Technical deep-dives from the team building enterprise AI that doesn't decay.
We pointed an autonomous coding agent at Apache Superset (500K LOC) and shipped 3 security PRs in under 8 minutes — including a Flask 2→3 major version upgrade that Dependabot abandoned.
2,847 build cycles. 3 laptops. $1,526 total cost. The real data on autonomous AI development — success rates, failure taxonomy, cost breakdowns, and five counterintuitive lessons from running AI coding agents for 6 months straight.
From self-healing infrastructure to self-improving code pipelines — five layers of autonomy, the real economics, and the technical architecture behind systems that operate themselves. With production numbers from our own stack.
Decision engines, feedback loops, and world models — the three pillars of autonomous architecture. With real code from our build engine that has completed 300+ autonomous builds.
Five layers of automated recovery, from intelligent retry to structural adaptation. How our build engine detected a memory leak, rewrote the fix, and resumed — all at 3 AM while we slept.
MĀRGA's routing cut LLM costs 81% by optimizing itself in production. Our build engine's first-attempt success climbed from 54% to 72% — without a code change. This is how.
312 builds. 1,847 auto-generated tasks. 67-hour autonomous streak. The operational playbook for running systems that run themselves — with every number from our production dashboard.
AI that improves AI, self-evolving codebases, autonomous feature development. The enterprise software paradigm shift is already underway — and we're building it today.
From alpha with 5 users to 10K concurrent — the performance optimizations, cost management strategies, monitoring patterns, and architectural decisions that got us there without a single hire.
Two people. Three AI products. Five microservices. Zero full-time engineers. What actually happened when we built an entire AI platform using autonomous coding agents — the wins, the disasters, and everything in between.
Case study: MĀRGA intelligent LLM router reduced API costs by 73%, achieved 99.97% uptime with auto-failover across 4 providers, and improved latency by 40%.
Case study: How RAKṢĀ + Datadog Code Security caught 46 SAST findings and 10 CVEs in AI-generated code before deployment. Zero secrets leaked.
Case study: How DevOps RAG reduced mean time to resolution from 45 to 15 minutes by making runbooks instantly queryable via AI-powered retrieval.
We built three production AI products across 5 microservices with two people. Here are the hard-won lessons about architecture, cost, quality, and the tradeoffs nobody warns you about.
How a 2-person startup uses autonomous build agents across distributed Mac nodes to ship like a 20-person team — 24/7, no sprints, no standup.
Self-healing test suites, behavioral probes, adversarial fuzzing — how autonomous QA systems verify AI products like LLM routers and security scanners without human intervention.
Parallel builds, dependency management, autonomous orchestration, and the architecture decisions behind shipping MĀRGA, RAKṢĀ, and DevOps RAG across distributed Mac nodes.
How we built a distributed AI platform across 3 machines, 5 microservices, and 3 cloud regions — with two people and an autonomous build engine that ships code at 3 AM.
Where Avyay is heading — the 5-pillar roadmap, autonomous software thesis, target market analysis, and competitive positioning for enterprise AI that doesn't decay.
Vector similarity isn't understanding. Here's why most RAG systems fail at enterprise scale — and how knowledge graphs turn retrieval into reasoning.
Your company's departments were designed for humans with limited working memory. AI agents are rewriting the rules.
A small classifier in front of your LLM stack can cut costs by 80% and actually improve output quality.
How we built a distributed inference architecture using consumer hardware, Tailscale, and Ollama.