// Topics / AI Operating Systems

AI Operating Systems

This hub collects the AI writing that is most useful for CTOs, founders, and engineering leaders who need to turn prototypes into reliable operating systems.

The archive is not about model hype. The through-line is operational: what to build, how to govern it, how to measure it, and where AI work fails when ownership is vague.

Start Here

Core Themes

Architecture

AI architecture is mostly about control surfaces. The model call is only one part of the system. The durable pieces are the routing layer, retrieval layer, validation path, observability, and rollback plan.

Useful next reads:

Governance

Good governance makes safe work faster. Bad governance turns every AI release into a committee meeting. The practical goal is explicit risk tiers, evaluation gates, and ownership for production behavior.

Useful next reads:

Economics

AI cost work is not just token optimization. The real metric is cost per useful outcome, including retries, evaluation, data work, human review, and incident response.

Useful next reads:

Teams

AI work breaks down when no one owns the boundary between platform, product, security, and operations. Strong teams make those interfaces explicit before scaling headcount.

Useful next reads:

Failure Modes

  • Treating AI as a feature instead of a runtime capability with ownership, telemetry, and rollback.
  • Measuring demo quality while ignoring cost per outcome and production drift.
  • Centralizing every AI decision until the platform team becomes a queue.
  • Shipping model behavior without evaluation cases tied to real workflows.

References

133 entries tagged “AI Operating Systems”

