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  • I Built a $20 Wi-Fi Desk Stock Ticker with an ESP32-C6 (No Soldering, No Code)

    I Built a $20 Wi-Fi Desk Stock Ticker with an ESP32-C6 (No Soldering, No Code)

    I kept a browser tab open just to glance at three stock tickers during the day. That tab cost me focus every single time I checked it, because a browser tab is never just a browser tab — it is a doorway to email, Slack, and forty other things. So I built the dumbest possible fix: a tiny Wi-Fi gadget that sits on my desk and shows live prices on a bright little color screen. No tab, no phone, no temptation. Just a glance.

    The whole thing costs under $20, needs no soldering, and after the first flash you never touch a computer again. The code is open source on GitHub, and below I will walk through exactly what it does, what to buy, and how to build one in an afternoon.

    ESP32-C6 desk stock ticker showing live stock prices on a 1.47-inch color LCD
    The finished ticker: live prices, day change, and an intraday sparkline on a 1.47″ screen.

    What it actually does

    It is a desk stock ticker. You give it a watchlist — AAPL,NVDA,SPY,BTC-USD, whatever you want — and it rotates through them on a 1.47-inch LCD. For each symbol you get the live price, the day’s change in both percent and dollars (green for up, red for down), and a little intraday sparkline so you can see the shape of the day at a glance.

    The details are where it earns its desk space:

    • Pre-market and after-hours prices. It pulls extended-hours data, so you are not staring at a stale 4 PM close all evening.
    • It throttles itself when the market is closed. Polling slows way down on nights and weekends, so it is not hammering your Wi-Fi or Yahoo’s servers for prices that are not moving.
    • Auto-dimming. The screen dims after a few minutes of inactivity to save power and protect the panel. Tap the onboard button to wake it.
    • A custom boot title. Mine says my name; one I gave away says “DAD’S TICKER.” It is a genuinely good gift.
    • Phone-based setup. After the first flash there is no app and no computer — you configure everything from a web page the device hosts itself.

    Prices come straight from Yahoo Finance’s public chart endpoint. No account, no API key, no monthly fee. If you have ever fought with rate limits on a “free” finance API — I wrote up four of them here — you will appreciate how refreshingly boring this data source is.

    What you need to buy

    This is the part I love: the entire bill of materials is one board and one cable.

    The board — Waveshare ESP32-C6-LCD-1.47. This is the clever bit. It is an ESP32-C6 microcontroller with the 1.47-inch color LCD already mounted on the same board — no wiring a display to a dev kit, no breadboard. Get the non-touch version; the ticker does not need touch and the non-touch board is cheaper. It runs around $12–15.

    The cable — a USB-A to USB-C cable. One real warning from building several of these: use an A-to-C cable, not a plain C-to-C one. Some C-to-C cables will not deliver power to this board and you will get a black screen and a very confusing ten minutes. An A-to-C cable from a phone charger just works.

    Optional — a 5V USB wall adapter. Any phone charger, power bank, or PC USB port powers it. You almost certainly already own one.

    That is the whole list. Under $20, and most of it is stuff you have in a drawer.

    Building one in an afternoon

    There are two paths, depending on whether you are starting from a blank board.

    If you received one already flashed (say, as a gift), there is no computer step at all. Plug it into any USB power, and it boots to a “SETUP” screen broadcasting its own Wi-Fi network called StockTicker-Setup. Join it from your phone, a setup page pops up, you type in your home Wi-Fi and your stock symbols, hit save, and it reboots into live prices. Done.

    If you are building from a blank board, you flash it once from a computer. The repo ships a flash.sh script that does everything:

    git clone https://github.com/dcluomax/stock-ticker-esp32c6.git
    cd stock-ticker-esp32c6
    ./flash.sh /dev/cu.usbmodem*   # macOS
    # ./flash.sh /dev/ttyACM0      # Linux

    The script installs the host tools (esptool, mpremote), downloads the MicroPython firmware for the ESP32-C6, erases and flashes the board, pulls down the display driver and fonts, and copies the app onto it. Reset the board and it drops straight into that same phone-setup flow. The one-time flash is the only moment a computer is involved.

