For most of my life I tried to be someone else. I'd find someone I admired, decide they were better than me, and copy them. That mindset pushed me into a business I never enjoyed and only started because I looked up to one specific guy. It failed. I felt completely lost.

Around that time I was obsessively tracking my sleep with a Whoop, trying to optimize it. I kept getting good recovery scores. And I was still exhausted, yawning through entire afternoons, dead by 2pm. That's when it clicked: the score doesn't do anything. It just confirms you slept well or badly. Cool. Now what? Knowing isn't fixing.

So I built the thing I actually wanted. It takes the data your wearable already collects sleep, recovery, heart rate, and turns it into a daily protocol instead of another number. It tells you what supplements to take based on your metrics, predicts your most productive hours and gives you the exact time window when you should do deep focus tasks and light focus tasks, it tells you how much caffeine you have in your system left based on your first coffee taken and notifies you when you should take the next caffeinated drink for maximum productivity, it even tells you when to nap so your energy lasts the whole day instead of crashing and much more...

It's on the App Store as RizeAI https://apps.apple.com/us/app/rizeai-maximize-your-energy/id6762402079. i built by myself, it's early stage right now, and I want honest feedback, what's confusing, what's missing, what you'd never use. Tear it apart.

Hi!

I've been working on this for the last few months and I finally have a Trial ready.

Instead of writing huge prompt paragraphs, you build your song visually.

Think of it like a DAW for AI music prompts.

Features include:

• 70+ music genres
• Genre blending
• Visual song structure
• Vocal editor
• Prompt optimization
• Works with Suno, Udio & AceStep
• 100% offline

The Trial is completely free.

👉 https://ko-fi.com/s/e4bcdc959b

Website:

https://ehdarkmuse.pages.dev/

I'd really appreciate any feedback or ideas for improving it.

Thanks!

I’ve been working on Murmur, a local text-to-speech app for Apple Silicon Macs.

The new feature I’m building is called Projects / Story Studio, and it solves a problem I kept running into:

TTS tools are fine for one-off clips, but messy for actual audio projects.

If you’re making a podcast segment, audiobook chapter, course lesson, ad, or game dialogue, you usually need multiple speakers, multiple takes, pauses, reactions, music, edits, exports, and a way to come back to the project later.

So I built a project-based workflow:

Write a script → assign voices → generate dialogue → edit clips on a timeline → add music/SFX → export final audio.

It supports things like:

• multiple scripts inside one project
• Host / Guest / Narrator / Character speakers
• inline tags like [pause], [laugh], [chuckle]
• per-block regeneration
• timeline editing with waveforms
• media lane for music and SFX
• ripple editing and gap tools
• WAV/M4A export
• transcript and stem export

Everything runs locally on Mac, so long scripts and voice samples do not need to be uploaded to a cloud service.

I’m still polishing the workflow and would love feedback from Mac users, especially people who make podcasts, audiobooks, courses, YouTube narration, or game dialogue.

JoeBro is a native macOS AI workspace that runs entirely on your machine. No cloud, no account, no telemetry, no third-party packages. Stdlib Python backend, memories in a local SQLite file. Nothing leaves your machine.

It builds up a picture of you as you chat: your projects, your preferences, the things you keep returning to. For a while that lived in a list, it was boring. So I rebuilt it as a graph.

Every memory is a node. Related memories cluster together, pulled by a physics simulation. Line length is conceptual distance. Node size is how connected a memory is — your biggest nodes are the things your assistant keeps coming back to. Hover any node and the full memory text pops up. Right-click to edit, pin, or delete.

The whole UI is liquid glass and you set a wallpaper behind it. The graph floats over whatever image you drop in — nodes, lines, hover cards, all of it. If your wallpaper is moody it looks stunning. Redditors who care about their setup will want to screenshot it immediately.

For me, seeing one project dominate the map as a massive hub node was a strange moment. It knows me. Not because someone trained it on my data, but because I *told* it things and it remembered. That's a different feeling entirely.

Stdlib Python, SwiftUI Canvas, hand-rolled force simulation, GPLv3. Fully offline. Point it at Ollama or any OpenAI-compatible endpoint and you're running.

Repo: https://github.com/joexk1/JoeBro

Hi we just launched the waitlist for yaven!

It's currently free for all beta testers :)

On its first iteration yaven:
- is an ai notification center, it prioritises your notifications across all apps (telegram, email, slack, imessage). It not only shows you whats urgent but it also shows you all the needed follow ups!
- one click drafts with full context
- A flows section where yaven will proactively start creating automations for you depending on your work.

