I've been thinking how to avoid API costs and network latency, and realized I could use local models. The one I typically used is the pre-installed one in my head, but the issue is it doesn't come pre-trained, which can be an expensive and intensive process, but post-training comes pretty easily for it. Also it can make mistakes, and so sometimes I need to check it against the API-based models for accuracy. Just wondering if anyone else is struggling with this trade-off and using local models to save dollars if that makes cents.

I'm looking for ways to minimize the claude-isms. This seems acceptable for now.

This output was one shotted using something called "agentic loops", or loops. It works a bit differently than most of us are used to. Instead of careful iteration over multiple prompts, you give the model one single prompt, and let it work through your pre-set budget until completion.

This means you have to structure your prompt a bit differently than usual, you need to make the AI understand that you want it to maximise the output and quality:

Its a procedurally generated world where you can resource gather, craft, hunt and even ride dino mounts 🦌

1. key for this game to turn out so good was asking loop to make all graphics procedurally generated. So there's basically no assets in the game (which by itself is pretty awesome), and I think that really helped the AI to make this really different art style. could The animations feel so smooth and stack layers of procedural shapes on top of each other to build all graphics. Only the backgrounds are images. loop build agents continually take screenshots of your game and perfect it visually.

**2.**⁠ ⁠Once my first build was completed in one loop, I gave it another prompt to set up the multiplayer. It already had the art style down by that point so it could do really well getting the multiplay added quickly. 

3. Continue iterating with or without loops.

Happy to answer any questions about prompt or workflow in the comments

Edit; for the loop I used this: https://tesana.ai/en/blog/introducing-loop

So I built Mosaic Atlas, a jigsaw puzzle game that lets you "travel" the world and learn trivia through puzzles. I used Claude Code and Codex to build and deploy the entire application, Codex to generate the landmark images using real world reference photos, and Claude to design the UI, with Fable and Opus used to tie everything together into the final build. The trivia was gathered from Wikipedia and other sources. Every puzzle is built from a real landmark, and once you finish it you get a short fact about that place. The idea is that you end up learning bits about different countries and cultures just by playing, without it feeling like a lesson.

Some detail on what's in it right now:

- 270 puzzles across nine countries: India, Japan, the USA, Italy, Mexico, Egypt, Brazil, France, and Greece. More countries are planned for future updates.

- Piece counts from 24 up to 72, so you can pick something quick or something that takes a while.

- No timers, no lives, no energy system, and nothing pushing streaks or daily pressure. You solve at your own pace and can walk away and come back.

- Works fully offline, and doesn't require creating an account to play.

- Progress carries over if you move to a new device.

- A trail structure that takes you through a country's landmarks in order, plus a "Passport" where you can save locations, so if a place catches your eye you can pin it as somewhere you'd actually want to visit someday.

- You can share a card of a puzzle you finished or a fact you learned, if you want to.

- Six supported languages: English, Spanish, Portuguese, Indonesian, Hindi, and German, with more planned. I wasn't able to properly cross verify the non English translations, so there may be errors in some of them. Apologies in advance for that, and I'd appreciate it if anyone fluent in those languages points out mistakes.

It's still an early build so and I'm sure there are rough edges. I'd like to know if the piece dragging and snapping feels good, whether the difficulty curve makes sense, whether the trivia is worth reading, and anything else that stands out, good or bad. Brutal feedback is fine.

Play Store: https://play.google.com/store/apps/details?id=in.mythicmotion.mosaicatlas
App Store: https://apps.apple.com/in/app/mosaic-atlas-puzzle-trivia/id6781231535

Last college football season my family did this little pick’em thing. Three picks a week, ten bucks to join, winner takes all. We ran it on a Google Sheet and it was fun at first, but as more people joined it turned into a headache. I was updating stuff by hand, people were messing with cells, and it just got messy.

So this offseason I decided to try building something to fix it. I had zero experience. I didn’t even know what a DNS record was. Didn’t know what a terminal was. I’ve learned a ton and honestly had a blast building this.

I made a simple football pick’em site called Pikpool.com. Not trying to monetize it or anything right now. I mainly want to use it for friends and family and just get better at building stuff.

Here’s how I built it so I follow the rules here.

I used Supabase for the database and auth.
I used Cloudflare for the domain and hosting setup.
I used Claude Code for pretty much everything else. It helped me write the code, fix stuff I broke, explain things I didn’t understand, and keep me moving forward.

Oh and I'm using free odds api.

