foxennyx

I’ve been using ChatGPT Work every day while building AI automation. I use it to design architectures, review code, refine workflows, research APIs, and solve backend issues. Instead of jumping between docs and tabs, I can stay focused and keep building.

@BlueGuardiancom Spain 0-2 agentina

Predict World Cup final Spain vs Argentina outcome! 🇪🇸 🇦🇷 We’re giving away $50,000 Instant Funded Account All you have to do is predict the correct score in comments and follow us One correct score wins! 👇

@AceTrader Agentina 2 - 0 Spain

FIFA World Cup FINAL GIVEAWAY! 🚨 Yes, @AceTrader predictions is back: 🏆 2 winners x 10K usd 🥇 1 winner x 1K usd Predict the exact score of the match 🇦🇷 Vs 🇪🇸 Drop the comment with your prediction 👇🏼 ⚠️extra entries if: RT, Like ⛓️‍💥: app.acetrader.com/predictions

Every department, installable. Developers - Superpowers → github.com/obra/superpowe… - Context7 → github.com/upstash/contex… - Skill Creator → github.com/anthropics/ski… - MCP Builder → github.com/anthropics/ski… - Webapp Testing → github.com/anthropics/ski… - Claude-Mem → github.com/thedotmack/cla… Designers - UI UX Pro Max → github.com/nextlevelbuild… - Taste → github.com/Leonxlnx/taste… - Frontend Design → github.com/anthropics/ski… - Transitions → github.com/Jakubantalik/t… - Web Artifacts → github.com/anthropics/ski… - Brand Guidelines → github.com/anthropics/ski… Marketing - 45 skills for copywriting, SEO, lead magnets, campaigns → github.com/coreyhaines31/… Social Media - 17 skills for posts, Reels, thumbnails, content ops → github.com/charlie947/soc… Finance - 8 skills for statements, reconciliation, audits → claude.com/plugins/finance Small Business - 31 skills for cash flow, payroll, invoicing, operations → claude.com/plugins/small-… Legal - 9 skills for contract review, NDAs, compliance → claude.com/plugins/legal Not prompts. Not wrappers. A real operating stack for Claude. Bookmark this and build your own. Follow @cyrilXBT

The New GPT 5.6 Sol is insane. ❤️‍🔥Just recorded a 21-min Full Tutorial on How To Build Premium, Cinematic Websites with GPT 5.6 Sol!

【Codexガチ勢に朗報】 OpenAI Build Week開催中！ 参加申請するだけで $100相当のCodexクレジットがもらえる 【正確な手順】 1. openai.devpost.com に参加申し込み 2. Resourcesタブから「request $100 in Codex credits」を申請 → 7月17日 12:00 PM PTまで（なくなり次第終了）

I’ve been using GPT-5.6 for coding, debugging, and building automation workflows. It understands context really well and helps me turn ideas into working projects much faster. Loving it so far! 🚀 #GPT56 #ChatGPT #OpenAI

Or… what if we gave you $100 in Codex credits if you tell us what you love about GPT-5.6 Sol or why you switched? Tweet it, claim your gift, enjoy more usage. First 10k get the free tokens! switch-to-codex.openai.chatgpt.site

@thsottiaux The GOAT Tibo

Hello. We have reached 8M active users across Codex and ChatGPT Work. We are once again resetting the usage limits for all. And we continue to not have the 5h rate limit as well, allowing everyone to explore the boundaries of GPT-5.6 Sol and discover how ambitious you can be. See you tomorrow for more updates on our growth!

The real cycle of $BTC You have about 3 months to prepare everything.

@Ryker_Crypto Hold and wait

@0xMrPinky Me

I’m back. 👀 As promised, the $50 → $100,000 challenge starts now. Zero risk — I’ll cover the $50 for every participant. Comment “Me” below for an invite. Comments lock in 24 hours.

