CyberCode Academy

CyberCode Academy
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Welcome to CyberCode Academy — your audio classroom for Programming and Cybersecurity. 🎧 Each course is divided into a series of short, focused episodes that take you from beginner to advanced level — one lesson at a time. From Python and web development to ethical hacking and digital defense, our content transforms complex concepts into simple, engaging audio learning. Study anywhere, anytime — and level up your skills with CyberCode Academy. 🚀 Learn. Code. Secure. You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

  1. -14 h

    Course 41 - Analyzing Attacks for Incident Handlers | Episode 3: Live Memory Forensics and Malware Analysis with Mandiant Redline

    🧠 Live Memory Forensics with Mandiant Redline — Study Template🔐 Core ConceptMandiant Redline is not just a memory capture tool — it performs a:Memory audit for rapid threat detection and triageUnlike basic tools:It analyzes live system stateIdentifies Indicators of Compromise (IOCs)Detects stealthy malware that bypasses normal APIs⚡ Why Redline Is PowerfulTraditional tools:Only dump memory → analysis comes later🔥 Redline advantage:Combines collection + analysisDetects:Hidden processesSuspicious driversMalicious memory artifactsNetwork anomaliesRedline = faster incident triage🧰 Phase 1: Collector Configuration🧠 What is the Collector?A portable package that you:generate on your analysis machinerun on the target (infected) system⚙️ Standard Collector SetupYou configure what data to collect.Key customizations:Strings extractionFinds readable artifacts in memorySHA-1 hashesUsed for file identification & threat intelDriver informationDetects rootkits / kernel-level malwareNetwork dataActive connectionsSuspicious endpoints🔥 Key insight:Proper collector configuration determines investigation quality🧪 Phase 2: Safe Malware Execution & Capture⚠️ Critical Requirement:You NEVER test malware on your real system.💻 Virtual Machine (VM) SetupPurpose:Isolate malware executionPrevent system compromise🔒 Network Configuration (VERY IMPORTANT)Use:Host-Only Network ModeWhy?Blocks internet accessPrevents malware from:spreadingcalling command & control (C2)infecting external systems🚨 Key insight:Misconfigured networking = real-world infection risk🧬 Malware Execution ScenarioSteps:Launch VMExecute malware sample (e.g., her.exe)Observe behavior👁️ Monitoring Tool:Process HackerUsed to:Inspect running processesDetect suspicious activityView memory usageIdentify injected code🔥 What to look for:Unknown processesHigh memory usageSuspicious parent-child relationshipsHidden or injected processes📥 Running the Redline CollectorAfter infection:Execute the collector packageGather:Memory artifactsProcess dataNetwork connectionsExport results for analysis🧠 Output Includes:Running processesLoaded driversNetwork connectionsMemory stringsFile hashes🔍 Investigation GoalUsing collected data, identify:Indicators of Compromise (IOCs)Malicious processesSuspicious connectionsSigns of persistence🔥 Key Concept ShiftThis episode teaches:Controlled infection → observation → evidence captureYou are not just analyzing — you are simulating an attack environment safely⚠️ Safety Principles🚨 Treat malware like:Active threat, not a fileMandatory precautions:Use isolated VMUse host-only networkingNever use host machineMonitor system behaviorDo not connect VM to production network🧠 Key TakeawaysRedline enables live memory auditingCollector must be properly configuredVM isolation is criticalHost-only networking prevents spreadProcess monitoring reveals real-time behavior🚨 Golden WorkflowInfect (safely) → Monitor → Collect → Analyze You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 41 - Analyzing Attacks for Incident Handlers | Episode 3: Live Memory Forensics and Malware Analysis with Mandiant Redline

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    Course 41 - Analyzing Attacks for Incident Handlers | Episode 2: Utilizing FTK Imager and Redline for Incident Handlers

