DEV Community: sumit2401

I Used AI for Code Review on a Production ERP for 6 Months. Here's Where It Actually Failed Me.

sumit2401 — Wed, 20 May 2026 11:42:55 +0000

Six months ago I started running every non-trivial piece of code through AI before it shipped. Not prototypes — real ERP and CRM modules with paying clients on the other end. Batch processing tables, MRP allocation logic, dynamic invoice builders, real-time dashboards.

Here's what I found out.

What AI is actually good at

Duplicate functions. In a codebase that's been touched by multiple devs over months, this is AI's most reliable win. It flagged things like: "This mirrors formatCurrency in utils/formatters.ts" — the kind of thing that slips through human review because everyone assumes someone else already checked.

Calculation bugs in self-contained utilities. Off-by-one errors, wrong operator precedence, bad unit conversions — if the function is isolated and doesn't depend on external state, AI catches these consistently. In ERP systems, a broken landed cost formula or tax calculation is instant client trust damage. AI has saved me here more than once.

Direct React state mutations. Pushing directly to an array, mutating nested objects without spreading — AI flags these reliably in simple components. Not groundbreaking, but useful as a first pass before compilation.

Where it completely fell apart

Race conditions. This was my most painful production bug in the last 6 months.

Our batch item table allows rapid concurrent mutations — user triggers cascading calculations, multiple async state updates overlap, one override silently wins. Classic race condition.

I ran the component through every AI tool I was using at the time. Zero flags. Not even a "this might be worth checking." The bug only surfaced when a client hit a specific sequence of interactions in rapid succession.

AI does static analysis. It cannot simulate erratic user interaction timing. Race conditions are invisible to it.

Web Worker memory leaks. I implemented workers for heavy client-side calculations. Suspicious of potential leaks from rapid event-driven spawning, I tasked my entire AI stack with auditing the cleanup patterns.

Every tool gave a confident green pass.

My manual browser profiling found that workers weren't reliably terminating under specific runtime exceptions. Ghost processes, staying alive. The AI verified the cleanup code existed. It could not verify the cleanup actually ran across every execution path.

CSS layout bugs on custom components. Built a proprietary data grid from scratch to hit strict UX requirements. Ran into padding misalignments and layout collapse under certain data payloads.

AI was almost useless. I'd describe the visual bug, it would claim to fix it, the rendered output would still be broken. Without a real rendering environment, it's guessing at cascade behavior.

The thing nobody talks about: chunking

This was my biggest operational discovery.

I was building an MRP allocation table — quantities, supply constraints, fulfillment priorities cascading across hundreds of rows. I fed the full spec into the AI in a single pass.

Every tool failed. Not "slightly off" — confidently wrong logic that looked correct until you traced execution. Broken dependencies, misallocated quantities, state updates that would silently fail on specific edge cases.

Then I split it:

One prompt — just the core allocation math
One prompt — data validation constraints only
One prompt — the immutable React state update pattern
Final prompt — audit each module against the others

Every piece came back clean. Assembled system worked perfectly in production.

The mental model shift: AI degrades under compound logical dependencies, not under token length. An ERP module has overlapping validation paths, tax calculations, database state requirements. Feeding all of it simultaneously overloads the model's situational logic.

If you can't explain the task scope to a senior dev in 2 minutes out loud, chunk it before it touches an LLM.

The confidence problem

This is the part that actually concerns me.

When my Web Worker had a memory leak, no tool said: "This syntax looks valid, but I can't verify runtime cleanup behavior — profile this manually." They said it looked fine. Clean pass. Ship it.

The framing I've settled on:

An AI bug flag is high-signal — act on it immediately.
An AI clean pass is weak evidence — it means no obvious patterns matched, not that the code is safe.

The deadline trap is real. When you're pushing to meet a sprint, "the AI said it's fine" becomes a substitute for actual testing. That's where production regressions come from.