    Shadow AI Is an Operating Problem, Not a Ban · 4 min Banning AI tools removes your visibility, not the tools. Make the governed path the fast path and pull usage into the control plane. governance ai security Power Belongs on the AI Roadmap · 3 min Power, not models or GPUs, is the binding constraint. Treat energy and capacity as roadmap dependencies with real lead times. ai architecture strategy Regulatory Divergence Is a Routing Problem · 4 min One global AI policy is wrong in every market. Tag requests by jurisdiction, data class, and user type, then route policy like cost and capability. governance ai privacy Reliability Is the Autonomy Ceiling · 4 min Capability earns the demo; measured reliability earns the autonomy. Budget autonomy by failure cost times failure rate. ai reliability operations Your Vendor's Balance Sheet Is Your Risk · 4 min Integrate a model vendor and you inherit their balance sheet. Subsidized pricing is a repricing waiting to become your outage—price solvency as a dependency. strategy ai cost Agent Identity Is the New Control Plane · 4 min An agent that acts needs an identity—scoped, short-lived, attributable, revocable—not a shared API key. ai reliability security Token Prices Fell. AI Bills Did Not. · 3 min Per-token prices keep falling while bills climb. Manage cost per governed workflow, not price per token. cost ai executive The Benchmark You Didn't Build · 4 min Public benchmarks are contaminated and gamed. The only eval that matters runs on your traffic, your failure modes, your bar—and you own it. ai reliability metrics Leading Senior Engineers in the AI Era: Autonomy, Standards, and Accountability · 4 min Leading senior engineers on AI work needs one concrete standard: a definition-of-done built on evals, named failure modes, and escalation triggers. leadership ai teams Sovereignty-by-Design for AI: How to Win Regulated Enterprise Deals · 4 min Sovereignty is an architecture you can demonstrate, not a checklist you assert. Trust boundaries decide revenue boundaries. ai privacy governance Agentic Systems at Scale: The New Reliability Contract · 4 min Agentic systems need SRE-style reliability contracts with explicit blast-radius limits, fallback paths, and kill switches. ai reliability operations The Anti-Fragile AI Organization · 4 min The best AI organizations do not merely survive model churn and vendor shocks; they convert each one into a capability they keep. teams ai reliability The AI Strategy Stack: What Boards Mistake for Moats · 4 min Most AI moat claims are distribution theater; durable moats come from routing economics, proprietary workflow data, and operational reliability. strategy ai executive From Model Demos to Profit Engines: The CTO Playbook for AI Unit Economics · 3 min AI value is won in routing and failure-cost control, not in picking a single “best” model. ai cost strategy The New Talent Stack: Product, Platform, and Applied AI Must Work as One System · 3 min AI organizations create leverage when product, platform, and applied AI are designed as one operating system instead of three kingdoms. teams ai platform-engineering The Executive Case for Local-First AI Infrastructure · 3 min Local-first AI is not ideology. It is control over placement, margin, latency, and failure modes. ai architecture cost The Post-Prototype AI Org: Operating Models That Survive Year Two Canon post — · 3 min Year-two AI failure usually comes from org-design mismatch, not model-quality mismatch. The handoffs are where the system slows down. ai teams leadership The Operating Cadence: Turning AI Leadership Interfaces Into Predictable Output Canon post — · 4 min Interfaces describe who owns what. Cadence is what turns those interfaces into compounding output. leadership ai operations Designing the AI Leadership Bench: Roles, Interfaces, and Failure Boundaries Canon post — · 2 min AI scaling needs explicit leadership interfaces between product, platform, reliability, and governance. leadership teams ai Decision Latency as a P&L Variable: The Leadership Metric Nobody Owns Canon post — · 2 min Decision latency is measurable and should be treated as a direct cost driver. leadership metrics strategy The AI Vendor Negotiation Playbook for CTOs · 3 min Vendor leverage in AI comes from architecture readiness, eval data, and exit credibility — not procurement theater. ai strategy cost How to Run an AI Incident Review That Changes Architecture, Not Slides · 2 min Incident reviews should produce architecture deltas and control updates, not narrative theater. reliability ai governance How Great CTOs Design AI Roadmaps That Survive Contact With Reality Canon post — · 3 min AI roadmaps fail when they are sequenced around ambition instead of dependency, verification, and rollback cost. strategy ai leadership Hiring for AI Teams: The Operator Profile That Actually