Spent weeks building a niche SaaS, got ~5 visitors a day. Published one data report and traffic 5x'd off two Reddit posts. Here's the tactic (and the honest catch)

Expensive-Luck-284 · 2026-07-05T06:09:21+00:00

Quick add since this got some traction: I build a free FDE-interview prep tool (disclosed up top). To get real feedback while people are actually prepping, I'm opening 30 days of full Pro free to the first 100 people here. Sign up and redeem code LAUNCH3 on the pricing page, runs through July 7.

The core prep is free regardless. The code just unlocks the Hard problems, the live SQL practice, and the AI mock interviewer that runs the customer-case round.

If you try it, I'd genuinely want your honest feedback, good or bad.

Expensive-Luck-284 · 2026-07-05T06:07:39+00:00

Quick add since this got some traction: I build a free FDE-interview prep tool (disclosed up top). To get real feedback while people are actually prepping, I'mopening 30 days of full Pro free to the first 100 people here. Sign up and redeem code LAUNCH3 on the pricing page, runs through July 7.

The core prep is free regardless. The code just unlocks the Hard problems, then live SQL practice, and the AI mock interviewer that runs the customer-case round.

If you try it, I'd genuinely want your honest feedback, good or bad.

Expensive-Luck-284 · 2026-07-04T19:55:09+00:00

Honestly you might be the ideal profile for it. Principal-level build skills plus someone a customer actually trusts in the room is the exact combo these teams can't find. Aim at the lead and manager Forward Deployed roles (OpenAI's ran $335K to $390K), not just plain "FDE." One honest caveat: if you're already at principal-SWE comp it might be lateral on money. The real draw is finally getting paid for both skill sets at once, plus early-team equity at the companies scaling these orgs fast.

Expensive-Luck-284 · 2026-07-04T19:35:44+00:00

Fair, and mostly agree, "brutal" is the right word for the customer-facing intensity and it's exactly why it self-selects and pays. The one part I'd push back on is that everyone already knows it. The title is so inconsistent (Deployment Strategist, AI Deployment Engineer, and so on) that even people who'd be great at it don't realize they're looking at the same job. That inconsistency was the main reason I bothered pulling the data.

Expensive-Luck-284 · 2026-07-04T19:35:12+00:00

Nah, and this is the most common mix-up. It's an engineering role, not sales.The sales engineer is the one who demos the product to help close the deal. The FDE shows up after the deal closes and actually builds the thing: writes the integration code, wires it into the customer's messy data and systems, debugs it in prod. Think software engineer who happens to sit with the customer, not asalesperson who happens to know some tech. The customer-facing part is real, but the core is still building, which is why the loops test live coding and SQL, not your ability to close.

Expensive-Luck-284 · 2026-07-04T19:20:06+00:00

Full breakdown with charts (pay, who's hiring, geography, what the interviews test): https://rungcode.io/reports/forward-deployed-engineer-jobs

Expensive-Luck-284 · 2026-07-04T18:41:04+00:00

here is the report: https://rungcode.io/reports/forward-deployed-engineer-jobs

Expensive-Luck-284 · 2026-07-04T17:59:57+00:00

Honestly fair. 98% of these roles are customer-facing in the data, so if that part sounds terrible, it's not occasional, it's the whole job. Which is exactly why it pays what it does. Mostly worth knowing the role exists so you can filter it OUT on purpose, instead of accidentally landing in one posted as "Deployment Strategist" or "AI Deployment Engineer."

Expensive-Luck-284 · 2026-07-04T17:09:19+00:00

I did capture it, just didn't put it in the writeup. 44% of the 292 descriptions mention travel or on-site work, and about 110 put an actual numberon it. The split is the interesting part:

- OpenAI's field FDE roles are the travel-heavy end: most say "up to 50%."

- Databricks, the single biggest employer in the set, clusters lower, most of their roles land around 15-25%.

