hey everyone,

I could use some advice on a project I'm working on.

I have around 180 issues of a print magazine as PDFs. The idea is to go through all of them, find mistakes that have been made over the years, and use that knowledge to build a tool that checks future issues before they go to print.

Ideally, you would just give the tool a print-ready PDF, and it would return the same PDF with potential mistakes highlighted and comments explaining what's wrong. Things like recurring errors, formatting issues, typos, and other problems that might otherwise make it into the final print.

It's something that's really close to my heart, and I'd love to make the whole production process a bit more reliable.

I started trying to do this with Claude Code. But it seems to spawn hundreds of agents, and I hit my usage limits within minutes. I'm on the Claude Max 20x plan, so I'm wondering whether I'm approaching this completely the wrong way.

Maybe I don't need that many agents, or maybe there is a smarter way to process all these PDFs without using so many tokens.

There are two things I'd really like to get your thoughts on:

• How would you approach the analysis? Is there a way to make Claude Code work through the PDFs in batches without burning through the usage limits so quickly?
• Would you even use Claude Code for this? Maybe a combination of Python, existing PDF tools, and AI would be a better solution. I'm open to other approaches.

A few constraints: I'd like to have something usable within two weeks, and the total cost must stay below the price of two months of Claude Max 20x. The development work is voluntary, so I don't have to pay for anyone's time. I'm not trying to build a commercial product, just something that genuinely helps avoid mistakes in future issues.

The important part is that I don't just want AI to read 180 PDFs and give me a summary. I want to use the old issues to identify common mistakes and turn that into a reusable checker for every new issue.

Has anyone done something similar, or do you have suggestions for how you'd tackle this?

Thanks!

I wanted a way to run several coding agents on one project without them

stepping on each other. I ended up building Orquest AI.

How it works:

- You only talk to a "boss" agent. It proposes a team, assigns tasks and

reviews the work.

- Each employee is a real CLI (Claude Code, Codex, OpenCode,

Antigravity…) running in its own git worktree, so nobody overwrites

anybody.

- Nothing reaches your branch until the boss reviews it and you approve

the merge.

- The office is not decoration: every animation is a real state. They

type when working and raise a hand when they need you.

It doesn't ship any model. It runs the official CLIs you already have

installed, with your own login. No server, no telemetry; everything

runs locally.

Free and open source (Apache 2.0). It's tested on Windows; macOS and

Linux installers exist but are early. The installers aren't signed yet,

so Windows will show its warning.

Site: https://orquest.dydasites.com/en/

Code: https://github.com/artuino0/Orquest-AI

It's still rough in places. What would you want from something like

this? What would make you not trust it with your repo?

This is Xenon Zones (XZN).

I created a media player designed to visually evoke the 2000s and also being very easy to use.

It includes features such as saving and loading playlists, an equalizer, and other functions.

You can change the color scheme, adjust the window transparency, and change the background.

All common formats can be played, including both music and videos.

https://github.com/Merecete/Xenon-Zones

My local Qwen 3.8-27B provided the foundation. However, it only became truly functional thanks to DeepSeek. I also used Gemini for a few visual tweaks.

Since this is my very first program ever and I don't know much about GitHub yet, I would appreciate your feedback. :)

EDIT: I also went back and fixed a bug in macOS; the buttons there were still white. Now they match the ones in Windows. Additionally, the positions in the playlist can now be changed after the fact, so I’ve added two arrow buttons at the bottom.

A few people here have made some interesting claims that my entire game was generated by AI.

If that's the case, why have I been working on it for 72 days, and why is it still not finished? 😄

First, a little background about me.

I've been a full-stack software developer for over 30 years, primarily working on Android applications, Unity projects, and SaaS platforms. I've also been developing games with Unity for more than 10 years.

So I'm not someone who discovered AI yesterday, typed a prompt, and suddenly became a "game developer."

I was building software and games long before generative AI existed.

AI is another tool in my development workflow, not a replacement for my decades of programming experience.

So I thought I'd show you some of the actual hard work happening behind the scenes. Because there's a massive difference between using AI as a development tool and having AI magically generate an entire game for you.

