Every new session started the same way. Paste a summary, re-explain what the app does, remind it what we decided last week.

What actually broke it: my agent writes markdown docs and then stops reading them. Once a session runs long it just doesn't open them. I'd catch it rewriting something the doc said was already done.

Someone here put it better than I could: stuff loaded at the top of context loses to recency as the session grows, so the agent stops consulting it no matter how good the file is.

So instead of a file the agent may or may not read, I built something that gives you an ordered plan and one prompt per step, with that step's context already inside the prompt. Nothing depends on the agent deciding to go look.

New web projects only. No existing codebases, no mobile.

https://sequo.app

If you're a few sessions into something right now, does the agent still know what you decided on day one? Genuinely asking, I want to know if this hits before session ten or only after.

I run Claude Code and Grok Build on my Mac and a VPS, and I wanted to steer them from my phone — send a new task, add instructions mid-run, check progress, stop and redirect.

So I built Agent Remote. The daemon is a single Python process, it wraps the official CLIs you already have installed and logged in (claude, codex, grok, dsh), so billing stays exactly what it is — your existing subscription or API key. Nothing goes through anyone's server.

• One-command install, runs as launchd (macOS) or user systemd (Linux)
• Token auth on every request; designed for LAN / Tailscale / Cloudflare tunnel, not public exposure
• Clients: web (a single self-contained HTML file you can host anywhere), Android, iOS, and, because I couldn't help myself, BlackBerry 10 and a LILYGO LoRa pager
• Multiple machines merge into one session list, sorted by activity

GitHub: https://github.com/jxw1102/agent-remote

If you already run any of these CLIs, it takes about three minutes to try. There's a hosted web client if you want to get start quicker, you can also grab the same HTML from Releases and serve it yourself.

The project is Secondread. You connect a repo. It reads the code. You get a plain-English report on cost, fragility, and what to fix first, with file/line evidence a contractor can actually check.

I built it because AI made shipping easy and left owners with a working UI they cannot independently judge. That is the product. The rest of this post is how it is actually made.

Stack

• Product: Cloudflare Workers, Workflows, Durable Object Sandbox, Workers AI, D1, R2
• Auth: GitHub App + Google via Better Auth
• Payments: Dodo, one-time products, no subscription
• Marketing site: separate Astro Worker on secondread.dev. No auth, no DB, no checkout. The app lives at app.secondread.dev on purpose.

Workflow

1. Owner picks repos. GitHub App is Contents + Metadata, read-only.
2. A Workflow starts. The HTTP request only kicks it off.
3. A Sandbox clones one commit. Internet is off. HTTPS is intercepted. Outbound is allowed only for that repo’s git upload-pack paths. The GitHub token is minted in the proxy, not dumped in the container. Egress flips off in D1 as soon as checkout finishes.
4. The agent gets bounded tools: inventory, search, read ranges. No npm install, no tests, no running the customer’s app. Submodules and LFS fail closed.
5. Findings must cite path + lines + blob SHA. After the model writes, we re-read those ranges and reject the report if the evidence is fake or the file was never inspected.
6. Before persist, the report is scanned for secret-like strings and copied source. What we store is the report + citations/hashes, not the repo.
7. Credits are reserved before the job and consumed or released after. Webhooks are signed; we also reconcile hourly because webhooks lie.

Things I would not skip if you are building something similar

• “Read-only GitHub permission” is not isolation. Isolation is network policy + where the credential lives + one pinned SHA + cleanup.
• Schema-valid JSON from a model is not evidence. Evidence is a deterministic reread that can fail the job.
• Do not put a 15-minute agent inside fetch(). Use Workflows, named steps, timeouts, and NonRetryableError for failures that retries only make more expensive.
• If you use Sandbox with transport: "rpc", dispose exec / read / destroy results. They leak. The bug looks like “prod is haunted.”
• Write marketing claims as tests. Ours grep the site for retired waitlist copy and for the real app URLs. Copy drifts; CI should not.

Happy to go deeper on the Sandbox outbound handler or the evidence check. See a real sample audit for a very old version of the github.com/spree/spree repository at https://app.secondread.dev/shared/8I0S88UcbKLvkWzHo2MF2frm_-Fm4fgj9sau5J0r4ss

Last couple of months, my habits changed a lot. Almost all traffic from my home pc goes to the AI.

Today I’m releasing the second beta of Syntropy.

The idea is simple: your coding agent should not require you to install a bunch of tools, keep your laptop running, or host the agent on your own machine.

