Elie A.

@AlfieJCarter CLAUDE

I just built the Claude For Small Business: Full Playbook. Feed it your Gmail, your calendar, and your accounting tool → it runs your weekly briefings, chases invoices, and preps your meetings → you get hours back every week without writing a single line of code. All inside one free reference document. Perfect for small business owners who are still processing their inbox by hand, chasing overdue payments manually, and spending time on tasks that repeat the same way every week. If you're running a small business in 2026, you already know the math: every hour spent on admin is an hour not spent on growing. Most people think AI tools were built for developers. This one was built specifically for owners who never want to touch code. This playbook solves it: → Which of the three Claude apps to actually use and why → The Small Business plugin: 31 pre-built skills and 12 connectors installed in four clicks → How to connect Gmail, QuickBooks, Slack, and Stripe in under 60 seconds each → The five built-in skills to run in your first week: Business Pulse, Friday Brief, Invoice Chase, Close Month, Tax Prep → How to customise any skill for your specific tools and business → Seven copy-paste custom skills with full build templates: /today, /research, /prep, email triage, SOP writer, client brief builder, DNA file builder → A daily and weekly Cowork routine that takes 15 minutes a day No coding. No technical background. No expensive tools beyond Claude Pro. What you get: - Full setup guide from download to first automated workflow - All 31 built-in skills explained with slash commands - Seven custom skill builds with complete copy-paste templates - A daily, weekly, and monthly operating rhythm - The DNA file builder that teaches Claude your entire business in one session Want it for free? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

@mikefutia META

I just vibe coded a Meta Ads creative analytics tool in Claude Code 🤯 It plugs into your ad accounts, AI-analyzes every creative you've ever run, and tells you exactly what's working, what isn't, and WHY. Built 100% in Claude Code. Perfect for media buyers and creative strategists managing high creative volume who can't afford to let a bad decision sit in-market for another week. If you're running 30, 50, 100+ active ads across accounts and your "creative review process" is still a shared Google Sheet where someone manually tags hook type and angle after the fact — You're making kill/scale calls a week too late. By the time you've watched the videos, tagged the assets, and built the analysis, the budget's already burned. This tool runs the entire loop for you: → Connect your Meta ad accounts in one click → AI watches every video and analyzes every static → Auto-labels each ad by asset type, messaging angle, hook tactic, and funnel stage → Win rate analysis broken down by every category → Kill/scale recommendations segmented by TOF, MOF, and BOF → AI-generated iteration recommendations for every underperformer No manual video watching. No guessing at what's working. No spreadsheets to track creative performance. What you get: - A full creative analytics dashboard pulling live from your accounts - AI classification on every ad you've ever run - Iteration priorities ranked by ads with real spend behind them - Weekly reports surfacing top and bottom performers with AI insights Built 100% in Claude Code as a real tool, not a one-off script. I recorded a full walkthrough showing exactly how this works and what every feature does, including ALL the prompts I used so you can build it yourself. Want access to all the prompts for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

@AlfieJCarter LIMITS

I put the entire 21 Ways to Stop Hitting Your Claude Limits into ONE Notion doc. 3 sections. No fluff. - Why you are hitting your limit in the first place: every message forces Claude to reread the entire conversation from the beginning - your 30th message costs more than your first 15 combined. One developer tracked a 100-message session and found 98.5% of tokens went to rereading history and only 1.5% went to generating the response. It is a context hygiene problem not a Claude problem - 9 general tips that apply everywhere: edit instead of following up so bad responses get replaced not stacked, batch all requests into one prompt so three tasks cost one context reload not three, fresh chat every 15-20 messages with a pasted summary, right model per task with Haiku for quick and Opus only for deep reasoning, extended thinking off by default since output tokens cost 5x more than input, files converted to markdown before uploading to reduce token weight by 60-90%, projects for repeated documents so they are cached not retokenised, session reset timing to start your 5-hour window early, and off-peak working since same prompts cost less when fewer people are on the platform - 8 Claude Code tips and 4 Cowork tips covering the segments most people never address: run /context before typing anything since most people are already 40-70k tokens deep before their first message, disconnect MCP servers not in use since one server can be 18k tokens of dead weight per turn, replace MCPs with CLIs for 40% token savings, use /clear between unrelated tasks as the single habit separating people who hit limits in 2 hours from people who never hit them, run /compact at 50% not 95%, use sub-agents with Haiku for grunt work and Opus for thinking only, keep CLAUDE.md under 200 lines, point Cowork at a clean dedicated folder only containing files relevant to the current task, and build a local memory system with instructions.md and memory.md that is portable across any tool This is the setup I would have KILLED for before burning Opus tokens on tasks Haiku handles identically, hitting my limit two hours into a session with no idea why, and paying for irrelevant session history on every new prompt across a full working day. Like + comment "LIMITS" and I'll send it over (must be connected for priority access)

