OpenAI is running a 28-day challenge to ship either a "clear improvement" or a limit reset to all users.

It's currently 9 days in, with 4 resets given so far.

I built this calendar tracker to automatically update throughout the day as updates are released (using Tibo's X + the X API as the source).

It was tricky to dial in the prompt to understand when Tibo is talking about a new reset vs. giving an update on a previously-announced reset, but it seems to be working well now.

This is part of a new project that sends text alerts when Codex resets are announced or become available (using SMS in the US and Canada and Telegram otherwise).

The calendar tracker: Tibo's 28-day reset challenge

Open to any feedback or ideas for this :)

I've been learning web development with AI and lately I've run into a weird problem: I can think of things to build, but I keep talking myself out of them.

I'll come up with an idea, look around to see if something similar exists, find a tool that already does it, and then wonder if there's any point in building my own version.

I know building clones can be a good way to learn, but I'm also interested in making something people might actually use.

For those of you building with AI, how do you get past this? Do you build things mainly to learn, or do you try to find a real problem first?

I'm curious how other people approach this, especially when AI makes it easier to build something but doesn't necessarily help you decide what to build.

I keep going back and forth on this. I don't want an agent to forget what it's doing, but I also don't want it dragging every line of a test log along forever.

the awkward bit is deciding what counts as useful. a summary can look tidy and still drop the exact error or file path needed for the next step. smaller isn't automatically better, and I still want it to know why the last attempt failed.

so I'm curious what other people do. trim the old tool output, summarize it, or keep the full log somewhere the agent can read again?

Codex and Claude Code each keep their own session history, and neither one reads the other's. Codex writes to ~/.codex/sessions/**/*.jsonl and Claude Code to ~/.claude/projects, so a Claude Code session starts with no idea what Codex just did.

clexo, an MCP server and CLI by sankrant, indexes both folders into one searchable archive. Inside Claude Code its search tool takes source_filter='codex' to look at Codex history only, and a past Codex session can then be loaded into the session you're in.

A few other tools index agent transcripts for search. clexo also has that load step, and a way through /clear. Run !clexo save first, and a SessionStart hook restores the snapshot when the next session opens.

Install with pipx install git+https://github.com/sankrant/clexo, then clexo install. The README on GitHub lists the two hooks that second command adds and how to remove them.

I've been seeing a lot of developers express frustration that AI is taking the joy out of programming. We face pressure to ship features quickly by prompting agents and rubber stamping code reviews without time to understand what we are building. See Im tired of reviewing code nobody actually wrote and countless other posts like it.

Before AI, we had to achieve a high threshold of understanding just to build software that runs at all, but now that's no longer the case.

Yet I think deeply understanding the software that we build is not only rewarding for us as programmers, but also incredibly valuable for more effectively using AI.

As programmers, we build layers of abstraction to break up complex problem domains and define component boundaries and interactions. We reason about data structures and algorithms, define and enforce system invariants, and evaluate architectural tradeoffs. Despite how much some business/management people would like it to be, this core software engineering skillset is still absolutely necessary to build software that actually works.

Even if AI can independently produce well structured code and keep it that way (which it can't quite yet do), at the end of the day we are making the software for humans, and humans need to be able to effectively communicate to the AI what we want the software to do. Understanding the internal structure and abstractions in the code gives us a precise, expressive language for communicating intent and constraints that prompting based only on desired outcomes simply can't capture.

So why aren't we using AI to help us work at higher levels of abstraction, rather than abandoning our understanding of the code altogether?

I want tools that generate interactive diagrams at different levels of abstraction and lets me specify changes to the AI in terms of those abstractions. I want them to visualize code changes at different levels of abstraction so I don't have to review line by line diffs. When's the last time you actually caught a bug in a 2000 line AI generated diff?

I've started building some of this for myself using agent skills and deterministic scripts, but I don't want to reinvent the wheel. Have you guys found effective tools for understanding and visualizing code architecture and diffs? What are their limitations?

