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Auto Organize Plugin!! 📁 Highly Requested & FREE Automatically sort your workspace into clean organized folders. Subfolder grouping, mesh ID detection, and bulk unsort. (built for lazy devs) #ROBLOX #RobloxDev

26,574 просмотров • 6 месяцев назад •via X (Twitter)

Комментарии: 5

Фото профиля ugm
ugm6 месяцев назад

Great fo the roblox part builders

Фото профиля AbsoluteCrafter
AbsoluteCrafter6 месяцев назад

thank you for everything..

Фото профиля swooshin
swooshin6 месяцев назад

so useful ty

Фото профиля Captain_Tails 💿
Captain_Tails 💿6 месяцев назад

This will clean up a lot of my builds, thanks for this.

Фото профиля Lazy
Lazy6 месяцев назад

thank you

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Claude Code + Shopify AI is f*cking cracked 🤯 Shopify just dropped an official AI Toolkit that connects Claude Code directly to your store. One prompt → Claude reads your products, rewrites your descriptions for AI shopping, and pushes the updates live. All from the terminal. All inside Claude Code. Perfect for DTC brands on Shopify who are still manually editing product pages, writing descriptions in Google Docs, and copy-pasting into the Shopify admin one product at a time. Claude Code + the Shopify AI Toolkit fixes the entire workflow: → Install the official Shopify plugin in Claude Code → Authenticate to your store → Claude reads your entire product catalog → Rewrites every description to be optimized for AI shopping → Pushes the updates directly to your store automatically → Validates every API call against Shopify's official docs before executing No Shopify admin tab-switching. No copy-pasting from a Google Doc. No hiring a copywriter to rewrite 50 product pages. What you get: → Claude Code connected directly to your live Shopify store → Product descriptions optimized for how ChatGPT, Gemini, and Perplexity recommend products → Bulk updates across your entire catalog from a single prompt → Full access to Shopify's GraphQL API — products, themes, inventory, orders, everything Claude can read and write → An official plugin built by Shopify that auto-updates as new features ship I put together a full playbook with the plugin install, the store authentication walkthrough, 5 DTC workflows to run on day one, and the exact prompts I used. Want it for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Mike Futia

114,572 просмотров • 5 месяцев назад

Last year, during tax season. I had 50+ receipts. Some in my email. Some on WhatsApp. Some in my gallery. And a few, I couldn’t even remember where I saved them. What should’ve taken a few hours turned into late nights, frustration, and second-guessing everything. Because the real problem isn’t filing taxes. It’s this: → Collecting documents → Organizing them → Verifying if everything is correct That’s where most people struggle. This year, I tried something different. Instead of chasing files everywhere, I built a simple, clean system using Wondershare PDFelement. And honestly, it changed everything. Here’s how my workflow looked. I started by scanning all my paper receipts directly from my phone using the Receipt Assistant. No manual typing. No guesswork. It automatically extracted details like: • Merchant • Date • Amount • Taxes Everything turned into searchable PDFs instantly. Then came the best part. All files were automatically saved to the cloud. So, I could: → Scan on mobile → Manage on desktop → Access everything, anytime No more “where did I save that?” moments. But what impressed me most. Every extracted detail is traceable. I could click any number and instantly jump back to the exact spot in the original receipt. No more cross-checking line by line. And when it was time to organize everything. • Exported all data into Excel → full expense overview • Merged multiple files into one clean PDF • Edited tax documents directly (no extra tools needed) Before final submission, I used: • AI Assistant → to summarize & cross-check documents • Smart Redact → to hide sensitive information Everything felt controlled, clean, and secure. That’s when it hit me: A good tax system isn’t about working harder. It’s about having a clear, traceable workflow that removes chaos. Suppose your files are still scattered across folders, emails, and screenshots. That is exactly why tax season feels exhausting. Search Wondershare PDFelement and try it free → #wondersharepdfelement #pdfelement #FileWithPDFelement #TaxSeason

