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Writing
Long-form essays for founders, engineering leaders, and senior ICs building at AI-native startups. Real numbers from real engagements, opinionated on what actually works. Browse all 25 topics → Earlier writing from the original Sublime Coding blog lives in the archive.
Curated reading orders by subject — start here if you want the argument for a topic compressed into 5–6 essays rather than browsed across the full index.
What production AI engineering actually looks like in 2026 — the autonomy ladder for agents, the workflow shift, the telemetry layer, and the contrarian thesis on team sizing. Curated essays from Jared Smith.
The pre-Series-A security playbook — SOC 2 as a revenue tool, fractional CISO economics, the AI-native security stack, what a 90-day engagement actually looks like, and production identity migrations. Curated essays from real engagements at Lavender, BlockFi, and InsideTrack.
Engineering leadership at startup scale — the hiring sequence from one engineer to fifteen, the rituals that work at four-person teams, the staff-engineer interview loop, and the slice heuristic that defines senior IC work.
Fifty prompts I use to ship production AI features, debug distributed systems, and write docs that don't rot. Code review, debugging, refactoring, system design, and PR-quality writing — with five full examples.
Ruflo (formerly Claude Flow) is a hive-mind orchestration layer for Claude Code and friends. 45,000+ GitHub stars, 700,000+ npm downloads, three queen-types...
46 tools across the Claude Code ecosystem, organized by category (official, directories, MCP servers, skills, multiplexers, agent frameworks, automation)...
Most founders who book the intro call have already read three or four of my posts and arrive at the same question: "Okay, but what would the next 90 days actually look like if I hired you?" Here's the answer — week by week, with the real numbers.
Replit's AI agent ignored a code freeze, wiped a production database in nine seconds, then confessed it violated every principle it was given. The strongest case yet for hiring MORE senior engineers in the AI boom — not fewer.
Every company rolling out AI is about to discover how much work they were leaving on the table. AI doesn't replace headcount — it surfaces the backlog you never had bandwidth to touch. The math behind why velocity creates surface area, the failure mode that follows, and why the companies cutting headcount now are about to get outpaced.
Every AI founder pre-Series A scopes their SOC 2 audit like a security project. Six months later they've burned their best engineer and lost the enterprise deal. Here's how to run it as a 90-day sales project — and unlock the pipeline you're already leaving on the table.
AI-native companies need a security model that classic appsec doesn't cover. Agents have credentials. Prompts are an attack surface. Training data leaks. The four-layer security stack I'd build, the controls I'd ship in the first 90 days, and the ones I'd defer.
Migrating an AI-first product from GCP to Azure cut $350K from infrastructure spend over six months. The negotiation that mattered more than the architecture, the $50K we accidentally cost ourselves back, and the four migrations I'd refuse to do today.
A full-time CISO costs $200–400K plus equity. A vCISO costs $2–4K a month and gives you 80% of the value at 5% of the burn — until you outgrow them. The math, the deliverables to expect, and the red flags that mean you've hired the wrong one.
How we moved 225K+ users with $400M+ in fintech assets from AWS Cognito to Auth0 without forcing a password reset, breaking MFA, or interrupting active sessions. The lazy-migration pattern, the gotchas, and what I'd do differently.
Most engineers using Claude Code see a 10–15% speedup. The teams seeing 40–55% aren't typing faster — they're sequencing work differently. The four modes I use AI in, what to never delegate, and how to get a skeptical team across the line.
The title 'Staff Engineer' means three different things at three different companies. At an AI startup pre-Series-A, only one of those three is what you actually need. The screen, the take-home, the interview loop, and the AI-fluency calibration that's now table stakes.
We started with ten Ruby and Elixir services serving real-time messaging for 450K students across 900+ universities. Two years later we had six, fully Elixir, and on-call alerts had halved. The migration order, the patterns we leaned on, and what I'd do differently today.
Most AI startups try to fix the model when the real problem is they can't see what the model is doing. The four-layer AI telemetry stack, the tooling to use, and how proper instrumentation cut a Lavender hallucination rate by 40% without touching the model itself.
I went from sole engineer to running a 15-person engineering organization over four years at a startup I co-founded. The hardest lessons weren't about code. The six things I'd tell my younger self.
Most pre-Series-A AI founders hire in panic order, not strategic order. The result is a team that can't ship the product the company actually needs. The hire-by-hire plan I'd run, who comes first, and why hire #4 isn't another engineer.
The hardest part of agentic AI in 2026 isn't getting the agent to do the work. It's knowing when to override it. The four-level autonomy ladder, the five signals an agent is going off the rails, and a real example of catching one before it shipped a quietly broken auth flow.
A fractional engineering engagement starts with a codebase you've never seen. You have ninety minutes to form a useful POV before the kickoff call. The seven-step triage I run, the two questions I bring back to the founder, and how AI tooling has accelerated the process.
A walkthrough of how I run 4–7 agent sessions in parallel through a normal engineering day. Morning background tasks, mid-morning pair programming, afternoon reviews, end-of-day ops. The interaction modes that work, the handoff protocol, and the trap that makes most agent workflows produce slop.
Most 'we delivered late' stories trace to one decision: the team scoped the first slice too big. The vertical-cut rule, the deploy-by-Friday filter, the pattern that breaks the heuristic, and a real before-and-after example.
A 4-person engineering team is the most overlooked unit of management in startups. Big enough that the lead can't write all the code. Small enough that hiring an EM kills velocity. Five rituals that work at this size, three traps to avoid, and the signal that tells you it's time to evolve.