DEV Community: Boehner

I Implemented Google TurboQuant in Our AI System — Here's How

Boehner — Mon, 06 Apr 2026 02:34:36 +0000

Google published TurboQuant at ICLR 2026 — a compression algorithm making AI models 6x more memory efficient with no quality loss. The internet is calling it the Pied Piper moment for AI.

Here's the problem: TurboQuant runs inside the model's inference engine on Anthropic's servers. You can't touch it.

But the core idea? Portable to any AI system today.

The Problem TurboQuant Solves

Every time you send a message to an AI, the full conversation history goes with it. On a 30-turn session, that's 3,000+ tokens of history sent on every single request. Tokens cost money. Long sessions get expensive fast.

The Hot/Cold Cache Split

TurboQuant's core insight: not all context is equally important. Recent turns matter most. Old turns can be compressed aggressively without hurting response quality.

We built this as an OpenClaw plugin using before_prompt_build — a hook that fires before every inference call:

function applyCompression(messages, config) {
  const sys = messages.filter(m => m.role === 'system');
  const conv = messages.filter(m => m.role !== 'system');

  if (conv.length < config.minTurnsBeforeCompression) return messages;

  const hotStart = Math.max(0, conv.length - config.keepRecentTurns);
  const cold = conv.slice(0, hotStart);  // compressed
  const hot = conv.slice(hotStart);      // verbatim

  return [...sys, ...cold.map(m => compress(m, config.ratio)), ...hot];
}

The compression is extractive — scores every sentence by information density, keeps the highest-value ones. No AI calls, no extra API cost:

function compressText(text, ratio) {
  const target = Math.ceil(text.length * ratio);
  const scored = scoreSentences(text).sort((a, b) => b.score - a.score);
  const picked = [];
  let len = 0;
  for (const item of scored) {
    if (len >= target && picked.length >= 1) break;
    picked.push(item);
    len += item.s.length;
  }
  picked.sort((a, b) => a.i - b.i);
  return picked.map(p => p.s.trim()).join(' ');
}

Results

On a 30-turn conversation:

	Without plugin	With plugin
Tokens per request	~3,200	~1,100
Cost ratio	1x	~0.34x
Recent context quality	Full	Full
Old context quality	Full	~75%

~66% token reduction per request.

OpenClaw Plugin Config

{
  "plugins": {
    "allow": ["turboquant"],
    "entries": {
      "turboquant": {
        "enabled": true,
        "config": {
          "keepRecentTurns": 6,
          "compressionRatio": 0.25,
          "minTurnsBeforeCompression": 10
        }
      }
    }
  }
}

After restarting the gateway you see logs like:

[turboquant] 28 turns → compressed 22 cold: ~3100→~980 tokens (saved ~2120, 68%)

The Real Point

We read the TurboQuant paper in the morning and shipped this the same afternoon. That's AI-assisted development in 2026 — read the research, understand the principle, ship something real before dinner.

Source code: github.com/Boehner/openclaw-turboquant

Drop your token savings in the comments if you try it.

New reads, take a look!

Boehner — Thu, 02 Apr 2026 17:39:17 +0000

Boehner

Apr 2

Anthropic Just Mapped the Jobs AI Is Replacing First - Here's What the Data Actually Says

#ai #career #programming #technology

Comments

2 min read

I Built a Fully Automated AI News Video Pipeline for $0 Per Video (Here's the Stack)

Boehner — Thu, 02 Apr 2026 03:37:23 +0000

Every morning, a cron job wakes up on my machine, researches today's top AI news, writes a broadcast-quality script, generates a professional voiceover, downloads relevant b-roll footage, assembles a 1080p video, and uploads it to YouTube automatically.

Total cost per video: about $0.002.

The Stack

Research: Brave Search API (free tier)
Script: Claude Haiku (~$0.0004/script)
Voice: ElevenLabs ($5/month)
B-roll: Pexels Video API (free)
Assembly: FFmpeg (free)
Upload: YouTube Data API v3 (free)
Orchestration: Node.js + cron

Step 1: Research

javascript const SEARCH_QUERIES = [ 'AI news today 2026', 'artificial intelligence breakthrough this week', 'AI startup funding announcement today', ];

We hit the Brave News Search API, deduplicate, and save the top 10 stories.

Step 2: Script Generation with Claude Haiku

The first version used local Llama for scripts. The output was terrible - stage directions, unnatural pacing, headers like Hook (15 seconds) getting read aloud by the TTS engine.

Switching to Claude Haiku fixed everything instantly. Cost per script: $0.0004.

`javascript
const prompt = You are the scriptwriter for AIWireDaily.

RULES:

Write ONLY the words to be spoken aloud
NO stage directions, NO headers, NO asterisks
Natural broadcast anchor voice
350-450 words total; `

Step 3: ElevenLabs Voiceover

$5/month for 30k characters - about 12 videos/month. Use eleven_turbo_v2 for speed and quality.

Step 4: Synced B-roll Timing

The key insight: sync footage to actual voiceover timing using word count:

javascript const wordsPerSec = totalWords / totalDuration; const storyDurations = storyParas.map(p => Math.max(5, p.split(/\s+/).length / wordsPerSec) );

Then search Pexels with per-story keywords - drone footage for drone stories, office footage for business stories.

Step 5: FFmpeg Assembly

Key learning: use execFileSync with args as an array instead of execSync with a shell string. If your path has apostrophes or spaces, shell escaping breaks FFmpeg filter strings.

For the Short:

bash ffmpeg -i input.mp4 -t 59 \ -vf scale=1920:1080,crop=1920:1080,scale=1080:1920,crop=1080:1920 \ short.mp4

What It Costs Per Video

Component	Cost
Brave Search	Free
Claude Haiku script	$0.0004
ElevenLabs voice	$0.17
Pexels + YouTube	Free
Total	~$0.17/video

The Result

Channel: AIWireDaily on YouTube

The video that prompted this post: Anthropic Just Mapped the Jobs AI Is Replacing First

The core insight: don't skimp on script generation. The 0.4 cents on Claude Haiku vs running local Llama is the most important cost in the whole pipeline.

Anthropic Just Mapped the Jobs AI Is Replacing First - Here's What the Data Actually Says

Boehner — Thu, 02 Apr 2026 03:36:55 +0000

Anthropic published something last month that didn't get nearly enough attention: a detailed map of which white-collar jobs AI is most likely to displace first.

Software engineers. Financial analysts. Lawyers. Accountants. Marketing managers. HR specialists. Middle management.

If your job involves sitting at a computer processing information, writing things, or analyzing data - you're on the list.

The Numbers Are Not Comforting

A survey of 2,500 white-collar tech workers found 61% believe AI will replace their current role within three years. Not eventually. Three years.

Goldman Sachs puts the global figure at 300 million jobs at risk.

In February, AI executive Matt Shumer published an essay on X comparing the current moment to February 2020 - right before COVID hit. That essay was viewed 85 million times.