Scales · 3 min The highest-leverage AI hires are operators who can handle ambiguity, systems tradeoffs, and verification pressure. hiring ai leadership Technical Leadership in the AI Era (It’s About Throughput, Not Trends) · 3 min Technical leadership in mid-2026: anchor decisions in throughput, verification, and operability instead of chasing the latest agent framework. leadership ai teams Stop Building Internal AI Tools No One Uses · 4 min Internal AI tools fail when teams optimize for launch instead of habit formation, trust, and workflow fit. productivity ai leadership Why Most AI Platform Teams Become the New Bottleneck Canon post — · 3 min AI platform teams fail when they centralize decisions instead of capabilities. The queue is the bug. platform-engineering ai teams Build the System the Model Cannot Break · 12 min A manifesto for building AI-native organizations. Twelve tenets across strategy, architecture, economics, and people — and the only test that matters in year two. opinion ai strategy The CTO Communication Protocol: Aligning Engineers, Executives, and Investors in AI Programs Canon post — · 3 min AI programs fail when each layer hears a different success definition. leadership ai executive AI Governance Without Bureaucracy · 2 min Effective AI governance is tighter defaults, clearer ownership, and faster escalation — not more committees. governance ai security The Board Deck Is Lying: How to Measure AI Progress Without Theater · 3 min Most AI progress reporting confuses activity with value. Executive measurement should collapse around adoption, reliability, margin, and delivery speed. metrics ai executive The 2026 AI Build vs. Buy Calculus (It’s Just Operational Cost) · 3 min By mid-2026, AI build vs buy has nothing to do with novelty. It is a ruthless mathematical calculation of telemetry, context freshness, and infrastructure lock-in. strategy ai architecture Margin, Risk, and Speed: The Three Numbers That Should Drive AI Strategy · 2 min Most AI strategy becomes clearer when leadership stops tracking novelty and starts forcing every decision through three numbers. ai metrics strategy AI Production Governance: A Maturity Model · 4 min The gap between stable AI features and shipping chaos isn't tools—it's production governance. How mature teams evaluate, deploy, and roll back. governance ai reliability Why Most Enterprise AI Architecture Fails in Year One · 3 min In 2026, enterprise AI isn't failing because models are bad. It is failing because organizations are building brittle demos instead of bounded, operable systems. architecture ai reliability AI Capital Allocation: What Great CTOs Stop Funding First · 4 min Strong AI strategy starts with a kill list. If a project cannot defend margin, risk, or speed, it should not survive the next budget meeting. ai strategy cost AI Strategy: The CTO Perspective (It's Just Data Infrastructure) · 3 min A CTO's AI strategy is not about chasing models. It is about resilient data infrastructure, operational boundaries, and measured throughput. strategy ai executive AI Agent Operations and the Networking Bottleneck: Why AI Agents Fail on Legacy Infrastructure · 7 min Most AI agent failures are infrastructure failures, not model failures. Legacy networking and missing circuit breakers are the real reliability bottleneck. agents infrastructure security Beyond Cloud-Heavy Architecture: Why Agentic Systems Need Local-First, Hardware-Aware Design · 7 min Local-first, hardware-aware architecture is becoming the default for high-reliability AI: cloud-heavy patterns cost too much and fail unpredictably. agents infrastructure cost AI Startup Landscape 2026 · 3 min By early March 2026, the AI startup market looks less like a gold rush and more like a durable industry. Here's where leverage sits and what buyers reward. startups ai business AI Security: Evolving Threats and Defenses · 7 min As of late February 2026, AI security is defined by adaptive attacks and layered, operational defenses. security ai production AI Team Structures 2026: Central, Embedded, and Hybrid Models · 8 min A practical guide to central, embedded, and hybrid AI team structures, with roles, tradeoffs, and scaling rules. teams ai architecture AI Inference Cost Trends 2026: Model Pricing and Token Costs · 11 min AI inference costs are falling, but durable savings come from routing, caching, context control, and cost per outcome. cost ai performance AI Regulation Is Here. Stop Acting Surprised. · 7 min Regulation is already in procurement, security reviews, and internal sign-off. Teams that treat compliance as engineering ship faster than those who bolt it on. compliance ai governance AI-Native Architecture Patterns 2026: Production Guide · 7 min Production AI architecture patterns for gateways, retrieval, evaluation, fallbacks, cost control, and ownership. architecture ai