misiti3780

is anyone doing this ?

I only read non-fiction, maybe my situation is unique.

Ryan Holiday is a stoic influencer who happens to have written a lot books, most of them not good.

I dont know you about you, but the reason I read is to learn and retain -- many studies havnt shown audiobooks are much worse than reading for retention.

Example: https://www.researchgate.net/publication/247504935_They_Hear...

same, i use the voice feature every day at the gym, talk to it for 1 hour, and then make anki cards based on what it has taught me, total game changer.

Doesnt an NVIDA Spark solve most of these problems? (at 5K)

why is SVD so important? i know it's important in general ML but seems minor for LLMs (LoRA?)

those are just people with bad manners.

sure, because you'd be offering me an idiotic arbitrage. your sentence doesnt prove it's useful.

what percentage of BTC transactions do you think are being used for international cash transfer in 2026 ?

what is your point?

i know exactly zero people using any of these markets, and 99% of the other people here can probably say the same.

neither of them are sane. BTC is useless, unless your trying to buy child porn, buy illicit drugs on the internet, or someone who bought it before the value exploded. eventually, the world will come around and it will go to zero, if quantum doesnt kill it first. im looking forward to that day.

anthropic's products are much better, anthropic and google will win the AI race, we wont even be talking about OpenAI in a few years when they run out of $ or compute and get acquired. They will be remembered by their Wikipedia page(s).

if i had to short 1 of the 3, it would be OpenAI

so how do stripe employees get liquidity? can anyone sell their secondary shares?

really - what am i missing?

go away. this isnt reddit

Anti-elon fud incoming....

you going to document this exchange in your diary ?

5/6/2026

Drank a cup of coffee; Made snarky remark to HN user.

Who keeps a personal diary in 2026?

Local governments have obvious incentives to encourage building, but the state of Florida itself does subsidize flood and hurricane insurance.

If you own a house or building in Florida and have a mortgage, you're required to carry it. Here's how a policy gets priced:

You go to a retail broker with your info. They pass it to a wholesaler, who puts the submission out into the market for quotes. Any carrier or MGA that wants the business prices the CAT and AOP (non-CAT) portions separately. Actuaries build models for the AOP side, while Verisk and Moody's model the CAT portion. Those two numbers get added together, plus some fees — and that's your annual premium.

From there, the insurers buy reinsurance on their portfolios. The reinsurers run those same models, do their magic, and come up with their own price.

Just an example, because no major hurricanes have hit the south east in a while, premiums are down 30% right now. All of the insurance companies are getting squeezed.

it has more character that SF

It's two monte carlo models that get refreshed every few years.

stick to software, you have no career in comedy.

i work in insure tech, in the E&S space, which is where all of the flood and wind polices gets placed. Actuaries have nothing to do with it --- the cost of hurricane insurance comes from Moody's RMS and Verisk AIR, the only two CAT models the carriers and re-insurance companies use. Actuaries price the non-cat risk.

what are you talking about, miami is actively investing in fixing this problem

https://www.nbcmiami.com/investigations/miami-beach-resilien...

Miami is not Cleveland, and SF sucks.

Senior ML Engineer | Ping Data Intelligence | REMOTE or ONSITE (Miami, FL) | Full-Time | https://www.pingintel.com Ping Data Intelligence is a dynamic startup based in Miami, FL, revolutionizing the property insurance sector with cutting-edge web technologies and ML-powered tools. Despite rapid growth, we retain the stability of a self-funded, profitable company. Role Overview: As a Senior ML Engineer at Ping, you will sit at the intersection of research and data engineering — designing, training, and deploying machine learning models that power our property attribute classification, document extraction, and geospatial products. This is a hands-on role for someone who can read a paper in the morning, prototype an idea by lunch, and ship it to production by end of week. You will own ML systems end-to-end: from data pipeline design and feature engineering through model training, evaluation, and production deployment. The role is remote-friendly, with the option to work onsite at our Miami, FL office.

Responsibilities: Design, train, fine-tune, and evaluate ML models (LLMs, classification, sequence models) for property insurance. Build and maintain robust data pipelines that feed training, evaluation, and inference workloads at scale. Develop rigorous evaluation frameworks — establish metrics, build rater alignment processes, and apply statistical methods to determine when a candidate model is genuinely better than production. Run controlled experiments, ablations, and A/B tests; communicate findings clearly with appropriate uncertainty quantification. Deploy models to production and own their performance, drift monitoring, and iteration cycles. Collaborate with the engineering team to integrate ML services into our backend (Django/Python) and frontend (React/TypeScript) products. Stay current with the ML literature and translate relevant advances into practical improvements for our products.

Required:

PhD in Statistics, Machine Learning, Computer Science, Applied Mathematics, or a closely related quantitative field (or equivalent research experience with a strong publication or production track record). Strong foundation in statistics — experimental design, hypothesis testing, Bayesian methods, and uncertainty quantification. Minimum 5 years of combined research and applied ML experience, with a proven track record of shipping models to production. Deep proficiency in Python and the modern ML stack (PyTorch, Hugging Face, scikit-learn, pandas, NumPy). Hands-on experience with LLMs, including fine-tuning (LoRA/QLoRA, full fine-tunes), prompt engineering, and evaluation. Strong data engineering skills — comfort building reliable pipelines over messy real-world data, working with SQL and columnar formats. Excellent debugging, problem-solving, and written communication skills.

Why Join Ping: Work directly on ML systems that touch real production traffic from day one. Collaborate with a small, senior team of insurance and tech veterans building products that are reshaping the property insurance industry. Enjoy the autonomy of a research role with the impact of an applied one — your models will be in production, used by real customers, and you will see the results immediately. Please apply at jobs@pingintel.com

I have a large open source project and noticed the number of LLM generate PR is making it unmanageable. Every two weeks, I go in, kill all of them and when someone complains or asks why, I realize it was a real person and then I merge it.

is anyone else seeing this / fixed this problem ?