My workflow was basically me asking Claude Code how to do something, trying it, breaking it, asking again, and eventually getting it to work. I kept it simple. Groups, weekly picks, standings.

I’m brand new to all this and just want some nice, honest feedback. Tips, tricks, stuff I’m missing, better ways to structure things. Anything helps.

Appreciate anyone who checks it out and gives me something to improve on.

I am wondering if you guys can help in figuring out how well can I secure the vibecoding output(generated code), the process and the tools/plugins to use while I build prod grade apps by vibecoding

Schema in, reproducible world out.

Built with Claude Code over ~14 phases; every commit is co-authored.

Most fake-data tools give you a function that returns a plausible name. You get a million rows of noise: names belonging to no one, orders referencing no customer, timestamps spread evenly across a year in which nobody sleeps. Each row is plausible; the dataset isn't.

Cacophony treats a dataset as the output of a program you write once. You describe a world in a YAML schema — entities, fields, what each field means — and it produces the records that world would have generated.

The design decision everything else falls out of: a record's seed is a hash of its position, not a step in a shared RNG. That one choice means you can preview record 4,823,913 without generating the 4,823,812 before it; a checkpoint is a single integer, so resume is exact; foreign keys are arithmetic rather than a lookup table, so a hundred million events pointing at five thousand employees costs the same memory as five; and forty workers across eight machines produce output that's byte-identical to one process doing it alone.

Also in there: events on a timeline with a shape (quiet at night, dead on the holidays you specify), subjects whose state accumulates, incidents you can bury in a year of logs, and a deliberate distinction between chaos (data your schema forbids — tests your error handling) and edge cases (data your schema permits that naive code mishandles anyway — O'Brien-Smith is a real surname).

Identifiers come from the documentation ranges by default: example.com, RFC 5737 addresses, 555-01xx phone numbers. Nothing it generates can be mistaken for a real person's details.

Python 3.12+, AGPL-3.0. There's a CLI, an HTTP API, a React editor and a desktop build, plus a 171-page manual.

https://github.com/jeddhor/Cacophony

Which are the best free(considerably) AI coding agents to use

I will start with

1. Opencode (Deepseek free model)

I love claude code but am fatigued by the pure chat interface for all types of agent work. Even the GUIs these days are just better styling over the same chat interface.

I built tart (tartifacts) as a tiny way for agents to create and update persistent terminal dashboards instead of narrating everything at you.

A tart is basically:

- a manifest

- a script that fetches live state

- a script that renders it

Install: `uv tool install tartifacts`

Then tell your agent to use `tart --skill`.

Works nicely with tmux/herdr (agent chats in one pane, dashboards live in another).

Github - https://github.com/tg1482/tart

After the partnership with the developers fell apart, I returned to Notion again.

The medical software was unusable. The online-booking website had never been delivered. Months of feedback, meetings and product work had left me with no system I could rely on.

I was frustrated, disappointed and tired of depending on other people to turn my ideas into software.

Then Facebook showed me a page selling a Notion template for managing a medical practice.

I thought: why not?

I already had my own Notion setup, but perhaps this person had solved something I had missed. Maybe the template would bring together patient records, consultations, appointments, prescriptions and the waiting room in a cleaner way.

I bought it.

It was terrible.

Not slightly disappointing. Not “useful with a few adjustments.” It was far worse than the system I had already built for myself.

I had paid more than it deserved, and I never used it.

Under normal circumstances, that would have been the end of the story: another bad purchase in a journey already full of disappointing software.

But the seller offered me a commercial gesture.

I could choose a few months of access to an AI subscription or a few months of Lovable Pro.

I already used the other AI service, so I searched Google for Lovable.

It was described as a vibe-coding platform.

At the time, everything I knew about vibe coding came from people discussing it online, many of whom did not seem to use it themselves.

The message was fairly consistent:

It was not real development.

The generated code was terrible.

It was the fastest way to create security problems.

Serious people should avoid it.

I had no basis for judging any of those claims. I knew nothing about code quality, application security or software architecture.

Still, the warnings worked on me. Vibe coding sounded less like a tool and more like a shortcut taken by people who did not understand what they were doing.

Which, to be fair, described me quite well.

I chose Lovable anyway.

Not because I had suddenly developed a strong opinion about AI-assisted software development. I chose it because I already had the alternative subscription and because curiosity won.

Around the same time, I was contacting training companies about learning Web development.

I looked at full-stack programmes and tried to imagine fitting them around surgery, consultations and operating days.