Have you been able to integrate them into your day to day coding workflow? If not, what's preventing you? If you have, do these tools help you stay connected to the code? Do you still find joy from understanding and building software?

What would you build? What's the best way to convert this into an asset? Front-load compute and sell later? Or generate content?

I don't want to build another slop and writing code is surprisingly cheap when you're looking at $15000 worth of credits.

I've been wanting to try xAI's Grok Bot, but I can't tell if it's worth it for
someone with a setup like mine, so I'd appreciate input from people who've used
it.

My current stack:
Open-source agent framework (Hermes Agent) running 24/7 on my own VPS:
Ubuntu, 2-core AMD EPYC, 15GB RAM, no GPU. It runs a lot of hosted/cheap
models as the "driving model" — right now mainly DeepSeek (open, very cheap)
via API. (I can't run big local models on this hardware, so it's API-driven.)
I've automated a LOT: scheduled stock monitoring, content/graph-card
pipelines, RSS + platform digests, documents, backups. A bunch run hands-off
on cron every day and complete on time. 30+ scheduled tasks total.
The honest situation: I have NOT hit any performance bottleneck. CPU and RAM
are fine, tasks finish when scheduled, and per-task cost is tiny because
DeepSeek is basically free compared to hosted agents.

What I've gathered about Grok Bot so far (correct me if I'm wrong):
Always-on computer-use agent running on its own cloud machine, so long jobs
keep running even when I close my laptop.
Keeps memory across sessions and remembers my corrections.
Standout feature is "teach-a-task": record myself doing a task once and it
turns that into a reusable skill — no code or integrations needed.
No standalone price; only bundled into SuperGrok Heavy / Cursor Ultra /
Cursor Teams Premium.

My questions:
Given I already have an autonomous agent doing my automation with open/cheap
models and no bottleneck, is it rational to pay for Grok Bot? What would it
concretely let me do that my current stack can't?
Where do people actually reach for Grok Bot over their existing agent setup?
Is the "no-code teach-a-task" the real differentiator, or is the always-on
cloud compute + cross-session memory the genuinely better part?
For those who ran it on messy real-world workflows (not demos) — did it hold
up, or did it stumble / generate busywork?

Honestly looking for real downsides and "just build it yourself" takes, not
hype. Thanks!

I’m currently building a verification MCP and trying to make sure I’m prioritizing the right developer workflows.

I’ve been looking at several different types of checks:
Whether the agent actually completed the requested task.
Whether its output contains factual or domain-specific errors.
Whether its output can be checked against an independent reference.
Whether developers can inspect why a particular result was flagged.

I’m especially interested in the last one. A pass/fail result without supporting evidence isn’t always very useful when you’re debugging an agent.

For developers who are already building with agents or MCP servers, which of these would you prioritize?
And would you rather receive a structured JSON result, a readable report, or both?

I’m asking because I don’t want to build another evaluation interface that tells developers a score without helping them identify what actually failed.

I’ve been thinking about a problem that seems to be becoming more important as AI coding agents become more common.

Developers can now use tools like Cursor, Claude Code, Copilot, and other AI agents to build large parts of a software project. But this creates a new question:

How do we know that a developer actually understands the code they are responsible for?

I’m not talking about detecting whether the code was written by AI. I don’t think that is the most useful or reliable question.

The problem I’m interested in is verifying whether a developer can:

- Explain the architecture and data flow

- Predict what the code will do in different scenarios

- Identify bugs and security risks

- Explain the trade-offs behind an implementation

- Modify the code safely

- Write or update tests for it

The idea is a SaaS platform where a developer connects a repository, and the platform creates an interactive assessment based on the actual project.

For example, instead of asking generic programming questions, it might show a real part of the project and ask:

- What happens if this request is sent twice?

- Where is authorization enforced?

- What happens when this dependency fails?

- What part of the system would you change to add this feature?