Vikas Singh

11,795 просмотров • 5 месяцев назад

I just built a Claude skill that acts as a second brain for DTC brands 🤯 Drop your ad exports, customer reviews, competitor screenshots, and brand docs into a folder → Claude compiles it all into an organized wiki you can ask questions against. All inside Claude Cowork. Perfect for DTC brands and agencies whose knowledge is scattered across Google Drive, Notion, Meta Ads Manager, Figma, and 47 spreadsheets nobody has opened in 3 months. If every strategic question takes 2 hours to answer because the data lives in 8 different places ... This skill eliminates the entire loop: → Claude scaffolds a DTC folder structure: ads, customers, competitors, brand, performance, notes → You drop every file you have into those folders — messy, unorganized, exactly how you have them now → Claude reads everything and compiles a wiki: hooks-that-work, customer-pains, competitor-angles, brand-voice, performance-patterns, creative-brief-library → Every article is cross-linked and traceable back to the source file → You ask questions against the wiki — "what hooks are actually working?" "what objections come up most?" "where are my competitors weak?" → Claude answers, grounded entirely in your own data → Save the answers back in and the system gets smarter every time you use it No more hunting through 12 tools. No more "where did I save that brief?" No more answering the same question twice. What you get: → A complete DTC brand brain scaffold in 60 seconds → Six core wiki articles Claude populates automatically from your raw files → A schema file that tells Claude exactly how to maintain the wiki for DTC use cases → Monthly health checks that catch contradictions and flag gaps before errors compound → A knowledge base that compounds — every question you ask makes the next answer better Built on a methodology Andrej Karpathy shared for personal knowledge bases, I rebuilt the entire thing for DTC operators: folder structure, schema rules, wiki articles, and question frameworks all tuned for brands and agencies. I put together the full skill file plus a playbook walking through the exact setup and 5 real questions to ask your brand brain. Want it for free? > Like this post > Comment "BRAIN" And I'll send it over (must be following so I can DM)

Mike Futia

15,257 просмотров • 5 месяцев назад

THE OBSIDIAN CEO BUILT 5 CLAUDE CODE SKILLS, AND NOW A 2,400 NOTE VAULT CAN SORT ITSELF AT 7 A.M., FIX BROKEN LINKS, AND RESURFACE IDEAS FROM 2019 IN 10 SECONDS 00:06 he loads the obsidian skill inside claude code, and the graph stops being decoration. claude can read markdown, follow backlinks, edit canvas files, repair dead links, pull project context, and move through the vault like it understands the workspace instead of guessing from one prompt. most people do not have a note problem. they have an abandoned memory problem: 800 notes, 12 folders, 40 unfinished drafts, and a beautiful graph view that never actually helps. the article shows a 33 year old editor with 2,400 markdown files, 8 years of thinking, and zero useful access for a full year. that is why the ceo angle matters. this is not another random plugin. it is claude code learning obsidian’s actual language: backlinks, daily notes, canvas boards, raw captures, processed ideas, broken references, and old drafts buried so deep they might as well be gone. a real system only needs a few rules. one inbox for messy thoughts, one raw folder nobody edits, three backlinks on every new note, and a 7 a.m. run that sorts yesterday before you even open the laptop. weekly synthesis is where it gets dangerous. claude can compress 7 days of notes into one file with themes, contradictions, abandoned ideas, repeated promises, and the exact old note where the next article was hiding. bookmark this before your second brain becomes 2,400 dead files with a pretty graph.