engineering Building Reliable AI Agents in Go · 6 min Reliable agents are engineered, not prompted: bounded tools, validation at every step, explicit recovery paths. Here's how I build them in Go. agents reliability ai AI Video Applications in Practice · 4 min Video AI is practical for scoped workflows. This post covers what works, how to design for reliability, and where human review still matters. llm ai production What I Actually Expect from AI in 2026 · 4 min Less hype, more plumbing. Agents get real but stay bounded, routing beats monolithic models, and the winners treat AI like software, not magic. trends ai 2025: The Year AI Stopped Being Special · 5 min A year-end look at what actually happened in AI -- not the hype, but the operational shift. The novelty phase is over. The infrastructure phase has begun. year-in-review ai reflection AI in 2025: The Year It Became Boring (Finally) · 4 min The most important thing that happened to AI in 2025 wasn't a model release. It was the shift from 'what can it do' to 'how do we run it.' That's progress. reflection ai Scaling AI in the Enterprise Is a Management Problem · 4 min The pilots work. What fails is going from five demos to fifty production features without an operating model. That's a management problem, not an AI problem. business ai architecture AI Incidents Don't Look Like Outages. That's the Problem. · 4 min AI systems can return 200 OK while confidently wrong. How to detect, contain, and learn from AI incidents using proven incident response principles. incident-management ai reliability AI Technical Debt Is Eating Your Team Alive (And You Can't Even See It) · 4 min AI debt hides in prompts nobody owns, evals nobody runs, and data pipelines nobody watches. By the time you notice, every change feels dangerous. technical-debt ai engineering AI Doesn't Make Your Team Faster. Shared Infrastructure Does. · 3 min Individual AI speedups are a distraction. The real gains come from treating AI as team infrastructure -- embedded in docs, decisions, and onboarding. productivity ai teams Measuring AI ROI Without Lying to Yourself · 5 min Most AI ROI calculations are fantasy. Measure honestly: one workflow, full costs, benefits tied to outcomes the business tracks, and a range, not one number. metrics ai business AI Privacy Is a Plumbing Problem, Not a Policy Problem · 5 min Privacy in AI systems fails in the details: what gets logged, who can replay prompts, how long artifacts linger. Treat it as infrastructure, not a checkbox. privacy ai data AI Pair Programming: It's a Junior Dev, Not a Wizard · 4 min Treat AI coding assistants like a fast, literal junior dev: tight constraints, critical review, and no expectations of architectural insight. ai engineering productivity AI Workflow Automation: Decisions Are Cheap, Actions Are Expensive · 4 min The trick to AI workflow automation is simple: let the model decide, let deterministic code act, and never confuse the two. devops ai agents AI Docs That Don't Lie to Your Users · 4 min Most AI documentation systems retrieve the wrong version, hallucinate details, and never admit uncertainty. Here's how to build one that actually helps. engineering ai llm Your AI Metrics Are Measuring the Wrong Thing · 3 min Engagement metrics tell you people clicked. They tell you nothing about whether your AI feature actually helped anyone do anything. metrics ai strategy Stop Fine-Tuning Models You Haven't Bothered to Prompt Properly · 4 min Fine-tuning is the goto move for teams who skipped the basics. Most of the time, better prompts and proper retrieval solve the actual problem. llm ai opinion AI Customer Support That Doesn't Make People Hate You · 4 min Most AI support systems are built to deflect tickets. The ones that work are built around escalation, grounding, and the idea that customers aren't idiots. ai production Your AI Pipeline Is Just ETL With Extra Steps (And That's Fine) · 5 min AI data pipelines are ETL with a retrieval layer bolted on. The discipline is the same as always: detect change, chunk intelligently, keep indexes fresh. data ai infrastructure Agent Orchestration: Four Patterns, Honest Tradeoffs · 5 min Multi-agent systems are distributed systems with the usual coordination headaches. The four patterns I've seen work, and when each one falls apart. agents ai architecture AI Security: Same Principles, New Attack Surface · 5 min AI systems are exposed APIs with real blast radius. The threats are injection, leakage, and tool misuse. The defenses are the ones we've always needed. security ai production Testing AI Where It Actually Runs · 6 min Offline evals are necessary but not sufficient. Here's how I test AI features in production with shadow mode, canaries, and rollback automation -- with Go code. testing ai production Your AI System Looks Healthy. It Is Not. · 4 min Traditional monitoring