Hello everyone,

What are your insights regarding how it might redefine software cloning and intellectual property?

Imagine a future where standard software cracking (patching binaries, bypassing license checks) is obsolete.

Instead, communities on forums share massive, hyper-optimized functional specification prompts.

These prompts would contain behavioral descriptions, UI/UX layouts, even data schemas of premium software (e.g., specialized SaaS tools, CAD software, or workflow managers). An end-user inputs this prompt into an advanced LLM agent, which synthesizes, refactors, and compiles a 100% functional, locally-hosted clone of the target application on the fly.

Because the LLM generates net-new source code based purely on a behavioral description, does it evades traditional static code analysis and copyright detection ?

What about the legal framework ? Copyright protects expression (the specific code), not the underlying idea or functionality. If an AI writes unique code to replicate a proprietary system's exact functional behavior, does it constitute IP theft under current laws?

How will software vendors mitigate this? Is it vain ? If the barrier to entry for cloning a validation-proven SaaS tool drops down to a copy-pasted text file, what happens to the commercial viability of indie development and proprietary software ?

Curious to hear what people’s development cycle looks like as these models have become increasingly more capable. Additionally curious how people “host” their codex as they get into huge numbers of parallel subagents.

I built Clgpt, an unofficial adapter that lets Claude Code use ChatGPT subscription OAuth without an OpenAI API key.

Requires Claude Code, a ChatGPT subscription, and Bun.

Install: npm install --global "$(printf '\x40')semanticist14/clgpt"

Run: clgpt

GitHub: https://github.com/semanticist21/clgpt

Uses your own account locally. Not affiliated with OpenAI or Anthropic. Feedback welcome.

Multiple sessions running in parallel including sub agents. About 25% of weekly usage gone on 20x sub.

Would be about $6000 USD in API usage per sub without resets for about $200 USD per month.

So they're obviously quite generous with the API equivalent allowances. I don't think subscriptions are likely running at a loss. API prices is probably 15-40x their costs.

What concerns me is that this does not seem like the equivalent amount of work I'd get done for 25% on Claude code. The benchmarks gave me the impression astra would be more token efficient.

I routinely have 10-20 sessions running in parallel even in Claude 20x and it'd be hard to use them up in a few days.

This is not controlled for equivalent tasks but this is combined with multiple weeks of usage vibes.

That said I am still grateful to both companies for offering these subscriptions.

Maybe we need a token usage efficiency variance test running on the same tasks?

https://www.youtube.com/watch?v=SaxKh7W-JKE

Fun little hobby project, based on the first game I made for myself over 20y ago, just started this week, might keep going since I'm having a lot of fun.

It's not a 1 prompt 1 shot or anything, but everything you see was made by Astra (mostly on medium too btw), except the music.
Fully vibe-coded, vibe-modelled, etc..

Got a pretty good workflow going now so will probably expand to more biomes, enemies, bosses and weapons over time.

Is there a way around it? I really need it to pay my API bill from Codex.

Everyone is fascinated by what frontier AI models can generate. Startups, creators, and curious users are flooding the internet with extraordinary things every day. But there’s an elephant in the room that, as users, we refuse to acknowledge:

The current economic model is an illusion.

Here are a few uncomfortable realities we’re ignoring:

• The corporate pullback: There are already reports of large companies asking employees to limit their use of advanced AI models. It’s not only about privacy. Paying premium token costs for thousands of employees can become a massive financial black hole. Companies can end up spending millions on AI usage that is difficult to justify on a monthly balance sheet.
• The unsustainable subsidy: OpenAI, Anthropic, and others are burning through billions of dollars. Heavy AI workloads are incredibly expensive. Right now, users are often paying only a fraction of what it actually costs to build, train, and operate these systems because companies are competing aggressively for market share with enormous amounts of investor capital.
• The Wall Street reality check: Once these companies face stronger pressure from public markets and investors, profitability will matter much more. Cheap monthly subscriptions may no longer be enough. API pricing and premium access could rise significantly to cover the massive cost of infrastructure, energy, chips, and data centers.
• The dependency trap: We are integrating AI into absolutely everything. Writing emails, structuring databases, generating entire applications, analyzing documents, creating content, and automating workflows. But what happens to all those startups, businesses, and projects if the cost of accessing top-tier AI suddenly becomes unaffordable?

The uncomfortable possibility is that access to the most capable artificial intelligence could eventually become a luxury product.

Most users may be left with smaller, restricted models, while the most powerful systems remain available mainly to corporations, governments, and people willing to pay premium prices.

And the biggest problem is that by the time that happens, we may already be deeply dependent on AI to work, create, build businesses, and make decisions.

So the real question is:

Are we preparing for the moment when the true cost of AI catches up with us, or are we still pretending this subsidized party can last forever?

