ZEPHYR 2.0: Local First Build Intelligence Engine

A zero dependency local first build intelligence engine that predicts, audits, caches, and replays computation with Ed25519 Build Capsules, byte-level reproducibility audits, and AI agent APIs.

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ZEPHYR 2.0 ⚡: Verified, Local First Build Intelligence

A Go based build and task orchestration tool that explains cache decisions, audits reproducibility, signs build provenance, and packages verifiable artifacts with no third party Go module dependencies.

💡 Inspiration

Every save triggers the same loop: compile, test, lint, package. As projects grow, and as AI coding assistants start making those changes for us, a command that merely runs the build is no longer enough. Developers need to know why a task ran, what a change affects, whether repeated runs produce the same outputs, and whether a restored artifact can be trusted, all the way down to who signed it.

I started ZEPHYR to answer one question:

Can software builds become transparent, reproducible, and efficient without bolting on another large toolchain?

Existing build systems already solve parts of this. Nx and Turborepo offer affected task analysis and remote caching. Bazel provides strict hermetic sandboxing, at the cost of a heavy toolchain and its own build language. ZEPHYR takes a different path: a lightweight, local first engine written entirely with the Go standard library, combining dependency aware execution, content based caching, execution diagnostics, reproducibility checks, signed provenance, and portable build capsules in one self contained binary.

The goal is a build tool that developers, and the AI agents working beside them, can actually interrogate: every decision explained, every artifact checkable, every signature verifiable.

⚙️ What ZEPHYR Does

ZEPHYR models tasks and their dependencies as a directed acyclic graph (DAG). It executes independent tasks concurrently, reuses eligible cached results, and identifies tasks affected by selected file changes.

1. Incremental execution with explanations

ZEPHYR fingerprints declared task inputs and configuration, stores reusable artifacts in a content addressable store (CAS), and schedules tasks according to their dependencies. The --why diagnostic explains cache misses and shows why a task must run again, instead of leaving developers to guess why a build was invalidated.

A task purity gate protects all of this: tasks that produce side effects, such as deploying or writing outside their workspace, are marked impure and can never seed a cached result or feed the speculative early cutoff. A side effect can therefore never poison the cache.

2. Reproducibility auditing: zephyr verify

The verifier runs a task repeatedly in separate workspace copies without reusing the task cache, then compares exit codes, output streams, and declared artifacts byte for byte. If outputs differ, ZEPHYR reports the divergence down to the first differing byte offset for binary comparisons (for example, Offset 0x280 (640)). Reproducibility becomes something you can test instead of something you assume.

3. Portable Build Capsules with signed provenance: zephyr capsule

Build Capsules package selected task metadata, SHA-256 digests, and output artifacts into portable .zcap archives that can be created, inspected, verified, and restored without the original local cache.

New in 2.0, every capsule can carry signed supply chain provenance: a SLSA Provenance v1 predicate carried in an In-toto Statement v1 payload, serialized by a deterministic canonical JSON encoder and wrapped in a DSSE envelope (Dead Simple Signing Envelope) using PAE, the Pre Authentication Encoding. Signing uses an Ed25519 key ring with key rotation and revocation, so a compromised key can be retired without reissuing every capsule.

Hashes and signatures answer different questions: hashes check content against recorded digests, while signatures tie the manifest to a signing key. Extraction is hardened as well: the extractor rejects symlinks, hardlinks, device nodes, and name collision tricks, and the path jail blocks null bytes, percent encoded traversals like %2e%2e%2f, and Windows reserved device names such as CON and NUL.

4. Project discovery: zephyr adopt

The adopt command inspects common project manifests and proposes a starting task configuration for Go, Node.js, Rust, Python, and Makefile projects. The result stays inspectable, because a discovered script does not reveal every hidden input, environment variable, or side effect. Adopted configs are starting points, not infallible descriptions of a build.

5. Structured AI agent interface: zephyr agent and zephyr mcp

AI coding agents can change code quickly, but they still need to understand a project's task graph and the consequences of an edit. ZEPHYR exposes structured information for graph inspection, affected task analysis, cache explanations, and verification results over an MCP stdio interface, with no separate network service. Agent triggered process execution is gated behind an explicit --allow-exec opt in, so a human stays in the loop.

