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AI Models & Agents
Foundation-model launches, agent workflows, benchmark analysis, and implementation playbooks for applied AI teams.
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Applied AI, cloud infrastructure, and engineering execution guides for modern product and platform teams.
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Explores solutions for large-scale organizations, focusing on tools and platforms that optimize operations, customer engagement, and business processes.
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Agent architectures, tool-calling patterns, and multi-agent deployment strategies for real-world workflows.
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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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Robotics & Physical AI
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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AI Models & Agents
Why Benchmark Accuracy Fails to Predict Operational Reliability in Autonomous Agents
Benchmark accuracy and operational reliability are different properties. Two 2026 papers show capability gains barely moved reliability.
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Developer Platforms
Driving Firefox Without CDP: Building a Lightweight Python Adapter over WebDriver BiDi
I wanted the small, direct feel of a raw Chrome DevTools connection in Firefox without pulling in a full automation framework. Firefox no longer exposes CDP, so I
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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.
Read story
Robotics & Physical AI
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.
Read story
AI Models & Agents
Measuring the Gap Between Visible UI Success and Persisted State in Browser Agents
I built RStackBench to test when browser agents look successful but fail the requested state: 288 conditions, 864 traces, and an open preprint.
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AI Models & Agents
Why Benchmark Accuracy Fails to Predict Operational Reliability in Autonomous Agents
Benchmark accuracy and operational reliability are different properties. Two 2026 papers show capability gains barely moved reliability.
Read story
Developer Platforms
Benchmarking Machine Learning on Consumer AMD GPUs: DirectML Performance and Workload Limits
Most machine-learning tutorials quietly assume NVIDIA. I wanted to know how far a consumer AMD Radeon RX 6600 could go on Windows without CUDA. I tested PyTorch through torch-directml, TensorFlow with the DirectML plugin, ONNX Runtime, matrix workloads, a full MLP training loop, and
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Cloud & Infrastructure
Skynet Verified DirectML Placement—Then Chose the CPU
An RX 6600 DirectML benchmark verified both LSTM nodes on DirectML, found CPU median latency lower, and withheld 1,062 unvalidated forecasts.
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Developer Platforms
Driving Firefox Without CDP: Building a Lightweight Python Adapter over WebDriver BiDi
I wanted the small, direct feel of a raw Chrome DevTools connection in Firefox without pulling in a full automation framework. Firefox no longer exposes CDP, so I built a roughly 700-line Python adapter that gives my code a CDP-style Tab API over WebDriver
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AI Models & Agents
Why Autonomous Review Agents Need Deterministic Guardrails Below Model Self-Assessment
A proof-backed field note on separating deterministic risk gates from neural review, using a real Gemini 3.5 and ChatGPT research run and official 2026 guidance.
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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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