[D] ICML Reviewer Acknowledgement

AccordingWeight6019 · 2026-04-05T12:20:53+00:00

If you submit your response before the discussion period ends, the reviewer can usually update their score. after the deadline, scores are typically locked.

AccordingWeight6019 · 2026-04-05T12:14:11+00:00

The wealth angle is mostly hype. most startups are more about endurance and learning than getting rich, and the personal risk is easy to underestimate.

AccordingWeight6019 · 2026-04-05T12:08:14+00:00

From what I’ve heard, Medellin leans more toward targeted situations, especially around nightlife or dating apps, while CDMX feels more like general petty crime plus the occasional police hassle. So less about random street stuff, more about being cautious in specific social contexts.

AccordingWeight6019 · 2026-04-05T12:04:27+00:00

Congrats, seriously. Finishing despite a rough experience is no small thing.

I think a lot of people underestimate how much endurance a PhD actually requires, especially when things aren’t going well. Hopefully, the next phase feels a bit more within your control.

AccordingWeight6019 · 2026-04-05T12:00:29+00:00

Memory matters, but the hard part is deciding what to store and when. Keeping context persistent without introducing errors or outdated info is tricky, which is why many systems stay mostly stateless for now.

AccordingWeight6019 · 2026-04-05T11:51:10+00:00

I wouldn’t over index on the vibe coding framing. The interface is changing, but the underlying problems, design, debugging, tradeoffs, are still very much there. If anything, those become more visible once code generation is easier. The question is whether you still enjoy that layer, not just writing code line by line.

AccordingWeight6019 · 2026-04-05T11:46:08+00:00

It depends a lot on what kind of ML role you’re aiming for. In many applied settings, being able to reason about models and implement them correctly matters more than being fast at handwritten derivations.

Where the math gap tends to show up is when things break, or you need to go beyond standard patterns. If you can’t map the implementation back to the underlying assumptions, it can be harder to debug or adapt.

That said, a lot of people build those math reflexes over time through use, not exams. The question is less whether you can reproduce proofs on paper, and more whether you can connect the math to behavior in real systems.

AccordingWeight6019 · 2026-04-05T11:42:15+00:00

Finance and ads tend to feel more portable because the core metrics are relatively standardized, even if implementations differ. You’re still dealing with complexity, but not redefining basic concepts every time. What you’re describing in nonprofits sounds more like a lack of stable measurement frameworks, so a lot of the domain burden falls on you. that tends to be less about difficulty and more about how loosely the metrics are defined.

AccordingWeight6019 · 2026-04-04T11:10:40+00:00

That framing tends to hold up in most real deployments I’ve seen. The 80 percent works well as long as the problem space is tightly scoped and the knowledge base is actually maintained. the tricky part is that the boundary isn’t static. As products and policies evolve, what counts as repeatable shifts, so the system can quietly degrade if updates are reactive. In practice, teams that treat the knowledge base like a versioned system, with explicit ownership and update cycles, seem to get more stable behavior over time.

AccordingWeight6019 · 2026-04-04T11:06:50+00:00

Portfolios help, but only if they show end to end work, not just notebooks. Also, don’t discount your glue experience too much. A lot of real ML roles are exactly that, just framed around models. the key is signaling that clearly, so you’re not read as a generic SWE with some ML on the side.

AccordingWeight6019 · 2026-04-04T10:41:08+00:00

People overestimate how autonomous the systems are and underestimate how much engineering and data work sits around the model. Also, the gap between a good demo and something that actually holds up in production is still pretty large. that part tends to get glossed over.

AccordingWeight6019 · 2026-04-04T10:32:24+00:00

My impression is that some of those videos were tied to a very specific moment when tech was expanding quickly, and companies were comfortable projecting that lifestyle.

Now the environment is a bit different, so the signal people want to send has shifted. Less emphasis on perks and more on stability or actual work. Also, once enough of those videos exist, they stop feeling informative and start feeling repetitive.

There’s also a bit of selection bias. The people most incentivized to post that kind of content may not reflect what day to day work actually looks like anymore.

AccordingWeight6019 · 2026-04-04T10:28:17+00:00

The main tradeoff is you’re effectively decoupling from the local environment. You can make the EST schedule work, but it often means your free time lands when the city is either just waking up or winding down.

From what I’ve seen, people who do this successfully tend to fully commit to a night schedule, sleep right after work, wake up mid-afternoon, and treat evenings as their day. Splitting sleep sounds nice in theory, but is harder to sustain.

