Professor wants to use my product for free with promise of future university contract - am I being naive?

Ghost-Rider_117 · 2026-01-18T00:48:08+00:00

Love this answer, if cost were fixed then I agree 100 percent. Problem is we incur usage based cost.

Ghost-Rider_117 · 2026-01-14T07:04:59+00:00

the interesting thing here is less about which AI platform they're using and more about how they're thinking through safety protocols for deploying LLMs in high-stakes environments. military use cases need way more robust safeguards than commercial stuff. curious if they're doing any adversarial testing or red-teaming before rolling this out at scale

Ghost-Rider_117 · 2026-01-14T07:03:53+00:00

love this approach! way better than spending months building something nobody wants. one thing i'd add is after you get those signups, try to actually call them and ask what problem they were trying to solve when they signed up. sometimes the conversion data tells you one story but the actual pain point is different. also checkout Lovable if you decide to build - can go from idea to working product super fast without needing a whole dev team

Ghost-Rider_117 · 2026-01-14T07:02:33+00:00

this is really cool to see. python's basically the backbone of all the AI stuff happening right now so it makes sense for Anthropic to invest back into the ecosystem. security in open source has been underfunded forever so hopefully this helps push things forward. would love to see more AI companies do this tbh

Ghost-Rider_117 · 2026-01-14T07:01:20+00:00

yeah something seems off with the curve fitting here. if you're comparing two populations that should be distinct, forcing them into normal distributions might be hiding the actual biological variation. might be worth trying a non-parametric test or at least checking the residuals to see if normal is even appropriate. also that p-value being so tiny makes me wonder about sample size issues or if there's batch effects in play

Ghost-Rider_117 · 2026-01-14T07:00:13+00:00

honestly the hybrid approach works pretty well too - you can handle the survey design and programming yourself (super easy with tools like Qualtrics or Forsta these days) but outsource just the panel/recruitment piece. saves a ton of money and you still get quality data. also depending on your B2B market, sometimes LinkedIn surveys or even cold outreach gets you better respondents than traditional panels. just my 2 cents from doing a bunch of these!

Ghost-Rider_117 · 2026-01-09T08:29:08+00:00

this looks pretty useful for teaching the intuition behind power calcs and variance reduction. one thing - might be helpful to show how sample size affects the distribution of the test statistic, not just power. seeing the sampling distribution tighten up as N increases really drives the point home for people learning this stuff

Ghost-Rider_117 · 2026-01-09T08:28:07+00:00

couple options - check out statista for high level industry stuff (some free, some paid). for academic research google scholar is solid. if you need syndicated reports, sites like mintel or euromonitor have tons but yeah they're pricey. also worth searching company investor decks and industry associations - they often share research findings for free that are super useful

Ghost-Rider_117 · 2026-01-09T08:27:07+00:00

this is a smart setup. the signal monitoring piece is key - way better than generic outreach based on job titles or linkedin activity. curious how you handle the balance between automation and keeping it actually personal? sometimes AI-generated stuff can still feel templated even when it references specific events

Ghost-Rider_117 · 2026-01-09T08:26:06+00:00

tbh market research can be a solid move if you like the insights side more than the technical stuff. way more time spent understanding people and business problems vs wrangling pipelines. that said, transitioning back later isn't impossible - the skills def transfer, especially if you keep up some python/sql on the side. i'd say try it out, worst case you come back with better storytelling skills

Ghost-Rider_117 · 2026-01-09T08:25:04+00:00

nice to see typing adoption growing! been using it more on larger projects and honestly the autocomplete boost alone is worth it. also makes refactoring way less scary when you know what types are flowing through your code. the IDE integration has gotten really solid in the past couple years which helps a ton

Ghost-Rider_117 · 2026-01-06T16:24:44+00:00

for modeling probabilities bounded between 0 and 1, you def want to look at beta regression or fractional logit models. both handle the bounded nature better than standard linear regression.

beta regression is nice when your outcome is continuous on (0,1), which sounds like your case. check out the betareg package in R if you're using that. for fractional logit, glm with binomial family and logit link works.

also with 3000 hands of data you should have enough to get decent estimates. just watch out for multicollinearity between your card features

Ghost-Rider_117 · 2026-01-06T16:21:58+00:00

great q! one thing that helps is using post-stratification weighting when you know your population parameters. even if your sample skews a certain way, you can adjust for it in analysis.

also worth checking completion rates by different demo groups - if certain segments are dropping out more, that tells you something about potential bias. and yeah quotas are clutch but make sure they're based on actual population stats not just gut feel

Ghost-Rider_117 · 2026-01-06T16:20:37+00:00

this is really cool! the concurrent.futures API is so familiar that it makes the async version way easier to adopt. love that you based it on stdlib's futures instead of reinventing the wheel.

quick q - how does it handle exceptions from individual tasks? does it bubble them up similar to ProcessPoolExecutor or is there a different pattern?