@MEXC_Thailand @Fatcatinvestors สมัครไม่ได้ครับ บัตร ปชช หมดอายุ แล้วผมอัพเดต kyc ใหม่มีแต่บอกว่า อ่านบัตรไม่ได้ รบกวนแจ้งทีมงาน mexc ตรวจสอบเรื่องการอ่านบัตร ปชช ด้วยครับ

@Fatcatinvestors MEXC มีแต่ให้ ซึ่งทางผมเพิ่งถึง 6% 😂

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@PlasmaThailand @Plasma @tether มีวิธีเพิ่มเข้า apple wallet ไหมครับ ผมเพิ่มบัตรไม่ได้แฮะ

บัตร Plasma One ได้ AI Cashback + ตั๋วเครื่องบิน Cashback แม้จะเป็น Tier สูงถึงจะได้สิทธิประโยชน์ด้านบน แต่สื่อได้เลยว่า @Plasma ที่ Backed โดย @tether มีสายป่านมหาศาล + connection ที่สูง ปัจจุบัน เราสามารถได้ดอกเบี้ยบน stablecoin $USDT บน Earn ประมาณ 3-5% เรื่อยๆ พูดแบบอวยคือ บัตรทุกใบไม่มีใครมี Backed ใหญ่เท่า Plasma One Card แล้ว 📌โหลดแอพ @Plasma One กรอกโค้ด "THTHTH" บัตรฟรีก็ได้ Cashback 2% แล้ว

@0xMrPinky Ce9dXVGEZhqcSYFZzmQW9S87xyPt7xdGTL2csUNZFi3e

Okay lets send more $ansem Show me your solana addresses. ITS AIRDROP SEASON.

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

Use Fable 5 as orchestrator and Opus + Codex to execute (to save fable usage): Fable 5 (max reasoning) = orchestrator Opus = deep reasoning subagent Sonnet = mechanical work subagent Codex = peer Sr. engineer, different perspective Setup: 1. Set Fable 5 as your main model In Claude Code: /model → Fable 5 → reasoning /effort to max 2. Create 2 subagents with /agents In Claude Code: deep-reasoner → pinned to opus "Use for reasoning-heavy phases, architecture, debugging complex issues, algorithm design. Think thoroughly, return a concise conclusion the orchestrator can act on." fast-worker → pinned to sonnet "Use for mechanical tasks, boilerplate, tests, formatting, simple edits. Execute efficiently." 3. Add OpenAI's official Codex plugin (install codex cli in your computer first), In Claude Code type: /plugin marketplace add openai/codex-plugin-cc /plugin install codex@openai-codex /codex:setup 4. Drop this in your CLAUDE.md in your folder: ## Orchestration workflow You (Fable) are the orchestrator. Plan, decompose, synthesize. Reasoning-heavy phases → deep-reasoner Mechanical work → fast-worker Codex (/codex:rescue --background) is a cracked engineer on par with deep-reasoner, from a different perspective. Treat as a peer, not a reviewer. High-stakes decisions: task Opus + Codex on the same problem in parallel, synthesize the best of both, without showing either the other's answer. Keep your own context lean. 5. Then prompt Fable like a tech lead: "Goal: [what you want] Context: [files, constraints] You're the lead. Delegate reasoning to deep-reasoner, grunt work to fast-worker, fresh-perspective problems to Codex. Show me your plan first, then execute." That's it.

@Ryker_Crypto why you not share lol

CRAZY ?? Meme on Solana season ? Meme season is leading by $ANSEM I turned from $1,000 to $63,000 in just few hours with $FROGBULL Made 63x in few hours so fast and crazy !

@uzmanfatihkaya Me

Söz verdiğim gibi geri döndüm :) $50 → $100,000 challenge'ına başlama zamanı Bu SIFIR RİSKLİ, her katılımcı için $50'yi ben karşılayacağım Geçen sefer yaklaşık 12 gün sürmüştü, bu sefer daha hızlı yapmaya çalışacağım Takip etmek istersen, aşağıya "Me" yorumla ve seni çağrı grubuna davet edeceğim Yorumları 24 saat içinde kilitleyeceğim