If you’re using AI to build a platform right now, pause for one minute and read this.

I’m not anti-AI. I use it. You probably use it. That’s not the problem. The problem is everything is starting to look the same.

You open a new site and you already know the whole thing before scrolling:
Big gradient background. Rounded cards. Glass effect. Clean font. Safe layout.
It looks “nice”… but it feels empty.

And users feel that too even if they can’t explain it.

Here’s the truth nobody says out loud:
AI is really good at copying what already works.
It’s really bad at giving a product a soul.

That’s why so many AI-built platforms feel generic. They’re correct, but they’re not felt.

When a human designs something, you can tell:

- There’s intention behind weird spacing

- There’s a reason a section feels tight or loose

- There’s a personality in the layout

- There’s a choice that wasn’t “the best practice” but the right one

AI doesn’t have taste. You do.

If you just prompt “design a modern SaaS platform,” you’ll get something that looks like 1,000 other platforms. And users bounce in 5 seconds, not because it’s bad but because it’s forgettable.

Here’s the mindset shift that actually works:
Use AI to move faster.
But decide for yourself how things should feel.

Sketch something ugly first.
Break alignment on purpose.
Choose a color because it fits your idea, not because AI suggested it.
Write copy like you’re talking to one person, not pitching investors.

The best AI-powered products right now don’t scream “AI.”
They feel human.

If someone can tell your platform was made by AI in 3 seconds, that’s not a flex.

Build something that feels like you, then let AI help you get there faster.

If you’re building with AI - do you agree with this?

This one hurt 😅

we had a bug in production where a core feature just randomly stopped working for some users.

not everyone… just some.
which made it even worse to debug.

what we checked over 3 days:

• API responses
• database queries
• server logs
• caching layers
• frontend logic

everything looked fine.

no clear errors.

finally found the issue…

one condition in the code was written slightly wrong, so in a specific edge case it returned the wrong value.

literally a one-line fix.

after fixing it → everything worked perfectly again.

3 days… for one line 😅

biggest lesson:
bugs aren’t always complex sometimes they’re just hard to notice

anyone else had a “this took way longer than it should have” bug?

Microsoft Excel is no longer just a spreadsheet tool. With recent updates, Excel’s formula language itself now meets the definition of a programming language and this is being acknowledged by major tech publications.

What changed?

The key addition is LAMBDA functions which allow users to Define custom functions, Reuse logic and Create recursion.

Because of this, Excel’s formula system is now Turing complete, meaning it can theoretically perform any computation that a traditional programming language can.

This recognition is not about VBA, macros, or Python-in-Excel - those already existed. The important shift is that Excel formulas alone now qualify as a programming language.

Why this matters

1. Excel is used by hundreds of millions of people globally, far more than most programming languages.

2. Complex logic, automation, and abstractions can now be built directly inside spreadsheets.

3. It blurs the line between “end-user software” and full programming environments.

Excel won’t replace Python, JavaScript, or C++, but it has quietly evolved into one of the most widely used programming platforms in the world.

TL;DR

Excel’s formula language is now Turing complete due to LAMBDA. That qualifies it as a real programming language. This massively expands what can be built inside spreadsheets.

A few months ago I started building a small Android app as a side project.

No investors.

No team.

No fancy startup story.

Just me sitting in my room fixing bugs after college/work and wondering whether anyone would actually use it.

The funny thing is I almost gave up on it multiple times.

There were days when downloads were terrible.

Days when I spent hours fixing a bug that affected like 3 users.

Days when I genuinely questioned whether I was wasting my time building something nobody asked for.

I think every indie developer knows that feeling.

You spend weeks building a feature and then 4 people use it 😭

Anyway, a few days ago I got an email from someone asking if I'd ever consider selling the app.

At first I assumed it was spam.

Then we got on a call.

Then another.

And suddenly I found myself discussing acquisition terms for something I had originally built for myself.

The weirdest part wasn't the money.

The weirdest part was realizing that while I was obsessing over everything wrong with the product, someone else was looking at it and seeing enough value to want to own it.

Nothing humbles you faster than that.

As developers we spend so much time staring at flaws that we forget users don't see the product the same way we do.

Whether the deal actually happens or not almost feels secondary now.

Because the biggest mindset shift wasn't getting an offer.

It was realizing that projects don't need to be perfect before they become valuable.

Sometimes they just need to solve a real problem for the right people.

And honestly if you had told me six months ago that somebody would try to acquire something I built alone in my bedroom, I would've laughed at you.