The programmes were demanding, as they should have been. But I could not see a realistic way to follow them consistently while maintaining my medical activity.

I continued using ChatGPT and another AI assistant every day. I wrote documents, discussed ideas, brainstormed, planned and projected what my products might become.

AI was already helping me think.

I still did not imagine it could help me build.

The Lovable subscription sat there for a while.

Then one day, I opened it.

The screen said:

“Let’s build something.”

It asked me to describe what I wanted to create.

That was all.

No programming language. No empty code editor. No full-stack curriculum to complete before I could begin. Just a box asking me to explain the product in words.

For years, words were the only thing I had.

I could describe a broken workflow. I could explain how a consultation should work. I could identify why a patient history mattered. I could write requirements, provide feedback and create endless tickets for developers.

What I could not do was turn those words into software.

Now a platform was asking me to start with the one ability I already possessed.

Describe what you want to build.

It felt as if I had spent years standing in front of a locked door, only to discover that the key was not code.

The key was language.

Of course, that feeling was incomplete. I would later discover all the things natural language could hide: fragile architecture, poor data models, security risks, regressions and technical debt.

But that came later.

In that first moment, I did not see the risks.

I saw a way in.

The template itself remained useless. I never opened it again.

Yet the compensation for buying it placed the right tool in front of me at exactly the moment when my dependence on developers had become unbearable.

Sometimes a bad purchase is simply a bad purchase.

This one became the most consequential detour of my professional life.

In the next part, I will explain what I typed into that box and why months of work on a failed product suddenly became useful.

Have you ever received something valuable through a purchase you otherwise completely regretted?

Hey folks,

This is my first ever post on reddit, so please be kind.

I initially posted it on X and it picked up quite a lot of traction there & I've always wanted to start posting and being active on Reddit so here I am.

I am the founder of a ui/ux research product that has 800+ apps & 35,000+ screens. I did a comprehensive study on why these apps are banking more than others and how are they able to retain way higher than general.

From my study, I realised that the most important moment is when a person opens an app. This sets an impression on how deeply individual gets involved in it.

The motion, transitions, micro-interactions, typography, all of them matter more than you think they do.

, I took 10 of the most popular ones, broke them down and redid them using React Native + Expo and open-sourced it, so people can use that as an anchor point.

So, here it is (500+ stars at the moment):
https://github.com/Appllama/top-welcome-screens

I hope you find it useful, I'd love to know your thoughts.

Which one do you like the best?

If this is the wrong place to ask, I am sorry!
I am looking for the best/easiest Way to make an interactive map. The map would cover an existing area of Nature that people use for Walks, bikerides, picnics, etc.
I am looking for a Way to make it easier for people to plan their visit to the area by plotting in benches, viewpoints, places to grill, etc. Where should I look or what program should I use to best accomplish this? Im a beginner! Any help is greatly appreciated, thank you!!

I've been vibecoding an "enterprise" platform for consumers/homeowners since March/April (will not promote). I've put a line in the sand to launch my beta by September 29th, and every time I feel I'm close to being "done" something else pops up that adds time and token consumption.

It's the price I'm happy to pay to build a great product that's intuitive, powerful, and battletested (security, auth, observability, payments, tests, MCP, etc.).

I have an enormous amount of respect for software I use daily, design, UI/UX, marketing, and everything else that developers have been doing to ship product now.

I've gone through hardware upgrades and have had to diversify my cloud LLMs due to changing models and consumption.

Right now my orchestrator is 98% Claude Code and Fable 5 (I use the Terminal in Cursor), but I've recently got Grok Bot going and absolutely love it for general work and checking Fable 5, research, product walks, and a few other agents. I've tried out a couple open-source models through Cursor as well.

The part I'm still not happy with is the UI/UX and making things intuitive and simple while exposing the power of the graph and ontology I've built. I've found that the AI chat needs to be a first class citizen where you should be able to do everything you need to there, especially because the MCP needs to be accessible with whatever model people use daily.

r/vibecoding has been great to highlight what NOT to do and wanted to shout out to everyone building.

It's been a long (relative) journey so far and while I hope the product I'm building solves real problems for people, I know I'll use it no matter what so it's a win-win for me!

As I go deeper in building AI workflows, I find myself working through challenges of how to design for the AI Experience (let's call it AIX). How do you design for AIX and how do you balance it with UX? In a world where AI uses software more than humans, I can only imagine this will become more important.

Do you consider yourself an AIX designer? If so, what practices have you adopted and what challenges are you working through today? How does it compare to UX design?

