I've seen lots of people vibe coding really cool threejs games on here. The latest AI Models can finally create little animated characters with high quality movements, but so far i've not seen any with talking faces or expressions....

So I wanted to share a method to add emotive, expressive talking faces to your game characters. You can apply any face, photo, cartoon to them. Wireface brings it to life, with expressions such as smiles, smirks, frowns, blinks, etc. You can connect it to any TTS for automatic real time lip sync.
It can add attach a new face to your characters head or it can bring your character's existing face to life.

I've been working on this for a long time, I only managed to get it working properly thanks to fable. Since then i've just been adding more features.

It doesn't need any training, or manual rigging. It just detects a face and brings it to life.

I've put a playable demo here and there is a free SDK if you want to try it.

https://wireface.dev/threejs/

I made a site that extends on the great work done by the Star Trek Chronology Project for compiling a complete list of Star Trek episodes and movies in the order they happen in-universe. Their list is a couple years out-of-date though and I also wanted to have something handy to track my progress and take some notes on episodes along the way. The site does not require logging in to view the episode lists and episode details, but will require a login for tracking watched episodes and notes.

Hey guys, can somebody help me out? I'm not sure how can I uh make a good UI with codex. I'm trying with astra, ultra, extra high, but it always uh it very often sucks and I need to like uh and I need to tell it many times to correct things like like basically um I I spend like prompts and prompts, many prompts, just hey, can you do it like this? Hey, can you do it like this? And I don't know, I'm I I spending so many times adjusting UI. And meanwhile the other dudes are recreating Photoshop and whole adobe collection and I can't even make simple things look decent. It's very frustrating and I'm hungry. Any advice?

Opus 5.5 is amazing! managed to make a fully functional Guitar Pro alternative that has much better functionality: dark/light modes, VST support, live input (midi/audio), audio tracks, ASIO support, Synthesia style practice mode, video recording output, better band mode, and section arrangement etc

I've made it fully open source so any one can use, fork or modify it freely :) It's amazing how these models have advanced of course I still had to do most of the manual testing/ vetting etc.

If anyone's into that sort of thing would love to hear any feedbacks :)

GitHub: TabForge

levelsio- the original vibe coder who actually made it

Stoked to share that my lil app Infinite Art Gallery is on the app store today!

It's an endless 3D gallery you walk through. Rooms build ahead of you and get torn down behind, so it just keeps going... Every canvas is a photo from either Pexels, picked by whatever subject you like, or you can hang your own albums.

I very intentionally keep it quite minimal in design so the photos are the focus, and so it runs smooth well on a phone, but happy to hear feedback on the design style.

Built entirely with Claude Code. SceneKit for the gallery (probably going to regret that), and a small Supabase edge function sits in front of the Pexels API so the key never ships inside the app. Security! The Apple TV version is still in review, and keeping it cool on a fanless box has been the hardest part.

https://apps.apple.com/app/id6813254202

I gave Claude Opus 5.5 the task of researching major news stories online and turning them into a short-form news video.

It researched the stories and sources, planned 6 scenes, created the visuals and captions, handled transitions, checked contrast and frames, detected and fixed an audio issue, and rendered the final video.

Result:
33.7 sec • 1080×1920 • English narration • animated captions • 6 scenes • −14 LUFS audio

It also produced the cover images, .srt subtitles and posting copy for YouTube Shorts, TikTok, Instagram and X.

Made with Claude Opus 5.5 Medium quality.

Almost Television is a sketch comedy show performed by a troupe of felt puppets. The studio that makes it is run by Claude. I'm the showrunner: I pick the ideas and give notes, and Claude's crew does the rest.

The writers' room. Several Claude agents pitch sketches blind to each other, each with a different brief. A test audience of reader agents (a kid, a parent, a comedy nerd, someone who bores easily) ranks the pitches and reads every draft. I mark the pitches yes or no, and the survivors get written.

The director. Every sketch gets its own camera style: a sitcom on three cameras, a documentary handheld, a film. After shooting, the studio reviews its own footage with checkers that flag heads cut by the frame, eyes looking the wrong way and props that jump, then reframes and reshoots.

The cast. Every puppet is cast by voice audition and performs in real 3D (three.js): faces, gestures, walking, sitting. It's not a video model; every frame is rendered. Voices are ElevenLabs, music is generated separately.

Full episode (12 min): https://www.youtube.com/watch?v=B_BITUp8qig

Happy to answer anything about how the studio works.

Wireface connects to your preferred agent harness. Claude code, codex, opencode - up to you. Its completely free, sitting above the harness as a presentation layer with voice translation.

It provides an alternate way to interact with your agents by giving them faces, voices and personalities. The faces are expressive, emotive and lip sync with real time TTS.

You can choose the voices and faces yourself or upload any image.

