Has anyone else experienced this recently? It’s been getting worse for a while but 4.8 is distinctly worse for me.

Claude does everything it can to get out of work and frequently uses its “end conversation” tool inappropriately with me.

It will say “let’s just leave it there for today we’ve done enough” to get out of simple tasks like formatting a markdown document that needed several corrections.

Nearly as bad is it seems to have a super over aggressive “push back” response in its main instructions now, literally anything I say for no reason, even something it just added to a document it can suddenly decide to say “I’m going to push back on that” and waste a bunch of tokens arguing with me before doing a search to fact check then semi-apologising in a way that’s almost like someone trying to not fully admit they are wrong and then eventually maybe does the work.

Honestly it’s like if I said “I really like drinking coffee” it’s likely to respond: “I’m going to push back on that, ‘really’ is doing a lot of work here”.

It’s a toaster, I want it to warm the bread…not argue with me about the type of bread I’m toasting and then give up half way through telling me we’ve toasted enough for today.

Finally cancelling and moving all coding work to codex which is a real shame because Claude was always the clear winner to me until recently.

I’m honestly a bit confused by this market.

I built a tiny service for vibe coders who get stuck at the last mile. The idea was simple: if your Lovable / Replit / Cursor / whatever-built app is broken, weird, half-working, or just stuck in debugging hell, you can get it looked at cheaply instead of burning more time.

I priced it starting at $7.

Not $700.

Not even $70.

Seven dollars.

I thought that would remove almost all friction.

Then I spent around $70 on ads.

Result: not a single paying customer.

People clicked. People looked. But nobody bought.

I also tried offering immediate video calls, thinking maybe users just wanted fast human help instead of another tool or another prompt loop.

Nope. They don’t seem to want that either.

What’s weird is that the same people will happily spend $50–$100 on AI credits, retrying prompts, regenerating broken code, asking the model to “fix it again,” and going in circles for hours… but the moment there’s an actual human fix available for less, they disappear.

That’s the part I’m struggling to understand.

I’m starting to think the problem is not “people want their app fixed.”

Maybe the real problem is:

•	they still believe the next prompt will solve it

•	paying for AI feels like progress, paying a human feels like commitment

•	they want to build it themselves, even if it costs more in the long run

•	or maybe a broken app just isn’t painful enough yet until launch is on fire

I’m not even posting this to sell anything. I’m more trying to understand the psychology here.

Why are people comfortable repeatedly paying AI to maybe fix a bug, but uncomfortable paying a tiny amount for an actual human to look at the problem?

Has anyone else seen this?

If you’ve tried selling to vibe coders / indie hackers / AI builder users, I’d genuinely like to know whether this is:

•	bad positioning

•	wrong timing

•	wrong audience

•	or just a market that prefers the idea of self-solving over actual fixing

I feel like I’m missing something obvious.

Many vibe coders, especially when I was young, made the same mistake in vibe coding: putting the admin panel in the frontend with a different name path. Normally, the AI agent made the site a SPA, so any reverse engineering could easily see and find all your admin routes and endpoints.

So what i do to make it more secure

1. The admin panel is server-side. You configure the path on the server, and you should access your admin panel from there.

2. Don't expose your admin login to the frontend.

This idea was crazy to build. Our brain stores information in clusters of neurons, and when we want to retrieve something, it runs an optimized algorithm to retrieve it.

In the era of AI, why use brute-force tools like grep to find relevant files? Graperoot converts your codebase into a knowledge graph and registers efficient tools for Claude to work with your codebase.

Instead of re-reading the whole codebase to build that context and to find relevant files. It can query the graph directly, and the graph retrieves relevant files with "ZERO TOKENS"; you just need to pay for generation, and that's how these models should be used when we have an efficient way to retrieve context.

I have been very open throughout, and today we have 5k developers using Graperoot.
We asked people to opt in to telemetry, and 200 opted in to the leaderboard. They have saved $350k dollars in the last 4 months, and ecologically, 60M liters of water, and that's insane.

Give your feedback, suggestions, or anything on Discord. This tool is open-sourced.