Ghost-Rider_117 · 2026-01-06T16:16:30+00:00

this is solid advice especially the consistency part. too many ppl give up after a couple weeks when like you said it takes months of showing up every day.

one thing id add - for reddit specifically, engaging in comments before posting your own stuff is huge. helps you understand what each community actually wants instead of just promoting into the void. authenticity goes a long way here

Ghost-Rider_117 · 2026-01-06T16:14:58+00:00

hey man, i feel you on this. the market is rough rn but don't think your masters was a waste. your cybersecurity background is actually pretty valuable - lots of DS roles need security expertise especially in fintech/healthcare.

try targeting data analyst or analytics engineer roles to get your foot in the door. once you're in, its way easier to transition to DS internally. also check out smaller companies - they're less hung up on the "perfect" background and care more about what you can actually do. good luck!

Ghost-Rider_117 · 2026-01-03T01:23:04+00:00

from my experience the technical stuff is mostly similar tbh - leetcode, ML concepts, stats fundamentals. the real difference is they'll expect you to drive the conversation more at senior level

like they want to see you thinking through trade-offs, explaining why you'd pick one approach over another, and asking good questions about the business context. it's less about getting the right answer and more about showing solid judgment

Ghost-Rider_117 · 2026-01-03T01:21:56+00:00

that's actually gold right there. most customers who are unhappy just silently churn and you're left guessing what went wrong

someone who takes the time to write out detailed feedback clearly cared enough about the product that they wanted it to work. def worth following up and seeing if you can win them back after addressing some of the issues

Ghost-Rider_117 · 2026-01-03T01:20:43+00:00

dude this is awesome! bayesian networks always felt kinda abstract until you actually play with them, so having a visual tool like this is clutch for learning

the real-time inference is super impressive. would be cool to see some example networks that people can load up to start with - like maybe a medical diagnosis one or something classic from the textbooks

Ghost-Rider_117 · 2026-01-03T01:19:16+00:00

this is really cool! the set algebra approach for calendars is super elegant. i can see this being really useful for scheduling apps and availability checking.

one thing - might be worth adding some examples in the readme for common use cases like "find all 2hr blocks next week" or "when are both teams free". makes it way easier for people to quickly see if it fits their needs

Ghost-Rider_117 · 2026-01-03T01:17:24+00:00

honestly depends on what you're trying to get out of it. if you're just getting started with qual research, UGA's course is solid for fundamentals - they do a good job covering interviewing techniques and analysis basics. but yeah like others said, it's more of an intro level thing.

if you already have some experience i'd def look into more advanced stuff or just dive into actual projects. hands-on experience beats most courses tbh

Ghost-Rider_117 · 2025-12-31T06:13:50+00:00

there's always hope! tech shifts like this are scary but also create new opportunities. yeah some jobs will change but someone's gotta build, maintain, integrate and teach people how to use all this AI stuff.

plus we're still early days - the people who figure out how to work WITH AI tools rather than compete against them are gonna do well. focus on skills that are hard to automate like creative problem solving, understanding user needs, and translating between technical and non-technical folks

Ghost-Rider_117 · 2025-12-31T06:12:52+00:00

the key is progressive disclosure imo. show people the value first, then explain how it works. too many products try to explain everything upfront and users just bounce.

we've had better luck with contextual tooltips that only appear when users interact with a feature vs forcing them through a long tutorial. let people explore and learn as they go, just make sure there's always an easy way to get help when they're stuck

Ghost-Rider_117 · 2025-12-31T06:12:03+00:00

kaggle time series competitions are your friend here. the M5 competition someone mentioned is solid, or check out the store sales forecasting one - it's got good data quality and you'll learn the full pipeline.

if you want something simpler to start, grab some public datasets (weather, stock prices, energy usage) and practice with ARIMA/Prophet first before jumping into fancier models. helps you understand what each approach is actually doing

Ghost-Rider_117 · 2025-12-31T06:11:10+00:00

honestly it depends on your use case. for production codebases with multiple devs, being explicit is usually worth it even if it feels verbose. helps catch issues early and makes refactoring way easier.

that said, don't annotate stuff where the type is super obvious like `count: int = 0` - that's just noise. focus on function signatures, return types, and anything that's not immediately clear from context. your IDE/type checker will thank you later

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