Blogs
2026
What the GPT-6 benchmarks mean for enterprise AI delivery.
How Anthropic's model performs on the hacker-opus test.
Benchmark results for Gemini 3.8 Flash on the AI-matic bench.
Harness engineering: boundaries, repair loops, and the verification gate that decides what ships.
Skills, component libraries, shader kits, icon sets, inspiration galleries, and design craft.
An evaluation framework for autonomous agents that iterate and improve on their own execution loops.
What Claude's watermarking approach changes for enterprise AI teams and provenance verification.
GoML's practical guide to graph engineering, knowledge graphs, and agentic traversal patterns.
Grok 4.6 benchmarked, framed as a cost-curve shift rather than a capability jump.
A good bookmark is a promise you make to a future self who has different interests and less time.
A read-through cache story about Memcached, Neon Postgres, and the seductive lie of a big number.
Testing Claude Opus 5 as the first frontier model GoML calls enterprise-ready.
The spiral took four evenings. The figure, nine minutes.
Both begin the same way: something is wrong and I do not know what.
Evaluating OpenAI's GPT-5.6 variants, Sol, Terra, and Luna across reasoning and latency.
An overview and internal evaluation of Grok 4.5 (high) performance and tooling integration.
Stand where they left you. Let the moths mistake you for the moon.
GoML's complete guide to Sonnet 5 architectures, context utilization, and latency optimization.
How Sakana AI's Fugu enables one unified API for smarter model routing and resilient production AI.
Why the orchestration harness matters more than the raw model.
A visualizer and blast-radius calculator for detecting and mitigating rogue AI agent behaviors.
Cutting a 3-hour AWS observability incident investigation down to 11 minutes with automated telemetry agents.
GoML's complete guide to configuring, deploying, and hardening the AWS DevOps Agent.
2025
A practical breakdown of Bedrock endpoint types, invocation patterns, and how to choose the right setup.
How decoding strategies — greedy search, top-k, nucleus sampling, and temperature — shape model output.
What each attention head is really learning, and why running several in parallel gives richer representations.
The scaled dot-product attention mechanism demystified — queries, keys, values, and the matrix math.
Start from raw tokens, walk through embeddings and positional encoding, and arrive at self-attention.
The exact math, Python, and ML fundamentals you need before diving into language models.