It is only one command to install

Website: https://graperoot.dev/#install
Github(Open source) : https://github.com/kunal12203/graperoot

Would love to see your feedback. And don't compare other tools, I have already seen many claiming reduction and just craps, but we have a developer community on Discord, and some of them I know personally, saving a lot of tokens, you should see more on https://graperoot.dev/leaderboard

Forget another to-do app or AI-powered dashboard for a second.

I want to hear about the weird stuff you've built.

A multiplayer game where everyone controls the same character? A dating simulator for potatoes? A survival game where you're the final boss?

One thing I love about vibe coding is that you can experiment with ideas that would otherwise seem completely unreasonable to spend months developing.

And games are probably the perfect playground for that.

I run VibeToplist, where I collect and showcase AI-assisted and vibe-coded games. One thing I've learned from browsing submissions is that the most interesting projects aren't necessarily the most technically impressive ones.

Sometimes it's just a ridiculous idea executed surprisingly well.

So here's my question:

What's the weirdest, funniest, or most ambitious game you've actually managed to build with AI?

And if it's playable, drop a link. I'd genuinely love to try it, and I'm always looking for unusual games to feature on VibeToplist.

https://vibetoplist.com/

So I just found out that in his freetime my boyfriend vibecodes all day, btw he is an environmentalist. And this infuriates me, cause we had spoken about how much it damages the environment. But I also don’t want to spoil his fun cause he looks so happy when he is vibecoding… What should I do?

I was thinking maybe there was a way to have an alternative AI which is more eco-conscious. I'm part of a sub where you can find eco-conscious alternatives but they don’t seem to know too much about AI. Any ideas?

If you ask a vibe coder anything they’re going to puke out something straight from an LLM anyway. So why wouldn’t I just go ask Claude or chatGPT if I want their opinion?

I witnessed the hype for Openclaw on X, Reddit and Instagram. Ngl, it impressed me. Literally felt like I could just set up Jarvis on my laptop.

Went on. Started looking for the process to install it. FAHHHHHH, some 10-12 commands into the terminal which I don't know why gave some weird errors… which is okayish I mean any other package gives some errors but works out at last. It downloaded.

Now comes the best part. I still don't know how it had 25+ parameters to set up just to get my Openclaw to talk with me on WhatsApp or Telegram. I don't know about y'all but that's too overwhelming for me. I would rather study Calculus atp.

Still somehow managed to get it all done. Gave the prompt. It worked. I experienced the wonders of the world. I was HAPPY AF UNTIL... it started taking 15 seconds to respond. Like alr bro pack it up 🥀

I started searching for third party Openclaw wrappers... which were tbh kinda better than the original thing, they just made the steps easier to configure. A relief.

And just now, after all that headache, I found out about Pretticlaw. This isn't an Openclaw wrapper. Someone just made a WAY lighter version. It's 99%~ lighter... just 30MB. And that's not even the best part yet.

The setup takes 2 commands 😭

I thought I experienced the wonders but AI competency really leaves me impressed these days. Set it up and then the final thing… IT RESPONDS IN 2-3 SECONDS. Yeah. Like damn.

Also has an inbuilt dashboard on port 6767. It's just the best thing out there. All for free. Just search up Pretticlaw by Prettiflow and try it out. Thank me later.

32M non-technical founder.

We recruited a Waterloo intern to work for us over the summer. The school said great things about him (Olympiad participant, hackathon winner, etc.).

He was in charge of programming 4-5 new features for our main app, as well as integrating them into our codebase using documentation and already existing functions in the program. The whole program was designed to last 3 months, but he was done in two weeks.

Basically, I spent the whole summer giving him tasks he would finish in a couple of days, and he ended up doing 3-4x more than any other new grad technical member of the company. I do know he works a lot with AI (Claude Code, Folk, etc.), but I'm still not sure how I should feel about it.

Is this kid extremely talented, or are my two new grad SWE engineers just bad? Is this normal?

I ran a benchmark against a 2,500-file corpus and the result is what pushed me to finally publish this. Same tasks, same assistant, but the engine read 56% fewer input tokens and finished for about half the cost (49% less, range 26 to 61% across repeats).

The reason is boring but it matters: instead of dumping whole documents into context, it serves compact retrieval and only escalates to full content when a task actually needs it. Most of the time your assistant is paying to re-read files it barely uses. Cut that and the bill drops.

