panabee
Joined 15 years ago
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Comment · 1 month ago
I think a lot about fixing broken VC-founder dynamics, and this post by Marc Pincus (https://x.com/markpinc/status/2089572143344599079) crystallized one plank of the platform.
The principle is simple. VCs are soccer stars, but founders play basketball.
Basketball and soccer share much in common. For instance, both involve teams dribbling, passing, and shooting a round ball. But successful abilities and traits in one may not translate to the other.
Think of each profession as a different sport. Venture, growth, and value investing all differ, and all differ from founding.
VCs are all driven and highly intelligent, but so are lawyers, bankers, and consultants. Talent isn't the issue.
Capital confers authority, but not expertise.
Based on resume alone, 80% of VCs would not earn board seats at their portfolio companies. Their experience and skills, much like consultants and value investors, were honed on a field different from the basketball arena where founders compete.
Here's a quick heuristic: sans capital, would you still hire the VC to sit on the board? If yes, wonderful.
This is no slight and works in reverse: 80% of founders would not earn the right to direct VC investments.
To clarify, great VCs are absolutely worth the premium and can reshape a startup's trajectory as all great advisors can. If you find a great VC, do not haggle. Strike a deal, and return to building.
The greatest VCs exhibit the same pattern, understanding their role on the startup team as advisors, not alphas. They are often understated and work tirelessly on behalf of their clients.
The worst VCs exhibit the inverse pattern and imagine themselves as the alpha, not appreciating how a talented peer could have replaced them without changing the exit. They are loud on social media and assume accomplishments from finance or FAANG map to the startup arena. These VCs should run funds on Wall Street, not advise founders in Silicon Valley.
How do we highlight good VCs without attacking bad ones? Many good VCs, as with many good advisors, prefer subdued profiles and dislike self-promotion. This is the challenge.
The original idea was to flag bad VCs, but such a system grants founders too much power to levy unjust charges and settle feuds.
After all, many disputes are legitimate and reflect bad founders. Founders, like all professionals, sit on a spectrum. The surge of big money has spawned plenty of bad ones who, sadly enough, do not represent the best of tech and innovation but rather greed and self-aggrandizement.
The Pincus post sparked a cleaner iteration.
The proposal is a public page/spreadsheet where only founders can post, only after an outcome or a certain number of years, and only with affirmative assessments. Nothing negative, nothing anonymous. Posts must certify no quid pro quo or other VC prodding.
Topics could include responsiveness, support during dark days, absence of alpha syndrome, and other key considerations.
Over time, good VCs should reveal a clear pattern and attract new founders: founders trusting them again with repeat business and consistent high marks across the portfolio, not only unicorns. Arguably, the strongest signal will radiate from the worst outcomes.
Critically, this system won't incite mob justice or expose VCs to unfair accusations, but can still suggest who to diligence more deeply.
The purpose is to spotlight good VCs who advance innovation and startups over time, letting their body of work rise to the top and garner proper recognition.
Of course, it penalizes newer investors and is vulnerable to gaming like any system, but it plugs a small gap. Founders want to find good investors based on data, but good investors dislike boasting.
Comment · 2 months ago
The overall dataset was very large (8,972,221 deaths), but only 1,348 ambulance drivers were included, and just 10 had Alzheimer’s disease as the underlying cause of death.
Such a small number of outcomes matter because statistical precision depends on the number of relevant events. With only 10 Alzheimer’s deaths, even a small change in the count could substantially alter the estimated association.
Taxi drivers provided more robust data, with 171 Alzheimer’s deaths among 16,658 decedents, but death-certificate Alzheimer’s mortality may not reflect how often taxi drivers develop the disease. Differences in diagnosis or cause-of-death reporting across occupations could influence the observed association.
The large overall sample does not eliminate uncertainty in the much smaller occupational subgroups, particularly among ambulance drivers.
Nonetheless, this is an interesting study and merits further investigation.
Comment · 5 months ago
How to notify you once v0 is ready -- just comment here?
