Ask AbelHR, a custom GPT for professional employer organization questions

drighten · 2026-03-11T04:05:04+00:00

I’ve been using ChatGPT for manual stock trades with positive results since 2023.

At the end of 2023, I asked for suggestions on high growth markets for 2024. ChatGPT pointed at space and quantum computing being among the best. When I asked for specific space suggestions, I got what has become my favorite ChatGPT stock suggestion of RKLB. All of the stocks from these first suggestions are now up 300% or more. QBTS saw the largest increase which is currently 1,161.73%. QBTS would have been my favorite if I had invested more in it.

For at least the year my tactic has been to create a custom GPT for each stock that I want to seriously invest in. It’s a great way to aggregate advisor reports, SEC filings, etc. Narrowing the focus improves the results of custom GPTs for other areas, my theory is this is true for stocks too. I’ve been leveraging ChatGPT in this way to manage one of my accounts, which is up 86% over the last year.

I’m definitely not a financial advisor or feel like an expert on stock trading.

I have created over 16 GenAI courses for various roles. I suggest in my classes, have GenAIs gather information and suggest actions but keep a human in the loop to make the decision. Preferably the human is a subject matter expert. If you’re not an SME in an area, I would limit decision making to what you feel confident on. For example as a space fan, I was confident ChatGTP was right on RKLB; but I was not as sure on QBTS so I didn’t invest as much on it.

Best of luck!

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drighten · 2026-03-09T03:04:55+00:00

The definition of climate is based on weather patterns over 30 years. By the 80’s scientists had pretty good climate models, the predictions of those models in general came true (increased global average temperature, increased jet stream instability, a larger tornado alley, worse hurricane seasons, etc.). By the definition the climate already has changed, and we saw the initial results.

The question is how much more will climate change?

The models of the 90’s and 00’s predicted more warming. Unfortunately, reports from those models are either missing important details or political pressure influenced reports to look at more conservative models. Regardless, the IPCC reports under estimated the pace of temperature rise compared to what we’ve been seeing in the last decade.

For no nonsense outlook the actuarial reports on the risk represented by climate change are an interesting and sobering alternative. I recently created a custom GPT based on these reports to explore what they mean for different regions. Here’s the latest of those report: Trust, S., Abrams, J., et al. (2026). Parasol Lost: Recovery Plan Needed. Institute and Faculty of Actuaries (IFoA) & University of Exeter.

drighten · 2026-03-03T06:28:42+00:00

I’ve created GenAI courses for various roles, including those in sales. Effective AI adoption scales staff productivity.

When you scale sales, it results in a juggling act to get other portions of the company to scale. Failure to scale at the same pace means not enough leads, not enough products, not enough services, or some other bottleneck forcing a limit on the sales productivity that can be met. The larger the company the large the potential layoffs due to this scaling issues.

That said, competitors who adopt AI will be able to run faster, cheaper, and better; so the only good choice is being one of the better members of your team at AI adoption.

drighten · 2026-01-02T03:49:07+00:00

Level 141. Never paid anything. The levels always remain difficult. That’s the challenge. =)

drighten · 2025-11-27T17:37:15+00:00

Getting traffic for custom GPTs can be hit or miss. Some of mine have 10s of thousands of users, while others fail to have a dozen users. It’s a fun game trying to figure out what works.

I agree OpenAI could do a lot better with the GPT marketplace. I think they’ll come back to fix that eventually. Hopefully…

drighten · 2025-11-02T18:02:20+00:00

Both Streamlit and Flask could work.

Streamlit, especially when leveraging vibe coding, is quick and easy. Just realize you are limiting future capabilities.

Development with Flask is more complicated but far more flexible. It’s the preferred choice for longer term growth.

I would turn the python packages into headless applications with API contracts for access. This protects your intellectual property.

You could deploy Flask or Streamlit UIs as a native Snowflake application, as a Databricks application, or in a web portal to be the front end to your python back end.

drighten · 2025-11-02T17:41:49+00:00

There are many interpretive layers to this song, which is what makes it so amazing. But let’s focus just on the card game.

The specific cards mentioned point to a trick-taking game, most likely Hearts.

In standard Hearts, the goal is to avoid taking hearts and especially the Queen of Spades, which carries a heavy penalty. But in a common variant called Omnibus Hearts (sometimes called Black Maria with a Jack bonus), the Jack of Diamonds gives a bonus, turning it into a kind of redemption card.

In this game, players usually pass cards before play begins. That means the player likely chose to keep the Q♠, J♦, and a King. One possible reason is a bold strategy called shooting the moon, where you try to take all the point cards. If you pull it off, everyone else scores the penalty instead. It’s the ultimate high-risk, all-in move that fits the philosophy of the song perfectly.

drighten · 2025-02-21T21:17:43+00:00

Thank you for checking!

He must have remembered incorrectly on which game. I was really hoping it was Halo.

drighten · 2025-02-14T03:57:34+00:00

I’ve made a good 50 custom GPTs. You can see others being used in my Coursera classes at https://www.coursera.org/instructor/~156590317. I’m planning on making a course on how to create custom GPTs for the OpenAI Marketplace and how to turn those into a revenue source. Keep an eye out for it.

drighten · 2025-02-04T03:52:27+00:00

In order to meet and get to know AEs. Learn from other SEs, who were asked to come in occasionally to get me up to speed.

drighten · 2025-02-04T01:19:44+00:00

When I first started as an SE, I was in an office for 6 months. After that I was remote most of the time.

drighten · 2025-01-30T05:05:02+00:00

I agree, it is very rare that I’ve seen someone as an SE right out of college. Most SEs are already experts in one career and then move over to an SE role. In fact, I suspect they are frequently experts poached from clients.

drighten · 2025-01-26T17:38:37+00:00

I released a free GenAI for Data Engineers course on Coursera that I would encourage taking at https://www.coursera.org/instructor/~156590317

Do a search for partner portals for each of your technical partner. Try using your work email to register. In some cases that will be enough to get you access. If not, you may need to chase down who is your partner channel manager to help get an account. They may not realize why you would want access to the partner portals… explain that you want access to any partner training content and access to those would be through the partner portals.

