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I’m rethinking my product: from AI blog writing to weekly build log recovery for indie hackers

I’ve been rethinking the product I’m building, and I’d like honest feedback from solo builders. The original idea was simple: Turn GitHub commits into AI-written tech blog posts. At first, that sounded useful. I like writing about what I…

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I’ve been rethinking the product I’m building, and I’d like honest feedback from solo builders.



The original idea was simple:



Turn GitHub commits into AI-written tech blog posts.



At first, that sounded useful. I like writing about what I build, but like a lot of developers, I

stop blogging the moment real work gets busy.



So I thought:

maybe AI can help bridge that gap.



But the more I worked on it, the more something felt off.



If commit messages are partially AI-written, and PR descriptions are partially AI-written, and then

an AI turns that into a blog post, you can end up with polished text that sounds good but feels

increasingly disconnected from the actual work.



That seems dangerous.



What I actually found useful was something else:

being able to look back and recover what I worked on that week.



Not “publish a perfect post.”

More like:




  • What did I actually build?

  • What changed?

  • What was the reasoning?

  • What did I miss?

  • What should I revisit next week?



So now I’m considering a narrower product:



A weekly build log copilot for indie hackers / solo developers.



The idea would be:




  • read recent GitHub commits and PRs

  • reconstruct the past 7 days of work

  • summarize key changes and decisions

  • give AI feedback on missing context, unclear reasoning, or possible risks



Private by default.

Public devlogs can come later, maybe.



Before I build more, I want to validate whether this is even a real problem for other people.



A few questions for solo builders here:




  1. How do you currently keep track of what you worked on each week?

  2. When you need to remember “what happened this week?”, what do you go back to?

  3. Are commit messages / PRs enough to recover the actual context?

  4. Is private reflection more valuable than public writing for you?

  5. Would you actually use a tool like this, or is manually prompting ChatGPT/Claude already good
    enough?



If you’re building solo and have 5 minutes to share your workflow, I’d really appreciate it.



Brutal honesty is welcome.

I’m trying to figure out whether this should exist at all.

SOC Incident Playbook: Vulnerability Remediation & Verification
title: Detect Exploitation - I’m rethinking my product: from AI blog writing to weekly build log recovery for indie hackers
id: 00d18e70-dd66-43a5-a0c4-99e0d75897c5
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-24
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
rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-24"
        description = "YARA Signature for "
    strings:
        $str = "I’m rethinking my product: fro" ascii wide
    condition:
        any of them
}
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Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich I’m rethinking my product: from AI blog .... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

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  • 3. Telemetrie & EDR-Alerts: Prozessaufrufe und Child-Processes auf anomale Shell-Spawns überwachen.
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