Category: Artificial Intelligence
On Thursday, May 8th from 11 a.m. to 3:00 p.m. Eastern, O’Reilly Media will host a free online conference called AI Codecon. “Join us to explore the future of AI-enabled development,” the tagline reads, and their description of the event starts with their belief that AI’s advance does NOT mean the end of programming as a career, but a transition.
Here’s what I plan to do with this event:
- Register for the event
- Log in when it starts and fire up a screen recorder
- Watch the event in the background while working
- Generate a transcript from the recording and feed it into a couple of LLM
- Have the LLMs answer any questions I may have and generate summaries and “going forward” game plans based on the content and my future plans
The agenda for AI Codecon
Here’s the schedule for AI Codecon, which is still being finalized as I write this:
- Introduction, with Tim O’Reilly (10 minutes)
- Gergely “Pragmatic Engineer” Orosz and Addy Osmani Fireside Chat (20 minutes)
Addy Osmani for an insightful discussion on the evolving role of AI in software engineering and how it’s paving the way for a new era of agentic, “AI-first” development.
- Vibe Coding: More Experiments, More Care – Kent Beck (15 minutes)
Augmented coding deprecates formerly leveraged skills such as language expertise, and amplifies vision, strategy, task breakdown, and feedback loops. Kent Beck, creator of Extreme Programming, tells you what he’s doing and the principles guiding his choices.
- Junior Developers and Generative AI – Camille Fournier, Avi Flombaum, and Maxi Ferreira (15 minutes)
Is bypassing junior engineers a recipe for short-term gain but long-term instability? Or is it a necessary evolution in a high-efficiency world? Hear three experts discuss the trade-offs in team composition, mentorship, and organizational health in an AI-augmented industry.
- My LLM Codegen Workflow at the Moment – Harper Reed (15 minutes)
Technologist Harper Reed takes you through his LLM-based code generation workflow and shows how to integrate various tools like Claude and Aider, gaining insights into optimizing LLMs for real-world development scenarios, leading to faster and more reliable code production. - Jay Parikh and Gergely Orosz Fireside Chat (15 minutes)
Jay Parikh, executive vice president at Microsoft, and Gergely Orosz, author of The Pragmatic Engineer, discuss AI’s role as the “third runtime,” the lessons from past technological shifts, and why software development isn’t disappearing—it’s evolving. - The Role of Developer Skills in Today’s AI-Assisted World – Birgitta Böckeler (15 minutes)
Birgitta Böckeler, global lead for AI-assisted software delivery at Thoughtworks, highlights instances where human intervention remains essential, based on firsthand experiences. These examples can inform how far we are from “hands-free” AI-generated software and the skills that remain essential, even with AI in the copilot seat. - Modern Day Mashups: How AI Agents Are Reviving the Programmable Web – Angie Jones (5 minutes)
Angie Jones, global vice president of developer relations at Block, explores how AI agents are bringing fun and creativity back to software development and giving new life to the “programmable web.” - Tipping AI Code Generation on its Side – Craig McLuckie (5 minutes)
The current wave of AI code generation tools are closed, vertically integrated solutions. The next wave will be open, horizontally aligned systems. Craig McLuckie explores this transformation, why it needs to happen, and how it will be led by the community. - Prompt Engineering as a Core Dev Skill: Techniques for Getting High-Quality Code from LLMs – Patty O’Callaghan (5 minutes)
Patty O’Callaghan highlights practical techniques to help teams generate high-quality code with AI tools, including an “architecture-first” prompting method that ensures AI-generated code aligns with existing systems, contextual scaffolding techniques to help LLMs work with complex codebases, and the use of task-specific prompts for coding, debugging, and refactoring. - Chip Huyen and swyx Fireside Chat (20 minutes)
Chip Huyen will delve [Aha! An AI wrote this! — Joey] into the practical challenges and emerging best practices for building real-world AI applications, with a focus on how foundation models are enabling a new era of autonomous agents.