    How it works under the hood

    The entire application is a single MicroPython file, main.py, and that is on purpose — it is small enough to read in one sitting. It handles the Wi-Fi connection, hosts the web config page (including a captive portal so the setup page auto-opens on your phone), fetches prices from Yahoo Finance, drives the display and the sparkline rendering, and runs a little state machine for the single BOOT button.

    The display is driven by the open-source st7789py driver, the runtime is plain MicroPython, and your settings live in a human-readable config.json that the web page just edits for you. There is no cloud service in the middle, no telemetry, and nothing to subscribe to. The device talks to your Wi-Fi and to Yahoo, and that is it.

    If you like this style of “small, single-purpose, no-cloud” project, it is the same philosophy behind the SEC insider-buying alert script I shared earlier — do one job well, own your own data, skip the SaaS.

    Who should build one

    • Anyone who checks prices too often. Moving the glance off your screen and onto a dedicated object is a genuine focus upgrade, not a gimmick.
    • People learning microcontrollers. This is an almost perfect first ESP32 project: it does something real and useful, but there is no soldering and the code is one readable file you can modify.
    • Gift-givers. Flash one, set the boot title to someone’s name, preload their tickers, and hand them a finished gadget that needs zero technical setup.
    • Tinkerers who want a starting point. The watchlist is just symbols, so it happily shows crypto (BTC-USD) or ETFs, and the single-file design makes it easy to fork into a weather display, a sports score ticker, or whatever you like.

    The bottom line

    This is not a trading tool and it will not make you money — for that you want an actual strategy, like the portfolio rebalancing bot I built with Python and the Alpaca API. The ticker is something smaller and, honestly, more pleasant: a calm, glanceable object that tells you what the market is doing without dragging you into a browser. Under $20, an afternoon of work, and the code is all free on GitHub. Grab a Waveshare ESP32-C6-LCD-1.47 and an A-to-C cable, and you will have it running before the market closes.

    Disclosure: The hardware links above are Amazon affiliate links. If you buy through them I earn a small commission at no extra cost to you. I only recommend the exact parts this project uses; the ticker software is open source and free. Stock prices shown by the device come from Yahoo Finance’s public endpoints, may be delayed, and are for glanceable, personal use only — this is not financial advice.

  • Free Number Base Converter Online — Binary, Hex, Octal, Decimal

    Convert numbers between binary, octal, decimal, and hexadecimal instantly with this free online base converter. Type a value in any field and the others update in real time — essential for programmers, students, and anyone working with low-level data. Runs entirely in your browser.

    How to Use This Base Converter

    1. Type a number into any of the four fields
    2. The other bases update instantly
    3. Invalid input for a base is flagged automatically





    Understanding Number Bases

    A number base (or radix) defines how many unique digits represent values. Computers use binary internally; programmers often use hex and octal as compact shorthand.

    • Decimal (base 10) — digits 0–9, the everyday number system
    • Binary (base 2) — digits 0–1, how computers store all data
    • Hexadecimal (base 16) — digits 0–9 and A–F, used for colors, memory addresses, and byte values
    • Octal (base 8) — digits 0–7, used in Unix file permissions

    Common Conversions

    • 255 decimal = 11111111 binary = FF hex = 377 octal
    • 16 decimal = 10000 binary = 10 hex = 20 octal
    • 8 decimal = 1000 binary = 8 hex = 10 octal

    Where You’ll Use Each Base

    • Hex — CSS colors (#FF5733), memory addresses, MAC addresses, byte dumps
    • Binary — bitwise operations, flags, low-level hardware programming
    • Octal — Unix/Linux file permissions (chmod 755)

    Privacy

    All conversions run in your browser using JavaScript’s built-in number parsing. Nothing is sent to any server.

    Recommended Reading

    Go deeper into how computers represent and process numbers:

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  • Free Text Case Converter Online — UPPERCASE, lowercase, Title & camelCase

    Convert text between cases instantly with this free online tool. Switch between UPPERCASE, lowercase, Title Case, Sentence case, camelCase, PascalCase, snake_case, and kebab-case — perfect for writers, developers, and data cleanup. Runs entirely in your browser.

