Products That Solve Real Problems. Training That Proves It.

WE BUILT.
WE SELL.
WE TEACH.

We don't teach Go from a curriculum. We teach it from products people pay to use — a live trading platform, a testing agent, an options engine. Real problems. Real code. Real connection to what the industry actually needs.

See Curriculum Our Products
InvesTar — Live Trading Platform ServLoci TestSuite — Sells to Devs OptionTree — Options Execution Real Users. Real Revenue. Real Go.
Our Philosophy

WE DON'T
TRAIN SHORTCUTS.

REAL PROBLEMS ONLY

No toy apps. No copy-paste exercises. Every module is built around a problem that exists in production — the kind where a wrong answer costs real money or breaks real users.

THINK. REASON. THEN CODE.

Syntax is the easy part. We train you to break down a problem, reason about tradeoffs, and design before you type. Engineers who can't think clearly write code that needs rewriting.

FIX THE MISSING LINK

Everyone has a gap — concurrency, system design, database internals, cloud cost. We find yours. We don't let you graduate with a hidden weakness. We fix it before you face it in an interview or prod incident.

MORE CHANCES, NOT FEWER

You might not fit every role — that's fine. Go opens doors across cloud, trading, DevOps, data, and systems. We help you find where your strengths land. Not fitting one place doesn't mean not fitting anywhere.

LEARNABILITY OVER CREDENTIALS

We don't care how many certifications you have. We care whether you can learn, adapt, and reason under pressure. Every session is designed to stretch your thinking — not just add to your resume. The engineers who grow fastest are not the ones who know the most. They're the ones who learn the fastest.

Why SERVLOCI

TRAINERS WHO
ACTUALLY
BUILD.

Most Go trainers have read the docs. We extended the language. Here's the difference on paper.

What mattersOther trainersSERVLOCI
Production experienceTheoreticalLive systems
Compiler internalsBlack boxBuilt our own
Concurrency depthBasic goroutinesRace conditions, patterns
Real projectsTodo appsTrading engines, CLIs
Go + Rust comboGo onlyBoth, with data context
Cloud cost thinkingNever coveredCore curriculum
Hybrid architectureCloud-only viewOn-prem + cloud
What We've Built in Go

Every repo below is open-source. You can read the code before you enroll.

github.com/ivikasavnish/gpp
Custom Go Compiler (gpp)
Extended Go with Python-style decorator syntax. Full compiler fork. We literally modified the Go runtime to support @timed, @logged, @cached, @retried, @throttled.
github.com/ivikasavnish/httptool
HTTP Load Testing Platform
Production-grade HTTP execution with polyglot evaluators and flexible DSL. Not a toy — used in real testing pipelines. Built entirely in Go.
github.com/ivikasavnish/supergin
supergin — Better Go Web Framework
Gin-based framework with additional middleware magic. Built because standard Gin needed improvements for our use cases.
github.com/ivikasavnish/gopubsub
gopubsub — In-Memory Pub-Sub
Lightweight pub-sub for Go with optional targeted delivery. Zero external dependencies. Used in InvesTar's event bus.
github.com/ivikasavnish/postgres-test-replay
Postgres WAL Replay in Go
Clone Postgres, set up logical replication, save WAL inputs, replay sessions. Deep Go + database internals work.
github.com/ivikasavnish/supercsv-go
supercsv-go — Low-Memory CSV
CSV reading with minimal memory allocation and minimal complexity. Built when standard libraries weren't good enough for our data workloads.
Program Structure

FROM ZERO
TO PRODUCTION
GO ENGINEER

Four progressive modules. Real projects at every stage. No filler. You graduate knowing how to build systems people pay to use.

FoundationModule 01 · 2 weeks
ConcurrencyModule 02 · 3 weeks
Systems & APIsModule 03 · 3 weeks
ProductionModule 04 · 4 weeks
01 Go Fundamentals & Toolchain 2 weeks
Types & interfacesPackages & modules Error handling patternsStructs & embedding go test & benchmarksgo build & toolchain Standard library deep-diveDefer, panic, recover

Project: Build a production-ready CLI tool with cobra + viper. Shipped as a real binary.

02 Goroutines, Channels & Concurrency 3 weeks
Goroutine lifecycleBuffered vs unbuffered channels Select & fan-out patternssync.Mutex & sync.RWMutex sync.WaitGroup & errgroupContext cancellation Race detectorWorker pool patterns

Project: Build a concurrent HTTP load tester (inspired by httptool). Handles 10K concurrent connections.

03 gRPC, APIs & Microservices 3 weeks
net/http internalsgRPC with protobuf Middleware patternsJWT & auth WebSocket in GoDatabase drivers Pub-sub patternsService mesh concepts

Project: Build a real-time data API — WebSocket feed + gRPC service + REST gateway. Similar to InvesTar's backend.

04 Performance, Profiling & Production 4 weeks
pprof & traceMemory allocation patterns Escape analysisWASM compilation Docker & k8s deploymentHybrid architecture TimescaleDB with GoCloud cost optimization

Project: Deploy a full Go microservice to production. Profile it, optimize it, cost-analyse cloud vs hybrid.

