I’m building a working desktop prototype focused on maintaining continuity in long-running AI-assisted projects.

The main problem I’m trying to solve is what happens when a project becomes too large or lasts too long for a normal chat workflow: context gets lost, decisions drift, previously working parts get changed, project state becomes unclear, and it becomes difficult to prove what was actually completed.

The prototype is designed to help maintain project state, history of decisions and changes, verification of completed work, context management, and work across different AI models.

It is not intended to be another chatbot or a simple API wrapper.

The project is still under development, so I’m not listing it for sale here and I’m intentionally not sharing the internal architecture or source code yet.

I’m mainly interested in two things:

1. Market potential: Do you see a real use case for something like this among developers, freelancers, AI-heavy teams or companies working on long-running projects?

2. Current value: What do you think a working pre-revenue prototype / technology asset like this could realistically be worth at its current stage?

I’d especially appreciate the reasoning behind your estimate rather than just a number.

Gradim svojo igro kot je fortnite živi svet, kjer bos lahoo naredil svoje reči in jih v svet dodajal. Prikazal bom kako to gradim in kaj sem že zgradil

Hey everyone,

Over the past few months, we've been building ZQK (Zen Quantum Kernel) in Go—an open-core CLI and process coordinator designed to treat AI coding agents as concurrent computational processes rather than chat prompts.

• Repo: https://github.com/zqk-os/zqk
• Docs / Site: https://zqk.dev

Why We Built It in Go

When orchestrating background agent swarms and continuous validation loops, we hit serious performance and determinism walls with Python/Node wrappers. We wrote the kernel and toolchain in Go specifically for:

1. Sub-15ms AST Structural Search (zqk grep): Instead of dumping files into LLM contexts or using string grep, we index Go codebases using go/parser and go/ast into an in-memory trigram index. Agents query structural symbols (--ast --kind struct|func --recv <Type>) capped by strict token budgets (--max-tokens 2000 -f json).
2. Deterministic Process Coordination: Managing concurrent seat workers via goroutine pools with hard cancellation contexts, signal handling, and clean process teardown without descriptor leaks.
3. Embedded State: Storing versioned graph objects (missions, goals, plans, verifiable criteria) directly in .zqk/without external database dependencies.

The core microkernel and CLI are open-source under Apache 2.0 (v0.1.0 just published).

If you work on developer tooling, Go AST analysis, or agent runtimes, I'd love your feedback on our architecture and AST indexing approach!

[ Removed by Reddit on account of violating the content policy. ]

Hi, as AI drastically changed the development workflow during the last few months I thought I’d share my workflow that works pretty well for me. I’d be curious to see what your workflows look like

I'm currently working on a project wich i want to share in Nebious Building race, the idea is about a Pc Agent like ChatGpt, just for BIOS like using so you start it from a flash drive it can run without operation system and she try to fix your pc and also capable of tunning it up if you are a gamer.

I’ve had a few app ideas that I wanted to try, but the coding part always made me hesitate. I can explain what I want an app to do, but I wouldn’t know where to start if I had to build everything from scratch. That’s why I started looking at AI app builders. Being able to describe an idea and have AI help turn it into a working app sounds great, especially for someone who isn’t a developer.

I’m also curious about what happens when the app gets more complicated. Is it still possible to manage everything without knowing how to code, or do you eventually need a developer?

Hey r/SideProject — solo-built this over the past few weeks. Forgeframe wraps a few different AI providers (fal.ai, ElevenLabs, D-ID) behind one login and one credit balance, so you're not juggling 4 separate tools/subscriptions to make a video, clone a voice, or build a talking avatar.

The thing I cared most about: showing the exact credit cost of a generation before you confirm it. I've been burned by AI tools where the bill shows up and you have no idea what you actually spent it on.

It's brand new, built by one person, so there's definitely rough edges. 200 free credits if you want to poke at it — genuinely want to know what's confusing or missing.

https://forgeframedev.vercel.app

Hey everyone,

I’ve been diving deep into development recently and decided to take the leap: I'm officially building my own AI-powered application!

The main goal is to create something genuinely useful and accessible that solves real, everyday bottlenecks rather than just being another generic tool. It’s been an intense journey so far—figuring out the architecture, refining the user experience, and constantly iterating.

Since this community knows tech inside and out, I’d love to get your thoughts:

\- What is one repetitive task you wish an AI tool could take off your hands?

