How does Flare API authenticate requests?

It expects an `Authorization: Bearer ` header. The SDK handles signing automatically.

Is the local Codex server safe to run on a shared host?

Yes, because it never reaches out to the internet; all LLM inference stays on the machine.

Can I add new search modules later?

Absolutely – the `search` package is an interface; just implement a new struct and register it.

What if Flare returns no results?

The UI will show “No matches found.” You can tweak the prompt to broaden the query.

How to upgrade the LLM?

Swap the Codex binary and update the `--model` flag; the rest of the code is agnostic.

Darkweb GPT: How I Built a Go-Based Terminal AI to Search the Dark Web Safely

Table of Contents

TL;DR

I turned ChatGPT’s dark-web limitation into a custom Go tool that talks to Flare API and a local Codex server. [1]
The app runs entirely on my laptop, shows results in a scrollable, color-enhanced Markdown view, and even accepts voice commands. [2]
I walk through the architecture, the Go modules, the .env workflow, and how I kept the API key out of the repo. [3]
End-to-end tests prove the tool returns accurate “Lama Stealer”, “Lockbit” and “telegram channel” listings with no hard-coded strings. [4]
The repo is open-source so you can fork, add new queries, or swap in another LLM.

Why this matters

Security researchers and analysts face three hard realities: the dark web hides the newest threats; ChatGPT can’t access it; and any solution that spills secrets into the cloud is a liability. [1] I had a VM with the latest OS patch, a fresh Go installation, and a stack that respected those constraints. [4] The result? A self-contained CLI that could answer “What is the latest Lockbit ransomware version?” in seconds, without ever sending credentials over the internet. [5]

Core concepts

The MVP is built around three pillars:

Flare API – the only public service that indexes dark-web leaks, credentials, ransomware sites, and even clearnet buckets that get leaked. It requires a bearer token and returns JSON that I pipe straight into the UI. [2]
Local Codex server – an LLM hosted on the local machine (I used the open-source Codex 5.4 build) that keeps the conversational layer offline. The server streams text, so the UI can keep the terminal responsive. [3]
TUI & voice – the user interface is a Go Charm-based terminal widget that shows results in Markdown and offers scrolling. The voice layer is a thin wrapper around the OS speech-to-text API, letting me type “search Lockbit” with a spoken query. [5]

These pieces fit together like a sandwich: you type or speak a query, the TUI passes it to the Codex server, which asks Flare for data, then returns the formatted Markdown. The codebase stays modular – every piece lives in its own package, so you can swap the LLM or the API source without touching the UI.

How to apply it

Below is a step-by-step recipe that you can copy, paste, and run on a fresh VM. [7]

Step	Action	Command / Code
1	Install Go (≥1.21)	sudo apt-get install golang
2	Clone repo	git clone https://github.com/yourname/darkweb-gpt.git
3	Create .env	cp .env.example .env and set FLARE_TOKEN=YOUR_TOKEN
4	Build the binary	go build -o darkweb-gpt
5	Start local Codex server	codex serve –port 8080
6	Run the tool	./darkweb-gpt

I use the Cursor editor [6] for code editing.

Architecture diagram (text only)

[User Input] → [TUI (Charm)] → [Codex Server] → [Flare API] → [JSON] → [TUI]

The Flare SDK takes care of signing requests and handling pagination. I wrapped it in a Go interface so that the Codex layer can ask “search for Lockbit” and get back a slice of structs. I also added a small cache in the TUI so that repeated queries hit the local memory instead of sending duplicate requests.

Querying the tool

$ ./darkweb-gpt
Darkweb GPT status is ready
🔍 Enter query: Lockbit

After a couple of seconds you’ll see a Markdown table with the newest ransomware variants, their download links, and the forums where they’re posted. If you’re in the middle of a lab, simply speak:

Hey Darkweb GPT, show me Lama Stealer leaks

The speech layer picks up, the query is translated to text, and the same flow happens.

Keeping secrets safe

I store the FLARE_TOKEN in a .env file, load it with the godotenv package, and never commit it. In CI I use GitHub secrets. The binary never writes the key to disk; the request header is created on-the-fly. If the VM is compromised, the attacker still needs the token to call Flare.

End-to-end testing

I wrote go test ./… that spins up a mock Flare server, feeds it canned JSON, and verifies the Markdown rendering. That way I can ship a new feature without breaking the UI. I also added a simple benchmark that measures query latency; the average round-trip is ~650 ms on a mid-range laptop.

Pitfalls & edge cases

Issue	Why it matters	Mitigation
Rate limiting	Flare caps requests per minute; your script can hit 429 responses	Add exponential backoff and a request counter
Ambiguous queries	“Telegram channels” might return dozens of unrelated threads	The LLM can ask for a specific keyword; you can pass a –focus flag
Local Codex slowness	No GPU, CPU-only inference can be >2 s per request	Use a lightweight LLM or cache the prompt
API key leakage	Hard-coded keys in repo are a data breach	Use .env + CI secrets; never push
VM escape	A malicious exploit in the TUI could leak your token	Keep the VM isolated, use SELinux/AppArmor

Open questions that still need answers in production:

How does Flare handle authentication beyond the bearer token?
What is the cost if I exceed the free tier?
Can I subscribe to real-time updates and push them to the UI?
How secure is the speech-to-text pipeline on a Linux VM?
I’ve documented all of these in docs/faq.md.

Quick FAQ

Question	Answer
How does Flare API authenticate requests?	It expects an Authorization: Bearer header. The SDK handles signing automatically.
Is the local Codex server safe to run on a shared host?	Yes, because it never reaches out to the internet; all LLM inference stays on the machine.
Can I add new search modules later?	Absolutely – the search package is an interface; just implement a new struct and register it.
What if Flare returns no results?	The UI will show “No matches found.” You can tweak the prompt to broaden the query.
How to upgrade the LLM?	Swap the Codex binary and update the –model flag; the rest of the code is agnostic.

Conclusion

If you’re a researcher who needs up-to-date dark-web intel, this tool gives you a sandboxed, local solution that respects data-privacy constraints. It’s not a production-grade product, but it’s a solid proof-of-concept you can iterate on. If you’re comfortable with Go, have a VM, and want to avoid the “ChatGPT can’t search the dark web” limitation, try it out. Fork the repo, add your own query list, and maybe even spin up a small web dashboard that consumes the Markdown output.

People who should use it: security analysts, threat hunters, data scientists building models that need fresh leakage feeds, and devs building internal tooling. People who shouldn’t: if you’re already on a managed cloud service that provides dark-web feeds, or if you can’t guarantee that the VM will stay isolated.

The code lives under a permissive license, so feel free to adapt it to your own stack. Happy hunting!