    🧠 Memory Analysis & Incident Response — Advanced Template🔐 Core ConceptMemory analysis is a high-impact forensic technique used during incident response to uncover evidence that is not available through disk or antivirus analysis.Key idea: Critical attack artifacts often exist only in volatile memory⚡ Why Memory Analysis Is CriticalTraditional methods may fail:Antivirus → may not detect advanced threatsDisk forensics → may show no malicious files🔥 What memory reveals:In-memory malwareActive attacker sessionsRunning malicious scriptsHidden processesMemory = ground truth of what is happening right now🛠️ FTK Imager (Memory Acquisition Tool)🧰 What it is:FTK Imager is a portable forensic tool used to:Capture live RAM (memory dump)Create disk imagesPreserve forensic evidence⚙️ Key Operational Notes:Must run on live systemRequires sufficient storage for outputRAM dumps can be several GBsShould minimize system interaction during capture🔥 Key insight:If you fail to capture memory properly, evidence may be permanently lost⚖️ Core Forensic PrincipleLocard’s Exchange Principle“Every interaction leaves a trace”In practice:Memory acquisition modifies the systemPerfect preservation is impossible🚨 Implication:Always document actionsMinimize system impactMaintain chain of custody🔍 Investigation Strategy (Holistic Approach)Memory analysis should NOT be isolatedCombine with:Log analysisRegistry forensicsDisk forensicsNetwork traffic analysis🔄 Workflow:Capture memory (FIRST)Analyze memory artifactsCorrelate with other evidence sourcesBuild full attack timeline🧰 Mandiant Redline🧠 What it does:Memory + system data collectionThreat hunting & analysis💡 Why it's important:Free toolCombines collection + analysisUseful for incident response scenarios🧪 Practical Scenario: Phishing AttackSituation:User exposed to phishing emailSuspicious activity detectedAntivirus shows nothingTraditional checks:Logs → inconclusiveRegistry → cleanDisk → no malwareMemory analysis reveals:Malicious process in RAMPowerShell activityNetwork connection to attackerPossible data exfiltration🔥 Key insight:Advanced attacks can fully operate without touching disk⚠️ Malware Handling & Safety🚨 Critical Warning:Treat malware like live explosivesBest Practices:NEVER analyze on host machineUse isolated virtual machines (VMs)Disable network or use controlled environmentSnapshot before analysisAvoid accidental execution🧠 Why this matters:Prevent infection spreadProtect corporate infrastructureEnsure safe forensic analysis🧬 Virtual Machine UsagePurpose:Safe sandbox environmentIsolated from host OSControlled execution of malicious filesTypical setup:VirtualBox / VMwareSnapshot enabledNo shared folders (or restricted)Limited network access🧠 Key TakeawaysMemory analysis reveals hidden threatsFTK Imager is essential for data acquisitionRedline is useful for analysis & investigationAlways follow forensic principlesSafety is non-negotiable🚨 Golden RulesCapture memory firstNever trust antivirus aloneCorrelate multiple data sourcesAlways use a secure analysis environmen You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 41 - Analyzing Attacks for Incident Handlers | Episode 2: Utilizing FTK Imager and Redline for Incident Handlers

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    Course 41 - Analyzing Attacks for Incident Handlers | Episode 1: Volatile Evidence, Forensic Tools, and Investigation Procedures

    🧠 Memory Analysis (RAM Forensics) — Study Template🔐 Core ConceptMemory analysis is a critical part of the incident response process, used to detect threats that do not leave artifacts on disk.Key idea: Some attacks exist only in memory⚡ Why Memory Forensics MattersModern threats bypass traditional disk-based detection:Fileless malwareExecutes directly in RAMLeaves no files behindMalicious PowerShell scriptsRun in memoryMinimal or no disk footprint🔥 If you only analyze disk → you may completely miss the attack🧬 Volatile Nature of RAMDefinition:RAM is volatile, meaning:Data changes constantlyData is lost when power is off🧾 Evidence Found in MemoryCredentials (passwords, tokens)Active network connectionsClipboard contentsBrowser sessions/historyRunning processesInjected/malicious code🔥 Memory = real-time snapshot of system activity📊 Order of VolatilityFrom MOST → LEAST volatile:CPU Registers & Cache (nanoseconds)RAM (live memory)Network data (connections, routing tables)Disk (persistent storage)🚨 Forensic Rule:Always collect data from most volatile → least volatile🔍 Investigation WorkflowStep 1: Acquire MemoryCapture RAM while system is liveDo this BEFORE shutdownStep 2: Analyze MemoryLook for:Suspicious processesCode injectionHidden malwareActive connectionsStep 3: Correlate FindingsCombine with:Disk forensicsNetwork analysisMalware analysis🔥 Memory analysis is part of a holistic investigation⚖️ Forensic PrincipleLocard’s Exchange Principle“Every interaction leaves a trace”In memory forensics:Capturing memory alters memoryPerfect preservation is impossible⚠️ Implication:Minimize impactDocument acquisition process🛠️ Memory Acquisition ToolsCommon tools used to dump RAM:FTK ImagerMandiant RedlineVelkosoft Live CapturerPurpose:Capture full memory snapshotEnable offline forensic analysis🧪 Practical ScenarioSituation:Suspicious outbound trafficData exfiltration to foreign IPsNo evidence on disk or registryWithout Memory Analysis:❌ No findingsWith Memory Analysis:✅ Identify:Hidden processesIn-memory malwareActive connectionsCredential artifacts🧠 Key TakeawaysMemory is volatile but criticalModern attacks are often filelessRAM contains live evidenceMust capture memory firstAnalysis must be correlated with other forensic domains🚨 Golden RuleDump memory first. Analyze everything else after. You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 41 - Analyzing Attacks for Incident Handlers | Episode 1: Volatile Evidence, Forensic Tools, and Investigation Procedures