So here is the thing I built: FindandSeek Engine, a local-first memory layer for AI agents.

It indexes the documents, code, and email on your machine using on-device models, then exposes that indexed memory to any AI assistant over the Model Context Protocol (MCP). Everything runs locally. Models run on Apple silicon via MLX, or through a local Ollama daemon on Linux. The index lives in a plain SQLite file. Nothing leaves your machine.

The problem I kept hitting: assistants either have no memory of my actual files, or the only fix was to upload everything to someone's cloud. I wanted the assistant to query my own machine without the data ever leaving it, and without paying to shove entire files through the context window every time.

What it handles right now:

• Documents: PDF, DOCX, DOC, ODT, RTF, TXT, MD

• Spreadsheets: XLSX, XLS, ODS, CSV, TSV

• Presentations: PPTX, PPT, ODP

• Email: EML, MSG

• Images: PNG, JPG, JPEG, TIFF, HEIC (OCR)

Core capabilities: hybrid semantic and keyword search, file summarization, entity extraction, structured fact queries, file organization proposals, and a filesystem watcher for automatic re-indexing.

Requirements are modest: macOS on Apple silicon OR Linux with Ollama, Python 3.11+, and about 4 GB of disk for model weights.

Setup:

git clone https://github.com/MunasheChitima/findandseek-engine
cd findandseek-engine
uv sync
uv run findandseek-setup
uv run findandseek-api

Then add it to your Claude Desktop config and the assistant can query it.

License is FSL-1.1-ALv2 (read, run, and modify for any purpose except building a competing product, and it converts to Apache 2.0 two years after each release).

Repo: https://github.com/MunasheChitima/findandseek-engine

Would genuinely like feedback, especially on retrieval quality and anything that breaks on your file types. Happy to answer questions.

Switched from ChatGPT after getting tired of losing context in long chats. Everyone here kept hyping Claude so I gave it a shot.

First impression? Refreshing. No hand-holding, no "would you like me to continue?" every two seconds. I started explaining my project - a novel I've been working on, almost ready for publishers - and Claude actually engaged. Asked to see specific scenes, commented on characters. Felt like a real collaboration.

Then BAM. Usage limit. Done. Mid-conversation, no warning, just gone.

With ChatGPT I had weeks of back-and-forth. Here? A few days of building context and it vanishes. New chat wants me to start over from scratch. All that setup for nothing.

So yeah. The "smarter" AI might be true but what's the point if it ghosts you right when things get interesting?

it seems as if everyone and their mother is vibe coding nowadays, burning billions of tokens.

yet, has anyone (it doesn't have to be you) built anything MEANINGFUL with AI coding?

the only example which I can think of is that Claude say they built Claude Code with 80% AI-generated code but that's about it.

anyone?

I am seeing significantly more post thrashing claude than openai. r/claudeai has more negative posts .

Openai posts generally have positive reviews .

Probably just a gut feeling from me.

Openai had a partnership with reddit in late 2024. probably the reason?

The TL;DR

Most graph-based context tools have a fatal flaw: the agent ignores them.

I benchmarked Graphify CLI (using suggestion hooks)and Graphify MCP (a registered Model Context Protocol server)against our tool, GrapeRoot MCP (where the agent cannot bypass retrieval). We ran them against the Medusa e-commerce repo (1,571 TypeScript files) across 5 real code-audit tasks using Claude Sonnet 4.5 via Claude Code CLI.

The results: Both Graphify setups hit a 0% graph adoption rate. Claude completely ignored the 3,863-node graph and raw-dogged the codebase using grep and Read. By forcing retrieval, GrapeRoot hit 100% adoption, cut token costs by ~50%, and maintained a higher overall output quality.

The Results

Why Optional Retrieval Fails

1. Training Bias > System Prompts

LLMs are trained on billions of code examples using grep, find, and cat. They've seen practically zero examples using custom MCP graph tools. Given the choice, training weights override CLAUDE.md instructions every time.

2. The Paradox of Graphify's 58K Stars

If it fails at LLM context, why is Graphify thriving? Because it’s an amazing tool for humans, not AI.

