Macalinao Studio — AI Systems, Implemented and Operated

Vibe · Software & Apps

01 · Live

DvYnT Studio (Web) · DvYnT Fluencer (iOS & Android) · Vibe-coded

The software I ship, not just the pipelines I run. DvYnT Studio is a browser AI-generation app; DvYnT Fluencer wraps the same app native for iOS and Android. One shared codebase, a render backend swapped per tenant, bring-your-own-key on mobile, and app-store compliance treated as a build gate. Vibe-coded — design tokens locked before the pages, shipped behind real auth.

What I'm learning

One codebase with a render backend per tenant beats forking the app per brand — the shell stays shared, only the wiring swaps.
A mobile AI app lives or dies on bring-your-own-key plus cloud backends; never ship a farm dependency to a phone.
App-store compliance — AI-consent, on-device moderation, provenance — is a build blocker, not a launch-day checklist.
Capacitor gets you iOS and Android from one web layer; the native shell is the last mile, not the app.
Vibe-coded sites ship fastest when the design tokens are locked before the pages — six iterations of this portfolio taught that rule.
Shipping a generation studio as a live product means deployment, auth, and tunnels are part of the design, not an afterthought.

DvYnT Studio · Preview →

Surfaces

WebiOS AndroidVibe-code

Stack

ReactVite CapacitorAstro

Posture

One codebase
Many tenants
BYO-key
Store-compliant

ZayNid · Multi-Pack Studio

02 · Live

One studio · 25 packs · 6 families · Ascend-Z

The all-in-one studio: a single app that turns an input into a finished deliverable across six families — media (music, film, comic, podcast), commerce (e-commerce, brand, UGC), publishing (children's books, courses, worldbuilding), 3D and game assets, platform tooling, and finance research. Shared engines underneath, capability-unlock pricing on top, white-label for resale. Branded Ascend-Z.

What I'm learning

A studio "pack" is a product unit — packaging capability as packs turns a tool into a catalog.
Shared engines beneath many packs is the only way one person ships twenty-five products.
Capability-unlock pricing beats per-seat for creative tools — people pay for the deliverable, not the login.
White-label is a first-class tenant, not an afterthought — build the multi-tenant seam before the second customer, not after.
Provenance-routing one engine (hobbyist input vs research input) is how a single pack serves a beginner and an analyst.

Marketplace · Preview → WebApp · Preview →

Families

MediaCommerce Publishing3D PlatformFinance

Posture

One studio
25 packs
Shared engines
White-label ready

Publishing & Press

03 · Live

1 Book · 5 Ebooks · Finance Packs · Owned Imprints

An online press publishing under imprints I own. From Plan to Public is a capital-formation book (imprint Torey Press) with a five-title ebook line; the Family-F finance packs turn its frameworks into fill-in deliverables — run as a live web-app — licensed through Captiva Equity. A draft loop plus a three-model editorial scan, typeset once to print-ready 6×9 and reflowable EPUB.

What I'm learning

A book is a system — a repeatable draft → scan → typeset pipeline beats writing each chapter by hand.
Print (fixed 6×9) and EPUB (reflow) are two products from one source; design for both from page one.
A three-model editorial scan — draft in one, critique in two others — catches what a single pass never will.
Productizing the frameworks (fill-in packs) is where a book stops being a PDF and becomes a tool people license.
Owning the imprint name means owning the shelf — an imprint you control beats renting a marketplace slot.

Ebook Storefront → Family-F WebApp →

Catalog

BookEbooks Finance Packs

Imprints

Torey PressCaptiva Equity

Posture

Draft loop
3-model scan
Print + EPUB
Owned imprints

AI Attendant · Sarvinoz

04 · Live

Voice Assistant · Phone, Discord & Web · Realtime Voice & Vision · Layered Memory

A speech-first assistant running in production on the studio's own GPUs — an open-weight reasoning model behind a self-hosted speech pipeline, reachable over a real phone line, Discord, and the browser. Every AI Influencer in the roster now carries a unique voice, turning each character into a distinct conversational presence. Layered memory — vector recall plus a relationship graph — lets it remember across calls and sessions, with web search wired in for fresh answers. The newest build adds sight: a video-call persona that sees through the camera and talks back in real time — a face that watches, listens, and responds on one live connection. A working demo is available.