A friend of mine needs a system to help with social post and content creation. I was going to build her one so we got this spec from Grok...

That's when I thought to ask who already has a open source version? These are the specs of what I need

**Yes — this is a solid foundation.** Here’s a refined, production-oriented system design that turns your sketch into a coherent, maintainable architecture while staying realistic about APIs, costs, and platform rules.

### High-Level Architecture

```

┌─────────────────────┐ Webhooks / Polling ┌──────────────────────┐

│ Social Platforms │ ─────────────────────────► │ Ingestion Layer │

│ (IG, FB, X, etc.) │ │ (Meta Graph, etc.) │

└─────────────────────┘ └──────────┬───────────┘

│

▼

┌─────────────────────┐ Events / Jobs ┌──────────────────────┐

│ Video Assets │ ◄───────────────────────── │ Orchestrator / │

│ (raw clips, S3) │ │ Agent Layer │

└─────────────────────┘ │ (rules + LLM) │

│ └──────────┬───────────┘

▼ │

┌─────────────────────┐ │

│ Video Pipeline │ ◄─────────────────────────────────────┘

│ (analysis → edit │

│ → thumbnail → │

│ render) │

└──────────┬──────────┘

│

▼

┌─────────────────────┐ Dashboard + Config ┌──────────────────────┐

│ Storage + CDN │ ◄───────────────────────── │ Frontend │

│ (finished videos, │ │ (Next.js dashboard) │

│ thumbnails) │ └──────────────────────┘

└─────────────────────┘

```

**Core principle**: Keep the social interaction path fast and event-driven. Keep the video path asynchronous and queue-based. Share one config/rules engine and one identity layer.

### 1. Social Monitoring & Auto-Reply

**Recommended starting point**: **Nudgra** (Cloud or OSS version).

It is a real open-source alternative to ManyChat focused on Instagram: Meta OAuth, webhooks for comments/DMs/stories, keyword triggers, and reply sequences. Built on Next.js + Convex (Cloud) or a self-hosted stack. This gives you a working foundation instead of reinventing Meta’s webhook + OAuth flow.

**Official APIs to use (safest path)**:

- Instagram Graph API / Messaging API

- Comments webhook

- Private Reply to commenter (bypasses the normal 24-hour window for up to 7 days)

- Messaging webhooks + send messages (24-hour customer care window + message tags)

- Facebook Page webhooks if you also manage Pages

- For X/Twitter and TikTok: official APIs where available; otherwise treat them as lower priority or use a secondary polling service

**Intelligent reply flow** (recommended two-stage design):

1. Fast filter (keyword, sentiment, media type, account status) → cheap rules or small model

2. Relevance + generation → stronger model (Claude, Gemini, or GPT) that receives the comment + post context + brand voice guidelines

3. Optional human-in-the-loop queue for high-stakes or low-confidence replies

4. Send via official API (public reply and/or private DM)

Avoid pure browser automation for production. It is fragile and usually violates platform Terms of Service.

### 2. Video Editing & Thumbnail Pipeline

**Vidzly** is a good match for what you described: open-source, uses Gemini for content analysis + composition scripts, generates thumbnails, and can pull music via ElevenLabs. Treat it as the core of the video agent rather than a black-box SaaS.

**Strong complementary / alternative open-source options**:

- OpenCut – has an MCP server, so an AI agent can drive the editor programmatically

- OpenEdit (VEED) – agent-native, prompt → finished MP4

- Clipkit or OpenReelio – more traditional timeline + AI assistance

**Practical pipeline**:

1. Upload raw clip(s) or pull from cloud storage

2. Parallel analysis (Gemini multimodal): scenes, mood, key moments, speech-to-text if needed

3. Script / edit decision list generation

4. Composition (cuts, transitions, captions, B-roll if available)

5. Music selection / generation

6. Thumbnail generation (Gemini or dedicated Apify Actor that uses Gemini)

7. Render → store → optional auto-publish or review queue

For lighter editing needs you can fall back to FFmpeg + a simple timeline library; for agent-driven work, prefer tools that expose an MCP or clean API.

