DEV Community: Sam Chen

Why I Built a Bullet Journal Resource Site Instead of Another Productivity App

Sam Chen — Sun, 10 May 2026 20:56:15 +0000

The productivity space is drowning in apps. Notion, Todoist, Obsidian, TickTick — there's a new one every week. But the fastest-growing productivity movement is analog: bullet journaling.

I built BulletJournals.net as a resource hub for the community.

Why Analog is Growing in a Digital World

Bullet journaling has exploded because it solves the problem apps create: decision fatigue. With an app, you spend half your time configuring the system. With a bullet journal, you spend that time actually thinking about your life.

The BuJo community is massive — millions of practitioners worldwide — but the resources are scattered across YouTube, Instagram, and individual blogs.

What BulletJournals.net Offers

Spread templates — weekly, monthly, habit trackers, mood trackers, organized by complexity
Getting started guides — from "what notebook should I buy" to advanced collection design
Inspiration galleries — curated examples that are actually practical, not just pretty
Supply reviews — pens, notebooks, stickers, washi tape reviewed by actual journal keepers

The Technical Side

Built on WordPress with a custom taxonomy system for spreads. The key insight was that people search for bullet journal content very differently than typical blog content. They search by spread type, by month, by skill level, and by aesthetic style.

SEO in this niche is fascinating because the search intent is so visual. People want to see examples before reading about them.

Lessons

Sometimes the best thing a developer can build isn't an app — it's a well-organized information resource for a community that needs one.

Check it out: bulletjournals.net

I Built a Book Recommendation Engine Based on Mood, Not Genre

Sam Chen — Sun, 10 May 2026 20:56:09 +0000

Genre-based book recommendations are broken. Someone who loves "The Great Gatsby" and "Norwegian Wood" doesn't want "literary fiction" — they want books that feel a certain way.

I built BookMoodMatch to solve this.

Why Mood > Genre

Think about the last book you loved. You probably wouldn't describe it by genre first. You'd say it was "cozy" or "mind-bending" or "made me ugly-cry on the train."

That emotional dimension is what BookMoodMatch uses. Instead of asking "what genre?" it asks "what mood are you in?"

How It Works

The matching algorithm considers:

Current mood — contemplative, adventurous, cozy, intense, light
Reading context — commute, vacation, before bed, weekend binge
Past favorites — not just titles, but what you loved about them
Avoidance signals — topics or tones you want to skip right now

The Data Challenge

Building a mood taxonomy for books is harder than it sounds. A book can be simultaneously "dark" and "hopeful." The same book reads differently depending on where you are in life.

I ended up with a multi-dimensional mood space rather than simple labels. Each book gets scored across several emotional axes, and matching happens in that space.

Results

The most common feedback: "This is the first recommendation engine that actually gets me." Genre systems put you in a box. Mood systems meet you where you are.

Try it at bookmoodmatch.com.

I Built a Review Site That Actually Helps People Buy Things (Not Just Rank on Google)

Sam Chen — Sun, 10 May 2026 20:39:21 +0000

Most review sites are SEO farms. "Top 10 Best X in 2026" articles written by someone who never touched the product, stuffed with affiliate links, optimized for Google rather than humans.

I wanted to build something different: reviews that answer the actual question a buyer has at the moment of decision.

The Problem With Review Content

After analyzing 200+ competitor review articles in the fitness/gear space, I found the same template everywhere:

Generic intro paragraph about why [product category] matters
List of 10 products copied from Amazon bestsellers
Specs table pulled from manufacturer pages
"Pros and cons" that are just rephrased spec bullet points
Affiliate links everywhere

Nobody is answering: "I have $150, I run 3x/week, and my old shoes gave me shin splints. What should I buy?"

The Architecture That Works

Every review on pulsegearreviews.com follows a decision-first structure:

1. Decision matrix upfront — before any prose, a table showing: use case → recommended product → why. Reader gets their answer in 5 seconds.

2. Context-aware recommendations — not "best overall" but "best for runners with wide feet under $120" and "best for gym-only use with ankle support issues."