What I'm learning

Turn-taking is harder than transcription — the assistant that interrupts itself loses every conversation.
A real phone line is the honest benchmark: telephony adds latency you can't refactor away, so the budget is spent before the model says a word.
Serving the LLM, TTS, and STT on shared GPUs is a memory-placement problem — capacity planning is VRAM planning.
Memory has layers: vector recall answers "what did we say," a relationship graph answers "how does it connect." An assistant needs both.
Model selection matters more than parameter tuning for TTS quality — and multi-language coverage locks the engine choice early.
Latency budget is real; sub-second response is the floor, sub-300ms first-syllable is the goal.
A voice influencer is a brand asset — it should be consistent across calls, sessions, even years.
Adding vision to a voice call is a latency problem before it's a model problem — perception, speech, reasoning and lip-sync all have to fit inside one turn.

Try the demo →

Approach

TTSSTT PSTNMemory Turn-taking

Coverage

Phone · Discord · Web
Live video + vision
Layered memory
All AI Influencers voiced

AI Image/Video In House

05 · Live

3D Character Modeling · AI Web & UX Design · AI Film

In-house creative production built on AI image generation. A single generated character sheet becomes a 3D asset — image to mesh, auto-staged and lit in Blender, finished with a photoreal diffusion pass. The same image work drives AI-designed web products (an AI generation studio, live in production behind its own auth) and long-form AI film storytelling, where characters hold identity across hundreds of shots.

What I'm learning

Image-to-mesh models turn one character sheet into a riggable 3D asset in minutes — the bottleneck moved from modeling to art direction.
Driving Blender programmatically (stage, light, camera, render) makes 3D output reproducible the same way workflow JSON made image output reproducible.
3D for geometry, diffusion for skin: a photoreal img2img pass over a 3D render gives consistent characters in controlled poses.
An in-house design console beats a pile of prompt files — one surface to drive image, upscale, relight and 3D keeps the whole team on the same tools.
Narrative film is consistency management — face anchors, seed discipline, and shot-level decomposition, not longer prompts.

Ernie Image → Krea 2 → Macalinao Studio →

Approach

Img→3DBlender Web/UXAI Film

Output

3D characters
Live web studio
Story-driven film

Hosted Stack

06 · Stack

Mail & Web Operations · Cloud Service · Backups · Disaster Recovery

The self-hosted operations layer — managed mail with proper authentication and an outbound relay, plus a portfolio-wide edge / DNS / proxy setup with structured contact-form sweeping. Backed by a managed cloud service tier, daily off-host backups, and a warm standby that turns disaster recovery into a DNS swap rather than a rebuild. All managed as data, all built to roll back per zone.

What I'm learning

Reputation is the metric that matters; everything else is upstream of it.
An outbound relay for transactional mail is a much better story than sending direct from the mail server.
The dry-run output IS the rollout plan. Anything you can't reproduce in dry-run won't behave the same on apply.
Parked domains are a contact-form attack surface most teams forget exists. Sweep them like any other input.
Edge rules outlive any single deployment script — write them as data, not as one-shot commands.
A managed cloud service tier earns its cost the first time a local node drops and traffic just keeps flowing.
A backup you've never restored from is a hope, not a backup — and "off-host" is the word in "off-host backup" that actually saves you.
Warm standby beats cold backup: when disaster recovery is a DNS swap instead of a rebuild, you'll actually reach for it under pressure.

Approach

MailWeb DNSEdge CloudBackups Recovery

Stack

SESAWS

Posture

Self-hosted
Managed deliverability
Warm standby
Phased rollout

Site Previews

QKV Token → DvYnT →

AI Stack

07 · Stack

Claude · Hermes Agent · Gateway · GPT-5.5

The agentic stack I implement against — a Claude / GPT-5.5 reasoning layer fronted by Hermes (the agent runtime) and a delegation gateway (the cron / task-routing tier). One default agent, many models, all calls audited at the edge. Underneath it sits a persistent second brain — a file-based knowledge wiki the agents read first and write back to, with a scheduled "dream sequence" pass that ingests new material and lints the index. That brain is now exposed over MCP — any agent harness reads and writes it through one server — and orchestration has grown from a single agent into a multi-agent workflow that fans work out and verifies it before it lands.

What I'm learning

A knowledge base the agent maintains itself (ingest → page → index → log) turns chat ephemera into institutional memory — sessions stop re-discovering the same facts.
Plain markdown beats a database for agent memory: greppable, diffable, and any model can read it cold.
Centralizing access through one gateway makes cost, audit, and rate limits enforceable. Direct keys per tool is the slow drift into chaos.
"Agent" is a verb (a thing that decides what to call) more than a noun. Picking one default keeps the verb consistent.
Tool inventory belongs in the agent layer, not in the prompt — keeps prompts small and tools swappable.
One agent, many models is easier to operate than many agents, one model.
Cron-driven agent delegation is dramatically simpler than event-driven for small teams. Most "real-time" requirements aren't.
Mass-update workflows require idempotency from day one — or the rollback story gets ugly fast.
Audit logging at the gateway saves arguing about what an agent actually called when something breaks at 2am.
Exposing the knowledge base over MCP means every harness reads the same brain — the memory stops being tied to one tool.
A multi-agent workflow that fans out and then verifies beats one agent iterating alone — the second pass is where the mistakes die.

