Insights on leadership, technology, and human-centered strategy, originally shared on LinkedIn and expanded for deeper exploration.
Because I work with clients, I work in different email systems (every kind of system). I can’t express the dread I feel modifying recurring events in outlook because of the many, many notification emails it generates. I can’t find a way to turn that off… Advice welcome.
In 1986, “No silver bullet”, told us the best developers are 10x more productive than average. Anecdotal evidence says high performing teams can be even more. That divide will grow with automation tools for those who can both use those tools effectively AND deliver with quality.
Using a purpose trained models with fewer parameters built of curated training content can out perform mega models for focused tasks. They would definitely be more efficient from a compute perspective. But if you include the training costs and the number of models, I don’t know which approach creates the larger carbon footprint.
Today, I successfully converted a code file from Elixir to Node.js, a language I'm more familiar with. I utilized LLM assistance to not only translate the code but also to create comprehensive unit tests, achieving over 90% coverage in just 30 minutes. While I still need to ensure the functionality of the converted code as I address other dependencies, this experience highlights the efficiency of leveraging AI in coding tasks.
Here’s the flow of prompts I used today to convert a code file from one language to another and provide test coverage. I list out the steps illustrated by my actual prompts.
This sounds like a breakthrough advancement in Quantum Computing…
“With (Microsoft’s) qubit-virtualization system, (they) were able to create four highly reliable logical qubits from only 30 physical qubits of the available 32 on Quantinuum’s machine.
Three fundamental criteria to advance from noisy, intermediate-scale quantum computing to reliable quantum computing are:
(They) have demonstrated, for the first time on record, that all three of the above criteria have been met.”
https://lnkd.in/gWyRcenS (via pivot5)
AI is a concept manifested as enabling tools. We can build the tools more or less responsibly. We can use tools to create more or less harm - less or more benefit - for few or many people. It is humans who will be the heroes, heels, and supporting players in this story.
Amazon “Just Walk Out” is an unsettling experience. Turns out it’s not advanced AI. More surveillance and hidden labor. It relied on more than 1,000 people in India watching cctv and labeling videos (theinformation.com) to ensure accurate checkouts.
As software developers, we are paid to apply knowledge practically. The domains we master grow more complex. Our days are overwhelmed with tasks. Use AI automation to reduce time spent on tasks and allocate more for learning. We can produce more value, build our craft, and remain employable.
Working intensively with AI code generation tools, hasn’t changed my basic vision of the relationship between the tool maker, the tool wielder, and the tool. The people need the skill and professionalism to craft good software before their tools will help them create it.
The new OMB policy:
Agencies need to disclose safeguards to ensure AI systems cannot endanger citizens’ safety or infringe rights.
Publish system code (foreseeable loopholes here) and performance metrics.
Create governance boards and an accountable executive with oversight on AI use.
Well done.