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    Course 40 - Web Scraping with Python | Episode 43: Mastering File Uploads and Reverse Image Search

    This episode is about a very specific but powerful capability in scraping:automating file uploads as part of a web interaction workflowIt sits at the intersection of browser automation + data extraction pipelines.📤 Core IdeaSome websites don’t just serve data — they require you to: upload a filetrigger processingthen return resultsSo scraping becomes:“submit file → wait for processing → extract generated output”📌 1. When File Upload Automation Is Needed🧠 Two real use cases:1) Content generation systems upload input file (image, document, dataset)site processes itreturns generated report or resultsExamples: image analysis toolsdocument convertersscientific portals2) Gatekeeping / workflow restriction bypass upload required asset to continue navigation:resumeprofile imageverification fileWithout upload → no access to next page🔥 Key insight:File upload is often a hidden navigation step, not just data input🧭 2. Why Selenium is Required HereNormal HTTP tools (like requests) struggle because: file upload interacts with OS file pickerJavaScript handles upload triggersUI must be “physically simulated”So Selenium is used to mimic real browser behavior.📁 3. The Critical Mechanism: This is the key HTML element: Instead of clicking it and selecting a file manually…Selenium bypasses the dialog entirely.🐍 4. The Core Technique: send_keys()🧠 How it works:You directly send a local file path into the input field.file_input.send_keys("/path/to/image.jpg") 🚨 Important limitation: must be a valid local pathfile picker window is NOT usedSelenium cannot control OS dialogs🔥 Key insight:Upload automation = bypass GUI → inject file path directly into DOM🧪 5. Example Workflow (Reverse Image Search Case)Using a tool like TinEye:Step 1: open pageSelenium loads upload interfaceStep 2: locate file inputFind: element with type="file"Step 3: upload fileUse send_keys(path)Step 4: trigger processingSite automatically starts analysisStep 5: extract resultsNow switch to Beautiful Soup: parse returned HTMLextract:matching sitesimage sourcesmetadata🔄 6. Full Pipeline ArchitectureThis episode is really describing a 3-stage scraping flow:1. Interaction layer (Selenium) upload fileclick buttonstrigger server processing2. Network processing layer (server-side) file analyzedresults generated dynamically3. Extraction layer (Beautiful Soup) parse final HTMLextract structured results⚙️ 7. Why This Pattern MattersThis pattern appears in: reverse image search enginesAI document analyzersresume screening systemsfile validation services🧠 8. Core Concept ShiftThis episode moves you beyond “web scraping” into:automated workflow injectionYou’re no longer just extracting data — you’re: feeding inputs into systemstriggering computationharvesting outputs🔥 Final TakeawayFile upload scraping is about:turning browser-only workflows into programmable pipelinesAnd the key trick is simple but powerful: Selenium handles interactionfile path injection replaces manual upload dialogsBeautiful Soup handles result extraction You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 43: Mastering File Uploads and Reverse Image Search

