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Tamiz Uddin2d ago • 12 min read
Why My Agent Refused 96 Times Before Getting It Right: Lessons from Building Reliable AI Agents in Production
Real-world lessons from 96 iterations to build a production-grade AI agent. Patterns, failures, and ...
#ai#machine#learning#my
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Tamiz Uddin6d ago • 6 min read
From Hype to Production: The Harsh Reality of Shipping AI Agents Beyond the Demo
Why AI agent demos look nothing like production systems — and the concrete engineering work that bri...
#ai#machine#learning#hype
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Tamiz UddinAug 24, 2026 • 5 min read
Planning Over Execution: Lessons from 157 Agent Runs and the Rise of Orca-Style Agent Fleets
Analysis of 157 multi-agent experiments revealing why planning architectures outperform naive execut...
#ai#machine#learning#planning
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Tamiz Uddin2d ago • 8 min read
"My Agent Refused 96 Times": Building Self-Editing Agents with Hard Failure Modes
Stop rewarding hallucinations. Learn to build LLM agents with deterministic self-correction loops th...
#ai#machine#learning#my
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Tamiz Uddin4d ago • 11 min read
From Goroutines to Agents: Lessons from 1M Concurrent Threads and the New Wave of AI Engineering
How patterns from massive Go concurrency at 1M threads apply to building reliable AI agent systems i...
#ai#machine#learning#goroutines
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Tamiz UddinAug 24, 2026 • 6 min read
The Solo Founder Simulation: Lessons from Letting an AI Agent Run a SaaS While I Audited Its Human-Like Mistakes
A technical deep-dive into running a SaaS with autonomous AI agents, exposing the 'human-like' error...
#ai#machine#learning#solo
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Tamiz Uddin5d ago • 12 min read
Why Your AI Agent Doesn't Have a Reasoning Problem—It Has a Memory Problem: A Practical Guide to Production-Grade Agent State
Most AI agents fail because of broken state, not bad reasoning. Learn how to build production-grade ...
#ai#machine#learning#agents
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Tamiz Uddin4d ago • 12 min read
What Happens After the Agent Replies: Archiving Prompt History for Reproducible AI Workflows
Learn why prompt archival matters, the architecture patterns for capturing and storing AI conversati...
#ai#machine#learning#happens
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Tamiz Uddin3d ago • 11 min read
From Monolithic LLMs to Autonomous Rust Agents: Building the Next-Gen Developer Stack with uv, RAGFlow, and zeroclaw
Architect a next-gen AI developer stack using Rust agents, uv for dependency management, RAGFlow for...
#ai#machine#learning#monolithic
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Tamiz Uddin6d ago • 6 min read
From Cherry Studio to Destroylist: Mapping the New Developer Stack for AI Agents, Privacy, and Lightweight Tooling
Explore how modern developers are shifting from heavyweight LLM wrappers to decentralized agent fram...
#ai#machine#learning#cherry
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Tamiz Uddin3d ago • 14 min read
From Prototype to Production: Hard-Won Lessons Building Multi-Agent Systems That Actually Ship
Practical production lessons for multi-agent systems — testing, observability, state management, and...
#ai#machine#learning#prototype
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Tamiz Uddin6d ago • 7 min read
Beyond the Agent Hype: Architecting Observability, Memory, and Guardrails for Production AI Systems
Move past LLM demo scripts. A deep-dive into the production architecture patterns for observability,...
#ai#machine#learning#beyond
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Tamiz Uddin5d ago • 8 min read
Why Your AI Agent Fails at Observability: A Debugging Framework for Memory, Tool Calls, and RAG
Diagnose flaky AI agents by tracking memory state, tool execution, and retrieval failures. A technic...
#ai#machine#learning#agents
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Tamiz Uddin1d ago • 6 min read
Beyond the Cloud Bound: Why Local-First and Client-Side Privacy Are the Developer's New OS
Explore why the future of developer tooling demands local-first architectures and client-side privac...
#ai#machine#learning#agents
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Tamiz UddinAug 23, 2026 • 3 min read
The AI Agent Reality Check: Why MCP Backdoors Fail in Production
Explore why Model Context Protocol (MCP) implementations degrade in production despite working demos...
#ai#machine#learning#agents
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Tamiz Uddin4d ago • 5 min read
Beyond the LLM: Why RAG Checklists, Agent Observability, and Lightweight Infrastructure Are the New Developer Stack
Production-grade AI isn't about prompts; it's about deterministic infrastructure, observable agents,...
#ai#machine#learning#beyond
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Tamiz UddinAug 24, 2026 • 5 min read
From SGLang to OpenLogi: The Developer Shift Toward Local-First AI Infrastructure
Explore the architectural pivot from high-performance serving runtimes like SGLang to self-hosted lo...
#ai#machine#learning#sglang
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Tamiz Uddin5d ago • 7 min read
From Hype to Hard Reality: What We're Learning About Shipping AI Agents in Production
The gap between AI agent demos and production reality. Hard lessons on observability, cost, evaluati...
#ai#machine#learning#hype
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Tamiz UddinAug 25, 2026 • 7 min read
Why Your AI Agent Fails in Production: Bridging the Memory, Testing, and Tooling Gaps
Production AI agents fail due to fragile memory, insufficient evaluation, and poor tool orchestratio...
#ai#machine#learning#agents
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Tamiz UddinAug 24, 2026 • 15 min read
The Memory Bottleneck: Why AI Agents Fail and How to Fix Them with Self-Driving Tooling
Explore why LLM agents fail due to context fatigue and how autonomous tool-use architectures fix the...
#ai#machine#learning#memory
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Tamiz Uddin2d ago • 5 min read
The Agent Paradox: Why Memory, Trust, and the Refusal to Act Are the Next Bottlenecks in AI Engineering
Exploring the three critical failure points in autonomous AI agents: context degradation, reliabilit...
#ai#machine#learning#agents
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Tamiz Uddin1d ago • 6 min read
From API Dependency to Local Inference: Why Developers Are Betting on On-Device LLMs in 2026
An analysis of the 2026 shift from cloud API dependency to local inference, covering on-device execu...
#ai#machine#learning#api
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Tamiz Uddin4d ago • 19 min read
From Chatbot to Agent: Why Your AI App Needs Observability, Better Memory, and Real RAG
A production field guide for building AI agents — observability, persistent memory, and real RAG bey...
#ai#machine#learning#chatbot
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Tamiz Uddin5d ago • 4 min read
From Hype to Production: The Hard Truth About Shipping AI Agents in 2026
Move beyond the demo hype. A technical deep-dive into the reliability, observability, and cost chall...
#ai#machine#learning#hype
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