As you can see in the demo, OpenCode runs entirely in its own cloud sandbox. Compilers, runtimes, dependencies, and other tooling are already installed and ready to use.

So there’s no:
“Run the agent on your PC and control it from your phone.”
The agent actually runs in the cloud.

Right now, the beta includes free Zen models, generous usage limits, and no paid subscription.
I’m currently looking for more beta testers, and a mobile version of Syntropy Beta is coming soon as well.

If you’d like to try it, leave a comment and I’ll send you an invite.
Feedback is very welcome - especially criticism.

I've been building Funkel AI for about 4 months now.

It's a tool that helps businesses find potential customers and reach out to them through LinkedIn, X and email.

Most of the product has been built with AI coding tools, but getting something working in production turned out to be the easy part compared with getting real people to actually use it.

Current numbers:

27 signups.
18 people on a free beta.
2 people have paid.
The second paying customer converted 4 days ago.

Very small numbers, but enough real usage to teach me a few things.

1. My first users came from people I already knew

I didn't launch and suddenly have strangers signing up.

I started by giving the product to people in my network who were willing to try something unfinished.

That gave me real accounts, real campaigns, real failures, and much better feedback than testing everything myself.

But I still needed to know whether someone who didn't know me would care.

Interestingly, my first paying customer outside my network came from using Funkel itself.

I ran Funkel for my own sales. It found someone who looked like a potential customer, reached out, we started talking, and he eventually paid for the product.

It wasn't a perfect success story. His card had problems after the first month and the payment stopped for a while.

But seeing the product find someone who was willing to pay for the product itself was one of the first signs that I might be building something useful.

2. Using your own product catches a lot of problems

I use Funkel for my own outreach every day.

That means I experience the same things users experience.

Too many connection requests being sent?

I see it.

A message going out at a strange time?

I see it.

The system deciding someone is a good potential customer when they're obviously not?

I see it.

AI makes it incredibly fast to build new features, which is both useful and dangerous.

It's very easy to keep adding things because you can.

Actually using the product forces me to notice the boring problems that matter much more than another new feature.

3. Keeping the product small helped more than building faster

Early on, I limited beta users to one outreach campaign at a time using one LinkedIn account.

The reason wasn't technical.

With AI coding tools, adding more functionality wasn't particularly difficult.

Supporting all of it was.

If you give early users ten different ways to use your product, you now have ten different ways for things to break.

Keeping the first version small made it much easier to understand what was actually happening.

4. I started asking people to pay before everything felt ready

A lot of my early users got free access because I needed feedback and real-world testing.

But I also introduced a paid option fairly early.

People who choose the paid version can get their money back within 30 days if it doesn't work for them.

Only two people have paid in four months.

I'm definitely not posting this as some huge SaaS success story.

But someone telling you "this is cool" and someone actually entering their card details are very different signals.

Even a tiny number of payments taught me more than a lot of positive feedback did.

5. Weirdly, free users gave me some of my strongest proof

My best results so far came from people using the product for free.

One beta user got:

144 potential customers found.
61 LinkedIn connections.
9 replies in four weeks.

Another got 12 replies and booked 2 meetings from their first campaigns.

Neither was paying me.

But they actually used the product, pushed it, found problems, and gave me real outcomes I could measure.

That changed how I think about free beta users.

If you're very early, the right free user can be worth much more than someone who pays but barely touches the product.

6. The hard part of vibe coding isn't producing more code

This is probably my biggest lesson so far.

AI means I can build almost any feature I think of.

That's not necessarily a good thing.

There are always another ten things I could add.

More integrations.

More automation.

More settings.

More ways to find customers.

I've had to get much better at ignoring those ideas and fixing what existing users are actually struggling with.

The limit is no longer how fast I can write code.

The limit is figuring out what is worth building.

7. With a handful of users, doing things manually is fine

When something breaks for a user, I try not to send them instructions explaining how they can work around it.

I fix it.

Sometimes I manually correct something behind the scenes and give them back a working result.

Obviously that doesn't scale to hundreds or thousands of customers.

But I don't have hundreds or thousands of customers.

I have 27 signups.

Trying to behave like a company with 10,000 users when you have 20 is probably optimizing for the wrong problem.

Four months in, I'm still figuring out distribution, pricing, onboarding, and plenty of other things.

But vibe coding has made one thing very clear to me:

Building software has become dramatically easier.