- on the video we showcase the automatic meeting briefs, which have been really useful for me as it pull from previous meeting notes and messages.

Two commands:
- ⌥A : ask anything on your screen, teach you something on a new app you're using, find information about a person, resolve a quiz, all with context of your work day, ask for a price comparison etc.
- ⌥D : draft a response anywhere in your computer. This has been so useful for cold outreach with full search of the person, responding to message that need information of my calendar/ work etc. from anywhere

there is a lot more to come and would love to have you all on this journey :)

you can become a beta tester or sign up to the waitlist here yaven.ai

thank you.

I've been building multi-step prompt chains for about 18 months. Workflows where the output of one prompt becomes structured input for the next prompt, which feeds the next, which feeds the next. The kind of thing that takes a vague input ("I have a business idea") and produces a deliverable output ("here's a positioning statement, market analysis, and brand foundation") through five or six prompts run in sequence.

For most of those 18 months my chains underperformed. Each individual prompt was solid. The chain as a whole produced output that drifted, lost focus, or contradicted itself between steps. I kept improving the individual prompts. The chain didn't get noticeably better.

The problem wasn't the prompts. It was that I was treating the chain as a sequence of independent prompts when it's actually a single engineering artifact with multiple stages. Different problem entirely.

The structural difference between independent prompts and chained prompts:

An independent prompt has one job: produce a useful output from a known input. The input is whatever you paste in. The output is whatever the user does next with it. The prompt doesn't care about either.

A chained prompt has two jobs: produce a useful output, and produce that output in a structure the next prompt in the chain can reliably consume. The output isn't for the user - it's for another prompt. That changes how it has to be designed.

Most chain failures happen at the join points. Prompt 1 produces output that's useful for a human reading it but doesn't have the structure prompt 2 needs. Prompt 2 has to either guess at the structure or do extra parsing work, which degrades its own output. By prompt 4 or 5, you've accumulated three layers of degradation and the final output is meaningfully worse than if you'd written one big prompt that did everything in one shot.

The four engineering principles I now apply to any chain:

1. Output schema, not output style. Each prompt in the chain has to produce output in a parseable structure, not just a readable structure. This usually means specifying the output format explicitly: a labelled section structure, a markdown table with named columns, a numbered list with consistent fields. The next prompt knows where to find each piece of information because the structure is enforced.

Independent prompt output: "Here's a positioning statement for your business..." Chained prompt output:

## POSITIONING STATEMENT
[one sentence]

## TARGET AUDIENCE
[paragraph]

## CORE DIFFERENTIATOR
[paragraph]

## ASSUMPTIONS REQUIRING VALIDATION
[bullet list]

The second version is parseable by prompt 2. The first isn't reliably.

2. Explicit handoff instructions. Each prompt should explicitly state what its output will be used for downstream. Not because the model needs to know, but because the discipline of writing it forces you to design the output for the actual use case rather than for general usefulness.

Adding a single line - "This output will be passed to a market research prompt next, which will use the target audience and differentiator sections to identify competitive positioning gaps" - changes the output meaningfully. The model produces the audience and differentiator sections with more analytical sharpness because it knows they'll be analysed, not just read.

3. Failure mode propagation. When prompt 1 fails or produces low-quality output, prompt 2 doesn't know it's working with bad input. It just produces output one tier worse than its input. By prompt 5 the failure has compounded silently.

Chains need explicit failure handling at each join. Each prompt should check that its input has the structure it expects and flag if it doesn't. If prompt 2 expects a "TARGET AUDIENCE" section and the input doesn't have one, prompt 2 should say so rather than improvising. This catches degradation at the source rather than letting it propagate.

4. State that doesn't drift. Long chains tend to drift away from the original brief because each prompt only sees the immediate previous output, not the original input. By prompt 5, the work has often quietly diverged from what the user originally asked for.

The fix is anchoring. Every prompt in the chain after prompt 1 should receive both the previous output and the original brief, with explicit instruction not to deviate from the original brief unless the previous prompt's analysis explicitly justifies it. This adds tokens but preserves coherence over the length of the chain.

A specific example of these principles in action:

I built a chain for taking a rough business idea through to a usable founding document. Six prompts: niche validation, positioning, market research, brand foundation, visual concepts, pitch outline. The chain works because:

• Each prompt outputs in a labelled section structure the next prompt parses by section name
• Each prompt's instructions explicitly state what downstream prompts will do with its output
• Each prompt validates the structural integrity of its input before processing
• The original brief is re-passed with each step, with explicit anchoring to prevent drift

The full chain takes a 30-second input and produces a 4-page founding document. The same six prompts written as independent prompts and run in sequence produce a document that's structurally similar but consistently lower quality - the audience definition drifts between steps, the differentiator gets reframed, the pitch outline doesn't match the positioning.

