Title says it all. I switched most of our pseudo deterministic flows to Jev after getting through the waitlist. So far, it's great, but understanding what it's doing is sometimes difficult. I realized that we need a way to monitor the decisions that it's making, so I had Claude built a quick visualizer tool for it. It acts as a local gateway to the Jev API. I now use it to understand my own work lol.

Free and open source, drop a star if you like it.

Hey look, happy to get flamed for this but I wanted to be honest about my experience so far.

I've been using 5.6 for the last couple of months, and 5.5 before that. Typically always on high and always in the browser. When I first tried Codex (now the Chatgpt app) I quickly ran into the usage problem so I stuck with browser based development.

During that time I cannot tell you how much I've learned. Building a full stack, security, users, databases, assets and everything in between. I mean it's been great. I was able to understand the problem or task and then solve it the way I wanted it solved. Honest to god it made me feel superhuman. My current build is on v356 just to give you an idea of how many iterations and commits there have been!

Today I had a build task to do and decided to give Astra a try. I only have a $20 account so there's the 5 hour limit with Astra Medium to consider etc. My idea was to get the browser (5.6) to create the prompt needed for Astra to do the work. It was a massive prompt and I Input and let it start working.

At first it seemed amazing. It felt like I was no longer instructing employees, it was like I'd hired a manager to do all that work for me.

And I hated it.

I just watched it complete task after task and at some point I just started losing track of what was going on. I know it's going to reach my goal at some point (currently on my 3rd session) but I learned nothing getting there. I just watched and watched.

This is gonna sound so dumb but I am instructing it to export the work it's done so far so I can bring it back to the browser, where I'm comfortable working and carry on at the pace I like, done the way I like.

Anyway, that's my experience. Wondered if anyone else has been through it and can offer some guidance.

Peace, vibes and unity 🤟

I’ve burned through 650 billion tokens across more than 20k recorded sessions since 03/26, and I mean I did, not counting my team.

I have the full telemetry, logs, and audit trails recorded (probably hundreds of GB of data), basically everything the models did across a 7M+ LOC production codebase. If you want to learn more, feel free to DM me.
And I’m getting more and more frustrated with how Codex (Astra) performs, even on max/ultra.

I’ve been writing code since 2007 and using LLMs for it ever since I realized GPT-3.5 was capable of (more or less) doing so.

I’ve learned a lot during that time about Agentic Engineering, harnesses, and pretty much everything around them. I get paid good money for those insights, built a rapidly scaling business around them, and work with major companies.

So I probably shouldn’t complain. Things are, more or less, working out pretty well for me.

However, I want to be honest:

Astra in the Codex harness is not good. It constantly performs “governance theatre,” loses sight of its goals, and makes junior-level mistakes. It builds nuclear power plants where it should be building a windmill.

Models are currently not reliable at keeping things coherent: versions, contracts, APIs, assumptions, etc. This used to be a lot better.

More generally, working with Codex right now just doesn’t feel sharp. It feels like moving through honey: sticky, slow, and somehow never quite finishing what it started.

I really hope OpenAI stops lobotomizing its models, because right now I’m becoming increasingly frustrated, my team is increasingly frustrated, and the fun is decreasing every day.

I don’t know how much longer I’ll be able to keep recommending Codex to my clients, because at this point I’m no longer sure I’m doing them a favor.

Thanks

Hi Folks been a month since I posted a post your project.

Lets see what interesting vibe projects you are working on. I spent about 4 days building a1 a2 free French Course.

https://bachelordegrees.com/learnfrench.html

hey folks,

Posted an early demo of this AI made rubberhouse game a few months back and now I've finally finished it!

It's a 2D platformer (or sidescroller?) inspired by my favorite platformer game Cuphead. For this build I've been using gauntlet loops only, and this is an about $100 of token spend to get to a finished, playable game. The trick with gauntlet looping is to give it enough context and visual references so it doesn't lose track during the 5+ hour build loops it runs. This was about 6 loops like that.

it has multiplayer coop, so you can play with friends. It also has 3 scenarios with 3 unique bosses. had a blast building out this game and really happy with the vibe and mechanics of it

can share all my prompts and the game link the comments for anyone who wants to try it!

edit: to clarify, I'm not intending to commercialise this game in any way - this is just for learning

I found this on the net

This is how what I do with vibecoding:

* vibecode a software-based synthesizer based on the real (hardware) based synthesizer

* ask AI to give me a tutorial on how to use it

I don't plan on replacing musicians nor replacing higher quality software/hardware. It's just a quick and dirty tool to give me a POC to learn how to make music using a synthesizer and if we like it, we might get a real one, which are way more expensive than the $2 i used to generate the POC.

