Alpha 0.1 · Codex Labs LLC

The OS that
knows itself.

Zeos is an operating system built from signal chains. Everything — hardware, software, identity — is the same pattern: signals in, transform, signals out. It discovers its hardware. It learns from experience. It gets better over time.

Z+ is its native language. is the only operator you need.

Drag to see the difference.

Same program. Same features. Thousands of times less code.

Microservices · Java/Go/Python · ~2M lines
16,667x less code
Z+ · 120 lines
// Timeline Service (Go) — 1 of ~200 microservices
package timeline

import (
    "context"
    "github.com/twitter/timeline/cache"
    "github.com/twitter/timeline/fanout"
    "github.com/twitter/timeline/storage"
    "github.com/twitter/timeline/pubsub"
)

type TimelineService struct {
    cache    *cache.RedisCluster
    store    *storage.Manhattan
    fanout   *fanout.Service
    pubsub   *pubsub.EventBus
    metrics  *metrics.Client
}

func (s *TimelineService) OnTweet(ctx context.Context, tweet *Tweet) error {
    followers, err := s.store.GetFollowers(ctx, tweet.AuthorID)
    if err != nil {
        s.metrics.Incr("timeline.fanout.error")
        return fmt.Errorf("get followers: %w", err)
    }

    for _, batch := range chunk(followers, 1000) {
        if err := s.fanout.Push(ctx, batch, tweet); err != nil {
            s.metrics.Incr("timeline.fanout.batch_error")
            log.Error("fanout batch failed",
                "author", tweet.AuthorID,
                "batch_size", len(batch),
                "error", err,
            )
            // retry logic, dead letter queue, etc.
            s.pubsub.Publish("fanout.retry", RetryEvent{
                Tweet: tweet,
                Batch: batch,
                Attempt: 1,
            })
        }
    }

    // Update author's own timeline
    if err := s.cache.Prepend(ctx,
        fmt.Sprintf("timeline:%s", tweet.AuthorID),
        tweet.ID,
    ); err != nil {
        return fmt.Errorf("cache update: %w", err)
    }

    s.metrics.Incr("timeline.fanout.success")
    s.metrics.Histogram("timeline.fanout.followers",
        float64(len(followers)))
    return nil
}

// ... plus 199 more microservices for:
// auth, search, trending, notifications,
// media upload, content moderation, DMs,
// rate limiting, analytics, ads, ...
"zp-comment">// chirp.zp — the whole thing

user : auth.identity "zp-operator">-> {
    voice,    "zp-comment">// what they say
    ears,     "zp-comment">// who they follow
    pulse     "zp-comment">// their activity
}

"zp-comment">// posting = emitting a signal
voice "zp-operator">-> post : {text, media, timestamp: t}

"zp-comment">// post goes three places at once
post "zp-operator">-> {
    vault.store,
    ears.followers "zp-operator">~> timeline,
    content.index
}

"zp-comment">// follow is a wire. that's it.
user_a.voice "zp-operator">-> user_b.ears

"zp-comment">// timeline IS the follow graph
ears "zp-operator">-> "zp-operator">| "zp-keyword">merge(by: timestamp) "zp-operator">| "zp-operator">-> timeline

"zp-comment">// like = "zp-keyword">tap that amplifies
post "zp-operator">~> like(user) "zp-operator">-> post.heat↑

"zp-comment">// trending = "zp-keyword">resonance
content.index "zp-operator">-> extract(topics) "zp-operator">-> topic_stream
topic_stream "zp-operator">-> "zp-keyword">resonance(threshold: "zp-number">1000,
    "zp-keyword">within: 1h) "zp-operator">-> trending

"zp-comment">// moderation is structural
post "zp-operator">-> safety "zp-operator">@ ai("toxic-detect.zdx")
     "zp-operator">-> "zp-keyword">gate(safe) "zp-operator">-> deliver

"zp-comment">// the whole backend. "zp-number">120 lines.
"zp-comment">// no microservices. no kafka.
"zp-comment">// just wires.

Signal graphs, not instructions

Z+ programs describe connections, not procedures. The OS resolves them simultaneously. No loops. No threads. No locks. The wiring IS the program.

Δ

The OS feels its silicon

Every core, every card, every bus — measured continuously. Not spec sheets. Real-time telemetry. Your machine optimizes for its actual capability, not its SKU label.

~>

Everything is the same pattern

32 programs across every industry. Healthcare, finance, robotics, gaming. All the same architecture: signal in → filter → route → signal out

32
Z+ programs
32K+
lines of kernel C
130+
kernel source files
38M+
conventional LOC replaced
15,000x
average compression

The entire language fits on a card.

Flow
signal flows
~> tap (observe only)
-x> sever (disconnect)
<-> exchange (request/response)
@ pin to hardware
Structure
| merge (converge)
{} fork (split)
Logic
gate pass or block
knee smooth transition
delta rate of change
resonance convergent evidence
Time
t t-1 t-2 temporal access
on_silence absence is data
baseline learned normal
sustained must hold for duration