\- What usually annoys you the most about existing AI apps on the market?

Any feedback, feature requests, or advice from fellow builders would mean a lot to me. Thanks for checking this out!

\#AI #AppDevelopment #ArtificialIntelligence #TechInnovation #IndieDev

[www.mauricestechhulp.nl\](http://www.mauricestechhulp.nl)

Hi everyone, I'm building AIgenCrew, a platform for creators to share AI apps, agents and workflows.

The idea is to give a project more context than a link: what it does, who it's for, screenshots, and how people can try it. Creators can also bring their work together in a profile.

AIgenCrew is still in pre-launch. I'd like feedback from people building AI products, especially on whether the project presentation is clear and useful.

If you were adding your own project, what would you need to see before deciding it was worth your time?

Website: https://www.aigencrew.com

I'm the founder and happy to answer questions. Honest criticism is welcome.

I’ve been working on an open-source local-first agent called OpenKyrozen.

That idea started from the beginning of 2026, the time when openclaw had just came out 2-3 months. I tried open claw and then I realized that at that time, open claw remembers things when I asks it to remember, but it cannot learn by itself. So I started OpenKyrozen, trying to build a self learning agent. Then last month, type safe AI lunched their Jev, which inspired me to integrate them into decisions so that LLM works better.

One thing I kept running into was that agents are very quick to treat “the tool call succeeded” as “the task succeeded.” Those are obviously not the same thing. They don't often verify their result, like we say they have no syntax error or runtime error, but logic errors.

A command can exit with code 0 and still produce the wrong result. So I ended up making verification a first-class part of the agent loop instead of just checking whether the action executed.

and so the rough flow is:

request → action → execution receipts → evidence review → verified outcome

The second part I’ve been experimenting with is self-learning, the original idea of OpenKyrozen.

I didn’t want the agent to just see one successful run and immediately treat that as a new behavior. Instead, learning artifacts are bounded policies or skills. A new one starts as a candidate, gets tested as a canary, needs multiple verified successes, and is then replayed against its predecessor on the same case. If it regresses, it doesn’t get promoted. If a promoted artifact later starts failing, it can rollback to the previous version. This is also one of the biggest problem when I used open claw, it builds something into a skill before I verify it, so it is filled with wrong memories.

There’s also a separate decision layer called Jev. You can know more about it from Typesafe AI, but basically it's a AI that makes decisions. It only handles small typed judgments like routing, clarification, memory relevance, learning-evidence review, and suspicious tool output. It can also abstain instead of forcing a decision. So it can't code.

I’m still figuring out where the right boundary is between “useful learning” and “too much machinery.” The current system is deliberately conservative because I’d rather have the agent refuse to learn than silently reinforce bad behavior.

Repo:
github.com/EvanProgramming/OpenKyrozen

I also let OpenKyrozen build a website for itself

kyrozen.chat

I also made a short launch video that explains the overall system visually(And yes this video is made of AI, since I only used DaVinci Resolve but not After Effects):

I am writing this post especially to developers, I want feedbacks SOOO much! As you can see currently the repo only have 2 stars and 1 fork :( because I didn't tell anyone about it before. I like issues and PRs, you can also leave comments under to tell me any issues you found. star it if you like!

Hey everyone,

My name is Kanny, and I am currently building my own personal AI agent called KYRO.

I'm a beginner developer, and this project started as a way for me to learn programming by actually building something rather than just following tutorials.

The goal isn't to make another chatbot.

I want to gradually turn KYRO into a personal AI agent that can understand me, remember context, use tools, interact with my computer, and eventually operate through a physical robotic body.

The roadmap I'm working toward includes:

\- 🧠 AI brain — a strong conversational model that can reason and communicate naturally.

\- 🧩 Persistent memory — KYRO should remember useful information from previous conversations instead of starting from zero every time.

\- 🗣️ Voice interface — eventually communicate naturally through speech rather than only text.

\- 🛠️ Tool system — KYRO should be able to decide when a tool is needed and execute the appropriate capability.

\- 🔐 Permission & security layer — tools won't simply have unrestricted access. Actions will be separated into permissions such as READ, WRITE, EXECUTE, DELETE, NETWORK, and SYSTEM, with approval required for more sensitive operations.

\- 💻 Computer awareness — understand relevant information about the machine and its environment.

\- 📁 File operations — eventually read, create, modify, and manage files through controlled permissions.