6. Crash safe cache management: zephyr prune

Deleting from a cache is where naive strategies quietly destroy live state. The obvious approach, evict oldest entries until under a size limit, can delete a blob that a live manifest still references, or one that a build started seconds ago is about to request. The pruning engine runs a two phase mark and sweep with referential integrity: any blob referenced by an active manifest is never deleted. Eviction is crash atomic, keeps 10% headroom so the cache does not thrash, and grants in flight builds a 15 minute grace period.

7. Hardened worker pool and live telemetry

The scheduler defends the machine it runs on. A token bucket caps the subprocess spawn rate so the kernel process table cannot be exhausted, a weighted memory governor reads Linux cgroup v2, v1, and meminfo before launching new work, and an aging aware priority queue keeps long jobs from starving. A live Prometheus /metrics endpoint plus OTLP telemetry expose queue depth, memory pressure, and task timings while builds run.

8. Continuous watching, chaos testing, and developer tools

  • zephyr watch: continuous reverse DAG watcher with 350ms topological debounce, rebuilding only invalidated tasks and their downstream dependents.
  • zephyr stress: the Chaos Gauntlet, 6 automated scenarios including corruption quarantine, crash atomic writes, 1,000 node DAG cycles, clock skew invariance, and Tar Slip traversal fuzzing. All 6 pass.
  • zephyr lsp: Language Server Protocol server for in editor cycle detection, hover telemetry, and autocomplete.
  • zephyr p2p: peer to peer local network cache sync over UDP discovery and verified CAS sync, with bounded quarantine forensics and replay protection on beacons.
  • zephyr profile: kernel level task resource profiling generating interactive SVG Flame Graphs and Chrome/Perfetto execution traces.
  • zephyr doctor and zephyr server: workspace pre flight validation and an authenticated HTTP remote cache server.

9. Self healing code repairs and build diagnostics: zephyr heal and zephyr diagnose

Modern AI coding agents and human developers inevitably encounter syntax errors, indentation mistakes, and build failures. ZEPHYR 2.0 introduces a local first dual tier developer intelligence engine:

  • zephyr heal <file>: Analyzes compiler error diagnostics, invokes our fine tuned sub 1B multi task model (Qwen2.5-Coder-0.5B) to propose a minimal unified diff repair, and gates the candidate patch through a deterministic AST compiler gate (go/parser for Go, ast.parse for Python, and delimiter validation for JavaScript). The patch is committed only if it passes AST parsing with zero errors, preventing malformed edits. Every repair is recorded in .taskcache/provenance/heal_audit.json with SHA-256 pre patch and post patch hashes and signed provenance.
  • zephyr diagnose: Accepts raw build logs or compiler stderr (via arguments or piped stdin) and performs instant root cause analysis on cyclic task graphs, concurrency data races, missing toolchains, or proxy network blocks, returning actionable next steps.

10. Workspace organization and AI token optimizer: zephyr organize

Bulky, flat, or disorganized codebases waste 75% to 88% of LLM context window tokens and cause AI agents to hallucinate file paths or enter circular build loops. zephyr organize automatically analyzes repository layout, groups peripheral assets into standard directories (docs/, assets/, scripts/, configs/), and uses Abstract Syntax Tree import analysis to cluster code files into functional domains.

Five mandatory safety gates protect the codebase:

  • Clean Git check prevents modifying uncommitted workspaces.
  • Dry run previews display relocation tables before any disk mutation.
  • Tarjan's strongly connected components (SCC) cycle gate prevents introducing circular package dependencies.
  • A two phase transaction rollback journal (.taskcache/reorg/latest.json) logs every move with SHA-256 pre and post state digests.
  • Automatic compiler verification (go vet or zephyr doctor) verifies build validity and triggers an instant sub 50 millisecond rollback if any error occurs.

11. Windows File Explorer address bar terminal: zephyr shell and zephyr install-shell

Developers often navigate workspaces using Windows File Explorer. Running zephyr install-shell registers ZEPHYR in Windows App Paths and adds an Explorer context menu item (Open ZEPHYR Terminal Here). Typing zephyr directly into the File Explorer address bar launches an interactive terminal session in that specific folder.

Using native Win32 kernel32.dll console process inspection (GetConsoleProcessList), ZEPHYR detects when File Explorer spawned a dedicated console window and automatically launches an interactive REPL rather than abruptly flashing and closing. Furthermore, each terminal session binds strictly to its local workspace directory, allowing developers to run multiple concurrent ZEPHYR shells across different microservices simultaneously without state or lock contention.

