PrivaPDF which is privacy-first pdf converter reached 400 users in very short time.I hope that we will reach thousands of people in few months.Thanks to everyone who used or gave feedback about usage!
Honest origin story: we were doing a lot of research through YouTube online courses, industry talks, foreign language content and kept running into the same frustration.
The information is there. It's just locked inside a 40-minute video with no way to extract it without actually watching the whole thing.
So we built TubeLens. It's a Chrome extension that sits inside YouTube and gives you a full toolkit for actually processing what you watch not just playing it.
When people first explore RWA tokenization, the first question that comes to mind is always about cost. And the answers they usually find are confusing. Some sources mention very low numbers, while others talk about extremely high investments. This creates a gap between expectation and reality, especially for founders who are planning to build a serious platform. To understand the average budget properly, you need to look at what you are actually building. An RWA tokenization platform is not just a software application. It is a system that connects real-world asset ownership with blockchain-based transactions, while also operating within legal and regulatory frameworks. Because of this, the cost is influenced by multiple layers working together.
Understanding the Real Average Range
If we look at current market trends and real project structures, most production-ready RWA platforms fall within a range of $100,000 to $250,000. This range typically includes a working tokenization engine, smart contract development, basic compliance integration, user dashboards, asset management, and secure infrastructure setup. However, this is only the starting point for understanding cost.
I built a multi-agent orchestration system and turned the dev exhaust (tests, Git commits, CLI docs) into a free ebook. It s not theory: it documents the architecture, failures, refactors and ops decisions that made it production-ready. 5 lessons that actually moved the needle 1. Architecture > prompts. The wins came from memory, quality gates, orchestration, and service layers not better prompts . 2. Hire teams dynamically. A Recruiter AI assembles the right agent team per goal/domain; hard-coding roles doesn t scale. 3. Unify orchestration. Consolidating multiple orchestrators into a Unified Orchestrator cut conflicts and latency, and improved completion rates. 4. Production readiness is a discipline. We built a Production Readiness Audit to stress security, scalability, and performance beyond it works on dev . 5. Load reveals truth. A load-testing shock forced pragmatic quality thresholds and better prioritization systems get smarter under stress. Questions for the community How are you deciding when to use structured vs adaptive orchestration at runtime? What s your bar for quality gates so you don t stall progress? Would you find more useful: a starter repo + checklists, or deeper chapters on monitoring/telemetry & cost control? Link (free beta): books.danielepelleri.com P.S. The ebook was compiled automatically from the project s tests, commits, and CLI-generated docs so the narrative mirrors the real workflow, not a cleaned-up case study.
Exciting milestone to share: Triponic has just been featured by DesignRush as one of the Top AI Productivity Tools of 2025!
For those who don t know us yet, Triponic is an AI travel assistant that saves hours of planning by generating personalized, polished itineraries in minutes, including flights, hotels near business districts, transfers, and even downtime activities.