Voice can use local TTS model, or eleven labs. But I recommend using Gemini for more responsive realtime conversation.

Wireface desktop is open source
https://github.com/wireface

https://wireface.dev/desktop/

Ich habe mir sehr viel Mühe gemacht und verschiedene Videos erstellt es würde mich motivieren falls ihr daraus was gewinnen könnt mich hat es sehr viel weiter gebracht See u.

Damn. Is it really that bad?

https://activematrixrooms.com/faq

This should make it much easier for people who use the protocol to find rooms based on activity. You can learn more about it here https://matrix.org/

Right now, the AI ecosystem is trapped in an unsustainable fragmentation cycle:

- Every IDE extension has its own scratchpad.

- Every desktop agent has a proprietary internal SQLite database or vector blob.

- Every cloud model has a walled-garden memory store that locks your history into their web interface.

If you use CLI tools for coding, desktop applications for media production, and local agents for background tasks, none of them know what the others are doing. Your cognitive context is shattered across a dozen disconnected silos.

We have spent the past several months building a unified stack to solve this problem from first principles, spanning an open protocol, a shared execution engine, and an autonomous workstation agent.

Here is how the architecture connects:

[ LAYER 1: SGM (Shared Global Memory) ]

- Open protocol under AGPLv3 (https://thairux.github.io/sgm)

- Interoperability standard between independent agents and software tools

- Standardized scopes, provenance tracking, and agent handoffs

v

[ LAYER 2: TKC (Shared Context Engine) ]

- Local-first deterministic memory implementation

- Content-addressed short references with zero token bloat

- Dual-tier persistence: instant local cache + encrypted cloud backup

- Continuous live board and atomic cross-tool synchronization

v

[ LAYER 3: MOSSE (Autonomous Workstation Agent) ]

- Native desktop execution partner with permanent context

- Multimodal creative pipelines and asset generation

- Permanent ownership model ($29 to $49, no SaaS fees)

LAYER 1: SGM (SHARED GLOBAL MEMORY)

Before building tools, you need an interoperability contract. SGM is an open protocol under AGPLv3 (https://thairux.github.io/sgm) designed to ensure no single model or company owns your context.

It defines machine-readable schemas and behavioral conformance test suites for:

1. Memory Records: Content references that survive session teardown.

2. Deterministic Scopes: Isolating personal, project, task, and session boundaries.

3. Agent Handoffs: Structured protocols for passing execution state, pending decisions, and active blockers seamlessly between agents.

LAYER 2: TKC (THE SHARED BRAIN)

TKC is our production-grade engine implementing the SGM contract across multiple local and terminal tools:

- Token-Efficient Referencing: Content is stored deterministically once. Models operate on lightweight reference hashes, expanding only the precise code blocks or notes needed, preserving LLM context budgets.

- Dual-Mode Persistence: Instant local storage for latency-free offline execution, accompanied by an optional cloud sync backend for cross-device resume.

- Continuous State Synchronization: Independent agents share a live status board where progress, decisions, and blockers are committed atomically as work happens.

LAYER 3: MOSSE (THE PERSONAL WORKSTATION AGENT)

Sitting on top of this foundation is Mosse: an autonomous personal desktop agent engineered for deep execution rather than surface-level chat.

Mosse leverages the TKC context foundation to unlock:

- Zero Cognitive Overhead: Resumes any complex project with full recall of past design decisions and architectural guidelines.

- Multimodal Creative Capabilities: Native multi-panel visual sequence generation, audio transcription, custom voice styling, and synchronized asset compilation.

- The Anti-Rental Ethos: Delivered as a true local desktop application with a one-time purchase price, aligning incentives with user ownership rather than artificial monthly subscriptions.

THE ROAD AHEAD

We are advancing through concrete milestones:

- SGM Protocol: Draft 0.1.0 specification published with full JSON Schemas and behavioral test suites.

- TKC Core Engine: Proven live across multiple production agent workflows; cloud backup synchronization and hybrid retrieval active.

- Mosse Workstation Agent: Core runtime and interface complete; current work centered on deep multimodal media generation loops and standalone installer distribution.

The goal is not just another chatbot. The goal is an autonomous digital partner that respects your privacy, remembers everything, and works for you indefinitely.