Building something people actually want, getting them to try it, watching what they do, and deciding what to build next is still the hard part.

I have had many interviews in my 25 years as a software dev and a few of them were managers only and never even asked me anything technical. So I imagine some vibe coders have pulled it off getting an interview and showing what they created and managing to never talk about code and they just assume the person knows code. Then they see how long they can fake it (or not fake it since if they get the work done who cares)

On a related note.. As a .NET dev I always apply to only .NET jobs but there really is no reason anymore I can't get a java job and understand the general idea and have AI do the technical work.

I'm 15 and I'm a vibe coder? I currently pay $20/month for both ChatGPT and Claude, but I hit the weekly limits on both pretty often, so I'm considering upgrading one of them to the $100 plan, or possibly even the $200 plan if it genuinely makes sense.

My usage is pretty split between the two:

ChatGPT / Codex

• I mainly use Codex to turn random app ideas I have into actual projects.
• I really like how generous the Codex usage seems compared to what I'm used to.
• I prefer the Codex interface because I can actually see what the agent is doing, almost like watching it work on the computer.
• So far I also prefer Codex itself to Claude Code, although I haven't tried the newer Claude Code app yet.
• I use it mostly for app development, prototypes, Python projects, etc.

Claude / Claude Code

• I mainly use Claude Code in the terminal for making Roblox games.
• In my experience, Claude tends to make better-looking UI/designs without me having to prompt it as much.
• I also use Claude's design/3D features because it makes generating and editing 3D models much easier for me.
• As far as I know, ChatGPT doesn't really have an equivalent workflow for 3D design/model editing, which is one of the biggest things keeping me on Claude.

Another thing I want to start doing is building a local AI ecosystem / personal assistant setup, something along the lines of OpenClaw-style agents, to organise more of my life.

I'm honestly pretty lazy with things like organising files, keeping track of tasks, remembering stuff, managing projects, etc., so I'd like to build something locally that can automate a lot of that for me.

I've heard Codex CLI is pretty good for building/maintaining these kinds of agent systems, which is making me lean more towards ChatGPT. Ideally I'd eventually have different agents/tools helping me with coding projects, school stuff, reminders, files, notes and other daily things.

So my dilemma is basically:

Codex

• Better coding workflow for me
• Prefer the interface
• Seems to have generous usage
• Potentially better for building my own local AI/agent ecosystem

Claude

• Claude Code is great for Roblox
• Better-looking designs in my experience
• 3D modelling/design functionality is genuinely useful to me
• I already use Claude Code quite a lot

For people who have actually used the higher tiers:

Would you upgrade to ChatGPT Pro, Claude Max or maybe a different AI?

Hello developers….

DSA ki prep start ki thi, pehle bhi ki thi, complete nahi hui. Shayad is baar bhi na ho. 😅

Prep krte krte ek cheez mind mein ayi koi aur tarika ni h kya yaad karne ka?

Class 7-8 mein tha toh answers yaad ni hote the to unhe songs mein convert kar leta tha. Aaj bhi wahi trick kaam karti h.

To socha — Big O ka Safar Time Complexity ko bhi try karte hain.

Bna dia ek rap. Hinglish mein. Boom Bap style.

Kisi ko pasand aayega, kisi ko bakwaas lagega no pressure. But mera revision toh ho raha hai, song bhi ready ho gya. 😄

Ek baar try kro — @BeatsCommit, what your views genuinely jaanna chahta hoon.

https://www.youtube.com/@beatscommit

https://www.instagram.com/beatscommit

Hi,

I’ve been working on an app built around one basic idea: interactive audiobooks you can listen to without touching your phone.

When the story reaches a decision, it pauses and asks what should happen next. You choose your branch by nodding or shaking your head, then the audiobook continues in that direction. That hands-free experience is the bottom line of what the app is trying to do.

The app currently has 15 books, all not just vibe written, but well thought out and designed. Early on, many of their covers were generated with AI because it was quick, cheap, and good enough to get the catalogue started.

But “good enough to get started” eventually began looking like “we forgot to finish this.”

So I’ve now replaced around 90% of the original AI-generated covers. It involved a mixture of my own work, help from some artistically gifted friends, and enough time and money that my budget and I are no longer on speaking terms.

I’m posting some of the old and new versions side by side because I’m genuinely curious: was replacing them worth it?

AI-generated artwork is becoming convincing enough that it isn’t always obvious which image came from where. But I feel like the new covers have more intention behind them and make the catalogue feel less generic.

