It pulls of data from a server, it doesn't calculate local.

Hot take on the $1.3M/month screenshot everyone is sharing: This isn't a story about how powerful agents are. It's a story about how undisciplined agentic development has become. 603B tokens. 7.6M requests. ~79K tokens per request on average. That's not reasoning. That's stuffing the entire world into context every turn because nobody built a retrieval layer. I was on stage at Tech Alley's AI meetup this week making this exact argument. The pattern is everywhere — an engineering leader reached out to me recently because he was melting Claude Opus 1m token context window forcing it to reason across multiple repos. Every turn re-read every file. The fix wasn't a bigger context window. The fix was a code-map and symbol graphs over MCP. When my agent ships a feature, it doesn't read 10 repos. It queries semantic search against pre-built code-maps and symbol graphs to get back exactly the integration surface it needs. Token spend drops by an order of magnitude. Output quality goes up because the context is signal, not noise. Frontier model pricing is subsidized today. When that ends, the teams without a context engineering layer are going to discover their AI strategy was actually a venture-funded hallucination. Cost-per-shipped-feature is the only KPI that matters. Not tokens. Not requests. Outcomes per dollar. The harness isn't the bottleneck. Context is. That's what we built OutcomeOps to fix. -> outcomops.ai

Why We Built OutcomeOps (And What Comes Next) Every major corporate evolution follows the same pattern: a set of best practices, a few tools, and a promise of transformation. DevOps did this. So did Agile. So did Cloud. But each time, 80% of enterprises missed the point. They adopted the tools, not the mindset. They automated pipelines without aligning outcomes. They measured deploys instead of value. Now AI is here, and it’s happening again. Read more and subscribe to our Substack: outcomeops.substack.com/p/wh… #outcomeengineering #outcomeops #aiengineering #orginizationalintelligence

What you'll learn - What prompt engineering actually is and what it isn't - Why "context engineering" doesn't just mean RAG - Why Anthropic Skills, OpenSpec, and GitHub Spec Kit are the local optimization trap - How ADRs, code maps, and grounded legacy code form the systemic layer - Why enterprises need the systemic layer above their dev tools Why this matters at enterprise scale Spec-driven tools work for one repo, one developer, one greenfield project. When you scale across teams, departments, legacy systems, compliance regimes, and decades of institutional knowledge, spec-driven approaches create silos. In LEAN terms: classic muda. OutcomeOps eliminates this waste value stream flows via reusable context; working software is grounded in codified intent. Read more: Context Engineering deep dive: outcomeops.ai/context-engine… - Escaping Local Optimization Anti-Patterns: outcomeops.ai/blogs/escaping… AWS Kiro, OutcomeOps & Spec-Driven Context Engineering: outcomeops.ai/blogs/aws-kiro… - OutcomeOps the platform: outcomeops.ai Brian Carpio (founder): briancarpio.com

Prompt engineering is a skill. Context engineering is a system. Here's the 60-second explanation of why the difference matters at enterprise scale and why tools like Anthropic Skills, OpenSpec, and GitHub Spec Kit are the local optimization trap. Context Engineering Patterns. We define both terms, name the spec-driven tools that solve context engineering at the repo level, and show why real systemic context engineering ADRs encoded, code maps queryable, decades of legacy code in Java, .NET, Python, even ABAP all grounded is the layer enterprises actually need. youtube.com/watch?v=WERjfVDZ… #ContextEngineering #PromptEngineering #AnthropicSkills #OpenSpec #GitHubSpecKit #LocalOptimizationTrap #OutcomeOps

Quick poll for platform/engineering leaders in regulated industries: Biggest blocker with current AI coding tools? A) Security/IP risk B) No org-specific context C) Compliance hallucinations D) Other (Answers in our new FAQ 👇) Tabnine is an AI coding assistant focused on developer-facing experiences — code completion, chat, and agents inside the IDE — with on-prem and air-gapped deployment options for security-sensitive environments. OutcomeOps is an AI code generation platform focused on organizational context and governance. It generates entire features from Jira tickets, enforces ADRs at chunk-time, and routes every change through a self-review and human PR approval flow. Tabnine helps developers write code faster. OutcomeOps helps enterprises enforce standards at scale. They solve different problems — some customers use both. outcomeops.ai/faq

DevOps KPIs are theater. OutcomeOps KPIs tied to business results that matter. Nobody cares how many times you deployed last week. MTTR, lead time, deployment frequency they’re just vanity metrics. #DevOps #Leadership #TechStrategy #BusinessImpact briancarpio.com/2025/09/06/o…