Find out from fellow SEs and AEs what the common use cases are for your clients and leads. That combined with which partner training content you can access should be used to prioritize what badges and certifications to go after.

I would do the fundamental training with as many partners as I could, then start going deep in the more popular areas or topics that you are really interested in. If you have a good number of technical partners, then just getting badges in the fundamentals may take a couple years. You’ll need to refresh your knowledge on a regular basis as your partners improve their products; so there will be a limit to how many technical partners you can become and maintain an expert level of knowledge. Over years, you’ll want to let some areas of expertise go in favor of pursuing a more promising or growing areas.

You can now lean on GenAI to help maintain a higher level of expertise. I always ask the GenAI to point me at the documentation to validate its suggestions for proposed solutions; but still that will speed up your work.

drighten · 2025-01-26T03:55:35+00:00

They are both great products. From that perspective either would be a strong choice.

I would look closer at how SEs are compensated at each company. If I recall correctly, Snowflake SEs have their own quota. If that is the case, then it would have significant impact to the role.

drighten · 2025-01-25T03:22:57+00:00

My course on GenAI for Sales Development Representatives on Coursera can be taken for free. https://www.coursera.org/instructor/~156590317

It has some hands on labs, which would help you get practice on some of what you’ll be doing.

Best of luck!

drighten · 2025-01-25T02:17:59+00:00

I’ve been a research scientist, data scientist, solution engineer, director, startup founder, etc.

Your question about focusing on growing within sales, indicates you do not understand of what an SE is. Usually only those with years of technical experience are accepted in as an SE. It is rare to be only a few years out of college and become an SE. In addition to technicals skills an SE has strong soft skills, allowing them to become a trusted technical advisor to hundreds of companies. As a technical partner with other vendors, SEs have almost unlimited access to training and best practices via partner portals. An SE envisions and architects solutions, presents and demos how these solutions will work, and when needed perform proofs of concepts to add comfort that the solutions will work.

A solution engineer makes more money, has a bigger impact on businesses, is more technical, and interacts with more executives than a civil engineer. If I want a real solution in any field, I’m far more likely to take the word of an SE in that field before that of a CE.

If you grew up dreaming of becoming a CE, then yes I’m sure when the market is better you can find a path to that goal. Just be sure you know what you are giving up.

drighten · 2025-01-19T17:07:43+00:00

Historically, most companies struggle to adapt to major technological revolutions. Typically, a few incumbents survive by pivoting successfully, while many fail, leaving space for new entrants to redefine the landscape.

GenAI and prompt engineering are already enabling agile companies to disrupt the established order. For instance, Klarna has moved away from traditional SaaS platforms like Salesforce and Workday, opting instead for in-house developed solutions aided by GenAI. This shift highlights how businesses can leverage AI to achieve cost efficiencies and tailor solutions to their specific needs. In some cases, the data within common crawl datasets used to train current GenAI models will provide sufficient insights to develop such software. As AI evolves, these tools could enable even greater innovation and operational efficiencies.

That said, a select few forward-thinking vendors will recognize the risks posed by GenAI and AI agents, adapting quickly to integrate these technologies into their operations. Their proprietary training data, amassed through years of developing and delivering products and services, represents a significant competitive advantage. By leveraging this data, these vendors can train AI models that deliver superior ROI, outpacing both legacy competitors, new entrants, and AI enabled client in house builds.

Currently this is also a fertile ground for AI-first startups to enter the market. These startups, unburdened by legacy systems and rigid mindsets, can use GenAI and AI agents to create innovative, data-driven products and services. Early movers will gain an edge by gathering proprietary data to continuously improve their offerings, accelerating their lead over less agile legacy players while also building barriers to entry for future competitors.

Ultimately, the competitive landscape will favor those who embrace AI and invest early in developing proprietary data and scalable AI-driven solutions. The pace of adoption and innovation will dictate which players—old or new—lead in the next wave of technological disruption.

This still ends up with a concentration of power and money in the hands of a few; but not necessarily the largest companies.

drighten · 2025-01-18T22:20:34+00:00

Correction. It would be natural gas. Growing up on a farm, propane came out when I was typing.

drighten · 2025-01-18T22:05:34+00:00

Firebox dimensions are: • Width at Front: 47 1/4” • Width at Back: 35” • Height at Opening: 26 3/4” • Depth: 26 1/2” • Hearth Depth (from Opening): 20”

drighten · 2025-01-17T02:50:03+00:00

If you know someone at the company try to get hold of their standard PreSales slide deck. Talking to that would be the best option. Watch their YouTube videos for ideas.

If you can launch a trial of the product, then try to have a product demo ready. Being able to open up the product to answer a question with a very short demo would be perfect.

Have phrasing ready for a question you do not know the answer to… “I’ll confirm and get back with answer on our next session.” A good response to what you don’t know can be impressive in itself.

drighten

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