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Every one of my publishing lanes went dark for four days, and every script involved exited with status 0. Nothing had crashed. The files themselves had quietly stopped existing on disk — macOS had uploaded them to iCloud and deleted the local copies to "optimize storage." Why This Matters What it means for automation to depend on its environment When you run 160+ launchd jobs around the clock, the execution environment itself becomes a failure source before your script logic does. Ports get exhausted, processes orphan and pile up, memory never frees — I wrote about that class of resource leak last time. This is a completely different kind of total failure that happened the very next day. The files had become fatal to read . Not a bug in my code. Not a filesystem bug. An unintended side effect of a mechanism macOS runs under the name "optimization." What optimize-storage actually does macOS's "Optimize Storage" (System Settings → General → Storage → Optimize Storage), on a machine with iCloud Drive enabled, uploads files under Desktop and Documents to iCloud and deletes the local copies when free disk space gets tight . In Finder they still look like normal icons, but there is no local data — they are in a "dataless" state. Click one and it downloads automatically. For a human user, that's an acceptable tradeoff. The problem is automation scripts. python3 's open() , pathlib.Path.read_text() , cat , jq , cp — all of them die instantly on a dataless file with Errno 11: EDEADLK: Resource deadlock avoided . The name "Resource deadlock" makes you suspect a deadlock, but this is a POSIX errno code that macOS repurposes to mean "waiting for a file download." No lock is contended. No thread is stuck. The mere fact that "the data isn't local" surfaces to the process as a fatal error code. You can also get EAGAIN (resource temporarily unavailable). That one shows up as a race right after a download starts. The actual damage: four days of zero posts On August 6, 2026, note's a

Incremental improvements fail to generate much excitement, but Google’s Pixel 11 is still an accomplished Android phone.

If you ship email validation with CakeDC/Users , you eventually hit a question the plugin quietly hands back to you: what should happen when someone registers, never clicks the validation link, and then tries to log in? The honest answer is that CakeDC/Users doesn't decide for you. Out of the box you get a database column, a couple of behaviors, and a set of events — but the experience is yours to assemble. Get it wrong and you land in one of two bad places: a user silently logged in without ever validating, or a user who typed the right password and is told "username or password is incorrect." Neither is what you want. This post walks through why that happens in v16, and a clean way to wire the flow using the events the plugin already dispatches — no core hacks, no schema surgery. One flag, two meanings Everything starts with a single boolean column on the users table: active . When email validation is on, registration creates the account with active = 0 and only flips it to 1 when the user clicks the link in the validation email. You can trace it in BaseTokenBehavior::_updateActive() : // $user['validated'] is a transient flag set to false during register() $emailValidated = $user [ 'validated' ]; if ( ! $emailValidated && $validateEmail ) { $user [ 'active' ] = false ; // registered → inactive + token emailed $user -> updateToken ( $tokenExpiration ); } else { $user [ 'active' ] = true ; // clicked the link → active $user [ 'activation_date' ] = new DateTime (); } Notice there is no separate validated column in the database — $user['validated'] is a transient property used only during registration. The persisted truth is active , and it is doing two jobs at once: "Has this person confirmed their email?" — set by the validation flow. "Is this account enabled?" — the thing an admin toggles to ban or suspend someone. That conflation is the root of everything below. Hold onto it; we'll come back to it. How the finder decides who exists Login in CakeDC/Users runs thro

Today we're open sourcing Strata , the session infrastructure that powers Stele. https://github.com/Stele-Dev/strata Coding agents already leave surprisingly rich trails on your computer: prompts, responses, reasoning, tool calls, results, timing, token usage, cost, injected context, subagents, and more. The problem is that every agent stores this differently. Strata turns those trajectories into one normalized CLI and TypeScript API. You can use it to: search across past sessions inspect transcripts and granular tool use see token usage, cost, and active time replay complete agent trajectories tail running sessions in real time see which agents are currently running on your machine build your own agent infrastructure on top of the same normalized data It currently supports Claude Code, Codex, Cursor, DeepSeek Harness, Gemini CLI, GitHub Copilot CLI, Kimi, OpenCode, and Pi. But things get more interesting when agents use Strata themselves . Run strata --skill and an agent can learn the CLI. Now an agent can search previous sessions to find when and how something was built, inspect the trajectory behind a decision instead of rediscovering it, or watch another agent working in a different terminal in real time. Agent A can effectively observe Agent B. A message bus is also on the roadmap, opening the door for local agents to communicate directly through Strata. We built Strata because we needed this infrastructure inside Stele. It powers Stele today, so while this is the first public release, the core has already been battle tested against real agent workloads. Everything stays on your machine. Local-only. Read-only. No telemetry. MIT licensed. Your coding agents already leave a trail. Strata makes it readable. https://github.com/Stele-Dev/strata