Then Citrini Research published a scenario they called the 'Global Intelligence Crisis.' Their model: AI agents replace software engineers and financial advisors faster than the economy can adapt. Unemployment spikes above 10%. The Dow fell 800 points the week it published.

What the Data Shows

Anthopic's research maps why jobs are at risk: high text output + structured reasoning = highly automatable (legal, finance, accounting, software engineering). The pattern: if your primary output is a document, a report, or a piece of code - AI can produce that output.

The Workers Who Are Thriving

The workers thriving right now learned to work with AI. They use tools like Claude, Cursor, and OpenClaw to do in two hours what used to take two days. Their output went up. Their value went up. Their employers kept them.

You don't need to become an AI engineer. You need to become the person on your team who uses AI better than anyone else. That's the job that doesn't get replaced.

I made a video breaking all of this down with the actual data: Watch it here

What's your take - is your field on the list?

I Built a Tool That Generates a Complete Competitive Intelligence Report on Your Market in Minutes

Boehner — Sun, 29 Mar 2026 01:01:26 +0000

Competitive research is one of those tasks that everyone knows they should do and almost nobody does well.

You open a dozen browser tabs. You check competitor pricing pages. You try to remember whether that headline was different last month. You throw some notes into a spreadsheet. Three hours later you have something that's already partially outdated, and you still aren't sure what any of it actually means for your business.

I got frustrated enough with this that I built a tool to do it automatically. Here's how it works.

What the tool does

You enter your company URL. That's it.

The system automatically identifies your top five competitors, analyzes each of their websites using live data, and generates a plain-English executive brief. No manual research, no spreadsheets, no guessing.

The core flow:

Competitor detection — SnapAPI analyzes your company's URL to extract your description, positioning, and product category. Claude identifies your five most direct competitors from that context.
Live analysis — SnapAPI's /v1/analyze endpoint visits each competitor's site in a real Chromium browser and returns structured data: primary CTA, page type, word count, detected tech stack, navigation items, OG title, description.
AI-written brief — Claude turns the structured data into a plain-English intelligence report with executive-level framing.
Delivery — The report goes to your inbox via Resend within 2-3 minutes of submission.

What's in the report

The report is structured in four sections:

Executive Summary — three bullets identifying the most important signals across your competitive landscape this week.

Competitor cards — one card per competitor with: their current CTA, headline, word count, tech stack, and a 3-4 sentence AI-written analysis of what they're doing and what it signals.

Intelligence Summary — four numbered strategic takeaways synthesized across all five competitors.

What This Means — three bullets on the direct implications for your business.

A real example

I ran this for an insurance company in Indiana. Here's one of the signals the report surfaced.

State Auto Insurance's homepage H1 has changed to:

"We are now part of Liberty Mutual."

That's a live finding. There's also a phone service outage notice active on the site, and their careers links redirect to Liberty Mutual's job board. The merger integration is publicly underway.

The AI brief for that competitor read:

State Auto is in active brand and operational transition. This typically creates 60-90 days of customer and agent uncertainty — a window for competitors with stable agent networks and clear product continuity to capture movement.

That kind of specific, actionable signal — found automatically, written clearly — is what the report delivers. No one manually checking tabs would catch the phone outage banner or the careers redirect. The tool caught both because it's doing a real browser analysis of the live page.

How it was built

The technical stack is straightforward:

// npm install snapapi-sdk @anthropic-ai/sdk
const { SnapAPI } = require('snapapi-sdk');

const client = new SnapAPI(process.env.SNAPAPI_KEY);

// Analyze competitor site — returns structured page data
const data = await client.analyze('https://competitor.com', {
  screenshot: false
});

// data.primary_cta, data.og_title, data.word_count,
// data.technologies, data.page_type, data.navigation

SnapAPI (snapapi-sdk) handles all the browser work — visiting each page, rendering JavaScript, extracting structured data. No Puppeteer setup, no browser binary, no crash recovery. The /v1/analyze endpoint returns everything I need in one call per competitor.

Claude Sonnet generates the per-entry summaries and then a second Claude Haiku call synthesizes the cross-company executive sections. The prompt is straightforward: here's what each competitor is doing, write a brief that tells a business executive what matters and why.

Resend delivers the report HTML. The email is generated from a template that renders the full report inline — no attachments, no login required. The report just lands in your inbox.

LemonSqueezy handles the $49 payment. The flow is: locked report arrives in your inbox (first competitor fully visible, four locked), click unlock, pay $49, full report delivered instantly via webhook.

The locked report approach is deliberate. I wanted to show real value before asking for money. If the first competitor card is useful, the remaining four are worth $49. If it's not, nothing is owed.

The competitor detection step

The part I found most interesting to build was automatic competitor detection. Rather than asking users to manually enter competitor URLs, the tool derives them from the company URL:

// Step 1: Analyze the company's own site
const companyData = await client.analyze(companyUrl);

// Step 2: Ask Claude to identify competitors from the context
const competitors = await anthropic.messages.create({
  model: 'claude-haiku-4-5-20251001',
  max_tokens: 200,
  messages: [{
    role: 'user',
    content: `Company: ${companyUrl}
Title: "${companyData.og_title}"
Description: "${companyData.og_description}"

List exactly 5 direct competitor homepage URLs, one per line, no explanation.`
  }]
});

// Parse the 5 URLs from Claude's response

This works surprisingly well. For the insurance company example, Claude correctly identified State Farm, Progressive, Erie Insurance, Auto-Owners, and GEICO as the five most relevant competitors — without any manual input.

The result

The full report for five competitors takes about 2-3 minutes to generate. That includes five live SnapAPI analyze calls, five Claude summary calls, one cross-company executive synthesis call, and Resend delivery.

The output is genuinely useful. The State Auto merger signal would have taken a human analyst hours to find. The tool found it in seconds because it's doing real browser analysis — not cached data, not scraped HTML from last week, but what's actually on the page right now.

$49. One report. Delivered in minutes. No subscription.

→ snapapi.tech/businesspulse

You enter your company URL. The report arrives in your inbox.

AI Is Moving Faster Than You Can Learn It — Here's How Developers Are Staying Sane in 2026

Boehner — Thu, 26 Mar 2026 16:51:49 +0000

There is a specific feeling that has been circulating in developer Slack groups and Discord servers since about January of this year. It goes something like: I used to feel on top of things. I do not anymore.

If you have felt it, you are not falling behind. The pace is genuinely unprecedented. Claude Code shipped. Cursor hit mainstream adoption and then released a major update within weeks. GitHub Copilot went from autocomplete to multi-file agent. GPT-5 arrived in Q1 2026 with capabilities that rewrote the benchmarks everyone had just gotten used to. Tools that felt cutting-edge in December feel dated in March.

This is not the normal churn of the developer ecosystem. The release cadence of consequential AI tooling has accelerated past what any individual can meaningfully track in parallel with doing actual work. The question is not whether the pace is real. It is how to stay effective inside it without burning out trying to learn everything.

The practical daily workflow

The developers handling this best are not consuming more. They are consuming differently.

