Peaka Digest #49 💪 A New Addition to Peaka 🗞️ Enhanced Data Table Functionality
Hey there,
After a brief pause around the New Year, we’ve picked up from where we left off, starting 2024 with a bang. We held a series of strategy meetings in the first two weeks of the year, clarified the details of our roadmap, and settled on some New Year’s resolutions.
One of those resolutions was to make marketing a priority for Peaka in 2024. Generating more leads and pursuing a multichannel marketing strategy will be top of the agenda this year. In line with this plan, we’ve made another addition to our marketing team, and Alara Dolunay has joined Peaka as a Digital Marketing Specialist! 💪💪💪
Alara has more than 5 years of experience in marketing and business development. Her previous work in such diverse fields as market analysis, SEO, and digital content production will be an asset to Peaka as we ramp up our marketing activities this year.
“We will make sure that Peaka's unique approach to data integration will get more recognition,” says Alara. She will help us grow Peaka’s online presence and has already made a difference in her first two weeks with the team.
Expect to see more and varied content from us in the coming weeks, particularly on LinkedIn!
Welcome aboard, Alara!
In this edition
• 🥳 What's New at Peaka:
• 🗞️ From Peaka:
• 📣 Community News:
You can now make your data tables more functional with our “action buttons.”
Action buttons are perfect shortcuts for predefined row-based actions in Peaka. By adding an action button to a row, you can edit or delete the record in that row or add new capabilities to your data tables.
For the scenarios you use frequently, simply set up your flows and trigger complex actions at the click of a button. For example, have Peaka shoot an email or Slack message with the record in a specific row when the predetermined criteria for that row are met.
Customizable action buttons illustrate how our "doing more with less" mantra works in real life.
Make sure you check out our 'What’s New' page to learn about all the recent connectors, features, and bug fixes we shipped. 👇
🗞️ From Peaka
Many founders neglect lean principles while interacting with customers, often focusing too much on customer support. We’ve explored a healthier way.
A discussion of how the modern data stack fails startups and why zero-ETL may be the solution to the data integration pains of these companies.
What’s the best way of handling data, through ETL or ELT? This blog post takes a look at both methods and their respective strengths and weaknesses.
Recommended by LinkedIn
When AI was all the rage, and people were busy creating AI-generated images using Midjourney or Lensa around this time last year, some people were touting AI’s potential for more critical use cases like drug discovery.
It turns out that drug discovery and development is a good candidate for AI implementation. That's because discovering a new drug and bringing it to the market is a long and expensive process that can take more than a decade and cost around $2 billion, according to recent estimates. The situation is especially dire for antibiotic development, where costs are sky-high, and the pipeline is shrinking due to the antibiotic resistance the bacteria are developing.
AI technology can offer us a way out of this conundrum. Thanks to the ML models trained on previous research data, thousands of compounds can be analyzed to identify the most likely candidates for killing certain bacteria. The compounds identified are further tested in the lab environment and clinical trials. However, the initial narrowing down of promising compounds from literally millions to a couple of hundred saves a lot of time and money.
A team of researchers led by James Collins, a professor at M.I.T.’s Institute for Medical Engineering & Science, have leveraged a deep-learning algorithm that screened millions of compounds for antibiotic activity. After testing 283 likely candidates on mice, the researchers were able to isolate several compounds that are effective against methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci.
The highlight of the study was the fact that the algorithm used was a type of explainable AI, as opposed to the usual black box that gives researchers no visibility into how AI came up with the result it did. The explainable nature of the model means that researchers will be able to re-create the study and improve it as in other scientific disciplines.
Hit the link below to read an interview with César de la Fuente, an assistant professor in the Department of Psychiatry at the University of Pennsylvania, to understand what this means for the future of drug discovery.
Do we officially have an AI bubble? Probably, yes. AI has become what “turbo” was in the automotive jargon of the 80s: It just means “good.” Maybe you are not looking for AI capability in the refrigerator you’re planning to buy, but you will get it anyway because why not?
Nothing lasts forever, so this AI hype we’re going through is bound to end at some point. What will we be left with when the inevitable happens and the AI bubble bursts? Is there anything we can salvage and carry over to the future from the craze?
Cory Doctorow speculates about tech bubbles and where AI fits among them in a blog post:
“Tech bubbles come in two varieties: The ones that leave something behind and the ones that leave nothing behind. Sometimes, it can be hard to guess what kind of bubble you’re living through until it pops, and you find out the hard way.”
The one red flag for AI’s future, according to Doctorow, entails unit economics:
The areas where AI is to create the most value also are the most risk-intolerant. Using AI to diagnose cancer or add autonomous driving capability to vehicles offers a lot of value, but these projects still require lots of guardrails and human supervision due to the high stakes involved. AI cannot be trusted to handle such critical tasks on its own, and adding human involvement screws up the unit economics because supervising and finally green-lighting the work AI has done costs more than a human doing it from scratch.
It’s not all doom and gloom with Doctorow, though. He thinks we will still have a significant number of people skilled in statistical analysis, PyTorch, and TensorFlow, who may lead different tech breakthroughs, when this golden age of AI is over. And that’s saying something when you think about the NFT craze, which has left behind collectors with nothing more than thousand-yard stares in their eyes and worthless digital ornaments in their hands.
OpenAI seems to have left the power struggles behind and is carrying on with its business plans after a short delay. The company finally launched its GPT Store, a marketplace for custom AI chatbots (GPTs) along the lines of Apple’s App Store, on January 10 with a selection of 3 million AI bots. The initial plan to launch it at the end of November had to be canceled due to Sam Altman’s ouster and rehiring.
The GPT Store will be featuring custom GPTs derived from OpenAI’s own ChatGPT by users who subscribe to OpenAI’s paid tiers. These paying customers will also get to use the GPTs developed by other people.
The GPT Store will be a major milestone in OpenAI’s journey, with the potential to solve one of the biggest problems for the company: Defending its turf. OpenAI has been facing severe competition from smaller and more specialized LLMs. The GPT Store will serve as a defensive moat for the company, creating a two-sided marketplace that caters to both creators of custom GPTs and people who need such tools. If the plan works, the two sides will feed off of each other, unleashing network effects every company aspires to have.
In addition to creating an ecosystem around OpenAI, the GPT Store will generate a new income stream for OpenAI, as creators will transfer some of their revenue to OpenAI under a revenue-sharing model.
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Thanks for reading!
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