Hector
Oviedo

The discipline Karpathy named at Sequoia AI Ascent 2026 in his 'From Vibe Coding to Agentic Engineering' talk: you orchestrate fallible agents instead of typing most of the code, and you keep the parts that decide whether the result is correct. Specs, architecture, diff review, evals, integration tests, security review, deployment.

A workflow is a sequence of steps, each one a set of instructions the agent carries out with the tools it is given, refined by iteration until it does the job reliably. A loop is open-ended: the agent plans, calls a tool, reads the result, and decides what to do next until the goal is met. Most real systems are both. Censurado runs a 14-step editorial sequence inside a 24/7 loop that drives 32 tools and falls back to another agent when one fails; it publishes on its own at elcensuradoweb.com. gamentic is the multi-agent version: a narrator plus one agent per character, writing to a state machine that only changes through 40 validated tools. open-research is the pipeline version: five agents passing work down the chain.

Agents do most of the typing. I do the architecture, the specs, the diff review, the tests, and the security pass, and I own the result either way.

Yes. I apply AI inside a product you already have, or build the product around it. On an existing product it is usually retrieval over your own data, an agent layer on top of what already works, or a vendor pipeline wired in: at Ohara I added multi-agent architecture, MCP integrations, and a multimodal layer over ten vendors to a platform that was already live. From scratch it looks like Censurado, gamentic, or open-research.

That pattern, yes: one engineer owning scope, build, integration, and deployment, customer-facing through the integration phase. It is how I have worked on contract for the last two years.

At Ohara I split a monolithic agent into a multi-agent architecture, decoupling logic, UI, and asset-generation streams so no single context window had to hold the whole job. gamentic runs a narrator plus one agent per NPC, each with bounded context and memory, over a world state that only changes through 40 validated tools. open-research runs a five-agent research pipeline on local inference.

Both, and they run in my own systems. The Censurado brain exposes 32 tools over MCP that an agent drives to research, write, and publish. telegram-bot-skill, blockchain-skill, and text-to-3D-skill each ship an MCP server, over stdio or Streamable HTTP. At Ohara, MCP middleware connected an autonomous coding agent to generative-media APIs, web3, and real-time data.

Agent Skills are how a generic coding agent learns your codebase, your stack, and your conventions. I have published more than ten (research, websearch, gmail, telegram-bot, blockchain, ocr, text-to-3D, among others) and I wrote 30+ instruction sets at Ohara before the format had a name. For a client the deliverable is the same: a fresh agent that delivers in your repo.

Hybrid retrieval built around the shape of your data: pgvector for semantic, ParadeDB BM25 for lexical, a LightRAG graph for entity-rich domains, reranking on top. rag-base is the working reference: 19 endpoints, BGE-M3, four rerank modes, no LangChain. Its eval harness reports hit@1, hit@5, and MRR, including the configurations where the reranker makes results worse.

Two decisions carry most of it: what the model is allowed to write, and what enters its context. At Ohara I removed hallucinated API code from generated apps by replacing free-written integrations with deterministic template injection, so the model filled a validated interface it could not deviate from. The same approach runs through my repos: state-machine grounding in gamentic, source-checked publishing in Censurado, explicit context budgets in noob-cli.

AMD is where I do the hard version. vllm-awq4-qwen serves Qwen 3.6-27B AWQ-INT4 on a Strix Halo APU (gfx1151, 128 GB unified memory) at 24.8 t/s with 256K context, which needed a custom HIP prefill kernel and 18 patches carried against vLLM. llama-vulkan-strix does the llama.cpp side on Vulkan with GGUF weights pinned to GTT. CUDA is the easier target and is equally in scope.

Yes. My degree module benchmarked YOLOv8, EfficientDet, Detectron2, and SAM2 head to head for detection and segmentation in video, with a frame-by-frame analysis frontend. In client work vision is usually one step inside a larger loop: inspection, monitoring, or document understanding feeding something downstream. That is how I scope it.

At Ohara I owned the orchestration layer across Hedra, HeyGen, Luma, Veo 3, Runway, GPT-Image, Flux, ElevenLabs, PlayHT, and Lyria 2, each behind one validated interface so vendors swapped without touching generated code. Locally: ai-music-studio plans an album and generates tracks and cover art, text-to-3D-skill takes a prompt to a rigged GLB, and comfyui-strix-docker fixes the silent CPU fallback stock images hit on gfx1151.

Security review is part of delivery. Unreviewed agent output has documented quality and security drift, so the diff gets read, load-bearing paths get integration tests, and anything that writes gets a dry-run mode and a permission gate. In my own skills, fetched pages and inbound mail are fenced as untrusted so a page cannot inject instructions into the agent.

10+ years of games and interactive products before AI: ActionScript and Lua at Globant for EA (NHL, UFC), slot frameworks at Santo Mate, casino games on dual-monitor hardware at Grupo Ayex, AR prototypes in Unity, a mobile JavaScript roulette system. Three.js and Phaser are normal scope, and gamentic and duplexity-3d apply that background to agent systems.

As CTO or technical co-founder for a team that needs a real product delivered: Agile delivery on a tight budget and a tight timeline, business requirements translated into scoped deliverables, stakeholder communication through the whole cycle, user validation once it is live. Not a fit: ML research or academic roles centered on model training, multi-week interview loops gated on live coding, and teams that ban agentic engineering.

Both. Contract is the default: a defined scope, a defined deliverable, a defined window. Full-time is open for the right team, particularly Applied AI Engineer or Forward Deployed Engineer roles at growth-stage AI companies.

USD 90 an hour for contract work, and USD 9,000 a month for a full-time engagement. Both cover the whole delivery: architecture, implementation, review, and the agent orchestration behind it. Longer windows and larger scopes are worth a conversation.

Business requirements gathered with stakeholders and translated into scoped deliverables; delivery in sprints with backlog management and product owners, QA, and UX in the loop; user validation, feedback analysis, and adoption monitoring once it ships. That cycle ran at Ohara, ADGS, and Grupo Ayex across distributed teams. Engagements here start the same way: one scoped workstream with a defined window, extended if the fit is right.

Based in Rosario, Argentina, working remote-global and async-first. US, EU, and AsiaPac overlap for sync work is normal. I have been paid through EOR and contractor arrangements for years, so the payroll side is well-trodden.

Remote engagements with EU companies are ordinary work. Spanish citizenship is in its final stage, which opens EU on-site work; until it lands, remote contractor or EOR arrangements both work.

No. Live coding measures hand-writing code instead of agentic engineering, which is how I deliver. What I can do is discuss architecture or work through a problem with you on a video call, and walk you through the architecture of any project on this page. My formation is the coursework I completed on the Applied AI degree at IU plus the portfolio on this page, all of it public on GitHub and verifiable commit by commit.

Not unpaid. A take-home is consulting work: scoped, delivered, and useful to you, so it goes at my hourly rate (USD 90) like any other engagement. Free alternatives are on the table: a call to discuss architecture or work through a problem, and a walkthrough of the architecture behind any project on this page.

Yes, and the privacy constraint usually decides the architecture: retrieval over the practice's own knowledge base, running on hardware you control if the data cannot leave. Intake and workflow automation, document analysis, and vision-augmented review are the common asks. The deliverable is a tool integrated into how the practice already works.