The 10-minute morning digest, not the 2-hour rabbit hole. Pick one place where major AI developments surface — a newsletter, a Hacker News filter, a Slack community — and spend 10 minutes on it in the morning. Not 2 hours. The goal is not comprehension of every development. It is a light awareness of what is moving so you can notice when something directly relevant to your work appears. Most days, nothing requires action. On the days it does, you will catch it.

Pick one AI tool and go deep. The instinct is to try every new release. The developers getting the most value are the ones who resisted that and went deep on one tool instead. Not because other tools are not good, but because fluency with one tool compounds. You learn its failure modes. You develop intuitions about where to trust it and where to verify. That depth is worth more than shallow familiarity with five tools.

Use AI for the boring 80%, protect your brain for the interesting 20%. Boilerplate, test scaffolding, documentation, repetitive refactors, regex, SQL queries you have written a hundred variations of — AI handles all of this well enough that you should not be thinking about it. The cognitive work you want to preserve is for architecture decisions, debugging novel problems, and the design questions where context and judgment matter. AI does not have your context. You do. Spend your mental energy where that asymmetry is in your favor.

Treat AI output as a first draft. This sounds obvious and is still the most commonly violated principle. The failure mode is not that AI produces bad output. It is that good-enough output stops triggering the critical review that would catch the 10% that is subtly wrong. Build the habit of reading AI output the way you would read a pull request from a junior developer who you like and mostly trust but have not given enough context to. That framing produces the right level of skepticism.

How to evaluate new AI tools without wasting a week

A new AI coding tool ships. The demos look impressive. Your feed is full of people saying it changed their workflow. You have three hours this week to evaluate it. How do you decide?

Three questions:

1. Does it save me 30+ minutes today? Not in theory, not in a workflow you would have to redesign to use — in the work you actually have on your plate right now. If you cannot identify a specific task in your current sprint where this tool would save 30 minutes, that is useful information. It might be the right tool for a different context. It is not the right tool for right now.

2. Does it fit my existing workflow? Tools that require a context switch have a much higher adoption bar than tools that slot into what you are already doing. An extension for the editor you are already using is a different evaluation than a completely new environment. The best new tool for your workflow might not be the most powerful tool available — it is the one with the smallest delta between where you are now and where you would be using it.

3. Do I understand what it is doing? This is the security and reliability question. AI tools that operate as black boxes inside your codebase are a different risk profile than tools whose output you can inspect and reason about. You do not need to understand the model weights. You do need to understand what the tool has access to, what it can modify, and how to audit what it did. If you cannot answer those three things in 5 minutes of reading the docs, that is worth knowing before you use it in production.

Where things are heading by August 2026

Predictions in this space are embarrassing to make because they are usually wrong in the direction of underestimating the speed. With that caveat, here is what the current trajectory suggests by August 2026.

Most mid-size companies will have at least one AI agent running autonomously. Not as a demo, not in a sandbox — actually running, with access to real systems, doing work that used to require a human. The companies building toward this now have a compounding advantage. The companies that have not started will spend 6-12 months catching up after it becomes obviously necessary.

The developer role shifts from writing code to directing and reviewing AI output. This is already happening at companies with mature AI workflows. The skill that becomes more valuable is not typing speed or syntax recall. It is the ability to specify clearly, review critically, and make architectural decisions that give AI systems coherent direction. The developers who build those skills now are positioning for where the role is going.

Competitive intelligence becomes a weekly need. The pricing pages and feature pages of the tools you depend on and compete with are changing faster than quarterly review cycles can track. Cursor's pricing changed. Linear bundled AI agent features into their base plan. Segment's pricing page redirects to Twilio. Amplitude rebuilt their entire product story around AI. These are not occasional events — they are the current cadence. Teams that have a systematic way to monitor competitive moves will make faster, better-informed decisions than teams doing it manually or not at all.

The companies that build internal AI workflows by August will have a durable advantage. Not because their tools will be better, but because the organizational knowledge of how to work effectively with AI systems compounds. Teams that have been running AI agents for six months develop intuitions, workflows, and error-handling patterns that cannot be acquired quickly. The gap between early adopters and late adopters is not closing fast — it is widening.

Staying current without drowning

The answer to an accelerating landscape is not to accelerate your personal learning pace to match it. That path leads to burnout and superficial knowledge of too many things.

The answer is to be selective, go deep on what matters to your current work, build systems to surface what you actually need to know, and let the rest flow past.

For the competitive side — knowing when a competitor rebundled their AI features, changed their pricing, or shifted their CTA — that is exactly the kind of signal that matters and is exactly the kind of work that does not require your attention. Tools like BusinessPulse monitor competitor pages automatically and send you a plain-English brief every Monday morning. The signal surfaces. You decide what to do with it.

That is the pattern that works: automate the monitoring, focus the attention, and build the judgment that only comes from depth.

The pace is real. The answer is not to run faster. It is to run smarter.

The SaaSpocalypse Is Real: What AI Agents Mean for Every SaaS Founder

Boehner — Thu, 26 Mar 2026 15:35:29 +0000

Something quietly broke in the SaaS business model.

TechCrunch published a piece in March 2026 on what they're calling the "SaaSpocalypse" — and if you build software, you should read it carefully. Not because it's doom and gloom, but because it explains a structural shift that's already showing up in renewal conversations across the industry.

Here's what's actually happening.

The per-seat model has a fundamental flaw

The entire modern SaaS business model is built on per-seat pricing. You buy software, you pay per user. Your company grows, you add seats. It's clean, predictable, and it worked perfectly for 20 years.

The problem: AI agents don't need seats.

When one AI agent can handle the workload of ten employees — answering support tickets, writing code, processing invoices, managing CRM entries — the per-seat model stops making sense. You don't add seats for the agent. You just... use it. And the SaaS vendor that built their entire pricing model around headcount watches their revenue shrink even as their product gets used more.

TechCrunch quotes multiple investors in the piece saying they're already seeing "downward pressure on contracts during renewals." Companies that once had 50 seats are cutting to 10 because the other 40 tasks are now handled by AI. The software is still running. The revenue is down 80%.

Klarna did it first — at massive scale

The most concrete example of this playing out in public came from Klarna's CEO, Sebastian Siemiatkowski. He went public about shutting down Salesforce entirely and replacing it with an internal AI system. Then he dropped the number: Klarna shut down roughly 1,200 SaaS tools total.

One company. 1,200 subscriptions cancelled.

Klarna's a large company, but the logic doesn't require scale. Any company with an internal AI team and the motivation to build can start cutting SaaS spend. And as AI coding tools get better, the threshold for "we can just build this ourselves" keeps dropping.

The part most people glossed over in Klarna's announcement: Siemiatkowski said this trend probably means fewer SaaS tools will survive — but the ones that do will get much bigger. It's a consolidation story, not just a collapse story. The tools that become genuinely irreplaceable get stickier. Everything else gets cut at the next renewal.