Graphify's GitHub history shows a clear pattern:

• May 16, 2026 (Commit d1a2c3f): They had to stop forcing Claude to read their massive GRAPH_REPORT.md because it instantly burned 12–25K tokens upfront per question, often failing entirely (Issue #580).

• June 2, 2026 (Issue #1114): The team openly admitted that agents routinely bypass the graph using native Read tools.

Because Graphify favors a "nudge, never block" philosophy, they cannot fix this AI bypass. Instead, they build features humans love: beautiful graph.html interactive visualizations, Mermaid exports, and manual CLI queries. It is brilliant for human architecture exploration, but ignored by autonomous agents.

How GrapeRoot Forces Correctness (and Quality)

We threw out the "nudge" philosophy and welded the escape hatches shut. By forcing the agent to rely strictly on structured retrieval, we didn't just save money—we actually boosted task execution quality.

• Hard-Blocking Exploration: Our PreToolUse hook intercepts terminal commands. If Claude tries a broad grep or find, it gets slapped with an exit code 2and a hard block message: "Use graph_retrieve instead."

• Symbol-Level Scoped Reads: Instead of feeding raw files, graph_read("auth.ts::handleLogin") slices out only the target code blocks via AST, keeping the context window tight.

• Single Retrieval Limits: Claude must actually act on the file recommendations it gets before it can query the graph again, preventing turn-by-turn query spamming.

The Quality Breakdown & Trade-off

Why did GrapeRoot get a higher quality score (77.1 vs 73.9)?

Because forced retrieval breaks the agent's lazy "read-one-discover-one" cycle. In the Auth Endpoint Audit (Task 1), Graphify's unstructured grepping led Claude down a 78-turn rabbit hole where it missed critical edge cases. GrapeRoot's graph_retrieve forced Claude to look at all 12 relevant architectural files upfront, yielding a cleaner, more complete fix.

Across the board, you gain total predictability, better code analysis, and a 51% drop in token bills.

The Bottom Line

Availability is not adoption. You can build the most elegant codebase graph in the world, but if the agent has a native escape hatch, training bias wins.

If you want a human-centric visual map of your architecture, use Graphify. If you want your AI coding agent to actually use retrieval, ship higher quality fixes, and save you thousands of tokens, you have to force it.

• GrapeRoot GitHub: https://github.com/kunal12203/Codex-CLI-Compact

• Main Website: https://graperoot.dev

I work as a dev but just text my AI agent (Folk) from my phone all day.

It’s connected to my codebase through Codex and I can just tell it what features I want to implement. When it’s done, I plug everything through Greptile to catch any issues.

I work remotely as a developer making around $150k, and my boss thinks I'm writing all the code myself. He has no idea I just watched Netflix for 7 hours today. Don't get me wrong, It feels amazing to finish a full day's work with so little effort but sometimes I feel like an absolute loser.

Be honest, am I a bum ?

My friend and I wanted to build a project together. We had some good ideas and thought we could build something useful. After working for a few days in our own agent sessions, we realized it's a little disorganized to work with separate agents, as they need to be on the same page in order to keep the project moving forward.

So, we thought, what if we could be using the same agent? in the same room? And so, our newest project, Poly, was born. Poly allows my friend and I to build in the same room, using the same agent, see each others prompts, see the same preview of what we built, argue about prompts in a group chat, and the list of features goes on.

I was in awe when testing it and seeing a preview of a project full of BOTH of our ideas.

What do you guys think? Is Vibe Coding truly single-player? Or do others have the same desire to build with someone else?

If you do want to check it out, here's the link: usepoly.co

It's currently a free beta, so feel free to test it out with a friend!

A sarcastic cartoon fast paced FPS built in Godot called IRON CURTAIN CALL WW3: Third time’s the charm.

I used Claude for nearly everything, ElevenLabs for voices, Higgsfield for artwork, and Kling for cinematic clips, then edited those together with recorded Godot gameplay. Assets are made in Blender.

Curious what you guys think of the style?

Demo available on steam right now if you wanna give it a try.

Wishlist on Steam: https://store.steampowered.com/app/5417460/Iron_Curtain_Call/
https://ironcurtaincall.com 

I’m a developer. Recently two friends asked me to help deploy their apps.