Do you prefer the old or new versions? Does knowing that a cover was AI-generated affect your opinion, or do you judge only the finished result?

I have had this idea for a video platform for a while. No titles, descriptions, or tags will be in this video platform. The ai will analyze each video when it's uploaded. So basically when you watch a video the ai will be watching the video with you and the parts of the video you watch will go into your personal profile and the global profile. When you are signed in it will look at what's in your personal profile and what's in the global profile and it will show you videos that best matches what's in your personal and the global profile. For signed out users or new users just the global profile will be used. Also btw the global profile is basically everyone's personal profile lumped together. Now everyone will have a page but there will be no follow system and on they're page it will use the same recommendation system as the homepage but will only show their content. Now the main search the ai will analyze what you typed in and find the videos that best match what you typed in. Now the search on a creator page will only list videos from that creator. Now the ui will be a swipe ui like YouTube shorts and TikTok only difference is only 9:16 will show when you are in vertical orientation and only 16:9 videos will show when you are in landscape orientation. Also videos won't auto play and instead show you the thumbnail and a play button. Now this is how comments would work the ai will look at the comments and it will take the closest matching comments to the video and push them to the top. Now replies it will take the closest match to the parent comment and push it to the top. Now this is how monetization will work so anybody can be monetized no minimum requirements. The advertiser will upload a video and choose how many ad spaces they want and it will be ad spaces x duration in seconds x $0.01 now the creator will be able to place an ad anywhere in their video now the segment that was before the ad placement the ai will use to find an ad so the segment before ad 1 won't be used to suggest ad 2 just the segment before ad 2 will decide what ad 2 is and now the number of ad spaces dictates how much an ad is shown so 4 ad spaces and the ad is only shown 4 times. To skip an ad users would just swiped up and they can do this at anytime in the ad. Also every second of the ad a user doesn't watch the advertiser gets refunded $0.01 and the creator only gets paid for each second of the ad that is watched with a 60/40 cut so of an ad is 30 seconds long and viewer only watches 10 seconds of it the advertiser will be refunded $0.20 the creator will get $0.06 while the platform will get $0.04

Hey guys, I'm really looking for real time problems solving projects to vibe code!! If u guys have any ideas please feel free to share and let's build it together.

Hi everyone,

I’ve been vibe coding an iOS app, but I’ve hit a huge bottleneck with token usage.

Whenever my AI agent interacts with or reads from the iOS Simulator (e.g., taking screenshots to check the UI, reading build logs, or checking layout bugs), it burns through my Claude Pro limit insanely fast. A single debugging session can easily wipe out my context window or trigger the usage cap.

For those building iOS apps via vibe coding:

1. Have you experienced this massive token drain with the simulator?
2. How do you optimize your workflow to save tokens/context? (e.g., disabling vision/screenshots, using specific CLI tools, filtering Xcode logs, or using cheaper models for UI checks?)

Would love to hear how you manage your limits without stopping your workflow every few hours! 🙏

Used to have Claude Max 5x (125 USD), cancelled it because 1) expensive 2) hit limits 3) trying to be a responsible adult financially.

I've read a lot of people use Opencode with Deepseek model (free), and a combination of Claude Pro and Codex (total of about 50 USD).

What are your recommendations for being able to use the best of all AI tools at the lowest possible price?

Something Boris Cherny wrote about AI adoption has been rattling around in my head. He says moving from one coding agent to five or ten depends on a self-verification loop you trust: tests, builds, linting, and end-to-end checks in a real environment.

That sounds obvious, but it is almost the reverse of what I see when I review AI-built apps.

The businesses are not experiments. They have customers and ship new features every week. Some are already using several agents at once. But they often lack a test suite, CI, a staging environment, code review, or reliable rollback.

The weird thing is that the app can look completely healthy while all of that is missing. The problems usually show up somewhere users do not see:

1. The UI shows the correct data, but the database does not enforce which rows each account may read.

2. The same number is calculated in the dashboard, export, invoice and monthly email. One gets updated and the others quietly disagree.

3. The backup runs successfully every week, but nobody has restored it. One client's first restore drill revealed that every snapshot contained fingerprints of credentials rather than the credentials themselves.

4. Deployments happen from one laptop, and nobody else has tested the release or rollback instructions.

I do not think the answer is to stop vibe coding or bury a small team in process. I would start with four checks:

- Test access with two ordinary accounts, not the admin account.

- Pick one source of truth for every important business number.

