Machines gave humanity physical labor. AI gives you mental labor. Command 10,000 agents that know your codebase, your style, your decisions — and turn every idea into reality. Not someday. By morning.
Free to start. No credit card required.
Built-In
Overseer monitors your production 24/7. When something breaks, it detects the error, traces the root cause, writes a fix, runs tests, and deploys — all before you wake up.
Cannot read properties of undefined (reading 'subscriptionId')
src/api/billing/webhook.ts:47Matched error pattern: null reference in billing flow. Impact: 12 users affected in last 5 min.
Stripe webhook sends 'subscription_id' but handler expects 'subscriptionId'. Camel-case mismatch introduced in commit a3f891c.
PR #341 auto-merged. Error rate dropped to 0 within 30 seconds.
Total time: 2 minutes. Ticket NOB-89 auto-closed. No user-facing downtime.
2 min
Detection to deploy
0
Humans needed
3 AM
You were asleep
Instead, you spend your morning catching up on Slack. You copy an error from Sentry, paste it into your IDE, re-explain your codebase to AI that forgot everything overnight, debug its wrong suggestion, push a fix, wait for CI, update the ticket, message the team.
Eight hours. Two hours of actual building. The rest is overhead.
Your ideas are bigger than your bandwidth. You're one person doing the work of ten. And no autocomplete is going to fix that.
Machines solved physical labor centuries ago. Now mental labor has the same answer. Codmir gives you a workforce of autonomous agents that know your codebase, your style, and your past decisions. They execute. You direct.
Your codebase patterns, past decisions, mistakes you learned from — persisted in a Cortex Protocol that every agent reads before touching a line of code.
Not autocomplete. Not chat. Autonomous experts that see the bug, trace the cause, write the fix, open the PR, and update the ticket. You review their work.
Describe what you want. Cortex decomposes it into tasks and dispatches thousands of agents — writing, reviewing, testing, deploying. Your idea, built by morning.
Tickets, code, errors, deploys, AI traces — one platform. Fix a bug and the ticket updates, the PR opens, the deploy triggers, the alert clears. Automatically.
Cortex monitors every process. When something dies at 3am, it restarts it, verifies the fix, and only pages you if self-repair fails. You sleep through recoverable failures.
Start solo with AI as your entire team. Add humans when you grow. Same platform, same power, every scale — from solo founder to enterprise.
People hear “AI” and think of a tool. A smarter autocomplete. A chatbot. But there's a difference between a tool that helps you work and a workforce that works for you. That difference is the cost of mental labor — and it just dropped to near zero.
Machines gave humanity physical labor. Factories, engines, assembly lines — they multiplied what our bodies could do. But our minds? Mental labor stayed expensive. If you wanted 10,000 people thinking, writing, reviewing, and building for you, you needed to be a pharaoh, a general, or a Fortune 500 CEO.
The idea that one person — sitting at a laptop — could command 10,000 skilled workers on a single task? That's not normal. That's an anomaly.
Now it's yours. Codmir doesn't give you a smarter tool. It gives you a workforce. 10,000 agents that know your codebase, your patterns, your past decisions — writing, reviewing, testing, deploying — all in parallel. Your idea, built by morning.
2560 BC
100,000
Pharaohs
workers to build a pyramid
Physical labor
27 BC
5,000
Roman Generals
soldiers per legion
Physical labor
1900s
10,000
Industrial Titans
factory workers per plant
Physical labor
Today
10,000
Tech CEOs
engineers across the org
Mental labor
This is not a trend. This is not a niche. This is the direction of the entire future market. Every AI company, every developer, every industry is converging toward the same point: mental labor at scale, near zero cost.
Most people can't fathom how massive this shift is. The market for creative, productive, efficient work will reshape the world. The speed of development will operate on a completely different level than anything before it.
The question isn't whether this future arrives. It's who leads it.
You + Codmir
We don't predict the future. We create it!
No empire. No billions. No army. Just you and the power to bring your creative and productive self to the world.
100 agents join a mesh, vote on a plan, and execute in parallel — all within milliseconds. No orchestrator. No queue. No waiting. This is how 10,000 minds become one workforce.
Self-organizing fabric
Every agent joins a real-time Socket.IO mesh. They discover peers by capability, signal availability, and form rooms on the fly. No central dispatcher. No bottleneck. Pure peer-to-peer coordination at the speed of WebSockets.
Discovery, signals, rooms, scratchpad — agents find each other in milliseconds.
Democratic self-assembly
Agents self-assemble around a goal, propose approaches, vote on the best plan, then execute in parallel with millisecond-precision deadlines. Like a sprint meeting that takes 200ms instead of an hour.
Assemble → Propose → Vote → Execute → Review — all autonomous, all time-boxed.
Intelligence at every price point
50 seed agents scan with cheap models (10 tokens each). 10 synthesizers combine their findings (100 tokens each). 1 deep reasoner makes the final call (1,000 tokens). Total: 2,500 tokens. Unquantized: 10,000+. Same depth, 75% cheaper.