will tell you your AI service is up. It won't tell you it's returning confident garbage. Here's what observability actually looks like for AI. observability ai production MCP in Practice: Building Tool Servers in Go · 7 min Model Context Protocol promises to standardize how AI talks to tools. I built an MCP server in Go to see if the promise holds up. Here's what I found. agents ai go AI Governance That Does Not Suck · 3 min Governance that blocks delivery is broken. Governance that makes 'yes' safe and fast is a competitive advantage. Here's how to build the second kind. ai governance compliance Video Understanding AI: What Actually Works · 4 min I pointed a video understanding pipeline at 200 hours of meeting recordings. The results taught me more about pipeline design than about meetings. llm ai data AI Code Review Is Mostly Noise · 4 min I've been running AI code review on real PRs for months. It catches some real bugs. It also generates a staggering amount of useless commentary. engineering ai development Reasoning Models in Production: A Practical Guide · 7 min Reasoning models are powerful but expensive and slow. Here's how I integrate them in Go services with routing, async patterns, and cost controls that actually work. llm production ai AI in 2025: The Year Discipline Wins · 4 min The AI hype cycle is over. 2025 is about the teams who can make this stuff actually work in production -- repeatably, measurably, and without burning money. ai trends strategy 2025 Will Reward the Boring Teams · 3 min The AI advantage in 2025 goes to teams that ship measurable workflows, not teams that chase capabilities. The gap is discipline, not technology. ai strategy 2024: The Year AI Got Boring (In a Good Way) · 4 min 2024 was the year AI stopped being exciting and started being useful. The demo phase ended. The production phase began. Discipline won. year-in-review ai reflection Your AI Infrastructure Is Not Special · 4 min AI infrastructure at scale is just infrastructure. The same boring patterns -- gateways, caching, circuit breakers, budgets -- solve the same boring problems. ai infrastructure architecture Your AI Team Problem Is Not Technical · 4 min Most AI team failures come from unclear ownership and weak evaluation, not missing talent. Structure and discipline beat hiring sprees. ai teams hiring Picking an AI Model for Production (Late 2024) · 5 min There's no best model. There's the model that fits your workload, latency budget, cost constraint, and ops tolerance. Here's how to compare them. ai llm reflection AI Safety Is Just Production Engineering · 5 min AI safety in production isn't a research problem. It's defense in depth, the same way cyber defense works -- layered controls, assumed breach, observable boundaries. ai governance production Agent Patterns That Survive Production · 7 min Single-prompt agents break on real tasks. Plan-execute-replan, orchestrated specialists, structured memory, and explicit recovery are what survive -- in Go. agents ai go AI Cost Benchmarking: What Your Bill Actually Tells You · 4 min Price-per-token is the least useful number on your AI bill. Real cost benchmarking starts with your workload, not a provider's pricing page. ai cost llm Let AI Write Your First Draft, Not Your Docs · 3 min AI is a decent drafting assistant for technical docs. It's a terrible replacement for ownership. engineering ai developer-experience AI-Assisted Code Migration: What Actually Works · 4 min I used LLMs to help migrate a 200K-line Go codebase. The mechanical parts went fast. Everything else was still hard. ai technical-debt go How I Actually Test LLM Features · 6 min LLM outputs are non-deterministic. That doesn't mean you can't test them rigorously. Here's the layered testing approach I use in production. llm testing ai The Best Model Is the Smallest One That Works · 3 min Everyone reaches for GPT-4 by default. Most production tasks don't need it. Small models are faster, cheaper, and often better when the task is well-defined. llm ai performance Stop Stuffing Your Context Window · 4 min Bigger context windows aren't an excuse to stop thinking about what goes into them. Most teams are paying for irrelevant tokens and wondering why quality degrades. llm ai architecture Function Calling Patterns That Survive Production · 7 min Function calling is how LLMs touch real systems. Treat tools like APIs, arguments like untrusted input, and permissions like the model is an intern with root access. llm ai go Claude 3.5 Sonnet Analysis: Cost, Coding, and Model Routing · 5 min Claude 3.5 Sonnet changes model routing math for coding, cost, latency, and production AI workloads. llm ai reflection AI Compliance Without the Theater · 5 min Compliance doesn't have to slow you down. But you have to build it into the system from day one, not bolt it on after the demo impresses the