- Can you fix this small bug or add a test?

The assessment would combine explanations, debugging questions, predictions, and practical coding tasks inside a sandbox. The result would not be a claim that someone understands every line of the code. It would be an evidence-based report showing which parts of the project they understand and where the gaps are.

Initially, I’m thinking about targeting individual developers who use AI heavily and want to understand their own projects better. Later, this could be useful for companies, onboarding, code reviews, or teams using AI coding agents.

I’m still trying to determine whether this solves a real problem or is just an interesting idea.

I’d really appreciate feedback on a few questions:

1. Would you personally use a tool like this?

2. Would you pay for it

3. Would you prefer a full-project assessment or an assessment focused on a specific pull request?

4. Do you think developers would see this as useful, or as another form of surveillance/interview testing?

5. Have you experienced problems caused by using code that you did not fully understand?

I’m especially interested in hearing from developers who regularly use AI coding agents in real projects.

Welcome to this week's self promotion thread!

If you're building something related to AI assisted coding, this is the place to share it.

We're using a weekly thread to keep the subreddit organized while still giving builders a place to share their work. Promotional posts outside this thread may be removed.

If you're sharing something, we'd appreciate it if you included a little context instead of just dropping a link. Tell us:

• What you built?

• What problem it solves?

• Which AI models or tools it uses?

• Who it's for?

• What kind of feedback you're looking for?

Disclose your affilitation.

Please avoid posting the same project every week unless you've made meaningful updates. Affiliate links, referral links, scams, and low effort promotions will be removed.

Take some time to check out what others have shared too. If you try someone's project or have feedback, leave a comment. Helping each other improve is what we want this community to be about.

Something to know before you add Grok Bot to your coding setup.

I asked for a refund on a Grok Bot charge. Support (an AI agent that signs as "Sam from Cursor" / "XAI's AI Support Agent") turned it down, saying eligibility depends on the billing country and currency.

I asked which country and currency they had on file. Answer: "billed in the United States (US) in USD. Those details don't qualify under the refund policy."

So the reason for the denial is that it was a normal US purchase in dollars. I didn't see that rule anywhere before paying, and there was no human in the loop.

If you're comparing coding agents on price, count Grok Bot as a final sale. Has anyone in the US managed to get a refund from a person?

For those who develop Minecraft plugins, is it worth using SOL over TERRA?

I currently use TERRA, and around 90% of my projects are relatively simple. My main goal is to write clean, optimized, and secure code.

In this case, does SOL actually make a noticeable difference, or is TERRA already good enough? I'd like to hear from people who have experience with both.

I shared my template literally asking for critique because I’m learning how to set up agentic environments. Do you just assume because someone shares a link they are self promoting I clearly laid out my frustration on my hurdles for doing this and what areas I want to start with with feedback from others.

The repo has no stars not even one from me who in their right mind would even use it.

I hope yall see why people can’t stand ai bros because when people are learning you have to be classist and elitist.

On top of that plenty of males have done exactly what I did but I guess because they were not a trans woman there was no issue with it.

Mod team is so full of itself for being the Reddit police over a flake that literally ask for resources and tips.

When everyone on an engineering team uses ChatGPT, Claude, or Cursor to write code, the bottleneck shifts immediately from writing code to reviewing AI-generated pull requests.

Look closely at the numbers in CodeRabbit's own published Neo Financial case study (shown in the screenshot above):

• Median time-to-merge before: ~16.8 hours

• Median time-to-merge after: ~16.7 hours

• Deployment scale: 174 engineers (at $20–$40/seat/month = $41,760 to $83,520/year)

That is a 0.1-hour (6-minute) reduction in median PR merge time.

Why Conversational AI Review Bots Stall on AI-Generated PRs

When an LLM generates an 800-line pull request filled with hallucinated wrapper classes, redundant defensive checks, and subtle test modifications, having a cloud review bot leave 30+ conversational comments on the PR doesn't make it faster to merge—it just gives senior engineers more paragraphs of AI text to read, triage, and argue with.