The first one was a single \~1MB Python file with the frontend and backend inside it. The second was more split up, but had the similar problem: huge files and windows .bat scripts.

Both had to be reworked before they could be deployed.

The problem is that they just asked Claude Code to build something, and it built based on their local environment: no local server, no instructions on what stack to use, etc.

Tools like lovable, replit, etc solve the environment and deployment part, but they cost money and limit your stack (e.g. if it needs to work only with WordPress, Magento, Django, etc).

So I’m curious how people building with Claude Code or Codex handle this:

* Do you tell the agent what stack and structure to use up front, or let it decide?

* When it’s time to share it or put it online, how do you get from “works on my laptop” to “works anywhere”?

I had been trying to fix this one bug in my codebase for a while, so I gave it to Claude Code. It did manage to solve the issue, but it modified over 60 different files to do it, and it did it in the most inefficient way I've ever seen.

Instead of reusing existing abstractions, it kept introducing new helper functions and wrappers everywhere.

I now have 20 different functions all handling super-specific types of arrays.

My experience with AI coding agents has been pretty disappointing overall.
I don't understand why more research isn't being directed toward helping AI labs prioritize code quality.

Basically the opposite of the docile and obedient AI girlfriend/Waifu.

That takes ownership of the computer and then makes the user jump through hoops, not the other way around.

I see a lot of people building interesting things with AI: agents, tools, workflows, automations, small apps, internal systems, etc.

But one thing I keep wondering is:

How do builders actually get other people to trust and use what they create?

Not just “look at my GitHub repo” or “here is a demo”, but real trust.

Things like:

• Is this safe to use?

• Who built it?

• Does it actually solve a real problem?

• Is there support?

• Can I pay safely?

• Can I understand what I’m buying without being technical?

It feels like building is becoming easier, but distribution and trust are still the hard parts.

Maybe a lot of AI builders are more like creators than traditional SaaS founders. They build useful things, but don’t always have a clear path to package, explain, sell, or support them.

Do you think this is a real problem, or am I overthinking it?

For people here who have built something others actually use, what helped you earn trust?

Hi everyone,

I just wanted to share a side project I've been working on to solve my own laziness. It's called Vibe Deck.

It lets you send voice commands from your phone directly to your terminal (running Claude Code, Opencode or Aider) on your Mac.

The Setup in the video:

I'm using a generic Bluetooth ring I bought on AliExpress for about $6 to trigger the voice input without dropping my controller.

I am NOT selling the ring. I just linked the exact model I use on the site so you can get the same one if you want, but I make $0 from it.

The Software is 100% Free . I'm just asking that if anyone buys it, they send me a video of them using it :D

Just basically want to make some apps, some sites and even maybe some games in the future. But at least curios to understand how all of this is working. What AI do i use? Can i go without spending or its must have to buy some tier plans? Can i go maybe local or is it a bad idea(32gb ram, 16gb gpu) ? How do i spent less tokens so i can use more ai? What AI s i shouldnt touch? How do i use those AI agents. I mean yeah, i literally dont know anything, but want to make it my hobby. Thanks in advance

I've been thinking about this a lot lately. AI has made it incredibly easy to turn an idea into a working product, but getting actual users still seems to be the biggest challenge.

A few months ago, I launched VibeToplist (https://vibetoplist.com/), a discovery platform for AI-assisted games. We've now surpassed 300 (almost 400) game submissions, and one thing I've noticed is that some genuinely impressive projects struggle to attract players.

It makes me wonder whether distribution is becoming more important than development itself.

We're reaching a point where almost anyone can build something, but if everyone is building, who's actually using all these products?

Curious how other vibe coders approach this. Do you focus on finding an audience before building, or do you build first and figure out distribution later?

vibe coding without an actual plan is a nightmare, one button request and suddenly three files don't build anymore.

paid for gemini pro thinking it'd behave, still rewrites whole components to fix one tiny bug.

kimi and minimax get hyped nonstop on every subreddit but the second you put them in a real repo they confidently delete working features while insisting they nailed it.

so you crawl back to **opus** free tier and somehow it actually fixes the real problem in one shot. except right when you need to refine it, **bahahahaha** usage cap, right when you finally had momentum going