Text & documents
Encoder/DecoderURL, Base64, HTML
Text convertercamelCase, snake_case...
Text DiffCompare two texts
LaTeXPreview mathematical formulas
MarkdownReal-time preview
MermaidDiagrams as code
DiagramsVisio-style visual editor, Mermaid import
String escaperJS, JSON, SQL, Regex, HTML, URL
Unicode / ASCIICharacter information
Numbers & maths
CalculatorExpressions, calculation sheet and plots
StatisticsCalculator, distributions, simulators
Physics SimulatorPendulum, energy, integrators
Base converterBase 2 to 64
Unit systembits, bytes, SI, IEC
Subnet / CIDRNetwork calculator
Timestamp and datesUnix, time zones, formats
Date calculatorDays between dates, exact age, day of the week
Data & formats
Format converterJSON, YAML, Properties, ENV, CSV
JSON FormatterFormat and validate JSON
Regex TesterRegular expressions
JSON DiffStructurally compare two JSONs
Code formatterSQL, CSS, HTML, JavaScript
DB StudioVisual Postgres database in the browser
SQL LabValidate, run and optimize SQL with real Postgres
Giant file viewerOpen huge CSV/logs without loading them
Visual ETL flowTransform data by chaining nodes
Log analyzerMake sense of a log: traces, patterns and AI reduction
Knowledge graphRDF, SPARQL and ontologies
Atoms and chemistryPeriodic table, orbitals and valence shell
Security
Text anonymizerRedact PII and secrets (reversible)
JWT InspectorDecode JWT tokens
Auth flowsBasic, JWT and OAuth2 step by step
Hash generatorSHA-256, SHA-384, SHA-512
Password & UUID generatorPasswords and UUIDs
Hidden text (X-ray)Invisibles, hidden instructions and fake letters
Agent file auditorCLAUDE.md, skills, MCP, hooks and permissions
Can I paste this?Secrets and cookies in a text, log or HAR before sharing it
GitHub Actions auditorCommand injection, unsafe pull_request_target, unpinned actions
Headers and CSP auditorCSP, HSTS, cookies (Secure/HttpOnly/SameSite) from a pasted curl -I
Kubernetes network reachabilityCan pod A reach pod B? NetworkPolicy, ports and broken references
MCP catalogue auditorContext cost, annotations, official registry compliance
Access LabIdentity, RBAC and governance — vendor-neutral, multi-cloud
SAML decoder and auditorSignature, expiry, audience, and the XML Signature Wrapping attack
Email headers and DMARC auditorSPF, DKIM, DMARC — from an email’s headers or its DNS records
Side-channel labAttack a secret by timing and a key by power analysis
Authentication flow labAttack a real JWT (alg=none, RS256→HS256 confusion) on an interactive canvas
Defense in depthThe 7 security layers, and what Jarroba already covers with a real attack
Development & DevOps
CronGenerator and validator
Docker → ComposeConvert docker run to Compose
Skaffold multi-configDependency graph and requires validation
Linux: permissions and userschmod, chown, useradd and groups
URLParse and build URLs
Random dataNames, emails, IPs, UUIDs...
ElectronicsCircuit simulator: DC, transient, AC, MOSFETs and breadboard
From the transistor to the chipFrom a cell's noise margin to the clock: SRAM, read path, IR drop, standard cell and setup/hold
Logic GatesAND, OR, NOT and more gate simulator
Architecture SimulatorDiagram CI/CD, cloud, AI/MCP and simulate load
Quantum simulatorQubits, gates, superposition and entanglement
HammingError detection
Nginx/Apache SPA configGenerate config to serve a SPA
SemverSemantic version ranges
CORS ExplainerAnalyse your server's CORS headers
API workbenchSend requests, convert collections and compare versions
Cert inspectorAnalyse X.509 certificates in PEM format
Rate LimiterSimulate Token Bucket and Fixed Window
Assembly simulatorSimplified MIPS ISA · step-by-step · registers & memory
Operating systemsScheduling and page replacement, step by step and with sources
AutomataFrom regular expression to NFA, DFA and minimal DFA, plus CYK, step by step
Capture labNetwork captures (.pcap) explained: TCP, DNS and HTTP, packet by packet
Context ForgePack and shield your project for AI
Visual terminalClick and get the command: Linux, macOS, PowerShell
Project workshopSimulate your Makefile: what it rebuilds and why
Project X-rayWhat Spring will do with your code, and which dependencies are stale
Spring DoctorField injection, and the rest of the Spring Boot conventions, file by file
Test auditorOf these passing tests, which ones cannot fail?
Manuscript auditorWhich numbers in your paper or thesis don't add up
Artificial Intelligence
LLM costsSimulate monthly spend
Practice validityWhat still holds and what was superseded
AgentsSpec, permissions, verification and evals
Local AI cameraDetects objects and alerts, without uploading anything
Local speech transcriptionAudio to text without uploading it, and measure the error
Does this text say anything?How much you can cut without losing a single idea
EmbeddingsSimilarity, 2D map and RAG
Local AIChat, summarization, translation & sentiment in-browser
TokenizerVisualize tokens, compare models, estimate costs
Probability for AIDistributions, softmax, Bayes and entropy
Gradient descentSGD, Momentum, RMSProp, Adam
Neural networkTrain an MLP and watch the boundary form
Mini-LLMTrain a language model with your text
Dimensionality reducerPCA, t-SNE and UMAP, live
Confusion matrix & ROCThreshold, precision/recall, ROC/AUC, PR
Clustering (k-means, DBSCAN)Discover groups without labels
Attention visualizerWhat each word looks at in a transformer
Convolution & CNN filtersImage filters and feature maps
Decision treeDecision regions + the tree
DiffusionThe noise behind Stable Diffusion
Regression & MLELeast squares, overfitting, ridge
Markov chainsStates, stationary distribution and text
Hypothesis testingp-values, t-test, χ², ANOVA and power
OCR — Image to textExtract text from images, 100% local
Finance
InflationOfficial CPI, purchasing power and savings in today's euros
Economy simulatorFirms, prices, monopolies and limited resources
Mortgage / loanPayment, real APRC of each offer and early repayment
Split expensesWho owes whom and how much
Self-employed (Spain)Social Security, IRPF, VAT and how much to set aside
Health & Wellness
BMI & healthBMI, ideal weight, BMR and sleep cycles
Productivity
PomodoroTime-block work technique
StopwatchWith laps, stages and history
Games & Entertainment
Dice3D dice from 2 to 9,999 faces, with projection
ScorekeeperPoints per player with game timer
Random pickerPick a random item from a list
Name generatorInvented with a Markov chain, from 8 traditions
TournamentLeagues, groups, knockouts and Swiss, with standings
Multimedia & design
Color HEX/RGB/HSLHEX, RGB, HSL, OKLCH and scales
QR CodeCreate and read · WiFi, contact, URL
ImagesResize, convert and compress
PDF ToolsMerge, extract, rotate, images to PDF
ChartsVisualize data with charts
WCAG ContrastAA/AAA and the colour that does pass
Spreadsheet auditorRecalculates and compares with what's saved
Business
Meeting costHow much does each meeting really cost?
SLA / UptimeAllowed downtime, error budget and alerts
Burn rate / RunwayRunway, default alive and break-even
A/B TestAnalyse, plan and don't fool yourself by peeking
DORA MetricsYour team according to every DORA report, 2019–2025
UTM BuilderUTM links with their predicted GA4 channel
Spaces and resourcesTables, racks, beds or desks: share them out and see if they hold up
How to use
Analysing an experiment

