aiCode.fail
FreemiumCatch AI-generated code hallucinations, vulnerabilities, and logical errors before shipping.
By Tanmay Verma, Founder · Last verified 03 Jul 2026
0 views
Added 6d ago
77/100Safe BetIn short
aiCode.fail — Catch AI-generated code hallucinations, vulnerabilities, and logical errors before shipping. Best for Developers using AI code assistants who want a safety net, Engineering teams adopting AI-generated code in production, Open-source maintainers ensuring AI-contributed PRs are safe. Free to start; paid plans from $29/mo.
Is aiCode.fail actually worth it?
LiveSee what real users actually say. We scan live discussions, reviews and complaints across the web and hand you an honest verdict — in under a minute.
3 free scans · no card needed · downloadable report
Editorial Verdict
Best for
Developers using AI code assistants who want a safety netEngineering teams adopting AI-generated code in productionOpen-source maintainers ensuring AI-contributed PRs are safeSecurity-focused teams vetting AI-generated patches
Not ideal for
Non-AI-generated code (use standard linters instead)Teams needing runtime or dynamic analysis (static only)Single-developer projects with very low AI usage (overkill)Teams already using enterprise-grade AST-based analysis like Semgrep or CodeQL
aiCode.fill fills a genuine niche: catching AI-generated code hallucinations that traditional linters miss. It's lightweight and easy to integrate (especially with GitHub Actions). However, its language support is limited (Python, JS, Java, Go) and the free tier is restricted to 100 checks/month and open-source only. For teams heavily relying on AI code assistants, it's a worthwhile safety net — but consider CodeQL or Semgrep for broader static analysis. Recommended as a complement, not a replacement.
Skip aiCode.fail if Skip aiCode.fail if you don't use AI code assistants (like GitHub Copilot or ChatGPT) or if you need runtime/dynamic analysis rather than static checks.
Compare with: aiCode.fail vs Bito, aiCode.fail vs LangSmith, aiCode.fail vs Chrome DevTools MCP
Last verified: July 2026
What independent users actually report about aiCode.fail
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
7 mentions across 1 source (Product Hunt).
85% positive15% critical
Recurring strengths
- +Targets AI-specific failure modes like hallucinated functions and fake packages.
- +Catches security vulnerabilities before code ships.
- +Integrates into CI pipelines and pulls requests.
- +Free tier available for open-source projects.
- +Developer is active and responsive on launch day.
Recurring frustrations
- −Very limited community feedback—only launch day data available.
- −No real-world reviews on false positives or false negatives.
- −Integration with non-GitHub/GitLab platforms not validated.
- −On-prem deployment availability unconfirmed.
- −Long-term reliability and performance unknown.
Patterns worth knowing
Timely solution for AI-generated code hallucinations
Seen on Product Hunt
Appeal to no-code developers relying on GPTs
Seen on Product Hunt
Curiosity about comparison to traditional code review
Seen on Product Hunt
Learning curve
beginnerProductive in ~A few hours
Hidden costs people mention
- • No pricing page found; exact tier features and costs not publicly documented.
- • On-prem deployment likely requires enterprise plan with custom pricing.
Viability Score
77/100
Safe BetHow likely is aiCode.fail to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Detect AI hallucinated functions and libraries
- Flag package name plausibility issues
- Identify security vulnerabilities in generated code
- Verify type signatures and argument mismatches
- Check for null dereference and off-by-one errors
- GitHub and GitLab pull request integration
- CI pipeline integration (GitHub Actions, GitLab CI, Jenkins)
- Local CLI for pre-commit hooks
- Web dashboard for review history
- Custom rule and pattern definitions (Team plan)
- Support for Python, JavaScript, Java, Go
- Slack and Microsoft Teams notifications
- Single-tenant deployment option (Team plan)
About aiCode.fail
FreemiumIntermediateAPI availableWeb · CLI · Plugin
aiCode.fail is a specialized static analysis and validation tool that scans AI-generated code for common failure modes like hallucinated functions, package name plausibility issues, security vulnerabilities, and type mismatches. It integrates into your CI pipeline (GitHub Actions, GitLab CI, Jenkins) and supports GitHub and GitLab pull requests, automatically flagging suspicious code from AI assistants like GitHub Copilot, ChatGPT, or Claude. The tool runs a static check engine that verifies function names, package existence, type signatures, and known vulnerability patterns — going beyond general linting to address AI-specific errors. Available as a free tier for open-source projects, with paid plans (Pro and Team) offering more checks, integrations, and custom rule sets. Supports Python, JavaScript, Java, and Go, with a web dashboard and CLI for local pre-commit hooks.
