Bakhtiyar

Hi my name is Bakhtiyar! Nice to meet everyone who landed on my page!

[Bakhtiyar @Dedaj371505]

@IvanRomanteev great project! keep going man

[Ivan Romanteyev @IvanRomanteev]

I BUILT TRUSTFLOW — AN AI SYSTEM THAT DOESN'T JUST ACT. IT PROVES WHAT HAPPENED.

After an intense engineering sprint, I'm excited to officially launch TrustFlow for the Meta Global AI Developer Hackathon 2026! 🏆

Here's the problem I wanted to solve:

What happens when an autonomous AI agent executes a financial operation, times out, and nobody knows whether the transaction actually happened?

Do you retry it?

What if the money was already transferred?

What if the agent confidently reports success without sufficient evidence?

This is where AI agents stop being just impressive demos — and start needing real engineering.

! INTRODUCING TRUSTFLOW !

An experimental framework for verifiable AI-agent workflows, built around one principle:

AI can propose. Humans authorize. Evidence proves.

Instead of blindly trusting an LLM's decisions, TrustFlow separates probabilistic reasoning from deterministic execution controls.

⚙️ WHAT'S UNDER THE HOOD?

🔹 Multi-step AI agent orchestration

🔹 Structured planning powered by Meta Muse

🔹 Deterministic policy and risk enforcement

🔹 Human-in-the-loop approval bound to exact SHA-256 plan hashes

🔹 Idempotent operations to prevent duplicate execution

🔹 Independent ledger verification

🔹 Recovery after timeouts and process crashes

🔹 Persistent workflow state, audit trails, and evidence tracking

🔹 Automated evaluation, regression testing, and browser-based verification

🤖 BUILT WITH META. POWERED BY META.

Meta Muse Code was a major AI-assisted engineering collaborator throughout development — helping with architecture, implementation, testing, debugging, and reliability improvements.

I also used OpenAI Codex for implementation work and independent engineering reviews.

Built TrustFlow through an intensive Muse Code-assisted engineering workflow. Our Meta Model API dashboard recorded 322M+ processed tokens and ~3.5K requests during the development period.

At runtime, TrustFlow integrates the Meta Model API with:

⚡ Muse Spark 1.3 — structured agent planning

🧠 Muse Wonder 1.0 — semantic review

📊 REAL TEST RESULTS

✅ 10 real Muse Spark API calls

✅ 1 real Muse Wonder semantic-review call

✅ Spark test matrix: 5/5

✅ 654 automated tests passed, 19 skipped

✅ Independent ledger checks and controlled crash-recovery demonstrations

The Wonder review used an offline-prepared synthetic scenario; full Wonder/mixed-model validation is not claimed.

All financial operations use synthetic funds. This is an engineering prototype, not a production banking platform.

💡 WHY THIS MATTERS

As AI agents move from answering questions to executing real-world actions, reasoning quality alone won't be enough.

We need authorization boundaries.

We need stable operation identities.

We need independent evidence.

And we need systems that can recover from failure without blindly repeating irreversible actions.

That's what TrustFlow is exploring.

🎬 WATCH THE FULL 3-MINUTE DEMO:
https://t.co/OGOPp7L87S

💻 OPEN-SOURCE GITHUB:
https://t.co/JYmlWMyZ84

👨‍💻 MY GITHUB:
https://t.co/4RFdrw4ixk
💻 MY LINKEDIN:
https://t.co/Mpxym3xYI2
❤️ If you find this project interesting, I'd genuinely appreciate a like, repost, or comment.

I'd especially love feedback from people working on AI agents, LLM infrastructure, distributed systems, and production AI.
What do you think is the biggest unsolved reliability problem in autonomous AI agents? 👇

#MetaAI #MetaHackathon #MuseCode #MuseSpark #MuseWonder #OpenAI #Codex #AI #ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI #AgenticAI #AIAgents #LLM #MultiAgentSystems #Python #MLOps #LLMOps #AIEngineering #FinTech

[Bakhtiyar @Dedaj371505]

Tiny mites. A big problem for beekeepers.

I built HiveLens using Meta’s SAM 3.1 + Muse Spark to count Varroa mites, assess comb photos, and show the evidence behind each finding.

Built for the Meta Global AI Developer Hackathon. Demo below 🐝