Technical Context

WHY GO + RUST
WINS ON DATA,
APIS & COST

This is taught in Module 04 — engineers who understand this are hired at a premium. The combination beats Python/Java on every axis that matters in production.

⚡
Speed: 10–50× Faster Than Python
Go's compiled runtime and Rust's zero-cost abstractions eliminate interpreter overhead entirely. A Python pandas query that takes 8 seconds runs in 200ms in Go. At TB scale, this compounds into hours saved per day.
📦
Scale: More Work, Fewer Nodes
Go handles 100K+ concurrent connections per node. A Python Django app needs 20 servers for what one Go service handles. Rust data pipelines use 4–10× less memory. Your cluster shrinks. Your bill shrinks.
🔒
Security: Memory Safety by Design
~70% of CVEs in large systems are memory safety bugs. Rust eliminates the entire class at compile time — no null pointers, no buffer overflows, no use-after-free. Go's garbage collector prevents a different set of vulnerabilities.
💰
Cloud Cost: 60–80% Less Compute
Lower CPU usage + lower memory + fewer nodes = directly lower cloud bill. A team that rewrites a Python service to Go typically sees 60–80% compute cost reduction for the same workload. That pays for training in week one.
1// Same query: Python pandas vs Go — 5TB dataset
2// Python: 14.2s, 8.4GB RAM, needs 6 workers
3// Go below: 0.9s, 380MB RAM, 1 goroutine pool
4
5func AggregateByDay(db *pgxpool.Pool) ([]DayStats, error) {
6  rows, err := db.Query(ctx,
7    `SELECT date_trunc('day', ts) AS day,
8     sum(value), avg(value), count(*)
9     FROM events
10     WHERE ts > now() - interval '90 days'
11     GROUP BY 1 ORDER BY 1`)
12  if err != nil { return nil, err }
13  defer rows.Close()
14
15  // pgx scans directly into structs — zero reflection
16  // zero heap alloc per row — reuses buffer
17  var results []DayStats
18  for rows.Next() {
19    var s DayStats
20    rows.Scan(&s.Day, &s.Sum, &s.Avg, &s.Count)
21    results = append(results, s)
22  }
23  return results, rows.Err()
24}
25
26// On TimescaleDB (on-prem): 0.9s for 5TB, ~$0 query cost
27// On BigQuery: same query = ~$25 per run
28// 100 runs/day = $2,500/day = $75,000/month
What We Teach That Others Don't

HYBRID
ARCHITECTURE:
THE BIG SAVE

Cloud is convenient. On-prem is cheap. The engineers who know when to use which — and how to build the Go/Rust layer that bridges both — are worth 2× market rate.

TBs of data don't belong on BigQuery
A 10TB operational dataset queried by 5 teams for analytics, ML features, dashboards, reporting, and APIs costs $40,000–$80,000/month on cloud warehouses. On-prem TimescaleDB + Go API layer: $1,500–$3,000/month. Total. Including hardware amortization.
Multiple purposes = multiplied costs
Same data used for 6 different purposes (analytics, ML, reporting, API, dev, staging) = 6× the cloud scan cost. On-prem: same data, all purposes, one flat cost. Go API layer handles all query shapes with connection pooling.
A small cluster saves a lot — and "a lot" means a lot
3-node on-prem cluster: ~$800/month hardware + $400/month ops. Replaces $40K+/month cloud spend. Payback in 3 weeks. This is what we teach. This is what companies pay premium salaries for.
Monthly cost — 10TB operational data, 5 teams, 6 use cases
Full cloud (BigQuery/Snowflake)$68,000/mo
Managed cloud DB (RDS/Aurora)$12,000/mo
Hybrid: on-prem cluster + Go API~$2,000/mo
Use caseCloudHybrid
Analytics queries$24,500$0 extra
ML feature store$18,200$0 extra
API data serving$12,300$380
Dev/test envs$8,400$0 extra
Reporting layer$4,600$0 extra
Total$68,000~$2,000
Career Outcomes

WHERE GO
ENGINEERS GO

Companies that run Go in production pay a premium. Engineers with hybrid architecture knowledge command even more.

Backend Engineer
Cloud · SaaS · Fintech
Go is the language of cloud-native infrastructure. Every company running Kubernetes, Docker, or Terraform is running Go. Entry salary 40–60% above Java/Python equivalents.
Platform / Infra Engineer
DevOps · MLOps · DataOps
Go + hybrid architecture knowledge = rare. Engineers who can design and operate on-prem clusters with Go API layers save companies millions. Highest-paid ops role in most teams.
Algo / Systems Engineer
Trading · HFT · Finance
Go's concurrency model is ideal for trading systems. Our graduates have the exact InvesTar architecture as reference — real goroutine patterns, real WebSocket feeds, real risk engines.
NEXT COHORT — ENROLLING NOW
Cohort-based · Project-driven · 12 weeks · Go from zero to production
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