    How to Use This Case Converter

    1. Type or paste your text in the top box
    2. Click any case button to convert instantly
    3. Click Copy to grab the converted text











    Text Case Formats Explained

    • UPPERCASE — ALL LETTERS CAPITALIZED, for headings or emphasis
    • lowercase — all letters small, for casual text or URLs
    • Title Case — First Letter Of Each Word Capitalized, for titles and headlines
    • Sentence case — First letter of each sentence capitalized, for body text
    • camelCase — firstWordLowerRestCapitalized, common in JavaScript variables
    • PascalCase — EveryWordCapitalized, used for class names
    • snake_case — words_joined_by_underscores, common in Python
    • kebab-case — words-joined-by-hyphens, used in URLs and CSS

    Naming Conventions in Programming

    • JavaScript / Java — camelCase for variables, PascalCase for classes
    • Python — snake_case for variables and functions, PascalCase for classes
    • CSS / HTML — kebab-case for class names and attributes
    • Constants — UPPER_SNAKE_CASE in most languages

    Privacy

    All conversions happen in your browser. Your text is never uploaded to any server.

    Recommended Reading

    Improve your writing and coding style:

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  • Free Password Generator Online — Create Strong Random Passwords Instantly

    Generate strong, random passwords instantly with this free online password generator. Customize length, character types, and exclude confusing characters. Uses your browser’s cryptographically secure random generator — passwords are never sent anywhere.

    How to Use This Password Generator

    1. Adjust the length slider (12+ characters recommended)
    2. Toggle uppercase, lowercase, numbers, and symbols
    3. Optionally exclude similar characters (i, l, 1, L, o, 0, O) for easier typing
    4. Click the copy button to grab your new password








    What Makes a Strong Password?

    Password strength is measured in entropy (bits of randomness). The more bits, the longer it takes to crack by brute force.

    • Length matters most — each added character exponentially increases cracking time
    • Character variety — mixing cases, numbers, and symbols enlarges the search space
    • Randomness — avoid dictionary words, names, and predictable patterns
    • Uniqueness — never reuse passwords across accounts

    Password Strength Benchmarks

    • < 40 bits — Weak, crackable in seconds to hours
    • 40–60 bits — Fair, suitable for low-value accounts
    • 60–80 bits — Strong, resistant to offline attacks
    • 80+ bits — Very strong, effectively uncrackable today

    Security Best Practices

    • Use a password manager to store unique passwords for every account
    • Enable two-factor authentication (2FA) wherever possible
    • Use a hardware security key for your most important accounts
    • Change passwords immediately if a service reports a breach

    Privacy

    This generator uses the Web Crypto API (crypto.getRandomValues) built into your browser — the same secure randomness used for encryption. Passwords are generated entirely on your device and never transmitted or stored anywhere.

    Recommended Reading

    Level up your security and privacy knowledge:

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  • Nvidia and Microsoft Just Teased a New Era of PC: What N1X and the GB10 Superchip Actually Mean

    Nvidia and Microsoft just posted the exact same thing on the exact same day, and if you have been watching this corner of the industry as long as I have, that is not a coincidence. Both accounts promised “a new era of PC” and dropped the coordinates of the Taipei Music Center, where Jensen Huang gives his GTC Taipei keynote at Computex 2026 next week. When the Windows account mirrors Nvidia’s marketing word for word, the message underneath is hard to miss: the long-rumored N1X laptop platform is probably about to show up, and it looks like it is running Windows on Arm.

    I have been digging into the chip this is all built around since the DGX Spark reviews landed, so I want to explain what is actually coming, where the hype outruns the silicon, and what I would buy today if you want to build for this platform before everyone else does.

    What N1X actually is

    N1X is widely understood to be the mobile version of the GB10 Superchip, the same part sitting at the heart of Nvidia’s DGX Spark mini-PC. GB10 pairs an RTX 5070-class Blackwell GPU with a 20-core Arm CPU complex that Mediatek helped design (ten Cortex-X925 performance cores and ten Cortex-A725 efficiency cores), and it hangs 128GB of LPDDR5X off a single unified memory pool that both the CPU and GPU share.