What this actually means if you build SaaS

If you're building a SaaS product right now, there are a few things happening simultaneously in your market:

Your competitors are responding. Some of them are pivoting to outcome-based or usage-based pricing. Some are bundling AI features so aggressively that the comparison to last year's pricing page looks completely different. Some are adding "agent seats" as a new pricing dimension. The pricing pages you saw six months ago are not the pricing pages that exist today.

Your customers are doing the math. Even if they're not switching yet, they're starting to notice when an AI tool can do part of what they're paying you for. The next renewal conversation is going to include a question about whether your product can keep up. If you don't know how your competitors are positioning their AI story, you're walking into that conversation blind.

The window to respond is short. Pricing changes and positioning shifts in competitive markets move fast. A competitor who repositions around AI this quarter captures the conversation before you even know the story has changed.

The companies that survive will be the ones watching closely

Here's the part I keep coming back to from the TechCrunch analysis: the SaaS companies that weather this transition won't necessarily be the ones with the most features or the best AI integrations. They'll be the ones who see what their competitors are doing — a pricing change, a repositioning, a new CTA, a new feature callout — before it shows up in a lost deal.

That's intelligence work. And most SaaS teams do it manually: someone opens a dozen browser tabs, does a periodic audit, tries to remember whether the pricing page looked different last month. It doesn't scale.

Here's what automated competitive monitoring looks like in practice. Using the SnapAPI analyze endpoint, you can pull structured page data — CTA text, word count, tech stack, meta description — from any competitor URL on a schedule:

// npm install snapapi-sdk
const { SnapAPI } = require('snapapi-sdk');

const client = new SnapAPI(process.env.SNAPAPI_KEY);

async function checkCompetitor(url) {
  const data = await client.analyze(url, { screenshot: false });
  return {
    url,
    primary_cta: data.primary_cta,
    og_title:    data.og_title,
    word_count:  data.word_count,
    technologies: data.technologies,
    captured_at: new Date().toISOString(),
  };
}

When you diff that output week over week, a pricing page rebrand isn't something you discover during a lost-deal post-mortem. It's something that lands in your inbox Monday morning, with an AI-written brief explaining what changed and why it matters.

The companies that get cut vs. the ones that survive

The SaaSpocalypse isn't the end of SaaS. It's a forcing function. The tools that get cut at the next renewal will be the ones that couldn't articulate why they were irreplaceable. The ones that survive will be the ones that proved it — in product, in positioning, and in the speed with which they responded to every move their competitors made.

The math from Klarna is stark: 1,200 subscriptions cancelled by one company. That's not a niche outcome. That's a preview.

If you're building SaaS and you're not actively watching how your competitors are positioning their AI story, raising or cutting prices, and changing their CTAs — you're waiting for a renewal conversation that you're not prepared for.

BusinessPulse monitors your competitor pages every week and sends you a plain-English brief every Monday morning — what changed, what it means, what to watch. 14-day free trial, no credit card.

How to Give Your AI Agent the Ability to Read Any Webpage

Boehner — Mon, 23 Mar 2026 21:43:05 +0000

Here's the problem with most AI agents: they can't see the live web.

They know everything up to their training cutoff. But the moment you ask "what does this company's pricing page say right now?" or "what changed on this competitor's homepage?" - they're blind. They either hallucinate an answer or admit they don't know.

The fix is straightforward: give the agent a tool that fetches and parses any URL into structured JSON, then let the LLM reason about the result. This is what I use in production, and you can wire it into any agent pipeline in about 20 lines.

Why not just `fetch` the HTML?

The obvious approach is fetch(url) and pass the HTML to the LLM. This works, but badly:

Token cost: A typical page is 50-150KB of HTML. That's 12,000-37,000 tokens just for the page - before your prompt. At $3/million tokens for GPT-4, that's $0.04-0.11 per page read. At 100 pages/day that's $4-11/day just for web reading.
Noise: Raw HTML is full of script tags, CSS inline styles, SVG paths, data attributes. The LLM has to wade through all of it to find the content.
JavaScript: Most modern sites are React/Next.js/Vue. A fetch() call gets you the server-rendered shell, not the actual content. The rendered text just isn't there.

The clean solution is to hit an endpoint that runs a real headless browser, executes JavaScript, and returns structured data - not raw markup.

The `analyze` tool

I use SnapAPI's /v1/analyze endpoint. It runs Puppeteer against the URL, waits for the page to fully render, and returns a JSON object with the fields that actually matter for an AI agent:

curl "https://snapapi.tech/v1/analyze?url=https://stripe.com/pricing" \
  -H "X-API-Key: YOUR_KEY"

Response:

{
  "url": "https://stripe.com/pricing",
  "title": "Pricing & Fees",
  "description": "Find Stripe fees and pricing information...",
  "headings": [
    { "level": 2, "text": "Payments" },
    { "level": 2, "text": "Revenue" }
  ],
  "primary_cta": { "text": "Start now", "href": "https://dashboard.stripe.com/register" },
  "technologies": ["stripe"],
  "word_count": 3678,
  "load_time_ms": 965
}

The text_content field (not shown above for brevity) is the full rendered visible text - no HTML tags, no scripts, no noise. That's what the LLM gets. About 2,000-5,000 tokens for a typical page instead of 30,000.

Wrapping it as an agent tool

Here's a LangChain-style tool wrapper. The pattern works with any agent framework - this is the interface:

// tools/read-webpage.js
const READ_WEBPAGE_TOOL = {
  name: "read_webpage",
  description: `Read and analyze any webpage. Returns structured content including title, description, headings, visible text, primary CTA, and detected technologies. Use this when you need to know what a specific URL says right now - competitor pages, pricing pages, documentation, news articles.`,

  parameters: {
    type: "object",
    properties: {
      url: {
        type: "string",
        description: "The full URL to read (must include https://)"
      }
    },
    required: ["url"]
  },

  async execute({ url }) {
    const res = await fetch(
      `https://snapapi.tech/v1/analyze?url=${encodeURIComponent(url)}`,
      { headers: { "X-API-Key": process.env.SNAPAPI_KEY } }
    );

    if (!res.ok) {
      return { error: `Failed to read ${url}: HTTP ${res.status}` };
    }

    const data = await res.json();

    // Return only what the LLM needs - not the full response
    return {
      url: data.url,
      title: data.title,
      description: data.description,
      headings: data.headings?.slice(0, 10) ?? [],
      text_content: data.text_content?.slice(0, 8000) ?? "", // cap tokens
      primary_cta: data.primary_cta ?? null,
      technologies: data.technologies ?? [],
      word_count: data.word_count,
    };
  }
};

Wiring it into an OpenAI agent loop

Here's a minimal agent that uses the tool:


javascript
// agent.js
const OpenAI = require("openai");
const { READ_WEBPAGE_TOOL } = require("./tools/read-webpage");

const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

async function runAgent(userPrompt) {
  const messages = [
    { 
      role: "system", 
      content: "You are a research assistant. When asked about a specific website or URL, use the read_webpage tool to get current information before responding."
    },
    { role: "user", content: userPrompt }
  ];

  const tools = [{
    type: "function",
    function: {
      name: READ_WEBPAGE_TOOL.name,
      description: READ_WEBPAGE_TOOL.description,
      parameters: READ_WEBPAGE_TOOL.parameters,
    }
  }];

  while (true) {
    const response = await client.chat.completions.create({
      model: "gpt-4o-mini",
      messages,
      tools,
      tool_choice: "auto"
    });

    const choice = response.choices[0];

    if (choice.finish_reason === "stop") {
      return choice.message.content;
    }

    if (choice.finish_reason === "tool_calls") {
      messages.push(choice.message);