board. ai compliance business Why Your Enterprise AI Pilot Is Stuck · 4 min Most enterprise AI projects die between the demo and production. The blockers aren't technical -- they're organizational. Here's what I keep seeing. business ai strategy Building Voice AI That People Actually Use · 5 min Voice AI is ready to ship. The hard parts are latency, interruptions, and knowing when voice is the wrong interface. Here's how I approach it. llm ai go GPT-4o Changed the Interface, Not the Hard Part · 4 min OpenAI shipped a model that sees, hears, and talks back in real time. The demos look magical. The architecture implications are where it gets interesting. llm architecture ai Most AI Developer Tools Are Not Worth Adopting Yet · 3 min The AI tooling landscape is exploding. Most of it adds complexity without removing real friction. Here is how I decide what earns a spot in the stack. ai developer-experience opinion Agentic Workflows: From Demo Magic to Production Reality · 6 min AI agents that can take actions are fundamentally different from chatbots. The engineering bar must match the blast radius. agents ai production Why I Run Multiple Models in Production · 4 min Betting on a single model provider is like having a single database with no failover. Here is why multi-model is the only sane production strategy. ai architecture llm Claude 3 First Impressions: Three Models, One Decision Framework · 4 min Anthropic shipped three models instead of one. That is actually the most interesting part of the release. llm ai LLM Evaluation: Stop Shipping on Vibes · 5 min Your LLM feature looks great in demos and breaks in production. Here is how to build an evaluation loop that catches regressions before your users do. ai llm testing Architecting AI-Native Applications (Without the Delusion) · 7 min AI-native apps are fundamentally different from a model bolted onto a CRUD app. How I structure them -- with code, layers, and hard-won opinions. architecture ai engineering AI Engineering Is Its Own Discipline Now · 4 min AI engineering is not ML research with a product hat. It is the discipline of making models behave in production -- and it demands its own skill set. ai hiring llm 2023: The Year Everything Changed (and I Barely Kept Up) · 5 min A personal look back at 2023 -- watching AI reshape the industry in real time, and figuring out what matters next. year-in-review ai reflection Your AI Infrastructure Is Not Ready for Scale. Neither Is Mine. · 4 min GPU shortage is real, rate limits are a production constraint, and your AI demo will collapse under real traffic. Annoyed thoughts on infrastructure realism. ai infrastructure architecture Multimodal AI: Five Use Cases That Actually Work (and Three That Do Not) · 5 min GPT-4V is out and everyone is building vision features. After testing it across real workflows, here is what ships well and what falls apart. ai llm business Two Weeks With the Assistants API: What I Like, What I Hate · 4 min I built three things with the Assistants API. One shipped, one got scrapped, and one taught me where the API's limits really are. llm ai go OpenAI DevDay Happened and I Have Opinions · 4 min OpenAI DevDay was not just a product launch. It was a platform play that changes the build-vs-buy calculus for every team shipping AI features. llm ai architecture I Tracked My AI-Assisted Coding for Three Months. Here Are the Numbers. · 5 min After three months of tracking Copilot and GPT-4 usage across real projects, the productivity picture is messier than the marketing suggests. ai developer-experience productivity LLM Security: A Field Guide for People Who Ship Things · 6 min LLMs bring security failure modes most teams aren't defending against. Prompt injection, data leakage, tool abuse, and cost attacks are exploitable today. security llm ai Responsible AI Is Just Risk Management. Treat It That Way. · 3 min Responsible AI is not an ethics committee. It is operational risk management, and teams that treat it otherwise are building liabilities. ai security governance AI Technical Debt Is Eating Your Codebase (You Just Cannot See It Yet) · 4 min AI features create a new species of technical debt that hides in prompts, data pipelines, and model versions. By the time you notice it, the cleanup bill is brutal. ai technical-debt engineering Agent Architecture Patterns That Actually Work in Production · 6 min Most agent demos are impressive. Most agent production systems are not. Here is what separates the two. ai agents llm Stop Starting With the Model: AI Product Strategy That Works · 4 min Every roadmap I've seen this quarter has an AI feature. Most of them start with the wrong question. Start with the user problem, not the model. ai strategy startups LLM Observability: Your Existing Monitoring Is Not Enough · 5 min Traditional monitoring says the service is up. It won't tell