I used to spend an hour every morning checking AI news and figuring out what to post about. Built a model that handles the whole thing automatically. What it does: - Monitors 4 sources daily: Anthropic changelog, Claude GitHub, Gemini release notes, AI influencer posts - An LLM filters the noise — only major updates make the cut - Generates a LinkedIn post, Instagram caption, and branded image for each update - Creates a ClickUp review card — I approve or discard in one click Saves 1 to 2 hours every single morning. Full live demo in the video. Stack: trigger.dev, FAL.ai, ClickUp

If you're doing manual lead research on LinkedIn, this might be the most useful build I've shared. I was spending 10-15 minutes per lead, 25 leads a day — that's almost 4 hours of research daily. Built a system that does it automatically the moment someone accepts a connection: - Scrapes their LinkedIn profile and recent posts via Apify - Scores them 1-10: cold, warm, hot, or ideal client - Writes a personalized DM based on their actual profile - Suggests 3 posts to comment on with ready-made comments - Updates Airtable in real time — CRM never falls behind Full enrichment in 40 seconds per lead. Stack: Dripify, Sales Navigator, n8n, trigger.dev, Apify, Claude Sonnet, Airtable

Sharing this here because I think a lot of you are building similar things. I built a personal AI executive assistant called Drew — not a chatbot, it actually executes tasks. In the video I show the full system live: - It scrapes LinkedIn profiles, scores leads, and writes personalized outreach - Generates YouTube subtitles, SEO titles, and descriptions from raw video - Creates LinkedIn and Instagram posts with branded images on demand - Gets smarter over time — learns my tone, priorities, and workflow The whole thing runs on Claude Code. I also show the file structure: CLAUDE.md, skills, tools, context files — everything. If you're thinking about building your own assistant or agent on top of Claude, this is the full breakdown.

Built this for the NYC commercial real estate space — sharing the full build here. The problem: agents were manually searching LoopNet every morning and spending hours on listings that would never close. What the model does: - Pulls every new NYC commercial listing from the last 24 hours at 9am automatically - Scores each property 0-100 based on cap rate, size, price range, and opportunity status - Flags DOB violations so problem properties are filtered before you waste a call - Enriches agent contact info and pulls owner name, address, and last sale price - Sends a daily email with only the high-priority deals Covers for sale and for lease across all NYC boroughs. Stack: n8n, LoopNet, DOB (NYC Dept of Buildings), Batch Data, Airtable

Been experimenting with Claude for automation lately — wanted to share what actually works for beginners. Most people overcomplicate this. Here's what I've learned: The only stack you need to start: → Claude = the brain (tells you what to do with the data) → claude code = the hands (actually does the action) First automation worth building: New form submission → Claude writes a personalised response using their answers → Auto-sent as email Takes 30 minutes to set up. Saves hours every week. What actually makes you better at this: → Give Claude more context, not less — it performs like the quality of your brief → Build one working thing before starting the next → The mistake everyone makes: automating something they don't fully understand manually first To go from beginner to expert: → Month 1: Prompting deeply + one simple workflow → Month 2–3: Chaining prompts, connecting APIs → Month 4+: Full agents, multi-step logic, real client work Took me a while to figure out the right order. Sharing so someone here skips the confusion. 📢Here's 1 to 2 points which even begginers should notice and do not do blindly?? Let's see if you can catch which points they're?? Happy to answer questions if anyone's building something specific ??