Tools

ClaudeHermes GatewayGPT-5.5 Second BrainMCP Multi-agent

Posture

One default agent
Many models
Audit at the edge

Generation Stack

08 · Stack

ComfyUI · GPT-Image2 · LTX-Video · AceStep 1.5 XL

The media-generation stack covering image, video, and audio — running as local generation on a private multi-node GPU farm. ComfyUI is the workflow editor; GPT-Image2 handles batch influencer renders; LTX-Video is the video diffusion runner; AceStep 1.5 XL covers music. LoRA fine-tuning is baked into the pipeline, and centralized orchestration handles model routing, throughput shaping, and node-level health.

What I'm learning

A saved workflow .json is more reproducible than any prompt — treat it like code, commit it, diff it.
Custom nodes age fast; pin the ones you depend on or accept the breakage.
Splitting the workflow at the latent stage (load, sample, decode separately) is the cheapest debugging move.
Influencer enrichment as structured JSON beats freeform prompt rewrites every time — and survives reruns.
Visual-DNA maps make characters consistent across hundreds of frames without re-prompting from scratch.
Video models reward thinking in shots, not seconds — pacing is a prompt input, not a render setting.
Music generation is most useful when the visual cut already exists — composition lives in the video, not the audio.
VRAM is the binding constraint on multi-model serving — utilization without VRAM tracking is a misleading green dashboard.
LoRA training stays cheap if the base model is locked and only the adapter cycles — full retrains rarely earn their cost.
Auto-load/unload is the difference between "real cluster" and "one big model that won't move."
Cost-per-generation only becomes a real number once node-level telemetry is wired in.
Long-running generations should checkpoint to disk; "cluster goes down at 80%" should not erase the work.
Throughput shaping per-tenant is the difference between "shared farm" and "one user starves everyone else."

Tools

ComfyUIGPT-Image2 LTX-VideoAceStep

Coverage

Image · Video
Audio · Music
LoRAs · Fine-tunes
Workflow as code

Local generation

Multi-node farm
Internal-only
Auto-routing

AI Music Idols

09 · Live

Albums & Music Videos · Character-led Acts · Live Sites

A small label of in-house AI idols — characters with discographies, not producers with stage names. Currently QKeyV (synth · dark pop) and DvYnT (electronic · cinematic), each with a live artist site. Both acts are voiced and attended. The production direction stays deliberately vocal-forward. Releases ship as albums plus visual companions.

What I'm learning

Prompt versions are album versions — v6 replaced v5 the same day; the per-track lane file is the studio session.
A production constraint ("vocal-forward, no auto-tune") survives only if it's written into every track's prompt lane, not held in your head.
Visuals tied to the audio cut from day one beat post-hoc music videos — every time.
Album shape forces a story; single-track-only acts lose continuity fast.
An idol is a character with a discography, not a producer's stage name — write the influencer, then the songs.
Voice consistency across releases is what keeps an act feeling like one act — a distinct voice identity per idol is now in place for exactly this.
The fan loop (release → social → response → next release) is the actual product, not any single song.

QKeyV → DvYnT →

Acts

QKeyVDvYnT

Output

Albums + videos
Voiced · Attended
Live artist sites
DSP distribution

AI Influencers, Models, UGC

10 · Live

Personalities · Rosters · Images · Websites · Voices · LoRAs

Multiple rosters of AI Influencers — female models, male brand ambassadors, music idols, and professionals — each with a consistent identity, unique voice, and ready-to-publish content.

What I'm learning

An influencer is a brand asset — versioned, signed off, retired the same way logos are.
When influencers multiply past a few dozen, the registry is the product.
UGC feel comes from imperfection on purpose, not from prompt-perfect renders.
Cross-platform formats cost less when designed up front than retro-fitted.