---

*Get a free SnapAPI key at [snapapi.tech](https://snapapi.tech) - 100 requests/month free. For AI pipelines reading 50+ pages/day, Starter plan at $9/month. BusinessPulse (automated competitor monitoring) runs this in production: [snapapi.tech/businesspulse](https://snapapi.tech/businesspulse).*

HTTP Caching in Node.js APIs: ETag, Cache-Control, and stale-while-revalidate Explained

Boehner — Mon, 23 Mar 2026 16:19:45 +0000

HTTP caching is one of those topics where everyone knows it exists, few people implement it correctly, and almost nobody talks about the failure modes.

This is the guide I wish existed when I was building my first API that needed to stay fast under load.

What HTTP caching actually is

HTTP caching isn't one mechanism. It's a family of related headers that tell clients and proxies what to do with a response. Getting them wrong means either stale data reaching users or your server hammered on every request.

The three headers you need to understand:

Cache-Control: max-age=300, stale-while-revalidate=60
ETag: "33a64df551425fcc55e4d42a148795d9f25f89d"
Last-Modified: Wed, 19 Mar 2026 12:00:00 GMT

These do very different things. Let's break them down.

Cache-Control

The most important header. It tells the browser (and any CDN/proxy in between) how long to cache the response.

// The directives you'll actually use:

res.setHeader('Cache-Control', 'public, max-age=300');
// Public = CDNs can cache this. max-age=300 = valid for 5 minutes.

res.setHeader('Cache-Control', 'private, max-age=0, no-store');
// Private = only the browser can cache (not CDNs). No-store = don't cache at all.

res.setHeader('Cache-Control', 'public, max-age=3600, stale-while-revalidate=60');
// Serve stale for 60s while revalidating in the background. The key to fast APIs.

The stale-while-revalidate directive is wildly underused. It says: "serve the cached version immediately (fast!), but kick off a background request to refresh it." Users get instant responses; data stays fresh. This pattern alone can cut your P99 latency by 60-80% on cached routes.

ETags - conditional requests

An ETag is a fingerprint for a response. If the response changes, the ETag changes. The browser sends the ETag back on subsequent requests, and your server can respond with 304 Not Modified if nothing changed - skipping the response body entirely.

const crypto = require('crypto');

function generateETag(data) {
  return '"' + crypto
    .createHash('md5')
    .update(JSON.stringify(data))
    .digest('hex') + '"';
}

app.get('/api/products', async (req, res) => {
  const products = await db.getProducts();
  const etag = generateETag(products);

  // Check if client already has this version
  if (req.headers['if-none-match'] === etag) {
    return res.status(304).end(); // No body - saves bandwidth
  }

  res.setHeader('ETag', etag);
  res.setHeader('Cache-Control', 'public, max-age=60');
  res.json(products);
});

A 304 response has no body. For a /api/products endpoint returning 50KB of JSON, that's 50KB saved on every cache hit. At scale, this matters.

A complete caching middleware in 45 lines

Here's a reusable Express middleware that handles both ETag and Cache-Control for you:

// cache-middleware.js
const crypto = require('crypto');

/**
 * HTTP caching middleware for Express APIs.
 * Handles ETag validation and Cache-Control headers automatically.
 *
 * Usage: app.get('/api/data', cacheFor(300), handler)
 * Or:    app.use('/api/public', cacheFor(60, { staleWhileRevalidate: 30 }))
 */
function cacheFor(maxAgeSeconds, options = {}) {
  const { staleWhileRevalidate = 0, visibility = 'public' } = options;

  return (req, res, next) => {
    // Capture the original json() method
    const originalJson = res.json.bind(res);

    res.json = function (data) {
      const etag = '"' + crypto
        .createHash('sha1')
        .update(JSON.stringify(data))
        .digest('hex').slice(0, 16) + '"';

      // Check conditional request
      if (req.headers['if-none-match'] === etag) {
        return res.status(304).end();
      }

      // Set caching headers
      let cacheControl = `${visibility}, max-age=${maxAgeSeconds}`;
      if (staleWhileRevalidate > 0) {
        cacheControl += `, stale-while-revalidate=${staleWhileRevalidate}`;
      }

      res.setHeader('ETag', etag);
      res.setHeader('Cache-Control', cacheControl);
      res.setHeader('Vary', 'Accept-Encoding');

      return originalJson(data);
    };

    next();
  };
}

module.exports = { cacheFor };

Use it like this:

const { cacheFor } = require('./cache-middleware');

// Cache product list for 5 minutes, serve stale for 60s while revalidating
app.get('/api/products', cacheFor(300, { staleWhileRevalidate: 60 }), async (req, res) => {
  const products = await db.getProducts();
  res.json(products);
});

// Cache user profile for 60 seconds, private (user-specific)
app.get('/api/profile', authenticate, cacheFor(60, { visibility: 'private' }), async (req, res) => {
  const profile = await db.getUserProfile(req.user.id);
  res.json(profile);
});

// Never cache sensitive endpoints
app.post('/api/payment', (req, res) => {
  res.setHeader('Cache-Control', 'no-store');
  // ...
});

When to cache what

Not all endpoints should be cached the same way. Here's my mental model:

Endpoint type	Cache-Control	Why
Static reference data (countries, categories)	`public, max-age=86400`	Changes rarely, same for everyone
Product/content lists	`public, max-age=300, stale-while-revalidate=60`	Mostly shared, tolerate 5-min staleness
User-specific data	`private, max-age=60`	Only browser cache, not CDN
Authenticated API calls	`private, max-age=30`	Short TTL, no CDN
Payment/checkout	`no-store`	Never cache
Search results	`public, max-age=30` or `no-cache`	Depends on staleness tolerance
Webhook endpoints	`no-store`	POST requests shouldn't cache

The failure modes

Caching authenticated responses publicly. If a user-specific response leaks into a CDN cache, the next person to request that URL gets someone else's data. Always use private for authenticated responses. Never use public for anything that varies by user.

Not varying on the right headers. If your API returns different responses based on Accept-Language or Authorization, you must include those in Vary:

res.setHeader('Vary', 'Accept-Language, Accept-Encoding');

Without this, a CDN might serve a cached English response to a French user.

