · 3s ● live PROVENANCE · why this output? Drew on 8 prior translations from this repo: scheduler.ts · queue.ts · platform-adapter.ts · +5 more
Pricing

Pricing coming soon.

We're still talking to early users before we lock in numbers. Join the waitlist and you'll get early-access pricing before we go public.

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FAQ

Things engineers ask first.

What does "self-learning" actually mean? Is my code training a shared model?
No. Your translations build a private vector index scoped to your account or team. The base LLM never trains on your data. "Self-learning" means the engine retrieves your prior translations as context for the next one — so accuracy compounds inside your workspace, in isolation from every other customer.
What does "Vertex AI tooling" do that other translators don't?
Most LLM-based translators emit code in one shot. CodeMorph 2.0 uses Vertex AI's tool-calling layer to run during generation — it can call the TypeScript compiler, query your monorepo for real import paths, scaffold Vitest cases, and re-query its own memory when confidence drops. The model checks its own work instead of hallucinating.
Which pseudocode dialects are supported?
Free-form English-style pseudocode, structured pseudocode (Wirth-style), and many flavors in between. The engine doesn't require a syntax — it parses intent, retrieves analogous prior translations, and produces TypeScript matching your codebase's conventions.
Can it produce languages other than TypeScript?
TypeScript is the v2.0 release target. Python and Go are in private beta. The engine is language-agnostic at the architecture level; tooling validators are what take time per target language.
How does it handle private code? Is there a self-hosted option?
Pro and Team store your AI memory vectors in our SOC 2 infrastructure with per-account isolation. Enterprise gets VPC-isolated deployments — your vectors and your prompts never leave your cloud account.
Who's building this?
CodeMorph 2.0 is built by Code Morph, a small team of engineers focused on AI-assisted developer tooling. We build in public — most of what we ship gets a teardown on our blog.

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