    That unified-memory design is the whole point. On a normal gaming laptop your discrete GPU gets its own walled-off pool of fast GDDR memory, and anything bigger than that pool has to be shuffled across the PCIe bus. On GB10 the GPU can address all 128GB directly. For AI work that is the difference between loading a 70-billion-parameter model and watching it crash with an out-of-memory error. You can run quantized large language models locally, on your desk, that simply will not fit on a 16GB or 24GB consumer card. (If you want capable local hardware that stays quiet under load today, see our guide to the quietest gaming laptops of 2026.)

    Why Microsoft showing up changes the story

    Until now GB10 has only existed as the DGX Spark, and the DGX Spark runs DGX OS, which is Ubuntu Linux. It is an AI developer sandbox, not a machine your accountant can use. The moment Microsoft puts its weight behind N1X, the entire Windows application ecosystem comes along through Windows on Arm, and this stops being a niche Linux box and starts being a general-purpose computer that also happens to have a serious local-AI engine inside it.

    This matters for Microsoft more than people realize. None of its existing Windows on Arm partners have shipped anything close to the GB10’s raw AI capability. Today’s Copilot+ PCs run small neural accelerators rated in the tens of TOPS; GB10 is a different weight class entirely, advertised around a petaFLOP of AI performance. If Microsoft has hardware this capable to target, it can finally build first-party local AI features that were never possible on the underpowered NPUs it has been shipping. (For where the current frontier AI models actually land, see our Claude Opus 4.8 review.) It is worth remembering that Microsoft has already confirmed Windows 11 26H1 will launch as an Arm-only release, so the company is clearly committing engineering runway to this architecture.

    Where I would pump the brakes

    I am not going to pretend the first wave of these machines will be for everyone. Two specifics keep me grounded.

    First, bandwidth. Because the CPU and GPU share one LPDDR5X pool, the GB10 GPU gets about 273 GB/s of memory bandwidth. That is generous for a unified design, but it is far below what a traditional laptop GPU pulls from dedicated GDDR. In practice you can game on GB10, but gaming is not what it is good at. The bottleneck shows up exactly where you would expect it to.

    Second, price. Every DGX Spark-class GB10 box I have seen is selling in the rough neighborhood of $4,000 to $5,000. Some of that premium comes from an exotic ConnectX networking card that almost certainly will not survive the trip into a laptop chassis, so N1X notebooks could shave cost there. But with memory and SSD prices where they are during the current silicon crunch, nobody should expect these to be cheap. A smarter product stack with 64GB options and trimmed core counts would help, and I suspect that is coming, but the launch hardware will be a premium purchase aimed at developers and AI professionals, not students.

    Who this is actually for

    If you fit one of these descriptions, the GB10 platform is genuinely interesting right now rather than a year from now:

    • You build with local LLMs. 128GB of unified memory lets you run and fine-tune models that will not fit on any single consumer GPU, with no cloud bill and no data leaving your desk.
    • You are an AI/ML engineer who wants the dev environment that ships first. The DGX Spark’s CUDA stack is the same architecture N1X laptops will inherit, so anything you build now ports forward.
    • You want a quiet, compact AI workstation. This is a desktop-class AI engine in a footprint smaller than a stack of books, not a screaming tower.
    • You are a researcher or founder prototyping agentic systems. A petaFLOP of on-desk compute means you iterate locally instead of renting H100 time by the hour.
    • You teach or demo AI. A self-contained box that runs big models offline is far easier to deploy in a classroom or a conference room than cloud credentials.

    What I would buy today

    Here is the honest part. N1X laptops are not on sale yet, and based on Nvidia’s own history the first units will be supply-constrained and expensive when they do arrive. If you want to start building for this architecture now, the shipping hardware is the DGX Spark and its OEM twins, which use the identical GB10 Superchip. Anything you develop on them moves straight onto N1X when the laptops land.

    The reference machine is the Nvidia DGX Spark with the GB10 Grace Blackwell Superchip, 128GB of LPDDR5X, and 4TB of NVMe storage. It is the cleanest way to get the full unified-memory experience and Nvidia’s own DGX software stack.