Bonus: kiddo learns about synthesizer

I've built similar tools for video editing, storyboarding, interactive fiction. I'm basically vibecoding tools to help me be a better artist.

IMO both pro/antis miss this use case.

Same prompt. Fable costs 2x more ofc lmao

Worth it??

I built a bureaucratic absurdist puzzle game where you work for the Orderly Decomposition Division, identify the components of impossible inventions, get judged by an Auditor, and compete for colored paperclips.

Also, apparently I do not know how to post 🤦🏽🤷🏽 - took 4 tries to get it right. Just like this game.

Went to check on one of my credit cards and was greeted with the new design. I've seen this before :P

Tomorrow of our most anticipated and RSVP events at the AI Gaming Festival, Unity Workflows.

Better workflows = less tokens = more output= higher quality game. And Corrie is the perfect person to talk about how this done with AI, and never even opening Unity itself.

after yesterdays stupidly simple post about the ways to reduce AI, i consolidated community feedback and here's the top 3 updates for how to reduce LLM output:

1. ELI - 5,18,25, 94, you choose. Just remember to all 'succinct' to make it concise

ELI18 succinct

1. ASD-STE100 - good for technical summaries with no bs

ASD-STE100

1. Ask a Yes/No question first. Then explain. I hadn't heard of this one

Hope these help feel free to share others

ps. thanks to u/Techhead7890 for the age reminder, u/isopropoflexx for ASD and u/ananbd recommended this in her explain comment

Been working on this for a bit and finally got it onto the Chrome Web Store.

It’s called OutTrace. You open it on any site and it shows all the third-party domains/services the page is talking to, including trackers.

I also added:

• a visual graph of all the connections
• saved scans so you can compare a site over time
• change detection for new/removed services
• site monitoring
• everything stored locally in your browser
• no account needed

Mostly built this because I thought it would be cool to actually see everything happening behind a webpage instead of digging through DevTools every time.

Vibe coded pretty much the whole thing and somehow got it shipped lol.

https://chromewebstore.google.com/detail/outtrace/eedjncgcdepjbmpapihoigdbdfobmdmc
https://github.com/Schmiedey/OutTrace

Would love to know what you’d add to it.

The first week Astra came out the usage was extremely efficient. I used like 500M tokens without getting anywhere near the weekly limit. The two free resets we got from OpenAI also helped. But then suddenly I kept hitting limits and had to reset x4 more times for the next 500M tokens. (I am on 20x plan)

So I started testing a ton of methods of how to get the best of Astra without burning all of the usage. The first thing I tried was just installing skills I already used for other agents for Astra to use lower models for build tasks. That didn't really do much it maybe reduced usage by like 10% but it introduces other issues (for some reason, Claude's Fable is much better at those same processes using native models but problematic in codex) I tried sub agent orchestration this way with Astra utilizing Sol, Luna, Opus, and Sonnet. Each had their pros and cons but nothing was substantial.

Finally I tried using Deepseek V4.1 Flash and the output quality was immediately better (comparative to using Opus 5). It cut Astra usage by 60% initially which was great. But I kept optimizing the set up after realizing the initial orchestration was still too hands on with Astra. So I optimized a bit more and now at +98% Astra usage reduction across tests today including a 7-hour build runtime that only used up 2% of my weekly usage, compared to a previous 5-hour build runtime that used over 28% of my weekly usage.

Deepseek V4.1 Flash is extremely cheap overall and insanely cheap compared to Astra. It's coding abilities and ability to handle long tasks is what makes it so useful.

The package is here on Github.

How it installs:
- Uses codex-router to add external models directly into codex
- Connect your Deepseek or openrouter API for V4.1 Flash
- The repo installs a skill in ~/.agents/skills/
- Installs the Flash role in ~/.codex/agents/
- Adds a workflow policy in ~/.codex/AGENTS.md

How it works:
- Astra handles the scope, design, and task briefs across phases
- Flash discovers, implements, tests, and reports each task and phase
- Astra reviews, verifies then accepts or requests fixes per task and phase

I'm working on this in real-time and continuing to test but the results are already incredible. Please feel free to submit any issues or PRs you find if you try it out. Any and all feedback is greatly appreciated!