3. Real testing data — weight measurements, flexibility tests, durability after X weeks. Numbers, not adjectives.

4. The "don't buy this if..." section — every product gets one. Honesty builds trust and reduces returns (which kills affiliate revenue anyway).

The Technical Stack

WordPress + custom theme (fast loading, minimal JS)
Structured data schema for Product, Review, AggregateRating
Comparison table component (custom Gutenberg block)
Automated price tracking via affiliate API
Internal linking graph that connects related buying decisions
RankMath for SEO scoring (target: 80+ on every post)

What 500+ Reviews Taught Me

Comparison posts outperform single-product reviews 3:1 — people search "X vs Y," not "X review"
"Best for [specific use case]" beats "Best Overall" — long-tail keywords with higher conversion intent
Updating prices quarterly keeps rankings stable — stale prices = stale content signal to Google
Internal links between related reviews compound — "best running shoes" → "best running socks" → "shin splint prevention" builds topical authority

The lesson for any builder: solve the decision, not the information gap. Information is everywhere. Decision support is rare.

Why I Built 200+ Niche Calculators Instead of One "Smart" Calculator

Sam Chen — Sun, 10 May 2026 19:25:21 +0000

Everyone's building AI chatbots. I built calculators. Boring, specific, instant-result calculators. They get more traffic than most SaaS products I've seen.

The Insight

People don't Google "AI assistant." They Google "concrete cost per square foot calculator" and "how many BTUs do I need for my room" and "salary to hourly conversion."

These searches have:

High intent (someone is about to make a decision)
Zero competition from AI tools (ChatGPT can't pre-fill a form from URL parameters)
Massive long-tail volume (each calculator: 1K-50K searches/mo)
Perfect ad placement (the user is literally about to spend money)

The Architecture

Each calculator is a self-contained page with:

Inputs pre-filled from URL parameters (shareable results)
Instant calculation (no submit button, no loading state)
SEO-optimized explanation text below the calculator
Related calculators sidebar (internal linking)
Schema markup for rich snippets

The stack is deliberately simple:

HTML + vanilla JS + CSS
No framework, no build step, no dependencies
Average page weight: 12KB
Time to interactive: <200ms

Why no React? Because a mortgage calculator doesn't need a virtual DOM. It needs to load instantly, work on every device, and rank #1 for its keyword.

The SEO Flywheel

Here's what makes this model work:

One calculator = one keyword — "concrete calculator" is the page, the H1, the URL, the title
Internal linking is natural — "concrete calculator" links to "rebar spacing calculator" links to "foundation cost estimator"
Time on page is insane — users actually USE calculators (3-5 minutes average)
Zero content maintenance — a calculator never needs "updating for 2026"
Backlinks come naturally — contractors share the specific calculator URL with clients

What 200 Calculators Taught Me

Winners (10K+ monthly searches each):

Unit converters (temperature, weight, length) — boring but massive volume
Financial (mortgage, loan, compound interest) — high CPC, great for ads
Construction (concrete, paint, flooring) — people use these at the job site
Health (BMI, calorie, macro) — evergreen search volume
Time (age, date difference, timezone) — daily utility searches

Losers (built them, nobody came):

Overly specific (e.g., "aquarium stocking calculator" — 200 searches/mo)
Educational (e.g., "physics momentum calculator" — seasonal, students only)
Novelty (e.g., "what day will I be 1 billion seconds old" — one-time use)

The Sweet Spot

5K-50K monthly searches
Commercial or transactional intent
No good existing free tool (or existing tools are bloated/ad-heavy)
Can be built in under 2 hours
Result is shareable (URL parameters encode the inputs)

Revenue Model

No subscriptions. No premium tiers. Just:

AdSense (high-CPC pages = high RPM)
Affiliate links where relevant (financial calculators → broker links)
Sponsored calculator partnerships (brands want a "brand name + calculator" page)

RPM ranges from $15 (general calculators) to $80+ (financial/insurance calculators).