    If you would rather buy the same silicon from an OEM, the ASUS Ascent GX10 packs the identical GB10 Superchip, 128GB of unified memory, and a stackable chassis so you can cluster two of them over Nvidia’s high-speed interconnect later. Same chip, same 128GB pool, slightly different box.

    Both are premium purchases, and I would only pull the trigger if local AI is real work for you rather than a curiosity. But if it is, owning the GB10 platform now means you are already fluent on the architecture the rest of the Windows world is about to discover at Computex.

    The bottom line

    A coordinated Nvidia and Microsoft teaser is not proof of anything, and I will update this once Jensen actually holds the thing up on stage. But the direction is clear enough to plan around: the GB10 architecture is leaving the Linux sandbox and walking into the Windows mainstream. The DGX Spark is how you get a head start, and N1X is what happens when that same engine fits in a backpack.

    Disclosure: Links to Amazon are affiliate links. If you buy through them I earn a small commission at no extra cost to you. I do not accept payment from manufacturers for placement. Specifications cited here are based on Nvidia’s published GB10 platform details and independent reporting; prices and availability change, so confirm current details on the product page before buying.

  • Claude Opus 4.8 Is Here: What Actually Changed, and When to Reach for It

    Anthropic shipped Claude Opus 4.8 on May 28, 2026 — at the same price as Opus 4.7. After spending real time putting it through coding, design, and strategy work, here’s an honest breakdown of where the new model shines, where it still trips, and how to actually get the most out of it.

    The one-line summary

    Opus 4.8 is not a reinvention. It’s a sharpening. Anthropic’s own framing is telling: sharper judgment, more honesty about its own progress, and the ability to work independently for longer. Those three things — not a headline benchmark jump — are what you actually feel when you use it. And critically, it lands at the same price as its predecessor, which reframes the whole “is it worth it” question.

    Where Opus 4.8 genuinely excels

    Three patterns showed up over and over:

    • Greenfield prototypes. Starting from a blank file, 4.8 is fast and confident. Give it a fuzzy idea for a tool and it’ll scaffold something usable in one shot more reliably than 4.7 did.
    • One-shot features. Self-contained units of work — “build this component,” “add this endpoint,” “write this script” — come out clean and complete.
    • Raw execution speed. It commits to a plan and moves. There’s less hand-wringing, less circling, more shipped output per unit of your attention.

    If your work looks like “spin up something new, fast,” this is the model’s home turf.

    Where it still struggles

    The honest part. Three weaknesses persist:

    • The last 10%. It gets you 90% of the way with startling speed, then stalls on the finishing details — the polish, the wiring, the “make it actually production-ready” pass. That final stretch still needs you.
    • Edge cases in existing codebases. Greenfield is its strength; the inverse is its weakness. Dropped into a large, established repo with its own conventions and gotchas, it’s more likely to miss the subtle constraints that a careful engineer would catch.
    • Hallucinations. Still present. It will occasionally invent an API, a function signature, or a fact with full confidence. If you’re wiring a model into real data, pick reliable sources first — see our test of 4 free stock market APIs. Trust, but verify — especially anything that looks like a precise external reference.

    4.8 vs 4.7 on strategy work: a surprise

    Here’s the counterintuitive finding. On business strategy and roadmap work — especially data-heavy analysis — Opus 4.7 is still the model to reach for. Newer doesn’t mean strictly better across every axis. 4.8’s gains are concentrated in agentic coding and fast execution; for dense, analytical strategy reasoning, the older model held its ground or did better. (Running models like this locally is exactly what Nvidia’s new N1X and GB10 Superchip are built for.)

    The takeaway isn’t “4.8 is worse.” It’s that “latest model” is not a synonym for “best model for this task.” Keep both in your toolkit and pick deliberately.

    The features shipping alongside the model matter as much as the model

    Two of these may change your workflow more than the raw intelligence bump:

    • Dynamic workflows with parallel subagents. The model can now spin up and coordinate multiple subagents working in parallel — decomposing a task into independent threads and running them concurrently instead of serially. For multi-part work, this is a real throughput multiplier.
    • Effort control in Claude.ai and Cowork. You can now dial how much thinking/effort the model spends. Low effort for quick lookups, high effort for hard problems. It’s a direct lever on the speed-vs-quality trade-off — and on cost.