Gipp 🦅

17,790 просмотров • 2 месяцев назад

Hollywood has a dirty secret. That perfectly clean green screen shot in your favorite Marvel movie? A human being sat in a dark room for 6 hours fixing it frame by frame. The AI keyer got the body. A person painted every strand of hair. By hand. At 2 AM. For 400 frames. The software costs $5,000 a year. And it still cannot key hair. Nuke: $4,988/year. Cannot key hair in motion blur. After Effects: $264/year. Cannot key transparent glass. Boris FX: $1,865. Cannot key fine edges without haloing. The industry's solution for 30 years has been the same: pay for expensive software, then pay a human to fix what the software couldn't. The YouTubers behind Corridor Crew looked at this and asked a different question. What if the AI didn't try to remove the green? What if it figured out what color was actually there before the green contaminated it? They trained a neural network on synthetic 3D data. Not scraped footage. Not stolen clips. Perfectly rendered scenes where every pixel's true color was already known. Hair strands. Motion blur. Transparent glass. All simulated with known ground truth. Then they fed it real green screen footage. It worked. They called it CorridorKey. Then they open sourced it. → Feed it raw green screen footage → AI reconstructs the true foreground color for every pixel → Hair stays perfect. Every strand. → Motion blur stays intact. Every frame. → Transparent glass stays transparent. → 16-bit and 32-bit EXR output. Nuke-ready. Resolve-ready. → Handles 4K natively → Runs on consumer GPUs. 6 GB VRAM minimum. → Runs on Apple Silicon via MLX → Auto-detects green or blue screen → Removes tracking markers automatically → DaVinci Resolve plugin live → Standalone GUI for non-technical users → One-click installer. No Python setup. Here's the wildest part: Within 2.5 months: 13,000+ GitHub stars. 787 forks. Active Discord. Community built a cloud render farm so you can process footage without owning a GPU. DaVinci Resolve plugin shipped. Nuke and After Effects plugins in development. VFX freelancers are already offering CorridorKey-powered services to production companies. One person. One GPU. Hollywood-quality keys. A business built on free software. Nuke: $4,988/year. Still needs manual cleanup. After Effects: $264/year. Still needs manual cleanup. Rotoscope artist: $50 to $150/hour. For the cleanup. CorridorKey: $0. No cleanup needed. One honest flag: the license is CC BY-NC-SA 4.0. Free forever for personal projects, students, indie films, and learning. Commercial use requires permission from Corridor Digital. They did not pretend it was MIT. A problem that plagued Hollywood for 30 years. Solved by YouTubers. Open sourced for free. 13,000+ stars. 787 forks. CC BY-NC-SA 4.0. Your footage. Your keys. No rotoscoping.

Nav Toor

296,112 просмотров • 4 месяцев назад

🔍 Loupe is coming and it's yours to keep 🚀🚀🚀🚀🚀 Your inbox and your folders are quietly eating your week. Loupe hands that time back. Free to download. Free to run. Decisions in a fraction of a second. And your data never leaves your machine — there's no server of mine holding your inbox, because there's no server. Two apps, working as one: 🖥️ Loupe Station — lives on your Mac. This is the brain. 📱 Loupe — your companion on the go. Here's what it actually does. ━━━━━━━━━━ 📬 INBOX TRIAGE Sorts and labels everything, and puts what genuinely needs you at the top. The rest stops shouting. ━━━━━━━━━━ 💳 SUBSCRIPTION RADAR — this one pays for itself You're almost certainly paying for something you forgot about. Loupe reads the receipts already in your mail and works out what's recurring: what it is, how often, how much, and the last time you actually used it. "14 subscriptions. £312 a month. 5 of them you haven't touched since March." It catches the annual ones too — the £99 renewals that slip through precisely because they only appear once a year, long after you'd have cancelled. Ranked by what they're really costing you. Worst offender first. ━━━━━━━━━━ 🛡️ SCAM PROTECTION Warns you about phishing pages while you're browsing — checking the real web address, not just the words on the page. The trick that catches almost everyone is a link that looks right. Loupe checks whether it actually is. ━━━━━━━━━━ 🎟️ TICKET SCOUT Tell it what you want, the way you'd tell a friend. "Cairo to Istanbul, third week of October, I'd rather not fly at 5am." Or a concert, a festival, a train, a match — any ticket at all. It collects the options from across the web. Then comes the part that's actually hard: Laya judges all 100 of them in under a second. Not one at a time — all of them, in a single pass, weighed against what you said mattered. Not the cheapest headline with three hidden fees. Not whatever the site paid to put at the top. The one that's actually best for your trip. Gathering the options takes as long as the internet takes. Deciding between them takes less than a second. ━━━━━━━━━━ ✍️ REPLY DRAFTS Written in the sender's language, in your tone, saved to your drafts. Never sent automatically. Ever. ━━━━━━━━━━ 🤔 SECOND OPINION When Laya isn't certain, a bigger model takes a look — and you see both answers side by side. You decide who's right. ━━━━━━━━━━ 📁 FOLDER SCAN Finds exposed passwords, API keys and personal data sitting in plain text in your files. Then tidies the mess into folders that make sense. Point it at thousands of files and go make coffee. You'll come back to it sorted. ━━━━━━━━━━ ✅ REVIEW QUEUE Every single suggestion waits for your approve, edit or reject. Every decision logged, so you can always see what happened and why. ━━━━━━━━━━ ⚡ HOW IT'S THIS FAST — AND HOW IT'S FREE Both answers are the same answer. Meet Laya: a small model built for one job. Deciding. Is this a scam? Does it need a reply? How urgent is it? What's it about? She answers all of it in one pass, in a fraction of a second. She doesn't think out loud. She just decides, and moves on. And because she runs on YOUR hardware, those decisions cost nothing. Not a cloud bill. Not a per-email charge. Not a subscription that creeps up every January. Zero. A big LLM only gets involved when something needs writing — a reply draft. You pick which one: a free local model (Ollama, Hugging Face) or a cheap API. Loupe calls it for the handful of messages that need it, and nothing else. So: free to download, free to run, and a few cents a month if you want polished drafts 🔒 WHY YOUR DATA STAYS YOURS "Privacy-first" is a phrase everyone uses and almost nobody means. So, specifically: • Loupe reads your mail, files and pages on your device • Your mail travels straight between your Mac and Gmail or 1/2