VyAyEy → Male UGC → AI Influencers → Studio Team →

Rosters

VyAyEyMale UGC AI InfluencersStudio Team

Capabilities

Voiced · Attended
Face-anchored
Brand-safe media
UGC-ready feeds

Use

Campaign assets
Social feeds
UGC seeding

AI Learning Course

11 · 44 Lessons · Live

4 Levels + 3 Specialist Tracks · 19 Live Playgrounds · Content as data, design as code

A from-the-ground curriculum on transformer-based AI, fully rebuilt as a generated static site. 44 lessons climb four levels — 01 Foundations, 02 The Transformer, 03 Training a Model, 04 Running Models — then branch into three specialist tracks: Image, Video, Voice. Lesson content lives as structured JSON; one Python builder renders every page through a single template — the curriculum is data, the design system is code. 19 live interactive playgrounds run inside the lessons, 24 lessons carry audio narration (125 mp3 clips), and 42 ERNIE-generated infographics illustrate the concepts. Current through 2026: agents & MCP, reasoning models & RLVR, flow matching, provenance & regulation. No login. No paywall.

Open the course →

AI Classroom (new) →

The climb · 4 levels, then 3 specialist tracks

01 Foundations — tokens, embeddings, the math under next-token prediction.
02 The Transformer — attention, Q/K/V, the block, what a forward pass actually does.
03 Training a Model — pretraining, RLHF, PEFT/LoRA, reasoning models & RLVR.
04 Running Models — inference, the KV-cache, agents & MCP, provenance & regulation.
Specialist tracks — Image (diffusion, flow matching), Video, Voice — each inheriting the same foundation.

Interactive system · lessons you can poke at

19 live playgrounds embedded in lessons — tokenizer, attention heatmap, LoRA rank slider, diffusion denoiser, KV-cache visualizer, and more.
Homogenized card system across every page: expandable concept cards, animated pipeline flow rails, count-up stat strips.
localStorage progress tracking plus a filter/search course map — resume exactly where you left off.

What I learned shipping the course

Curriculum survives best when anchored on principles rather than specific products — model names date; the residual stream and the diffusion process won't.
A common foundation that all specialist tracks inherit beats duplicating the basics per track — mirrors how the systems actually work.
Content-as-data pays off at scale: 44 lessons as structured JSON rendered through one template means a design change is one edit, not 44.
Interactive playgrounds teach what prose can't — dragging the LoRA rank slider explains low-rank adaptation faster than any paragraph.
Staying 2026-current is a maintenance contract, not a milestone — agents, RLVR, and regulation will need revisits; the JSON structure makes those cheap.

Curriculum

44 lessons
4 levels
3 specialist tracks

Interactives

19 live playgrounds
Concept cards
Flow rails · Stat strips
Progress tracking

Media

125 mp3 clips
24 narrated lessons
42 ERNIE infographics

Format

Self-pacedNo login AudioPlaygrounds

Source

github.com /
ryzenx570 /
LLM-Understanding
macalinao.com/learning

Security, IDS, Hardening

12 · Live

SOC Posture · LLM-written Daily Briefs · Intrusion Detection · Runbooks

Continuous security work across access policy, an ongoing threat-tracking notebook, an intrusion-detection layer, versioned hardening checklists, and a monitoring runbook. An LLM reads the night's logs and writes the daily security brief, with a continuous monitor sweeping between briefs at 15-minute cadence; every new service passes a security check-in before it ships. Real attack analysis feeds the runbook back instead of drifting into archive.

What I'm learning

A threat database without a monitoring runbook is a graveyard. Alerting is what makes the data load-bearing.
An LLM that reads the logs every morning and writes three paragraphs turns "the audit log exists" into "the audit log gets read."
A security check-in gate on every new service catches the boring 80% — default creds, open ports, missing rate limits — before exposure, not after.
Hardening checklists need versioning the same way code does — each revision should explicitly subsume the last, with a diff.
Brute-force at the perimeter is still the floor of what you'll see, and rate-limit tooling is still the cheapest mitigation.
Snapshot completion does not equal recovery. Restore drills uncover gaps the snapshot job hides.
IDS signal-to-noise ratio drives whether you'll act on it — tune for false-positive cost, not detection rate.
Deploy scripts are part of the attack surface — a mirror-style rsync will happily publish your internal docs if you let it.

Approach

HardeningIDS Rate LimitsRunbook

Stack

CloudflareMail Certs

Cadence

Daily LLM brief
15-min monitor
Weekly audit

AI systems,
implemented & operated.

Twelve projects, three stacks, one workshop.

How a project actually moves.

Route, don't proliferate

One-shot rate is the cost line

Dry-run is the spec

AI Influencer = structured DNA

Memory is the binding constraint

The runbook is the alert

AI systems, implemented & operated.

Twelve projects, three stacks, one workshop.

How a project actually moves.

Route, don't proliferate

One-shot rate is the cost line

Dry-run is the spec

AI Influencer = structured DNA

Memory is the binding constraint

The runbook is the alert

AI systems,
implemented & operated.