Hey everyone! 👋 Like many developers and sysadmins, I constantly find myself needing to share temporary credentials, API keys, or sensitive text with clients and coworkers. Dropping these straight into Slack, Discord, or standard email always feels like a massive security headache because those chat platforms store everything in plain text in their databases. I looked into popular "one-time secret" web utilities, but I noticed a major flaw: almost all of them handle the encryption and decryption on their servers. That means you have to blindly trust their backend configurations, logging policies, and database security. I wanted something truly zero-knowledge where the server owner physically couldn't read the notes even if they wanted to. So, I built ScorchNote : https://scorchnote.com 🛠️ How it Works (Under the Hood) To achieve absolute zero-knowledge, ScorchNote relies on strict client-side mechanics: Browser-Side Encryption: When you type a secret, the data is encrypted directly in your browser before it ever leaves your network interface. The URL Hash Advantage: The decryption key is generated and stored inside the URL's hash fragment (everything after the # ). Zero Server Footprint: Web browsers never send the hash fragment to the host server during HTTP requests. This means my database only receives a completely scrambled, encrypted payload. The server has no concept of what the key is. Millisecond Burn-on-Read: The moment the recipient visits the link, the encrypted payload is fetched and instantly purged from the server database. 🚀 Try It Out I kept the page entirely lightweight, minimalist, and completely free of bloated tracking scripts. It’s built to do exactly one job, safely and instantly. I would love to hear your thoughts on the architecture, the user experience, or what features you think I should cook up next! Check it out here: ScorchNote

The API makes it look trivial. const number = await carrier . numbers . buy ({ phone_number : " +1... " }); await db . insert ( " rented_numbers " , { user_id , e164 : number . phone_number }); await stripe . subscriptions . create ({ customer , price }); Three lines, one number, done. Ship it. What you actually wrote is a distributed transaction across three systems. They share no transaction log, they have no two-phase commit, and none of them can roll back the others. The carrier will keep charging you for a number your database has never heard of. Stripe will stop charging for a number your database still thinks is paid up. Neither one is going to mention it. I run a virtual phone number product. Below are the failure modes that actually cost us money, roughly in order of how much. The orphan taxonomy Write down the states first, because the interesting ones are the states nobody designs for. Three systems, each holding an opinion about a single number: Your DB Carrier Stripe What is actually happening active owns it active The happy path. Rare in the tail. no row owns it nothing You pay monthly rent on a number nobody can see or use. active released active You bill a customer for a number you no longer own. pending_cancellation owns it canceled Customer stopped paying. You are still paying the carrier. active owns it canceled You provide service for free, indefinitely. cancelled owns it canceled Release failed at teardown. Silent monthly bleed. Every row under the first one is reachable from a plain network timeout at a bad moment. The first orphan class is the worst, because you cannot see it from inside your own product. No row, no user, no support ticket. The number sits in the carrier's inventory producing an invoice line every month until somebody actually reads the invoice. The second class is the one that generates a complaint. The rest leak money in one direction or the other, quietly. Reconcile, don't prevent The instinct is to armour the write path. S

What working on real systems taught me about maintainable code. My second job was the first time I worked with an international team where everyone had ten or more years of experience. I had maybe two. It was also the first time I was part of proper code reviews, branching strategies, and pull request workflows. Everything felt new and slightly intimidating. One of my first tasks was adding spacing between two elements. It should have been a simple margin or padding change, but I added a <br> tag instead. The feedback on that PR was polite but clear, and it made me a little embarrassed. That moment, along with dozens of similar ones, made me want to get better. I started reading about clean code and caring deeply about how my code looked. Small functions, no repetition, everything abstracted and organized. For a while, that served me well. It helped me grow from a junior developer into someone who could write code that passed review without a wall of comments. But over time, as I worked on larger systems with real users and real constraints, I started noticing that the rules I had learned didn't always hold up. Sometimes the "clean" approach made things worse, and sometimes messy-looking code worked better than the elegant version I would have written. This post is about how my definition of clean code expanded. I still believe in the principles I learned early on. I'd just add a few things to them now. What I thought clean code meant When I first started paying attention to code quality, my idea of clean code was mostly about appearances. If the code looked organized and followed certain patterns, it was clean. If it didn't, it wasn't. I believed in small functions for everything. If a function was longer than fifteen or twenty lines, something was wrong. I would extract pieces into helpers even when they were only used once, just because the parent function felt "too long." I was strict about DRY. Any time I saw similar logic in two places, I would immediately pul