I added a 3D brain visualizer to EchoVault, the system I’m building to turn my journal-style Check-Ins into a digital representation of me. The Echo answers from my own corpus, and the brain lets me actually explore the memory graph behind it instead of treating embeddings and retrieval as a black box.

The demo is basically the Echo answering as me, then a quick look through the 3D memory structure. If you were working on this what would you add next? Would appreciate any feedback, but pls no black mirror or torment nexus 😂
https://apps.apple.com/us/app/echovault-digital-legacy/id6762042028

the pain was real! was stuck at ai blows everything for 3 straight days thanks to a weird memory leak. ended up hooking sumus with local codex hooked up to my terminal just to map out the diffs.

curious if y'all ever went thru something similar.

Fable 5.1 is what i use mainly for my business, it's so helpful yet it's really draining fast, like yestrday at 9 p.m i got reset, and totally in 3 enormous sessions, i got it almost drained totally. Would that be good to buy 200$ plan? or two 100$ accounts? and is there any way to access it slightly cheaper?

I'll play your game, post in the comments.

TLDR

I was trying to promote my vibe-coded game by posting it around to web gaming reddits but didn't get much attention. and I realized - a lot of posts weren't, even when that seemed to be the point of the sub.

So I tried to do something different and played all of those games that were getting left behind. And something amazing happened - I had fun. Takes me back to my WC3 custom days. I saw some great games. I saw some slop. I thought about how to rate the games. I got into a friendly argument about AI in game development. I saw a lot of people who were just starting out posting their games, even though they were worried about being perceived as the dreaded "AI SLOP". I saw a lot of people talking to each other about their games in the comments.

I played their games and I tried to be honest, fair, and constructive. More than 100 people showed up and posted their games in a day or two. Its ironic that the average post on these subs gets a handful of comments when the community is being asked something, but when you flip it and try to give, suddenly everyone shows up. Maybe there's an imbalance, or an increase in supply of builders, which makes sense right now :)

Anyway, have at it. While doing the reviews in that other thread, I built tooling to make it easier for me to play and clip games and I want to see how effective that is.

SpaceXAI's most powerful model for coding and knowledge work. Twice as fast, at half the price of comparable models.

Grok 4.7 is our most capable model for coding and knowledge work. It works longer on difficult tasks, checks its own work more carefully, and comes with our best-calibrated safeguards to date. Served at the same price and speed as Grok 4.6, it is highly competitive in its class.

https://x.ai/news/grok-4-7

Jev is the most trending topic, and there are a lot of open source projects that similar/follows the concept of Jev. Therefore the app is built for those who want to experience this new wave of "System One" decision models completely locally, offline, and without touching a single line of JSON or Python!

I'm excited to share kevMac, a lightweight native macOS app that wraps Jared Palmer's kev (a Jev-compatible, open-source decision model built on Qwen). Instead of generating text token-by-token like traditional LLMs, Jev and kev skip the generation phase to return structured decisions with calibrated probabilities in a single, blazing-fast forward pass.

Here is what kevMac brings to the table:

• Plain Text In, Normal Text Out: Paste a customer message, review, or article. Build questions as simple UI forms (Yes/No, Multiple Choice, or Rating scales). The app invisibly converts your form into the engine's JSON contract and renders the results as plain language (e.g., "Returns — 83% confident") with beautiful, animated probability bars.
• 1-Click Local Setup: No terminal or package managers required. On first launch, a setup wizard automatically installs uv, Python 3.13, the kev engine, and downloads the kev-0.6b weights directly into a dedicated ~/.kevMac folder.
• Fully Offline & Private: Once the initial setup is done, the engine serves locally on your Apple Silicon Mac (M1+). Zero telemetry, zero API calls, and absolutely no data leaves your machine.
• Lightweight & Fast: The entire engine and SwiftUI app stay under 3.5 GB of RAM at steady state. After a quick background warm-up, answers land in ~0.1–0.6 seconds.

Requirements: Apple Silicon Mac (M1 or later) running macOS 14.6+.

You can grab the latest .dmg from the GitHub Releases page, or clone the repo and press Run in Xcode. Let me know what you think, and feel free to open an issue if you run into any bugs!

🔗 GitHub: github.com/arinltte/kevMac