@AlfieJCarter MARKETING

If you don't have my "Claude Design and Claude Skills Marketing Playbook" yet... The one I built to run every repeated marketing task from one brief with skill files across brand extraction, campaign planning, social content, carousel design, animated video, multi-skill orchestration, Notion library sync, and Kanban task board setup... Just comment "MARKETING" and I'll DM it to you for free (must follow)

@lukepierceops BLUEPRINT

Automation consultants charge $15K for what Claude Code now does in 2 hours. I know because we're the ones who used to charge it. Here's the exact process: Step 1: Discovery (20 min) → Paste your org chart, tool stack, and top 3 bottlenecks → Claude interviews you with clarifying questions → Outputs a full process inventory ranked by time cost Step 2: Workflow Mapping (15 min) → Describe any department's daily operations in plain English → Claude builds a complete process map → Every manual handoff, redundant step, and automation trigger flagged Step 3: Opportunity Audit (10 min) → Feed it the workflow map output → Returns your top 10 automation opportunities → Ranked by ROI, complexity, and build time Step 4: Architecture Design (20 min) → Claude designs the full system architecture → Which tools connect where, what the data flow looks like → Agents for complex logic, linear flows for the repetitive stuff Step 5: Build (ongoing) → Claude writes the actual workflow JSON → Self-documents everything as it builds Step 6: The output. A live dashboard your whole team can work from. → Clickable process maps for every department → Automation opportunities ranked by ROI → Implementation progress by phase → KPIs updated in real time → One link you share with clients, freelancers, or your team to execute This is what we hand every client at the end of discovery. The .md file is what makes all of it possible. Without it, Claude guesses. With it, Claude builds like a $15K consultant. Like this post, RT and comment "BLUEPRINT" and I'll send you the full prompt stack and the .md file we use internally. (Must be following so I can DM you) 🎁 Bonus: The first 100 people get a real Precision AI Blueprint — an actual sample audit doc from a client engagement so you can see exactly what the output looks like.

@IslamRashi2000 Founder

I built 7 Claude Skills that replaced 6+ hours of weekly marketing work. Now I’m giving them all away. The Founder Skills Vault: → Viral Content Generator → Content Pillar Generator → LinkedIn Content Analyzer → Lead Magnet Idea Generator → LinkedIn-to-X Converter → Warm DM Strategist → Lead Qualifier This isn’t a prompt pack. These are production-grade tools that run inside Claude. It’s the same system I used to grow 7,000+ followers in 3 months. Want it? → Like + RT → Comment “FOUNDER”

Stop worrying about AI replacing you. Worry about this instead: Are you + AI > others + AI? That gap is your moat.

@mikefutia Statics

I just vibe coded a static ad generator in Claude Code that creates 100+ Facebook ads in minutes 🤯 One competitor ad + your product photo + your brand kit = dozens of on-brand variations, each targeting a different customer persona. Built 100% in Claude Code. Perfect for DTC brands and agencies who need more statics at scale. The problem: You find a winning ad concept and want to test 20 variations of it. That means briefing a designer, waiting days, getting back 4 options, giving notes, waiting again. Or doing it yourself in Canva for hours. This tool solves it: → Upload any competitor ad as your reference template → Add your product photos and brand kit (colors, fonts, logos) → AI generates 10 customer profiles from your brand research → Pick how many variations you want (10, 20, 30) → Tool generates on-brand ads with persona-specific copy for each one No designer back-and-forth. No Canva templates. No generic "Shop Now" on everything. What you get: → Ads that mirror winning concepts in your brand's voice → Copy targeted to specific customer pain points and personas → Multi-brand/client support with saved brand kits → Reusable customer profiles you build once and generate from forever I recorded a full walkthrough showing exactly how this works, including ALL the prompts I used so you can build it yourself. Want access to all the prompts for free? > Like this post > Comment "STATICS" And I'll send it over (must be following so I can DM)

🚨BREAKING: Someone just solved LLM testing's biggest problem. It's called DeepEval and it gives you answer relevancy, hallucination detection, and G-Eval metrics that actually work. - Run evaluations 100% locally (no data leaves your machine). - Test agents, RAG systems, and production responses with human-level accuracy. 100% Opensource.