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    Course 40 - Web Scraping with Python | Episode 42: Web Authentication and Automated Form Input Submission

    This episode is essentially about turning “login-protected websites” into programmable sessions and then controlling full form workflows like a real user.🔐 Core IdeaModern scraping stops being “download HTML” and becomes:“Authenticate → maintain session → interact → extract”This is the foundation of scraping anything behind a login wall.🍪 1. Session Cookies (Staying Logged In)🧠 What they are: Small identifiers stored after loginTell the server: “this is the same user”Without them: every request looks like a new visitorlogin state is lost immediately🐍 How requests handles itYou use a session object:session = requests.Session() Why this matters: cookies persist automaticallyall requests share authentication statemimics a real browser session🔥 Key insight:A session object = a “fake browser memory”🧾 2. CSRF Tokens (Hidden Security Gate)🧠 What they are: random hidden string in login formsprevents fake automated submissionsUsually found in: hidden fieldsform HTML source🕵️ How scraping handles it: Request login pageExtract CSRF token from HTMLInclude it in POST requestExample flow:# Step 1: get page r = session.get(login_url) # Step 2: extract token (XPath / parsing) token = extract_token(r.text) # Step 3: submit login session.post(login_url, data={ "username": "...", "password": "...", "csrf": token }) 🔥 Key insight:CSRF tokens force scrapers to behave like real browsers that “see” the page first🧭 3. Selenium for UI InteractionOnce login flows become JavaScript-heavy or interactive, requests is not enough.So Selenium is used for:real browser simulation🔘 4. Handling Form Controls🔵 Radio Buttons only one option selectableused for choices like gender, type, categoryAction: locate element.click()☑️ Checkboxes multiple selections allowedtoggles true/false stateAction: click to toggle stateoptionally check if already selected📋 Dropdown MenusHandled using Selenium’s Select class:Options: select by visible textselect by value attributeselect by indexExample logic:from selenium.webdriver.support.ui import Select dropdown = Select(element) dropdown.select_by_visible_text("Option A") 🧠 5. Real Login Automation FlowThis episode combines everything into a full pipeline:Step-by-step: Open login page (Selenium or requests)Extract CSRF token (if exists)Fill credentialsSubmit formMaintain session (cookies)Access protected pagesExtract data⚙️ 6. Element Location StrategyTo interact with UI elements, you rely on: ID (best case)XPath (fallback, most powerful)CSS selectors🚨 7. Key Concept ShiftThis episode moves you from:Simple scraping: request pageparse HTMLTo authenticated automation: simulate login flowsmaintain identityinteract with UI controls🔥 Final TakeawayThe real skill here is:reconstructing the entire user authentication lifecycle in codeOnce you can: handle cookiesextract CSRF tokensautomate UI formsYou can access: dashboardsprivate data portalsaccount-based systemsdynamic user contentIf you want, I can next: combine ALL your episodes into a full advanced scraping architecture (professional blueprint)or show a real-world end-to-end system (login → scrape → clean → store → analyze)or design a portfolio-grade Scrapy + Selenium hybrid project for you You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 42: Web Authentication and Automated Form Input Submission

  6. -5 j

    Course 40 - Web Scraping with Python | Episode 41: Mastering GET and POST Form Submissions