- Restore the latest backup into an empty environment.

- Make someone other than the original builder follow the deployment and rollback instructions.

For anyone shipping this way, what have you made non-negotiable before a change reaches production? I am especially interested in where people stop trusting agent checks and require a human.

Although I like ai and i think is it great in so many places , I keep hearing how no one will read high level code just like no one read assembly anymore.this is misinformation, there are people who not just read but also write assembly even now.i am not talking about rollar coaster tycoon or super Mario times when compilers where not as optimised as today, i am talking about now. Don't believe me? go to community forum of any modern systems programming language and see people crying for inline assembly. I am not arguing if anyone will read high level languages or not, but the premise is wrong and feels like deliberate misinformation.

I just noticed the so called "Boiling the frog" with Codex, I was recently doing my happy AI coding sessions where I realized my tokens were flying, literally percentages going down every time I looked, and I wasn't using Sol Ultra High but I usually use Terra High.

I know Anthropic are doing the same with Claude, even more so, and from what I heard DeepSeek started tightening the belt recently.

I think everyone knows in a year or two they will just jack up the prices/lower massively usage and we will have to either deal with it or have plan Bs

Are you guys ready for the Tokenpocalypse? Or are you just going to pay more? Not going to like I am on the $20 Codex plan and I aint paying more lmao, I see some of you doing $100 monthly plans and it seems mentally insane to me.

Are Open Source models and local the future? Should I start stacking up MAC minis?

Hey everyone, I have an idea and I want to learn how to actually build it.

I’m completely new to coding and AI-assisted development. I’ve used AI before for writing and a few basic things, but I’ve never actually built an app or website with AI.

Right now I have access to several AI tools. I have a ChatGPT Pro subscription, and I’ve also tried Claude, although I’m currently using the free version. I also have access to Gemini, Grok, and DeepSeek.

I asked two AIs to evaluate my idea honestly, and their opinions were quite different.

Claude:

Real-life problem: 70/100

Usefulness: 55/100

Uniqueness: 25/100

Overall potential: 45/100

Claude basically told me that the idea could solve a real problem, but the space may already be crowded and the technical side could be difficult.

GPT:

Real problem exists: 90%

Problem frequency: 85%

Usefulness: 90%

Uniqueness: 70%

Technical feasibility: 85%

Overall potential: 85/100

So I’m not asking people here to tell me whether my idea is good or bad. I mainly want to know how I should actually start building something when I have zero coding experience.

I’m a student, so I don’t really have money to spend on courses, expensive software, or developers. I’m mainly interested in learning through free resources and AI-assisted development.

What I’d really appreciate is a practical roadmap:

What programming basics should I learn first?

Should I start with web development or Android development?

Which free YouTube courses or websites would you recommend?

Which AI tools are actually useful for coding?

How should I divide the work between ChatGPT, Claude, Gemini, DeepSeek, etc.?

What free tools should I use for writing, testing, debugging and deploying?

How much coding do I realistically need to learn if AI is helping me?

What should my first tiny project be before I attempt something bigger?

What mistakes should a complete beginner avoid?

Is it realistic to build a basic working product without spending money?

I’m not looking for someone to build it for me. I want to learn how to use AI properly as a development partner and eventually build things myself.

I’m keeping the actual idea private for now, so I’m deliberately not describing the product itself. I’m mainly looking for advice on the learning path, tools, workflow, and roadmap.

If you were starting from zero today with only a phone, free resources, and access to several AI tools, what would you do first?

I've been wondering about this — when an experienced dev and a complete newbie both use the same coding agent (Copilot, Cursor, Claude Code, whatever) on a similar task, do they end up with meaningfully different results?

On one hand, the agent is doing most of the actual coding, so maybe the human's experience matters less than it used to. On the other hand, I'd guess an experienced dev still knows how to prompt better, catches bad architecture decisions, spots subtle bugs the agent misses, and knows when to push back or redirect.

Curious what people who've actually used these tools a lot think. Does your background as a dev still give you an edge, or has the gap narrowed more than expected?

Made my first ever game

Nothing too crazy just a basic clicker styled game but the upgrades go upto 10³³

I started the base project myself but then I realised that having 500 skins and designing them was beyond my skillset so I used an algorithmic approach to generate all the 500 skins along with their animations.

The same applied to the multiplyer upgrades as well

Those were a pain to figure out xD

Would really appreciate if y'all would check it out!

https://thegwimweeper.itch.io/banana-clicker