@mikefutia KLING

Kling 3.0 for AI UGC videos is absolutely insane 🤯 I spent 25,000+ credits in Kling perfecting the ultimate prompting framework to get the best AI UGC outputs possible. And the 3.0 update just made everything even better. Perfect for DTC brands and agencies who want high-quality AI UGC without paying $500/video for actual UGC. Here's what Kling 3.0 unlocks for AI UGCL → "AI Director" system that understands full scripts and auto-schedules camera angles (shot/reverse shot) in one generation → 3 to 15 second cinematic clips with full temporal coherence → Improved character and element locking so your subject stays consistent across shots and angles → Native 4K output for both video and stills — actually usable for professional ad creative And best of all: perfect character consistency ACROSS different shots. What this means for AI UGC: - Multi-shot storytelling in a single generation cycle - Longer clips that actually hold together - Consistent characters across your entire ad - Output quality that's ready for paid media This is the closest AI video has gotten to replacing a real shoot for performance creative. I recorded a full breakdown of the prompting framework I built after burning through 25,000 credits. Want access to all the prompts I use to create AI UGC with Kling? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

@mikefutia AMAZON

I just generated 7 Amazon listing creatives from a single product photo 🤯 One CeraVe image. 7 high-converting listing images. All with Nano Banana Pro. No agency, no designer, no 2-week turnaround. Perfect for Amazon sellers and e-comm brands who need listing creatives fast. Here's the problem: Amazon listing images cost $500-$2k per set. You wait weeks for revisions. You get generic "benefit callout" templates. Half of them don't even match your brand. And if you want to test variations? Start the whole process over. This workflow changes that: → Upload one product photo → AI generates hero images, benefit infographics, comparison visuals → "Why Choose [Brand]?" trust panels → Lifestyle shots and pack visuals → Before/after graphics → All brand-consistent, all Amazon-ready No agency invoices, no revision rounds, no waiting. What you get from one product image: - Hero image for main listing - Benefit-led infographics - Comparison visuals ("Why Choose...") - Trust and ingredient panels - Lifestyle + pack shots - Secondary images built to convert One photo. Full listing set. Minutes. I built a workflow template so you can do this yourself. Want the full Nano Banana workflow? > Like this post > Comment "AMAZON" And I'll send it over (must be following so I can DM)

@mikefutia REELS

This Instagram Reels AI agent is absolutely wild 🤯 It scrapes trending Reels in your niche, analyzes them with AI, and extracts every creative insight you need. All inside n8n + Airtable. Perfect for DTC brands & agencies who need to know what's working on Instagram before they create content. Here's the problem: Manual Instagram research takes forever. You're scrolling for hours, screenshotting videos, manually noting hooks, trying to remember what worked. And by the time you act on it, the trend is dead. This n8n automation solves it: → Enter a keyword (e.g., "skincare", "fitness", "productivity") → AI scrapes trending Instagram Reels automatically → Writes all videos to Airtable with views, likes, comments → Click "Analyze Video" button in Airtable → Gemini watches each video and extracts: Hook, Proof Point, Theme → Click "Analyze Comments" for instant comment insights No manual scrolling. No spreadsheets. No missing trends. What you get in Airtable: → Video URL, creator handle, performance metrics → AI-extracted hooks (what stopped the scroll) → Proof points (what built credibility) → Creative themes (the narrative structure) → Comment insights (what the audience is asking) Built 100% in n8n. Want the complete n8n template + Airtable base? > Comment "REELS" > Like this post And I'll send it over (must be following so I can DM)

You no longer need to leave Python to write high-performance hardware kernels. Learn how to use Pallas in Keras to author custom ops that lower to Mosaic for TPUs or Triton for GPUs: keras.io/guides/define_…

With 40% of AI initiatives failing to deliver ROI, the stakes for product leaders have never been higher. Our live session tomorrow highlights the top AI disruptions and provides a roadmap for navigating new business opportunities: gtnr.it/4gqIgto #GartnerHT #AIDisruption #Strategy #Webinar

@mikefutia MCP. This sounds actually quite interesting.