    How to actually get the most out of it

    The model is only half the equation. The harness around it — how you prompt, structure context, and verify — is the other half. A few principles that pay off:

    1. Play to greenfield. When you can, frame work as a fresh, self-contained build rather than a surgical edit deep inside legacy code. You’ll get cleaner output.
    2. Own the last 10% yourself. Plan for it. Let the model sprint the first 90%, then budget your own time for the finishing pass. Don’t expect it to nail production polish unattended.
    3. Verify anything that looks like a fact or an API. Hallucinations haven’t disappeared. Build a verification step into your loop for external references.
    4. Use effort control intentionally. Don’t burn high effort on trivial tasks, and don’t starve a hard problem with low effort. Match the dial to the difficulty.
    5. Lean on parallel subagents for decomposable work. If a task splits cleanly into independent pieces, let the model fan them out.
    6. Keep 4.7 around for data-heavy strategy. Route deliberately. The right model depends on the task, not the version number.

    The verdict

    Opus 4.8 is a confident, fast, judgment-improved iteration that’s especially strong at greenfield building and one-shot features — and it costs the same as 4.7, which makes it an easy default for that kind of work. But it’s not a clean across-the-board upgrade: the last-10% problem and hallucinations remain, it’s weaker inside messy existing codebases, and 4.7 still wins on data-heavy strategy.

    The smart move isn’t “upgrade and forget.” It’s treat 4.8 and 4.7 as different tools, lean into 4.8’s execution speed where it’s strong, and keep a human in the loop for the finish and the facts. Same price, sharper edge — just know which edge you’re using.


    Based on early hands-on testing reported by Lenny Rachitsky and Anthropic’s official Opus 4.8 announcement (May 28, 2026). Opinions and framing are my own.

  • Free Diff Checker & Text Compare Online — Find Text Differences Instantly

    Free Online Diff Checker & Text Compare Tool

    Compare two blocks of text and instantly see what changed. This free diff checker highlights additions, deletions, and unchanged lines — perfect for comparing code versions, config files, or any two documents.

    Features

    • Line-by-line text comparison
    • Color-coded additions (green) and deletions (red)
    • Optional whitespace-ignoring mode
    • Swap inputs with one click
    • Works entirely in your browser — nothing uploaded






    Use Cases for Text Comparison

    Diff checking is essential for code reviews, tracking configuration changes, comparing API responses, or verifying document edits. This lightweight tool gives you instant results without installing any software.

    Tips for Effective Diffing

    • Use “Ignore whitespace” for comparing code with different formatting
    • Swap inputs to see changes from the other perspective
    • For large files, use a dedicated tool like VS Code’s built-in diff

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  • Free SQL Formatter & Beautifier Online — Format SQL Queries Instantly

    Free Online SQL Formatter & Beautifier

    Format messy SQL queries into clean, readable code instantly. This free tool properly indents SELECT, JOIN, WHERE clauses and uppercases SQL keywords — making complex queries easy to read and debug.

    Features

    • Automatic indentation of SQL clauses
    • Uppercase keyword conversion
    • SQL minification for production use
    • Supports all major SQL dialects
    • 100% client-side — your queries never leave your browser







    Why Format Your SQL?

    Well-formatted SQL is easier to review in pull requests, debug during incidents, and maintain over time. This tool uses the popular sql-formatter library to handle complex nested queries, CTEs, and window functions correctly.

    SQL Formatting Best Practices

    • One clause per line (SELECT, FROM, WHERE, JOIN)
    • Indent join conditions and subqueries
    • Uppercase SQL keywords for readability
    • Align column lists vertically in large SELECT statements

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  • Free YAML Validator & Formatter Online — Check YAML Syntax Instantly

    Free Online YAML Validator & Formatter

    Validate your YAML syntax, fix indentation issues, and convert between YAML and JSON formats instantly. This free tool uses the popular js-yaml library for accurate parsing — perfect for Kubernetes configs, Docker Compose files, CI/CD pipelines, and any YAML workflow.