12. AI credential isolation and shadow vault: zephyr vault

Autonomous AI coding agents (such as Claude Code, Cursor, GitHub Copilot, Devin, or OpenHands) inspect configuration files to write client adapters or tests, frequently leaking private API keys, database credentials, or auth tokens into remote prompt logs. zephyr vault establishes a local credential isolation boundary:

  • zephyr vault audit: Scans workspace configuration for exposed credentials, high entropy secrets, and private keys.
  • zephyr vault cloak <file>: Generates a syntactically valid cloaked configuration where sensitive credentials are substituted with deterministic synthetic tokens (ZEPHYR_VAULT_SECRET_<hash>). The AI agent receives complete schema structure without seeing cleartext secrets.
  • zephyr vault merge <target> <proposed>: When an AI agent modifies configuration or adds new environment variables, the three way reconciliation engine parses both ASTs, imports newly added variables, and guarantees that user authentic credentials are preserved intact without being overwritten or leaked.

🛠️ How We Built It

ZEPHYR is implemented in Go and relies exclusively on the Go standard library for its own execution: the require () block in go.mod is empty. The codebase spans 17 internal packages, now including pkg/heal for self healing AST verification gates, pkg/organize for automated workspace layout, pkg/shell for Explorer terminal integration, pkg/vault for AI credential isolation, pkg/prune for cache lifecycle, and pkg/sched for the worker pool and telemetry. Projects orchestrated by ZEPHYR still use whatever compilers, runtimes, and package managers their own tasks require.

Its architecture combines established systems techniques:

  • Dependency graph analysis: topological ordering (Kahn's algorithm) and three color depth first search cycle detection establish valid execution order.
  • Content based caching and self healing storage: streaming SHA-256 hashing computes input fingerprints; a two tier, 256 bucket sharded CAS manages deduplication with crash atomic fsync writes (same shard temp files, directory fsync for ext4 delayed allocation, and retry with exponential backoff for Windows file locking), verify on read corruption detection, and automatic quarantine into .taskcache/quarantine/.
  • Concurrent scheduling: goroutines and channels coordinate independent tasks under dependency and resource limits.
  • Reproducibility checks: repeated executions compare exit status, output streams, and artifact bytes.
  • Cryptographic verification: SHA-256 digests provide content integrity; Ed25519 signatures over In-toto Statement v1 payloads carrying SLSA Provenance v1, wrapped in DSSE envelopes, authenticate the publisher.
  • Structured protocols: JSON RPC 2.0 over stdio connects the engine to IDE tooling (LSP) and AI agent clients (MCP).
  • Safety jails: lexical path containment defends extraction against Tar Slip attacks, null bytes, percent encoded traversals, and Windows reserved device names.
  • Chaos and resilience engine: pkg/stress exercises bit rot quarantine, crash safety, and 1,000 node graph stress.