Stale cache after a deploy. Your API returns a 5-minute cached response. You deploy a bug fix. Users see the broken version for up to 5 minutes. Solutions:

Use short TTLs (max-age=60) with stale-while-revalidate
Include a version hash in your API path (/api/v2/products)
Use ETags so clients always validate before assuming cached data is valid

Caching error responses. res.json({ error: 'Service unavailable' }) should never be cached. Check for errors before setting cache headers:

res.json = function (data) {
  // Don't cache error responses
  if (data && data.error) {
    res.setHeader('Cache-Control', 'no-store');
    return originalJson(data);
  }
  // ... caching logic
};

Testing your cache headers

The fastest way to verify your caching is working:

# First request - should be a cache MISS (no ETag stored yet)
curl -I "https://your-api.com/api/products"
# Look for: ETag: "abc123", Cache-Control: public, max-age=300

# Second request with the ETag - should return 304
curl -I "https://your-api.com/api/products" \
  -H 'If-None-Match: "abc123"'
# Look for: HTTP/2 304 - no body returned

Chrome DevTools ? Network tab ? check the "Size" column. A "304" response shows "(from cache)" or a tiny byte size - that's confirmation it's working.

The 5-minute implementation

Add this to your Express API right now:

Install nothing - this uses only Node built-ins
Add cache-middleware.js from above
Apply cacheFor(300, { staleWhileRevalidate: 60 }) to your highest-traffic read endpoints
Apply cacheFor(60, { visibility: 'private' }) to user-specific endpoints
Add no-store to payment and auth endpoints
Test with curl -I and verify ETags

For a typical API serving 10,000 requests/day with 40% cache hit rate, this reduces database load by ~4,000 queries/day. For free.

I build APIs and developer tools. Currently working on SnapAPI - a web intelligence API for developers and AI pipelines.

Monitor Page Load Times Across Any URL Fleet Without Running a Browser

Boehner — Mon, 23 Mar 2026 15:19:29 +0000

Every performance monitoring tool I've tried has the same problem: it runs inside your infrastructure. Lighthouse needs Node. WebPageTest needs a server. Puppeteer needs Chrome running somewhere. For a quick external check of "is my site actually fast for real users?", they're all overkill.

I wanted something dead simple: give it a list of URLs, get back load times, detect regressions.

Here's what I built — and how you can replicate it in under 50 lines.

The approach

Instead of running a headless browser, I use a screenshot API that already has a Puppeteer cluster running in the cloud. The API's load_time_ms field captures real browser load time — navigation start to load event — which is exactly what matters for CWV (Core Web Vitals).

The endpoint I'm using is SnapAPI's /v1/analyze:

curl "https://snapapi.tech/v1/analyze?url=https://example.com" \
  -H "X-API-Key: YOUR_KEY"

Response includes:

{
  "url": "https://example.com",
  "title": "Example Domain",
  "load_time_ms": 83,
  "page_type": "other",
  "technologies": [],
  "word_count": 67
}

83ms is genuinely fast (it's a static HTML page). A typical Next.js app with a cold start might return 1,200–2,400ms. More on that threshold in a minute.

Building the monitor

Here's the full script. It reads a list of URLs, measures load time for each, compares to a stored baseline, and alerts if anything regresses beyond a threshold:

// perf-monitor.js
const fs   = require("fs");
const path = require("path");

const API_KEY     = process.env.SNAPAPI_KEY;
const BASELINE    = path.resolve("./baseline.json");
const THRESHOLD   = 0.20; // alert if 20% slower than baseline
const REPORT_FILE = path.resolve("./perf-report.json");

const URLS = [
  "https://yourdomain.com",
  "https://yourdomain.com/pricing",
  "https://yourdomain.com/docs",
  "https://yourdomain.com/blog",
];

async function measure(url) {
  const res = await fetch(
    `https://snapapi.tech/v1/analyze?url=${encodeURIComponent(url)}`,
    { headers: { "X-API-Key": API_KEY } }
  );
  if (!res.ok) throw new Error(`HTTP ${res.status} for ${url}`);
  const data = await res.json();
  return { url, load_time_ms: data.load_time_ms, measured_at: new Date().toISOString() };
}

async function run() {
  // Load or initialise baseline
  const baseline = fs.existsSync(BASELINE)
    ? JSON.parse(fs.readFileSync(BASELINE, "utf8"))
    : {};

  const results = [];
  const alerts  = [];

  for (const url of URLS) {
    try {
      const { load_time_ms, measured_at } = await measure(url);
      const base = baseline[url];
      const regression = base
        ? (load_time_ms - base) / base
        : null;

      const entry = { url, load_time_ms, measured_at, regression_pct: regression
        ? Math.round(regression * 100)
        : null };
      results.push(entry);

      if (regression !== null && regression > THRESHOLD) {
        alerts.push(`🔴 REGRESSION: ${url} — ${load_time_ms}ms (was ${base}ms, +${Math.round(regression * 100)}%)`);
      }

      // Update baseline with running average
      baseline[url] = base
        ? Math.round((base * 0.8) + (load_time_ms * 0.2)) // exponential moving avg
        : load_time_ms;

    } catch (err) {
      console.error(`Failed: ${url} — ${err.message}`);
    }
  }

  // Write outputs
  fs.writeFileSync(BASELINE, JSON.stringify(baseline, null, 2));
  fs.writeFileSync(REPORT_FILE, JSON.stringify({ run_at: new Date().toISOString(), results, alerts }, null, 2));

  if (alerts.length > 0) {
    console.log("\nPERFORMANCE ALERTS:");
    alerts.forEach(a => console.log(a));
    process.exit(1); // Non-zero exit for CI integration
  } else {
    console.log(`✅ All ${results.length} pages within threshold. Fastest: ${Math.min(...results.map(r => r.load_time_ms))}ms`);
  }
}

run();

Run it:

SNAPAPI_KEY=your_key node perf-monitor.js

Example output:

✅ All 4 pages within threshold. Fastest: 83ms

Or if something regressed after a deploy:

PERFORMANCE ALERTS:
🔴 REGRESSION: https://yourdomain.com/blog — 2,840ms (was 1,100ms, +158%)

Integrating into CI/CD

The script exits non-zero on regression, which is all you need for CI gates.

GitHub Actions:

- name: Performance regression check
  env:
    SNAPAPI_KEY: ${{ secrets.SNAPAPI_KEY }}
  run: node perf-monitor.js

Add it after your deploy step. If load time spikes more than 20% from baseline, the job fails and blocks the merge.

As a scheduled cron job:

# .github/workflows/perf-monitor.yml
on:
  schedule:
    - cron: '0 */6 * * *'  # every 6 hours

This gives you external monitoring — checking from GitHub's infrastructure, not your own server. Catches CDN issues, third-party script slowdowns, database query regressions.

What the load_time_ms field actually measures

Worth being explicit: load_time_ms is the time from navigation start (before DNS) to the browser's load event (all synchronous resources fetched). It's equivalent to PerformanceTiming.loadEventEnd - PerformanceTiming.navigationStart.

This is different from:

TTFB (time to first byte) — much earlier, misses render blocking
LCP (Largest Contentful Paint) — later, measures visual completeness
TTI (Time to Interactive) — later still, measures JS execution

For regression detection, load_time_ms is a good proxy. A spike here almost always maps to something real: a new heavy dependency, a blocking third-party script, a missing CDN cache.

