Playbooks for the bugs modern software teams actually ship, especially when AI accelerates the diff. Every article shows the manual fix and the Securie run that makes it continuous: prove the exploit, open the fix PR, retest, gate deploys, and keep the evidence.
Your coding AI can explain risk. Securie is the independent security engineer that proves the exploit, writes the tested repair, blocks unsafe deploys, and gives you evidence customers can trust.
From a growing sample of publicly-reachable Supabase projects we've audited, the same seven mistakes come up every time: RLS off on at least one table, service-role key in the client, missing tenant scoping, default-allow policies, no policies on storage buckets, exposed JWT secret, and over-broad anon-role grants. Fixes for each.
We ran 500 authentication-related prompts against Claude Opus 4.7, GPT-5.4, Gemini 2.5, and DeepSeek V3.2. 92% of the generated code had at least one security bug. Here is the catalog of the top seven recurring mistakes.
Moltbook leaked 1.5 million API keys, 35,000 emails, and 4,060 private messages in 72 hours. Wiz's disclosure showed the root cause: a single Supabase table without row-level security. Here is the timeline, the exact bug, and the ten-minute hardening walkthrough for your own app.
The Next.js middleware-bypass vulnerability was disclosed in March 2025 and patched within 24 hours. One year later, forty percent of public Next.js apps are still running vulnerable versions. Here is why, and the two-minute check to run on yours.
We reran the 2025 study against Claude Opus 4.7, GPT-5.4, Gemini 2.5, and DeepSeek V3.2. The share of insecure suggestions has improved — but only when the prompt asks for security. The prompts that reliably produce safer code are short and we have them in this post.
Request access to Securie, the AI codebase maintenance engineer for business-critical software. Public OSS starts free, private repos start at $39/month with Starter, and tested repairs, evidence, and Business+ on-demand replay scale from there.
Every major study in the last twelve months has measured the same thing: 40 to 62 percent of code produced by modern AI assistants contains a real security vulnerability. Here is what that looks like in practice, and why traditional SAST tools miss most of it.
A look at Securie's managed review architecture: specialist routing, bounded escalation, sandbox verification, and evidence signing.
It's 3 AM. You scrolled X and saw a tweet about a Lovable / Bolt / v0 app leaking customer data. You start wondering if yours is next. Here is the exact checklist to run in the next 30 minutes — what to check, what to fix first, and how to stop having this problem.
A solo founder's API key got scraped from a public commit and used to run gpt-4 calls for two days before they noticed. Total damage: $4,217. Here is the postmortem — how the key leaked, how to detect this, and how to prevent it from happening to you.
A prospect just emailed asking 'is your app secure?' You don't have a real answer. Here is the honest playbook — what to say, what evidence to point at, and how to turn this question from a deal-stopper into a deal-accelerator. Written for solo founders who don't want to lie.
Every AI-generated Next.js app ships with middleware.ts that looks like it gates admin routes. Half of them do not actually run on the routes they think they run on. Here is the 5-minute test, the canonical bugs, and the fixes — written for solo founders who do not want to read the matcher RFC.
Cursor wrote your authentication code. It compiled, it works, you shipped it. But you haven't actually checked whether it's secure. Here is the 30-minute audit checklist — six bugs to look for, three commands to run, and the one tool that does this on every PR forever.
You're about to ship the app you built with Lovable, Bolt, v0, Cursor, or Replit. Before you press deploy, run this 60-minute checklist — 12 items that catch the bugs that actually leak data on launch day. Written for solo founders who don't want to learn security.
If you're starting a project today and need to pick one AI coding tool, the right answer depends on three things: what you're building, how technical you are, and what you'll do once it's shipped. Here is the honest breakdown across Cursor, Lovable, Bolt, and v0 — what each is best at, what each gets wrong, and the tool-by-tool tradeoffs nobody tells you up front.
You built it. It works. You're about to launch. Here are the 14 things solo founders most often get wrong on launch day — from forgetting to set spending limits to shipping with a default Cursor secret in source. The honest playbook for shipping an AI-built app in 2026.
