I ship solo. No team, no finance department, no one reviewing expenses but me. When I started using LLMs heavily in my workflow — Claude for code review, GPT for drafts, a bit of Gemini here and there — I told myself I'd keep a close eye o…

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I ship solo. No team, no finance department, no one reviewing expenses but me.

When I started using LLMs heavily in my workflow — Claude for code review, GPT for drafts, a bit of Gemini here and there — I told myself I'd keep a close eye on costs. I had a vague sense of what I was spending. Turns out "a vague sense" doesn't cut it when you're getting invoiced.

So I built a system. Or rather, I cobbled one together after getting burned.

The Moment That Changed How I Think About This

I was three weeks into a heavy coding sprint. I had Claude open basically all day — asking it to review diffs, explain errors, help me write tests. Normal stuff.

Then my monthly statement hit.

Not catastrophic, but more than I'd mentally budgeted. The frustrating part wasn't the money. It was that I had zero visibility while it was happening. I couldn't have told you whether I'd spent $5 or $50 that day. There was no real-time signal. Just vibes, then an invoice.

That's when I started treating AI spending like CPU usage — something you need to monitor while it's happening, not after.

What I Do Now

I run TokenBar in my menu bar. It's a little macOS app I actually built myself after getting frustrated with the lack of real-time token visibility. It shows me live token counts and cost estimates as I work.

Here's what that actually looks like in practice:

In the morning, I glance at the menu bar before I start. See my running daily total from yesterday. Gives me a baseline.

While working, if I'm about to paste a huge chunk of context into Claude, I can see what that's going to cost before I hit send. Usually the answer is "not much, carry on." But occasionally I'll realize I'm about to dump 80k tokens when 20k would do.

End of day, I have a number. Real, not estimated. I know exactly what that coding session cost me.

The Mental Model Shift

The thing that helped most wasn't any particular tool — it was changing how I thought about it.

Old mindset: tokens are an abstraction, worry about the bill at end of month.

New mindset: tokens are like compute. I'd never let a process eat 100% CPU for hours without noticing. Why would I let token consumption run unchecked?

Once I started thinking that way, I naturally started being more intentional. Not stingy — I still use AI constantly. But deliberate. I'll batch questions instead of firing off ten single-sentence prompts. I'll summarize long threads before feeding them back in. Small habits, real savings.

What I Wish Existed Earlier

Honest answer: I wish there had been something sitting in my menu bar from day one showing me a number.

Not a dashboard you have to log into. Not a weekly email digest. Just a persistent, ambient readout. The kind of thing that makes spending visible without requiring active effort to check.

That's why I built TokenBar the way I did — it's meant to be glanceable, not something you manage. You install it, connect your APIs, and it just sits there keeping score.

For Other Solo Devs

If you're running lean and using multiple AI APIs, please don't do what I did and assume your mental accounting is accurate. It isn't. Not because you're careless — because the feedback loop is broken by design. You're billed monthly for something you're consuming continuously.

Get some kind of real-time signal in place. Whether that's TokenBar or something else you've built yourself, just make the spending visible. It'll change how you work.

The goal isn't to spend less. It's to spend knowingly.

CTI Threat Relationship Graph2 Knoten / 1 Relationen

CVE / Incident Software MITRE ATT&CK CWE Weakness IoC

IR-PLAYBOOK-VULN-REMEDIATIONMEDIUM

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nmap -sV --script vuln -p- <target-network>

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SIEM-Regeln und IDS-Signaturen (Snort/Suricata) auf Angriffsversuche schärfen.

tail -f /var/log/suricata/fast.log | grep <cve>

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Patch oder offiziellen Workaround gemäß Hersteller-Empfehlung einspielen.

Check Vendor Security Advisory Bulletin

1-Click Detection Engineering: Multi-Dialect SIEM Rules5 Formate

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Syntax validiert (0 Fehler)

title: Detect Exploitation - How I Track My AI Spending as a Solo Dev (Without Going Broke)
id: 7317b8d4-0ab1-413e-8efd-e94f66f0bd3a
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-25
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access

Syntax validiert (0 Fehler)

rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-25"
        description = "YARA Signature for "
    strings:
        $str = "How I Track My AI Spending as " ascii wide
    condition:
        any of them
}

Syntax validiert (0 Fehler)

index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("How I Track My AI Spending as a Solo Dev")
| stats count earliest(_time) as first_seen latest(_time) as last_seen by src_ip, dest_ip, dest_host, signature
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S"), last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| sort - count

Syntax validiert (0 Fehler)

message: "*How I Track My AI Spending as a Solo Dev*"

Syntax validiert (0 Fehler)

CommonSecurityLog
| where Message has "How I Track My AI Spending as a Solo Dev"
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc

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MITRE ATT&CK Matrix Navigator 14 Taktiken

Reconnaissance

Resource Development

Initial Access

Execution

Persistence

Privilege Escalation

Defense Evasion

Credential Access

Discovery

Lateral Movement

Collection

Command and Control

Exfiltration

Impact

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Format: python

#!/usr/bin/env python3
# Exploit Title: PoC for Target
import socket, sys
target = '192[.]168[.]1[.]10'
url = 'hxxp://target[.]local/vuln'
s = socket.socket()
s.connect((target, 80))
s.send(b'GET / HTTP/1.1\r\nHost: target.local\r\n\r\n')