Remote: Yes, open to remote or hybrid

Contract: Yes

Technologies: LLMs, agentic systems, RAG, semantic search, recommendation systems, computer vision, workflow automation, Python, TypeScript, React, Node.js, PostgreSQL, Kubernetes, Docker, AWS, GCP

Résumé/CV: linkedin.com/in/carloschinchilla | github.com/calufa | medium.com/@calufa

Email: calufa@gmail.com

Staff AI Engineer / former CTO with 20+ years of software experience and 10+ years building AI systems across LLMs, agentic workflows, semantic search, recommendation systems, computer vision, generative media, and infrastructure.

I have built and led multiple zero-to-one AI products, including a WhatsApp finance copilot, invoice automation platform, AI-powered budgeting app, dental computer-vision product, text-to-video AI platform, recommendation infrastructure, and semantic search systems.

I was previously CTO at YC and Techstars-backed startups, and two companies I helped build were later acquired by OpenAI and LDC/private equity.

Recently, I have been focused on AI-native workflow platforms, company-brain systems, agentic search, LLM tool use, sandboxed execution, structured outputs, evaluation, human-in-the-loop workflows, and AI systems that turn messy business processes into reliable software.

Comments URL: https://news.ycombinator.com/item?id=48439155

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=47318708

Points: 1

# Comments: 0

- Problem: AI UI generators are high-fidelity by default → teams bikeshed aesthetics before structure is right.

- Idea: use ASCII as an intentionally low-fidelity “layout spec” to lock hierarchy/flow first.

Why ASCII: - forces abstraction (no colors/fonts/shadows)

- very fast to iterate (seconds)

- pasteable anywhere (Slack/Notion/GitHub)

- editable by anyone

Workflow:

- describe UI → generate ASCII → iterate on structure/states → feed into v0/Lovable/Bolt/etc → polish visuals last

It also facilitates discussion:

- everyone argues about structure/decisions, not pixels

- feedback is concrete (“move this”, “add a section”), not subjective

More advanced setups could integrate user/customer support feedback to automatically propose changes to a spec or PRD, enabling downstream tasks to later produce PRs.

Comments URL: https://news.ycombinator.com/item?id=46538628

Points: 168

# Comments: 93

Location: San Francisco, CA

Remote: Yes Willing to relocate: Open to discussion

Technologies: AI Product Development, Distributed Systems, Machine Learning, LLMs, Kubernetes, NLP, Computer Vision, React, Python, Node.js, GCP, Generative AI, Infrastructure, and DevOps.

Résumé/CV: https://drive.google.com/file/d/1iWBggtOlNWqAtvPms8CEgU3qbkV...

AI Portfolio: https://docs.google.com/presentation/d/19Irye4I4iJKzNN56PqIp...

LinkedIn: https://www.linkedin.com/in/carloschinchilla/

Email: calufa@gmail.com

I’m currently open to new challenges and opportunities. If you know anyone looking for a consultant, VP, staff, or a software engineer with experience in AI, I’d love to connect.

Comments URL: https://news.ycombinator.com/item?id=36117213

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=36033280

Points: 1

# Comments: 0

You can also follow us on Twitter: - https://twitter.com/nius_tv - https://twitter.com/BeyondEarthTV

In April we will be launching our mobile app; a next-gen non-linear TV news aggregator for mobile devices, that converts articles on topics that interest our users into short-form video using AI and trusted media content.

Sign up for our mobile app beta testing program at https://nius.tv.

Thank you, Carlos Chinchilla Founder at NIUS.TV

Comments URL: https://news.ycombinator.com/item?id=26020596

Points: 3

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=23576373

Points: 1

# Comments: 0

We have taken a similar approach on NIUS.TV. We've even taken it further by synthesizing an AI anchor.

Check it out:

- https://twitter.com/nius_tv

- https://twitter.com/beyondearthtv

The way we synthesize our videos is a little different from Baidu in practice, but similar on a high level.

For video synthesis we use an event-driven architecture using K8s jobs, and a custom job scheduler built in-house that manages all the steps of the video creation process.

Additionally, because GPUs are expensive and knowing that we synthesize our stories in batches through the day, we built a k8s-cluster-builder, that spins up and down the k8s cluster based on pending stories. This saves us a tremendous amount of cost per month, as the cluster only runs when there is actual work for it. As a bootstrapped project, costs are really important.

Our current video synthesis pipeline is comprised of 20+ steps, this includes speech-synthesis, image generation, speech+text aligners (for captions), language models, text generation, among others.

From my experience building the product, and despite the fact that AI is expensive (training wise), it is feasible to build AI-powered products using off-the-shelf DL technology without having large resources.

Comments URL: https://news.ycombinator.com/item?id=23262689

Points: 2

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=22981664

Points: 2

# Comments: 0