JSON formatters, regex testers, Base64 encoders for frontend devs.
Cloud cost calculators, GPU estimators, SRE tools for infrastructure teams.
Privacy-first browser tools for everyday dev tasks. No signup. No tracking. All client-side.
Field-tested engineering investigations — the tax behind every cloud bill, the math behind every outage.
Observability = 15-25% of cloud spend. Datadog cardinality trap ($100/mo per tagged metric), Splunk ingest waste (93% never queried), trace head-sampling blindness. Self-hosted Grafana: 53% cheaper at 200 hosts.
VBR encoding peaks at 2× nominal. HLS protocol overhead +15%. Live vs VOD: fundamentally different formulas. Encoder economics: H.264→H.265→AV1 cost/benefit at scale.
Fan-out tail amplification: 10 backends × P99=50ms = composite 120-180ms. Protocol stack, serialization, queueing, and the physics floor. Five latency budget traps.
JVM RSS = Xmx × 2.2. Request=limit wastes 43% capacity. CFS throttling creates self-amplifying P99 spikes. Runtime memory models + VPA framework.
Per-request rate = decoy. Data transfer + throttling retry amplification = 5-30× multiplier. Self-hosted NGINX breakeven at ~500 rps.
Lambda's pricing page says $10.50/month. Your bill says $427. Five structural taxes: VPC cold starts, provisioned concurrency, CloudWatch Logs inflation, cross-AZ egress, Step Functions orchestration costing 12.5× the Lambdas it orchestrates.
A 3-year Standard RI is not a discount — it's a 36-month cash advance to AWS. The breakeven depends on one variable: spot interruption rate. A 1% move flips a $200K commitment from savings to loss.
Each NAT Gateway costs $32.85/mo per AZ in idle charges alone. Three independent cost vectors — hourly, data processing, cross-AZ double-count. The VPC Endpoint arbitrage.
The bottleneck isn't TFLOPS — it's memory bandwidth. Model throughput across 12 models × 7 GPUs × 4 quantization levels, with real benchmark data.
DNS propagation isn't one number — it's a distribution shaped by TTL, ISP resolver caching policy, and geographic anycast topology. Resolver-by-resolver timing model.
Five structural leaks between LLM pricing pages and your credit card — workload ratios, tokenizer variance, unused prompt caching (90% off), batch discounts (50% off), and retry overhead.
K8s says 110 pods per node. Your CNI says 30. The four binding constraints modeled across EKS, GKE, and AKS — with the DaemonSet tax and bin packing math.
2.71 quintillion UUIDs for a 50% collision chance. The math says never. Every documented production collision traces back to a bad entropy source.
RAID 6 rebuild is only 13% slower than RAID 5. The URE survival gap is five orders of magnitude. Stop comparing the wrong number.
We're the people who get paged at 3 AM. These are the calculators we reach for ourselves — research-backed, client-side, zero barrier to entry.
All calculations run in your browser. No data ever leaves your machine. No server-side processing, no logs, no tracking.
Every formula is documented and sourced. We cite our data — Imperva, Cloudflare, Akamai, IEEE, and academic papers.
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We're the people who get paged at 3 AM. These are the calculators we reach for ourselves.
51 calculators encoding the back-of-the-envelope heuristics we use to avoid provisioning disasters — GPU OOMs, egress billing shocks, RAID rebuild windows that exceed MTTDL.