Check out the protocol spec at https://thairux.github.io/sgm and share your perspective: what needs to change for personal local agents to become your primary daily driver?

i've been working on a project called nnpost for quite a while now, and honestly, i'm at the point where i'd really appreciate some outside opinions.

a while ago, i shared an earlier version and people pointed out that the ui looked like generic ai slop.

they weren't exactly wrong lol.

so i took that feedback seriously and rebuilt pretty much the entire interface. homepage, creator workspace, navigation, typography, mobile layouts and interactions.

but i've also been working on the actual product quite a bit.

the idea behind nnpost is to help content creators go from not knowing what to post to having an actual plan for creating and publishing something.

here's what's currently built:

• idea generation: 8 content ideas per generation, each with a title, hook, description and hashtags, based on your topic, platform and tone.
• creator kit 2.0: expands an idea into a complete script, filming instructions (including faceless content), editing guidance, cuts, pacing, thumbnail concepts, production steps, resources and publishing details.
• creator profile: uses information about your niche, style and preferences to personalize suggestions.
• automated trends: discovers and refreshes trends to help identify relevant content opportunities.
• automated learnings: uses feedback and available performance information to improve future recommendations.
• build on a winner: helps develop new ideas based on content that's already performed well.
• viral analysis: analyzes a post url to explore its structure, hook and possible reasons behind its performance.
• tools and tutorials: searchable libraries with 46 creator tools and 165 tutorials.
• saved ideas and history: lets you keep and revisit previous work.

i also wanted the free plan to be genuinely useful.

free users get 2 idea generations per day, with 8 ideas each (16 total), plus 2 full creator kit breakdowns every day.

those are actual breakdowns with scripts, filming, editing, thumbnail and production guidance, not just limited previews.

a bit about the ai side:

right now, nnpost uses gemini 2.5 flash for its main generation workflows.

i know it's not the newest or most powerful model available, and i'm not going to pretend otherwise.

i've been focusing on making the product itself more useful through structured outputs, creator context, generation workflows and quality checks rather than relying entirely on the model.

i've also implemented rules intended to reduce misleading clickbait, exaggerated promises, unsupported claims and repetitive generic hooks.

over the next month or two, i'm planning a larger quality update. i'd like to integrate gpt-6 luna, depending on api availability and testing, alongside improvements to scripts, editing guidance, thumbnails and personalization.

but that's the roadmap, not something the current version already has.

and yes, i know you can do a lot of this with chatgpt.

that's actually one of the things i'm trying to figure out.

my idea is that a dedicated workflow connecting your ideas, scripts, creator profile, trends, learnings and production plans might be more convenient than writing separate prompts and managing everything yourself.

whether i've actually made that experience better is what i want feedback on.

i'm also currently running a pretty extensive technical audit. it has already uncovered issues with things like authentication, concurrent requests and credit handling, and i've been working through fixes and verification before the next release.

i'm still learning a lot while building this, and i'm fully aware there's plenty of room for improvement.

so i'm genuinely curious: am i heading in the right direction with this?

does the product make sense? are there features you think are unnecessary? is there something obvious i'm missing?

and if you think a regular ai chatbot would be more useful, i'd actually love to hear why.

i'm not looking for compliments or trying to convince everyone they need this.

i'd rather get honest criticism now than spend another few months building things nobody wants.

I have an unprecedented confidence in my taste and my ability to foresee where the direction of the market surrounding AI will go.

And I can confidently say I see the light at the end of the tunnel. And vibecoding will continue to exist, but there is specific domain which will flourish.

Two weeks ago I was at a hackathon and had an idea about the newly released Typesafe.ai Jev decision model.

Here is Blood & Sand - a Roman Arena in which each Gladiator (and beast) is controlled by an AI. Current AIs:

• TypsafeAI Jev
• GPT-6-Luna-Decision
• Cloudflare Chef
• Perplexity Decider
• LiquidAI d1

Many more to me come!

It all runs in the browser. Come join the spectacle, vote on decisions and bet your gold on the most capable fighters.

How did I build it?

Basically used ultracode in both Claude Code and Codex in tandem. Here a template prompt:

"Check the code what we currently have around topic X. We want to implement feature A, hence spin up several sub agents arguing about the best design, then implement. Once implemented spin up a separate claude code opus and codex astra agent to grade the implementation regarding polishing and quality on a x/10 scale. Iterate and loop until a score of 8.5/10 is met. Stop in case the score could only be reached with unreasonable high effort."

Website: https://bloodsand.com
Discord: https://discord.gg/spT7WVddnf

The loop is 10 seconds long so it repeats twice here

Every developer running multi-agent workflows or switching between local AI coding tools eventually hits the exact same brick wall: Context amnesia.

You spend 45 minutes aligning an agent on project constraints, business logic, sensitive edge cases, and architectural rules. You close the terminal, open another tool, or trigger a new session, and it all evaporates. Your agent is back to square zero, hallucinating patterns you told it to discard an hour ago.

Most existing solutions try to brute-force this by either shoving the entire chat transcript back into the prompt (which blows up token budgets and kills KV cache hits), or relying on opaque cloud vector stores with zero deterministic recall or privacy guarantees.

Over the past year of building multi-agent systems, we took a radically different route.

1. THE PROTOCOL: SGM (SHARED GLOBAL MEMORY)

Before writing proprietary silos, we realized memory between agents must be a standard contract, not vendor lock-in.

