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Would you let an AI write SQL against your production database?
I wouldn't either. So when I built Quanta — a workspace where you ask questions in plain English and get back a live dashboard — the model never touches your data directly.
Every query it generates is SELECT-only and runs read-only. If one fails, the system feeds the exact database error back to the model and lets it correct itself once before anything reaches you. Credentials stay encrypted with AES-256-GCM and never leave the vault.
Connect Postgres or MySQL, switch between Gemini, Claude, and GPT, and build dashboards from 12 chart types. It's multi-tenant now too. Orgs, roles, and per-workspace AI spend caps.
Curious how other devs handle the "LLM + real database" problem. What guardrail would you add?
great idea! waiting to use this.
The self-correction loop is such a smart touch. Only thing I'd add is a statement_timeout + forced LIMIT so a valid-but-heavy query can't tank prod. Ideally run against a read replica. Great build.
Good calls. Some of it's already in, the rest is going on the roadmap. Thanks for the thoughtful feedback!
Happy to help
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This is a B2B SaaS application for digital recording of working hours, organization of teams and automatic calculation of wages on construction sites. It solves the biggest problem of construction companies: manual writing of hours in notebooks, "smuggling" of workers and complicated calculation of wages at the end of the month.
Can u tell me about videos, what is better solution for u :)
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Both ain't bad, sparkling 🤩
After using AI extensively for the last 1-2 years, I've noticed an interesting limitation.
When I ask AI to generate 10 test cases for a scenario, usually only 3-4 are highly relevant. The remaining test cases are often generic, duplicated, or based on assumptions that don't match the actual business scenario.
The problem isn't just accuracy—it's also cost(Extra Money, Water, Electricity etc).
Even the irrelevant output consumes tokens, which means:
• More tokens used
• Higher API costs
• More time spent reviewing and filtering responses
I don't think the solution is to stop using AI. Instead, we need to use it more efficiently.
A few things that have helped me:
✅ Provide complete business context and acceptance criteria.
✅ Mention constraints and assumptions explicitly.
✅ Ask for only "Top 5 high-priority test cases" instead of asking for everything.
✅ Generate test cases iteratively rather than in one big prompt.
✅ Ask AI to first identify missing information before generating the answer.
✅ Use AI as an assistant, not as a replacement for domain knowledge.
AI is incredibly powerful, but better prompting and better context are still the keys to saving both tokens and money.
Has anyone else experienced the same thing while using AI for testing or development?
Have you tried testing a feedback loop with specific domain tweaks? Curious if it improved relevance!
I Built two-sided marketplaces in Bubble and Flutter. The ones that scale share the same 5 things:
Model roles before UI, Reasy (Flutter + Supabase) has 4 roles with different permissions. Get this wrong early, you're rebuilding the data model by week 6.
Trust layer before the feed, agents on Reasy can't go live without license verification. No gate = nobody trusts anybody.
A hard auto-approval rule, ≥95% match = auto-connect, below that = manual approval. Cuts admin load in half.
Cold start is a product problem, not a marketing one, on Aptavo, supply (rates, availability) is seeded before demand ever shows up.
Post-deal flow built before launch, on Aptavo, payment only releases after the customer approves job completion. That's what brings people back.
Most no-code marketplaces don't fail at tech. They fail at trust.
What's the pattern you always build in from day one?
Strong list. I'd add one more point, build for operational visibility from day one.
Every marketplace eventually needs an internal dashboard where the team can see disputes, failed payments, stalled bookings, verification status, and user activity without digging through the database.
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