Why this matters more than it sounds:

Most prompt engineering content focuses on single-prompt optimisation. The economic impact of well-engineered chains is much larger because chains can replace whole workflows that previously needed human coordination between stages. A six-prompt chain that runs reliably is worth more than 60 individually-excellent prompts run by hand, because the human coordination cost between independent prompts is enormous compared to the marginal output difference.

The chains that actually run reliably in production aren't sequences of optimised individual prompts. They're single engineering artifacts where the join points are designed at least as carefully as the prompts themselves.

If you want to see a working example of a chain engineered with these principles, I built a six-prompt sequence for taking an idea to a business founding document. Each prompt is structured to feed the next, with the join points designed explicitly. Free, signup-gated: https://www.promptwireai.com/businesswithai

Worth running it on a real idea you have rather than a hypothetical, because the chain's reliability shows up most clearly when the input is specific.

AGY Builders,

We are all trying to build useful and scalable workflows for our AGY CLI and ecosystem, but the speed at which we need to learn, build, and deploy new things is incredibly overwhelming. If you are feeling that pressure, you are in the right place here at r/GoogleAntigravityCLI.

Over the past few weeks, I have been testing an "AGY WIKI OKF" setup that I put together myself (after inviting some members of this community to collaborate; mod is not proud). I know some folks might hesitate to trust a tutorial from a random Redditor, but I wanted to share this with the community anyway because it actually works.

I was able to build this because I am all-in on Google and the Antigravity Ecosystem. I’m a truly AGY—I am not some ultra-smart, 10x developer, but I know how to work hard, I dig for the right information, and I iterate.

AGY WIKI OKF | The Idea

To build a frictionless, token-efficient knowledge WIKI engine that transforms static documentation or notes (information) into an active, intelligent collaborator—orchestrated entirely by Antigravity CLI.

The core philosophy is simple: treat knowledge management as a clean pipeline and tokens as a premium, finite resource.

By anchoring this architecture to Google’s Antigravity CLI, the AGY WIKI OKF bypasses heavy middleware and complex UI layers, delivering a hyper-focused AI partner built entirely for execution speed, context hygiene, and minimal footprint.

Why adopting AGY WIKI OKF matters:

• Stay organized (AGY OCD): Structured Markdown and YAML keep the chaos in check.
• Save tokens: Doing more with less context window bloat.
• Scale shareable knowledge: Making it easy to pass context and logic between different LLMs.
• Humans and Agents working together: One standardized, readable format that works perfectly for both of us.
• BYOD (Bring Your Own Data): Own your context. Port it to the newest model, platform, or OS instantly.

The Tools

• Antigravity CLI
• Obsidian : The IDE for the Knowledge bank
• Obsidian Web Clipper:
  • Official Site
  • Chrome Web Store
  • Firefox Add-on

The WIKI

In the agent-first era, a WIKI is no longer just a static graveyard for human notes; it is the operational hard drive for your agents. By maintaining a highly structured WIKI, you ensure that every piece of context is stored in a clean, machine-readable format. This means that whether you are testing a new modular skill or spinning up a specialized agent, your AGY CLI knows exactly where to find the precise context it needs to generate autonomous action, moving you far beyond simple, reactive conversational text.

Reference: Gist on Knowledge Representation

Google Open Knowledge Format (OKF)

Google’s Open Knowledge Format (OKF) feels like the exact missing piece we've needed for orchestrating multiple AI agents effectively. It provides a vendor-neutral, interoperable standard for storing and sharing organizational knowledge.

Why this is huge for orchestration:

1. The "Lingua Franca" for Agents: Any agent can read it out of the box without platform-specific integrations.
2. Seamless Context Passing: Specialized agents can access, update, and pass the exact same foundational context back and forth.
3. Human-in-the-Loop Oversight: Because OKF is just Markdown and YAML, it’s inherently readable and auditable.
4. Scalable Knowledge: It acts as a shared, living library that grows alongside your agents.

AGY WIKI OKF Integration

Structuring an AGY Wiki using OKF revolutionizes how complex knowledge is shared. By standardizing documentation with concise Markdown and YAML frontmatter, OKF provides a unified taxonomy for cataloging AGY CLI slash commands or skills It is highly token-efficient, stripping away bloated formatting and maximizing context window limits.