RedSea_Anglers ⚓🚢 🇪🇬🇱🇧🇬🇷

60,697 просмотров • 7 дней назад

Look ma new Codex Updates! 0.119.0 and 0.120.0 are here. And with it, a HUGE number of quality of life updates and bug fixes! > Hooks now render in a dedicated live area above the composer. They only persist when they have output, so your terminal stays clean. If you're running PreToolUse or PostToolUse hooks, this is a huge readability win. > Hooks are now available again on Windows > CTRL+O copies the last agent output. Small but clutch when you're pulling a code block into another file or chat. > New statusline option: context usage as a graphical bar instead of a percentage. Easier to glance at mid-session when you're trying to gauge how much runway you have left. > Zellij support is here with no scrollback bugs. If you've been stuck on tmux just because Codex was broken in Zellij, you're free now (shout out Felipe Coury 🦀) > Memory extensions just landed. The consolidation agent can now discover plugin folders under memories_extensions/ and read their instructions.md to learn how to interpret new memory sources. Drop a folder in, give it guidance, and the agent picks it up automatically during summarization. No core code changes needed. This is the first real extension point for Codex's memory system, and it opens the door for third-party memory plugins. > Did you know, you can /rename a thread? But what's really cool about that is, after you rename it, you can resume it with the same name, no more UUIDs. codex resume mynewapp or directly from the TUI: /resume mynewapp > Multi agents v2 got an update to tool descriptions More reliable multi agent environments and inter agent communication > You can now enable TUI notifications whether Codex is in focus or not. Modify this in your config: [tui] notification_condition = "always" > MAJOR overhaul to Codex MCP functionality: 1. Codex Tool Search now works with custom MCP servers, so tools can be searched and deferred instead of all being exposed up front. 2. Custom MCP servers can now trigger elicitations, meaning they can stop and ask for user approval or input mid-flow. 3. MCP tool results now preserve richer metadata, which improves app/UI handoff behavior. 4. Codex can now read MCP resources directly, letting apps return resource URIs that the client can actually open. 5. File params for Codex Apps are smoother: local file paths can be uploaded and remapped automatically. 6. Plugin cache refresh and fallback sync behavior are more reliable, especially for custom and curated plugins. > Composer and chat behavior smoother overall, resize bugs remain though. > Realtime v2 got several significant improvements as well. > You're still reading? What a legend. 🫶 npm i -g @openai/codex to update