    This episode is essentially teaching you how to reverse-engineer web forms into programmatic HTTP requests, which is one of the most important skills in practical scraping.🧭 Core IdeaWeb forms are just structured HTTP requests.So instead of thinking:“I’m filling a form”You should think:“I’m constructing a GET or POST request that mimics what the browser sends”🌐 1. GET Forms (Simple & Scrape-Friendly)🧠 How they work: User input is appended to the URLParameters are visible in the address barExample structure:https://site.com/search?query=batman ✅ Why GET is easy for scrapingBecause you can: copy the URL directlymodify query parameters manuallyreproduce requests with requests.get()🐍 Typical scraping workflow: send GET requestretrieve HTML responseparse with BeautifulSouprequests.get(url, params={...}) 🔥 Key insight:GET forms are basically:“URL-based APIs disguised as search boxes”🔒 2. POST Forms (Hidden & More Complex)🧠 How they work: data is sent inside the request bodynot visible in the URLoften used for:loginsgovernment portalssecure searches🚫 Why POST is harderBecause: parameters are hiddenstructure is not obvious from URLrequires inspecting browser internals🕵️ 3. How to Break Down a POST FormThe episode teaches a key skill:Step 1: Use Developer Tools open Network tabsubmit the form manuallyinspect the request payloadYou extract: form fieldshidden inputsrequest headerspayload structureStep 2: Rebuild request in PythonYou convert the captured form data into:requests.post(url, data={...}) Step 3: Parse responseOnce server returns HTML: use BeautifulSoupextract structured data⚙️ 4. GET vs POST (Critical Comparison)FeatureGETPOSTVisibilityURL visiblehidden bodyEase of scrapingeasymedium–hardUse casessearch, filterslogin, secure formsDebuggingsimplerequires DevToolsReproducibilityvery highmoderate🧠 5. Core Skill You’re LearningThis episode is not really about forms.It’s about:translating human browser actions into raw HTTP requestsOnce you master that, you can scrape: search enginesdashboardsgovernment databaseslogin-protected portals (when permitted)🚨 Important InsightMost “scraping difficulty” is not HTML parsing.It is:understanding how the request is built before HTML even exists🔥 Final TakeawayGET and POST forms are just two ways websites accept input: GET → visible, simple, reusablePOST → hidden, structured, requires inspectionOnce you can replicate both:You can reproduce ~80–90% of real-world web interactions programmatically You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 41: Mastering GET and POST Form Submissions

  7. -6 j

    Course 40 - Web Scraping with Python | Episode 40: Introduction to Advanced Web Scraping: Tools and Tactics

    This episode is essentially about moving from “simple scraping” → “interactive web automation + session-aware extraction”, where websites behave more like applications than static pages.🧠 Core Idea of the CourseStandard scraping fails when websites: require logindepend on session state (cookies)use forms instead of URLsrely on user interaction (buttons, uploads, checkboxes)So the goal becomes:Make your scraper behave like a real user inside a real browser session🔐 1. Core Concepts: Why “Advanced Scraping” is DifferentUnlike basic HTTP scraping, advanced targets introduce state and interaction:Key obstacles: 🔑 Login walls🍪 Session cookies🧾 Form submissions (GET / POST)☑️ UI controls (checkboxes, radio buttons)🧠 JavaScript-driven behavior👉 This turns scraping into web automation engineering, not just parsing.🧭 2. Strategy ShiftInstead of:“Fetch page → parse HTML”You now do:“Simulate a real user → maintain session → interact → extract final state”This introduces 3 critical layers: Network layer (Requests)Session layer (cookies, authentication)Browser layer (Selenium automation)🔧 3. Tools Used in the Course🟢 RequestsUsed for: login requests (when simple)form submissions (POST/GET)session handling with cookies🟡 Beautiful SoupUsed for: parsing returned HTMLextracting structured data after interaction🔵 SeleniumUsed for: full browser automationJavaScript-heavy pagesclicking, scrolling, uploading files📓 Jupyter NotebookUsed for: step-by-step experimentationdebugging scraping logic interactively🔐 4. Key Technical Skills Covered🧾 Form HandlingYou learn to automate: login formssearch formsmulti-field submissionsIncludes: GET vs POST behaviorpayload constructionform field mapping🍪 Cookie ManagementCritical for: staying logged inmaintaining sessionsaccessing personalized contentYou learn: how cookies are createdhow to persist them across requestshow servers use them to identify users☑️ UI Element InteractionAutomation of: checkboxesradio buttonsdropdown menusThis turns scraping into:“simulate human decisions programmatically”📤 File Upload AutomationOne of the most advanced parts:You can automate: image uploadsresume submissionsdocument uploadsUsing Selenium to: locate file input fieldssend file paths directly to browser elements⚙️ 5. Environment SetupBefore anything works, the course ensures:Required installs: requestsbeautifulsoup4seleniumvia pipChromeDriver setup: matches Chrome versionallows Selenium to control browseracts as bridge between script and browser engine🧠 Big Picture ArchitectureThis course is essentially building:A full browser-controlled scraping system with session awarenessPipeline: Selenium opens browserUser-like actions (login, clicks, forms)Cookies/session storedPage becomes personalizedBeautiful Soup extracts final structured data🚨 Key InsightThis is where scraping becomes:not “data extraction” but “web application interaction engineering”🔥 Final TakeawayThe major shift in this episode is:From passive scraping: download HTMLparse contentTo active automation: behave like a usermaintain identity (cookies)interact with UIextract final state You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 40: Introduction to Advanced Web Scraping: Tools and Tactics

