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Introduction:
Traditional technical interviews – Leetcode, live coding, algorithm puzzles – are rapidly losing relevance as AI coding assistants (Claude Code, Cursor) can solve them in under a minute. Applied Computing, a UK AI startup backed by $40M+ and Imperial College London alumni, has pioneered a take‑home task that tests exactly what matters in 2026: how well an engineer steers AI, overrides its output when context demands, and delivers working solutions at scale. Their approach filters for expert AI direction skills, not memorized syntax.
Learning Objectives:
- Understand why AI‑augmented take‑home tasks better assess real‑world engineering than Leetcode.
- Learn practical techniques to steer AI models for secure, context‑aware code generation.
- Master Linux/Windows commands and workflows that integrate AI pair programming into CI/CD, cloud hardening, and vulnerability mitigation.
You Should Know
1. AI‑Augmented Code Review & Override Techniques
The post highlights a critical shift: AI can identify algorithms, but it lacks business context (e.g., “one user vs. ten thousand providers”). Engineers must know when to override the model.
Step‑by‑step guide to applying this in your workflow:
- Generate initial solution using Cursor or Claude Code with a prompt like:
`”Write a Python function to handle rate‑limiting for an API. Assume 10,000 concurrent providers.”` - Review for context gaps – AI may produce an in‑memory token bucket that fails under distributed load.
- Override by specifying Redis backend and cluster awareness:
Override: add Redis for shared state across instances import redis r = redis.Redis(host='localhost', port=6379, decode_responses=True)
- Validate with a Linux command to simulate high concurrency:
Send 10,000 parallel requests seq 1 10000 | xargs -P 100 -I {} curl -s http://localhost:5000/api -o /dev/null
On Windows (PowerShell) :
1..10000 | ForEach-Object -Parallel { Invoke-WebRequest -Uri http://localhost:5000/api -UseBasicParsing } -ThrottleLimit 100
2. Building a Context‑Aware AI Pair Programming Environment
To replicate Applied Computing’s assessment, set up an environment where AI handles 50% of the problem solving while you direct the rest.
Linux setup (Ubuntu 22.04+):
Install Cursor editor (or VS Code with Continue extension) wget -qO- https://download.cursor.sh/linux/appImage/x64 | tar xz Install Ollama for local AI (optional, for privacy) curl -fsSL https://ollama.com/install.sh | sh ollama pull codellama:7b
Windows (WSL2 or native):
Install via winget winget install Cursor.Cursor Or VS Code + Continue extension code --install-extension continue.continue
Step‑by‑step AI steering workflow:
- Decompose task into high‑level requirements (e.g., “build an energy consumption API with JWT auth”).
- Let AI generate first draft – prompt: `”Write a FastAPI endpoint with JWT, using async DB calls.”`
- Inject context – add environment variables for production vs. development:
`”Modify the code to read JWT secret from a vault (HashiCorp Vault) when deployed in cloud.”`
4. Run security scan on AI‑generated code:
bandit -r ./generated_code/ -f json -o report.json
- Prompt Engineering for Secure & Scalable AI Output
The engineer who passed Applied Computing’s task emphasized where you push back on AI. Poor prompts lead to injection vulnerabilities or race conditions.
Example of a weak prompt:
`”Write a SQL query to get user data.”`
AI might output vulnerable code: `f”SELECT FROM users WHERE id = {user_id}”`
Strong, steerable prompt:
`”Write a parameterized SQL query in Python using psycopg2. Prevent SQL injection. Include error handling for large datasets (>1M rows).”`
Step‑by‑step for API security hardening with AI:
- Ask AI: `”Generate an Express.js route that accepts a user ID and returns account balance.”`
- Override the AI’s lack of input validation by adding:
app.get('/balance/:id', (req, res) => { const id = parseInt(req.params.id, 10); if (isNaN(id) || id < 0) return res.status(400).json({error: 'Invalid ID'}); // ... rest });
3. Test for injection using a Linux one‑liner:
curl "http://localhost:3000/balance/%3B%20rm%20-rf%20%2F" Should return 400, not delete files
4. Automating Take‑Home Testing with AI‑Generated Unit Tests
Applied Computing’s task size is tuned so that AI does the heavy lifting, but the engineer must design the test plan.
Step‑by‑step to build an AI‑powered test pipeline:
- Prompt AI (Claude Code): `”Generate Pytest unit tests for the attached rate‑limiting module. Cover edge cases: concurrent requests, token refill, and negative timeouts.”`
- Review tests – add missing scenarios like Redis connection failure.