The Prompt for Building an AGY WIKI OKF

AGY CLI WIKI OKF PROMT EXAMPLE

/grillme I want to initialize a brand-new, empty Obsidian vault from scratch that adheres strictly to the Open Knowledge Format (OKF) standard, with the specific intent of potentially open-sourcing or sharing this architecture later. I want a purely blank, skeletal framework with no pre-populated data. Please grill me to define the optimal architectural blueprint for this vault. I need you to interrogate me on: Do not generate the directory structure or files until you are satisfied that you have captured all my requirements for a production-ready, shareable knowledge base. 
Core Directory Hierarchy: How should we structure the root (e.g., /concepts, /resources, /indices, /log) to be intuitive for external users? Template Strategy: What base boilerplate templates do we need to ensure every new file is automatically OKF-compliant and structured for consistent metadata? Workflow Logic: Since this is a fresh start, what processes should we bake in for capturing information vs. refining knowledge that could be easily documented for others? CLI Integration: What specific file locations or configurations do we need to ensure this vault plays nicely with the Antigravity CLI from day one? Open-Source & Contributor Documentation: What files should we create to make this a "deployable" standard? Please include requirements for: A README.md with installation and usage instructions. A CONTRIBUTING.md that defines how to add new concepts or schemas. A "System Architecture" document that explains the logic behind the folder structure and metadata fields, ensuring anyone who clones this vault understands how to extend it.

The Final File Structure

AGY WIKI OKF
    ├── .agyrc
    ├── ARCHITECTURE.md
    ├── CONTRIBUTING.md
    ├── README.md
    ├── .agy
    │   └── .keep
    ├── .obsidian
    │   ├── app.json
    │   ├── appearance.json
    │   ├── core-plugins.json
    │   └── workspace.json
    ├── 00-Inbox
    │   └── .keep
    ├── 10-Projects
    │   └── .keep
    ├── 20-Areas
    │   └── .keep
    ├── 30-Resources
    │   ├── .keep
    │   └── Google Antigravity Documentation.md
    ├── 40-Archive
    │   └── .keep
    ├── 99-Meta
    │   └── Templates
    │       ├── Base_Template.md
    │       ├── Project_Template.md
    │       └── Resource_Template.md
    └── Clippings

TL;DR

• AGY WIKI OKF: Organizes your information (context) , AGY CLI commands, skills  behaviors, and A2A workflows into a token-efficient, shareable format that reduces inference costs for any LLM.
• Open Knowledge Format (OKF): Provides a standardized, vendor-neutral way to share context (Markdown + YAML), preventing platform lock-in and eliminating data fragmentation.

AGY Builders, I genuinely want your input on this. Please comment, grill me, roast me, ask questions, or give me your raw feedback on this AGY WIKI OKF setup. We are building the foundation to organize and share our data in the BYOD era. Let's build the future together.

Hey everyone,

If you're not sure who yolo-auto.com is, we are the guys who are pushing unlimited access to qwen3.6-35b-3a for $6 a month flat fee.

Just a heads up. We're planning on giving YOLO-Auto an upgrade and are currently evaluating which new models we can add to our $6/month unlimited tier. We're currently eyeing Qwen/Qwen3.5-122B-A10B. Given that, we're thrilled to announce that our very first FREE AI Weekend on YOLO-Auto is just around the corner!

🚀 1. Unlimited means unlimited.

No token meter. No "you've used 90% of your quota." Just use the models as much as you want for $6/month. No misleading limited plans where 5h usage is 30% of your monthly budget lol (Im looking at you opencode go).

🧠 2. 128k context, 2 generations at once, 24/7

⚡3. Fast, simple, and built for power users.

OpenAI-compatible API, chat interface, and hopefully soon 122b. We have a simple yolo-auto-desktop open source where you can enter your api key and start immediately.

🎉 4. Free AI Weekend is coming.

For one weekend, all users (no credit card required) will be able to use Qwen/Qwen3.5-122B-A10B for free. All you need to do is make an account. If you've been curious about YOLO-Auto, this is the best time to see what it's like. Currently Projected for weekend of July 11th.

🔒 5. No data retention

We save 0 prompts. Just like electricity passing through a wire, once your prompts leave the cache, it's gone. No training on data. We don't sell or share any of your data with anyone.

❤️ Help shape what's next.

The service is improving every week based on community feedback. Every subscriber helps fund better hardware, better models, and more features.

If you've been waiting to try an unlimited AI service without worrying about token costs, keep an eye out—we'll announce the details Free AI Weekend soon. Our services will remain the same (35b still live and free tier still available).

See you there!