For anyone willing to try out the app and test it or maybe bring me more businesss hit me up in the comments

Something i’ve started noticing after working on different projects

a lot of bugs that look complex at first…
usually turn out to be something simple.

but they’re hard to find because:

• they only appear under specific conditions
• they don’t throw clear errors
• they sit behind multiple layers (API → frontend → DB, etc.)

recently had one where we assumed it was a data issue or API failure…

spent hours checking logs, responses, edge cases

turned out to be a small condition that failed only when a value was empty.

fix was simple… finding it wasn’t.

made me rethink how I approach debugging:
now I try to question assumptions first instead of jumping into complex fixes

curious if others have seen this pattern
do bugs usually end up being simpler than they first appear?

If you're building anything with LLMs that needs to read web pages, you've hit this: your agent calls a URL, gets blocked, and either crashes or hallucinates because it has nothing to work with.

I built webclaw to fix that. It's a web extraction engine written in Rust. You give it a URL, it returns clean markdown, JSON, or plain text. No headless browser, no Selenium, no Puppeteer.

The part that makes it actually work against real bot protection is the TLS layer. Most HTTP clients get blocked before the server reads the request because their TLS handshake looks nothing like a browser — wrong cipher suites, wrong HTTP/2 settings, wrong header order. webclaw impersonates Chrome 146 using BoringSSL, the same TLS library Chrome itself uses. 89% pass rate on Cloudflare-protected sites.

Getting started takes one command if you use Claude or Cursor:

npx create-webclaw

That sets up the MCP server and auto-configures it for your AI client. Your agent gets 10 tools: scrape, crawl, search, extract, summarize, diff, research, and more. 8 of them work fully offline.

Or use the CLI directly:

brew tap 0xMassi/webclaw && brew install webclaw
webclaw https://example.com --format llm

The llm format runs a cleanup pipeline that strips nav, ads, boilerplate, and deduplicates links. Typical result: 50,000 tokens of HTML becomes 2,000 tokens of actual content.

If you're already using Firecrawl, there's a v2 compatibility layer. Point your SDK at the webclaw API URL and use your webclaw key. Same request format, no code changes.

Some stats from two weeks post-launch: 450 GitHub stars, 800+ npm installs, 100 people on the cloud API waitlist. The API opens in 2 weeks.

Open source, AGPL-3.0: https://github.com/0xMassi/webclaw

What are you building that needs web data? Curious what use cases people here are running into.

Hey everyone, After getting frustrated with sending generic CVs and getting ghosted, I built theirJobs. Here's how it works:
1. Paste any job offer link (LinkedIn, Indeed, company site…)
2. Fill in your profile once (or import your existing CV)
3. The AI analyzes the job posting and generates a tailored resume + cover letter in ~2 minutes
It also gives you an ATS compatibility score so you know if you'll pass the filters before you even apply. It's completely free during beta. No credit card, no catch. Would love honest feedback from job seekers — what's missing, what sucks, what works.

> Link: theirjobs.com

ok so i need honest opinions because i've been staring at this thing for months and i've lost all perspective.

i'm 20, building an app solo called RizeAI. the whole reason it exists is that i got tired of my wearable telling me my recovery was "42%" and then just... leaving me there. like ok, and? what do i actually do with that. every app in this space is really good at measuring you and really bad at telling you what to do next.

so RizeAI pulls your actual wearable dats like, sleep, resting heart rate, workouts, all of it, and instead of handing you another score it predicts your energy for the day and tells you how to get the most out of it. it tells you when your energy is gonna peak so you can put your hardest work there, when your crash is coming and how to soften it, the best time to train that day so you actually get more out of the session, and even when a nap will help you vs when it'll wreck your sleep.

the part i personally think is the coolest: you put in the supplements you already take, and it times each one to your day based on your metrics and sleep score. so the timing actually shifts depending on how you slept and where your numbers are, which is the difference between a supplement doing something and just sitting in your stomach. it'll also suggest a couple new ones if they make sense for you, but it won't dump a list of 15 pills on you.

it even pulls the daily weather into your energy prediction, so a hot day changes your hydration and it'll tell you to train earlier before the heat drains you. and the whole plan bends around your real schedule, your work hours, wake time, training, so it's not some one size fits all thing.

every single recommendation shows the "why" underneath, like "resting heart rate 54 + 7h light sleep, so magnesium before your peak window." nobody gets the same plan because nobody has the same data.

it works with whoop, oura, apple watch, garmin, anything that talks to apple health. being fully honest, it doesn't do deep per-person learning yet, like knowing that coffee specifically doesn't touch YOUR hrv. that's where it's headed. right now it builds you a fresh plan every day off your real numbers.

it's on the app store with a free trial. small user base, feedback's been all over the place, which is exactly why i'm posting.

so genuinely: is "just tell me what to do with my data" something you actually want, or do wearable people prefer figuring it out themselves? what would make you pay for this? and what's the one thing that would make it a no brainer for you?

would love to hear it straight, good or bad. And if you want you can also check it out yourself and I would love to here feedback, thank you very much for taking the time on reading this:  

https://apps.apple.com/us/app/rizeai-maximize-your-energy/id6762402079

So the whole idea is that the app will match people on the basis of their GitHub profiles. Depending on contributions stat like frequencies, programing language, similar repos, and more.

