I switched to a password manager (bitwarden) about 7 years ago. I have over 200 accounts (not all of them use my @gmail). it would take me weeks to convert those accounts to a new domain, if the application could even support it.
I will admit, many of the accounts are not needed any more. but the process will still be emotionally boring to filter through that.
> ... it would take me weeks to convert those accounts to a new domain ...
I did the same with about the same amount of accounts and it took me the better part of a Saturday. Even if you were really slow and needed five minutes per account, 200 accounts would still only take about 17 hours.
I don't think that's a lot of effort. You could easily spend that time fixing something around the house or garden, which often might not have nearly as big of an impact on personal agency.
If there are growth opportunities for the company, selectively choosing the top 90% YoY, minimizing backfills (in theory...) will result in a company full of high achievers that can execute on that growth vision.
If the company is shifting into maintenance mode, cutting 40% of the staff is the right move, but definitely hurts shareholders b/c they valued the company as growth, not maintenance.
My understanding is the original value these markets create is the ability to hedge risk.
If you're worried an event may impact you materially (like cat 5 hurricane in Florida), then you can place a bet that the event will happen, thus hedging some risk if it does happen.
Insurance companies can participate in these products for the same reasons.
Or if you need to hedge against an event that isn't insurable. For example, if you are a high level democrat party leader and you will lose your job if a republican wins, you might take a bet to hedge your risk if your party looses the next cycle.
Weather derivatives have existed much longer than prediction markets. search for “catastrophe bonds” (normally called “cat bonds” in the markets) if you want to find out more. There is also insurance and reinsurance.
Insurance is what normal people use to hedge weather risk. The insurers use an combination on reinsurance and cat bonds issuance and the reinsurers use cat bonds and weather derivatives.
I seriously doubt there is meaningful weather hedging volume on prediction markets by comparison.
The vast majority of bets on Kalshi (90% according to another user) are sports bets. There's no risk being hedged here; it's just gussied-up sports betting.
A substantial portion of the other bets on the market are other trivial events of no financial significance. For instance, the second insider case described in the article involved the contents of Mr. Beast videos.
> For example, if you are a high level democrat party leader and you will lose your job if a republican wins...
This would probably constitute insider trading, as the party leader has a direct role in their party's election results.
My example is not great. A better one would be an employee at an oil company may be personally impacted depending which party is elected and they want to create their own hedge.
on sports betting, people's income depends on if a team wins or looses. If the team didn't make it to the playoffs, then their bonuses (or income) is reduced. Sports betting enables these people to smooth out their income, instead of all or nothing.
I agree, these tools are frequently abused by gamblers (or better: the tools abuse gamblers), but unlike your typical casino game, there is utility in these services for certain groups of people.
I find it interesting how American-accented people publish on social media how to access non-linked FBI files related to the Epstein leak, by updating a URL.
When Engineering Budget Managers see their AI bills rising, they will fire the bottom 5-10% every 6-12 months and increase the AI assistant budget for the high performers, giving them even more leverage.
In my case, over the last 3 years, every dev who left was not replaced. We are doing more than ever.
Our team shrunk by 50% but we are serving 200% more customers. Every time a dev left, we thought we're screwed. We just leveraged AI more and more. We are also serving our customers better too with higher retention rates. When we onboard a customer with custom demands, we used to have meetings about the ROI. Now we just build the custom demands in the time we took to meet to discuss whether we should even do it.
Today, I maintain a few repos critical to the business without even knowing the programming language they are written in. The original developers left the company. All I know is what is suppose to go into the service and what is suppose to come out. When there is a bug, I ask the AI why. The AI almost always finds it. When I need to change something, I double and triple check the logic and I know how to test the changes.
No, a normal person without a background in software engineering can't do this. That's why I still have a job. But how I spend my time as a software engineer has changed drastically and so has my productivity.
When a software dev say AI doesn't increase their productivity, it truly does feel like they're using it wrong or don't know how to use it.
> Today, I maintain a few repos critical to the business without even knowing the programming language they are written in. [...] No, a normal person without a background in software engineering can't do this.
Of course they can - if you don't know any of the tech-stack details (i.e. a "normal" user), why can't someone else who also doesn't know the tech-stackc details replace you?
What magic sauce do you possess other than tech-stack chops?
In the future, they might be able to. Not yet though. I still have a job.
When a non software engineer can build a production app as well as I can, I know I won’t be working as a software engineer anymore. In that world, having great ideas, data, compute, and energy will be king.
I don’t think we will get there within the next 3-4 years. Beyond that, who knows.
Could you provide some details on your company, code base, etc? These are wild claims and don’t match the reality I’m seeing everywhere else.
How big is your team? How many customers? What’s your product? Can we see the code? How do you track defects? Etc.
Part of the reason I’m struggling with this is because we’d be seeing OpenAI, Anthropic, etc. plastering these case studies everywhere if they existed. Instead, I’m stuck using CC and all its poorly implemented warts.
Not OP, but I am seeing this in my current company.
Companies are charged per token, which means heavy AI users deliver more and stress budgets. They recently announced significant payroll costs over the past ~3 years.
Those savings I think will partially be reclaimed by AI companies, enabling the high performers more ai model usage.
By those metrics, Microsoft lost 20% of it's value due to hopping on the AI coding assistance train.
I'm not saying it is the case, just making it apparent how unreliable it is to measure productivity by comparing what's happening at the lowest level in a company to its financials.
My issues with the super posts is its really hard to grep for relevant information.
I've tried asking ChatGPT to recommend projects based on my interests, but ChatGPT heavily hallucinated or projected my interests onto irrelevant projects (for example, a project might be about developing a new programming language and chatgpt was like, "you could use this in your soap making hobby!").
The Who's Hiring posts have community sponsored indexers and its easy for me to query job titles relevant to me, but keyword search is not as useful here.
Author here. Good point—OpenSearch (which is based on FAISS) is actually included in the VectorDBBench results, so you can see how it compares there.
That said, horizontal scaling for vector search is relatively straightforward; the real challenge is optimizing performance within a given (single-node) hardware budget, which is where we've focused our efforts.
I will admit, many of the accounts are not needed any more. but the process will still be emotionally boring to filter through that.
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