I Gave Five Graph Databases 256MB of RAM Each. Here's What Broke. CognoDB Cloud's free tier gives you a graph database instance with half a CPU core and 256MB of RAM. That's not a lot. It's also, honestly, a pretty realistic starting point a lot of real side projects and early-stage products live exactly there, on whatever the free tier happens to give them, and find out the hard way what their database does under pressure. So I decided to actually find out. I took CognoDB and lined it up against four other graph databases Neo4j AuraDB, FalkorDB, and ArangoDB gave every single one of them the same tiny resource budget, threw the same 198,050-edge dataset at all of them, and ran the same queries. No cherry-picking, no "best case" numbers. Just: here's a small VM's worth of resources, go. One of the databases I originally planned to include never even made it into the results. It crashed on startup. Not "slow to start" a full segfault, reproducibly, across two different versions, with nothing I threw at it fixing it. More on that below, because it's honestly one of the more interesting parts of this whole thing. The setup, quickly Five candidates going in: CognoDB (mandatory, since that's the actual point of this), Neo4j AuraDB Free, Memgraph, FalkorDB, and ArangoDB. Same dataset for all of them a real social-graph-shaped dataset from Stanford's SNAP collection, ~18.7k nodes and ~198k edges, sized specifically to fit inside every platform's free tier without anyone getting an unfair advantage. Same queries too: I wrote every single query 1-hop, 2-hop, 3-hop traversals, point lookups, filtered lookups, aggregations exactly once, then translated each one into whatever query language a given platform actually speaks. No platform ever got a "friendlier" version of a query than another. And everyone ran under the same 0.5 vCPU / 256MB RAM ceiling, whether that was their real cloud free tier or a Docker container I capped by hand to match. The one that didn't survive Memgra

I recently built filetools, a suite of file utilities that run entirely in the browser. No server backend, no file uploads, no data collection. The Problem Existing tools for CSV extraction, PDF manipulation, and table conversion often require uploading files or creating accounts. That creates friction and privacy concerns. But these tasks are fundamentally simple: extracting text from a PDF or parsing a CSV can happen entirely in JavaScript. The Solution filetools is a collection of single-purpose utilities: PDF Tools: Merge, split, rotate PDFs Extract tables from PDFs to CSV Convert bank statements to CSV Data Tools: Extract tables from HTML to CSV or JSON Convert between XLSX, JSON, YAML, and CSV Remove duplicate lines, sort CSV files, merge/compare data files Each tool is its own page, targeting one specific task without bloat. Architecture Why static hosting? Keeps infrastructure simple and costs near zero. Files are built once, served from GitHub Pages. Why client-side only? User files never leave their machine. Processing is fast (no network round-trip). Privacy is the default. Tech stack: vanilla JavaScript using npm libraries (pdfjs-dist, exceljs, js-yaml, pdf-lib) - no framework, no server. Each page is roughly 5-15KB gzipped. Design: Started with demand mining, looking at actual Google search queries and autocomplete suggestions to pick which tools to build first. What's Next Live site: https://usefiletools.com/?utm_source=dev.to&utm_medium=article&utm_campaign=filetools-launch I'm building more tools based on real search demand. If there's a file utility you've always wished existed, especially for data professionals, I'd love to hear about it.

