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Built a comprehensive Excel-based US Payroll Management System designed to organize employee payroll data, automate payroll calculations, and provide clear management insights through an interactive dashboard.
The system includes payroll summaries, employee records, payment tracking, automated calculations, KPI reporting, and visual analysis to make payroll data easier to manage and monitor.
Built with advanced Excel formulas, PivotTables, data validation, charts, dashboards, and automation-focused workflows.
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"We can't afford a data analyst."
I've heard this from 3 different startups this year.
So I built one.
Metriva is an AI-powered business analyst that:
📊 Turns messy spreadsheets into executive dashboards
🧠 Writes C-level business briefs automatically
📈 Forecasts trends + detects anomalies
💬 Answers questions about your data in plain English
🎯 Finds your Pareto 80/20 — who drives 80% of your revenue?
No SQL. No Python. No data team.
Just upload your file and get answers.
Built with Flask, vanilla JS, and a lot of late nights.
🔗 Try it: metriva.ai1.vercel.app
#AI #BusinessIntelligence #Founder #IndieHacker #DataScience
The brief-writing piece is interesting — curious how you handle the validation step. AI-generated language tends to sound confident even when the underlying data is sparse or the trend is ambiguous. One pattern that helps: have the model output a confidence signal alongside the...
One quarterly number almost got double-counted across two reports.
Two dashboards were quietly tracking overlapping data. Caught before it went out now there's one source of truth the reports pull from, not two that can disagree.
→ A good number is only as good as the one place it actually comes from
Great catch before it shipped. I like the “one source of truth” framing - pulling reports from one validated source makes it much harder for a duplicate dashboard to quietly skew the number again.
In 90 days, AI stopped being a tool I use and became a teammate I manage.
Here's what actually changed, from where I sit as a software engineer:
→ Agents went mainstream. JetBrains found 90% of professional developers use coding agents weekly and 68% daily. Claude Code usage doubled since January (18% → 39%). Codex went 3% → 16%.
→ Models learned to drive computers. GPT-6 Astra (Sep 3) was pitched on computer use and software engineering, and shipped with gated access over its cyber capabilities.
→ Output limits blew open. Gemini 4 Argon (Sep 30) can write up to 1M tokens in one response, up from 64K. Also gated at launch.
→ Agents did real, checkable work. Dozens of Claude agents formalized Fermat's Last Theorem in Lean in 11 days: 13M lines, machine-verified. (Formalization, not a new proof. The verification is the point.)
→ The plumbing grew up. MCP's July spec went stateless, so agent tooling now deploys like ordinary web infrastructure.
→ Open weights kept pace. Alibaba released Qwen3.8-Max as a 2.4T-parameter open-weights model.
My take: code generation is getting cheap. Judgment isn't.
Anthropic's agentic coding report says engineers use AI in roughly 60% of their work but can fully delegate only 0–20% of tasks. The bottleneck has moved to architecture, review, testing, security, and keeping things alive in production.
That's where I spend my time: shipping AI features (agents, RAG, LLM integrations) into real products, with the guardrails that make them survive contact with users.
What's one task you'd hand to an agent tomorrow, and one you never would?
#AI #SoftwareEngineering #AIAgents #LLM #FullStack
managing agents instead of just using them is exactly the shift I've felt too ngl