DEV Community: Albidev

Your User Count Is a Lie

Albidev — Thu, 09 Apr 2026 14:55:09 +0000

And it’s not a bug. It’s your model.

You open two dashboards.

One says 847k users.

The other says 612k.

That gap? It’s not noise.

It’s structural error.

The real problem

Identity systems fail in two ways:

inflate users
merge different people into one

Most teams catch the first.

The second is worse. It silently corrupts everything.

Why this happens

You’re using a relational model to represent something that isn’t relational.


sql
SELECT DISTINCT user_id FROM events

Your Model Is 94% Accurate. It's Also Making Terrible Decisions.

Albidev — Wed, 01 Apr 2026 14:41:59 +0000

Your model hit 94% accuracy. And then it made the worst possible decision in production.
Here's what nobody tells you about building ML systems that actually work.

The Problem Nobody Talks About
You train the model. You evaluate it. The numbers look great.
Then it goes live and starts recommending things that make zero sense in the real world.
Not because the math is wrong.
Because accuracy and decision quality are not the same thing.

A model can be statistically excellent and practically useless. Worse, it can be confidently wrong, which is the most dangerous state in any automated decision system.

What Most Projects Get Wrong
Most ML projects stop here:
raw data → model → prediction → done

That's not a decision system. That's a calculator with good PR.

A real decision system looks more like this:
raw data → feature engineering → model → explanation
→ decision logic → scenario simulation → outcome

Notice what's in the middle: explanation and scenario simulation.
That's where the real work lives.

The Project: Full Lifecycle, Zero Shortcuts

(Open loop: stick with me. The scenario simulation part alone will change how you think about model deployment.)

This project covers the full lifecycle of a production-grade ML decision system:

Data ingestion: raw, messy, realistic input
Feature engineering: transforming noise into signal
Model training: nothing fancy, just solid
Prediction explanation: why did the model say that?
Decision simulation: what happens under different policies?

Everything is reproducible. Everything reflects real production constraints: data drift, uncertainty, and policy trade-offs.

The Insight That Changes Everything
Here's the uncomfortable truth:
A model that performs well statistically can lead to catastrophic outcomes in practice.

Why? Because models optimize for the metric you gave them, not for the outcome you actually want.

You trained on historical data. But the world drifted.
You optimized for precision. But the cost of a false negative is ten times higher.
You trusted the prediction. But you never asked why it was made.

This project makes all of that visible.

Pattern Interrupt: Quick Question
When was the last time you tested what your model recommends under an economic shock, a data drift event, or a policy change?

If the answer is "never", you're not alone. But you're also flying blind.

Decision Quality Over Accuracy
The core shift this project forces:

Don't ask "is the model accurate?"
Ask "does the model lead to better decisions?"

Those are different questions. And they have different answers.

The project lets you explore scenarios where:
A high-accuracy model produces bad outcomes
A simpler model outperforms because it handles uncertainty better
Policy trade-offs change which prediction is actually "right"

That last one is the most underrated insight in applied ML.

What You'll Walk Away With
A reproducible pipeline you can fork and adapt
A framework for separating model performance from decision performance
Tools for explaining predictions, not just making them
A simulation layer to stress-test decisions before they hit production

No fluff. No toy datasets. Designed to reflect what production actually looks like.

The Real Flex
Anyone can train a model.
Building a system that knows when not to trust its own predictions, that's the actual skill.

This project is for developers who want to stop optimizing for leaderboard scores and start optimizing for real-world outcomes.

Drop a comment: Have you ever had a high-accuracy model fail in production? What broke first, the model or the decision logic around it?

Your AI Agent Is Not Broken. Your Runtime Is

Albidev — Tue, 24 Mar 2026 22:32:10 +0000

We lost a 4-hour agent run because a worker restarted mid-step. No logs. No recovery. The agent had called six tools and was halfway through a document pipeline. When the worker came back up, it started from zero. That’s when we stopped debugging the LLM and started debugging the runtime.

The Real Problem

Most frameworks, LangChain-style orchestrators, and prompt chaining libraries stop at the LLM call. They solve the conversation, not the execution loop. In production, agents fail silently: queue errors, worker restarts, malformed tool payloads, runs that leave no trace.

Retries, logs, cron checks – none of that fixes the root cause. The model is fine. The runtime is where things die.

Production-Ready Requirements

State persistence – every step and tool invocation written to durable storage. No memory caches. No stdout logs.
Decoupled execution – agent thinking and tool execution separate, queue-based, no blocking. Typed, validated tooling – catch malformed payloads at the boundary. Runtime bombs avoided.
Horizontal scalability – add workers without touching agent logic.
*Observability *– structured telemetry for every step, tool call, duration, and output. How Runloop Solves It

Stack: Bun, PostgreSQL, Redis + BullMQ, Zod, OpenTelemetry.

Bun: high-throughput I/O for agent workloads, low memory per worker.
PostgreSQL: source of truth. Persisted runs, replayable and auditable.
BullMQ + Redis: stateless workers, queue-based execution, retry policies, deduplication.
Zod: tool schemas validated at runtime, TypeScript autocomplete, serializable manifest. -OpenTelemetry: tracing at run and step level, easy integration with Grafana, Jaeger, Datadog. Architecture Core Runtime – manages state, transitions, recovery. Tool Registry – centralized repository, register once, available globally. Worker System – executes steps, persists results, stateless. Getting Started

docker-compose up -d cp .env.example .env bun install

Define a tool, launch an agent, and get a fully traced, persisted run in minutes.

0Albiere / Runloop

The production-ready runtime for AI agents. Persistent state, distributed execution, type-safe tooling, and built-in observability — built on Bun, PostgreSQL, and BullMQ.

⚡ Runloop v1

The Production-Ready AI Agent Runtime.

Stop building experimental scripts. Start building resilient, scalable, and persistent AI agents that actually survive production workloads.

🚀 Why Runloop?

Most AI frameworks focus on the LLM call. Runloop focus on the Execution Loop. It provides a robust runtime for AI agents, built on the fastest modern stack.

🏎️ Bun-Native Speed: Leverages the high-performance Bun runtime for blazing-fast execution and low overhead.
🛡️ Production-Grade Persistence: Every run, step, and tool result is backed by PostgreSQL. Never lose an agent's state or history again.
📦 Distributed Task Orchestration: Powered by BullMQ and Redis. Scale your agent workers vertically or horizontally with ease.
🛠️ Type-Safe Tooling: Define your tools using Zod schemas. Get automatic validation and perfect TypeScript autocompletion.
📊 Built-in Telemetry: Integrated tracing and monitoring to understand exactly what your agents are doing at every…

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