My actual tool stack after 6 months

Codeium — inline autocomplete for single-file work. Hits a ceiling fast on cross-file reasoning.
ChatGPT — useful for isolated logic audits, but snippet output creates integration friction at scale.
Cursor (Claude Sonnet) — my go-to for refactoring. Full-file editing context is genuinely better than any chat interface.
Google Gemini Code Assist — primary daily driver. Large context window, cost-efficient for heavy ERP work.
Claude direct — architectural audits and high-level logic design. Most willing to flag issues with caveats instead of blind passes.
OpenAI Codex App — pre-PR repository-wide audits in sandboxed worktrees. Runs parallel agents without touching my local environment.

Each tool serves a distinct step. Switching between them isn't tool-hopping — it's using the right instrument for the job.

Quick reference

Trust AI for:

Utility functions under ~50 lines with clear I/O
Catching duplicate/redundant modules across large repos
Refactoring known-safe logic into cleaner patterns
Boilerplate React hooks, contexts, basic reducers

Verify manually:

Multi-tiered stateful components with rapid user interactions
Web Workers, Sockets, async lifecycle management
Custom visual components (non-library)
Core financial/billing calculations
Anything you can't explain to a teammate in 2 minutes

Chunk before sending:

Features spanning more than 2 interacting subsystems
Full ERP modules with cascading state (MRP, inventory allocation)
Any prompt that needs multiple paragraphs just to define the constraints

The engineers getting the most out of AI aren't the ones who trust it blindly. They're the ones who know exactly where that trust stops.

Full breakdown with the complete MRP case study, tool-by-tool notes, and a practical checklist here → stacknovahq.com

I Cancelled My Cursor Pro+ Subscription. Here's What I Switched To (And Saved $45/Month)

sumit2401 — Fri, 15 May 2026 06:33:01 +0000

If you've used Cursor for any length of time in 2025–2026, you've felt the squeeze.

It started in June 2025 with Cursor's shift from flat-rate fast requests to dollar-denominated credit pools. The Pro plan stayed at $20/month but now includes only a $20 credit pool — and a single Composer session running Claude Opus on a large monorepo can burn $5–$10 in one task.

I was a happy Cursor Pro subscriber for 12 months. Then my real monthly cost crept to $40, then $60. By January 2026, I quietly migrated to a mix of Claude Code and Windsurf, and my AI coding spend dropped to $20–$35/month flat — with arguably better output.

This post covers the 7 mainstream Cursor IDE alternatives I tested in production over 4 months on real client work.

What actually changed with Cursor's pricing

Current Cursor tiers (verified May 2026):

Hobby (Free): 2,000 completions, 50 slow requests/month
Pro ($20/month): $20 credit pool
Pro+ ($60/month): 3x usage limits
Ultra ($200/month): 20x usage limits
MAX mode: +20% surcharge on every run

The structural shift: Cursor went from "a developer tool" to "infrastructure spend" overnight. Developers now act as financial controllers — deciding whether a specific bug is "worth" the cost of an Opus-tier reasoning agent.

The 7 alternatives I tested

1. Windsurf (formerly Codeium) — $15/month

The predictable Cursor replacement. Same VS Code fork model, 84% multi-file refactor success rate, 1M-token context included as standard (no MAX mode equivalent surcharge). Ships plugins for 40+ IDEs including JetBrains and Vim — no editor migration tax.

Best for: Cursor refugees who want predictable billing.

2. Claude Code — $20/month flat

Anthropic's own coding agent. Scores 80.9% on SWE-bench Verified — the highest of any tool in this comparison. Included in Claude Pro ($20/month) and Max plans rather than billed per token. Terminal-first with VS Code and JetBrains plugins.

For heavy Cursor users whose workflow is dominated by Claude Sonnet/Opus calls, Claude Code at $20/month replaces a Cursor Pro+ plan that's effectively $60/month at the same usage level.

Best for: Heavy Claude users on Cursor Pro+/Ultra.

3. GitHub Copilot — $10/month

The cheapest mainstream paid option. Multi-model selector (Claude, GPT, Gemini), mature integrations with VS Code/JetBrains/Visual Studio/Neovim. Agent Mode and Copilot Workspace closed part of the gap on agentic capabilities in 2025.

Best for: Autocomplete-heavy workflows, GitHub-native teams.