    Features

    • Real-time YAML syntax validation with detailed error messages
    • YAML to JSON conversion
    • JSON to YAML conversion
    • Works entirely in your browser — no data sent to any server








    When to Use a YAML Validator

    YAML is whitespace-sensitive, making it easy to introduce subtle bugs. Use this validator when editing Kubernetes manifests, Ansible playbooks, GitHub Actions workflows, or any configuration file. Catching errors before deployment saves hours of debugging.

    Common YAML Mistakes

    • Mixed tabs and spaces (YAML only allows spaces)
    • Incorrect indentation levels
    • Missing colons after keys
    • Unquoted special characters

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  • I Built a Python Script That Alerts Me to SEC Insider Buying — Here’s the Code

    Last October, I noticed three directors at a mid-cap biotech all bought shares within the same week. The stock was down 40% from its high. Two months later it doubled on a pipeline update. I didn’t trade it — I found out from a Twitter thread three days after the filings hit EDGAR.

    That stung enough to make me build something. SEC Form 4 filings — the ones insiders must file within two business days of buying or selling company stock — are public data. They’re free. They hit EDGAR before any news outlet picks them up. And parsing them with Python takes about 80 lines of code.

    Why Insider Buying Actually Matters

    Insider selling is noise. Executives sell for a hundred reasons — taxes, divorce, a new house. But insider buying with personal money? There’s exactly one reason: they think the stock is going up.

    Academic research backs this up. A study by Nejat Seyhun (University of Michigan) found that stocks with cluster insider buying — three or more insiders purchasing within a 30-day window — outperformed the market by 7-13% annually over the following 12 months. That’s not a small edge.

    The challenge is timing. Form 4 filings hit EDGAR at unpredictable times. Some land at 4:01 PM, others at 2 AM. If you’re checking manually, you’re always late.

    The EDGAR Full-Text Search API

    SEC opened up their EDGAR full-text search API (EFTS) in 2023. No API key required — just a proper User-Agent header with your email. Rate limit is 10 requests per second, which is generous.

    Here’s the endpoint that matters:

    GET https://efts.sec.gov/LATEST/search-index?q=%224%22&dateRange=custom&startdt=2026-05-26&enddt=2026-05-27&forms=4

    But raw search is messy. The better approach: hit the recent filings RSS feed for Form 4s, then parse each SGML/XML filing for the transaction details.

    The Script: 80 Lines That Do the Work

    import requests
    import xml.etree.ElementTree as ET
    from datetime import datetime, timedelta
    import time
    
    HEADERS = {
        "User-Agent": "YourName [email protected]",
        "Accept-Encoding": "gzip, deflate"
    }
    
    def get_recent_form4s(hours_back=4):
        """Fetch recent Form 4 filings from EDGAR."""
        url = "https://www.sec.gov/cgi-bin/browse-edgar"
        params = {
            "action": "getcurrent",
            "type": "4",
            "dateb": "",
            "owner": "include",
            "count": 40,
            "search_text": "",
            "output": "atom"
        }
        resp = requests.get(url, params=params, headers=HEADERS)
        return resp.text
    
    def parse_form4_xml(filing_url):
        """Parse a Form 4 XML filing for purchase transactions."""
        resp = requests.get(filing_url, headers=HEADERS)
        time.sleep(0.15)  # respect rate limits
        
        try:
            root = ET.fromstring(resp.text)
        except ET.ParseError:
            return None
        
        ns = {'o': 'http://www.sec.gov/cgi-bin/viewer?action=view&cik=...'}  
        # Form 4 XML uses default namespace
        
        issuer = root.find('.//issuerName')
        insider = root.find('.//rptOwnerName//value')
        
        transactions = []
        for txn in root.findall('.//nonDerivativeTransaction'):
            code = txn.find('.//transactionCode//value')
            shares = txn.find('.//transactionShares//value')
            price = txn.find('.//transactionPricePerShare//value')
            
            if code is not None and code.text == 'P':  # P = Purchase
                transactions.append({
                    'issuer': issuer.text if issuer is not None else 'Unknown',
                    'insider': insider.text if insider is not None else 'Unknown',
                    'shares': shares.text if shares is not None else '0',
                    'price': price.text if price is not None else '0'
                })
        
        return transactions

    The key filter: transactionCode == 'P'. That’s a direct open-market purchase. Ignore ‘A’ (grant/award), ‘M’ (exercise), ‘S’ (sale). You only want executives spending their own cash.