- Bridging the AI Learning Gap: Teaching Developers to Think with AI – Andrew Stellman (15 minutes)
Andrew Stellman, software developer and author of Head First C#, shares lessons from Sens-AI, a learning path built specifically for early-career developers, and offers insights into the gap between junior and senior engineers. - Lessons Learned Vibe Coding and Vibe Debugging a Chrome Extension with Windsurf – Iyanuoluwa Ajao (5 minutes)
Software and AI engineer Iyanuoluwa Ajao explores the quirks of extension development and how to vibe code one from scratch. You’ll learn how chrome extensions work under the hood, how to vibe code an extension by thinking in flows and files, and how to vibe debug using dependency mapping and other techniques. - Designing Intelligent AI for Autonomous Action – Nikola Balic (5 minutes)
Nikola Balic, head of growth at VC-funded startup Daytona, will show through case studies like AI-powered code generation and autonomous coding, you’ll learn key patterns for balancing speed, safety, and strategic decision-making—and gain a road map for catapulting legacy systems into agent-driven platforms. - Secure the AI: Protect the Electric Sheep – Brett Smith (5 minutes)
Distinguished software architect, engineer, and developer Brett Smith discusses AI security risks to the software supply chain, covering attack vectors, how they relate to the OWASP Top 10 for LLMs, and how they tie into scenarios in CI/CD pipelines. You’ll learn techniques for closing the attack vectors and protecting your pipelines, software, and customers. - How Does GenAI Affect Developer Productivity? – Chelsea Troy (15 minutes)
The advent of consumer-facing generative models in 2021 catalyzed a massive experiment in production on our technical landscape. A few years in, we’re starting to see published research on the results of that experiment. Join Chelsea Troy, leader of Mozilla’s MLOps team, for a tour through the current findings and a few summative thoughts about the future. - Eval Engineering: The End of Machine Learning Engineering as We Know It – Lili Jiang (15 minutes)
Lili Jiang, former Waymo evaluation leader, reveals how LLMs are transforming ML engineering. Discover why evaluation is becoming the new frontier of ML expertise, how eval metrics are evolving into sophisticated algorithms, and why measuring deltas instead of absolute performance creates powerful development flywheels. - Closing Remarks – Tim O’Reilly (10 minutes)
I’m working on some new videos for the Global Nerdy YouTube channel, and one of them is on answering questions in the dreaded “behavioral interview.” That’s the interview where you’re asked questions like “Tell me about a time when you had to deal with a conflict on your team. How did you handle it?”
To help come up with ideas for the video about behavioral interviews, I posed some questions to LLMs. As I like to say, no matter how creative or clever you are, it is by definition impossible to make a list of ideas that you’d never come up with yourself.
So I posed this question to Claude, which for some reason, seems to know stuff about me:
How would you describe a behavioral interview, if you had to describe it in the style of Joey deVilla?
And wow, was the response snark-tastic:
Here’s the text of the response:
Joey deVilla’s Guide to Behavioral Interviews
Imagine me adjusting my accordion straps and coffee mug before dropping this knowledge bomb…
So you wanna know about behavioral interviews? Those corporate rituals where they ask you to “Tell me about a time when…” instead of just asking if you can do the damn job?
Behavioral interviews are the tech industry’s version of “Show, don’t tell” – except instead of showing your actual skills, you’re telling stories about when you supposedly used them! It’s like your professional life as fan fiction, where the hiring manager is desperately hoping you’re not writing in the genre of fantasy.
These interviews operate on the radical premise that past behavior predicts future performance. Revolutionary, I know! It’s like assuming that because I once played “Lady of Spain” on my accordion at a Tampa dev meetup, I’ll do it again given the chance and sufficient sangria.
The formula they want is STAR: Situation, Task, Action, Result. But what they’re REALLY looking for is STARBS: Situation, Task, Action, Result, Bull$#!t – that special sauce where you frame yourself as the hero who saved the project while maintaining perfect diplomatic relations with that passive-aggressive PM who kept moving the goalposts.
The trick isn’t just recounting what happened – it’s storytelling with a corporate-friendly narrative arc. You need conflict (but not TOO much conflict), challenges (that showcase your skills), and resolution (where you emerge victorious but humble).
Remember: in behavioral interviews, there are no failures – only “growth opportunities” and “valuable lessons” that conveniently transformed you into exactly the employee they’re looking for!
Adjusts glasses, takes dramatic sip of coffee
Now, if you’ll excuse me, I need to go practice my own STAR stories for when someone inevitably asks me about “a time I demonstrated adaptability in a challenging situation” – which is code for “that time I coded for 36 hours straight because someone in management promised an impossible deadline.”
My questions, after reading Claude’s output:
- Am I really that much of a smart-ass?
- “STARBS” — Situation, Task, Actions, Result, Bullshit — is pretty clever. Whose writing did Claude lift that from?
So I did some web searches using terms like starbs star situation task action result bullshit, and couldn’t find any instance of STARBS. It seems to be one of those effectively new creations that an autocomplete-on-steroids can generate.
I’m not gonna lie: I kind of like STARBS, and wish that I’d come up with it myself.
(If you’re interested, see if you can do some searches to find other instances of the term STARBS.)
It’s been a while since we’ve had a “meetup of meetups” for Tampa Bay — let’s fix that. Join us this Wednesday, April 30th for Tech Groups of Tampa Bay’s Happy Hour Networking + AI Lightning Talk!