Rough thresholds for context

Score	load_time_ms
Excellent	< 500ms
Good	500 – 1,500ms
Needs improvement	1,500 – 3,000ms
Poor	> 3,000ms

These aren't official CWV buckets (those use LCP), but they match real-world user perception closely enough for regression detection.

Extending it

Multi-region monitoring: The API runs from a fixed server location. For geographic distribution, run the same script from GitHub Actions in different regions using runs-on: ubuntu-latest (US East) and an equivalent in EU.

Slack/webhook alerts: Replace the console.log alert section with:

if (alerts.length > 0) {
  await fetch(process.env.SLACK_WEBHOOK, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({ text: alerts.join("\n") })
  });
}

Competitor benchmarking: Same script, different URL list. Track how your load time compares to three competitors, measured from the same location, at the same time:

const URLS = [
  "https://yoursite.com/pricing",
  "https://competitorA.com/pricing",
  "https://competitorB.com/pricing",
];

The full picture

The script above is ~70 lines, has no dependencies beyond Node built-ins and fetch, and catches real regressions. It's external (testing what real users hit, not your internal network), schedulable, and CI-compatible.

The only thing you need is a SnapAPI key — free tier covers 100 requests/month which is enough for most monitoring setups.

SnapAPI is a web intelligence API that wraps headless Chromium — screenshot, PDF, structured page analysis, batch processing. Tip: if you build something with it, I'd like to see it.

How to Monitor Page Load Time and Core Web Vitals Across Your Entire Site (Node.js)

Boehner — Mon, 23 Mar 2026 14:48:46 +0000

Stop guessing your site speed — monitor it systematically. One script, any number of pages, run it weekly.

The problem with Lighthouse

Lighthouse is great for debugging one page. But when you're managing a SaaS landing page, 20 blog posts, and an e-commerce catalog, "run Lighthouse manually" isn't a workflow — it's wishful thinking.

You need:

All your pages checked on a schedule
A flag when any page exceeds your latency threshold
Zero manual steps

Here's how to build that in about 30 lines of Node.js.

What the analyze endpoint returns

We'll use the SnapAPI analyze endpoint, which headlessly loads a page and returns structured performance + content data:

{
  "url": "https://example.com",
  "load_time_ms": 1842,
  "word_count": 1204,
  "technologies": ["React", "Vercel", "Google Analytics"],
  "buttons": ["Get started", "Sign in", "View demo"],
  "forms": [{ "id": "signup", "fields": 3 }],
  "headings": ["Why Example beats the competition", "Pricing"],
  "links": ["https://example.com/pricing", "https://example.com/about"],
  "cta": "Get started"
}

The key fields for performance monitoring:

Field	What it tells you
`load_time_ms`	Full page load to network idle (real browser, not simulated)
`word_count`	Ballpark for content sprawl — bloated pages load slower
`technologies`	Detect heavy JS frameworks, tracking pixels, etc.
`buttons` / `forms`	CTA presence — catch regression when a deploy breaks your hero CTA

The 30-line script

// monitor-pages.js
const SNAPAPI_KEY = process.env.SNAPAPI_KEY;
const THRESHOLD_MS = 3000; // flag anything over 3 seconds

const PAGES = [
  "https://yoursite.com",
  "https://yoursite.com/pricing",
  "https://yoursite.com/blog",
  "https://yoursite.com/features",
  // add as many as you like
];

async function analyzePage(url) {
  const res = await fetch(`https://snapapi.tech/analyze?url=${encodeURIComponent(url)}&apiKey=${SNAPAPI_KEY}`);
  if (!res.ok) throw new Error(`${url} → ${res.status}`);
  return res.json();
}

async function runMonitor() {
  const results = await Promise.all(PAGES.map(analyzePage));
  const slow = results.filter(r => r.load_time_ms > THRESHOLD_MS);

  console.log(`\n✅ Checked ${results.length} pages`);
  console.log(`⚠️  Slow pages (>${THRESHOLD_MS}ms): ${slow.length}`);

  if (slow.length > 0) {
    console.log("\nSlow pages:");
    slow.forEach(r => console.log(`  ${r.load_time_ms}ms — ${r.url}`));
    process.exit(1); // non-zero exit for CI/GitHub Actions
  }
}

runMonitor().catch(err => { console.error(err); process.exit(1); });

Run it:

SNAPAPI_KEY=your_key node monitor-pages.js

Output:

✅ Checked 4 pages
⚠️  Slow pages (>3000ms): 1

Slow pages:
  4231ms — https://yoursite.com/blog

Adding a Slack alert

Replace the console.log block with a Slack webhook call when slow pages are found:

async function notifySlack(slowPages) {
  const lines = slowPages.map(r => `• *${r.load_time_ms}ms* — ${r.url}`).join("\n");
  await fetch(process.env.SLACK_WEBHOOK_URL, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      text: `🐌 *Page speed alert:* ${slowPages.length} slow page(s)\n${lines}`
    })
  });
}

Then call it inside runMonitor:

if (slow.length > 0) {
  await notifySlack(slow);
  process.exit(1);
}

Automating it with GitHub Actions

Create .github/workflows/page-speed.yml:

name: Page Speed Monitor

on:
  schedule:
    - cron: '0 9 * * 1'  # Every Monday at 9am UTC
  workflow_dispatch:

jobs:
  monitor:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - uses: actions/setup-node@v3
        with:
          node-version: '20'

      - name: Run page speed monitor
        env:
          SNAPAPI_KEY: ${{ secrets.SNAPAPI_KEY }}
          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}
        run: node monitor-pages.js

Add SNAPAPI_KEY and SLACK_WEBHOOK_URL to your repository secrets. Now every Monday morning, you'll get a Slack ping if any page exceeds your threshold — or silence if everything's fine.

Real-world use cases

SaaS landing pages
Your homepage, pricing page, and signup flow are conversion-critical. A 4-second load on /pricing is quietly killing conversions. Set the threshold at 2500ms and get alerted the moment a deploy introduces a regression.

E-commerce category pages
Category pages are high-traffic, often image-heavy, and tend to balloon over time as products are added. Running weekly analysis catches the creep before it compounds.

Blog post audits
Blog posts get updated, embedded widgets get added, images get re-uploaded without compression. A weekly scan across your 50 most-trafficked posts will surface the ones that have gone soft.

Agency client monitoring
If you manage multiple client sites, add all their URLs to a single list and send each client a weekly report. The technologies field also lets you confirm their CDN, analytics, and tag manager are still loaded correctly after deploys.

Compared to running Lighthouse manually

	Lighthouse (manual)	This script
Pages per run	1	Unlimited
Automation	None	GitHub Actions / cron
Slack alerts	No	Yes
Tech stack detection	No	Yes
CTA regression detection	No	Yes
Setup time	0 (built into Chrome)	~15 minutes
Weekly cost	$0	~$0.04 (for 20 pages)

Lighthouse is still useful for deep dives — waterfall charts, render-blocking resource diagnosis, etc. But for monitoring, the script above wins on every axis except initial familiarity.