    Detecting Cluster Buying

    A single insider buying $50K of stock might mean nothing. But when the CFO, a board member, and the VP of Engineering all buy within two weeks? That’s a cluster, and historically it’s the strongest signal.

    from collections import defaultdict
    
    def detect_clusters(purchases, window_days=30, min_insiders=3):
        """Find stocks with multiple insiders buying in a window."""
        by_ticker = defaultdict(list)
        
        for p in purchases:
            by_ticker[p['ticker']].append(p)
        
        clusters = []
        for ticker, buys in by_ticker.items():
            buys.sort(key=lambda x: x['date'])
            unique_insiders = set()
            
            for i, buy in enumerate(buys):
                window_start = buy['date']
                window_end = window_start + timedelta(days=window_days)
                insiders_in_window = set(
                    b['insider'] for b in buys
                    if window_start <= b['date'] <= window_end
                )
                if len(insiders_in_window) >= min_insiders:
                    clusters.append({
                        'ticker': ticker,
                        'insiders': insiders_in_window,
                        'total_value': sum(
                            float(b['shares']) * float(b['price'])
                            for b in buys
                            if window_start <= b['date'] <= window_end
                        )
                    })
                    break  # one cluster per ticker
        
        return clusters

    Running It on a Schedule

    I run this every 4 hours via cron on my home server (a Beelink mini PC that draws 15W — perfect for always-on scripts). When a cluster is detected, it pushes a notification through ntfy:

    # crontab -e
    0 */4 * * * python3 /home/scripts/insider_scanner.py 2>&1 | logger -t insider

    Total infrastructure cost: $0. SEC data is free. Python is free. The mini PC was a one-time purchase I already had running my homelab.

    What I’ve Learned Running This for 7 Months

    Some patterns that showed up in my data:

    • Cluster buys after 30%+ drawdowns are the highest-signal events. Insiders buying the dip with conviction usually know something about the recovery timeline.
    • Dollar amount matters more than share count. A CEO buying $2M of stock is more meaningful than a director buying $15K. I filter for purchases over $100K per insider.
    • Ignore scheduled 10b5-1 plan purchases. These are pre-programmed and carry no informational value. Check the footnotes in the filing — they’ll mention if it’s a plan purchase.
    • Biotech and small-cap clusters have the highest hit rate in my experience. Large-caps move too slowly for this to give you an edge.

    Limitations (Be Honest With Yourself)

    This isn’t a magic money printer. Insider buying signals work on a 3-12 month horizon, not days. You need patience. Some clusters lead nowhere — maybe the insiders were wrong, or a macro event overwhelmed company fundamentals.

    I use this as one input into a broader process, not as a standalone trading strategy. It’s particularly useful as a “where to look” filter — when I see a cluster, I dig into the company’s fundamentals, recent earnings calls, and technical setup before deciding anything.

    If you want to go deeper on systematic approaches to market signals, I publish daily analysis (narrative detection, sector rotation, macro scoring) in my free Telegram channel. No fluff, just data: join Alpha Signal here.

    Full Source and Next Steps

    The complete script with ntfy notifications, cluster detection, and a SQLite database for historical tracking is about 200 lines. I’d recommend Python for Data Analysis by Wes McKinney if you want to extend this with pandas for backtesting the signals against price data.

    Key improvements I’m still working on:

    • Cross-referencing cluster buys with free stock APIs to auto-pull the price chart context
    • Filtering out Form 4 amendments (they duplicate the original filing)
    • Adding options grant exercises that immediately convert to holds — these are sometimes disguised conviction signals

    The SEC gives you the data for free. The edge is just showing up before everyone else reads about it on Twitter.

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