Mathematical decision engines, Markdown formatters, and developer utilities for growth engineers, writers, and founders. 100% client-side with zero data storage.
Interactive Diagnostic Engine: scores 107 tactical growth tasks and builds a personalized 30-day P0/P1/P2 Roadmap with 1-click AI prompts and strategic frameworks.
Priority Score = (Effectiveness × 3) / (Complexity + Cost + Time) × Multipliers
Open, preview, and edit Markdown (.md) files with real-time GFM preview, KaTeX math formulas, Mermaid diagrams, and instant export to Word DOCX, HTML, or PDF.
100% Client-Side GFM + CommonMark Parser
Convert Markdown (.md) files into publication-ready PDF documents with 2x Retina resolution, LaTeX math, code highlighting, and custom page breaks.
100% Client-Side 2x Retina In-RAM Engine
Convert PDF documents to clean Markdown (.md) online in your browser. Extracts text, tables, headings, and lists with 100% client-side privacy.
100% Client-Side PDF.js In-RAM Parser
Convert Markdown (.md) to Microsoft Word (.docx) documents online with native styling, One Dark code blocks, LaTeX math formulas, tables, and zero server uploads.
100% Client-Side docx.js In-RAM Engine
Convert Microsoft Word (.docx) documents to clean Markdown (.md) online in your browser. Extracts headings, tables, lists, and code with 100% client-side privacy.
100% Client-Side Mammoth + Turndown Engine
Convert Markdown (.md) to clean, styled HTML5 code and fragments online. Features real-time HTML source inspection, KaTeX math formulas, One Dark syntax highlighting, and 100% client-side in-RAM privacy.
100% Client-Side Marked + DOMPurify In-RAM Engine
Convert HTML code, web pages, and CMS rich text into clean GitHub Flavored Markdown (.md) online. Features live Markdown source and visual preview, GFM tables, KaTeX equations, and 100% client-side privacy.
100% Client-Side Turndown + GFM In-RAM Engine
Create, edit, and format GitHub Flavored Markdown (GFM) tables online with an interactive spreadsheet grid, smart paste from Excel/Sheets, KaTeX math support, and AI token minifier.
100% Client-Side In-RAM Spreadsheet Grid Engine
Convert CSV, Excel (XLSX, XLS), TSV, and Google Sheets to clean GitHub Flavored Markdown (GFM) tables online. Features multi-sheet support, auto-delimiter detection, and AI token minifier with 100% in-RAM privacy.
100% Client-Side SheetJS + PapaParse In-RAM Engine
Convert Markdown tables to CSV, TSV, and Excel (XLSX) spreadsheets online. Features multi-table workbook extraction, custom delimiter control, and 100% in-browser client-side privacy.
100% Client-Side In-RAM GFM Table Parser & SheetJS Engine
Interactive Markdown cheat sheet and syntax reference with 1-click copy, instant keyword search, live sandbox preview, and GFM/KaTeX support.
100% Client-Side Interactive Syntax & Sandbox Engine
Convert Markdown (.md) to clean LaTeX (.tex) code and standalone documents online. Features math formula preservation, professional booktabs tables, and 100% in-browser client-side privacy.
100% Client-Side In-RAM LaTeX AST Compiler
Convert LaTeX (.tex) papers, equations, and tables to clean GitHub-Flavored Markdown online. Features in-RAM privacy, KaTeX math preservation, and zero server upload.
100% Client-Side In-RAM TeX Parser & Math Engine
Universal LLM token counter and text-to-tokens estimator. Calculate BPE tokens, word density, and separate input/output inference costs across models with 100% in-browser privacy.
Tokens ≈ Chars / 3.95 (General) | Cost = (Tokens / 1M) × Rate
Calculate exact Target ROAS, break-even thresholds, and required profit margins across Google & Meta campaigns.
ROAS = Gross Revenue / Ad Spend
Convert seamlessly between gross profit margin percentage and markup on cost. Avoid dangerous pricing errors.
Margin % = (Price - Cost) / Price
Itemize cost of goods, warehouse rent, software tools, taxes, and ad spend to model net operating profit (EBITDA).
Operating Profit = (Rev - COGS) - Fixed OPEX
Model customer acquisition backward from LTV to calculate maximum allowable CAC, CPL, and max PPC click bids.
Max CPC = (Net LTV / 3) × (CR₁ × CR₂)
Evaluate margin-adjusted Customer Lifetime Value, benchmark your LTV:CAC ratio health, and track CAC recovery speed.
Payback Mo = CAC / (ARPU × Gross Margin %)
Model token consumption COGS, Inference Efficiency Ratio (IER), true gross margins, Net LTV:CAC, and safe hybrid token quotas.
IER = Revenue / Direct Inference COGS (GPUs + Token APIs)
Model customer acquisition cohorts from inbound traffic down to net contribution profit using ARPU, ARPPU, and ROMI.
Profit = UA × (ARPU - CPA_visitor)
Cross-calculate acquisition costs, expected CTR, and conversion rates to stress-test your paid media budget.
CPA = CPC / Conversion Rate
Estimate the number of referring domains required to rank in the Top 10 based on keyword difficulty and target DR.
Link Gap = Competitor Med. Ref. Domains - Current
Forecast monthly Google Search ad spend from keyword demand and compute margin-based break-even CPC bids.
Max CPC = (AOV × Margin %) × CR %
Simulate 25 real-time scenarios analyzing Net Profit and ROAS across fluctuating CPC bids and conversion rates.
Net Margin = (Clicks × CR × AOV × Margin %) - Budget
Calculate statistical significance, two-tailed Z-scores, p-values, confidence intervals, and required sample size per variant.
Z = (p_B - p_A) / SE_pool
Forecast broadcast campaign revenue, benchmark Revenue Per Subscriber (RPS), calculate ESP ROI, and model list decay.
ROI = ((Email Revenue - ESP Cost) / ESP Cost) × 100
Model compounding competitor link growth, portfolio decay (link rot), required monthly velocity, and capital budgets to rank in Top 10.
V_mo = [RD_target(1+g)^T - RD_curr(1-δ)^T] · δ / [1 - (1-δ)^T]
Simulate passage extraction probability, exponential positional decay (t½ ≈ 350 tokens), and multi-engine viability across Google AI Overviews, Perplexity, and ChatGPT Search.
S_cit = 0.15·S_len + 0.25·S_pos + 0.20·S_fd + 0.15·S_edr + 0.10·S_dr + 0.15·S_fmt
Model total annual software expenditure across Ahrefs, SE Ranking, and Semrush. Quantify seat fees, credit overages, net migration savings, and reallocated link budgets.
ΔTCO = [S₁ + U₁·C_seat + O_credits] - [S₂ + U₂·C_seat + A_pack]