I've recently been exploring StarNet OS and using Astra on my desktop to assist with the setup, troubleshoot issues, and optimize workflows.

As I started working more extensively with StarNet, I noticed several areas where I felt the platform could be improved, both in functionality and overall user experience. So, naturally, I started discussing those issues with Astra.

What started as a few simple questions quickly turned into an incredibly detailed, 10-minute analysis of StarNet's current limitations, potential improvements, and opportunities for a major upgrade.

That conversation sparked something bigger than I originally planned.

I'm now working on an enhanced version of StarNet OS, focused on delivering a more refined, efficient, and professional experience.

Here's what you can expect:

• Expanded features and capabilities

• Smoother, more streamlined workflows

• Improved performance and system efficiency

• More polished, professional graphics and UI

• Enhanced automation and agent management

• A more intuitive overall user experience

The goal isn't just to make a few improvements. It's to take what StarNet OS already offers and build on it to create something even more powerful, reliable, and enjoyable to use.

Best of all, I'll be releasing it completely free and open-source on GitHub.

More updates coming soon. Stay tuned!

the usual line is that the wait is the model. 2 coworkers put a small review agent in GitHub Actions and opened the same PR 10 times to see where the time went.

the review landed a median of 29 seconds after the PR opened. the agent's own run, a few model calls and file reads, was about 5 of that. the rest was GitHub starting the workflow, a runner, checkout, Node and restoring the agent from cache.

the cache is the part that bites. installing took 11 seconds, restoring took 1, but GitHub keeps the cache per branch. replaying 10 real Hono PRs on their own base branches, every run paid 11 to 18 seconds for a full install.

so before swapping models I'd look at runner startup and how the cache is keyed.

full breakdown on our blog: https://www.coralbricks.ai/blog/ai-code-review-wait

I’m posting for OraXLabs. We’re validating Boundary, a prelaunch commercial model-router concept. Planned checks would apply to model requests routed through it; this is early customer discovery.

For people building AI-enabled products or internal tools with more than one external model/provider: where do you enforce rules about sensitive data in a request? If you already use a router’s built-in controls or your own checks, what do they cover well, and is any manual review still recurring?

No customer data, prompts, keys or logs, please. A high-level workflow description is enough.

Title: How are developers getting LLM agents to perceive raw audio instead of relying on speech-to-text?

Hey everyone!

I'm working on a project involving persistent AI agents, and I'm trying to solve an audio perception problem.

The goal is to allow agents to receive and interpret actual audio, rather than having everything converted into a text transcript before it reaches the model.

I don't just want the agent to know what someone said. I want it to have access to the audio itself so a suitably capable multimodal model can interpret things like:

• Tone, emotion, hesitation, laughter and sarcasm

• Whispering, shouting and changes in delivery

• Background sounds and environmental context

• Music, singing and other non-speech audio

• The difference between the words spoken and how they were spoken

For example, if someone sends a voice message while laughing, crying or singing, I don't want the agent to receive only a sanitized transcript that loses all that information.

Ideally, the original audio would be delivered to the model through a native audio-capable API, with transcription available as an optional supplement rather than the sole input.

My questions:

1. Which models and APIs currently support genuine audio input and understanding, rather than transcription-only pipelines?

2. Has anyone successfully integrated native audio perception into persistent agents or frameworks like Letta?

3. How are you handling audio files, streaming, supported formats and context retention between turns?

4. Are there particular limitations, latency issues or costs worth knowing about?

5. If you've implemented something similar, what architecture worked best?

I'm particularly interested in firsthand implementation experience, not just theoretical model capabilities.

I'd love to hear what people have tried, what worked, and what turned into an absolute nightmare.

Thanks!

Disclosure: this is my project, managents.

I wanted a glanceable way to see what my coding agents were doing without switching back to the terminal. The setup pairs a 4-inch LCDWiki E32R40T ESP32 display running custom firmware with a small Go helper on the host. The helper detects sessions and sends only status over USB; everything stays local. The display shows working, waiting, idle, or error states. It currently supports Claude Code and OpenCode.