Claude + Facebook Ads MCP is absolutely WILD 🤯 This AI system turns Claude into your personal ads analyst using MCP integration. And generates full client reports in seconds. Perfect for agencies & ecomm operators who hate wrestling with Ads Manager. But instead of manually pulling data, building charts, and formatting reports... Just give Claude one natural language prompt and get a complete visual storytelling report with insights. Each report includes account performance, campaign breakdowns, demographics, and actionable recommendations. All with a single prompt. Here's what it does: → Connects directly to your Facebook Ads account → Pulls all performance data automatically → Calculates ROAS, CPA, conversion rates on demand → Creates interactive charts and visualizations → Generates beautiful reports → Builds everything from scratch in real-time Built with Claude MCP. Zero manual work. Want the complete MCP setup? Comment "MCP" + like and I'll send it over (must be following so I can DM)

Think all embeddings work the same way? Think again. Here are 𝘀𝗶𝘅 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝘆𝗽𝗲𝘀 of embeddings you can use, each with their own strengths and trade-offs: 𝗦𝗽𝗮𝗿𝘀𝗲 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 Think keyword-based representations where most values are zero. Great for exact matching but limited for semantic understanding. 𝗗𝗲𝗻𝘀𝗲 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 The most common type - every dimension has a value. These capture semantic meaning really well, and come in many different lengths. 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗲𝗱 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 Compressed versions of dense embeddings that reduce memory usage by using fewer bits per dimension. Perfect when you need to save storage space. 𝗕𝗶𝗻𝗮𝗿𝘆 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 Ultra-compressed embeddings using only 0s and 1s. Super fast for similarity calculations but with reduced accuracy. 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 (𝗠𝗮𝘁𝗿𝘆𝗼𝘀𝗵𝗸𝗮) These embeddings let you use just the first 8, 16, 32, etc. dimensions while still retaining most of the information. This ability comes during model training: earlier dimensions capture more information than later ones. You can truncate a 3072-dimension vector to 512 dimensions and still get great performance. 𝗠𝘂𝗹𝘁𝗶-𝗩𝗲𝗰𝘁𝗼𝗿 (𝗖𝗼𝗹𝗕𝗘𝗥𝗧) Instead of one vector per object, you get many vectors that represent different parts of your object (like tokens for text, patches for images). This enables "late interaction" - comparing individual parts of texts rather than whole documents. Way more nuanced than single-vector approaches. 𝗦𝗼 𝘄𝗵𝗶𝗰𝗵 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗵𝗼𝗼𝘀𝗲? • Dense for general semantic search. • Matryoshka when you need flexible performance/cost trade-offs. • Multi-vector for precise text matching. • Quantized/Binary when storage and speed matter most. Modern vector databases (like Weaviate 😄) support all of these approaches, so you can experiment and find what works best for your use case. Want code examples or deep dives for any of these? Drop a comment on which one and I’ll send it over.

How small can a BERT get without losing its power? 📱🤯 Meet EI-BERT: an ultra-compact framework for edge NLP that combines token pruning, cross-distillation, and quantization. ✅ Just 1.91 MB — smallest ever for Natural Language Understanding (NLU) tasks! It's already been serving — it got integrated into Alipay's live Edge Recommendation system back in January 2024 and is now handling the app's recommendation traffic across 8.4 million daily active devices.

postgres MCP + mermaid/notebooks have completely replaced database clients for my ad-hoc data analysis. being able to query data from my db and visualize it directly in cursor makes everything so much easier. here’s my setup:

🚨 BREAKING: NVIDIA just exposed the dirty secret about LLMs. Their new paper proves SLMs outperform massive models in real-world applications. AI researchers are quietly pivoting overnight. 10 wild findings that change everything:

The potential of Customer Service AI is immense, yet realizing its benefits requires a focus on strategic use cases. Learn how to enhance operations and customer experiences by prioritizing AI-driven initiatives: gtnr.it/4oKNmV9 #GartnerCSS #CustomerServiceAI #AI #CX