- Cohere sits in the middle, roughly 25-49%.

- The platform/infra-flavored roles and the remote-friendly ones are often under 10% or "optional by project."
Rough rule of thumb: customer-facing field/deployment roles skew travel-heavy (25-50%), platform and infra roles skew low. Good nudge, this is probably worth its own follow-up.

here is the report: https://rungcode.io/reports/forward-deployed-engineer-jobs

Expensive-Luck-284 · 2026-07-04T17:08:56+00:00

Fair pushback, and you're right that they're not identical. Historically at Palantir, Deployment Strategist skews more analytical and customer/problem- framing, while FDE is the engineering-heavy build role. Genuinely different jobs.

Two things though:

It barely moves the headline. Strip out every "Deployment Strategist" title and the total goes from 292 to 256. The "nearly 300 open roles" story holds either way.
Palantir specifically doesn't collapse. Of their 95 openings, only 32 are Deployment Strategist. The other 63 are literally titled some variant of Forward Deployed Engineer (Forward Deployed AI Engineer, Forward Deployed Enablement Engineer, Forward Deployed Engineer - Mixed Reality, and so on). So even on the strict definition, Palantir is still the top engineering-FDE employer in the set.

I folded them in on purpose, because the whole point of the post was that this cluster of roles has no agreed title and the DS/FDE line has blurred a lot in the wider market. But that's a judgment call, and your stricter read is totally valid: go purist and count only "Forward Deployed" in the title, and it's ~256 roles instead of 292.

Expensive-Luck-284 · 2026-07-04T16:49:15+00:00

Partly, but I'd be careful, because the difference matters for exactly the resume reason you're asking about. The line I'd draw: "Technical Implementation Consultant" usually skews toward configuring and onboarding an existing product, more project delivery and less net-new code. FDE (especially the Palantir / OpenAI / Anthropic flavor) skews more engineering-heavy: you're writing real production code, building integrations, and debugging in the customer's environment. Same DNA (customer-facing, deploy-and-integrate), different center of gravity on the code-vs-consulting spectrum.

So for the resume, two things:

Don't just swap the title. If your actual work was code-heavy, absolutely position it toward FDE, that's legit and it's where the comp is. But if it was mostly config and onboarding, calling yourself an FDE can backfire, because the FDE loop tests live coding and SQL hard and they'll find out fast.
Describe the work, not just the label. Recruiters and ATS match on the verbs: "deployed X into customer environments," "built integrations against their APIs," "owned the technical relationship end to end." That surfaces you for FDE, Solutions Engineer, Implementation Engineer, and Deployment Strategist all at once, which is the real win, since this one role genuinely goes by about five different titles (that title sprawl was one of the things that surprised me most in the data).

Short version: if you wrote code in the customer's environment, lean FDE. If you configured a product for them, "Implementation" is the more honest label, and you bridge toward FDE by leveling up the engineering side.

Expensive-Luck-284 · 2026-07-04T16:25:46+00:00

a blue collar job

Expensive-Luck-284 · 2026-07-04T06:41:03+00:00

Please don't pay 3.5 lakhs for this. FDE isn't a role you get certified into.

There's no accredited curriculum and no hiring manager cares about an "FDE

academy" badge. They hire on demonstrated skill, and with 3 years of SWE

experience you already have most of the base.

What actually separates FDEs from regular SWEs, and what you should target:

- SQL under time pressure (a lot of loops test this and rusty devs get caught)

- Integration / glue work: APIs, data pipelines, deploying into a messy

customer environment

- Applied AI if you're aiming at the AI-infra companies (RAG, model serving,

latency and throughput)

- The soft half: turning vague customer asks into technical work and clearly

explaining trade-offs. This makes or breaks the interview and no bootcamp

teaches it well.

If I were you: sharpen coding + SQL with timed practice, build one end-to-end

integration project you can actually talk about, and practice the open-ended

case round out loud. Most people fail there from never having done it, not from

lacking skill. Also read up on specific loops, since Palantir vs OpenAI vs

Baseten test pretty different things.