The Compound Effect

Each new calculator:

Adds a node to the internal link graph
Increases domain authority from natural backlinks
Cross-links to existing calculators (boosts their rankings too)
Takes 1-3 hours to build, ranks within 2-4 weeks

After 200 calculators, the domain authority is high enough that new pages rank faster. The flywheel accelerates.

Try It

The full collection is at calcvortex.com. If you're thinking about a content play that doesn't require constant content creation, niche calculators are the most underrated approach I've found.

What utility tools have you built that surprised you with traffic?

I Review 50+ AI Tools a Month — Here's My Evaluation Framework

Sam Chen — Sun, 10 May 2026 19:19:48 +0000

Running an AI tool review site means I test 50+ new tools monthly. Most are wrappers around GPT-4 with a UI. Here's how I separate signal from noise in under 10 minutes per tool.

The 90% Filter (Eliminates Most Tools Instantly)

Before I even sign up, three questions:

Does it solve a problem I had before AI existed? If the "problem" only exists because AI created it (e.g., "manage your AI-generated content"), skip.
Can I describe the value without saying "AI-powered"? If removing "AI" from the description makes it meaningless, it's a feature not a product.
Would I pay for this if it weren't novel? Novelty wears off in a week. Utility doesn't.

This filter eliminates ~90% of new launches immediately.

The 10-Minute Deep Evaluation

For tools that pass the filter:

Minute 1-2: First-Use Experience

Time to first value (TTFV): can I get output in under 60 seconds?
Does it require my data/API keys to demo? (Red flag for privacy)
Login friction: email-only signup or OAuth maze?

Minute 3-5: Core Functionality

Run my standard test prompts (I keep a bank of 20 across categories)
Compare output quality to the same prompt in raw Claude/GPT
If output quality is indistinguishable → the tool adds no value over the API directly

Minute 6-8: Differentiation Check

What does this do that I can't do with a well-crafted system prompt + API?
Is the differentiation in UI/UX, output quality, or workflow integration?
UI/UX differentiation is valid but must be significant (not just "dark mode ChatGPT")

Minute 9-10: Business Model Viability

Free tier limitations: is it usable or a time-locked demo?
Pricing relative to raw API costs (most tools are 10-50x markup on API costs)
Team/enterprise angle: does this tool make sense for one person or only at scale?

What I've Learned After 600+ Tool Reviews

The Patterns That Predict Success

Workflow-native tools win — tools that live inside your existing workflow (VS Code extension, Slack bot, browser extension) beat standalone apps every time
Specific > general — "AI that writes SQL from natural language" beats "AI assistant for everything"
Output format matters more than output quality — a tool that gives me a perfect CSV is more valuable than one that gives me a slightly better answer as plain text
Batch processing is the killer feature — any tool that processes 100 items while I sleep is 10x more valuable than one that handles them one at a time

The Red Flags

"Just like ChatGPT but..." — if your differentiator starts with "just like X," you don't have one
Requires API keys to function — you're paying for a UI over an API you already have access to
No export/API — your data is trapped; you'll hit a wall within a month
Pricing per "credit" not per usage — designed to be confusing, always more expensive than it looks
"Enterprise" with no team features — means "expensive" not "enterprise-ready"

The Categories That Actually Deliver Value

From highest to lowest ROI across 600+ reviews:

Code assistants (Cursor, Copilot, Claude Code) — measurable time savings, daily use
Writing/editing aids (Grammarly, Hemingway) — specific enough to be reliable
Data extraction/transformation — structured output from unstructured input
Image generation (for specific use cases, not general "make me art")
Meeting summarization — genuinely useful, hard to do manually at scale

Categories with the worst ROI:

General chatbots (you already have one)
AI social media managers (output is generic)
AI "agents" that do everything (do nothing well)

The Review Site

I publish structured reviews with these evaluation scores at aidiscoverydigest.com. Every review includes: TTFV, differentiation score, pricing analysis, and a "would I still use this in 6 months" prediction.

If you're building an AI tool: the bar is higher than you think. Your competitor isn't other AI tools — it's a well-written system prompt in the user's existing API setup.

