Thanks! At first I was using OpenAI's deep research to just give a summary and overall score 1-10, but I realized that could not be iterative and future proof as new evidence comes to light.
So after some thought, I switched to a system of individual evidence gathering and weighting each piece of evidence. I've given the models some basic starting points for types of evidence (for instance a donation has a default weight of 8/10), but have given the models leeway to make relative judgements.
After all evidence is collected, the weights and confidence that the evidence is accurate (usually very high) are put into a formula to derive a final score. No recency bias. The nitty gritty:
-Each row contributes direction × weight × confidence × status_factor, where disputed is cut in half and there is no recency decay.
-All signed contributions are summed into S, and total support mass goes into M.
Final score is 50 + 50 * (S / (M + 4)), clamped to 0-100.
-That +4 prior mass keeps thin but unanimous evidence from producing extreme scores too easily.
-Neutral evidence (direction = 0) doesn’t push the score up or down, but it does increase M, which pulls the result back toward 50.
As for the ladder - I think that is a good idea, but in a controlled manner because of the token cost and potential for abuse.
Maybe you genuinely have no use case for ChatGPT. Maybe you just haven't been creative enough to figure out how to use the tech as it currently is. Forget AGI. What it is capable of right now, for me, and countless others in countless fields of work, already saves more time per day than everything else combined. There's certainly nothing more important than my time. That's a pretty powerful product.
Perhaps people can feel like they have special talents or powers in one field while still feeling apprehensive and anxious when it comes to other facets of life ie. I don't know that success in business equates to confidence on the dance floor.
I'll add to your anecdotal evidence by saying I am a vegetarian and I am interested in this. So somebody does really want this, just not people "around" you.
-Chiefs win the Super Bowl
-Bruins win the Stanley Cup
-Nets win an NBA title
-Yankees win World Series
-People continue to spell the opposite of win, incorrectly, en masse, by “sounding it out”
Generally...he'll have to lower the price on the remaining 3 shares to sell them it looks like as there was only demand for 7 items @ $10. Or he can simply wait until someone values them at $10. Conversely, if someone is willing to buy more than is available, people will probably make more available, just at a higher price.
$10 PAGE RPM is the key here people. Not a great metric to go by. Ad impression RPM is the number to compare. I'd be interested to know the average number of ads/page you need to display to achieve a $10 rpm. At any rate, you can get that on AdSense no problem with very little hassle and net 30 payments.
They offer an excellent API for a very reasonable price and offer it free to people if you're interested in a more comprehensive solution for free. Also, take a look at IEX if you're interested in getting into financial APIs.
Came here to say this. It's great. I use it for my portfolio tracker - https://stockdaddy.io/ - it's quite powerful in terms of breadth of APIs and data available.
https://www.seedinvest.com is really good for this. They are the best at vetting companies and only allow a small amount (something like 3 out of 1,000) companies that apply raise funds through the Reg A (and other various regulations passed in the JOBS Act) exemption and I believe these are now available to accredited and non-accredited investors in some form.
So after some thought, I switched to a system of individual evidence gathering and weighting each piece of evidence. I've given the models some basic starting points for types of evidence (for instance a donation has a default weight of 8/10), but have given the models leeway to make relative judgements.
After all evidence is collected, the weights and confidence that the evidence is accurate (usually very high) are put into a formula to derive a final score. No recency bias. The nitty gritty:
-Each row contributes direction × weight × confidence × status_factor, where disputed is cut in half and there is no recency decay.
-All signed contributions are summed into S, and total support mass goes into M. Final score is 50 + 50 * (S / (M + 4)), clamped to 0-100.
-That +4 prior mass keeps thin but unanimous evidence from producing extreme scores too easily.
-Neutral evidence (direction = 0) doesn’t push the score up or down, but it does increase M, which pulls the result back toward 50.
As for the ladder - I think that is a good idea, but in a controlled manner because of the token cost and potential for abuse.