Roman Travnikov TravnikovDev

If it’s not in Git, it didn’t happen.

I run a bunch of n8n automations for clients. One server hiccup nuked a workflow last year and I had to rebuild from memory. Never again.

Now I back up every n8n workflow to GitHub using the official template workflow. It calls the n8n API, exports all workflows as JSON, then commits them to a repo on a schedule.

What gets persisted: every workflow definition - nodes, connections, triggers, settings, tags, active flag - one JSON per workflow. What doesn’t: credentials and execution history. That’s exactly what you want.

Setup is simple: create a private repo, generate a GitHub token with repo scope, add it to n8n credentials, import the template, point it at your repo. I group files by tags and use timestamped commit messages.

GPT-5.1 is about to hit.

OpenAI says rollout starts Nov 12 for Pro, Plus, Go, and Business, with wider access right after. November just got interesting.

Last month I shipped a multi-agent lead-gen tool in Next.js. The constant tradeoff: fast but shallow vs slow but smart.

GPT-5.1 literally names this split. Instant for rapid, natural replies. Thinking for stickier reasoning when the task gets gnarly.

In practice, that means fewer brittle prompts and fewer babysitting hacks. Test writers, RAG flows, schedulers - less glue code, more signal.

TypeScript won, but not how you think.

In 2015 I was still slinging ES5 and jQuery. TypeScript felt academic, with triple-slash refs and a compiler that yelled at me. Most startups I touched wanted speed, not types.

Back then, adoption was a rounding error - think 1-5% of devs touching TS, based on early surveys and npm noise. JavaScript owned basically 100% because, well, everything compiles to it.

By 2020 the tide flipped. React, Angular, and VS Code made types feel natural. Stack Overflow and State of JS put TypeScript around 25-35% regular usage, with JavaScript still near 70%. Refactors stopped being gambling.

2025 feels like the default changed. Most greenfield React/Next projects I see start in TS. Call it 45-55% of developers using TS monthly, while JavaScript remains universal as the runtime. npm installs for typescript grew by roughly an order of magnitude since 2017.

LLMs don’t just code, they pick winners.

On a recent freelance dashboard, I asked an LLM for a responsive layout. It returned React + Tailwind + shadcn/ui without me saying it. I stopped fighting it, vibed with the stack, and shipped the first draft in hours.

Why Tailwind? Deterministic atomic classes (px-4, flex, gap-2) map cleanly from text. JSX + className means no context switching into CSS files. And the web is flooded with Tailwind examples, so models are fluent.

The ecosystem sealed it: Vercel v0 openly defaults to React + Tailwind + shadcn/ui. Cursor/Bolt.new demos lean the same way. Even niche generators brand themselves Tailwind-first.

Adoption signals match the vibe: State of CSS shows Tailwind leading in retention, and npm trends put it shoulder to shoulder - often ahead - of Bootstrap.

Every October, npm whispers: Black Friday.

I watch the charts like weather. Stripe, @stripe/stripe-js, shopify-api, algoliasearch tick up, then flags libraries join. In my last ecommerce build we saw the same curve inside our CI.

The rhythm is consistent: late-Oct climb, small Thanksgiving dip, rebound into Cyber Monday. Pull Oct-Dec on npmcharts and you’ll see the weekday-weekend sawtooth too.

Why? Traffic is unforgiving. Vercel’s 2024 recap: 86.7B requests, 1.94M RPS, five-nines uptime. The winners made BFCM feel like a normal Friday.

What moves right before peak: payments and commerce, flags, Sentry and queues like BullMQ, rate-limiter-flexible, load tools Artillery/Autocannon, Playwright/Puppeteer, i18next/Nodemailer, plus Next.js and next-auth updates.

Stop pretending AI isn't taking tickets.

Boilerplate, code translation, test scaffolding, even log triage - AI is chewing through them. 84% of devs use it now and half use it daily, but trust slipped and debugging time went up, per Stack Overflow.

In my last Next.js project, an assistant cranked out a data table and tests. Looked clean... until pagination broke on edge cases. I felt faster, then lost the afternoon. METR found experienced devs 19% slower with AI on realistic tasks. Same energy.

Secret 1 - Treat AI like a fast junior. Use it for scaffolds, examples, docs, translations. JetBrains says 9 in 10 users save time. Don't let it design your system. You own invariants.

Secret 2 - Industrialize guardrails. Tests first. Feed context - architecture notes, API contracts, golden examples. Tag AI-origin PRs, keep them small, run SAST and license checks. GitClear saw duplication and churn up. Faros/DORA: throughput rises only with real quality gates.

AI won’t invent a language. It’ll grow one.

The signs are here: DreamCoder learns libraries, AlphaDev/AlphaTensor find new algorithms, MLIR invites new dialects, and Triton/Mojo show we can mint languages for performance. That’s not sci-fi. That’s Tuesday.

What’s coming is a two-world stack: a machine-native interlingua tuned for optimization and verification, plus human views that explain, constrain, and teach. Think types, contracts, cost models - with equality-saturation style rewrites picking the best form.

High-level or low-level? Both. We’ll write intent, specs, and tests; the system will decide memory, parallelism, and kernels. Code won’t be just text - it’ll be a bundle: intent, spec, implementation graph, evidence, and a literate view.

Sci-fi called it: Banks’ Marain shaping thought, Snow Crash as firmware, Ted Chiang on time. Same vibe - a language that changes what we notice.

Me, MacBook and my content fabric on n8n

I didn't build a system to post more. I built it so every minute online compounds.

My content fabric is a tiny n8n engine that turns sparks into native LinkedIn posts in minutes. It keeps me consistent, repurposes what resonates, and shows me what to ship tomorrow.

Why here? LinkedIn is where work happens - 65M decision-makers, 10M C-suite, and ~46% of B2B social traffic. Not reach. Relevance.

Also, it's civil by design. Real names, real jobs. You can run experiments without anonymous drive-bys. People may disagree, but they do it like adults.

AI isn't killing frontend. Sloppy workflows are.

Lifehack 1 - Make AI your junior dev. Keep a context file with stack, tokens, a11y rules, APIs, budgets. Feed it to chat. Use it for scaffolds, refactors, tests, docs. Guardrails: lint, types, Playwright, Lighthouse CI. Keep a bad-patterns list.

Lifehack 2 - Own the irreplaceables. Bake budgets into CI for LCP/INP/CLS and bundle size. Run axe in PRs. Go server-first on critical paths, push work to the edge, and keep client JS lean with modern CSS and progressive enhancement.

Lifehack 3 - Ship AI UX, not just AI code. Recent client: semantic search with natural language filters, token streaming, cancel and undo. Prompt-injection checks, PII redaction at the edge, and a deterministic fallback when models hiccup.

One-week plan: Day 1-2 write context and checklists. Day 3-4 build prompt templates for codegen/refactor/review. Day 5 ship one small AI pattern with telemetry.

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