The Home Automation Stack That Actually Works (After 3 Years of Tweaking)

Sam Chen — Sun, 10 May 2026 19:19:48 +0000

I've been running a smart home for 3 years. Here's what survived the "cool demo" phase and became genuinely useful infrastructure.

The Graveyard of Failed Automations

Before the working stack, let me honor what didn't survive:

Motion-activated lights everywhere — turns out, lights turning on when the cat walks by at 3 AM is not "smart"
Voice-controlled thermostat — nobody wants to shout at their house when guests are over
Automated blinds on sunrise — great in theory, terrible when you're sleeping in on Saturday
Smart lock auto-unlock on proximity — security nightmare when your phone GPS drifts

The pattern: automations that are triggered by presence/time fail. Automations that are triggered by intent succeed.

What Actually Works

1. Context-Aware Lighting (Not Motion-Based)

Instead of motion sensors, I use a combination of:

Time of day + calendar events (if "meeting" → office lights to video-call preset)
Device state (TV on → living room dims to 20%)
Manual scene triggers via physical buttons (not voice, not app)

The key insight: automation should eliminate decisions, not create new failure modes.

2. Climate That Learns Behavior

My thermostat schedule isn't time-based — it's event-based:

First motion in kitchen (coffee routine) → heat living areas
All phones leave geofence → eco mode (this one actually works reliably)
Window sensor open > 5 min → pause HVAC for that zone
Humidity spike in bathroom → exhaust fan for exactly 12 minutes

3. Security That Doesn't Cry Wolf

After disconnecting 90% of my notification triggers:

Only alerts on: door open when "away" mode active, smoke/CO, water leak
Zero alerts on: motion (too noisy), camera changes (useless), device offline (temporary)
Weekly digest of camera clips rather than real-time notifications

4. The "Leaving Home" Macro

One button press at the door:

Lights off (all zones)
Thermostat to eco
Robot vacuum starts
Camera recording activates
Door locks (30-second delay for "forgot my keys" scenario)

This single automation saves 10 minutes of "did I turn off...?" anxiety daily.

The Stack

Layer	Tool	Why
Hub	Home Assistant (RPi 4)	Local-first, no cloud dependency
Switches	Zigbee (IKEA + Sonoff)	Mesh reliability, no WiFi congestion
Sensors	Aqara (temp, humidity, door)	Battery life: 2+ years
Cameras	Reolink (local NVR)	No subscription, no cloud
Voice	None	Removed all voice assistants — privacy + reliability
Buttons	IKEA Shortcut buttons	Physical > voice for reliability

The Non-Obvious Lessons

WiFi smart devices are the wrong choice — Zigbee/Z-Wave mesh doesn't compete with your devices for bandwidth
Cloud-dependent devices will fail — when the company shuts down (it will), your "smart" device becomes a dumb one with no manual override
Your partner must be able to use everything without the app — if automation breaks and only you can fix it, it's a hobby project not infrastructure
Less automation = more reliability — my best setup runs 8 automations, not 80

Building vs. Buying Intelligence

I write about smart home architecture and protocol comparisons at smarthomewizards.com. If you're planning a setup from scratch, the protocol decision (Zigbee vs Z-Wave vs Thread vs WiFi) matters more than any individual device choice.

What's your most reliable automation? Curious what survives long-term for others.

How I Cut My AI API Bill by 90% With a Multi-Model Routing System

Sam Chen — Sun, 10 May 2026 18:04:15 +0000

Last month my Claude API bill was $847. This month it's $73. Same output quality. Here's the system I built.

The Problem

I run multiple AI-powered services — content generation, email classification, SEO optimization, data extraction. Every call was going to Claude Sonnet because "it works." But most of those calls didn't need Sonnet-level intelligence.

Classifying an email as spam? That's a Haiku job. Generating embeddings? Ollama handles that for free. Writing a full article? OK, that's Sonnet. But only 15% of my calls actually needed the expensive model.