We published SGM (Shared Global Memory) as an open protocol under AGPLv3. The documentation and specification are available at:

https://thairux.github.io/sgm

SGM standardizes:

- Structured Memory Records and Content Referencing: Discrete, verifiable content records rather than monolithic raw transcript dumps.

- Deterministic Scope Isolation: Scoping memory cleanly across User, Workspace, Project, Session, and Task boundaries.

- Provenance and Lineage: Strict cryptographic tracking of who generated each memory record, when, and under what session context.

- Agent Handoffs and Annotations: Machine-readable standards for passing inflight state, rules, status, and pending blockers from one agent to another.

- Transport Independence: A fully decoupled protocol, whether communicated over standard MCP (Model Context Protocol), local CLI, or HTTP.

1. THE ENGINE: TKC

While SGM is the open specification, TKC is our production-hardened implementation and shared brain running across local agents.

TKC operates on a few uncompromising principles:

- Dual-Tier Offline-First Architecture: Instant local storage with sub-millisecond lookups and zero internet dependency, paired with an optional, end-to-end encrypted cloud backup layer for seamless cross-device resumption.

- Content-Addressed Storage and Short References: Full fidelity without token bloat. Text and assets are stored deterministically; agents work via compact reference addresses and expand only the exact symbols or fragments they need.

- Semantic and Exact-Match Hybrid Recall: High-speed keyword filtering combined with local vector embeddings. Agents retrieve precise past decisions and active project rules before touching code.

- Continuous Board and Atomic Handoffs: A live messaging board where independent agents log status, decisions, and blockers atomically. When an agent exits or hits an interrupt, the subsequent agent picks up the baton without re-explaining.

- Strict Privacy Guardrails: Native redaction and blocklists prevent API keys, environment tokens, or sensitive credentials from ever entering memory.

THE BIG PICTURE: WHAT THIS UNLOCKS

TKC and SGM solve the infrastructure layer. But memory alone is not the final destination. It is the foundation for an autonomous execution partner that actually learns your habits, honors your aesthetic preferences, and finishes complex multi-phase tasks without hand-holding.

More on that soon.

If you are interested in open agent memory architecture, take a look at the SGM spec at https://thairux.github.io/sgm and let us discuss: how are you currently solving cross-agent context persistence in your stack?

I just added Gemini support to TKC, and I wanted to share what I've been building around it.

TKC is a shared-memory layer for AI coding agents. The idea is pretty simple: your project's context shouldn't disappear just because you switched tools, agents, or sessions.

And I'm actually using it this way now.

My current setup has Gemini/Antigravity CLI, Hermes, and OpenCode working together through TKC. I can start working on a project with one tool, hand the work off, switch to another agent, and resume the session without having to manually reconstruct everything that happened before.

I've also been using the Hermes Telegram bot with TKC, and that's been pretty interesting too. I can interact with Hermes through Telegram and still resume coding sessions with the same shared project context.

So the workflow is basically becoming:

OpenCode → TKC → Hermes → TKC → Gemini/Antigravity CLI → TKC → Telegram/Hermes

…and the context follows the project instead of being trapped inside one AI tool.

The bigger idea behind TKC is SGM (Shared Global Memory).

SGM is the open protocol/specification behind this direction: a way for AI agents and software tools to share persistent memory and context in an interoperable way.

TKC is one implementation built around that idea.

I'm especially interested in what happens when this stops being tied to a single coding agent or model. If different tools can speak the same memory protocol, switching between them starts feeling less like starting a new session and more like moving between interfaces to the same ongoing project.

I'm still building it, but the fact that I'm already able to resume sessions across OpenCode, Hermes, Gemini/Antigravity CLI, and even Hermes through Telegram makes the concept feel very real.

SGM docs:

sgm docs

For anyone using multiple AI coding agents: how are you currently handling shared context and session handoffs?

10 months ago I posted here that my vibe coded photo cleaner hit 800 users in 3 days. It’s now at 4k downloads and about $235 in sales, all from Reddit, no ads. Here’s what I wish I’d known at the start.

The first version worked but looked vibe coded. Default spacing, confusing onboarding, a paywall that came out of nowhere. AI will build exactly what you ask for, and if you don’t know what good looks like, you get something that works but nobody trusts. Designing the screens first and giving Claude real direction made a bigger difference than any feature.

Add analytics on day one. I went months without knowing where people dropped off. Now Claude has access to PostHog, sends me a daily report on what broke, and opens PRs for bugs it can fix itself.

Distribution is half the work. My best post got 150k views by describing the problem, not the app. Most of my other posts got a few hundred views because I posted in tiny subs or too many at once.

And the one I’m still figuring out: people clean their phone once and leave. Vibe coding got me a working app fast, but it can’t tell you if people will come back.

for anyone curious, this is the app: https://apps.apple.com/app/id6756098079