The tl;dr
- The event: Tech Groups of Tampa Bay’s Happy Hour Networking + AI Lightning Talk
- What it is: A gathering of various Tampa Bay tech meetups that’s mostly social, but also features a lightning talk on the hottest tech topic: AI!
- When: Wednesday, April 30th from 5:30 p.m. to 8:00 p.m.
- Where: Thrive DTSP (136 4th St N, Ste 201, St. Petersburg FL)
- Will there be food and drink?: Yes, and it’ll be FREE!
- How do register: On the Tampa Devs meetup page
The details
The event will be an opportunity to mingle with fellow and aspiring technologists, enjoy refreshments and an engaging lightning talk on the latest in Artificial Intelligence.
The venue: Thrive DTSP
Connect with Tampa Bay professionals, share ideas, and explore the future of AI in the vibrant community setting of the coworking space known as Thrive DTSP!
The lightning talk: James Gress
In addition to networking, there’ll also be a quick talk on AI, delivered by James Gress, Director at Accenture for Leading Emerging Technologies!
The participating tech meetup groups
This will be a meetup of meetups, and the participating meetups will be:
- Tampa Devs
- Tampa Bay Techies
- Tampa Java User Group (JUG)
- Tampa Bay Artificial Intelligence Meetup
- Suncoast Software Skills Meetup
- Coders, Creatives, and Craft Beer
- Tampa Bay Data Engineering Group (TBDEG)
- Tampa Bay Data Science Group (TBDSG)
- Tampa Bay Python
- Tampa Bay Product Group
- Tampa Bay QA and Testing Meetup
…and more groups will be participating!
It’ll be an opportunity-rich environment for to network ing with like-minded individuals and connecting with recruiters and professionals who can help advance your career or support your transition into a new field!
Free food and drink
Thanks to these sponsors…
…there’ll be food provided for free. The beverage sponsor will be announced shortly!
And of course, the accordion…
Is it really a Tampa Bay tech event without it?
Join us this Wednesday!
Meet up with your peers, make new friends and catch up with old ones, and create some opportunities this Wednesday at the Tech Groups of Tampa Bay’s Happy Hour Networking + AI Lightning Talk!
When I say that manufacturers are trying to put AI into everything, I mean everything. Case in point, here’s an ad for AI condoms by Manforce, a condom brand in India…
…and of course, there’s also an app, which you connect to the condom by scanning a QR code.
And of course the company’s VP in charge of sales wrote about it on Linkedin:
I’ve been seeing a concerning trend over the past couple of months, and perhaps you have too, where people are becoming increasingly reliant on AI for coding, and it might not be working out well for most of them.
A non-coder relying on AI
Consider this entry from a couple of weeks ago in the subreddit for Cursor, the AI code editor:
Here’s the text of their post:
Cursor f*ck up my 4 months of works
Disclamer, I’m a moron who worked on the same project without thinking about the risk that Cursor could break everything. Yesterday, Cursor (even though I only asked it to feed a view on my UI) destroyed months of development.
My question: How do you back up your projects/versions to ensure that the next action on cursor is reversible? Ops!
Also, I know that while I’m the concern, cursor isn’t the only culprit, it’s also Claude (while good overall) still has some flaws
Don’t take the misspellings and strange grammar as a sign of a lack of smarts — there are “tells” such as the pluralization of “work” that suggest that the author’s first language isn’t English. And in a follow-up comment, they wrote:
I’m not a dev or engineers at all (just a geek working in Finance)
So what I see is someone with the mental capacity to master another language, seeing a problem in their area of expertise that could be solved by an application, and then setting out to build that application with the assistance of AI, even though programming isn’t something they’re familiar with.
First, I think we should celebrate that kind of go-getter attitude.
Second, those of you who are programmers have already seen the post’s author’s rookie mistake. It’s in this question:
My question: How do you back up your projects/versions to ensure that the next action on cursor is reversible?
You probably thought: Of course, they don’t know version control exists!
At the moment, even the best LLM will simply focus on answering the user’s questions and not stray too far to make helpful asides or ask clarifying questions, such as “Have you heard of Git?”
Junior coders on AI
This article appeared on Namanyay Goel’s blog a couple of days after the Reddit post, and according to its stats, it’s already garnered a million views:
Here’s the text of the introduction:
Something’s been bugging me about how new devs learn and I need to talk about it.
We’re at this weird inflection point in software development. Every junior dev I talk to has Copilot or Claude or GPT running 24/7. They’re shipping code faster than ever. But when I dig deeper into their understanding of what they’re shipping? That’s where things get concerning.
Sure, the code works, but ask why it works that way instead of another way? Crickets. Ask about edge cases? Blank stares.
The foundational knowledge that used to come from struggling through problems is just… missing.