Supabase and Firebase are the two backend defaults for AI-built apps. Here is the honest comparison — what each is best at, where each one's bugs hurt most, and which one to pick for your specific stack.
If you're a solo founder budgeting for your first AI-built SaaS, this is the honest cost breakdown — every line item, every free tier, every gotcha that turns a $50/month plan into a $1,200 surprise. Written for the moment before you pick your stack.
Most auth tutorials show you how to add a login button. This is the guide that shows you how to add auth that actually works — what to wire up, what AI tools get wrong, and the bugs you ship if you copy-paste the first Stack Overflow answer.
Adding Stripe to a Next.js app is a 30-minute task. Doing it without shipping a webhook-bypass bug, a leaked secret key, or an unsigned-event vulnerability takes another 30 minutes. Here is the real walkthrough.
If you're picking where to host your AI-built app, the three big choices are Vercel, Netlify, and Cloudflare Pages. Here is the honest breakdown — pricing, limits, lock-in, and which one is right for which kind of project.
Most launch tweets get 12 likes and 0 paying customers. The first 10 paying customers come from a specific kind of focused outreach, not from a launch. Here is the playbook — what to do, what to avoid, and the question every prospect asks that catches solo founders flat-footed.
Three solid auth options, three different shapes. Clerk is the polished hosted product. NextAuth (now Auth.js) is the open-source DIY. Supabase Auth is the integrated default if you're on Supabase. Here is the honest comparison with the bugs each one ships at high frequency.
The decision every solo founder faces in 2026 — keep AI-assisted coding or hire your first engineer. The honest framework, with the calculation that actually predicts which path gets you to product-market fit faster.
Your launch tweet went viral, or you got featured on Hacker News, or a YouTuber linked to your demo. Now 50,000 people are visiting in an hour and your app is dying. Here is the playbook for surviving the first traffic spike — what fails first, what to fix in the moment, and how to prepare for next time.
Most solo founders price their AI SaaS by guessing. The result: either paying customers leave when they realize the value, or the founder discovers their unit economics are negative because every user costs more in OpenAI fees than they pay. Here is the actual framework that works.
If you sell internationally, the boring tax + compliance work eats your time. Lemon Squeezy and Paddle become Merchant of Record, handling sales tax + VAT in 60+ countries. Stripe stays the platform but pushes the tax work back to you. Here is the honest comparison for solo founders.
When your AI-built app errors in production, the temptation is to log everything so you can debug. The result: most error logs in AI-assisted apps leak API keys, JWTs, password hashes, and customer PII into log aggregators that anyone with read access can grep. Here is the right pattern.
AI agents are now writing tests, reviewing code, fixing bugs, and even deploying. The hype says they replace engineers; the reality is messier. Here is the honest map of what AI agents do well in software engineering today, where they break, and what production deployment actually looks like.
Adding an AI chatbot to your SaaS is a 60-minute task. Doing it without leaking customer data, getting prompt-injected, or burning $4,000 in OpenAI fees is another 60 minutes. Here is the real walkthrough — what to wire up, what to redact, and what to watch for in production.
AI code review is one of the cleanest AI-agent deployments — bounded scope, structurally verifiable output, immediate value on every PR. Here is the honest comparison of the four real choices in 2026, with the evidence angle most reviews skip.
AI agents in production extend your attack surface in specific, predictable ways. Prompt injection at runtime, tool-scope abuse, RAG poisoning, data exfiltration through chained tool calls. Here is the honest map of what attackers do and what defenses actually hold.
Model Context Protocol servers are the new standard way to give LLM agents tool capabilities. The protocol shipped in late 2024 and now powers most AI-agent deployments. This is the practical guide — what MCP is, what it enables, and the security envelope every production deployment needs.
AI tools are now in every team's workflow. Most companies have no policy. The result: leaked customer data, leaked source code, leaked strategic plans, leaked salary data. Here is the practical guide to writing an AI usage policy that actually gets followed.