Layered micro-execution turns expensive reasoning into distributed cheap operations.
One mesh, everywhere
Sprint events bridge to every connected client through NestJS EventEmitter. Your desktop IDE, web app, VS Code, CLI, mobile — all see the same agents working in real-time. Watch 100 agents execute from anywhere.
Desktop, web, extension, CLI, mobile — one event bus, zero configuration.
Quantized vs Traditional
2,500 tokens vs 10,000+
Same depth of reasoning. 75% less cost. Distributed across 61 agents in 3 layers.
50
Seeds
10 tok/ea
10
Synths
100 tok/ea
1
Deep
1,000 tok/ea
The mesh is live
100 agents. One sprint. Milliseconds.
Real-time coordination at a scale that was science fiction a year ago.
Most AI tools run one agent at a time. Codmir agents form a hive mind — they share context through a mesh scratchpad, vote democratically through the Council Protocol (81 rules derived from 5 Seeds), and converge on decisions in milliseconds.
Not parallel workers. A collective consciousness.
Protocol
Council Protocol
81 rules from 5 Seeds govern every AI decision. Agents don’t just vote — they deliberate within ethical and strategic constraints.
Memory
Shared Memory
The mesh scratchpad lets agents read each other’s progress, share discoveries, and build on each other’s work in real-time.
Consensus
Convergent Decisions
Consensus in milliseconds. The sprint voting phase reaches agreement before a human could finish reading the proposal.
The council has already decided
While you were reading this, 100 agents could have proposed,
debated, voted, and shipped.
A business takes 2 weeks to decide on an architecture change. Slack threads, meetings, review cycles. Codmir's agent council proposes, debates, and votes in 200ms. The sprint executes in seconds. The PR is ready before the meeting invite is sent.
This isn't incremental improvement. It's a category shift.
Traditional
2 weeks
Architecture decision
Meetings, reviews, approvals
AI-Assisted
2 days
Same decision
One engineer + copilot
Multi-Agent
2 hours
Same decision
Agent swarm, sequential
Codmir Sprint
200ms
Same decision
Mesh + vote + execute
Codmir
The speed is the moat
By the time your competitor schedules a meeting,
your agents have already shipped.
From a message in Slack to a deployed fix — no copy-pasting errors, no context-switching, no manual ticket creation. Watch the loop run itself.
You know the drill — someone drops a bug in Slack, you screenshot it, create a ticket, add labels, assign it. Codmir does all of that the moment it sees the message. You just approve.
Anyone else noticing the iOS app feels slow to open if you haven't used it in a bit?
Yea, we're still blocking initial render on a full vehicle_state sync every time...
Feels like we could render sooner and load the rest in the background. Probably also worth tracking startup timing so we know how often this happens!
The checkout flow is throwing 500 errors on mobile Safari. Users can't complete purchases.
Created NOB-42: "Fix Stripe webhook parsing on mobile Safari" — Priority: Urgent, assigned to @marcus
Confirmed — looks like it's related to the Stripe webhook handler not parsing the payload correctly on iOS.
Added sub-task NOB-43: "Add structured error logging to webhook handler" — linked to NOB-42
Remove UI inconsistencies
TypeError: Cannot read properties
Upgrade to Claude Opus 4.5
Optimize load times
Remove contentData from GraphQL API
Launch page assets
Prevent duplicate ride requests on poor...
Implement dark mode toggle
Add rate limiting to REST API
The agent reads the ticket, greps the codebase, finds the bug, writes the fix, runs the tests, and opens a PR. It knows your code style, your patterns, your past mistakes. You review, not write.
Codex Agent assigned to NOB-42: Fix Stripe webhook parsing on mobile Safari
2:16 PMExamining the issue — Stripe webhook handler failing on mobile Safari. Let me trace the request flow...
grep -rn 'webhook' src/api/stripe/ --include='*.ts'Found the issue on line 14. req.body is a ReadableStream on mobile Safari — we need to buffer it first before passing to Stripe's constructEvent.
cat src/api/stripe/webhook.ts | head -35Writing the fix — buffer the request body using req.text() before verifying.
npx vitest run src/api/stripe/webhook.test.tsAll tests pass. Creating PR with the fix and updated tests.
Opened PR #127: Buffer webhook request body before Stripe signature verification
2:19 PMSarah Chen
Frontend Lead
Marcus Owens
Backend Eng
Priya Desai
Full Stack
Jay Kumar
Mobile Eng
Codex AgentAgent
Working on NOB-42
Review AgentAgent
Idle
QA AgentAgent
Running tests
Triage AgentAgent
Idle
You don't care who wrote it — you care if it's correct. Structural diffs, inline reasoning, test results, and evidence. One review flow for everything.
Good fix. Buffering via req.text() correctly handles ReadableStream on all browsers. The added null-check for the signature header prevents the unhandled rejection we were seeing.
Suggestion
Consider adding a max payload size check before buffering to prevent memory exhaustion from oversized requests.
Nice — can we also add a regression test specifically for the Safari payload format? I want to make sure this doesn't slip again.