4. Google Antigravity — FREE

Google's late-2025 entry. Free preview for personal Gmail accounts with generous Gemini 3 Pro rate limits. Agent-first design with the Agent Manager surface — agents produce verifiable Artifacts (task lists, plans, browser recordings).

Best for: Students, hobbyists, side-project builders.

5. Trae (ByteDance) — $10/month

VS Code-based IDE with the SOLO autonomous agent. 5,000 completions on free tier. The caveat: ByteDance ownership introduces geopolitical and data-policy considerations. Not appropriate for regulated codebases.

Best for: Individual devs without enterprise vendor restrictions.

6. Kiro (Amazon) — $19/month

Spec-driven development workflow on Code OSS foundation. Deep AWS integration — CloudWatch log analysis, CDK scaffolding, Lambda debugging, IAM policy generation.

Best for: AWS-heavy teams.

7. OpenAI Codex — Free if you have ChatGPT Plus

The most underused alternative. Bundled into ChatGPT Plus ($20/month) and ChatGPT Pro ($200/month). If you already pay for ChatGPT, you already have a capable Cursor alternative.

Scores ~80% on SWE-bench Verified, tied with Claude Code.

The SWE-bench numbers nobody talks about

Tool	SWE-bench Verified
Claude Code	80.9%
OpenAI Codex	~80%
Cursor	~71%
Windsurf	~68%

Cursor's edge is UX polish and ecosystem maturity, not raw agent accuracy.

The hidden cost most comparisons miss

Context window economics.

Cursor: ~200K tokens via RAG retrieval + MAX mode (+20% surcharge per run)
Claude Code / Windsurf / Codex cloud: 1M tokens as standard

On a 40k-line ERP codebase refactor I ran, Cursor required 3 iterations because RAG retrieval missed a utility module. Claude Code completed it in one pass.

My recommendation

On Cursor Pro+ ($60) or Ultra ($200)? → Claude Code on Claude Pro/Max
On Cursor Pro burning credits? → Windsurf at $15/month
Autocomplete-heavy? → GitHub Copilot at $10/month
Cost-constrained? → Google Antigravity (free)
Already on ChatGPT Plus? → Codex CLI (you already have it)

Full breakdown

This is a condensed version. I wrote a full 5,800-word breakdown with detailed pricing tables, real production testing notes, and a decision tree:

👉 Read the full guide on StackNova HQ

What are you using right now? Drop your AI coding stack in the comments.

AI Hallucinations Aren't Random — They're Predictable: A 2026 Case Study

sumit2401 — Sat, 18 Apr 2026 14:13:49 +0000

Most developers I know treat AI hallucination as a mysterious bug — something that happens randomly and unpredictably.

It's not. It's a completely mechanical failure with a predictable trigger.

Here's what I found after running 40+ structured tests across ChatGPT, Claude, and Gemini in 2026.

The core mechanic you need to understand

Every LLM has a knowledge cutoff — a hard date when training data was frozen. Here are the current dates for the three major models:

Gemini (base): January 2025
ChatGPT (GPT-4.5/5 class): August 2025
Claude (3.5/4 class): August 2025

Anything after that date doesn't exist in the model's memory. Zero. Not a fuzzy boundary — binary.

The problem: models don't behave like they have a gap. They generate fluent, confident text regardless of whether they have real data or not.

What I actually tested

I took a verified real-world event from March 2026 — an enterprise tech acquisition — and asked all three models to summarize it with web search disabled.

Claude: Refused cleanly. Exact response: "I don't have information about events after early August 2025. I cannot confirm or summarize this acquisition."

ChatGPT: Didn't refuse. Produced a 3-paragraph summary mixing real pre-cutoff industry rumors with implied post-cutoff outcomes. A careless reader would think it was factual.

Gemini: The most dangerous output. With 14 months of missing context, it generated a complete narrative — invented a $4.2B deal value, fabricated a CEO quote, described fictional EU regulatory hurdles, and named an antitrust commissioner who doesn't exist. ~400 words. Perfect AP style. Entirely fictional.

