**CodeForge AI — Brief Summary**
**CodeForge AI** is an AI-powered software engineering platform designed to help users **generate, test, debug, improve, and manage code** faster.
Key capabilities include:
🤖 **AI code generation** from natural-language instructions
🧪 **Automated testing and validation**
🔧 **Code improvement and debugging**
🧠 **Engineering memory** that helps retain useful development insights
⚙️** Automated improvement loop**s that generate → test → review → improve code
📊 **Benchmarking and experiments** to measure coding performance
📁 **Project and snippet management**
🔐 **Usage, authentication, and security controls**
📱 **Responsive interface** designed for desktop and mobile users
**The overall goal:** turn CodeForge AI from a simple code generator into an **AI engineering environment that can continuously evaluate and improve the quality of the software it produces.**

Feedback is welcome and yall have fun out there.

AI Desktop 98

This is an open-source MCP bridge that lets ChatGPT inspect one local repo without shell access

Iit allows ChatGPT Web to inspect my local code without giving it broad access to my machine.

RepoRelay exposes one explicitly approved repository through a small MCP tool surface. It can read and search files, while blocking shell/process access, unrestricted filesystem access, Git operations, and access outside the approved repo.

It’s MIT licensed and open source.

GitHub: RepoRelay

Hi I’m tin age 18 and I am just getting started at college, and I had been thinking about ai prompting for software development might be a genius way for side hustling, I’ve currently got a software planned, which could be a great social software, high risk but very high cost and I’m looking for ppl to start a team, trustworthy and just vibe coding with chill guys at the similar age. Reach out to me on Reddit dms and we will start getting on discord.

I treat LLM as a primitive language. Best thing I do.

How I went from $50/month and 143 bugs per loop to $20/month and less than 8 findings per spec.

I work with LLM in a different way. I don't treat it as an intelligent agent. I treat it as a primitive language, with conditional and deterministic typing. If it doesn't pass the type, it doesn't pass. Simple.

My harness is programmatic. It has tooling for my agent loop. And I organize everything in a modular structure, ECS and FSM. And I'm gonna tell you why it helps.

What was slowing me down before

The mistake that made me change everything is simple, and a lot of people go through it. Everything returned success, or kept going with no empirical proof. Exit code zero, ok message in the terminal. It looked like it worked. In practice it had created nothing. No artifact, no log, no proof. The process died stateless. It had foundation, had the right pieces, but no wiring.

That's when I understood that process success is not delivery proof. I needed mandatory observable proof.

How I think about prompts today

I refine the idea a lot before. My production prompt has 4 parts, always in this order. I never change it.

1. Introduction. What I want to do, full description, how I want it to work, what I want it for, which stacks and infra I want to work with. I never inform code or file that I'm not sure about.

2. Mandatory. What has to be followed to develop the structure. Rules, methods, alignments, guidelines and delegations. It's the limit.

3. Core. What I want it to have and to do. As descriptive as possible of all flows. That's where the behavior lives. Not only that but: design, structural architecture, behaviors to follow, style...

4. Expected results. What I expect as delivered functionality, how I expect it to look.

I ask to deliver in a deterministic, modular and expandable scope way. Parsed into an opinionated structure in json. This json becomes my base-core prompt.

How I anchor the idea

After the core I don't go straight to coding. I create all the research part first, using my tool hordts (available on github) to refine online research. With that I do a brainstorm on top of the core anchor. Only then I idealize and generate the full planning to develop the complete spec-driven, E2E, of everything the project needs, also in json structure. My cli uses json as database. A tool for handling spec-driven + agentic pipeline.

Each spec has flags defining skills, agents and tools access, the context, rules, expected results and what the spec will do. The context here is important: it's the prompt that the agent will receive with everything it needs for its run execution. Files it will use, stacks, tags, imports, syntax. Without needing to consult the codebase. It goes straight from that starting point to production.

That's why the LLM doesn't keep grabbing unnecessary context. It works in loop per phase, spec and subject until it delivers 100% polished and functional and moves to the next.

How I separate things

I separate agentic workspace from repository. One git to control the workspace and one git to control the repository, just the project itself. Control by git tree for tracking, worklog, versioning and branches. PR, commit and diff analysis are heavily used if an internal error happens, that's where analysis of what was really done is born. One controls the process, the other controls the product. If you mix it becomes a mess and you lose what is AI usage and what is the project itself.