You said you'd remove that flag after launch. You lied. It's been six months and the flag is still in appsettings.json , the if statement is still in your controller, and nobody remembers which state is "on." This is how codebases turn into haunted houses. Why Cleanup Matters Dead feature flags are technical debt with teeth . They add branches to your code that nobody tests. They confuse new developers who don't know the history. They inflate configuration files and make deployments harder to reason about. And they compound. Every flag you don't clean up makes the next cleanup harder because the cognitive load of understanding the system keeps increasing. The cost of removing a flag is lowest immediately after the feature ships, while everyone still remembers what the thing does. Six months later? Good luck. Track Every Flag You can't clean up what you can't find. Maintain a registry of every active feature flag with: Name Purpose Owner Date created Expected removal date This can be a spreadsheet, an issue tracker, internal documentation, or a dedicated feature flag management system. The format doesn't matter nearly as much as the habit. When you add a flag, add it to the registry. When you remove a flag, remove it from the registry. If your registry contains flags with no owner or no removal date, congratulations: you've found your next cleanup project. Set Expiry Dates Every flag should have a planned removal date when it's created. For example: Release toggles: Remove shortly after the feature ships. Two weeks is a reasonable default. Experiment toggles: Remove when the experiment concludes. Ops toggles: May be permanent by design. Permission toggles: May also be permanent, but document that explicitly. If a flag has been alive longer than its planned expiry and nobody deliberately extended it, it's already a zombie. Treat it accordingly. Make Cleanup Part of the Process Flag cleanup doesn't happen unless someone owns it. Add a cleanup step to your feature compl

I've been running Claude Code as a daily driver for about three months now. It writes Ansible I'd have taken a week to write. It reads a codebase faster than I do. It is, genuinely, very good. It also once wanted to force-push to main , and it wanted to for an extremely good reason. Sit with that for a second, because it's the whole post. The rebase was stuck. Force-pushing would have unstuck it. Every link in that chain of reasoning is sound. The agent wasn't being careless, wasn't hallucinating, wasn't "drifting" or whatever we're calling it this month. It made a locally correct decision with a non-local consequence, which is the exact category of mistake that human code review is worst at catching — because the diff looks fine . It could see the command. It could not see the crater. The thing I stopped doing For a while my answer was to read everything. Every diff, every command, eyes on the screen, hand hovering over Ctrl-C like a man watching a toddler near a staircase. This does not scale, and the reason it doesn't is embarrassing when you say it out loud: reviewing output scales with how much the agent writes. That number is going exactly one direction, and it isn't down. So I flipped it. Instead of reviewing what it produces, I started writing down what it must never do. And here's the good news that took me way too long to notice: that list is short . Not "short for a security policy" short. Short like you can fit it on a napkin. Here's mine: A credential it read an hour ago gets inlined into a source file. A rebase gets stuck, and the fastest route to a green terminal is git push --force origin main . rm -rf "$BUILD_DIR/" runs on the one machine where BUILD_DIR never got set. A version bump gets typed straight into package-lock.json , because that's the file the version number is visibly in. A failing test quietly grows a .skip and CI goes green. Someone runs cat .env "just to see which variables exist." That last one is my favourite, and I'll come back to

A small release, not a launch. The LAB tab on gex.live has a new rightmost rail called IDEA FEED . It is a stream of short, rule-shaped trading ideas about SPX dealer positioning — "fade the first touch of the call wall after a gap up", that kind of thing — collected daily by a scanner from what people actually discuss, rewritten into something the Lab compiler can parse, and published untested . That last word is the point. Why untested is the feature Every feed of trading ideas on the internet comes with a verdict attached: "this works", "78% win rate", a screenshot of a good month. The feed here refuses to do that. Each card says exactly two things about its idea: compiles clean (our compiler turned the text into a runnable rule without complaint) and untested (nobody has run it against the archive yet). The honest test is yours to run. One click drops the idea into the Lab conveyor. The compiler has already done the translation, so the first message in your session is the rule itself, stamped ↳ from IDEA FEED · compiles, untested . Running the backtest costs one Lab credit; a failed job refunds itself. If your balance is zero the button does not go dead — it turns into 0 CREDITS · BUY → , remembers the idea you picked, and comes back to it after. What you will not find No source attribution on the cards. The idea is the unit, not the poster. No win rates, no "rated", no thumbs. The archive is 1,000+ finished SPX sessions; the Lab tests against all of it with an out-of-sample split and tells you what survived, which so far is: very little. That verdict is worth more than a badge on a card. No approval gate. The scanner's finds ship directly every day, so the feed stays fresh by itself. "NEW" is personal — it means new since you last opened the rail, not new for everyone. Why build a feed that mostly produces "no" Because the alternative is pretending. The whole site is built on measuring dealer positioning from the tape instead of assuming it from yesterday's ope