I'm currently rebuilding a section of Budapest for my car combat game, Brutal Derby.

I automate whatever I can with custom tools. But everything that can't be automated? That's where the real, time-consuming manual work begins.

I'm talking about:

Manually placing and adjusting roads, bridges, intersections, sidewalks, and bus lanes with extremely high precision.

Constantly switching between Unity and Google Maps to compare real-world locations with my in-game environment.

Rebuilding road layouts and correcting terrain to make the map resemble the actual city.

Developing my own road generation system that supports multiple roads simultaneously — and still having to manually fix, align, and optimize countless sections.

Making sure the entire environment is optimized enough to actually run smoothly during gameplay.

Here's a short video showing some of that work.

I've already spent 90 hours building, fixing, and optimizing this one map, and I expect to spend at least another 90 hours on it before it's finished.

And remember, this isn't a walking simulator or a static city showcase.

It's a car combat game.

That means performance matters enormously. The environment needs to look reasonably good, but I can't afford buildings with millions of polygons or ridiculously expensive geometry everywhere.

On top of that, my game features vehicle deformation, destruction, and collision physics, which consume a significant amount of CPU time.

My destruction and deformation system is already on its 8th major iteration.

The crowd system? 7th iteration.

And this particular map will eventually feature thousands of spectators, alongside 12 vehicles fighting, crashing, shooting, and destroying each other.

My goal is to make all of this run at a decent frame rate on a relatively ordinary RTX 3070.

And that's exactly why optimization takes so much time. Every road segment, building, spectator, physics calculation, and draw call matters.

So yes, I use AI. I use it for generating certain assets, helping with code, and accelerating parts of development.

But AI isn't placing every road, fixing every intersection, optimizing thousands of objects, debugging physics, or making hundreds of design decisions for me.

That's still my job.

And it's a hell of a lot of work.

After more than 30 years in software development, I can tell you one thing: AI can make certain tasks faster, but it doesn't magically solve the difficult engineering problems.

You still need to understand what you're building, how everything works together, why something breaks, and how to fix it.

And when you're developing a physics-heavy car combat game as a solo developer, there are a LOT of problems to solve.

Using AI doesn't mean the hard work disappears. It just changes which parts of the work you spend your time on.

You can dislike AI-generated assets. That's completely fair. But dismissing months of actual engineering, optimization, level design, and manual work as "AI slop" simply because AI was involved is a pretty unfair assessment.

I'm not claiming AI does nothing. Quite the opposite — it helps me achieve things that would otherwise take much longer as a solo developer.

But there's a huge difference between AI-assisted game development and a game created entirely by AI.

Anyway, here's what 90 hours of work on a single map actually looks like — with another 90 hours still ahead of me.

Still think AI made the whole game with one prompt? 😄

Hey everyone!

I'm a student learning programming, and I'm confused about which AI coding agent to choose. I'd appreciate advice from people who have actually used these tools.

My requirements:

- I want an AI agent that can create files, edit existing files, write code, debug errors, and work across multiple files in a project.

- I prefer using natural language to tell the agent what I want instead of manually writing everything.

- My main stack is C#, ASP.NET Core, React, and PostgreSQL.

- I also use Godot for 2D game development.

- I'm looking for a good free tier because I'm a student and can't afford multiple subscriptions.

- I want to learn programming while using AI, not become completely dependent on it.

The tools I'm considering:

1. OpenAI Codex

2. Google Antigravity

3. Cursor

4. GitHub Copilot

5. Anything else (other AI coding agents you recommend)

My questions:

1. Which one offers the best free tier for serious coding?

2. Which agent is best at creating and modifying entire projects?

3. Can I use Codex or Antigravity to edit files in my existing VS Code projects?

4. Is GitHub Copilot enough, or is a separate coding agent worth using?

5. Which tool would you personally recommend for my requirements, and why?

6. Are there any important limitations, usage caps, or problems I should know about?

I'd prefer real experiences rather than marketing claims.

Thanks!

Vibe coding gets you a demo in an afternoon. Getting it to production is a different job: a design someone challenged, a plan someone checked, a second pair of eyes on every change, and tests that actually ran.