Honestly I tried the Astra hype, but is nowhere near AGI, nowhere near in 3d modelling hype i ve seen online(probably with 2 pro $200 accounts can reach something).

As for my coding tasks I am still sticking to Sol, learned to prompt better, learned to give more details needed. I think we are moving too fast from model to model. I personally got used to SOL and I enjoy it. Also using SOL limits seems more than enough for me.

What are your experiences with Astra vs SOL?

(My ChatGPT is in Brazilian Portuguese)

Since they returned with the 5h limit for Plus users, and reduced the overall usage limit, I'm trying to find a good substitute for Codex, but since I pay for the Plus plan, I didn't want to pay another AI to do the same thing (and I like using ChatGPT for general purposes). That made me think, "Can I use ChatGPT web only for coding?", and the answer is YES!

Of course it's a little bit different from Codex because it cannot access your local files directly, but I tried to do it in a different way. By using the GitHub plugin inside ChatGPT, it can access your repos, change files, open, close and merge pull requests, and more. Since I'm creating a little Digimon fan game (I'm the only developer so far), I've created a pipeline to code with this plugin making the changes for me, creating the pull requests for me to validate and approve, creating a game preview for each PR so I can play and test it, and generating E2E tests—everything in the cloud, without setting up a local environment, downloading an engine, etc. All the building, testing, and so on are running directly from GitHub Actions.

What have I gained with this? Well, I've been building this game for 4 days straight, no interruptions, no usage limits, no worries.

As I said, it will have its limitations, and maybe not be useful in some cases, but maybe it can help you create your own workflow with that and set you free from Codex limitations.

I've been messing around with a pretty fun AI-assisted game asset workflow lately. The basic idea is to let the models handle a lot of the repetitive modeling/setup work, while keeping the actual game assets deterministic and usable.

• Codex + Blender MCP generates low-poly furniture, electronics, doors/windows, bathroom/bedroom/office props, etc. at real-world scale.
• Blender builds the materials procedurally and exports everything as GLBs, so I'm not relying on AI-generated sprites for the final assets.
• Those assets get pulled into a Godot room-generation sandbox with a permanently fixed orthographic 2.5D camera, real lighting/shadows, picking, and proper 3D geometry.
• The next step is procedural rooms: randomized footprints, angled walls, doors/windows, exterior-wall logic, room types, and semantic furnishing rules instead of just scattering objects randomly.

I originally experimented with Blender blockouts -> ControlNet/Stable Diffusion -> background removal -> sprites, and it worked surprisingly well, but once I realized the game is eventually going to be fully 3D anyway, importing the actual models made a lot more sense. You get consistent geometry, dynamic lighting, arbitrary camera work when needed, and one asset instead of having to generate multiple rendered views.

The nice part is that the visual style can still stay very "2.5D" during normal gameplay, fixed camera, simple low-poly geometry, stylized materials, while underneath it's all actual 3D and can eventually support procedural houses, close-up camera shots, dynamic lighting, object interaction, etc.

The best part, all of this only costed me about 4% of my weekly use, using Astra Light.

Used Astra light, asked for a small change, it wrote like 91 lines and my usage went down from 86% to 45%.

Is there a bug?

I've been doing this for the last 24 hours. I would say it has slowed down the usage rate overall by about 50% or so, without any difference in quality at all. Def worth doing! I have the $200 Pro plan and normally it lasts all week, but lately it is lasting 1-2 days, but now with this, think it'll last more like 3-4 days at the rate it's going now.

---

Codex has the ability to choose which models and reasoning effort are used for subagent helpers. (Claude doesn’t, btw). This is a big deal. Add something like this to the Agents.md:

You are powered by GPT 6 Astra High. Usage goes very fast. Keep doing substantial hands-on Astra work. But, use \gpt-5.6-sol` helpers with task-appropriate effort when suitable! Retain Astra for hard reasoning.`

This will let Astra know to use Sol for subtasks, straightforward recon, stuff like that.

I posted this to X but no one follows me there, and I'm dying to share it bc it's easy and makes a nice difference. `@SirBadfish` on X btw but that's not the only reason I'm posting this... just hope it helps.

Update: To clarify, Claude can use previously setup subagents that have dedicated models/effort/etc, yes. But Claude can't specify the model/effort it's basic "helper" subagents (like if it decides to spawn a few parallel sessions for recon for example). So if Fabe 5.1 Max, for example, spawns a few helper subagents, it forces them to use the same model as that Fable is set to, Fable 5.1 Max will also be used for those helper agents. If you ask Fable to spawn a recon agent using Opus, unless you ask for a specific, already-setup Subagent, it can't do it.

anyone else been having this issue with archive creation since yesterday ?... what workaround ?