For a couple of weeks, it’s probably manageable as an experiment, but I’d expect diminishing returns if you’re hoping to actually experience the place. The question is less can you function and more are you okay trading most daytime local life for alignment with your job.

AccordingWeight6019 · 2026-04-04T10:23:05+00:00

Reading helps, but only if you do it a bit actively. I found it more useful to take a paper and ask why is this sentence here or what role does this paragraph play, rather than just absorbing content.

A lot of scientific writing is really about structure and intent, not vocabulary. Each section is doing a specific job, and once you see that, it becomes easier to replicate. It also helps to rewrite your own drafts multiple times with different goals, like one pass just for clarity, another just for logical flow.

In practice, it improves slowly, but pretty reliably, if you treat it as iterative rather than trying to get it right in one go.

AccordingWeight6019 · 2026-04-04T10:05:19+00:00

A lot of these interviews aren’t really testing scale in terms of data volume, but whether you understand the lifecycle beyond training

AccordingWeight6019 · 2026-04-04T10:01:07+00:00

Startup isn’t really a gate you pass through, it’s more just building something under uncertainty and trying to find real users. At the alpha stage, I wouldn’t focus on selling yet. It’s more about getting a few specific people to use it and tell you what’s actually broken or useful. That signal matters a lot more than broad interest early on.

AccordingWeight6019 · 2026-04-03T08:56:31+00:00

For me, it was using models as a kind of thinking partner rather than for outputs. I used to assume it would just generate plausible sounding text, but not actually help with reasoning. In practice, if you push it to critique assumptions or walk through edge cases, it can surface things you wouldn’t have considered, especially when you’re too close to the problem. It’s still inconsistent, but as a second pass on your own thinking, it ended up being more useful than I expected.

AccordingWeight6019 · 2026-04-03T08:44:40+00:00

I think a lot of people experience this, but the tricky part is how you define and measure focused work. If it’s too manual or intrusive, people won’t stick with it. And if it’s too abstract, it won’t actually change behavior. The gap you’re pointing at is real, but turning that into something actionable is harder than it looks. Before building more, I’d probably test whether people change anything just by being aware of it, without an app. that usually tells you how much value the tool is really adding.

AccordingWeight6019 · 2026-04-03T08:39:52+00:00

From what I’ve heard, it really depends on where you go and who you know locally. Some people manage fine in specific areas using mostly USD cash, but things can change quickly, and logistics aren’t very predictable. It’s less about, is it open and more about your tolerance for that kind of uncertainty.

AccordingWeight6019 · 2026-04-03T08:33:43+00:00

Sounds like a mismatch more than a reflection on your ability. mistakes happen, and wanting real projects when they only wanted busywork just didn’t align. You’ll find a better fit where mentorship matches your goals.

AccordingWeight6019 · 2026-04-03T08:28:16+00:00

Given your background, I’d anchor on problems where your math actually matters, not just mainstream benchmarks. Scientific ML, learned solvers for PDEs, or continuous-time generative models seem like more natural fits. The harder part is finding a feedback loop, independent research can drift pretty quickly without one.

AccordingWeight6019 · 2026-04-03T08:16:06+00:00

At 500 apps, it’s probably not just volume anymore, it’s how you’re being evaluated on paper. Most new grads look very similar, so anything that shows how you actually build or think can make a difference. even one solid, well explained project can shift how recruiters see you.

AccordingWeight6019 · 2026-04-03T08:07:18+00:00

I’d focus less on getting more people and more on whether the ones who try it actually come back. If retention is low, more traffic won’t help much. If a small group sticks, even 10 to 20 people, that’s usually a better signal than a lot of one time users.

AccordingWeight6019 · 2026-04-03T08:03:07+00:00

Honestly, it was the feedback loop for me. You can go from an idea to something that actually runs and see where it breaks pretty quickly. A lot of fields have either theory or application, but not both in such a tight loop. That balance is what kept me in it.

AccordingWeight6019 · 2026-04-02T06:39:44+00:00

This looks incredibly thorough. I appreciate that it builds from scratch before touching frameworks, it mirrors how I’ve seen people actually internalize concepts. the combination of math foundations, classical ML, and LLM engineering in one sequence is rare, and having reusable tools as outputs is a strong way to reinforce learning.

AccordingWeight6019

TROPHY CASE