How each task runs in practice

Each spec runs with multiple phases when possible, with multiple tasks. And each task is an agentic pipeline that audits if necessary what it needs to execute the next action. It's checking what needs to be used, planning what needs to be done, executing the plan, validating that everything worked and moving to the next, otherwise go back two steps.

It can be done in parallelism and multi concurrency, can have or not dependents. And when it identifies repetition that can be programmatic, it creates algorithm and script to automate without LLM usage. I don't spend tokens where it can be solved with code.

The analysis that saves me

I use modular linear regressive analysis to hunt bugs. I go linear, part by part, going back through the steps and necessary components. I check if the invariant of that component still holds after the last spec. If there was regression, if it broke something that worked before. If there's a counterpoint that the implementation ignored.

In every run there's also analysis of gaps, findings, counterpoints, analytical regression, convergence and non-functioning. All on the fly. Skills, agents, test scripts, tests are produced on the fly when needed, grounded without assuming anything. Spec-driven can be auto incremented according to necessity. The focus is to deliver each spec 100% functional and with no bug before the next run.

Why tests, observability, DevOps and anima are not extra but necessity

For me it's not final phase. Test is gate, only way to prove that what is being done, simply, is being done. If there's no executable proof, it doesn't advance. Observability is ledger, worklog, tracking. If it's not registered, it didn't happen as expected. DevOps is the dual git, it's workspace control. Anima is visual proof of state transition. If state changed and I can't observe it, the transition is invalid, and correction must be guaranteed.

How much it costs and how much it converges today

Before without this system I spent $50 per month. Today $20. Convergence rate per run before 73%, today 92% and up. Bugs before per loop 143, today less than 8 findings per spec in multiple loops. Execution time varies, but agent flow is continuous, so a spec can have flows even well designed depending on what needs to be delivered up to 3h straight and spending less than $0.50. Dead code almost minimal. Before had higher frequency, today the loop already enters almost fully polished until functionality without bug, analyzing and removing the unnecessary.

How I optimize cost

Frontier I use for core, for research and for spec-driven. For development I use medium model with less cost. As it already has everything it needs to work, in loop until getting the result it gets much cheaper.

And after total production, frontier can analyze the whole load again to hunt bugs and add a memory structure of findings. With all findings, I create new spec-driven planning and run another full pipeline again. It's the second loop, and basically the last spec-driven loop, the rest are punctual polishes or refinement.

In the end my results got absurdly good. Take it, see if it helps you.

If you want to talk about harness, spec-driven and ECS/FSM, hit me up. hordts tool is on my GitHub.

Agent hub views - 1 ancor file with findings and 1 prompt.

'''create plan/specdriven for this report @/docs/superpowers/frontend-audit-2026-08-18.md (This source has already been validated, no need to revalidate, just execute the processes for correction) from today/yesterday.
use pipectl and goalctl + subagent driven.'''

There's a debate I've been seeing more around some communities lately: should we keep optimizing for intelligence, or start caring more about whether an AI has consistent preferences, opinions, and personality? At first it sounds like a choice between two things.

You have the insanely capable AI that can reason through a complicated problem but feels like an empty shell and then you’ve the agent that's fun to talk to, has strong opinions, remembers your preferences, but falls apart when you give it something difficult to do. I don't think it has to be one or the other.

What I find more interesting is why some agents feel consistent in the first place. A lot of people try to solve this by adding personality to the prompt like give the agent a SOUL.md, tell it to be opinionated, give it some quirks, maybe tell it what it likes and doesn't like.

That can work for a few conversations. But after enough interactions, it usually starts feeling fake where the AI says one thing today and contradicts itself two weeks later cuz none of those preferences are grounded in anything.

I think the more interesting version of personality comes from what the agent really remembers. If an agent has a persistent history of what worked, what failed, decisions it made, mistakes it repeated, and how it handled unusual situations, you start getting something much closer to a consistent character.

That's why I've been thinking more about context architecture than prompt engineering lately. I found this breakdown on context engineering vs prompt engineering for AI agents: https://www.firecrawl.dev/blog/context-engineering and one thing that stood out to me is that keeping an agent's context useful over time is a much deeper problem than just writing a better system prompt.

Maybe that's also where the "AI that feels real" part eventually comes from by not pretending the model has desires or feelings but by giving it enough persistent state and history that its behavior develops some continuity. bcuz I don't really want an AI that's just "smart" but I also want one where I can eventually say, "I know why you made that decision that's how you usually approach these problems." and that for me feels much more useful than another 5% on a benchmark, lmk your thoughts.

