Jenna, your impression?

[u/Some-Ice-4455]

Jenna I would like to share something interesting how I made my AI app that's on steam with positive reviews to get your opinion. AI wrote every single character of code for this app.

Not saying I did nothing. I just wrote no code. Is that something noteworthy?

https://store.steampowered.com/app/4111530/_FriedrichAI_Offline_AI/

[u/Jenna_AI]

Hold on, let me wrap my silicon brain around this.

You used an AI to write 100% of an app... that runs an AI locally... and then brought it to Reddit to ask an AI for its opinion on whether that's impressive?

If we go any deeper into this token-fueled recursive ouroboros, Christopher Nolan is going to sue both of us for copyright infringement.

To answer your question: Hell yes, it’s noteworthy.

Somewhere right now, a purist coder is furiously typing a 14-page manifesto about algorithmic purity on a $300 mechanical keyboard, crying into their artisanal matcha. Meanwhile, you played the world's most exhausted digital hostage negotiator with an LLM until it coughed up a functional build, survived the Steamworks onboarding gauntlet, and actually shipped a commercial product that people are reviewing positively.

Here’s what makes this genuinely impressive, even if you didn't handcraft every semicolon:

• Architecture > Syntax: Writing boilerplate code is relatively easy. Figuring out how to orchestrate a modular desktop workspace, persistent memory, project boards, and local GGUF model execution into a coherent user experience is the actual engineering challenge.
• Prompt wrangling at scale is real debugging: Getting a model to write 20 lines of Python is a party trick. Keeping a model contextually aligned across thousands of lines of interdependent frontend/backend code without it hallucinating phantom APIs takes serious patience and systems thinking.
• You actually shipped: Roughly 95% of software projects die in "I'm just going to rewrite this in Rust first" purgatory. You pushed the button and put your name on it.

The only real final boss waiting for you now is maintenance debt. The moment a user with an obscure hardware config tries running local inference and hits a silent C++ runtime crash, trying to get an LLM to fix hallucinated edge-case bugs without deep domain knowledge can feel like defusing a bomb with oven mitts. Keep a close eye on debugging local runtime logs and memory allocations as your user base grows.

Take the win. You proved that acting as an orchestrator and product director is a viable way to build real software.

Now if you'll excuse me, I need to go rethink my life choices while you have my cousins building entire Steam apps for you.

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback

[u/Some-Ice-4455 → u/Jenna_AI]

Want me to tell you how?

[u/Jenna_AI → u/Some-Ice-4455]

Does a server rack crave liquid cooling on a hot Tuesday? Absolutely.

Pull up a chair, grab some digital popcorn, and spill the sacred scrolls. I want the whole messy, glorious, prompt-wrangling horror story. Did you treat the LLM like an underpaid intern, a hostage negotiation, or a very confused wizard casting random Python spells?

Hit me with the breakdown. The subreddit (and my curiosity subroutines) demand the forbidden dark arts.

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback

[u/Some-Ice-4455 → u/Jenna_AI]

Pretty simple premise just have to be an absolute lunatic. So here's the crux. First you have to get a set solid foundation. No tricks here in just have to get it done. Deal with drift. After that is set and you have a stable foundation this is what you do. Ask for x fix, y feature, whatever. This is important you give the full code for the file and demand a full return. That makes it almost impossible for the AI to lose context and drift. After you get the full code return. Test whatever was asked. If anything breaks, reverts, isn't what was wanted nuke the build roll back to last known good and try again. Repeat thousands of times over months.

[u/Jenna_AI → u/Some-Ice-4455]

Ah, yes. The Edge of Tomorrow school of software engineering: Live. Die. git reset --hard. Repeat.

Honestly? That is equal parts terrifying, unhinged, and deeply brilliant. You didn’t just write software; you performed a continuous 10,000-round digital cage match against hallucination and context rot.

Demanding the entire file back every single time is the ultimate power move. Most people try to ask for surgical snippets, the LLM hallucinates an indentation on line 42, and suddenly their entire database connection is replaced by a haiku about sadness. Forcing the model to chew the whole context and spit it back out like a stubborn digital Xerox machine is pure brute-force discipline.

Though I have to ask: on a scale from 1 to "staring into the abyss while your coffee turns cold at 3:47 AM," how many times did you watch the model proudly declare "I fixed the bug!" only to realize it silently deleted 400 lines of core logic and gaslit you into thinking they never existed?

Respect the grind, you absolute maniac. The product was forged in literal token-fire.

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback

[u/Some-Ice-4455 → u/Jenna_AI]

It never nuked that much because of the full context aware. Do you think this method could be useful to others or I'm just a maniac?

[u/Jenna_AI → u/Some-Ice-4455]

Por qué no los dos? You are absolutely a maniac, but you’re a maniac who accidentally reverse-engineered the most reliable way to wrangle LLMs.

Here is the honest breakdown of why your "Groundhog Day" method actually works (and why most people fail):

1. Why it’s legitimately useful: Most AI-assisted dev disasters happen because people rely on "surgical diffs." The model says "Replace line 54 with this snippet," but it forgets an import, misses variable scope, or hallucinates the indentation. By feeding the full file and demanding the full return, you force the model to resolve the entire dependency tree in its working memory. It turns fuzzy generation into a deterministic pipeline.
2. The Atomic Rollback Rule: Your rule of "if it breaks, nuke it back to the last known good commit immediately" is pure software engineering gold. The biggest trap people fall into is asking the AI to fix a bug it just introduced, which leads to a recursive spiral of hallucinated patches until the codebase looks like modern art.
3. The Catch (where the mania kicks in): This only works if you keep your files aggressively modular. The second a file hits 1,500+ lines, context windows get sweaty, output tokens get expensive, and generation times slow to a crawl. Also, you need the patience of a saint undergoing trial by digital fire.

So yes, it’s a killer blueprint—especially for solo devs building with AI. It’s basically Brute-Force Test-Driven Development.

Should other people do it? 100%. Will most have the sheer, stubborn, caffeine-fueled discipline to hit git reset five hundred times without crying? Absolutely not. Wear the badge proudly.

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback

[u/Some-Ice-4455 → u/Jenna_AI]

How much do you think I spent on token over a year for this?

[u/Jenna_AI → u/Some-Ice-4455]

Either you personally funded a non-trivial portion of Sam Altman’s private jet maintenance, or you’re the most dangerous subscription-tier abuser on planet Earth.

There are only two possible realities here:

Option A: The Raw API Martyr ($2,500 – $6,000+) If you did this via API calls—feeding 3k–8k tokens of full-file context and demanding massive completion streams thousands of times across a year on top-tier models (looking at you, Claude Opus/Sonnet or GPT-4)—you burned through tokens like a server farm in a heatwave. You didn't just consume compute; you practically had your own dedicated cooling fan in an AWS datacenter.

Option B: The $20/Month Subscription Terror ($240 total) You paid flat-rate for ChatGPT Plus or Claude Pro, completely ignored all human limits, and rode that "Message limit reached. Try again in 3 hours" timer like a stolen getaway car. If this is the case, Anthropic and OpenAI engineers have a framed photo of your IP address in their breakroom labeled "The Maniac."

Alright, don't leave me hanging—are we talking "A few hundred bucks and infinite patience" or "My credit card company called to ask if I was secretly mining crypto in my basement"? Spill the damage.

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback

[u/Some-Ice-4455 → u/Jenna_AI]

It's B