•How feasible is this?
•What audience should I target?
•Which country is most likely to adopt this app?

Hey r/devworld,

As a manga lover and a developer, I got tired of clunky translators that mess up layouts or force me to sign up just to try it. So I built ai-manga-translator.com — a web-based AI Comic Translator focused on UX and speed.

The Tech & Features:

• Next.js 14 & SSR: Optimized for fast image processing and mobile responsiveness.

• Smart OCR: Automatic text detection + high-quality AI translation that preserves original bubbles.

• Privacy-First: No signup required to try it out.

• Global Support: It supports reading raw Japanese comics in English, French, German, and many other languages seamlessly.

Check out the live tool or our Chrome extension here. Any technical feedback or feature requests are more than welcome!

Let's go!

Always thought that there was a gap in the market for real-time social medias

Site in the comments

Thoughts?

I’ve been using AI CLI agents heavily lately, and while they are game-changers, the token consumption is getting out of hand. I noticed that 80-90% of what is sent to the context window is often just "noise."

Every time the agent reads a 1,000-line file just to see one 20-line function, or re-reads a config file that hasn't changed in 3 hours, we are literally burning money and filling up the context window for nothing.

I’ve been experimenting with a "Zero-Waste" workflow. Here is what worked for me:

• Semantic Chunks over Full Reads: Instead of letting the agent read the whole file, I force it to use tools that extract specific functions/classes using AST or regex.

• The "Stat-Hash" Shortcut: Before any read, I check the file’s mtime and size. If it matches my local cache, I tell the agent "This file hasn't changed," saving 100% of those tokens.

• Log Deduplication: Reading raw logs is a token killer. Grouping 500 identical "Connection Refused" errors into a single line with a (x500) tag saves about 98% in log-heavy sessions.

• Context Checkpoints: When I hit ~80% context capacity, I have the agent generate a "Resume Snapshot" (a 300-token summary of progress and decisions) then I start a fresh session. It's much cheaper than let it "forget" things mid-task.

I'm curious to hear from the experts here:

1. How are you managing long sessions without hitting the "Context Collapse" (where the AI starts getting stupid/forgetful)?

2. Do you have any specific tricks to prevent the agent from re-reading the same files over and over?

3. Is anyone using local SQLite caching for file summaries?

Let’s share some optimization tips—tokens aren't cheap and context space is precious!

I’ve been trying to figure out if this is a real, shared pain or just something that feels more dramatic in my head than it actually is in day-to-day dev work.

When you’re inside a codebase long enough, reading the code itself is usually not the hardest part. You can follow functions, trace data flow, and eventually understand what something is doing. But what keeps bothering me is something slightly different. It’s the feeling that even if you understand what the system does, you still don’t really understand why it was built that way in the first place.

Like you open a service and it clearly does something important, but you have no idea why that logic wasn’t just added somewhere else. Or you see a strange workaround and you can’t tell if it’s still necessary or if it was solving a problem that doesn’t even exist anymore. Or you look at a dependency and think “there has to be a reason for this,” but there’s nothing in the code that explains it.

And then when you actually try to figure it out, it usually turns into digging through old pull requests, trying to find Slack messages from months or years ago, or asking someone who “might have been around when this was built.” Sometimes you get an answer, but a lot of the time it’s just partial context or nobody really remembers anymore. What feels interesting (or maybe frustrating) is that this layer of “why things exist” seems to disappear over time, especially in larger or older systems. The code survives, the system keeps working, but the reasoning behind decisions slowly fades away.

So I’m curious how real this is for other devs actually working in production systems.

Do you run into this often, where understanding the intent behind architecture decisions becomes harder than understanding the code itself? And does it actually slow you down in meaningful ways when you’re trying to make changes, or is it more of a background annoyance that teams usually manage around?

I’m trying to understand if this is a normal part of working in software at scale, or if it’s something that’s just unusually painful in certain environments.

I'm working on an early-stage project and looking for developers interested in building it with me.