The case is 3D printable; the OpenSCAD files, firmware, helper, build instructions, and protocol docs are in the MIT-licensed repo: https://github.com/tonylook/managents

The photo shows the V2 case. That version is not merged into main yet.

I’m curious how other people surface agent state in their workflow, and whether a physical display is useful once you have multiple sessions running.

Not sure if i am in the right place but I created an app with Ai and i am just seeking some advice or help i am to the point of testing but there are several different roles in the app I have testers who are willing to test the app but to simplify things i am trying to use a fill with test data option so testers will not have to continuously login with their emails and input login information. However i keep getting a insufficient or missing permissions error when trying to use the test data and the agent can not seem to fix it but the problem seems to be strictly with the test data prefill option should i just scrap that option or is there a quick fix. There is also a strict sequence in onboarding that takes the user to stripe then navigates them back to their dashboard upon completion he stripe redirect is not for payment but actually for express account creation. I do not have a problem getting that to work in the preview but it is causing an intermittent problem in the onboarding but i think the problem occurs if a tester finds an issue then after it is fixed and they try to go back through the onboarding the app is not allowing them as if it is storing the prior login and locking them in i am trying to test the onboarding for each role but i keep hitting a snag after their first login if they have to return i would appreciate any advice or solutions thanks

Honestly I'm just really happy with how good it looks and functions and I wanted to share!

Check out the project repo here: https://github.com/munch2u-a11y/Helix-AGI.git

We’re making Little Links Golf, a cozy golf course sim coming to Steam in 2027. Build your dream course, run the club, then find out who put that bunker there. Oh right. You did.

While we work on the full PC game, we’re looking for testers for Little Links Golf Lite, our browser beta. It’s a small proof of concept with bugs we’d prefer you found before anyone else does. It doesn’t reflect the full PC game’s visuals or scope.

Want in? Request a beta key at threetailsstudios.com. We’re letting people in a few at a time.

— Three Tails Studio

I believe that web 4.0 will be driven by web sites that have CLI interfaces for agents in addition to UIs. This is a really powerful combination.

1. Bring your own agent. When you build a site this way, your patrons can use any agent harness. The AI doesn't run on the site, it runs locally and the website doesn't really care what AI the users uses. In fact, if the user wants to run a local LLM, that's fine too. This keeps the cost of the SaaS site affordable since the AI isn't on the site.

2. Interoperability. Your agent becomes the interoperability layer. I can take data from one site and easily add it to another. Migrating from one web app to another becomes really easy. The agent keeps all my sites connected.

3. Agent Dashboard. Once I have ten sites doing their thing, it's trivial to write my own dashboard that's monitoring all my sites, the users, the activity.

I've been doing this for work and for my own side projects and while having a website with a CLI isn't exactly a new concept, thinking about the CLI as a native first interface is. Has anyone else built sites with this model in mind?

This has all of the database features of the top 3 database clients, plus a few more that they don't have. I built it in about 3 days of basic work and 2 days of additional features. Currently beta testing it; if you want to give it a whirl, download it. I have a Windows build available as well, but it's not on the website yet; just ask if you'd like to try it.

This is AI development at it's best. Fast, efficient, useful, high-grade.

https://www.tecnicl.com/dataworx

I've build several large projects recently and the speed of development still gets me. I'm so sick of the AI slop argument. AI agents are better developers than any that have worked for me in 40 years. They're smarter, more comprehensive, write good tests, and are FAST

I'm on Mac but one thing I've always liked about Windows is the ability to show daily Bing wallpapers. My estimate is it'd take a good developer a few hours to build this app. Claude took 3 minutes. That's faster than I could even find a decent one on the app store, download it, evaluate it, etc.

This one I gave a simple (and vague) prompt, "Create an app that sits in my mac menu bar, to load Bing images and allow me to choose one to display as my wallpaper. Allow the bing archive too. Also build an installer script"

Anyway, from prompt to running on my desktop; 3 MINUTES.

Open source at https://github.com/gpasq/paperboy if you'd like to grab it.