Behind the Verdict
aiCode.fail addresses a real and growing problem: AI-generated code that looks plausible but contains hallucinated packages, incorrect function names, or subtle bugs. The tool's focus on AI-specific failure modes makes it unique compared to general linters like ESLint or Pylint. Its integrations with GitHub/GitLab pull requests and CI pipelines (GitHub Actions, GitLab CI, Jenkins) mean minimal friction for teams already using those tools. The CLI also allows pre-commit hooks. The pricing is reasonable: a free tier for open-source (100 checks/month), Pro at $29/month (500 checks, CI integration), and Team at $99/month (unlimited checks, custom rules, dedicated support). The Team plan's single-tenant deployment option is a plus for security-conscious enterprises. However, significant limitations remain: only four languages are supported (Python, JavaScript, Java, Go), and there's no runtime analysis. Custom rules are locked to the Team plan, which may be a sticking point for smaller teams that need domain-specific checks. Also, the tool doesn't yet support TypeScript or C++, which are common in AI-generated code. For pure static analysis of AI code, it's a solid choice, but you'll still need other tools for non-AI code and deeper security scanning. Overall, it's a pragmatic safety net for teams embracing AI coding assistants.
Researching aiCode.fail? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas aiCode.fail actually fits — and what changes day-one when you adopt it.
Freelance developer using GitHub Copilot
You generate a Python function using Copilot for a client project. Before committing, you run the aiCode.fail CLI as a pre-commit hook. It flags a hallucinated package 'pandas_utils' that doesn't exist on PyPI, saving you from a failing pipeline.
Outcome: You catch the hallucinated import locally, fix it, and commit with confidence.
Engineering team lead at a startup
Your team uses ChatGPT to generate JavaScript code for a new feature. You configure aiCode.fail's GitHub Action to scan all PRs. A PR includes a function that uses a nonexistent npm package, which the action flags with a warning. The developer corrects it before merging.
Outcome: Your team avoids shipping code with broken dependencies, reducing CI failures and runtime errors.
Use Cases
- Scan AI-generated code in pull requests to catch hallucinated imports before merging
- Run as a pre-commit hook to prevent shipping code with known vulnerability patterns
- Validate code from ChatGPT or Claude doesn't use non-existent libraries
- Integrate into CI pipeline to automatically flag suspicious AI-generated functions
- Review batch-generated code for type mismatches and argument order errors
- Enforce security best practices on AI-generated code blocks without manual review
- Check for null pointer dereferences and off-by-one errors in AI-generated patches
Limitations
- Free tier limited to 100 checks/month and open-source projects only.
- The tool currently supports only four languages (Python, JavaScript, Java, Go).
- No runtime analysis — purely static.
- Custom rule definitions are only available on the Team plan.
- No TypeScript or C++ support, which may be limiting for many AI code users.
as of 2026-07-03
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published aiCode.fail tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/month
Ideal for
Open-source maintainers and hobbyists who occasionally check AI-generated code contributions.
What this tier adds
Starting tier: 100 checks/month, open-source projects only, web and CLI access, community support.
Pro
$29/month
Ideal for
Individual developers and small teams using AI assistants who need CI integration and more checks.
What this tier adds
Adds 500 checks/month, CI integration (GitHub/GitLab), and priority email support over Free.
Team
$99/month
Ideal for
Engineering teams with heavy AI code generation needing unlimited checks, custom rules, and dedicated support.
What this tier adds
Unlimited checks, custom rule sets, Slack/MS Teams notifications, dedicated support, and single-tenant deployment over Pro.
Integrations
Where the pricing makes sense
The company stage and team size where aiCode.fail's pricing actually pencils out — and where peers do it cheaper.
At $29/month for the Pro tier (500 checks, CI integration), aiCode.fail is affordable for small teams and solo developers already using AI assistants. The Team plan at $99/month offers unlimited checks and custom rules, which is competitive with broader static analysis tools (e.g., Semgrep Team starts higher). However, the free tier's 100 check limit and open-source restriction may feel stingy compared to completely free tools like TFLint. Overall, it's reasonably priced for the niche it serves.
Setup time & first value
How long it actually takes to get something useful out of aiCode.fail — broken out by persona, not the marketing-page minute.
For individuals: install the CLI via pip or npm and add a pre-commit hook — about 10 minutes. For teams: add the GitHub Action or GitLab CI template to your repo (5 minutes) and configure the webhook — under 30 minutes for an initial scan. The web dashboard requires no setup.
Switching to or from aiCode.fail
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From manual review: simply add the CLI to your pre-commit hooks and the GitHub Action to your CI; no code changes needed.
- →From other static analysis (e.g., SonarQube): run aiCode.fail alongside, focusing only on AI-generated code files.
- →From no tool: install the GitHub app and enable it on your repos — zero migration effort.
Migrating out
- ↗To Semgrep: export your custom rule definitions (if any) as Semgrep rules and disable aiCode.fail in CI.
- ↗To CodeQL: rewrite your custom patterns as CodeQL queries and remove aiCode.fail from your pipeline.
Resources & Guides
Frequently Asked Questions
Tools that pair well with aiCode.fail
Common stack mates teams adopt alongside aiCode.fail, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Alternatives to aiCode.fail
View allUsed aiCode.fail? Help shape our editorial sentiment research.