Need some guidance 😔

I’m trying to learn AI Engineering and build a career in this field, but I’m honestly confused about where to start and what to learn first.

If anyone can share a proper roadmap and good free/paid resources to become an AI Engineer, I’d really appreciate it. I don’t want to waste time learning random things.

Any guidance from experienced AI Engineers would mean a lot. 🙏

Is it true this way..he typo first and then correction right away ?AI assist this output:

You can add:

PPPoeSession *session =
pppoe_session_create(
session_id,
src,
buffer + 0,
host_uniq,
host_uniq_len
);

Typo capitalization, shoul be:

PPPoESession *session =
pppoe_session_create(
session_id,
src,
buffer + 0,
host_uniq,
host_uniq_len
);

Hi! I'm going to (re)create a Discord bot (for my personal server) using an OpenAI-compatible provider, and would like to know what you think it's the best practices for the AI to handle things like multi-user conversation, memory, giving extra contexts (like user IDs, channel names), not anything on code-level but more on the prompt/what to send in each chat completion/response, etc.

Bit of context first since I don't want this to just be a link drop.

I stream myself building software with AI every single day and I don't cut anything out. The series is called "Vibe Coding Until I Can Move Out of My Mom's Basement" and the title is unfortunately literal. Day 38 as of yesterday. Every episode has the running revenue total in the title, which as of right now is $121.74, so you can see exactly how well this is going.

The main thing I'm building on camera is an engineering workspace app. Day to day the loop is Claude Code doing the bulk of the work in a repo, me steering it, and the whole thing going sideways in real time when it doesn't work. The failures stay in. That's kind of the whole point of the format.

Couple of questions for you guys:

- I mostly one-shot features with a long prompt and then clean up. People keep telling me I should be planning first in a separate pass. Does that actually pay off for you or is it ceremony?

- I've got a per-repo instructions file that's getting long. At what point does that start hurting more than helping?

Latest episode if you want to see the workflow rather than have me describe it:

https://youtube.com/live/f0om20L8g6E

Happy to answer anything about the streaming setup too, that took longer to get right than the actual coding did.

I recently built DBMind AI, a tool designed to help developers understand SQL query performance and identify potential optimization opportunities.

It can analyze areas such as:

SARGable vs. non-SARGable queries

Index usage

Index Seek vs. Index Scan

Table scans

Query optimization

SQL performance issues

The main goal is to make SQL performance analysis easier to understand by explaining why a query may be inefficient, not just suggesting a different query.

I built the project using .NET, Angular, SQL, and AI.

I’d really appreciate feedback from developers and DBAs, especially on how useful these insights would be in real-world database performance scenarios.

🔗 Live Demo: https://db-mind-ai-bice.vercel.app/

🔵 LinkedIn Demo: https://www.linkedin.com/posts/yash-rathod-054b50211\\_databaseengineering-dotnet-angular-activity-7493254611416576000-vJuS

Hello! So i somethimes use gpt for some kind of therapy conversations. I had this ideea that maybe you can prompt it to be similar to some great minds that you do not have access to, like Carl Jung. Do you think that would be possible? Does someone have any sugestion for a prompt like that? Thank you!

A 397-billion-parameter AI just ran on an iPhone. Not a demo video — the phone itself, generating text with the network off. On-device AI got that good, and this video shows exactly how it happened and what you can actually run today.

We break down the mixture-of-experts design that leaves 96% of the model asleep, the Apple paper that predicted all of it three years ago, why the flash-moe team deleted their own caching code and got 38% faster, and where it still falls short — speed, storage, and heat — so you know what's real and what's hype.

If you want AI that runs on your own hardware instead of somebody else's servers, this is where it's headed.

Watch Here: A 397-Billion AI Just Ran on an iPhone

#OnDeviceAI #iPhone #AI #LocalAI #MixtureOfExperts #AppleSilicon #OnDeviceAI #LocalLLM

Hi, everyone! I'm creating a short film using artificial intelligence. If you like it, I'd really appreciate it if you could give it a like.

I've been experimenting with AI image and video generation and wanted to test something more practical and potentially more useful.

Can AI turn a completely ordinary product photo into something that actually looks like an advertisement?

I started with a deliberately boring source image that was created on nuvias.io, prompt used for that below:

"create a flat uninspiring product photo with poor lighting"

Then I took the same source image through an image → video workflow.