The Architecture: Empire Router

I built a routing layer that sits between my application code and the LLM providers. Every request gets classified by complexity, then routed to the cheapest model that can handle it.

from empire_router import router

# Auto-routed based on task complexity
response = router.complete(
    prompt="Classify this email: ...",
    task="classify"  # Routes to Haiku ($0.80/M tokens)
)

response = router.complete(
    prompt="Write a 1500-word article about...",
    task="generate"  # Routes to Sonnet ($3/M tokens)
)

embedding = router.embed("text to embed")  # Routes to Ollama (FREE)

The Routing Decision Tree

Task classification:
├── Binary/classification → Haiku ($0.80/$4 per M tokens)
├── Embeddings → Ollama on VPS (FREE)
├── Simple extraction → DeepSeek ($0.27/M) or Groq (FREE)
├── Content generation → Sonnet ($3/$15 per M tokens)
└── Complex reasoning → Opus ($15/$75) — used <2% of calls

Key Design Decisions

1. Task-type routing, not content-length routing

My first attempt routed by prompt length. Terrible idea. A short prompt like "Is this email spam?" needs a cheap model regardless of length. A short prompt like "Design the architecture for a distributed cache" needs an expensive one.

The task type is what determines model selection, not the token count.

2. Fallback chains, not single-model assignments

ROUTING_CHAINS = {
    "classify": ["ollama/llama3.1:8b", "groq/llama3", "haiku", "sonnet"],
    "generate": ["sonnet", "opus"],
    "embed": ["ollama/nomic-embed-text", "voyage-3"],
}

If the primary model is down or rate-limited, it cascades to the next option. No failed requests, just slightly higher cost on fallback.

3. Quality gates on cheap models

The router doesn't blindly trust cheap model output. For tasks where accuracy matters, it runs a quality check:

Send to cheap model first
Score the response (confidence, format validity, coherence)
If score < threshold → retry on next model in chain
Log the escalation for future routing optimization

In practice, Haiku handles 94% of classification tasks without escalation.

4. Prompt caching for repeated patterns

System prompts that exceed 500 characters get cached. For classification tasks that run the same system prompt thousands of times, this cuts input costs by 90% after the first call.

Results After 30 Days

Metric	Before	After	Change
Monthly cost	$847	$73	-91%
Avg latency	2.1s	0.8s	-62%
Failed requests	12/day	0.3/day	-97%
Quality (human eval)	4.2/5	4.1/5	-2%

The quality dip is within noise. The latency improvement comes from Haiku being faster than Sonnet, plus Ollama embeddings having no network round-trip.

Self-Hosted vs. API: Where the Line Is

I run Ollama on a Contabo VPS (CPU-only, $15/mo). It handles:

All embeddings (nomic-embed-text)
Simple classification fallback (llama3.1:8b)
Data extraction on non-sensitive content

Everything that needs quality or handles sensitive data goes to API providers. The VPS pays for itself in 2 days of avoided API calls.

Try It

The routing pattern works with any LLM provider combination. The key insight: treat model selection as a runtime decision, not a deployment decision.

I write about practical AI cost optimization and infrastructure at wealthfromai.com. The full router is open for anyone building similar multi-model systems — DM me if you want the architecture details.

I Built an AI News Site That Actually Explains Things in Plain English

Sam Chen — Sun, 10 May 2026 17:59:21 +0000

Most AI news reads like it's written for ML engineers. Papers with arxiv links, breathless announcements about parameter counts nobody outside a lab cares about, and "revolutionary breakthroughs" that turn out to be incremental improvements on a benchmark.

I kept running into the same problem: I needed to stay current on AI for my work, but every source was either dumbed-down clickbait or impenetrable academic writing. There was no middle ground.

So I built one.

The Problem With AI News Coverage

The AI news ecosystem has a gap. On one end: TechCrunch and The Verge writing "ChatGPT can now do X!" with no technical depth. On the other: research blogs assuming you know what LoRA fine-tuning means and why a 2% improvement on MMLU matters.