  8. 19 août

    Course 40 - Web Scraping with Python | Episode 39: Overcoming Challenges and Optimizing Performance

    This module is essentially the “real world survival guide” for web scraping — it moves away from pure tooling and focuses on what actually breaks scrapers in production and how to behave responsibly while scraping at scale.🚧 1. Real-World Scraping ProblemsModern websites actively defend themselves against automation, so scraping is rarely “just code and go”.🚫 Bot RestrictionsWebsites may block automated traffic using: User-agent detection (recognizing Selenium / bots)Behavioral analysis (click speed, navigation patterns)🧩 CAPTCHAsA major anti-bot mechanism: Designed to distinguish humans from automationOften blocks login pages, search pages, or high-value data🌐 IP BlockingIf you: send too many requestsscrape too fastignore rate limitsThen servers may: temporarily block your IPpermanently blacklist it🕳️ HoneypotsHidden traps inside websites: invisible linksfake endpointsnon-visible HTML elements👉 If your bot clicks them, it gets flagged instantly.🔄 Dynamic Structure ChangesWebsites constantly evolve: HTML layouts changeclass names get renamedelements move or get removedThis causes:Scrapers to break without warning♾️ Infinite ScrollingInstead of pages, content loads as you scroll: requires scroll automationrequires dynamic request handlingoften tied to JavaScript APIs🧪 2. Data Quality & ReliabilityScraping is not just about collecting data — it’s about ensuring it’s usable later.Recommended practice: build test cases for scraped outputvalidate structure before savingensure consistency across runsWhy? Because bad scraped data can: corrupt datasetsbreak ML pipelinesproduce misleading analytics⚡ 3. Performance Optimization TechniquesThe module introduces practical speed improvements:🖼️ Disable Images prevents browser from loading heavy assetsdrastically reduces page load time💾 Browser Caching reuse previously loaded assetsavoids redundant downloads🧠 Headless BrowsersRun Chrome without UI: faster executionlower memory usageideal for automation servers🧹 Proper Resource CleanupImportant rule: driver.quit() → closes everything (safe cleanup)driver.close() → closes only current tab👉 Not quitting properly can leak memory and processes.⚖️ 4. Ethical Scraping GuidelinesThis is the most important conceptual layer.📄 robots.txt compliance defines what bots are allowed to accessignoring it can violate site rules or laws🧠 Rate limiting (be a “polite bot”) avoid rapid-fire requestsprevent server overload🕒 Off-peak scraping run jobs during low traffic hoursreduces impact on real users🎭 Transparency principleA “good bot” should: not disguise malicious intentnot impersonate real usersbehave predictably and responsibly🧠 Core Philosophy of the ModuleScraping is not just a technical task — it’s a system interaction problem with ethical constraintsSo you need three layers: Technical robustness (avoid breaks)Performance efficiency (don’t waste resources)Ethical compliance (don’t abuse systems)🔥 Final TakeawayModern scraping isn’t about “how to extract data” anymore.It’s about:how to extract data without breaking systems, getting blocked, or violating rules You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 39: Overcoming Challenges and Optimizing Performance
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À propos

Welcome to CyberCode Academy — your audio classroom for Programming and Cybersecurity. 🎧 Each course is divided into a series of short, focused episodes that take you from beginner to advanced level — one lesson at a time. From Python and web development to ethical hacking and digital defense, our content transforms complex concepts into simple, engaging audio learning. Study anywhere, anytime — and level up your skills with CyberCode Academy. 🚀 Learn. Code. Secure. You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

Informations

  • Création
    CyberCode Academy
  • Années d’activité
    2025 - 2026
  • Épisodes
    340
  • Classification
    Tous publics
  • Copyright
    © Copyright CyberCode Academy
  • Site web de l’émission
    CyberCode Academy

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