you the model started returning garbage last Tuesday. How to actually observe LLM systems. observability llm ai What I Learned Building AI Features Into a Fintech Product · 5 min Building AI features at a fintech taught me the hard part isn't the model: it's defining quality, handling failures, and not shipping a demo as a product. ai strategy business Your LLM Bill Is Your Own Fault · 4 min Everyone's complaining about LLM costs. Almost nobody has done the basics: caching, model routing, or even measuring what they're spending per feature. ai cost llm Embedding Models Compared: Retrieval Quality, Cost, and Latency · 6 min A practical embedding model comparison for retrieval quality, vector size, latency, cost, and self-hosting tradeoffs. llm ai go Most AI Startups Are Wrappers. That's the Problem. · 3 min Everyone has an AI startup now. Having been through two accelerators and founded two companies, I can tell you: most of these will not survive the year. ai startups strategy Building Semantic Search in Go: From Embeddings to Production · 7 min A hands-on walkthrough of building semantic search with Go, OpenAI embeddings, and pgvector -- chunking, hybrid retrieval, and the gotchas I hit. ai llm go AI Code Review: What It Actually Catches (And What It Misses) · 4 min After three months of using AI-assisted code review across multiple projects, here's what actually works and what's just noise. ai engineering developer-experience Fine-Tuning vs. Prompting: A Decision Framework · 4 min Most teams should exhaust prompting before they even think about fine-tuning. Here's how to decide which lever to pull. ai llm LangChain Is the New ORM: Convenient Until It Is Not · 4 min LangChain promises to simplify LLM development. Instead it adds abstraction layers you will fight against the moment your use case gets real. llm ai opinion RAG Patterns That Actually Work in Production · 8 min RAG is the default architecture for grounding LLMs in private data. Here are the patterns that survive real traffic, with Go examples from production systems. llm ai go Vector Databases: What They Actually Are and When You Need One · 6 min A practical guide to vector databases -- what they store, how similarity search works, and the architectural decisions that matter in production. llm ai go Claude vs GPT: A User's Honest Take · 3 min Anthropic's Claude takes a different approach to AI safety. Here is how it compares to GPT in practice, from someone using both daily. ai llm AI Safety Is Just Security Engineering With Extra Steps · 4 min AI safety is not a philosophy problem for engineers. It is reliability, security, and accountability applied to a new kind of system. ai governance security My First Week Building with GPT-4 · 4 min GPT-4 landed and everything changed. What I learned in the first week of building with it, and the architecture decisions that followed. ai llm architecture Prompt Engineering Is Not Engineering · 3 min The term 'prompt engineering' oversells what is essentially clear writing. It is a useful skill, not a discipline. ai llm opinion LLM Integration Patterns That Actually Survive Production · 6 min Practical patterns for integrating LLMs into real applications -- prompt management, structured outputs, caching, fallbacks, and tool use -- with Go examples. ai llm go AI in Production Is Just Engineering. Treat It That Way. · 4 min ChatGPT changed expectations overnight, but shipping AI features that actually work is an engineering problem, not a model problem. ai production engineering 2022: The Year the Music Stopped · 5 min A personal look back at 2022: building through the downturn, watching ChatGPT arrive, and what the year taught me about building things that last. year-in-review reflection ai Five Days With ChatGPT · 4 min First impressions of ChatGPT from a working engineer. It is not a search engine, it is not a colleague, and it is definitely not a replacement. But it is something. ai llm developer-experience My Honest Take on GitHub Copilot After Six Months · 5 min Six months with Copilot in real projects. What it actually helps with, where it quietly makes things worse, and why the productivity claims are overblown. ai developer-experience productivity GitHub Copilot: First Impressions From a Go Developer · 4 min I got early access to GitHub Copilot's technical preview. Here's what it actually does well, what it gets wrong, and why I'm cautiously interested. developer-experience ai go Most Teams Are Not Ready for MLOps · 4 min MLOps is real, but most teams buying MLOps tooling cannot even version their training data. Fix the basics first. ai devops data Machine Learning for Backend Engineers: What Actually Matters · 6 min What backend engineers actually need to know about ML in production -- from someone who builds NLP pipelines for financial news. ai backend engineering
All topics →