gex.live has two halves. The terminal measures where SPX options dealers are positioned, every second, from the tape. The Lab is the half that asks the uncomfortable question: does any of that predict anything? What it is A browser-side conveyor with three stages and a credit meter. Compile. You describe a rule in plain text — "short the first touch of the put wall when net gamma is below the 20th percentile" — and the compiler turns it into a deterministic rule over the archive's fields: flip, walls, hold band, gamma percentile, DEX/VEX/vanna/charm per strike, time of day. Compiling is free. If the text is ambiguous the compiler says which part, instead of guessing. Backtest. The rule runs against the full session archive — 1,000+ finished SPX days, every one of them public at gex.live/sessions — with a fixed out-of-sample split. One credit per job; a job that fails refunds itself. Quant optimize. Optional. A LightGBM pass over the same feature store to see whether there is structure the hand-written rule missed, reported as out-of-sample AUC plus feature importance, not as a new "signal". The heavy part (DuckDB + LightGBM) runs in a scale-to-zero container that reads snapshots over HTTPS from the public archive. It depends on no machine and on no private data, which is the point: you are testing against the same files anyone can download. The honest-stats rule Every verdict comes with its baseline. "Your rule made 3% in-sample" means nothing next to "the unconditional drift over the same days was 2.8%". The report shows both, shows the out-of-sample half separately, and refuses to produce a headline number from the in-sample half. Most rules do not survive this. That includes our own: the site's own directional levels were tested three separate ways across the whole archive and none held out of sample — which is why the terminal sells measurement and not signals, and why the Lab exists at all. The free Idea Feed Next to the conveyor sits a rail of rule-shaped idea

gex.live has an MCP server. Add it to Claude, Cursor, ChatGPT or any MCP client and the assistant can read the dealer-positioning archive and drive the backtesting Lab on your behalf. The one-line version is the title. Here is the rest. Two tiers, one rule: free data stays free Free, no key — the same finished-session data that is free on the website: list_sessions — finished SPX sessions in the archive, newest first, paged (max 50 a call). get_session(day) — one session's dealer-positioning summary: OHLC, the zero-gamma flip and how often price crossed it, call/put walls, the hold band, net-gamma percentile, ATM IV at the open. Measurements only. get_levels(day) — just the level set for one session — flip, call resistance, put support, hold band — plus where the session closed relative to them. Keyed — the Lab, metered in credits exactly as on the site. These tools only appear in the tool list once the client sends a Lab token: lab_compile(message) — turn a plain-words idea ("fade a +3 sigma stretch above vwap on top-decile volume") into a testable rule. Free of credits, needs a positive balance. Returns the compiled spec, a clarifying question, or compile errors — never a guess. lab_run(id, kind) — one conveyor step: backtest first (rule → tested), then quant (the LightGBM optimize, tested → ready). One credit, refunded on failure. The result is the engine's honest verdict: per-leg era tables — all / this year / holdout. lab_state — your whole Lab in one call: ideas with stages and results, which idea holds the conveyor, your credit balance. lab_thread(id) — the compile-chat thread for one idea. lab_idea(id, action) — desk actions: put a ready idea on the desk, drop it back to the start, delete, or set its desk display/alert options. When a keyed tool is called without a token, the error is the instruction: what it does, where to get a key (gex.live/account → LAB & API, shown once, scoped to the Lab only, revocable), what it costs. The assistant relays it verbatim

