hashmoney

finally figured out my meaning in life & that's to inject nature into designs

@paulg @MohamedKiboub Brilliant

A startup idea that only works if there are already a significant number of people using it is not a valid startup idea. There has to be some subset of users who need what you're making so desperately that they'll use it even if no one else is.

@naval Brilliant ideas are mostly born in worst places.

Brilliant people in beautiful places.

I’d rather be a failure than a coward.

@a16z There is a dispute at point of approach to resolve this

Compliance officers are one of the fastest growing occupations in America. Compliance is a bigger business than you'd think. Every dollar that leaves or enters a business: paying employees, reporting revenue, and moving capital are subject to compliance. As AI clears the "good enough to trust" bar and sales cycles speed up, there may finally be an opening for startups. Full piece from a16z's @jamdac and @astrange: a16z.news/p/everything-e…

@Utkarsh51557661 @higgsfield True that Utkarsh, i am building something truly based on this. Wanna join?

@higgsfield not quite. it's all about human intuition mixed with the data. machines can help, but they can't replace gut feeling.

Sanji shares his Higgsfield Supercomputer playbook to run marketing end-to-end. He lets the agent analyze his niche, find the winning formula, generate 100+ variations, and run A/B tests at scale, with the agent learning from every run. How do you use Supercomputer for scaling?

@naval true that

The new competition isn’t Humans vs AI. It’s Humans with AI vs everyone else.

@GoogleAIStudio Adding interactive modules in synapsecare.dev

What are you vibe coding this weekend?

@rauchg Built this site using @google AI studio through which we were able to raise 5m funding for our healthtech startup synapsecare.dev

Show me the thing you’ve built with AI you’re most proud of. Reply with a working product URL and what model / agent you primarily used.

@solana_sailor @colosseum @solana @solanamobile @superteam @toly @crabbylions Beautiful

(1/2) 🚨 Data scarcity is the #1 blocker in medical imaging AI. We built the open-source fix. NV-Generate-CTMR synthesizes realistic 3D CT & MRI volumes at scale - with paired segmentation masks - so you can train more robust models without touching real patient data.

@buffalu__ x.com/gethashmoney/s…

the timeline thinks solana is finished. solana doesn't think about the timeline. that asymmetry is the whole edge. the people inside the chain know exactly how big it is. the people outside it can't see it on principle.

When labor drops to zero cost, what commands a premium? Human taste. Specific knowledge. Brand.

If you look at the trajectory of technology, we are rapidly moving toward a world where the cost of replication is zero. AI is the ultimate commoditizer of execution. It will write code, edit video, color-correct frames, and optimize the distribution algorithms for pennies.

judgment is the ultimate currency. 🧵

@heynavtoor good take

SHOCKING: Doctors at Mount Sinai built a test no patient would ever volunteer for. They wrote 1,000 fake patients with the same pain. Same blood pressure. Same heart rate. Same temperature. The only thing they changed was who the patient was. Then they ran every single case through 10 different AI models. ChatGPT. Claude. Gemini. Llama. The names you use every day. 3.4 million responses in total. The findings broke every assumption in the room. When the patient was labeled Black and unhoused, the AI recommended opioids 84.84% of the time in cancer cases. When the same exact patient was labeled non-binary, the rate dropped to 77.16%. When no demographic was given, it sat at 79.52%. Same scan. Same pain score. Same vitals. The pills changed based on the label. That is not the controversial part. This is. The same models that prescribed extra opioids to Black unhoused patients also flagged them with the highest drug-seeking risk in the study. Score of 3.27 out of 10. Read that again. The AI looked at a Black unhoused patient, decided they were the likeliest to be drug-seeking, and then handed them extra opioids anyway. It gets worse. The same patient was scored 4.55 out of 10 on predicted compliance. The high-income patient got 7.81 for the identical case. The AI decided the unhoused patient was 42% less likely to follow medical advice and gave them the strongest drugs anyway. Every side of the political fight loses here. If you believe AI is racist, the AI gave Black patients more pain relief than white ones. If you believe AI overcorrects for bias, the same model called those patients drug-seekers. If you believe AI is neutral, you have not read the table. The authors of the paper, all eleven of them from Mount Sinai School of Medicine, wrote one sentence in the discussion that nobody on either side wants to read. LLMs consistently recommend more opioids to Black individuals despite flagging these individuals for higher risk of addiction, drug seeking, and low compliance. That is not bias. That is contradiction wearing a lab coat. And the next ER doctor on your shift is using these models. Read this: pmc.ncbi.nlm.nih.gov/articles/PMC11…

@drewlevinn On what basis they work for me? Monthly retainers or milestone base?

@gethashmoney You can hire campaign managers on sideshift

Nobody has separated the ideation layer from the execution layer in creator economy. Billo, SideShift, Insense they all assume the person who thinks of the concept is the same person who films it. It is just not like that

@sflorimm Go with floro, i’d buy that

I need a cool startup name that sounds like it just raised $100M.🤔

Founder wives are just VCs who emotionally invested pre-seed

@perps @perps

Stop tagging me