Enter the visitors and conversions of each variant (A is the control). First the traffic split is checked (SRM): if it doesn't match the requested one, the result isn't reliable and you need to find the cause. Then you get the difference with its confidence interval, which tells you how much, not just whether. With little data or a rate at 0% or 100% Fisher's exact test is used, because the normal approximation declares winners that aren't.

What the p-value is (and isn't)

The p-value is the probability of seeing a difference like this or larger if there were really none. It is not the probability that B is better: the Bayesian part gives that, P(B > A). With more than two variants it is corrected for multiple comparisons (Holm or Benjamini-Hochberg): comparing several variants at once gives more chances to win by luck.

A/B TestAnalyse, plan and don't fool yourself by peeking
A/B testAnalyse with confidence intervals and an exact test, plan the sample size and see how much peeking early misleads you
VisitorsConversionsSplit % (optional)Rate
A Control (A)
12.00 %
B
14.00 %
Traffic split consistent with the requested one (SRM: p = 1.0000).
These data don't show any difference
Difference (B − A)+2.00 pp95% CI: -0.95 pp … +4.96 pp
Relative lift+16.7 %95% CI: -7.1 % … +46.5 %
p-value0.1836two-proportion z · z = 1.33
P(B better than A)90.8 %Bayesian, uniform priors

The p-value is NOT the probability that B is better: it is the probability of seeing a difference like this if there were none. And it only holds if you fixed the sample size beforehand and didn't stop on seeing a good result (“Peeking early” tab).