One thing I'm finding early success with is to define how the system can know if this statement is being met. Frequently I will include in the prompt e.g. "research what makes good high quality engineering practices and derive how to tell if those practices are being followed".
Directly telling it my team's values would be better, if we have it developed (like the style guide you mentioned) ... but that's a lot of work, the reasons that hasn't happened before are just as true now, and honestly - there's a lot of overlap with the generic research result.
> Are these changes good, high quality, good engineering practices, in line with known best practices and the style guide
The problem is it's impossible to predict success in an early stage company. Optimal way to ensure profit is to place many bets and hope one pays off enough to cover the costs of the losers. Asymmetrical outcomes are a requirement for sane strategic investors.
If all you can do is buy lotto tickets, you are better off buying insane payoff opportunities than trying to pick which lotto tickets will have a higher frequency of payoff, but a much lower yield.
The point though is that these are not really lotto tickets. A lot of high-tech startups are working on monetizing some obscure technology, or inventing a more efficient way to do a task, or something along those lines. They are actually providing value, other than the payoff from their sale or IPO. I think we as a society are failing somewhat to give an appropriate monetary value to that kind of contribution.
Skin in the game seems to be a large factor. Government workers tend towards better job security (less of a stick) and don't tend to get bonuses (smaller carrot) than private sector.
This is of course by design, since favoritism is generally acceptable in a private organization but (the perception of it) poison to govt roles.
Disclaimer, I have gotten much value out of their previous project, jobstart.
1/ it's based on personal advice so needs to scale on both sides. I've spoken w the founders and they legit.
2/ I intend to get my work to pay for it under "high quality outsourced good management". I don't expect to use it for a new job, but to perform better in my current.
3/ it is my understanding that it's a scaling thing.
5/ in my job, a 10% improvement in efficiency Uncompounded is worth thousands of dollars in direct costs per month. Well worth it, based on my jobstart experience w the same team.
If the service ($) actually develops an employee to a higher level of performance, the employer gets the benefit of a more senior hire who is already at their company instead of having to recruit ($$) and on-board ($$) one & hope they work out ($$$).
Even if they need to give the current employee a healthy raise ($$) to retain them, they'd still probably net save money.
(in theory)
If some high % of employees who use this tool discover they really ought to be working elsewhere, well, that's probably better for the industry's cost/productivity as a whole, so everyone wins in the end yayy!! But sure maybe it's optimal for an employer to hide this sort of tool if they rely on employees having inefficient market information to retain them & that somehow fits with their ethics...
(Looking at you, the giant companies that got sued for their illegal informal non-competes...)
Personally, if the goods quality is high, I don't think the designers or Brandee's ought to capture the majority of the value.
The "credit" (my money) goes to the people that made and got the product to me.
I think the appropriate level of discussion is "at what level is art conodditised" to which I would say "I have no idea, but somewhere before it's printed on clothing and sold to me".
Other people who value different aspects of fashion are likely to disagree, but in general I don't highly value the creativity in clothing
I agree, thank you for peovoking this thought. It is raw and if so I apologize.
This is where hinting is important. Metadata. That sequence if I know it's a phone number, or a sequence of increasing digits, depends a lot on metadata.
Given some reasonable sample size, i believe machine learning could provide hints as to some of the common types of formats. Semi automated data hinting or structuring?
There is a bidirectional connection between interpreting your data and how your data is structured
Is it possible to use your data column to statistically hint at metadata characteristics by some sort of clustering, then use that to automatically clean input data?
Did you mean that it's not common in root beer? Wiki says that root beer has little to no caffeine.
Root beer is so sweet that I would think it could mask the bitterness. I wonder if the plants that originally were brewed into it just didn't have any caffeine.
Do the ingredients to coke naturally have caffeine, or was that a substitute for the original alkaloid? I.e., is decaf coke because the removed the caffeine, or is caffeinated coke that way because it was added?
Coca-Cola lists caffeine as an ingredient in the UK.
I don't know why caffeine was originally added - maybe for flavour, maybe for the stimulant effects, possibly because of having to not have cocaine in it anymore...
Directly telling it my team's values would be better, if we have it developed (like the style guide you mentioned) ... but that's a lot of work, the reasons that hasn't happened before are just as true now, and honestly - there's a lot of overlap with the generic research result.
> Are these changes good, high quality, good engineering practices, in line with known best practices and the style guide
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