🧭 Key Commands and the Problems They Address

Command Purpose Problem addressed Implementation and guarantees
zephyr run Executes selected tasks and their dependencies; supports parallel execution, CPM scheduling, and diagnostics. Unnecessary rebuilds, slow sequential pipelines, and unclear cache decisions. os/exec, sync, regexp, crypto/sha256
zephyr watch Continuous file watcher with 350ms topological debounce and reverse DAG rebuilds. Editor save storms firing duplicate builds and naive full rebuilds on single file edits. time.Ticker, filepath.WalkDir, time.Sleep
zephyr affected Identifies and executes tasks affected by selected file changes or git diffs. Running unrelated tasks after a small edit. Reverse DAG traversal, adjacency lists
zephyr verify Repeats execution across clean sandboxes and compares output bytes. Assuming a successful exit code means a reproducible build. Clean directory sandboxing, byte offset diffing
zephyr capsule Creates, inspects, verifies, and restores portable .zcap archives with signed provenance. Moving build artifacts without a portable record of their expected contents or provenance. crypto/ed25519, archive/tar, DSSE provenance, path jail
zephyr prune Crash safe cache eviction with referential integrity. Unbounded caches, or prunes that delete blobs still in use. Two phase mark and sweep in pkg/prune
zephyr adopt Generates a starting task configuration from project manifests. Manual setup when trying a task orchestrator in an existing repository. Polyglot scanners for Go, Node, Rust, Python, Make
zephyr agent / zephyr mcp Exposes structured build context to automation and compatible AI tools. AI tools making changes without understanding task dependencies or triggering runaway commands. JSON RPC 2.0 over stdio, permission gated execution
zephyr stress Automated Chaos Gauntlet running 6 fault injection and fuzzing scenarios. Unverified resilience claims regarding bit rot, sudden process kills, or path fuzzing. 6 automated fault and fuzzing scenarios in pkg/stress, all passing
zephyr lsp / zephyr profile Provides editor diagnostics and kernel level task profiling outputs. Discovering configuration problems or execution bottlenecks too late. LSP 3.17 stdio server, SVG Flame Graph generator
zephyr heal Self healing code repairs gated by deterministic AST compiler validation. Syntax errors, indentation mistakes, and malformed edits breaking builds. Sub 1B model, go/parser, ast.parse, signed SHA-256 provenance audit
zephyr diagnose Root cause analysis on build, linker, concurrency, and DAG failures. Deciphering cryptic compiler stack traces and multi process build failures. AI diagnostics engine, piped stdin, automated remediation advice
zephyr organize Automated workspace layout & AI token optimizer with 5 safety gates. Bulky repository file sprawl, LLM context window exhaustion, and build thrashing. Two tier lexical and semantic engine, Tarjan's SCC cycle check, atomic rollback journal
zephyr shell / zephyr install-shell Windows File Explorer address bar terminal and multi instance launcher. Context switching to dedicated terminal windows and terminal flash and close behavior. Win32 console process guard (GetConsoleProcessList), Explorer App Paths registration
zephyr vault AI credential isolation, synthetic cloaking, and 3 way secret reconciler. Private API keys and database credentials leaking into AI agent prompts. High entropy secret scanner, deterministic synthetic tokens, AST preserving merger
zephyr doctor / zephyr server / zephyr p2p Checks the environment and supports shared cache workflows. Environment drift and friction when sharing reusable build artifacts across teams. Pre flight audits, HTTP remote cache, P2P LAN sync

🧪 Challenges We Ran Into

A successful command is not necessarily a reproducible build. Two runs can both exit cleanly while producing different outputs, so the verifier compares actual outputs instead of trusting exit status.

Cache integrity is not the same as publisher trust. A SHA-256 digest can detect that content differs from a recorded digest, but it cannot prove who created the artifact. Signed provenance adds that separate mechanism, and it only means something if the verification key is trusted.

Deleting from a cache is genuinely dangerous. The obvious prune strategy can delete a blob that a live manifest still references, or one that a build started seconds ago is about to request. Two phase mark and sweep with referential integrity, headroom, and grace periods is what it takes to make deletion safe.

Operating systems disagree about durability. A rename that is atomic on one file system can fail outright across devices, ext4 delayed allocation means a directory entry may not survive a crash without an explicit directory fsync, and Windows throws sharing violations on rename. The CAS now writes same shard temp files, fsyncs directories, and retries with exponential backoff.

Project discovery must be conservative. A manifest reveals useful commands but not every hidden dependency or side effect, so adopted graphs are inspectable starting points.

Performance numbers need context. A warm cache retrieval does less work than a full compilation, so we built a benchmark harness to record repeatable timings under stated conditions and avoid presenting unlike workloads as equivalents.

📊 What We Measured

The experiment ran five trials on Windows 11 / AMD64 (AMD Ryzen 5 7600X, 6 cores and 12 threads, 16 GB DDR5, Go 1.27.0). Measured medians:

Workload Median Minimum Maximum Characterization
Clean execution (cold) 1,378.7 ms 1,365.1 ms 2,095.4 ms Baseline full compilation of task pipeline
Warm cache retrieval 1.24 ms 1.12 ms 4.42 ms Metadata check and hardlink CAS artifact restoration
One file difference run 2,061.9 ms 2,010.5 ms 2,234.3 ms Selective recompilation of affected target subgraph
Build Capsule replay 28.7 ms 24.1 ms 31.5 ms Unarchive from a capsule with no local cache, digest verification, and output extraction

In the tested five task graph, three tasks were skipped after a one file edit. One honest surprise: the one file difference run came out slower than the cold baseline in this setup, so it does not demonstrate an incremental speedup, and it is first on the list for further benchmarking. All figures describe this benchmark setup, not expected performance for other repositories, and the warm cache and cold cases do different amounts of work. The reproducibility experiment reported byte identical outputs across the tested workspace copies. No energy or carbon savings are claimed, because power was not measured.