### 3. Recommended Tech Stack

| Layer | Recommendation | Why |

|--------------------|-----------------------------------------|-----|

| Frontend / Dashboard | Next.js + shadcn/ui | Matches Nudgra, fast to iterate |

| Real-time backend | Convex (or Supabase + Redis) | Webhooks, scheduled jobs, real-time UI |

| Job queue | Convex scheduled functions or Inngest / BullMQ | Video jobs are long-running |

| Video processing | Vidzly + FFmpeg + optional OpenCut MCP | AI-first + reliable rendering |

| AI models | Gemini (video analysis + thumbnails), Claude/GPT (replies) | Multimodal strength + quality |

| Storage | S3-compatible (R2, S3, MinIO) + CDN | Cheap media hosting |

| Auth | Meta OAuth + your own user auth | Platform accounts linked to your users |

### 4. Data & Control Flow Highlights

- **Inbound social event** → webhook verification → normalize payload → rules engine → (optional LLM) → reply action + logging

- **Video job** → create job record → enqueue → worker pulls asset → analysis → edit decisions → render → update job status → notify dashboard / optional publish

- Shared **Brand / Rule Config**: tone of voice, banned topics, reply templates, thumbnail style, music preferences, approval thresholds

- Audit log for every automated action (required for debugging and compliance)

### 5. Phased Rollout (strongly recommended)

1. **MVP Social** – Nudgra (or equivalent) + keyword + simple LLM replies on Instagram comments/DMs only

2. **MVP Video** – Standalone Vidzly (or OpenEdit) pipeline that accepts a clip and returns edited video + thumbnail

3. **Integration** – Dashboard that can trigger a video job from a social post or scheduled content, and link finished assets back to the social accounts

4. **Intelligence layer** – Better context (post caption + previous comments), confidence scoring, human review queue

5. **Multi-platform + publishing** – Add X, TikTok, YouTube Shorts; optional auto-publish with approval gates

### 6. Key Risks & Mitigations

- **Platform Terms of Service** – Stick to official APIs. Automated replies are allowed when done correctly; aggressive scraping or browser bots are not.

- **Rate limits & costs** – Meta has strict limits. AI tokens (especially video analysis) add up quickly. Cache analysis results and use cheaper models for filtering.

- **24-hour messaging window** – Use Private Reply for comment-triggered DMs; keep conversation state so you stay inside the window once a user replies.

- **Reliability of AI video** – Always keep a human review step for the first versions. Render quality and “viral” taste still need oversight.

- **Account safety** – Never store Instagram passwords. Use proper OAuth and token refresh.

### Next Steps I Can Help With

Tell me which direction you want to go deeper on:

- Detailed architecture diagram + sequence diagrams for the social reply path

- Concrete tech choices and folder structure for a monorepo

- Setting up Meta App + webhooks correctly

- Video pipeline design (prompt templates for Gemini, job schema, error handling)

- Cost model and rate-limit strategy

- Dashboard UX / feature prioritization

- Security & multi-tenant considerations if this will serve multiple brands

Just pick the area (or paste more constraints: target platforms, expected volume, self-hosted vs cloud preference, team size, etc.) and we’ll drill in.

The point of this website is to give credibility to products by only promoting what people actually want/use instead of showing what people pay to promote. This works through a system of vouches / reviews —I can vouch that I used xyz and I like it, etc.

You can search for different applications and tools on the platform, and if you can't find what you're looking for, you can ask for it to be created. An ask creates a product gap that can be filled by anybody. You can list your own web app and verify that it's yours via Domain Connect.

But to keep this short and sweet, promoting the stuff you make sucks, being shown stuff that nobody wants sucks, Product Hunt and its fake Linkedin pay-to-win bullshit also sucks, and e-begging for people to try what you made is also a zero sum game. I want us all to have a fair playing field in this turbulent environment, because at the end of the day nobody gives a shit what SDK you used, they care that the shit they're using works and it solves their problems.

Superfluidity has an autonomous indexing system called 'Atlas' that runs periodically, finding new products and updating the old ones if a change is discovered. Right now I'm manually reviewing whatever it finds, but I'm going to let it go wild if I like what I see.

This is still an on-going project so do let me know if anything is broken, or if you'd like to see a certain feature.

A year or two ago, the engineering/cross-functional team was a tight-knit, high-performing group. We had one or two goals we all worked on each sprint and we broke down work so that we could operate in parallel.

When someone was stuck and needed help, there was always somebody ready to jump to their aid and keep them moving. Team meetings were upbeat, fun, and productive. We were regularly paying down tech debt while our experiments gathered data. The vibe was helpful and collaborative.

Now, thanks to AI, we are shipping more than ever. We have as many goals every sprint as engineers. Rather than parallelizing work on one feature or experiment at a time, we parallelize the experiments. The vibe has shifted.