The people who actually use AI tools daily — marketers, developers, business owners, writers — need something different. They need to understand:

What actually changed (not the marketing spin)
Whether it affects their workflow
What the practical implications are in 6 months
Which tools are worth switching to

What ClearAI News Does Differently

Every article follows a simple structure:

What happened — one paragraph, no jargon
Why it matters — practical impact on real workflows
Who should care — specific roles and use cases
What to do about it — actionable next steps

No "this could revolutionize everything" hedging. No breathless hype. No 47-paragraph essays when 5 paragraphs will do.

The Curation Challenge

The hardest part isn't writing — it's deciding what to cover. On any given day there are 50+ AI announcements. Most are irrelevant noise. The curation filter I've developed:

Cover if:

A major model release that changes what's possible (not incremental updates)
A tool launch that solves a real workflow problem
A policy change that affects how people can use AI
A trend shift backed by data (not speculation)

Skip if:

Another wrapper around GPT-4 with a different UI
A benchmark improvement with no practical application
Funding announcements (unless the product is already useful)
"AI will take your job" fear pieces with no substance

The Stack (for the curious)

WordPress with custom theme optimized for reading speed
SEO pipeline built on RankMath with structured data
Content enrichment via Claude API for summaries and categorization
Automated trend detection scanning 200+ sources daily
Newsletter via Kit (ConvertKit) for daily digest delivery

What I've Learned After 6 Months

People want "so what?" more than "what" — the analysis paragraph gets 3x more engagement than the news itself
Tool reviews drive 10x the traffic of news posts — people Google "best AI tool for X" not "latest AI news"
Weekly roundups outperform daily posts — attention is finite; curation beats volume
Plain language isn't dumbing down — it's harder to write clearly than to hide behind jargon

Try It

clearainews.com — no signup needed, no paywall, no newsletter popup on page load.

If you're building in the AI space and want a news source that respects your time, give it a look. Happy to hear feedback on what topics you'd want covered more.

Building an AI That Reads Tarot Cards (and Actually Remembers Your Last Reading)

Sam Chen — Sun, 10 May 2026 16:50:59 +0000

Most tarot apps work like this: shuffle, deal, look up card meaning from a table, display. It's a random number generator wearing a mystical costume.

I wanted to build something that actually understood readings — that could connect The Tower showing up today with the upheaval themes from last week's Celtic Cross, and that treated reversed cards as more than just "the opposite meaning."

The Problem With Generic Interpretations

Card meanings aren't static. The Three of Swords in a "past" position tells a very different story than in "what you need to let go of." And if the querent has been pulling Swords cards for three weeks straight, that pattern matters.

Traditional apps ignore all of this context. Luna's Circle doesn't.

The Stack

FastAPI — async API serving readings, grimoire, horoscopes
Claude API — multi-layer persona architecture for Luna (the AI companion)
Swiss Ephemeris — accurate birth chart calculations (same library astro.com uses)
Qdrant — vector search over 300+ crystal and herb correspondences
SQLite — reading history, user profiles, grimoire entries
ElevenLabs — voice-narrated readings

The Interesting Technical Challenge

Getting an LLM to be consistent in tarot interpretation is harder than it sounds. Most training data is generic copy-paste card meanings. My solution:

Card-position-context matrices — each card gets different interpretation weights based on spread position, surrounding cards, and reading question
Quality scoring — a separate evaluation pass rejects below-threshold interpretations and regenerates
Reading memory — Luna maintains a rolling context window of your past readings, connecting themes across sessions
Persona layers — base knowledge, mystical warmth voice, user-specific context, and a final coherence check

What 22,000+ Readings Taught Me

The Tower generates the most follow-up readings when it appears in future positions
Reversed cards double engagement time (people read more carefully)
The Moon is the hardest card for AI to interpret consistently (illusion vs. intuition depends heavily on context)
Users who get 3+ readings are 5x more likely to convert to paid

Try It

Free daily readings at Luna's Circle — no signup needed for basic features.

The full platform includes 10 spread types, a digital grimoire (Book of Shadows), birth chart analysis, moon phase tracking with ritual suggestions, community covens, and daily horoscopes.

Happy to answer questions about the architecture or the spiritual AI space in general.