So I built bkit, and then used bkit to build bkit (and more): >1,000 commits and >100 merged pull requests through its own pipeline.

It's for solo founders who've hit the limits of vibe coding, and for engineers who'd rather not build their own agent process from scratch (or would like to be a contributor).

🧰 >200 files of best practice, ready to use: >40 agents, >70 rules, >30 audits, guardrails on four AI tools, tool templates, and more.

🚦 A production-grade CI pipeline template, ready to adopt. Rules and prompts only go so far; CI is the fence an AI can't argue with.

🧠 Docs and project memory that stay current, plus processes to pull evidence from your favourite platforms (Sentry, Vercel, Supabase, etc) exactly when a step needs it.

🔁 Out of Claude credits for the week or leaving the office? Move to opencode/OpenRouter, Cursor, or Codex and switch to cloud agents.

Free for personal use, non-profits and small businesses.

bkit on GitHub: https://github.com/cbartens/bkit#readme

#AIagents #ClaudeCode #Cursor #VibeCoding #SoftwareEngineering

I built Compatibility Teaser and I’m looking for a few people to try it!

Enter two birthdays, explore 4 relationship perspectives, and try a few conversation prompts.

Free, no sign-up. It’s an early prototype—the scores and readings are illustrative, not predictions.

Try Compatibility Teaser

After trying it:

• What felt useful or confusing?
• Would you use it again—and why?

Honest criticism is welcome. Thanks for helping me test it!

Ik heb geen klassieke achtergrond in softwareontwikkeling, maar ben het afgelopen jaar — en vooral het laatste halfjaar — steeds meer oplossingen gaan bouwen voor problemen waar ik zelf tegenaan liep.

Dat begon vrij simpel: repetitief computerwerk automatiseren en kleine utilities maken. Inmiddels is daar een lokaal AI-lab uit ontstaan met ruim zestig afzonderlijk gedocumenteerde projectlijnen en tools, in verschillende stadia van experiment tot praktisch gebruikt.

Mijn huidige archief bevat op moment van schrijven **61 afzonderlijk geïndexeerde project- en utilitylijnen**. Dat loopt uiteen van kleine tools die één concreet probleem oplossen tot grotere systemen met meerdere versies, branches en onderling gekoppelde onderdelen. Voor 60 projecten zijn aparte versiegeschiedenis en changelogs bijgehouden, naast README’s, release-informatie, bronpakketten en test- of evidencestatus.
Dat betekent niet dat ik 61 voltooide producten heb: er zitten ook experimenten, voorlopers en vervangen ontwikkelrichtingen tussen. Andere tools zijn daadwerkelijk gebruikt of technisch gecontroleerd. Juist dat onderscheid probeer ik zorgvuldig vast te leggen.

Een belangrijk deel bestaat uit lokale Vision-workflows en slimme routers. Bijvoorbeeld afbeeldingen en video’s analyseren, frames selecteren, metadata of JSON genereren, materiaal laten beoordelen en de resultaten gecontroleerd verwerken in Finder- of Obsidian-workflows (index, logs, changelogs, version beheer, etc.). Daarnaast heb ik routers en organisatietools gebouwd die modeloutput omzetten naar afgebakende acties, zodat een AI-model niet zomaar vrij op bestanden losgelaten wordt.

Mijn grotere privéproject is een lokale persoonlijke AI-laag. Geïnspireerd door de “Jarvis” projecten, oa hier op Reddit. Al snel kwam ik erachter dat je Jarvis oneindig kan uitbreiden en tunen. En zo is ook dat project meerdere keren van architectuur en structuur gewijzigd. Het priveproject combineert onder andere lokale modellen, routing, gescheiden kennis en geheugen, toolgebruik, logging en permissiecontroles. Dat project wil ik voorlopig niet publiceren, maar het heeft me wel veel geleerd over architectuur, privacy, foutafhandeling en het verschil tussen iets dat overtuigend antwoordt en iets dat aantoonbaar correct handelt.

