I think it's wild that AI has turned software developers into QA testers.

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better: Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better: Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better: Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work! In summary: - As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding. - Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

The ability to explain why we shouldn't build something is becoming a legitimate technical skill. It probably always was.

It’s clear that the best people to direct agents are people who know how to code and build products. No agent is skilled at recursive self-improvement (make this software/product better independently) and I don’t see anybody making progress on this front.

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To be right a lot you need to have the courage to change your mind when the new data says you were wrong. I'll go against the grain here and say the company is doing the right thing by hiring people back. But I'll also say that I hope they are paying those that boomerangs a lot more to compensate them for this churn.

Companies are downsizing aggressively but the people they retain are more important than ever because they know where all of the dead bodies are buried. They will have to give outsized rewards to desperately keep people in bad situations if companies cut too far. If you’re worried about being laid off, it's a good time to be the only person that knows how the critical systems work.

You can’t say coding is solved but then have a flaky product. Coding will be solved when there are zero bugs, and your system has 100% uptime.

My first thoughts about Jev: 1. The value is in the near-real-time generation of discrete options with associated distributions. You could probably do better on your own for specific domains, but doing it on the fly and at what seems to be arbitrary high quantity is truly amazing, if the quality is there. I suspect a lot of load is going to shift from agent to Jev. 2. A new tool chain will emerge to combine with LLMs. Like other tooling in AI, the first ones will be wonky, people will piece together something that works, then the frontier labs will incorporate this new hybrid architecture in their own products. I suspect Jev will be a hot acquisition target if what they have announced is true. I believe the frontier labs can develop similar on their own but I suspect it will take on the order of a year to do so. In AI time that’s a decade. 3. I am curious what type of bench marking and qualitative measures will surface. This is a big unknown-unknown but really exciting. 4. A good decision maker that won’t blow up scope (as my buddy @kunchenguid puts it with current LLMs “I ask it to make me a meal and it remodels my kitchen”) gives me real line of sight to getting humans out of the loop for recursive self-improvement. Currently, there’s no credible way to point a swarm of agents to make a system better without supervision. Good judgement is the critical piece to taste. If Jev is for real, that moat humans have may disappear. I truly believe this marks a paradigm shift. Buckle in folks.

I don’t know whether we're going to generate technical debt at a record pace or whether it's going to go extinct.

The value of knowing how to build something is tanking while the value of knowing what shouldn’t be built is skyrocketing.

The funniest possible outcome is that senior engineers become more valuable because we see what happens when unskilled people direct agents.

The senior engineer's natural predator is a meeting that could have been a Slack message. "Here we observe the senior engineer in his natural habitat. He senses the calendar invite before it arrives. Watch closely... he declines with 'no agenda, no attenda.' A remarkable display of survival instinct."

Software developers will be cooked when the AI labs start laying off their developers. If they are hiring (and their availability isn’t 100%) there’s still a need.

What’s the best way to birdie?

Companies flattening management while expecting engineers to have more influence is a pretty strong signal about where the IC role is going.

We spent decades complaining that the documentation didn’t exist, and now that AI can trivially generate it, nobody even reads the code anymore.

ADD + AI = means I am so effective at stuff that doesn’t matter.

The weird thing about modern work is that everyone says they're overwhelmed, yet somehow everyone still finds time for another meeting.