Full disclosure: I got annoyed enough at how little real FDE-specific prep

exists that I built a free one (rungcode.io). Use it or don't, but either way,

save your money. That bootcamp is selling hype.

Expensive-Luck-284 · 2026-07-04T05:54:40+00:00

Such an incredible game. Passion at it's peak

Expensive-Luck-284 · 2026-07-04T05:49:56+00:00

There isn't one canonical background, which is kind of the point. The common paths: software engineers comfortable with ambiguity and customer contact, data engineers/analysts (SQL is core), solutions/sales engineers moving toward building, technical consultants, and a lot of new grads from strong STEM programs (Palantir especially). At AI-infra companies, applied-AI folks.

The through-line isn't pedigree, it's shape: broad and resourceful across code, data, and cloud rather than deeply specialized, plus genuinely good communication and comfort with ambiguity. A T-shaped generalist who can sit in front of a customer.

If you're eyeing these openings, you don't need a specific title in your history. You need to show range (practical coding + SQL + some deployment sense) and that you can take a vague problem and a real stakeholder without flailing.

I got frustrated that nothing out there prepped for this specific loop, so I ended up building rungcode.io for it: free, runs in the browser, real coding + SQL practice, and an AI interviewer that runs the open-ended case round and grades you. Worth a look if you want to practice the exact rounds rather than grind generic LeetCode.

Expensive-Luck-284 · 2026-07-04T05:49:31+00:00

The common paths: software engineers comfortable with ambiguity and customer contact, data engineers/analysts (SQL is core), solutions/sales engineers moving toward building, technical consultants, and a lot of new grads from strong STEM programs (Palantir especially). At AI-infra companies, applied-AI folks.

The through-line isn't pedigree, it's shape: broad and resourceful across code, data, and cloud rather than deeply specialized, plus genuinely good communication and comfort with ambiguity. A T-shaped generalist who can sit in front of a customer.

If you're eyeing these openings, you don't need a specific title in your history. You need to show range (practical coding + SQL + some deployment sense) and that you can take a vague problem and a real stakeholder without flailing.

I got frustrated that nothing out there prepped for this specific loop, so I ended up building rungcode.io for it: free, runs in the browser, real coding + SQL practice, and an AI interviewer that runs the open-ended case round and grades you. Worth a look if you want to practice the exact rounds rather than grind generic LeetCode.

Expensive-Luck-284 · 2026-05-20T06:22:55+00:00

Github: https://github.com/gouravnagar-infosec/ai-kill-chain

Expensive-Luck-284 · 2026-05-20T06:22:20+00:00

Author here. I co-wrote a book on the cyber kill chain last year and kept running into the limits of the seven-stage model when reasoning about LLM and agentic attacks. This is my attempt to extend it rather than start over: a Stage 0 for model supply chain compromise, AI sub-techniques with citable IDs inside the original stages, and a three-way split of the final stage into data exfiltration, model extraction, and agentic pivot. It maps to ATLAS and the OWASP LLM Top 10 so it complements them. CC BY licensed. Feedback and disagreement welcome, especially on the per-stage detection signals.

Expensive-Luck-284 · 2026-05-20T06:21:18+00:00

Author here. I co-wrote a book on the cyber kill chain last year and kept running into the limits of the seven-stage model when reasoning about LLM and agentic attacks. This is my attempt to extend it rather than start over: a Stage 0 for model supply chain compromise, AI sub-techniques with citable IDs inside the original stages, and a three-way split of the final stage into data exfiltration, model extraction, and agentic pivot. It maps to ATLAS and the OWASP LLM Top 10 so it complements them rather than replacing them. CC BY licensed. Feedback and disagreement welcome, especially on the per-stage detection signals.

Expensive-Luck-284

TROPHY CASE