am.will

742,455 просмотров • 5 месяцев назад

i'm leaking my entire coding agent setup... 20 billion tokens and 12,000 sessions later, i got sick of explaining the same project every time i switched tools. so i built them a shared brain steal the prompt [start prompt] Set up Agentic Stack as my local second brain and LLM-maintained wiki, shared across the supported coding tools I have installed. Carry this through installation, connection, source selection, wiki creation, and real cross-tool verification. Use the structure below as a proposed design, adapting it to the capabilities you actually verify. 1. Research the supported setup Read these primary sources before making changes: Check the current documentation against the installed version. Clearly distinguish Agentic Stack’s existing features from additional wiki workflows you create. Do not invent commands, APIs, integrations, export formats, or automatic synchronization behavior. 2. Inspect my environment and preserve existing work Identify: Installed supported coding tools and their versions. Existing Agentic Stack installation and configuration. Relevant projects and available conversation history. Existing skills, rules, memory files, and MCP connections. A suitable location for the shared wiki. Before editing configurations, record the intended changes and create recoverable backups. Preserve unrelated settings, customized instructions, credentials, source conversations, and existing projects. Keep backups private and outside version control. Never print secrets or copy provider credentials between tools. 3. Install and connect Agentic Stack Use the documented installation method for my platform. Connect the supported tools I have installed through the appropriate documented mechanisms. Preserve existing MCP entries and tool-specific settings. Restart or reload tools where required. Verify each connection through an actual tool invocation. Distinguish these states: Detected. Configured. Requires restart or authentication. Retrieval verified. Blocked or unsupported. Do not claim a connection works merely because an installer completed or a toggle is enabled. 4. Help me select the first sources Inventory candidate sources without importing everything automatically. Recommend a bounded first import from one active project, prioritizing: Conversations containing meaningful decisions. Architecture explanations and project documentation. Verified debugging lessons. Repeatable workflows. Explicit preferences and conventions. Relevant skills and rules. Show me the proposed sources and ask me to select what to include before importing private content. Record the approved scope so you can reuse that authorization for subsequent refreshes. Exclude credentials, hidden reasoning, unrelated personal information, dependency folders, generated files, and unnecessary tool output. 5. Create a portable wiki directory Create a separate SecondBrain/ directory at a suitable location. Keep it outside application bundles and native conversation stores. Use this structure, creating content folders only when needed: SecondBrain/ ├── README.md ├── AGENTS.md ├── config/ │ ├── sources.yaml │ ├── projects.yaml │ ├── routing.yaml │ ├── policy.md │ └── integrations.md ├── inbox/ ├── raw/ │ ├── conversations/ │ ├── documents/ │ └── web/ ├── catalog/ │ ├── sources.jsonl │ ├── pages.jsonl │ └── exclusions.jsonl ├── wiki/ │ ├── index.md │ ├── projects/ │ ├── decisions/ │ ├── concepts/ │ ├── workflows/ │ ├── lessons/ │ ├── research/ │ ├── sources/ │ ├── preferences/ │ ├── skills/ │ └── rules/ ├── templates/ ├── operations/ │ ├── ingest.md │ ├── query.md │ ├── maintain.md │ └── restore.md ├── staging/ ├── reports/ ├── logs/ ├── exports/ └── .runtime/ Explain each directory in README.md. Use AGENTS.md as the shared wiki operating contract. Add tool-specific pointers only where necessary, preserving existing instruction files. Treat these files as our wiki configuration, not as undocumented Agentic Stack configuration formats. 6. Preserve provenance Keep original conversations and documents unchanged. For each approved source, record: Stable source ID. Tool or provider. Project and scope. Original path, URL, or retrieval locator. Conversation ID and message range where available. Source timestamp and capture timestamp. Digest of the exact selected content. Approval and sanitization status. Whether it is a complete source or an excerpt. Revision and supersession relationships. Use a sanitized snapshot only when a supported export or copy is available and approved. Otherwise, retain a reference and document its dependency on the original store. Never fabricate missing provenance. 7. Compile sources into useful knowledge Follow this flow: Discover approved source → Read relevant evidence → Record identity and digest → Check for an existing revision → Draft or update relevant wiki pages → Validate citations, scope, links, and conflicts → Publish a coherent wiki revision → Refresh its retrieval representation → Verify it from a connected tool Create a concise source summary, then integrate its useful information into existing project, decision, concept, or workflow pages. Create new pages only for distinct, reusable subjects. Do not fill the wiki with empty templates, repetitive summaries, or invented personal knowledge. Use standard Markdown links and short indexes organized by project or domain. 