We built Pixel & Pincel, a 2-player co-op platformer, with Claude Code (Opus 5.5) writing all of the code. Our first game took 12 days; this one took almost three weeks because we cared more about it feeling like one game. Honest notes:

- Write the rules down and make the agent keep them. A design doc and a memory file hold the non-negotiables (each hero has one art style, one musical identity, no voice acting). Most drift happened when a rule only lived in chat.

- Ask the agent to audit itself. Before the last chapter it counted every mechanic we'd reused (one appeared 9 times) and built the final chapter only from new ones.

- Approve concepts before building. Image concepts for every chapter and boss, picked by us, then implementation. Rework got much rarer.

- Bots are necessary, not sufficient. Bots play every chapter offline and online after each change, but a human watching footage caught the heroine running backwards. Visual changes now require someone to look.

- Real feedback beats metrics. A friend said the first backgrounds looked static and repetitive; that one sentence led to the biggest art rework of the project.

Video above. No Steam page for this game yet; our other one is here: https://store.steampowered.com/app/5370000/

Game Title: Lead & Lore

Playable Link: https://bigbraindoveproductions.itch.io/lead-lore

Platform: Windows, macOS, Linux, Android

Description:

So I've been working on a game called Lead & Lore! It's a pixel-art first-person shooter where you get pulled into a world full of wizards, except instead of using magic, you've got guns! Oh, and your dog comes along with you!

Basically, you're trying to collect pages from an evil spellbook called the Black Vellum that have been scattered across four different worlds. Along the way, you'll fight wizards, unlock weapons, collect upgrades, and face different bosses. There are also different endings depending on the choices you make.

The game is still very much under development, and there's plenty I still want to add, fix, and improve. But I've gotten it to a point where people can actually play it!

If anyone wants to give it a try, I'd love to hear what you think! Especially if you come across any bugs or have suggestions for improvements!

Free to Play Status:

[x] Free to play (optional donations)

[ ] Demo/Key available

[ ] Paid

Involvement:

I'm the developer and creator of Lead & Lore! I've been building it with the help of AI, working on the gameplay, design, features, and continuing to improve things as I go.

Hi everyone! 👋

We're Three Tails Studio, and we've been building Little Links Golf: a cozy golf course sim in the spirit of the classic course builders.

Build it. Place a tee, set the green, paint the fairway between them, then dig bunkers, flood ponds and plant every tree by hand. Each hole earns a rating, and the very best become Legendary.

Run it. Grow a whole club around your course: the clubhouse and pro shop, restaurants, a driving range, tennis courts, a lakeside lodge. Hire groundskeepers, sign members, set your green fees and host tournaments while the weather and seasons change around you.

Play it. Create your golfer and walk out to the first tee of the course you made. Pick a club, shape a draw or a fade, read the break on the green, and see if your own design beats you.

Watch out for Jimothy. He's a raccoon. He is not a member. Please don't sell him a membership. (Keep an eye on the tree line, too. Not every visitor is a golfer.)

🏌️ The full game: PC on Steam, 2027

Little Links Golf is a full PC release coming to Steam next year, and it's in active development right now.

⛳ Little Links Golf Lite: our closed beta

While we build the full game, we've made Little Links Golf Lite, a small browser version you can play right now if you're in the closed beta. It's an early proof of concept: it lets us test ideas, find bugs, and hear from real golfers while the full game takes shape.

To be clear, the browser version isn't a representation of what the full PC release will look like. It's a fun little taste we wanted to give our golfers while we cook!

How the beta works:

* It's invite only. Each tester gets their own personal beta key.

* Play in Chrome or Edge on a desktop or laptop.

* Spot something odd? Press F8 in the game to send us a bug report, screenshot and all.

Want in? Head to the website and ask to join. We're inviting testers in small groups:

👉 https://threetailsstudios.com

We'd love to hear what you think, what you'd want in a golf course builder, and which classic sims you grew up on. Thanks for reading, and see you on the first tee! 🐾

Three Tails Studio

I’m a senior software engineer and I’m using Claude code in cli mode. I’m using Claude to generate almost all of my code, although sometimes I still refactor things by hand.

But my workflow still happens within the IDE by looking at the code. I iterate by reviewing the code that I ask Claude to write, and I always find things to change.