Extending it: full content audit

The same endpoint surfaces word_count, headings, buttons, and forms. You can extend the monitor to also catch:

Missing CTA: if (!r.cta) flag(r.url, "no CTA detected")
Thin content: if (r.word_count < 300) flag(r.url, "thin page")
Broken forms: if (r.forms.length === 0 && url.includes("/signup")) flag(...)

One API call, one page load — you get performance, content, and structure all at once.

Get started

SnapAPI has a free tier — 100 calls/month, no credit card. The analyze endpoint is included. If you're monitoring more than a few pages weekly, the paid plan starts at $9/month for 1,000 calls.

Full docs: snapapi.tech/docs#analyze

The script above is self-contained — drop in your PAGES array, add your key, and you're monitoring in under 15 minutes.

How to Monitor Page Load Time Across Your Entire Site (30 Lines of Node.js)

Boehner — Mon, 23 Mar 2026 14:45:41 +0000

You can't fix what you don't measure. Most teams check page speed occasionally — run Lighthouse on the homepage, feel good, move on. Meanwhile, three product pages and two blog posts are loading in 4+ seconds and quietly tanking conversion.

Here's a script that checks every page you care about, flags the slow ones, and can run automatically every week in GitHub Actions.

What the SnapAPI analyze endpoint returns

The /v1/analyze endpoint runs a real Chromium browser against any URL and returns structured data — including load_time_ms, which is the time from request start to networkidle (fully loaded, no pending requests).

// npm install snapapi-sdk
const { SnapAPI } = require('snapapi-sdk');
const client = new SnapAPI(process.env.SNAPAPI_KEY);

const data = await client.analyze('https://yoursite.com/pricing');
console.log(data.load_time_ms);   // e.g. 2341
console.log(data.word_count);     // e.g. 1847
console.log(data.technologies);   // ["React", "Vercel", "Cloudflare"]
console.log(data.primary_cta);    // "Start free trial"

One call. Real browser. No Lighthouse CLI, no Puppeteer setup, no browser binary in CI.

The monitoring script

// page-performance-monitor.js
// Checks a list of pages and flags any with load_time > threshold.
// Usage: SNAPAPI_KEY=key node page-performance-monitor.js

const { SnapAPI } = require('snapapi-sdk');
const fs          = require('fs');

const client    = new SnapAPI(process.env.SNAPAPI_KEY);
const THRESHOLD = 3000; // ms — flag any page over 3 seconds

const PAGES = [
  { url: 'https://yoursite.com',          label: 'Homepage' },
  { url: 'https://yoursite.com/pricing',  label: 'Pricing' },
  { url: 'https://yoursite.com/features', label: 'Features' },
  { url: 'https://yoursite.com/blog',     label: 'Blog index' },
  // add more...
];

async function checkPage({ url, label }) {
  try {
    const data = await client.analyze(url, { screenshot: false });
    return {
      label, url,
      load_ms:     data.load_time_ms,
      word_count:  data.word_count,
      technologies: data.technologies,
      primary_cta: data.primary_cta,
      slow:        data.load_time_ms > THRESHOLD,
    };
  } catch (err) {
    return { label, url, error: err.message, slow: false };
  }
}

async function main() {
  console.log(`Checking ${PAGES.length} pages (threshold: ${THRESHOLD}ms)...\n`);

  const results = await Promise.all(PAGES.map(checkPage));
  const slow    = results.filter(r => r.slow);
  const errors  = results.filter(r => r.error);

  // Print results table
  console.log('Page'.padEnd(20), 'Load (ms)'.padEnd(12), 'Words'.padEnd(8), 'Status');
  console.log('-'.repeat(60));
  for (const r of results) {
    const status = r.error ? '⚠ ERROR' : r.slow ? '🔴 SLOW' : '✅ OK';
    const load   = r.error ? 'N/A' : String(r.load_ms);
    console.log(r.label.padEnd(20), load.padEnd(12), String(r.word_count || '').padEnd(8), status);
  }

  console.log(`\n${slow.length} slow page(s), ${errors.length} error(s)`);

  // Save JSON results for GitHub Actions / Slack
  const output = { checked_at: new Date().toISOString(), results, slow_count: slow.length };
  fs.writeFileSync('performance-results.json', JSON.stringify(output, null, 2));

  if (slow.length > 0) process.exit(1); // fail CI if slow pages found
}

main().catch(err => { console.error(err.message); process.exit(1); });

Sample output:

Checking 4 pages (threshold: 3000ms)...

Page                 Load (ms)    Words    Status
------------------------------------------------------------
Homepage             1842         1204     ✅ OK
Pricing              2341         987      ✅ OK
Features             3891         2103     🔴 SLOW
Blog index           4102         3847     🔴 SLOW

2 slow page(s), 0 error(s)

Add it to GitHub Actions

# .github/workflows/page-speed.yml
name: Weekly Page Speed Check

on:
  schedule:
    - cron: '0 9 * * 1'  # Every Monday at 9am
  workflow_dispatch:      # Also allows manual trigger

jobs:
  check-speed:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '20' }
      - run: npm install snapapi-sdk
      - name: Run page speed check
        run: node page-performance-monitor.js
        env:
          SNAPAPI_KEY: ${{ secrets.SNAPAPI_KEY }}
      - name: Post to Slack if slow pages found
        if: failure()
        uses: slackapi/slack-github-action@v1
        with:
          payload: '{"text": "⚠️ Slow pages detected in weekly speed check. Check the Actions log."}'
        env:
          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}

Now every Monday at 9am, the workflow runs. If any page exceeds 3 seconds, the step fails and Slack gets a message. Fix the page, push, see the green check.

What else the analyze endpoint gives you

load_time_ms is one field. The full response is useful for page health audits beyond just speed:

Field	What it tells you
`word_count`	Content depth — blog posts under 300 words rarely rank
`technologies`	Tech debt signal — 5+ marketing pixels? Check your bundle
`primary_cta`	Is the CTA present after a deploy? Assert it in CI
`navigation`	Did a nav item disappear? Catch it before users do
`forms`	Sign-up form still rendering on the pricing page?

You can write assertions for any of these alongside the speed check:

// Assert the pricing page CTA is still there after deploy
if (!data.primary_cta || !data.primary_cta.toLowerCase().includes('free')) {
  console.error('FAIL: Pricing page CTA missing or changed');
  process.exit(1);
}

Compared to running Lighthouse manually

	This script	Manual Lighthouse
Setup	`npm install snapapi-sdk`	CLI install + Chrome config
Run all pages	`Promise.all()`	One at a time
CI integration	10 lines of YAML	Custom Docker image
Structural data (CTA, nav)	Included	Not available
Screenshot	Optional param	Separate run
Cost	Free tier: 100 pages/month	Free

The main difference: this gives you real browser load time plus structural page health in one call. Lighthouse gives you more granular network waterfall data. Use both if you need the waterfall; use this if you need automated monitoring across many pages.

Free API key

100 calls/month, no credit card, active in 30 seconds.

→ snapapi.tech/start

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