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Pulse60 — Fully automated AI/Tech news YouTube Shorts pipeline. Script → XTTS v2 voice → SDXL Turbo visuals → FFmpeg composition with Ken Burns effects and synced captions. Zero manual editing, single GPU, ~3 min per episode. Live at youtube.com/@pulse60ai
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I built Travero: Commute as a smaller,
sharper version of the original Travero idea.
Travero started as an app for seeing weather along your driving route, based on where you’ll actually be at each point of the trip. That works really well for longer drives, but I kept thinking there’s a more frequent use case hiding inside it: the daily commute.
Most people don’t need a full navigation app every morning. They already know where they’re going. What they need is a quick answer before leaving:
Is traffic bad? Is there an incident? Will weather affect the drive? Should I leave now or wait a bit?
So Travero: Commute became a more focused product around that exact moment.
The app is built around saved Home and Work locations, one-tap commute checks, traffic status, incidents, weather along the route, alternative routes, and quick summaries that are easy to understand before you get in the car.
One important shift was treating weather as a supporting layer, not the main feature. For commute, traffic is the real priority. Weather still matters, but only when it affects the route.
I also wanted the app to feel more like a daily habit than a trip planner. Something you can open quickly in the morning or after work, check the route, and decide whether to leave now, wait, or take another way.
This project was a good reminder that sometimes the best product direction comes from narrowing the idea, not adding more features.
Travero: Commute is still part of the same bigger Travero vision, but with a much clearer daily use case.
Awesome work👏🔥🔥
I tested the same cinematic prompt across multiple AI image models to compare how each system interprets character, lighting, realism, and mood.
No predefined face.
Just pure AI interpretation.
Tools included:
ChatGPT, Kling AI, Higgsfield, Hunyuan, Wan 2.7 Pro, Reve, Meta, Morphic, Flux 2 Pro, Seedream 5, Nannobanna, Recraft V4 Pro, and more.
The result became less of a comparison…
and more of an AI personality experiment.
Part 2: real identity consistency test 👀
Looks quite impressive!
Agentic AI Workflow for Real Estate Lead Enrichment (USA)
I built a production-grade, engineering-first workflow that turns minimal “clues” (company name, property address, or Tax ID) into a structured owner/contact dossier.
Stack: self-hosted n8n, OpenAI (GPT-5), Airtable (UI + database), Google Search, RealTrack (closed system)
Input → Output: 1–3 clues in → a complete enriched profile in ~60–120 seconds (with citations/links to sources) ready for sales outreach and due diligence.
The Problem (How the team worked before)
The client’s manual process looked like “crossfire research”:
- Find a company name + street / Tax ID
- Open multiple platforms/tabs
- Cross-check signals, collect evidence
- Compile a dossier in spreadsheets/CRM
This was slow (20–45 minutes per request), error-prone, and hard to scale.
I delivered an end-to-end agentic workflow that automates the same multi-platform research the team did manually — but faster and with consistent formatting.
1. ~90% reduction in pre-call research time per lead
2. ~60–120 seconds per lookup vs 20–45 minutes manually
3. ~$0.02 per enrichment request (token cost; excluding optional external services)
4. Self-hosted n8n: dramatically lower operational costs vs cloud pricing at scale
Amazing job Oleh!
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