The idea came from something I kept noticing while working with larger Figma files the UI might look great visually, but the structure underneath slowly becomes messy over time.
Most AI tools give you an output and ask you to trust it. We'd rather show you the work. When you type a ticker into CoreSight, here's exactly what happens in the background:
Resolving SEC CIK: Every public company has a unique identifier in the SEC database. The agent finds it first, so everything that follows pulls from the right source.
Fetching SEC filings: 10-K, 10-Q. The raw financial truth, straight from the source, not a third-party summary.
Getting market price: Live data. The analysis reflects what the stock actually costs right now, not yesterday's close.
Extracting financial statements: Income statement, balance sheet, cash flow. Structured and ready for analysis.
Computing metrics: P/E, P/S, P/FCF, margins, debt ratios. The numbers that actually tell you if a stock is priced fairly.
Searching web for context: numbers don't exist in a vacuum. The agent searches for recent news, product launches, and market developments that could affect the analysis.
Generating AI analysis: Everything gets synthesized into a valuation verdict with a bull case, bear case, and clear reasoning.
Populating spreadsheet: The output lands in a structured spreadsheet you can explore, edit, and build on.
Solo founder here, launching Torziva on March 12th.
I kept seeing the same problem online shoppers can't visualize how clothes look on their body, so they guess, buy multiple sizes, and return most of it.
Torziva lets fashion stores add AI virtual try-on in minutes. Customers upload their photo and instantly see any outfit on themselves before buying.
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.