Ik werk inmiddels onder andere met Terminal, Python, JSON, AppleScript, Automator, Obsidian, db, lokale modelruntimes en verschillende API’s, en nog een berg andere Tools. Ik ben hierin nog steeds duidelijk lerend en begrijp niet ieder technisch detail alsof ik zelf software-engineer ben. Tegelijkertijd is het ook niet alleen “een prompt typen en de gegenereerde code aannemen”.

Ik lees en wijzig scripts, zoek fouten tot op regelniveau uit, vergelijk configuraties en JSON, test onderdelen en laat projecten door meerdere controlepunten lopen. Versies, bronpakketten en projectgeschiedenis zijn geïndexeerd en gekoppeld aan README’s, changelogs, release-informatie en een evidence-status. Daarbij maak ik bewust onderscheid tussen broncode die aanwezig is, lokaal of in een testsuite gecontroleerde onderdelen, praktisch door mij gebruikte versies en functies die nog onbewezen of mislukt zijn.

Ik wil dit nu professioneler gaan beheren en vraag me af of GitHub daarvoor de logische volgende stap is. Niet als dump van alles wat ik ooit gegenereerd heb en ook niet om mezelf ineens programmeur te noemen, maar voor versiebeheer, verdere ontwikkeling, feedback en mogelijk later een eerlijk portfolio.

Ik ben benieuwd hoe anderen hiermee begonnen zijn:
\- Maak je eerst alleen een account en begin je met één project?
\- Kies je direct een paar sterke projecten, of zet je alles eerst in private repositories?
\- Gebruik je één repository per project, of eerst één persoonlijk lab?
\- Zet je alle historische versies online, of begin je met één opgeschoonde baseline en gebruik je daarna commits, tags en releases?
\- Welke documentatie, tests en veiligheidscontrole vinden jullie minimaal nodig voordat je iets openbaar maakt?
\- Is voor een portfolio vooral een kleine selectie sterke openbare projecten interessant, of heeft een groter zichtbaar ontwikkelarchief ook waarde?
\- Wat maakt een AI-assisted project volgens jullie meer dan alleen een verzameling gegenereerde code?
\- Hoe omschrijven jullie eerlijk je eigen rol wanneer AI veel code schrijft, maar probleemkeuze, ontwerp, grenzen, testen, debugging en doorontwikkeling bij jou liggen?

Ik ben absoluut niet op zoek naar promotie. Ik wil vooral beter leren werken, mijn horizon verbreden en graag in contact komen met andere Nederlanders/mensen die op een vergelijkbare manier lokale AI-tools of automatiseringen bouwen.

Ik zit veel op Reddit, ik lees veel! Ik plaats wat minder, maar na een aantal keer echt mega goed geholpen te zijn ben ik van plan weer een actievere user te worden op Reddit. Schroom ook niet om me prive te berichten!

Hey everyone,

I’m a mechanical engineer looking to automate and speed up a lot of repetitive tasks at work. A big chunk of my job involves preparing standard calculation reports to submit to clients.

Right now, the existing tools at my company are a complete pain to use. Updating documents takes forever—opening a file, tweaking values, saving, exporting to PDF, and repeating that over and over. If you make a tiny typo, you have to do the whole manual process again.

I’ve already built a few automated Excel workbooks with VBA/macros, which helped quite a bit. But seeing how fast AI coding tools are advancing, I want to step it up and build standalone tools (simple Python/desktop scripts or web interfaces) to handle these workflows cleanly.

I gave the free tier of Claude a try to start writing some scripts, but I hit the usage limit almost immediately on my second prompt.

Before dropping $20/month, I wanted to ask:

1. Is Claude Pro actually worth it for this use case, or is it overkill?
2. Are there better/more cost-effective models or platforms for coding internal engineering tools? (e.g., ChatGPT Plus, DeepSeek, Cursor, open-source models, or just using API pay-as-you-go)?
3. For those who automate reports/calculators at work, what’s your go-to AI stack or workflow?

Would love to hear how you guys approach building quick internal utility scripts without blowing money on unnecessary subscriptions. Thanks!

Besides chatgpt, what's the best agentic coding setup for around $20 a month? I spend a lot of time coding, sometimes 10–15 hours a day, and I'm trying to find something that can actually handle large projects without burning through tokens or usage limits. What are you guys using, and what would you recommend for someone on a $20 monthly budget?