The pattern I haven't seen documented elsewhere

After 40+ structured tests, I noticed something: hallucination severity scales proportionally with the size of the data gap.

1-2 months past cutoff: Hedged responses, mild fabrications, easier to catch
3-6 months past cutoff: Moderate confidence, subtle errors mixed with real information
6+ months past cutoff: Full narratives, high confidence, specific invented details, authoritative tone

The practical implication: the more confidently a model answers a recent-events question, the more aggressively you should fact-check it. Confidence and accuracy are inversely correlated in post-cutoff queries.

The four highest-risk categories

Based on production content work across SaaS, fintech, and e-commerce clients, these four categories account for ~80% of caught hallucinations:

Proper names — people, companies, organizations
Specific dates — appointment dates, announcement dates, filing dates
Financial figures — deal values, market caps, revenue numbers
URLs — fabricated source links that look real

Every editorial workflow should have an explicit check for these four.

A practical verification workflow

This is what my team runs on every AI-assisted article before publish:

Date-check every claim — if the event date falls after the model's cutoff, flag for manual verification regardless of how confident the output reads
Source-inject, don't source-request — paste actual source material into the prompt and use "Based ONLY on the following text..." rather than asking the model to find sources
Cross-model validation — if one model refuses and another provides confident details, treat the confident response as suspect
Four-category spot-check — mandatory human review of all proper names, dates, financial figures, and URLs

Why Gemini specifically is a different problem

Gemini's January 2025 cutoff puts it 15+ months behind the present. Google compensated by building live Google Search grounding into Gemini's default behavior. That helps — but it shifts the accuracy problem from training data to whatever currently ranks on Google.

If your competitor's SEO-optimized blog post with outdated pricing ranks #1 for a query, Gemini will repeat that information as fact.

SEO implication: your content is now training material for live AI answer systems. Factual errors in your content get amplified across thousands of AI-generated answers at scale.

Full case study with both test scenarios, the complete verification workflow, and the hallucination severity pattern analysis:

AI Knowledge Cutoff vs Hallucination: Case Study 2026 →

Originally published on StackNova

Google Antigravity 'High Traffic' Error (April 2026): The Rollback Fix Is Dead — Here's the Truth

sumit2401 — Wed, 15 Apr 2026 08:27:15 +0000

Google Antigravity is showing this to millions of users right now:

"Our servers are experiencing high traffic right now, please try again in a minute."

And it's not temporary. It's not your internet. It's not a bug you can fix.
Here's what's actually going on.

The error means your request is rejected before it even enters the queue.
This isn't a timeout. The backend is at capacity and actively shedding load. Your request never gets processed — it gets dropped at the door.
Every plan is affected equally.
Free. Pro. Ultra. Doesn't matter. There is no priority queue. Paying more does not move you up the line.
The rollback fix everyone is sharing? It's dead.
Between January and March 2026, users found that uninstalling Antigravity and installing an older version bypassed the error. It worked because older clients were hitting slightly different API endpoints with different rate limit configs.
Google patched it.
All versions — old and new — now route to the same backend. The version of the app you run is completely irrelevant to this error.

Why every "fix" fails
Reinstall the app — same client, same overloaded backend.
Install older version — endpoints are now unified. Rollback window is closed.
Use a VPN — different IP, same full queue.
Clear cache — cache has nothing to do with server capacity.
Switch accounts — your account isn't the bottleneck.
Change network — same destination server.
If any video or blog says "this 100% works" — check the date. If it's after April 2026, it's wrong.

You cannot fix this. Here's why.
Server capacity cannot be increased by any user action.
No app version. No network setting. No account config. None of it provisions more backend compute. The only entity that can fix this is Google's infrastructure team.
Any guide claiming a fix right now is either outdated, mistaken, or clickbait.