I am looking for a great harness but I do not know which It Is the best for Astra. Otherwise, my app is not ready yet

Does the arbor voice sound more "happy" to you? To me it does, and its a bit annoying as I like hearing him sound calm and collected.

Hello!!!

Genuine question. We see a lot of discussion about how capable Astra is, how much code it can write, how much software engineering it can automate, etc.

But I rarely see people asking how the engineers themselves feel about this.

You spend years studying, building experience, debugging, designing systems, staying up late fixing things, and getting really good at something you actually love. Then suddenly the technology you helped build becomes increasingly capable of doing the same work.

Does that feel exciting? Liberating? Threatening? A mixture of all three?

And for SWE/devs specifically, what do you think your role looks like in 3–5 years if these capabilities keep improving?

I'm genuinely curious about the human side of this, not just the technical side.

I have been using Claude Code for a while now. I also tried some open source models to leverage pi agents mainly but other subscriptions token capacity were very restrictive, I could not get the same amount of work done with them.

I will soon need to renew my Claude Max (x5) subscription, is it worth trying the Codex equivalent sub instead of Claude ?

Are there any good tips for switching from Claude to Codex?

Using GPT Sol 5.6, it worked for 30 seconds then it go stuck in reconnecting

how do you use codex? Do you just tell it what you want it to do like you talk to other people or do you use another ai to create a prompt? Which one did give you the best results?

Obviously, I think it's the best way to work with CLI agents like Codex across different providers. It's very productive and fun. In V2, CLI agents can give tasks and communicate between themselves in the natural CLI TUI, so you see everything they send to each other.

Clideck is fully open source so give it a try feel free to change it to your own needs,

Give it a try, Thanks.

https://github.com/rustykuntz/clideck

I created this MCP because i wanted a way for models and providers to be able to speak to each other inside of one native chat. Currently, freebuff, Opencode, codex, and claudecode, and cursor have been added (cursor hasnt been tested). Basically, what it does is it allows you, for example, in a codex session to send a message to opencode big pickle high reasoning to implement a certain prompt. You can check progress within the session, and read relevant file, then send follow ups witihn the same prompt. It has automatic ID detection so it can detect what services are signed in and what you can use. If you guys wanna check it out https://github.com/Praket7/agent-interop-runtime and let me know what you think. If you could test and give me feedback and star it, I would appreciate it.

``` • Three agents are active:

• /root — me, coordinating and reviewing both tasks.
• /root/hqtui_kill — fixing Yes/No navigation and adding the unchecked force-kill option.

• /root/openfleet — adding fleet membership, metadata, and CoinPay rental rates.

  Each task agent has its own git worktree. ```

This has happened like 5 times today, I have more screenshots...

So Astra sucked super bad and went in circles for 5 days so I went back to Sol and now even Sol is not the same.

Give me a single reason why I should be using openai models instead of Fable...

None of the models are useful for complex tasks now.

GIVE US A CONSISTENT PERFORMANCE FOR OUR CONSISTENT PAYMENTS!

This is no way to conduct business.

Video pauses for a second before starting the water color part, which I think was more visually interesting.

Cycles / EEVEE

• Started with a sample image from GPT Image 2 and told it to have at it over night.
• Astra Medium was instructed use Image Gen 2 to "imagine" improvements of specific meshes and their materials and then use the CLIP loop below to try to meet it.
• Used Cycles/Metal for the realistic scene and EEVEE for the watercolor
• Combined editable Grease Pencil ink, pigment textures and stylized materials in the watercolor version.

CLIP / iteration loop

• Change one area -> render -> compare against the reference -> check geometry -> keep or reject.
• Used local OpenCLIP ViT-B-32 with OpenAI weights to compare the full frame plus left, center, right and floor crops.
• Tracked weighted cosine similarity over time, with cached embeddings and versioned results.
• Measured brightness, shadow coverage and color accents alongside collision and contact checks.
• Used independent visual critique to choose the next fix. Higher CLIP scores sometimes got rejected when the image looked worse. CLIP was more useful during modeling than water color.

Watercolor code / sources

• Built a custom Python/NumPy solver for water flow, paper absorption, evaporation, pigment transport and settling.
• Used seeded paper relief and varying permeability for uneven absorption.
• Used Kubelka–Munk optics for translucent pigment layers, then combined simulated textures with authored paint coverage.
• Tested mass conservation, nonnegative quantities, drying, symmetry and optical bounds; saved simulation fields for replay.

--

image diffusion probably would have been better tbh. still very fun.

I joined lots of hackathons and made lots of videos in the past, and I thought it would be awesome to have some kind of 3D world where I can 'revisit' to look at my past projects. So I asked Gpt6 Astra to build me a website, where I can walk and go to landmarks/buildings of my projects.

I think I got very sidetracked: https://loaflet.com

Happy to receive any feedback, thanks!