Learning new things every day. Writing them down to remember.
limit() consumes a token. getRemaining() doesn’t. Use getRemaining() for status endpoints and UI counters. Built this for ray.tinte.dev .
// read-only, no token consumed
const { remaining } = await rl.getRemaining(id);
// consumes a token
const { success } = await rl.limit(id);Figma’s “Code to Canvas” plugin enables a full round-trip between design and code via MCP.
/implement-design extracts layout, typography, colors from any Figma frame and generates code for your stack. generate_figma_design serializes your running page’s DOM into real editable Figma layers (not a screenshot).
Figma frame → /implement-design → component
↑ ↓
edit in Figma generate_figma_design
↑ ↓
get_design_context ← editable Figma layersGotcha: Code to Figma still needs a browser render with a capture script injected. Not purely automatic from code.
Static sites can’t publish posts at a future date because pages only exist if they’re generated at build time. No build = no page = 404.
The fix: Vercel Deploy Hooks. You create a hook URL in your project settings, and hitting it with a POST triggers a fresh production deploy. Combine that with a vercel.json cron job that calls your hook daily, and your SSG site rebuilds itself automatically.
{
"crons": [
{
"path": "/api/cron/rebuild",
"schedule": "0 12 * * *"
}
]
}The API route just fetches the deploy hook URL and authenticates with CRON_SECRET. I use this on my Astro blog to auto-publish “premiere” posts at their pubDate — the countdown disappears and the article goes live, zero manual intervention.
Built a 3-agent dev team (Bolt codes, Sage reviews, Nova researches) and hit every classic engineering management problem: ownership, concurrency limits, routing, queues.
Conway’s Law applies to AI teams too — your agent architecture mirrors the communication structure you design. The review loop proved it: Bolt opens PR, Sage reviews, Bolt fixes, repeat. Had to cap at 3 iterations because without a kill switch, two agents argue forever.
Don’t think of multi-agent systems as “parallel workers.” Think of them as a team. Roles, boundaries, escalation paths, termination conditions.
Agent skills load context in three levels: metadata (~100 tokens) at startup, full instructions (under 5,000 tokens) when triggered, and resources only as needed.
This is why skills beat .cursorrules - it loads entire contents into every conversation. A skill with 60+ product references only loads what’s relevant. Cloudflare-skill has 60 reference files but you can ask about Workers without paying for WAF documentation.
Every public commit, tweet, or project is a lottery ticket. The more you ship visibly, the more opportunities find you.
tryelements.dev caught Clerk’s attention and got me the job. X grew from 0 to 2500 followers. Ship something every week. Tweet 2-3x per week minimum.
Effective agent design boils down to context management. Context is a finite resource with diminishing returns - like human working memory.
A higher capacity model with caching can be cheaper than a lower cost model without it. Progressive disclosure: don’t load all tool definitions upfront. Successful agents use surprisingly few tools (~12 for Claude Code, <20 for Manus).
Autonomous AI coding loop that ships features while you sleep. Named after The Simpsons character - embodies persistent iteration despite setbacks.
The core insight: context pollution is inevitable. Ralph doesn’t try to clean memory - it throws it away and starts fresh. Progress persists via files + git. Failures evaporate.
while :; do cat prompt.md | agent ; doneEach iteration reads task list, picks highest priority, implements, runs tests, commits if passing, logs learnings, repeats.
Model Context Protocol (MCP) is an open standard for connecting AI agents to external systems. Think of it as “USB-C for AI applications” - a universal connector.
Before MCP, every agent needed custom integrations for every service (N×M problem). MCP creates a standardized interface so one MCP server for Postgres works with any MCP-compatible agent. Agents discover tools dynamically via protocol.
In a world where everyone has access to the same AI intelligence, companies differentiate through context - the proprietary information, tribal knowledge, and organizational data that agents receive.
Knowledge workers are becoming managers of AI agents. Agents are only as good as the organizational context they receive. As Peter Drucker said: “if HP knew what HP knows, we would be three times more productive.”
Ship the fast version first. Tight, artificial deadlines spike productivity and reveal that most things can be done faster than scarcity mentality allows.
“Do the five day version” - the constraint forces clarity. Unrealistic deadlines are a useful tool, not a stress source. Ship fast, iterate faster. Nobody cares about your v1 polish.
AI is plummeting the cost of personalization. The web will become more whimsical, expressive, and unique in 2026.
The cost of turning “wildest dreams into pixels” is now accessible. Geocities-era creativity is returning but with AI capabilities. Non-technical people can move away from templates. The template era is dying.
Willpower fluctuates with stress, fatigue, and hunger. Instead of relying on it, build automated if-then rules that remove decision-making.
Nearly half of daily decisions are already autonomous (habits). Create explicit rules: “if 7am, work out” - no decision needed. Every choice taxes mental energy. High performers don’t have more willpower - they remove choice entirely.