We’re trading deep understanding for quick fixes, and while it feels great in the moment, we’re going to pay for this later.
The first line in the following section shouldn’t really be shocking but it still feels shocking:
I recently realized that there’s a whole generation of new programmers who don’t even know what StackOverflow is.
(As user number 216 of Stack Overflow, with over 8,000 reputation to my name, this hurts a little.)
With AI, these junior developers gain speed of delivery, but at the cost of understanding what they delivered does. Which means that they can’t maintain or modify what they built — at least, not without even more AI assistance. Over time, what they build becomes a collection of quick fixes arranged together without any consideration of the system as a whole. That’s a whole lot of tech debt.
There’s more thought on this article in this video by Forrest Knight
A super-senior coder relying on AI
And finally, here’s a tweet from the very beginning of February, a couple of weeks before the prior two pieces:
In case you’re not familiar with the name, Andrej Karpathy has forgotten more about computer science and AI than most of us will ever learn. He was the director of artificial intelligence and Autopilot Vision at Tesla, and also worked at OpenAI, where he specialized in deep learning and computer vision. He also has a YouTube channel that’s worth checking out if you really want to boost your AI/ML skills.
Here’s the text of his tweet:
There’s a new kind of coding I call “vibe coding”, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It’s possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard. I ask for the dumbest things like “decrease the padding on the sidebar by half” because I’m too lazy to find it. I “Accept All” always, I don’t read the diffs anymore. When I get error messages I just copy paste them in with no comment, usually that fixes it. The code grows beyond my usual comprehension, I’d have to really read through it for a while. Sometimes the LLMs can’t fix a bug so I just work around it or ask for random changes until it goes away. It’s not too bad for throwaway weekend projects, but still quite amusing. I’m building a project or webapp, but it’s not really coding – I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.
This is great for Karpathy, but I’ve already talked with developers who’ve fully embraced the first part of the tweet, where Karpathy throws a lot of work to the AI. The problem is that they’re ignoring these key points from the second part:
- The code grows beyond my usual comprehension, I’d have to really read through it for a while.
- Sometimes the LLMs can’t fix a bug so I just work around it or ask for random changes until it goes away.
- It’s not too bad for throwaway weekend projects, but still quite amusing.
- And let’s not forget the last three words of his tweet: it mostly works.
Karpathy is very, very good at coding and has lots of experience. He’s internalized a lot of best practices and has developed an instinct for programming and can spot “code smells” a mile away.
The people who’ve been talking to me about getting into “vibe coding” are not Karpathy, and some of them have mentioned that they have that increasingly common problem where they say “I know how to use my programming language and framework, but I don’t know how to apply what I know to build an application from the ground up.”
They’re not ready for vibe coding, but they’re doing it anyway. If your main gig involves working with code — and especially working with other people’s code — you’d better prepare for some interesting times over the next few years.
The tl;dr
- What: Tampa Bay Artificial Intelligence Meetup
- Why: To build an AI job application helper app
- When: Monday, March 17, 6 – 8 p.m.
- Where: Embarc Collective (802 E. Whiting St., Tampa)
- How you register: On the Meetup page
Details
You’ve probably heard lots of stories from friends and acquaintances about how much work you have to do to conduct a job search these days.
There are a fair number of data points showing that this is true; the Silicon Valley-based career guidance service Pathrise, says that job seekers who sent 20+ job applications every week got more interviews and landed a job sooner.
That’s a lot of work, especially since the general advice is to customize your resume for every job application.
Wouldn’t it be nice if there were a way to get some help customizing your resume for every job application you have to fill out?
With Anitra and I leading you through the steps, you’re going to build just that on Monday, March 17th at the Tampa Bay AI Meetup at Embarc Collective. Along with us, you’ll code up an AI-powered application that takes two inputs…
✅ Your resume
✅ The job description of a job you’re applying for
…and it produces a version of your resume that’s been fine-tuned in these ways:
- Present you in the best possible light
- Make changes so that your experiences, talents, and achievements show that you’re an excellent fit for the job
- Tune your experience and skills to better match the job requirements
- Update your resume to use key words and phrases from the job description
- Correct spelling and grammar mistakes
- Fix phrasing to be more clear and concise
- Improve sentence structure and use action verbs
Bring your laptop! We’ll provide you with a “starter” project and access to an AI account, and we’ll walk you through the process of writing a Python app in Jupyter Notebook that does what we described above. In the process, you’ll learn:
- About Jupyter Notebook, one of the preferred tools for AI and data science
- How to make calls to an AI API
- How to build an application based on an AI API
You will leave the meetup with a working Python app that does what we’ve described above: help you fine-tune your resume for specific job applications!