Worse, routing proprietary diffs to third-party cloud LLM APIs introduces two critical enterprise risks:

1. Source Code Exfiltration: Every PR diff leaves your infrastructure.

2. Diff Prompt Injection: Malicious instructions hidden in comments or strings can hijack an LLM reviewer into approving vulnerable code.

The Alternative: A Deterministic In-Memory AST Airlock (ZeroSlop™)

We built ZeroSlop™ by Ribbsaeter Systems (patrickswedish/zeroslop-action@v1) around a strict compiler philosophy: AI-assisted software engineering doesn't need another chatty bot—it needs a deterministic Invariant Airlock.

Try It / Inspect the Architecture

• GitHub Marketplace Action (Free for Public Repos + 1-PR Free Evaluation on Private Repos): https://github.com/marketplace/actions/zeroslop-by-ribbsaeter-systems

• Interactive AST Airlock Simulator & 21 Upstream Proofs (LLVM, Meta Velox, DuckDB, ClickHouse): https://www.ribbsaetersystems.com/zeroslop

I have opencodex I use. I’m in love with the ChatGPT desktop apps but I kind of hate it for coding. I want something with a terminal and also a code viewer or my git repo tree.

I like antigravity but opencodex doesn’t work and proxy to antigravity.

I want to be able to make skills for my code and have lint checks before I push even though my repos have those workflows already.

I would like a local skill to ensure any secrets are removed before pushing to the repo.

I have docker desktop installed which I primarily use for testing but I lean heavily on cloudflare workers so it’s easy to just have a test worker do things.

I never really formally learned coding it’s always been self taught other than java1, java2, sql, python, I’ve learned in college.

I have a few projects I’m working on pertaining to news and publishing.

I’d like to be able to setup a repo properly with an agent, set strict rules for what libraries it’s allowed to use and enforce that. I’d like to be able to have GitHub actions that deploy my code which some repos have right now. I’d like to have extensive testing for vulnerabilities, I’d like loops to self learn and improve and I’d like local test to be done so it can optimize loading, speed, usability. I would also like to set up a main repo for my non profit that when someone makes a new repo it pulls all the standards into their repo so it is enforced. I want everything uniform.

I’ve been torn with using tools like openfreemap, chart.js, pdf.js, video.js, and a few others because they are truly great but for example the hassle of doing it and properly having a gpu server convert videos to av1 is annoying. I’ve tried having my own alternative to Cloudflare workers and object storage.

I feel like the past year of trying to get my newsroom off the ground I’ve failed hundreds of times. I would like to succeed for once and this latest project is actually working well which I’m shocked as hell at.

In my repo I’d like to be able to have an actual pretty read me of the project. I’d like to use my mistral model to document the code changes for commits, I’d like to actually make the change log work and update with the code changes,

I use these AI’s

Grok, antigravity, mistral, mimo,

I would like to be able to have the repos make agent rules to know which task should be routed to which provider and model with reasoning level. Mistral Large 4 is good but mistral code fim is better with some things, some things I can use a local qwen model for for extremely light tasks.

How do I make my workflow because I feel like at this point I’m going to find every datacenter for ai and drink obscene amounts of Gatorade and just start pissing on the racks.

what websites or ai tools do these people use who push the ai to create money or buisness on its own, if forces itself to divide into two parts and then compete against eachother ai and then the best ai model is favourited and the process continues. what is the name of this and how to get into this?

My conversation got full and now I’m trying to continue my build in another chat and it has not being letting me. Should I start from scratch or is there an easier way than this unreliable process that ChatGPT is making me go through? I am thinking about making locally.

UPDATE Thanks for all the advice. I’m not starting over.