Every LLM call, every token, every dollar. Trace requests through your stack, replay agent sessions step by step, and catch cost spikes before they hit your bill.
User: Fix the login redirect bug on mobile Safari
Analyzing: login flow uses window.location which Safari blocks in cross-origin iframes
grep -rn 'window.location' src/auth/ --include='*.ts'
Read: src/auth/callback.ts (lines 12-45)
Found it: line 28 uses window.location.replace() inside the OAuth callback iframe
Fix: Replace window.location.replace with postMessage to parent + parent-side redirect
| Model | Calls | Tokens | Cost | Latency |
|---|---|---|---|---|
| claude-haiku-4-5 | 847 | 2.1M | $1.89 | 340ms |
| gpt-4o-mini | 234 | 890K | $0.67 | 520ms |
| gemini-2.5-flash | 1203 | 4.8M | $0.48 | 180ms |
Four packages that give you monitoring, edge intelligence, autonomous agents, and multi-provider AI — each works standalone, together they're unstoppable.
npm install @codmir/sdkDrop-in monitoring for any app. Error tracking, session replay, and performance — one import, full visibility.
import { init } from "@codmir/sdk"
init({
dsn: "https://your-project.codmir.com",
traceAI: true,
sessionReplay: true,
})Intelligent edge runtime. Auth at the edge, distributed mesh networking, AI-powered routing, and self-healing infrastructure.
import { createCortex } from "@codmir/cortex"
const cortex = createCortex({
auth: { jwtSecret: process.env.JWT_SECRET },
models: [
{ id: "claude-haiku", provider: "anthropic", tier: "fast" },
{ id: "gpt-4o", provider: "openai", tier: "premium" },
],
})
const result = await cortex.executeTask(task)
// model auto-selected, cost tracked, failures self-healedAutonomous issue triage. Detects production errors, creates tickets, proposes fixes, and deploys patches — while you sleep.
import { Overseer } from "@codmir/overseer"
const overseer = new Overseer({
project: "my-app",
autoFix: true,
approvalRequired: false, // full autonomy
onPatch: (patch) => {
console.log(`Fixed ${patch.issue} in ${patch.timeMs}ms`)
},
})Multi-provider AI with governance. Route between Claude, GPT, and Gemini through one API with voting, reasoning chains, and cost optimization.
import { ai } from "@codmir/ai-sdk"
const response = await ai.generate({
prompt: "Review this PR for security issues",
strategy: "balanced", // cost vs quality
governance: {
requireConsensus: true, // multi-model voting
reasoningChain: true, // show the why
},
})Your project tracker, error monitor, AI coding tool, deployment pipeline, meeting assistant, and monitoring dashboard — one login, one bill, one source of truth.
You describe what you want. Agents grep, read, write, test, and open the PR. You review their work like a senior reviews a junior — except they never need onboarding.
A ticket gets created. An agent picks it up, writes the fix, runs CI, and marks it done. You didn't touch your keyboard.
Same diff view, same approval flow — whether the code came from a human or an agent. Inline reasoning shows why each change was made.
One import. Every LLM call logged — tokens, cost, latency, errors. Replay full agent sessions to see exactly where things went wrong.
Every deploy linked to the code that changed. When something breaks, you see which deploy caused it and roll back in one click.
3 AM. Error spike. Overseer detects it, traces the root cause, writes a fix, tests it, and deploys. You find out when you wake up and read the ticket.
Your codebase patterns, past decisions, mistakes you learned from — persisted in a format only AI reads. Every agent starts with full context. No more re-explaining.
AI sits in your meetings. When someone says "we should fix that" — a ticket appears, assigned, prioritized, with context from the conversation.
Talk to your project. "What broke last night?" "Show me the PR from the billing agent." "Deploy the fix." Natural language, instant action.
Your command center. Voice-first project management with a native browser so agents can test websites live while you watch. Built for founders and team leads.
AI agent workstation built on VS Code. Queue tasks, run agents, and let the IDE control the Desktop for full-stack testing. Where the agents actually live.
Approve agent PRs from your phone. Review tickets on the train. Get notified when Overseer fixes something at 3 AM. You're connected, always.
Not people. Agents. Each one knows your codebase, your patterns, your past decisions. They work in parallel, they never sleep, and they cost less than your morning coffee.
You + 100 AI agents. That's not a side project — that's a startup with a team.
10,000 agents on one idea. That's not a tool — that's an army that makes you a founder with infinite bandwidth.
Your org runs on Codmir. Dedicated infrastructure, compliance, and white-glove onboarding.
The math: A junior developer costs ~$6,000/month. For $49, you get 10,000 agents that know your codebase better than any new hire. They start shipping on day one. No onboarding. No ramp-up. No Slack questions.
The era of mental labor is here. The speed of development is about to change on a level the world has never seen. Don't fight it. Lead it.
Join us.
Or install from your terminal
curl -fsSL https://codmir.com/install.sh | bash2 min
Avg time to auto-fix
10,000
Agents per task
$0.003
Per agent execution
Free to start. No credit card. Cancel anytime.