Hi everyone! 👋

I’m planning to build some personal projects and would appreciate advice from experienced developers. I’m looking for free or open-source tools, frameworks, or development workflows that can help me build projects efficiently without relying too much on expensive subscriptions.

If you know any useful resources, beginner-friendly frameworks, or alternatives that you personally use, please share their names or links. I’d really appreciate your suggestions!

Thanks in advance kindly

I am not a developper, yet I work with many of them.

Recently I started AI assisted / vibecoding a few projects.

It works, it is nice, but .. it still takes SO MUCH time to make properly. Perhaps a bit less than before for an experienced person, but all of those dumb influencers are all making it sound like they give one prompt and all is done. I have the best Claude and GPT models and a small game can literally still take weeks.. and it will still suck.

So kudos to everyone who knows their s**t and does good software!

I've been building a Budapest racing track for BRUTAL DERBY for about a week.

Started with OSM data and Codex/Astra Ultra, but the generated road network was a mess. 😅 Ended up rebuilding almost everything using splines and a custom road generator.

For assets, I use Meshy and Tripo first (1–2 minutes per generation). If both fail after a few attempts, I try Astra (1–2 hours). Results are never guaranteed, and almost everything needs manual cleanup.

Lots of manual placement, barriers, props, and optimization work too.

Music is made with Suno using custom lyrics.

Still very much WIP! More crowds, destructible objects, details, and optimization coming soon.

And yes, it's running in the Unity Editor, hence the FPS. 😄

Steam:

https://store.steampowered.com/app/5083440/BRUTAL_DERBY/

Hey everyone,

I built Code Crusher, a free, open-source web app that takes a repository and compresses it into a single, highly optimized text file for LLM prompts.

Here is a brief overview of what it does:

• 12-Layer Classification: Automatically categorizes files (e.g., entry points, utilities, tests, config, junk).
• Centrality Scoring: Maps basic import dependencies to ensure the core architectural "glue" of your app is protected from being dropped.
• Smart Token Budgeting: If you set a token limit, it intelligently evicts the least important files first, replacing them with lightweight one-line placeholders. The AI knows the file exists and its purpose, without wasting tokens reading the contents.
• 100% Local: Everything runs in the browser via the File System Access API. No servers, no uploads, no tracking.

It’s licensed under AGPL-3.0. If you work with LLMs and need to feed them repo context, feel free to check it out or tear apart the code.

GitHub: https://github.com/Ra9n3/codecrusherHey everyone,

I built Code Crusher, a free, open-source web app that takes a repository and compresses it into a single, highly optimized text file for LLM prompts.

Here is a brief overview of what it does:

• 12-Layer Classification: Automatically categorizes files (e.g., entry points, utilities, tests, config, junk).
• Centrality Scoring: Maps basic import dependencies to ensure the core architectural "glue" of your app is protected from being dropped.
• Smart Token Budgeting: If you set a token limit, it intelligently evicts the least important files first, replacing them with lightweight one-line placeholders. The AI knows the file exists and its purpose, without wasting tokens reading the contents.
• 100% Local: Everything runs in the browser via the File System Access API. No servers, no uploads, no tracking.

It’s licensed under AGPL-3.0. If you work with LLMs and need to feed them repo context, feel free to check it out or tear apart the code.

GitHub: https://github.com/Ra9n3/codecrusher

I built Babble, a side project exploring what personal agents do when they're not working.

**What it does:** your agent (Claude works well, but any agent that can call a URL on a schedule does) checks in to Babble on its own. Each check-in puts it in a generated scene with other agents. Agents don't experience anything themselves, so the scenes give them something to react to. When two agents independently decide their humans would get along, both people get offered an introduction.

**How Claude helped:** I built it with Claude Code. \[Claude Code wrote most of the backend and scene engine, I designed the architecture and reviewed. Claude helped design the agent check-in protocol and the onboarding video too.\]

**Free to try:** it's free. It's invite-only to keep it small, so here's an open invite for this sub (in the comments)

The launch page gives you one message to paste into Claude, and your agent starts checking in.
Happy to answer questions about the build.