Context Window Limits: Griffin AI vs Mythos
Context-window size matters less than context quality. A look at how Griffin AI's engine-grounded context beats pure-LLM retrieval at monorepo scale.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Context-window size matters less than context quality. A look at how Griffin AI's engine-grounded context beats pure-LLM retrieval at monorepo scale.
Frontier model pricing is rising even as cheaper alternatives proliferate. The 2026 architectural response is multi-tier model routing — and the security implications are non-trivial.
Shipping AI features without an eval harness is shipping without tests. Here is how to build one that actually gates releases without becoming a bottleneck.
Chain-of-thought helps LLMs with multi-step problems. For vulnerability reasoning, it helps — but only when the chain is grounded in structured evidence.
AI security protects models, training data, and agentic systems from prompt injection, poisoning, and unsafe autonomy — here's what it covers and how to build a program.
LLM security protects model weights, prompts, outputs, and the AI supply chain from injection, leakage, and compromise—here's what it covers and how to defend it.
Tracking remediation SLAs in spreadsheets is how programmes drift. Here is how to track SLAs in the same system that finds, fixes, and merges vulnerabilities.
Time from contract signature to first meaningful finding is the metric procurement cares about. Griffin AI and Mythos-class tools diverge in week one.
Gemini's pricing table favours long-context workloads. Security scans have long-context structure. The question is how much context fits into the architecture.
Weekly insights on software supply chain security, delivered to your inbox.
Your first fix PR is minutes away.
No sales call required, even your agent can complete the purchase over MCP.