Comment · 5 months ago
How to notify you once v0 is ready -- just comment here?
Comment · 6 months ago
Will aim to ground the framework -- Cancer Mini-101 for Engineers -- in personal use cases. I hope it will be helpful for you.
Comment · 6 months ago
Great question. The bar for proof in biomedicine is naturally high. I only shared facts because so much is unknown.
If you can find a lab exploring the question, maybe you can support them by helping to raise money for experiments.
As a fun intellectual exercise, dive into the topic and challenge yourself to think about what kind of experiments could shed more light on the subject.
Comment · 6 months ago
I will aim to put together a Cancer 101 for engineers, not sure how to share. Maybe I'll post here or will post to our biomedical GitHub so it can evolve over time?
Comment · 6 months ago
For people questioning why to involve GPT and AI assistants:
GPT and AI assistants cannot be fully trusted, but they can personalize learning.
The chief challenge for the framework/handbook will be resolving how to personalize guidance into cancer research while grounding knowledge in trustworthy sources.
For instance, the framework will anchor abstract, dry biological concepts in personally meaningful tracks. Imagine someone you care about is battling lung cancer — the framework may orient learning around the molecular drivers and signaling pathways at play, or perhaps how to explore the treatment landscape while respecting established practices. If you're fortunate enough to not know someone affected by cancer, GPT can help find a personal angle.
The sheer depth of information is staggering. People devote entire careers to niche specialities, and these experts still don't know everything in their niche because our understanding of human biology and disease is constantly evolving. Adapting depth should also depend on the individual and can only be achieved via AI. Static curriculums do not maximize learning in 2026.
Comment · 6 months ago
On second thought, I will publish something regardless of interest.
It will be an "Cancer for Engineers" framework, delivered via free, open-source Custom GPTs and Claude Skills. (Gemini gems are less reliable in our experience.)
The goal: to ease engineers into cancer via AI personalized introductory curriculums with varying time commitments to enable deeper independent investigation or fast exits if interest wanes: 4 hours, 8 hours, 12 hours.
Basically 1-3 hours per week for a month.
The reason I think some engineers may find cancer interesting, aside from the societal impact:
The human body is like a complex operating system. Cancer is a severe runtime error. Tracing root causes -- like genetic mutations, signaling errors, or immune evasion -- has many parallels to diagnosing system failures.
BTW if anyone from Kaggle/GDM is reading this, we are having issues submitting a benchmark paper for NeurIPS based on the Kaggle Benchmark.
Google models seem to get a different scheduling priority, ironically, enough and take >20 hours to complete a benchmark task that other models like Opus 4.6 finish in <1 hour -- same code path, same task. Would love help if possible since the abstract deadline is Monday (It's last minute because we didn't originally plan to submit this, but someone suggested it.)
Comment · 6 months ago
Here are more fascinating facts about caffeine and cancer.
Caffeine affects the immune system via at least two opposing mechanisms.
Mechanism 1: A2A receptor antagonism (immunostimulatory) Tumors and damaged tissues release adenosine, which engages the A2A receptor on immune cells and signals them to stand down. Caffeine antagonizes (i.e., blocks) this receptor.
Mechanism 2: Raising intracellular cAMP (immunosuppressive) Caffeine also inhibits phosphodiesterase, the enzyme that hydrolyzes (i.e., breaks down) cAMP. cAMP accumulates inside immune cells, which acts as a "calm down" signal.
Note: both mechanisms are dose-dependent. At dietary caffeine levels, A2A antagonism likely dominates, whereas PDE inhibition is weak and mainly relevant at higher concentrations. However, the net immune effect in the tumor microenvironment remains unproven.
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If you would like to learn more, I can outline a framework for technical folks to ease in and become more informed on cancer. Gaps abound. The more people who understand cancer, the faster we get to cures. Moreover, personalized cancer treatment is the obvious future. Knowledge acquired now may pay off later (but hopefully not needed).
X.com/panabee
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Five years of researching cancer from the outside and where we can go from here: https://x.com/panabee/status/2096357777434374638