3. Run tests continuously using a Linux watchdog:
while inotifywait -r -e modify ./src/; do pytest ./tests/; done
4. Windows alternative (PowerShell + FileSystemWatcher):
$watcher = New-Object System.IO.FileSystemWatcher -Property @{Path='.\src'; IncludeSubdirectories=$true; EnableRaisingEvents=$true}
Register-ObjectEvent $watcher 'Changed' -Action { pytest .\tests }
- Cloud Hardening for AI‑Deployed Models (Energy Sector Context)
Given Applied Computing’s Orbital model runs in complex industrial environments, hardening the AI deployment pipeline is critical.
Linux commands to secure an inference API endpoint (e.g., on AWS EC2 or Azure VM):
Restrict API to only HTTPS with modern ciphers sudo ufw allow 443/tcp sudo ufw deny 80/tcp Rate‑limit using iptables (prevents AI model DoS) sudo iptables -A INPUT -p tcp --dport 443 -m limit --limit 10/minute -j ACCEPT Run security audit with Lynis lynis audit system
Windows Server (IIS or Nginx on Windows):
Block HTTP, enforce HTTPS via URL Rewrite
Add-WebConfigurationProperty -Filter "system.webServer/rewrite/globalRules" -Name "." -Value @{name='Redirect to HTTPS'; patternSyntax='Wildcard'; wildcard=''; redirectType='Found'; url='https://{HTTP_HOST}/{R:1}'}
Install and run OWASP ZAP for vulnerability scan
Invoke-WebRequest -Uri "https://github.com/zaproxy/zaproxy/releases/download/v2.15.0/ZAP_2.15.0_Windows.zip" -OutFile "ZAP.zip"
- Mitigating AI‑Generated Vulnerabilities (Code Injection & Insecure APIs)
AI often produces code with unsafe defaults – e.g., disabling SSL verification or using eval(). The expert engineer knows how to find and fix these.
Step‑by‑step remediation workflow:
- Static analysis with Semgrep (supports AI‑generated code patterns):
semgrep --config auto --severity ERROR ./generated_code/
- Common AI mistake: `requests.get(url, verify=False)` – override by forcing:
import ssl; context = ssl.create_default_context() requests.get(url, verify='/path/to/cert.pem')
- Command to check for hardcoded secrets in AI output:
grep -r --include=".py" --include=".js" -E "(API_KEY|SECRET|PASSWORD)\s=\s['\"][A-Za-z0-9]{16,}" .
What Undercode Say:
- Key Takeaway 1: Leetcode and live coding are obsolete because AI solves them instantly. The real skill is steering AI with context and overriding its incorrect assumptions.
- Key Takeaway 2: Take‑home tasks should be too large for a human alone but achievable with AI direction – this filters for engineers who can orchestrate AI, not just use it as a crutch.
- Analysis (10 lines): Applied Computing’s approach signals a broader shift in technical hiring. By 2026, companies that fail to update their assessments will lose top talent to those that test AI‑augmented problem solving. The ex‑Monzo engineer’s preference for this task proves that senior engineers value modern, realistic evaluations. However, there’s a risk: candidates must already know how to prompt and override AI – a skill still unevenly distributed. Over time, we’ll see certification tracks for “AI direction” similar to cloud certs. Security teams must also adapt: AI‑generated code introduces new vulnerabilities (e.g., hallucinated dependencies, insecure defaults). The step‑by‑step commands above (bandit, semgrep, iptables) become mandatory in any AI‑driven SDLC. Finally, this model scales beyond energy – any domain with complex context (finance, healthcare, defense) will adopt similar assessments.
Prediction:
Within 18 months, 70% of tech companies will replace Leetcode rounds with AI‑steered take‑home tasks. Third‑party platforms will emerge to benchmark “AI collaboration coefficient.” Simultaneously, we’ll see a spike in AI‑aware supply‑chain attacks – malicious prompts hidden in code repos that trick AI into generating backdoors. Defenders will respond with runtime AI oversight layers (e.g., LLM firewalls that validate output before execution). The engineer of 2027 won’t be judged on memorized algorithms, but on their ability to orchestrate, secure, and override artificial intelligence – exactly as Applied Computing has predicted.
▶️ Related Video (70% Match):
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Basic Verification: Pass ✅