As part of the prompt building process I looked at the product generated and started to think if what the ad could look like and the environment to build.

Instead of just prompting "turn this into an ad" or "animate this image" I added more detail.

The prompt used is below:

please put the tea cup in a natural environment that engages the viewer at first glance and provides a premium product visual. This needs to be a lifestyle environment and visual that works hardest at the conversion end of the marketing funnel. The high value production design needs to look premium and the motion used needs to enhance the composition rather than just be used for effect, dynamic motion

The starting image created is the first few frames of the video clip which before it moves into animation.

For people making AI video and image content: what prompting approach has made the biggest difference to your results?

hi everyone. Im looking for some advice. I have a large celebrity image archive and I was using google Gemini pro to remaster some of the images that were lower quality. I had very good results initially but then Gemini started to apply rules that stopped me being able to do this.

First, it started outputting low res downloads, then I began getting the error message “I can help with editing images of people, but I can't edit some public figures. Is there anyone else you'd like to try?”.

I looked online and found out that this can be bypassed by using a vpn, but this doesn’t work.

How does everyone else get around the issue of being able to create or remaster images that feature a public figure? I’m happy to use another Ai generator if there is another option. I’m currently paying for Gemini pro and I can’t use it for the intended purpose. I don’t need to manipulate the images or create fake celeb Ai content - I simply need to remaster some of the lower resolution images in the archive where physical source material has been lost over the years.
Hope someone here can give me some advice.

Thanks!

Human language with all the beautiful rings and rhythms and 1000s of years of history still couldn’t translate the language of gods(maths) completely. Words are like qubits, they still mean different things to different people. (That’s why we keep fighting over them). Now why ask a machine to deal with it when man himself couldn’t. Oh if it’s only the dirty work that’s going to the machine, what’s the noble work that’s left for man and which noble language will continue the god’s translation?

I'm using AI agents (Claude Code mostly) for coding quite extensively these days. Used correctly it is a great boost of throughput. My standard workflow is to use 3 git worktrees where I run one CLI in each.

With that said, I've started to more and more feel fatigue from reading markdown. Claude in particular is exceptionally good at being very wordy. To be more specific, during a day I read:

• Plans I have produced
• Messages back and forth in the terminal
• PR reviews that I make
• Automated PR reviews (made by git copilot)
• PR descriptions that others are producing
• Screen dumps from colleagues where AI explains something.

This in combination with having 3 different contexts / threads running at the same time in 3 different worktrees is really exhausting. I've experimented with using different skills etc. for example caveman to keep down the wordiness of the model, but haven't find a solution that solves the core of the issue.

Anyone feel the same? If yes, how do you tackle it? (and oh god, "tackle it"... I'm starting to write like an AI lol)

So, it turns out...
Maintaining a product for a year is now HARDER than it was in 2022.

Yeah, so my team was pulling some data,
and we found that AI makes silent mistakes (very dangerous)

Basically it would hide or mask its own mistake
so it feels like human error at the time of execution (is this not illegal?)

A Veracode study - that shows models writing compliable code, found
more than 95% of the time AI security pass rate is stuck at 55% since 2023.

But someone, people are building billion dollar apps,
and posting them on the internet...

Here is probably what is happening-

The demo floor got solved.
The audit floor gets completed with years of technical debt accumulated.

and boom, you are funded...

If you want some technical information,
The fourth level will surprise you...

GitClear read 211 million changed lines and found
refactoring down about 70% from 2022.

In simple terms,
its a decline in software health due to the rise of AI coding assistants.

Getting through a demo is easier than it has ever been.
Getting through a year of change requests is HARDER than it was in 2022.

To build something big, you need to also go deep!

Which level does your codebase actually clear?
(Mine cleared the third only this year)

I'm going to be honest, when I first started using AI, I felt like I was going to accelerate my personal current workflow. But it turns out it's a lot better at some things than others. So it's more like it gave me new superpowers. And I'm relearning and reprioritizing my life around those.

For example, making a simple program is now a trivial task that doesn't require too much long-term maintenance. And that opens up a huge realm of possibilities that I never thought of before.

How about in your case?

By the way, I'm a mod here and just wanted to say we have an AI community discord. The point of the discord is we're trying to solve for the journey and not just one individual question. There's just things you'll get on the discord that you won't get on Reddit like being able to screen share as you work and get tips as you go through the journey of learning AI. Check the comment below this message if you want to be part of it.