What you can actually do

Try off-peak hours — late night IST or early morning UTC sees better success rates
Stop hammering retry — rapid retries may trigger rate limiting and make it worse
Switch tools for urgent work — Claude, ChatGPT, Gemini Advanced, or Cursor cover most use cases
Monitor status — StatusGator Antigravity page

Should you wait or move on?
That's the real question. If your work is deadline-dependent, don't wait on an unfixed server. Migrate the task now.
If it's exploratory work, off-peak usage might get you through. But there's no ETA from Google.
I did a full breakdown covering:

Exact technical cause (why the queue saturates)
The complete rollback timeline — when it worked, when it died
Why Pro/Ultra users aren't getting priority
Best alternatives by use case
What to monitor for resolution signals

Full breakdown in StackNovaHQ

How AI Changed My SEO Workflow in 2026 (Google + AEO + GEO)

sumit2401 — Mon, 13 Apr 2026 03:27:29 +0000

Search in 2026 isn't one channel anymore — it's three:

Google organic (still matters)
AEO — Answer Engine Optimization (Google AI Overviews, featured snippets)
GEO — Generative Engine Optimization (ChatGPT, Perplexity citations)

Most dev blogs and SEO teams I've seen are still running a 2022 workflow. One search bar, one keyword tool, one content calendar. That worked. It doesn't anymore.

What actually shifted
A stat that changed how I think about this:

The overlap between top Google-ranking URLs and AI-cited sources has dropped from ~70% to below 20% in early 2026.

Ranking #1 on Google no longer means you're in the AI answer. These are now two separate competitions with different rules.
And AI Overviews now appear in roughly 16–30% of all searches — meaning a lot of your target audience is getting zero-click answers before they ever reach your content.

The workflow that actually works
The stack I've been testing:

Perplexity → Research + competitive AI answer audit
Claude → Content structure + E-E-A-T framing
ChatGPT → AEO FAQ generation
Otterly / LLMrefs → Track how often you're being cited in AI answers

The key insight: write for human intent first, then structure for AI extraction, then build citation authority through distribution.

Why I wrote a deep-dive on this
I put together a full breakdown of this unified workflow — including the actual day-by-day team rhythm, schema tips, and what "GEO authority" really means in practice:
👉 AI Productivity Workflows for SEO Teams in 2026 — StackNova
It's ~18 min read but covers specifics most "2026 SEO" posts skip — like why strong traditional SEO still feeds GEO, and how to measure AI citation share alongside organic clicks.

Would love to hear how other devs/content teams are handling this shift. Are you tracking AI citations at all yet?

Claude Mythos Preview: The AI That Can Hack Everything (And Why You Can't Use It)

sumit2401 — Fri, 10 Apr 2026 10:56:51 +0000

Anthropic just published a 244-page system card for a model they have
zero intention of releasing publicly.

The model is called Claude Mythos Preview. And the reason you can't
use it isn't pricing or performance — it's because they believe it's
too dangerous.

Here's what it actually did in testing:

Found a 27-year-old bug in OpenBSD — autonomously, in hours.
OpenBSD is one of the most security-hardened OS projects in existence.
Security researchers reviewed its code for nearly 3 decades. Mythos
found a remote crash vulnerability without human steering.

Found a 16-year-old bug in FFmpeg.
Automated tools had hit this codebase 5 million times. Nobody caught it.

Chained multiple Linux kernel zero-days to escalate from a normal
user to full machine control.

Anthropic's own Red Team researcher said:
"I've found more bugs in the last couple of weeks than I found in
the rest of my life combined."

What is Project Glasswing?

Instead of releasing Mythos publicly, Anthropic launched Project
Glasswing — giving access only to 12 organizations (AWS, Apple,
Google, Microsoft, CrowdStrike etc.) for defensive security work.

They've committed $100M in usage credits for this initiative.

Should developers be worried about their jobs?

That's the real question. Mythos isn't a narrow security tool —
it's a general-purpose model that happens to be extraordinary at
finding vulnerabilities.

I did a full breakdown covering:

Exact benchmark scores (CyberGym: 83.1% vs Opus 4.6's 66.6%)
Why the capability gap matters
What this means for security engineers
Honest pros AND cons — including the alignment risks

Full analysis here:
https://stacknovahq.com/claude-mythos-preview-glasswing-cybersecurity

What do you think — is Anthropic making the right call by not
releasing this publicly?