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Hey HN,

I'm the developer behind LocaFlow. Here's the backstory:

I've built several iOS apps over the past few years. Every single one stayed English-only because I dreaded the localization process. The typical workflow:

1. Open Localizable.strings as a source code 2. Copy-paste pieces of strings to ChatGPT or Claude manually 3. Copy-paste translations back 4. Test everything 7. Repeat for each language and every app update

This would take me 8+ hours per app. I kept putting it off.

Last quarter, I finally decided to localize one of my apps. Halfway through the Saturday I spent on it, I thought "I'm a developer... why am I doing this manually?"

So I built LocaFlow.

What it does: - Select your the app project on your computer - AI translates to 50+ languages - Takes about a few minutes instead of hours/days

What's different: - No "bring your own API key" friction (I handle translation API costs) - Preserves formatting (variables, plurals, special characters) - Simple pricing: $19/month (or generous free plan) - Built by a developer who uses it for his own apps

Technical details: - Uses AI for translation (better context understanding than Google Translate) - Validates string formatting before/after translation - Handles iOS plural forms, Android string arrays, etc. - Can process batch translations (entire app at once)

Happy to answer questions about implementation, pricing, roadmap, or anything else.

Try it out: https://locaflow.dev (free tier available, no credit card required)

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Hi HN,

Since AutoGPT in 2023, I’ve been uneasy about fully unsupervised AI agents. I see the productivity upside, but “kick it off and walk away” felt risky.

Recently, the “Ralph Wiggum loop” pattern has gone viral. The idea is simple: An autonomous coding agent runs repeatedly until all PRD items are complete, with fresh context each loop and state stored outside the model in git, JSON, etc.

What bothered me was this part: what protects the system while I’m AFK?

Traditional AI-assisted dev today looks like: AI writes code → human reviews → CI scans → human fixes

What I wanted instead: AI writes code → security scans immediately → AI fixes issues → repeats until secure → escalates if stuck

So I built a prototype that embeds security scanning directly inside the agent loop. The agent runs tools like Semgrep, Grype, Checkov, etc. inside its own session, sees the findings, and iteratively fixes them before anything is committed.

The loop looks like this:

PRD → Agent → Scan → Pass? → Commit Fail → Fix → Retry (3x) → Escalate to human

A few design principles that mattered:

* Baseline delta: pre-existing issues are tracked separately. Only new findings block commits. * Sandbox constraints: no network access, no sudo, no destructive commands. * Human override: nothing is fully autonomous. You can step back in at any point.

Is this bulletproof? Definitely not. Is it production-ready? No. But it’s a starting point for applying DevSecOps thinking to autonomous agents instead of trusting “AI magic.”

Repo link: https://github.com/agairola/securing-ralph-loop

Would love feedback from folks experimenting with agent loops, secure automation, or AI-assisted development gone wrong.

Happy to iterate.

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I built a free snake game which is controlled by your head.

good for your neck health, thanks for give a try.

I wanted a way to lower the friction of contacting elected officials - find your reps, grab their contact info, and send something without starting from scratch every time. Users can create and share letter templates, so one person can draft something good and others can use it for their own outreach. Built with Astro.js on Cloudflare Pages, Neon PostgreSQL, OTP auth. Planning to add voting records and campaign finance data next. https://github.com/anomalousventures/democracy-direct

One month ago, OpenAI deactivated my ChatGPT account without warning. 3+ years of conversations—gone.

I tried everything. Emailed every OpenAI address I could find. Their response? "Use our data export tool." The catch? You need an active account to export your data.

Classic.

So I built a browser extension that lets me save any conversation from ChatGPT, Claude, or Gemini with one click. Markdown format, stored in one place.

Turns out it solved another problem I didn't expect: I often ask the same question to all three AIs, then forget which one gave me the best answer. Now they're all in one place.

It's been my personal tool for now. Nothing fancy—just scratching my own itch. If there's interest, I'll consider publishing it. Could easily extend to other AI assistants too.

Anyone else paranoid about their AI chat history now?

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There needs to be a universal format to share conversations with AI that captures tokens from multiple sources in one stream of conversation.

Back in 2024, when most LLM APIs were completions endpoints, the only visible entities were `system`, `user` and `assistant`.

Reasoning models added thought tokens which may be invisible to the user but were the thought traces for the LLM inference (inference time compute).

Now with agents making tool calls, and there are other entities also participating and influencing the flow of a conversation.

I took a stab at vibe-composing a `SKILLS.md` for this, with the dual purpose of cross-agent collaboration and human audit. - Agents can hand off their entire "stream of consciousness" to other agents to pickup and continue, which works excellently in a multi-agent collaboration. - Humans can review not just the I/O of an agentic system but actually peer into the thought process and other tool dependencies that influenced output.

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