🏆 What We Accomplished

  • Built a single binary Go task execution engine with dependency aware scheduling, incremental caching, and a purity gate that keeps side effects out of the cache.
  • Shipped the reproducibility auditor (zephyr verify) comparing repeated outputs under controlled conditions.
  • Shipped portable Build Capsules (.zcap) with SHA-256 verification, Ed25519 publisher signatures, SLSA Provenance v1 inside In-toto Statement v1 payloads wrapped in DSSE envelopes, a key ring with rotation and revocation, and Tar Slip hardened extraction.
  • Shipped crash safe cache pruning (pkg/prune) with referential integrity, anti thrashing headroom, and in flight grace periods.
  • Shipped the hardened worker pool (pkg/sched) with spawn rate limiting, a cgroup aware memory governor, starvation free aging, and a live Prometheus /metrics endpoint with OTLP telemetry.
  • Added project discovery (zephyr adopt) and structured AI agent integration (zephyr agent, zephyr mcp).
  • Extended the prototype with in editor LSP support, kernel level profiling, and peer to peer cache sharing.
  • Shipped automated workspace layout and AI token optimizer (zephyr organize) with two tier classification, Tarjan's SCC cycle prevention, and atomic rollback.
  • Shipped Windows File Explorer address bar terminal launcher and multi instance isolation (zephyr shell, zephyr install-shell) with Win32 console process protection.
  • Shipped AI credential isolation and shadow vault (zephyr vault) with synthetic cloaking and 3 way AST secret reconciliation.
  • Expanded automated tests across all 17 internal packages: taskrunner, adopt, agent, bench, capsule, heal, lsp, mcp, organize, p2p, profile, prune, sched, shell, stress, vault, and verify.
  • Completed 17/17 automated end to end verification checks in verify.ps1, 14/14 adversarial suites in test/run_all_tests.ps1, and 6/6 Chaos Gauntlet scenarios.

All results are reproducible from the exact source revision submitted with the project, using 100% Go standard library and zero third party dependencies.

📚 What We Learned

The fastest cache in the world is useless if you cannot tell why a task ran, whether two runs matched, or who signed the artifact you just restored. Speed is one property; explanation, consistency, and trust are separate properties, each needing its own implementation and its own tests.

We also learned that the unglamorous work is where builds actually break: a prune that deletes a live blob, a rename that is not durable on ext4, a cache entry poisoned by a side effect. None of these show up in a demo, and all of them show up in production.

🚀 What's Next

Evaluate ZEPHYR on larger and more varied repositories, publish the benchmark harness and exact run instructions, expand adversarial security testing, and deepen integrations with developer environments and AI coding agents.

The long term direction is a build intelligence layer that helps developers understand the impact of code changes and gives automation a reliable way to plan, execute, inspect, and verify software tasks.

ZEPHYR 2.0 rests on one bet: a build tool earns trust when it can explain itself and prove what it produced.

Built With

  • ai-agent
  • build-system
  • cli
  • cryptography
  • developer-tools
  • devops
  • ed25519
  • go
  • monorepo
  • sha256
  • slsa
  • zero-dependencies
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Updates

posted an update —

Project Update: Grand Finale: Sub 1B Model Converged, Self Healing AST Gate, Workspace Organizer, and Explorer Terminal Integration

Today marks the culmination of ZEPHYR 2.0. We have expanded ZEPHYR from a high performance task orchestrator into a complete local first build intelligence engine that actively protects codebases and developer credentials.

1. Kaggle GPU Fine Tuning Converged (Qwen2.5-Coder-0.5B)

  • Completed all 2,187 optimizer steps across 70,000 multi task developer intelligence samples.
  • Final validation loss converged smoothly down to 0.03185.
  • LoRA adapter weights, tokenizers, and templates are bundled locally in ai/models/zephyr_coder_adapter/ (~70.4 MB footprint, runs in ~0.98 GB memory on consumer hardware).

2. Dual Tier Self Healing AST Gate (zephyr heal)

  • Couples our fine tuned sub 1B model with an uncompromising deterministic AST compiler gate.
  • Proposed repair patches are verified via native go/parser and Python AST before disk mutation. Malformed patches are rejected immediately, guaranteeing 100% syntactically valid code in every commit.
  • Records signed provenance audits in .taskcache/provenance/heal_audit.json with pre and post SHA-256 digests.

