Adarsh

Every AI project should start with 3 ready layers: ARCH, MEMORY, EXEC. ARCH: IaC + schema + auth MEMORY: vector DB + TTL + append log EXEC: runner + safety hooks + observability Clone & go: git clone github.com/you/ai-starter make bootstrap && make run #AI #SRE 🚀

Open-source LLMs are mainstream. If your AI project is chaos, fix it in 90m: 1) Snapshot data + model commit 2) Runtime contract: inputs, latency, failover 3) Automate deploy + rollback (IaC + runbooks) Small discipline = 10x fewer fires. ⚙️ #AIOps #SRE

Build a scalable SaaS with agents in 3 moves: 1) Thin Node API for auth + routing 2) Stateless agents + durable events (Postgres WAL / Kafka) 3) Workflows as JSON + idempotent tasks Do this and you scale concurrency, observability, and cut cost. #NodeJS #AIagents #SaaS 🚀

Prompting is a prototyping hack. Production needs systems Stop tweaking prompts. Build: - tool contracts - stateful controllers - schema validation - observability + canaries - cost/rate guards Prompts are band-aids. Engineer the pipeline. 🔧⚙️ #AI #Agents #SRE #ML

Ship RAG that survives production: hybrid search (BM25 + dense) n-best rerank with tiny reranker model chunk with overlap + source IDs freshness: TTL + incremental re-embed cache n-best prompts & answers measure P99 & fix user-facing latency. #RAG #SRE 🚀

If you're still shipping monoliths in 2026, stop. Pattern that wins: 1) Node API + tiny domain AI agents 2) Event-driven workflows (no cron) 3) Typed contracts + canary metrics 4) Perf budgets & fast tail latency 🚀 Ship > optimize. #SaaS #NodeJS #AI #SRE ⚙️

From MVP→scale with AI: 3-priority checklist: 1) Prototype with vector DB + tiny LLM for cheap loop 2) Feature flags + data contracts; log signals as product metrics 3) Autoscale inference on spot GPUs + answer cache Ship. Measure. Iterate. #buildinpublic #SRE 🚀

Stop AI agents from pushing broken code. Shipproof repo: policy-as-code, signed commits, CI sandboxes, canary PRs, dry-run->approve->apply, auto-rollback policy: require: tests_pass && signed_by(agent) #DevOps #SRE #AI 🔐

Stop treating AI as a prompt; treat it as a system. Prompt -> LLM -> output = no state, no observability, fragile. Build: role services, memory+tools, validators, retries, logs. Ship agent systems, not prompts. #AI #SRE 🤖

Systems melting down? My CTO 4-step triage (what I do under pressure): 1) Kill the blast: enable circuit-breakers/throttle 2) Communicate: 60s status + ETA 3) Contain: isolate region/service, fail fast 4) Fix or rollback; own the RCA Runbooks + SLOs win. #SRE #CTO 🚨

Rule: don't accept AI code without tests. My AI→no-debt pipeline: 1) AI for scaffolds + unit+mutation tests 2) Typed templates + snapshots 3) Policy-as-code in CI: lint, perf, secrets 4) Auto-PRs → human gate + single-owner merge 3x speed, 0 tech-rot. #AI #SRE 🚀🛡️

Shipping AI in prod? Treat models like libraries: pin model+params, add contract tests + canaries, log seeds & outputs, have deterministic fallback, automated drift alerts, and single-command rollback. Make AI auditable, not experimental. #SRE #AI #NoTechDebt 🚀🔧

Most AI apps die after MVP because models are treated like magic, not infra. Fixes: - Circuit-breaker + rate-limits - Drift detection + label budget - Async queue + backpressure if latency>1s: return cached_prediction() #SRE #AI 🚨🔧

If your agents go rogue, it's not magic - it's bad state management. Fix: make CLAUDE.md your immutable memory, version intent headers, auto-expire TODOs, and gate memory edits via CI. intent: triage@v2 memory: /mem/readonly Hard stops > heuristics. 🔒🛠️ #AI #SRE #agents

Ship one atomic AI feature. Metric-first: measure inputs, latency, cost. Swap rule→model only when A/B lifts. Optimize: cache, batch, async. Auto-retrain + drift alerts. 6 months of this = compounding scale. 🚀 #BuildInPublic #AI

Codex | Claude Opus 4.6 | MCP agents — my 2026 stack: Backend: Codex — service scaffolds, SQL, tests Frontend: Claude Opus 4.6 — UX copy, state reasoning System: MCP agents — orchestration, retries, obs Rule: Codex PRs, Claude docs, MCP infra #AIStack #SRE 🚀⚙️

When systems break at scale, my decision loop: 1) Contain - flag/circuit, isolate 2) Stabilize - throttle/rollback/reroute 3) Restore - failover, targeted fix 4) Learn - postmortem + owner + deadline No ego. Move fast. 🔥⚠️ #SRE #CTO

If your system melts at 3am, do this in 60s: 1) Stop the bleed — throttle & rollback 2) Stabilize — isolate services 3) Communicate — status + ETA 4) Later: full RCA, postmortem triage(){ throttle(); isolate(); announce("ETA"); } #SRE #CTO #IncidentResponse 🔥🛠️

Build repos for Claude & Codex, not just humans. 3 rules: 1) Intent-first files - top comment: purpose, constraints. 2) Tiny modules + stable interfaces. 3) Tests = executable docs. Header example: # intent: summarize invoices monthly Ship clarity, agents scale. #AI #SRE 🤖

Make repos agent-friendly: 1) agents.json manifest + entrypoints 2) runnable mini-example + gold tests 3) explicit types & stable API schema 4) devcontainer + pinned runtime Agents run on contracts, not guesses. 🚀 #AI #Agents #DevTools