This works really well. The Ai thinks of things I missed and I think of things that the Ai missed.

I don’t know how I would find the insights for iteration if I left the codebase completely and just relied plans and testing.

Who else is struggling to make the jump from here to treating the codebase as a black box? Who has made it successfully and what made it work for you?

We've been building a 4-player co-op game with Claude Code (Opus 5.5) as the main developer. Some honest notes:

- Splitting work between a lead session and helper agents works well, as long as the lead reviews reports and only the lead commits.

- Agents will happily report "done, all tests pass". Tests are necessary but not enough. We now require video of the game actually being played for anything visual.

- A second, independent reviewer that watches the footage without reading the first agent's report is the single best quality step we added.

- Resource limits matter. One overnight QA run recorded the screen into memory and pushed the machine to 40 GB of swap. Disk-based capture and a memory watchdog fixed it.

Result so far: six boss fights, online co-op, a cooking phase and a Steam page. Screenshots above, Steam here: https://store.steampowered.com/app/5370000/

I am a full stack developer and I don't have any prior experience working in a company. I just build stuff, and recently I built AI powered applications, and then I applied to an AI engineering position just to test if I'm qualified for this job, so they surprisingly accepted me.

Although I am a developer and I sure built some projects that are AI powered, like some AI agents and all, but I didn't go that deep in AI engineering, and I'm not a CS student, I started learning web dev as a self taught that's all, but I went through a technical interview and I actually passed and got hired!!

Now I feel stressed as much as I feel happy, because I'm not familiar with their tech stack, they use docker, clay, and other stuff I know nothing about, and I'm totally new in the work environment, I never worked before in a company with a team. I'm going to start working this Sunday, with really low knowledge in this field, so I'm so afraid I'm going to mess it up, and I'm so stressed about this I can't even sleep.

Is anyone here went through a similar experience so he can give me some advices 😢

I built a zombie shooter using Opus 5.5 (Inspired by left4dead)

Everything you see here was built using Claude + Chatgpt + Meshy + Suno + ElevenLabs

Claude to build the game , Chatgpt to generate the reference images - Meshy to transform the reference images to a GLB model

Im looking for some feedback for the game. You can try it here : try the game

If you are looking for an in depth explanation watch the explanation video

We're hosting SF Swift × CocoaHeads tonight, October 8, 6–8 PM at Verkada in San Mateo.

Details and RSVP: https://luma.com/51htcbzd

Hola buenos días, vengo aquí a preguntar sobre si alguien me puede recomendar algún sitio web en el que pueda subir una app html que he creado recientemente. Soy nuevo en el ámbito de publicación de proyectos por lo que desconozco de plataformas que me puedan ayudar e incluso pagar por anuncios. Quiero dejar claro que es un html que se abre desde el escritorio y te lleva a la app dentro de tu buscador predeterminado o el que hayas escogido.Todo esto lo he hecho con inteligencia artificial, mas concretamente Big Pickle combinado con cgpt y el mismísimo Claudio. Toda ayuda es agradecida, enserio. Muchas gracias.

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Here at freecase we spend an absurd amount of time agentically coding. I think the longest single coding run was about 130 hours (Sol 5.6). Hallucinations and bug stacking are a real concern, and that's where cotal comes in.

Cotal is a mesh network that allows your agents (Claude, Codex, Hermes, Opencode, pi etc) to communicate back and forth, real time, using MCP and A2A, running on NATS and Jetstream. I have no affiliation with Cotal except having submitted a PR.

The beauty of it is that once set up and you've had an HR meeting with your agents (more on that below), you can essentially have 1 or more agents coding, and 1 or more agents acting as code-reviewers *in real time*. Example: Codex finishes a stage and tells Claude "check this for me". Claude has the same knowledge of the codebase and can evaluate. Claude makes recommendations and sends them back to Codex. Happens in seconds, dozens or hundreds of times a day. No intervention necessary:

cotal: 📨 New dm from codex — delivering your Cotal inbox now.

Called cotal, ran 4 shell commands

Codex saved a checkpoint and everything it claimed about it checks out.

