ddevnyc

Forward Deployed Engineer | AI Transformation | Agentic Systems | Previously at frontier labs

Remote: Yes

Willing to relocate: No

CV: Ask

Email: d.dev.nyc+hnjobs@gmail.com

I help organizations evolve into AI-native businesses by redesigning processes, decision-making, and operating models around LLMs, agentic systems, and intelligent automation. I enjoy partnering with leadership to identify high-leverage opportunities, define AI strategy, and translate ambitious transformation goals into processes that drive organization-wide adoption.

My focus is applying frontier AI capabilities—LLMs, agents, and intelligent automation—to transform how companies operate: redesigning business processes, augmenting teams, automating knowledge work, and building agentic workflows that create measurable business impact.

I specialize in bridging the gap between business strategy and technical execution. I work with executives, operators, and technical teams to identify high-value AI opportunities, translate ambiguous problems into scalable solutions, rapidly prototype systems, and drive adoption across teams.

My expertise includes enterprise agentic workflows, AI-native operating models, AI orchestration, workflow automation, autonomous business processes, and production AI deployment.

I'm particularly interested in helping companies rethink how work gets done — building AI-first workflows and augmenting decision-making where humans and AI collaborate effectively. I enjoy working across strategy and implementation, taking ideas from discovery through deployment, adoption, and continuous improvement.

Looking for Forward Deployed Engineering opportunities where technical excellence, customer partnership, and AI-driven organizational transformation are equally important. If that's the transformation your company needs, reach out via email.

Think? I know it

Haha really? How do you hook up midi to the UI?

ahhhh, that's such a great example

Forward Deployed Engineer | AI Transformation | Agentic Systems | Previously at frontier labs

Location: EU/US Remote: Yes Willing to relocate: No CV: Ask Email: d.dev.nyc+hnjobs@gmail.com

I help organizations evolve into AI-native businesses by redesigning processes, decision-making, and operating models around LLMs, agentic systems, and intelligent automation. I enjoy partnering with leadership to identify high-leverage opportunities, define AI strategy, and translate ambitious transformation goals into processes that drive organization-wide adoption.

My focus is applying frontier AI capabilities—LLMs, agents, and intelligent automation—to transform how companies operate: redesigning business processes, augmenting teams, automating knowledge work, and building agentic workflows that create measurable business impact.

I specialize in bridging the gap between business strategy and technical execution. I work with executives, operators, and technical teams to identify high-value AI opportunities, translate ambiguous problems into scalable solutions, rapidly prototype systems, and drive adoption across teams.

My expertise includes enterprise agentic workflows, AI-native operating models, AI orchestration, workflow automation, autonomous business processes, and production AI deployment.

I'm particularly interested in helping companies rethink how work gets done — building AI-first workflows and augmenting decision-making where humans and AI collaborate effectively. I enjoy working across strategy and implementation, taking ideas from discovery through deployment, adoption, and continuous improvement.

Looking for Forward Deployed Engineering opportunities where technical excellence, customer partnership, and AI-driven organizational transformation are equally important. If that's the transformation your company needs, reach out via email.

i bet they're good for it. however robotics aren't electronics. that's a completely different space.

what sorts of things have you designed with it?

> AFAIK, everything we can "know" can be written down, one way or another, even analog circuits.

writing down circuit diagrams is like cooking about music.

> what SOTA LLMs are you referring to?

I have done a survey among analog electronics designers just a couple weeks ago and they all said that their forays into LLMs were great for digital electronics, code, and firmware, but for analog they were pretty terrible, with a variety of LLMs, according to everyone.

spice is a very bad simulation. it's not like a unit test or a VM. it works very differently to the real thing, only approximating it in some highly controlled situations.

The contribution of Excel, the most popular programming environment in the world, is undoubtable - therefore, not only has Excel accomplished something (popularize programming among non-programmers) but it's also easily measurable (it has done so more than any other technology in existence). It _is_ a genuine contribution to the world, by the tool itself, not just by what people use the tool for.

So, I disagree with this line of criticism - I think you very well can do those things.

> Get BigCentralThing to add it

Making a workflow central to your business dependent on the goodwill and efficiency of an external entity is a very bad idea!

It's good that you've found a partner who is willing to put in the work, but that is luck rather than what normally happens.

Do you believe this might have been Citadel grooming SA to implode like that?

Looking back on the project, what do you think could be some interesting things one can learn from it?