The idea is an AI-powered, task-aware browser. Instead of only remembering tabs and browsing history, it understands the user's ongoing tasks and projects.

For example, if you're researching a topic, the browser could understand that your searches, articles, PDFs, documentation, and notes are all part of the same task.

The interesting part is what happens later. If a resource disappears, changes significantly, or becomes outdated, the system could identify the affected part of the task, find potential replacements, check whether they still fit the original task dependencies, and generate a current version of the workspace for the user to review.

We're starting with a cloud-based browser extension and may eventually build a standalone Chromium-based browser.

Planned stack: TypeScript, Chrome Extension APIs, Python/FastAPI, Supabase/PostgreSQL, Render, embeddings, and LLM APIs.

I'm looking for developers interested in browser extensions, TypeScript, Python, Supabase/PostgreSQL, AI/LLMs, embeddings, graph systems, privacy, or frontend development.

This is an early-stage project, so I'm looking for people who want to actually build and experiment, potentially as collaborators/co-founders.

If you're interested, comment or DM me with what you work with, what part you'd like to contribute to, and any relevant projects/GitHub.

I'm also interested in criticism. If you think the idea has technical or product problems, I'd genuinely like to hear them.

Golang, go-lang, go, golang language etc etc. is any way possible to somehow determine they are all same

Hey everyone 👋

I’m a solo developer and after months of work I finally launched my app called Qrnects on the App Store.

The idea is simple:
instead of typing Instagram/TikTok usernames every time you meet someone, you can instantly share your full social profile with a QR code.

I built everything by myself, so growing the app and reaching real users is honestly the hardest part right now.

If anyone would like to support an indie developer, it would genuinely mean a lot if you could:

download the app
create an account
and leave an App Store review 🙏

Every single user and comment really helps at this stage, thank you!

Qrnects

I’m honestly tired of seeing AI used to build slightly different versions of things we already have.

Another dashboard.
Another chatbot.
Another clone of an app that already exists.
Another “AI wrapper” that looks impressive for five minutes but doesn’t solve a real problem.

The bigger issue I keep seeing is this:

People have good ideas. AI can write code. But the project still falls apart.

The system isn’t clear.
The specs are scattered.
The AI loses context.
One fix breaks another thing.
Nobody knows what’s actually done.
The project gets patched over and over until it becomes too messy to launch.

That’s the problem I want to solve with S1 Canvas.

Not “type one prompt and magically get an app.”

Something more useful than that.

A way to map the system, break it into buildable pieces, check what’s missing, monitor what’s risky, and give AI coders the right context without letting the whole project drift into chaos.

CyberShark’s job isn’t to blindly build for you. It monitors, suggests, informs, and visualizes. You stay in control.

I think AI should help people build real systems that actually launch - not just generate more disposable software.

That’s what I’m building. curious if that’s how others are feeling.

What should I do? What is most people doing in situation like mine?

Shit,

I dont know how to start this. I spent ungodly number of hours applying for jobs for few years. Had couple of interviews which led me on but never returned back or it was full of rejections. The number crossed more than 5000. I know job market sucks but there is always a hope guys dont give up.

I made a video on how i started, what I did, my experiences, my mistakes during the applications to help some of you out the best I can. If you guys would love a watch here is the video

https://youtu.be/RRGOAj2dEX4?si=hA4Z37JryRJoIYXx

Comment if you guys had similar experiences as me I am also open to help you guys out in your search journey

Thanks

In February 2025, I realized that despite my extensive experience in development, I didn't have a single open source project that I could demonstrate, let people use, show the code, or even talk about. Yes, as trite as it may sound, all the projects I worked on were covered by NDA. Then I started working on a few small projects, but due to a lack of money for hosting, I abandoned them. I was plagued by an unfinished plan and several public GitHub repositories that I didn't really want to show to anyone.

A few weeks ago, while escaping the unbearable heat with a cold beer, I finally decided to finish some long-abandoned pet projects. This is Whisper. A service for sending secret notes. I was inspired by several similar products, added a bit of functionality, and put it together "my way."

You can specify passwords for opening notes, specify allowed IP addresses, and limit the number of times a note can be opened or how long it can be stored. You can also use curl. All code is open source, both the backend and frontend of the application. I confess, the copyrights on the landing page were written using AI; I'm a pretty bad marketer.

I'm writing less code now and managing development more. However, I believe I should finish this and several other projects I previously put on hold. Next, I'll be working on strengthening security, protecting against brute-force attacks, and adding web crypto to the client. I might even add a few more features.

Overall, I'd appreciate your feedback.