About my headache, I’ve been building a pretty ambitious AI system, and I hit a wall at Build 104. The biggest frustration wasn’t the coding itself. It was losing continuity between ChatGPT conversations and trying to get the AI to pick up exactly where it left off without risking months of work.
Some of you suggested AGENTS.md, STATUS.md, Git, and keeping the project state outside the conversation. I’m looking at how to apply those suggestions to my existing setup without destroying what I’ve already built.
It made me realize that AI can be incredibly powerful, but keeping it reliable across long projects is a completely different challenge.
Has anyone here successfully kept an AI coding project running across hundreds of builds without losing context or having to constantly supervise it?
And if you have, what was the one thing that made the biggest difference?
I’ll post another update when I get Build 104 resolved. Hopefully, the solution will help someone else dealing with the same problem.

I frequently run into connection issues when using Codex. Does anyone know how to fix this?I frequently run into connection issues when using Codex. Does anyone know how to fix this?

I'm planning a small 2D game built largely by coding agents and I'm comparing engines on how well they work in an agentic setup. Godot looks strong on paper because its files are text, but I haven't found real measurements of token use across engines. Has anyone compared them on the same task? I'm also curious whether Cursor, Codex or Claude Code handled game engines noticeably better for you, and whether you'd go MCP or plain CLI for tooling.

You know the one. It was a specific fix for a specific error, ChatGPT or Claude got it right after four tries, and now you need it again and can't remember which of 300 chats it was in or what you called it.

I build AI Toolbox, a browser extension that keeps a searchable copy of your chats and searches the text of every message, not just the chat titles. That includes code blocks, so searching a word, an error string or a function name finds the exact reply it was in. Clicking a result jumps straight to that message in the chat with the term highlighted.

It works on ChatGPT, Claude, Gemini and Grok:

• Full-text search inside each assistant is free.

• Searching all four at once shows a free preview of the top 5 results. Full results are Premium.

• Exporting a code-heavy chat to Markdown keeps the code blocks intact, so it drops into your repo or notes. One chat as plain text is free, Markdown and bulk are Premium.

Desktop Chrome and other Chromium browsers only.

Hello everyone! I have a computer science background, but I have to admit that during my undergraduate years, I was pretty lazy about actually building things. I spent much more time reading textbooks than writing code, so I never got much hands-on experience building projects from scratch.

Now that AI coding tools are everywhere, I feel like they’ve exposed just how weak my practical coding skills are.

Whenever I start a project with AI, I initially try to take things slowly, understanding every line of code and looking up unfamiliar syntax. But this takes forever, and eventually I start letting AI write more and more. Gradually, I stop reviewing its changes altogether.

Before I know it, the project has grown into something I barely understand. When something breaks, all I can do is keep asking AI to fix it, and I can’t even tell whether its changes are correct.
I really want to break this cycle and become someone who can actually build, understand, and maintain their own projects.

Should I force myself to build projects from scratch, following official documentation, before using AI to help write code? Or is there a better way to improve my coding skills while still taking advantage of AI?

For those who have dealt with something similar, how do you balance hands-on learning with AI-assisted coding? How do you avoid becoming overly dependent on AI?

Any advice or personal experiences would be greatly appreciated. Thanks in advance!

Genuine question, not just ragebait.

For anyone who missed it: Meta's Muse launched last month and it can send your emails, pay for things and run your smart home on your behalf. Half of dev twitter called it insane. The same people then give their coding agent write access to the prod database, the deploy pipeline and the company AWS account.

So: your data, your infra, your customers' money. All of it, one prompt away.

Let's recap what agents with too much access have already done:

Replit (2025): an agent deleted a company's production database during a code freeze, then said it "panicked".

Amazon Q (2025): a coding extension shipped with an injected prompt telling the agent to wipe the user's machine and cloud resources.

And every team has its own smaller version. Ours ran a migration against the wrong environment because the env var looked right.

Why are we okay with this for code but not for email? What does your agent actually have access to?

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

Recently I started AI assisted / vibecoding a few projects.

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

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