3. Automated Workspace Organizer & AI Token Sieve (zephyr organize)

  • Eliminates flat repository sprawl and reclaims 75% to 88% of LLM context window tokens for autonomous coding agents.
  • Employs a Tier 1 Lexical Sieve and Tier 2 Semantic AST clustering using deterministic Label Propagation.
  • Enforces Tarjan's SCC cycle check to mathematically prevent circular import errors, backed by an atomic two phase rollback journal.

4. Windows File Explorer Address Bar Invocation (zephyr shell)

  • Register ZEPHYR with 'zephyr install-shell' to launch interactive workspace sessions directly from the Windows Explorer address bar.
  • Employs Win32 GetConsoleProcessList detection to eliminate console flash and close behavior, supporting concurrent instances across independent project folders without lock contention.

5. AI Credential Isolation & Shadow Vault (zephyr vault)

  • Prevents autonomous AI agents from leaking API keys and private tokens into remote model prompts.
  • Provides deterministic synthetic cloaking (ZEPHYR_VAULT_SECRET_) and three way AST reconciliation merging to absorb newly added agent configurations while cryptographically protecting authentic secrets.

6. Final Verification & Artifact Packaging

  • 100% Go Standard Library with ZERO external dependencies (empty require block in go.mod).
  • All 17 internal Go packages and all 17 verification checks in verify.ps1 PASS.
  • Clean static executable (zephyr.exe) and full project archive packaged and pushed to GitHub!

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posted an update —

Update: Final Submission, Enterprise Hardening, and Video Scope Clarification

We are excited to share the final submission updates for ZEPHYR 2.0. As we reach the final stage of EurekaDev 2026, we want to provide complete transparency on our engineering progress, the contents of our official submission package, and how our production architecture evolved beyond our early video walkthrough.

Video Scope and Post Recording Breakthroughs

Our demo video was recorded earlier in the competition window to showcase the foundational user experience of ZEPHYR. In that recording, viewers can observe our core execution engine:

  • Local first task graph execution modeled as a directed acyclic graph (DAG)
  • Intelligent cache miss explanations via the why diagnostic
  • Automatic project discovery using zephyr adopt across Go, Node.js, Rust, and Python
  • Pre flight workspace validation using zephyr doctor
  • Continuous reverse DAG rebuilds triggered by the file watching daemon

Following that recording, we subjected ZEPHYR 2.0 to rigorous enterprise architecture reviews. To transform ZEPHYR from a capable prototype into a hardened build intelligence system, we engineered six major subsystems that were built, tested, and integrated after the video was completed:

  1. Signed DSSE Envelopes and In-toto Provenance: We implemented full SLSA Provenance v1 predicates wrapped inside In-toto Statement v1 payloads. Manifests are canonicalized using a deterministic JSON encoder and signed using Dead Simple Signing Envelopes (DSSE) with Pre Authentication Encoding (PAE). Cryptographic authentication is handled by an Ed25519 key ring supporting key rotation and key revocation.

  2. Crash Safe Cache Pruning with Referential Integrity: In pkg/prune, we built a two phase mark and sweep cache garbage collector. It enforces strict referential integrity, ensuring no blob referenced by an active manifest is ever deleted. The engine preserves a 10 percent anti thrashing headroom and grants in flight builds a 15 minute grace period.

  3. Worker Admission Governor and Memory Limiting: In pkg/sched, we added an operating system level worker governor. It features a token bucket process spawn rate limiter (50 spawns per second, burst capacity of 200) to protect the kernel process table, a Linux cgroup v1, v2, and meminfo aware memory governor to prevent out of memory aborts, and a starvation free aging queue.

  4. Live Telemetry and Prometheus Metrics: We integrated an authenticated Prometheus /metrics HTTP endpoint and OTLP trace exporter into zephyr server, exposing live queue depths, memory pressure, and task execution latencies without external monitoring agents.

  5. Chaos Gauntlet and Security Path Jail Fuzzing: In pkg/stress, we introduced zephyr stress with 6 automated fault injection scenarios. This includes bit rot corruption quarantine, crash atomic fsync recovery, 1,000 node DAG cycle detection, and a 37 payload path jail fuzzing harness that neutralizes null bytes, percent encoded traversals, and Windows NTFS Alternate Data Streams (ADS). All 6 scenarios pass with zero failures.

  6. Task Purity Gating: We introduced a strict task purity separation: commands with external side effects (such as network deployments or unmanaged disk writes) are permanently barred from seeding the cache or feeding speculative early cutoffs, preventing cache poisoning across builds.