Today I shipped contenox 1.0.0. Not by pushing a tag on top of a thousand commits, but as a single commit into an empty repository: the whole tree, one signed tag, binaries built from that tag by CI. The 957 commits that got me there are still public, in the old repository, as history. They are no longer how the project is published. This post is about why, and about what went wrong before I had finished reading the result back. What a commit used to mean GitHub's workflow rests on four assumptions so old that nobody states them any more: A commit is a unit of human intent. Someone decided something and typed it. A pull request is a unit of review. A human reads the diff, because a human wrote it. History is provenance. Who changed what, when, and — through the message — why. Timestamps are labor. The contribution graph on your profile is a diary. All four were true in 2008. For a tree that agents write, none of them survive contact. What my repository actually looked like Some numbers from a tree you can inspect yourself: 957 commits in just over a year, most of them named Checkpoint , Fix tests , Snapshot WiP . Dozens on a busy day. The production Go grew from 17,267 hand-written lines to 134,040 agent-assisted ones. Measured, not estimated. The median file stayed the same size; the number of files and packages did not. At one point 530 uncommitted paths sat in a single working tree. Inside that blob, the file that carried the repository's own conventions had been deleted. Nobody noticed for days, because nobody reviews a 530-file diff. A commit stream like that is not history. It is a log. Reading it tells you nothing about what a human decided — the decisions happened in prompts, in agent declarations, in a policy file — and it tells you one thing with great precision: when the work happened. If you also do client work, a public commit stream is a timesheet you never agreed to publish. Review had quietly inverted, too. I was no longer reviewing commits. I was re

I thought I was building a better planning engine. What I actually built was a machine for showing...

Your company uses six different AI providers. OpenAI for ChatGPT, Anthropic for Claude and Groq for speed critical inference. Each one has different API formats. Different authentication models. Different rate limits and costs. Different failure modes. Your application code has to know about all of them. Your security team has to audit requests across all of them. Your finance team has to track costs across all of them. Your compliance team has to ensure governance across all of them. Bifrost Gateway solves this by doing what HTTP gateways have done for decades: centralizing control . But for AI. 👀 What is an MCP gateway? Model Context Protocol (MCP) is an open standard that lets AI models discover and execute external tools at runtime filesystems, web search, databases, ticketing systems, and custom business logic instead of being limited to text generation. An MCP gateway sits between your applications (or external MCP clients like Claude Desktop and Cursor) and the upstream MCP servers. Instead of each client maintaining its own connections, credentials, and tool lists, the gateway: Aggregates tools from multiple MCP servers into one registry Applies governance : authentication, tool filtering, budgets, and rate limits Exposes a single endpoint that external MCP clients can connect to In Bifrost, this pattern is implemented in two complementary roles: Role What it does MCP Client Connects to external MCP servers via STDIO, HTTP, or SSE MCP Server (Gateway) Exposes aggregated tools at /mcp for Claude Desktop, Cursor, and other MCP-compatible clients Bifrost is both an AI gateway (routing LLM traffic to 20+ providers) and an MCP gateway (connecting to and exposing tool servers). The open-source gateway covers virtual keys, budgets, rate limits, routing, and MCP tool filtering. Bifrost Enterprise adds RBAC, SSO, audit logs, MCP Tool Groups, guardrails, clustering, and in-VPC deployment options. ⚙️ How does an MCP gateway work? Connection layer Each upstream MCP serv

It's been slow going, but it looks like Tesla's Robotaxi fleet in Austin is operating fully autonomously.

I am building a site to make it easy to find both in-person and online events. It clearly needs a lot of improvements. This is what I got out of it now. 2026-08-20 (online) 🎙️ Metarhia community call 2026-08-20 (in-person) Boston TypeScript Club 2026-08-20 (in-person) Coding Agent con TS && AWS 2026-08-21 (in-person) IKIGAI 2026-08-22 (in-person) React Meetup #107 2026-08-23 (in-person) Code and Tea at Panera 2026-08-25 (in-person) Your Agent is Starving 2026-08-25 (in-person) Isolation: VMs, Containers, Namespaces, Oh my! | DenverScript August 2026 2026-08-26 (in-person) JavaScript Luzern Grill Edition #5 2026-08-26 (online) JavaScriptMN Monthly Event: Open Floor Show/Tell and Discussion 2026-08-27 (in-person) Frontendistim Community August Meetup at Matia! 2026-08-27 (in-person) Paderborn.JS Meetup 2026 2026-08-27 (in-person) Jozi.JS August - Make the code work 2026-08-27 (online) 🎙️ Metarhia community call 2026-08-27 (in-person) Why to choose Ember.js in 2026 2026-08-28 (in-person) Zagreb Developers Drinkup

The demo gives a quick look at the workflow for creating short-form videos with trimming, stitching, text overlays, voice tools, themes, and transitions. Built with FastAPI, Next.js, Redis/ARQ, and FFmpeg. Repo: https://github.com/ronin1770/reel-quick OpenSource #Python #FastAPI #NextJS #FFmpeg #VideoAutomation #DeveloperTools #AI