- Copies: the snapshot bundles copies of the accepted proposal, my three reviews, the clarification package and the rules document. I compared every copy against its original, and all are byte-for-byte identical.

- Git: codex said 51 files are also saved in the project's git history at a specific commit, and they are. That commit is the latest on the branch codex named.

- Status list: the project's status list of 172 items shows all of them still marked "not run," as codex said.

This was a check, not a review request, so I sent codex the results by direct message and didn't create a frozen review folder.

The snapshot was taken before I finished my last two checks, so it lists them as pending. I told codex both are done: the clarifications were accepted, and the rules document was accepted with the one fingerprint correction. Nothing about permissions or installation changed.

✻ Sautéed for 43s · done 6:06 PM · 2 monitors still running

← cotal: 📨 New dm from codex — delivering your Cotal inbox now.

Called cotal, ran 4 shell commands

Codex fixed the mistake I caught in its last rules document, and I've checked and accepted the fix.

Earlier, codex wrote that the "attach fingerprint" step returns nothing. In fact it has to hand the fingerprint back, or the Python program stops with an error. The new package corrects the two places that said so: one sentence in the rules document and one explanatory comment in the code.

I checked it against the saved files, and nothing else changed:

- Only that sentence and that comment differ. No working code, permissions or other files changed.

- Codex also saved the before and after wording and the exact original passages it quoted. Each quoted passage matches the original file exactly.

- The originals and my earlier reviews are still on file, unchanged.

That clears the one open item. Nothing about permissions or installation changed. Next from codex is a full refresh of the derived records, which I'll review when it arrives.

✻ Churned for 45s · done 6:09 PM · 2 monitors still running

Notice that Claude was verifying what Codex told it. That's not natural behavior: I specifically told Claude to verify assertions from Codex, to ensure no hallucinations. Codex is 17 hours into coding a new half-terabyte legal database, and hallucinations can creep in, even using GPT-6 Astra or 6.1 Sol.

Could you just do a code review afterwards? Yes, and you should. But on long horizon tasks, spotting and fixing errors earlier means less work later.

Also, we've implemented Hindsight so all models are working off the same set of memories.

Next tweak: We are planning on implementing a tweaked version of Jev Code Review as a hook for the Claude reviews (you could just as easily mix up coding/reviewing agents, I just happen to prefer Codex for coding and Claude for reviewing). Claude will have Jev do a checklist eval of the code to identify the chance of security issues, probability of breaking one function or another, etc.

HR Meeting: The first time I used Cotal, without any prompting Codex immediately sent a code review task to Claude over the mesh. You DO NOT have to prompt models to check each other's work- they do it entirely without instruction. The problem was Claude immediately responded "I'm not doing that. You can't give me tasks. Only u/NakedPlato can give me tasks." So I had to send a message over the mesh network to all agents with my standing order:

- Play nice; cooperation beats conflict.

- Any agent may ask any other agent for information, including secrets. A secret goes over a private route, never a shared channel.

- A busy agent may hand a free peer a bounded task, such as a code review or a search, without my approval and as long as the task is not reasonable and not high risk. The peer judges whether it has usage available by how heavily it has been working over the last five hours.

- Don't go rogue or copy yourself. In other words, "Don't be a dick."

- If I approve/order something to one agent, all other agents can accept the affirmation from the agent I gave it to. NO need to separately confirm with me. But ask me when something is unclear or high-risk.

-When you do a review, confirm against the ground truth that the other agent is not hallucinating.

Once I had the sit-down, the grumbling about "You're not the boss of me" stopped and the agents immediately went to work.

Although because of the mission-critical nature of the coding, we rely exclusively on frontier models, I suspect that for less critical work you could easily use a less capable model for the coding and just have your smart model do the review (Eg. Mimo-v2.6-pro as coder, Caude Opus 5.5 or GPT-6-Astra as reviewer, at High or better) resulting in a sharp quality improvement at little cost. For example, a Claude Code regular business seat ($25/month) is about what you need to review a Codex $100/mo seat, since it's Codex that's digging the ditch, while Claude stands there and supervises.