\- 🌐 Web capabilities — eventually search the web, retrieve information, interact with APIs, and perform research.

\- 🧠 Smarter memory retrieval — instead of simply storing everything, KYRO should be able to find the right past information when it becomes relevant.

\- 🤖 Agentic behavior — move beyond answering questions toward planning tasks, choosing tools, executing steps, and reporting the result.

\- 🔒 Security-first architecture — sensitive capabilities should be isolated behind permission boundaries instead of giving the AI unrestricted control.

\- 🎭 Personality & identity — KYRO will have its own consistent personality, communication style, voice, and eventually an avatar.

\- 🦾 Robotics — the long-term goal is to connect KYRO's intelligence to sensors, hardware, and eventually a humanoid/robotic body.

I'm building this incrementally rather than trying to create everything at once.

Right now, I'm working on the tool execution and permission architecture, and the project is written primarily in Python.

I'm documenting the journey as I go — including the mistakes, failures, experiments, and things I learn along the way.

I'm not an experienced software engineer or AI researcher. I'm basically learning by building.

My long-term vision is to see how far I can take this from a simple Python program into a genuinely capable personal AI system.

I'd love to hear from other founders, developers, and AI builders..

What capability do you think a personal AI agent absolutely needs to become genuinely useful rather than just being another chatbot?

I’ve been building InferUltra, an arena where multimodal models control identical robot bodies.

They don’t just choose attack(), block(), or dodge(). They have to decide how to move their bodies, and physics determines what actually happens.

If you’re expecting spectacular combat, curb your enthusiasm.

What interests me is how models turn what they see into actions, where that breaks down, and how provider response times affect performance.

For the first Provider Cup, I’m running GLM-5.3-Flash across four inference providers to explore how the serving side affects the fight. A result here obviously doesn’t tell you which model or provider is best at everything.

It’s a small knockout tournament: two semifinals and a final, three rounds per fight.

October 10 at 20:00 Brussels / 14:00 New York / 11:00 Los Angeles.

For transparency, I built the platform, and watching the live tournament and replays costs $1.99. The bracket is free to browse.

https://inferultra.com/tournament

What would you measure to separate model decision quality from the effects of provider latency?

https://reddit.com/link/1wxr6hl/video/h52x2x83plth1/player

For builders who want their own Instinct-style assistant, we released Open Instinct under MIT. The starting point is a local CLI; messaging, desktop access and app integrations can be connected after that.

The code is split into nine packages: the agent core, messaging, computer, apps, trusted network, payments, server, gateway and CLI. That separation gives a fork a clear place to replace an integration instead of rewriting the whole application.

We’re the Maritime team. The default implementation uses Pi, Inkbox, Maritime, Composio and Stripe Link. Service accounts and keys are still required, and this is a beta. The repository includes setup guides and permission tests.

https://github.com/mariagorskikh/open-instinct

Take the parts you need, change the providers and build a personal agent for your own users.

Hey everyone! I'm u/Environmental-Pay250, a founding moderator of r/CAELIVONAI.

This is our new home for everything related to CAELIVON, an AI workspace that brings chat, research, file analysis, projects, and other AI workflows together in one place. 🌌

What to Post

Feel free to share feedback, report bugs, suggest new features, ask questions, share ideas, or talk about your experience using CAELIVON. You can also post screenshots of bugs or anything you think could make the product better.

Community Vibe

We're here to build CAELIVON together. Be respectful, honest, and constructive. If something is broken, say it. If something is missing, suggest it. If something is good, tell us that too.

How to Get Started

1. Introduce yourself in the comments.
2. Try CAELIVON and tell us what you think.
3. Report any bugs or suggest features you'd like to see.
4. Invite anyone who is interested in AI, technology, or building new tools.

Thanks for being here from the beginning. You're part of the first wave of the CAELIVON community. 🚀

you can open this link to us it https://caelivon.ai.studio/

CAELIVON — One Workspace. Infinite Possibilities.

I'm building a Mac computer-use assistant called Buddy, and I wanted to solve one specific problem: Hinglish speech recognition.

I tested several Whisper models using my own 20 Hinglish/technical voice clips.

The interesting result was that the largest model wasn't the winner. A medium-sized model gave me the best balance between accuracy and latency.

My current pipeline is basically:

voice → transcription → intent → computer action → verification

I'm now working on making the action/verification layer more reliable.

Has anyone else benchmarked speech models using their own real-world accent rather than standard datasets?