Some people are confused as to what this is, so I thought I'd bring up this sentence from the Readme:

> PISIGuard runs entirely in your browser. It spots names, email addresses, phone numbers, credit card numbers, passwords, API keys, and more, replaces them with safe placeholders, then puts the real values back into the AI’s reply. From your point of view, it’s mostly invisible; from the AI’s point of view, the sensitive stuff was never there.

I suggest putting it at the top of the readme!

I think it's a very smart system, and could easily extend beyond AI use. Please continue working on it!

I would love a controller like that for my editor, or my LLM. That would make coding so much more fun.

Absolutely amazing presentation, the interactive examples alone are worth the click. Thank you very much!

There's enough inbreeding^W distillation happening that whatever OpenAI tone you're thinking of is probably Claude's tone (and vice-versa)

I'm sure I don't speak just for myself when I ask, can you link the presentation?

I know I am very very alone in this, but I've always found regex very readable. It's just not _quickly_ readable. You can't look at 20 characters of regex and read it and understand it as quickly as you would 20 characters of English. The biggest issue, I think, is people trying to do that. Regex is very information dense, it should be approached with the care of a mathematical formula or a sudoku rather than English prose.

I think it's absolutely amazing to have insight into long-term goals like this for open source projects. For one thing, it can help you plan your tech stack, and can even be a source of inspiration on what sorts of topics to learn and what sorts of research to do.

The overall structure of the document is no different than any other tech reference or RFC that I've read over the decades

This is really regrettable. I think the biggest issue is that once such laws or case law are in place, it is really really impossible to repeal them, or to replace them with something more modern. So we're still applying horse laws to cars 100 years later.

Smelly, rotting fish sauce that needs to be handled in hazmat suits? In Europe that's called a "condiment".

Does MoE help with multimodality? Can it in general enable reasoning in imagery (technical drawings, diagrams, schematics) rather than text-based?

I am particularly curious how multimodal models will work with types of knowledge that are inherently non-text. For example, SOTA LLMs really suck at electronics, especially analog electronics.

Is MiniMax H3 capable of logical / technical reasoning, or is it purely art oriented?

I think this presents a highly fascinating scenario:

1. A vulnerability that presents itself very rarely

2. If it presents itself, it is fatal to the organization

So, few targets are affected, but the outsized effect means that the overall economic impact is not insignificant. How does one keep track of that?

One method is to use a sort of "kirchhoff's law" approach, similar to google page rank. The electrical analogy would be:

- the attacker has a high potential, let's say 1000V. that attacker is the root of a rose tree, going from top to bottom, branching out towards the bottom.

- nodes impossible to exploit present edges with infinite resistance

- nodes that can be exploited present finite resistance (perhaps very low)

- once a node is exploited, its node branches out to other things that now become accessible and may or may not be exploited

- real economic impact presents a connection to ground, measured as 1/($economic impact) ohms resistance

Once you have a structure like that, you can measure the hypothetical current that would be flowing, and measure the severity of such exploits.

Sometimes a single path through a complex system can blow up vital, important parts, and this could be one way to measure it in some sort of objective manner.

Yes - I question the wisdom of that.

Thanks.

I question the wisdom of this limitation in monthly recurring threads. I think it should be special-cased for those specific instances.

One might think that a structure language is really desirable, but in fact, one of the biggest methods of functioning behind intelligence is stupidity. Let me explain: if you only innovate by piecing together lego pieces you already have, you'll be locked into predictable patterns and will plateau at some point. In order to break out of this, we all know, there needs to be an element of randomness. This element needs to be capable of going in the at-the-moment-ostensibly wrong direction, so as to escape the plateau of mediocrity. In gradient descent this is accomplished by turning up temperature. There are however many other layers that do this. Fallible memory - misremembering facts - is one thing. Failing to recognize patterns is another. Linguistic ambiguity is yet another, and that is a really big one (cf Sapir–Whorf hypothesis). It's really important to retain those methods of stupidity in order to be able to achieve true intelligence. There can be no intelligence without stupidity.

I've met countless Juliuses over the years. I kept track of the companies, and the Juliuses. My biggest revelation is that every company that was being in some substantial capacity led by a Julius (either at C level, VP, or high up in management) ended up one of two ways:

1. Shut down or shutting down (e.g. team reduced by > 50% since I've been there)

2. Julius removed, endlessly seeking work, keeps getting fired, and can't find a place to call home

The meteoric rise of the Julius is an exception - sooner or later their lucky streak ends and they face the cliff of adversity, towering above them with no way to climb it - no skills to help him actually do it.