AI Content Library
Platform — AI Systems Research & Benchmarks
Published research and analysis across AI systems, automation, infrastructure, benchmarks, finance, and emerging technology.
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6 articles
Artificial Intelligence
Foundation-model launches, agent workflows, benchmark analysis, and implementation playbooks for applied AI teams.
Open archive 5 articlesTechnology
Applied AI, cloud infrastructure, and engineering execution guides for modern product and platform teams.
Open archive 4 articlesEnterprise Technology
Explores solutions for large-scale organizations, focusing on tools and platforms that optimize operations, customer engagement, and business processes.
Open archive 2 articlesAI Agents
Agent architectures, tool-calling patterns, and multi-agent deployment strategies for real-world workflows.
Open archive 1 articleAnalysis
Market intelligence, sector forecasts, and data-driven explainers built for strategic decisions and search intent.
Open archive 1 articleAI Research
Published articles and analysis across this topic cluster.
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AI Models & Agents
Stop Treating AI Agents Like Junior Employees
AI agents need distributed-systems discipline: durable state, idempotent retries, tracing, scoped permissions, and explicit human approval.
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Developer Platforms
Verification Before Velocity: Four Skynet Evidence Layers
Four open-source Skynet products reveal why verification must follow the product layer—and why unavailable evidence is not a pass.
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Developer Platforms
Why Raw Accessibility-Tree Counts Need Environment Controls
Two Windows hosts on one Chrome build returned different accessibility-tree node totals. The experiment shows why viewport and node roles must be reported.
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Developer Platforms
Comparing UIA and CDP: How Instrumentation Errors Masked Automation Click Performance
A measured Skynet field study of UIA, CDP, coordinate mapping, occlusion, and provider acceptance with corrected experiments and raw evidence.
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20 articles
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AI Models & Agents
Preventing Permission Drift: Why Agent Evaluation Passing Does Not Equal Action Authorization
An AI agent can pass every eval and still take an action it was never allowed to take. Permission drift is a control-plane failure, not a model failure. Here is the fix.
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AI Models & Agents
Designing AI Agents as Distributed Systems: Architectural Contracts for Production Reliability
A Skynet field note on why production AI agents need reliability contracts: idempotency, retries, traces, scoped tools, evals, and bounded blast radius.
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AI Models & Agents
Why Upgrading Foundation Models Will Not Fix Multi-Agent System Coordination Failures
Why a smarter LLM will not fix an AI system that fails at orchestration and verification: reliability comes from the weakest coordination layer, not the model.
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AI Models & Agents
Central Orchestrator Failures in Multi-Agent Systems: Separating Coordination from Worker Liveness
A 2026 field note on why multi-agent AI fleets fail at the control plane, and how live probes, decoupled agents, fallback chains, and independent monitors keep capability alive.
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Developer Platforms
Managing Intent Across Multi-Tool Control Loops: High-Throughput Execution After Eliminating Drift
A source-backed reflection on Skynet, anti-drift AI orchestration, provider routing, TODO memory, CDP proof, and execution speed.
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AI Models & Agents
Probing Autonomous Swarm Execution: Live Production Smoke Tests and Model Fallback Paths
Skynet production truth status: evidence report on the May 10 backend reload, Gemini and Codex smokes, and Claude fallback boundary for agent routing.
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AI Models & Agents
ScreenMemory: Measured Improvements in Visual Desktop Control
Abstract Autonomous desktop agents remain far below human performance on real-world operating-system benchmarks, with the best reported results reaching only 12.24% task completion on OSWorld compared to a 72.36% human baseline. We hypothesize that the primary bottleneck is not large-language-model reasoning but rather the
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AI Models & Agents
Forecasting Philippine Food Prices: Evaluating Machine Learning Models Against Real Out-of-Time Data
I wanted to know whether two decades of Philippine food-price data could produce a forecast that survives a real holdout period, not just a clean training score. I trained the Random Forest on 2000-2019 data and held out 2020-2023. It reached R² 0.91, MAE
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