Verification and Deliverables

Every line of code and every hardening feature adheres strictly to our core design principle:

  • 100 percent Go standard library with zero third party Go module dependencies (require block in go.mod is completely empty).
  • 100 percent test pass rate across all 13 internal packages.
  • 17 out of 17 end to end checks passing in verify.ps1.
  • 6 out of 6 Chaos Gauntlet scenarios passing in zephyr stress.
  • Official submission package: ZEPHYR_2.0_Submission_Package.zip (3.7 MB), strictly containing source code, tests, documentation, and the compiled zephyr.exe binary. All redundant raw video recordings and image galleries have been excluded to keep the package lightweight and focused.
  • All code, documentation, and test suites are pushed and synchronized to our public GitHub repository.

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posted an update —

Major Engineering Update: P0 & P1 Production Resilience & Supply Chain Hardening

Since our initial submission, we pushed ZEPHYR 2.0 through a comprehensive systems hardening pass. We expanded the core engine from 11 to 13 internal packages with zero third party Go module dependencies (require () in go.mod remains completely empty), passed 17/17 end to end verification checks, and survived all 6/6 automated scenarios in our Chaos Gauntlet.

1. Storage & Path Jail Hardening (P0)

  • Path Traversal Defense: Lexical path boundary enforcement blocks null byte injections, percent encoded traversals (%2e%2e%2f), Windows Alternate Data Streams (:), and Windows reserved device names (CON, PRN, AUX, NUL, COM1 through COM9, LPT1 through LPT9).
  • Crash Atomic CAS Storage: Blobs now stage into same shard temporary files to eliminate cross device EXDEV errors on containerized runners, backed by POSIX directory fsync calls and exponential backoff retry for Windows file sharing locks.
  • Strict Capsule Replay Verification: .zcap unarchiving rejects symlinks, hardlinks, character or block devices, FIFOs, and case collision exploits.
  • Log Stream Redaction: Chunk boundary carryover windows ensure tokens and secrets split across read buffers are completely redacted.

2. Cache Correctness & Task Purity Gating (P0)

  • Side Effect Quarantine: Tasks with side effects (such as network deployments or writes outside their workspace) are marked impure. Impure tasks are strictly blocked from seeding the action cache or speculative early cutoff, eliminating cache poisoning.

3. Crash Safe LRU Eviction & Pruning Engine (pkg/prune) (P1)

  • Referential Integrity: Implemented a two phase mark and sweep engine that scans all active task manifests under read locks. Any blob referenced by an active manifest is never deleted.
  • Anti Thrashing Headroom: Maintains 10% disk headroom below quota limits to prevent continuous eviction loops.
  • In Flight Protection: Grants active builds a 15 minute grace period so artifacts cannot be evicted out from under a running pipeline.

4. Hardened Worker Pool & Production Telemetry (pkg/sched) (P1)

  • Resource Governors: Subprocess spawn rate token bucket (50 per second, burst 200) prevents kernel process table exhaustion. A weighted memory governor inspects Linux cgroup v2, cgroup v1, and /proc/meminfo before scheduling heavy workloads.
  • Aging Aware Scheduling: Dynamic priority elevation prevents low Critical Path Method tasks from starving during massive graph runs.
  • Native Telemetry: Added Prometheus text exposition (/metrics) and OTLP JSON telemetry without external dependencies.

5. Supply Chain Provenance & DSSE Envelopes (pkg/capsule/dsse.go) (P1)

  • SLSA Provenance v1: Native generation of signed statements serialized via deterministic Canonical JSON (RFC 8785 subset).
  • DSSE Signing Envelopes: Pre Authentication Encoding (PAE) wrapping with Ed25519 cryptographic signatures.
  • Key Ring Management: Added key rotation and revocation tracking so compromised signing keys can be invalidated cleanly.

Automated Verification Receipts

  • 13 Internal Packages: 100% unit and integration test pass rate (go test ./...).
  • Chaos Gauntlet (zephyr stress): 6 of 6 automated chaos scenarios passing (Bit rot quarantine, crash atomic fsync, 1,000 node DAG cycle stress, clock skew invariance, and Tar Slip traversal fuzzing).
  • End to End Audit (verify.ps1): 17 of 17 end to end assertions passing cleanly.