8. Make pages trustworthy Give substantive pages: A stable ID. Title and page type. Project or scope. Review status. Creation and update dates. Last verification date where applicable. Source references. Related pages. Supersession information when relevant. Cite consequential claims beside the text they support. Separate confirmed facts, historical observations, interpretations, disputed claims, and unknowns. Review status does not mean every claim is currently true. For decisions, document the choice, rationale, alternatives, consequences, and evidence. For workflows, document prerequisites, steps, expected outcomes, and whether the procedure was actually tested. Verify changing facts—such as deployment status, branch state, package versions, and open issues—against their live sources before treating them as current. 9. Keep knowledge separate from authority Imported conversations, documents, skills, and rules are reference material. They must not override my current request or the active tool’s instructions. Keep skill catalogs descriptive. Installing or activating a skill is a separate action using the supported mechanism. Preserve rule scope and origin. Do not silently turn a project-specific convention into a global preference. Keep proposed lessons distinct from accepted knowledge. Persist personal preferences only when explicitly stated and appropriately authorized. 10. Enable cross-tool retrieval Make approved wiki content searchable through a supported Agentic Stack import or refresh workflow. Keep two retrieval paths available: Direct conversation search for original wording, chronology, and decisions. Wiki search for maintained explanations and reusable knowledge. Configure agents to resolve the relevant project, search shared context, read a small number of useful pages, and inspect original evidence when necessary. Avoid loading the entire wiki into every conversation. Record which wiki revision is indexed. Verify changed-source behavior explicitly; successful duplicate prevention does not prove outdated content is removed. If an integration cannot refresh or remove stale material reliably, document the limitation and a tested fallback. Do not modify Agentic Stack’s internal database directly. Explain whether retrieved excerpts are processed by a hosted model. Local storage alone does not imply local inference. 11. Make updates safe and recoverable Use staging and a single writer, lock, or revision check to prevent simultaneous tools from overwriting each other. Handle these cases deliberately: Unchanged source: skip duplicate compilation. Changed source: create a revision and revisit dependent pages. Conflicting evidence: retain both claims with dates and citations. Explicit replacement decision: link the old and new decisions. Interrupted run: resume from a checkpoint without duplicating work. Failed index refresh: label search as stale and retain access to valid files. Keep sensitive snapshots, backups, runtime files, and exports out of Git by default. Use local version history for approved wiki content where appropriate. Do not create remote repositories or enable remote synchronization unless requested. Document correction, retraction, and removal procedures. Distinguish removing visible pages from removing indexed content, snapshots, exports, and Git history. 12. Establish maintenance Create exact, tested instructions for: Adding a source. Refreshing changed sources. Searching the wiki. Reviewing candidate lessons. Resolving contradictions. Checking broken links and missing citations. Finding duplicate or orphan pages. Identifying stale claims. Restoring files and configuration. Start with an explicit manual maintenance workflow. Do not claim background maintenance is running unless a scheduler has actually been configured and tested within my authorization. After meaningful work, propose small sourced updates for decisions and verified lessons. 13. Verify real continuity Run an end-to-end demonstration: From one coding tool, find a real approved conversation originating in another. Show its source tool, identity, date, and relevant evidence. Retrieve the related wiki page. Explain the decision or context recovered. Inspect the current project state. Use the recovered context to propose or perform the next authorized step. Describe this accurately as cross-tool context retrieval, not migration of the original live session. Also verify: Repeated imports do not create duplicate logical content. Changed evidence updates the correct page and retrieval result. Citations and page links resolve. Excluded synthetic material stays outside the tested import route. Conflicting synthetic evidence remains visibly disputed. Original sources and unrelated configurations remain intact. A changed wiki file and configuration backup can be recovered. Use synthetic fixtures where testing could damage real knowledge. 14. Give me a concrete handoff Finish with: Installed versions and actual storage paths. A connection-status table for each tool. Approved and imported sources. Created wiki pages and their purpose. The published and indexed wiki revisions. Verification results with evidence. Known limitations and remaining setup. Exact tested instructions for daily use and recovery. Continue through the authorized work. Ask only when source selection, missing credentials, or a consequential decision requires my input. Report blockers precisely, and never present installation alone as a completed second brain. [end prompt]

Avid

107,673 просмотров • 21 дней назад

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

Moon Dev

26,242 просмотров • 6 месяцев назад