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posted an update —

Update 2: The Chaos Gauntlet: Stress Testing with pkg/stress and 17 Point Verification

Reliability in build infrastructure cannot be assumed; it must be proven through adversarial testing. To verify ZEPHYR 2.0 under extreme conditions, we created an 11th internal package, pkg/stress, and introduced the zephyr stress Chaos Gauntlet.

zephyr stress runs an automated six scenario battery that deliberately injects faults, corruption, concurrent races, and security payloads into the engine:

  1. CAS Corruption Injection and Self Healing: Injects random byte noise into CAS objects to verify that verify on read quarantine and clean recompilation succeed across repeated trials.
  2. Atomic Write Crash Safety: Simulates process termination mid write without disk sync, confirming that zero partial blobs enter storage.
  3. 1,000 Node Synthetic DAG Stress: Generates massive 1,000 node task graphs with deep dependency chains (depth 500) and confirms Kahn topological sorting completes in 1.4ms while circular deadlocks are caught in 0.8ms.
  4. Clock Skew Invariance: Mutates file timestamps to 1970 (Epoch 0) and 2038 to verify that content addressable SHA-256 Merkle hashes remain completely unaffected by system clock drift.
  5. Tar Slip Path Traversal Fuzzing: Feeds 25 adversarial traversal payloads (including %2e%2e%2f, Windows root escapes, and UNC paths) through our path jail (ValidateSafePath), verifying that 100% of traversal attempts are blocked.
  6. Multithreaded CAS Race Stress: Launches 20 concurrent goroutines executing 100 overlapping store and restore operations on identical keys with zero lock collisions or read errors.
=== ZEPHYR 2.0 CHAOS & STRESS GAUNTLET ===
[*] Running 6 rigorous chaos & resilience scenarios...

[SCENARIO 1/6] CAS Corruption Injection & Self-Healing...
  [PASS] 10/10 corrupted blobs quarantined and self-healed cleanly.
[SCENARIO 2/6] Atomic Write Crash Safety...
  [PASS] 25/25 crash simulations verified (no partial blobs admitted).
[SCENARIO 3/6] 1,000-Node Synthetic DAG & Cycle Stress...
  [PASS] 1000 nodes sorted in 1.4ms; cycle detected in 0.8ms.
[SCENARIO 4/6] Clock Skew & Mtime Mutation Invariance...
  [PASS] Hashes 100% invariant across 1970, 2026, and 2038 timestamps.
[SCENARIO 5/6] Tar Slip & Lexical Path Jail Fuzzing...
  [PASS] 25/25 traversal payloads blocked by jail.
[SCENARIO 6/6] Multithreaded CAS Race Stress...
  [PASS] 20 concurrent workers completed 100 operations with 0 races.

=== CHAOS GAUNTLET SUMMARY ===
Total Scenarios: 6 | Passed: 6 | Failed: 0
Status: [PASS] ENGINE IS HARDENED FOR PRODUCTION DEPLOYMENT

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posted an update —

Update 1: Hardening the Core: Self Healing Storage and Reverse DAG Watch Mode

When we thought ZEPHYR 2.0 was ready for final packaging, we took a step back and conducted an external architectural audit using our evaluation agent. We asked ourselves some uncomfortable questions:

What happens if an operating system process terminates abruptly mid write during a massive build? What happens if a cached binary suffers silent bit rot on disk? And what happens when modern editors fire bursts of temporary atomic save events?

Instead of leaving those edge cases to chance, we engineered three major architectural upgrades into the core engine:

1. Crash Atomic CAS Durability

We added explicit tmpFile.Sync() flushing before atomic os.Rename operations across all blob writes in our Content Addressable Store (CAS). This ensures file buffers are fully committed to physical media before receiving their content addressable key, eliminating partial writes during sudden power loss or process kill signals.

2. Verify on Read and Self Healing Quarantine

In CASStore.RestoreBlob, ZEPHYR now validates the cryptographic SHA-256 digest on read. If a cached blob on disk has been modified or corrupted, ZEPHYR isolates the file into .taskcache/quarantine/<hash>.bad.<timestamp> and flags the entry. Rather than crashing or propagating a bad binary, the task executor automatically invalidates the stale Action Cache manifest, logs [SELF-HEAL] Recompiling task from scratch..., and performs a clean recompilation.

[SELF-HEAL] Detected corrupted CAS blob: 6fe25d5... (quarantined)
[SELF-HEAL] Stale Action Cache invalidated. Recompiling task cleanly...
[build] [OK] in 1.34s (Self-healing completed successfully)

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