Are there good guides about how to write Agents or good repos with examples? Also, are there big differences between how you would write one in Codex cli vs Claude code? Can there be run on it interchangeably?

> I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media.

2026 is just when it picks up - it'll get exponentially worse.

I think 2026 is the year of Business Analysts who were unable to code. Now CC et all are good enough that they can realize the vision as long as one knows exactly the requirements (software design not that important). Programmers who didn't know business could get by so far. Not anymore, because with these tools, the guy who knows business can now code fairly well.

> - How much of society is bottlenecked by digital knowledge work?

Any qualified guesses?

I'm not convinced more traders on wall street will allocate capital more effectively leading to economic growth.

Will more programmers grow the economy? Or should we get real jobs ;)

Great point about expansion vs speedup. I now have time to build custom tools, implement more features, try out different API designs, get 100% test coverage.. I can deliver more quickly, but can also deliver more overall.

I don't see the AI capacity jump in the recent months at all. For me it's more the opposite, CC works worse than a few months ago. Keeps forgetting the rules from CLAUDE.md, hallucinates function calls, generates tons of over-verbose plans, generates overengineered code. Where I find it a clear net-positive is pure frontend code (HTML + Tailwind), it's spaghetti but since it's just visualization, it's OK.

> The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic.

Does this not undercut everything going on here. Like, what?

> What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows a lot.

No doubt that good engineers will know when and how to leverage the tool, both for coding and improving processes (design-to-code, requirement collection, task tracking, basic code reviewal, etc) improving their own productivity and of those around them.

Motivated individuals will also leverage these tools to learn more and faster.

And yes, of course it's not the only tool one should use, of course there's still value in talking with proper human experts to learn from, etc, but 90% of the time you're looking for info the LLM will dig it from you reading at the source code of e.g. Postgres and its test rather than asking on chats/stack overflow.

This is a trasformative technology that will make great engineers even stronger, but it will weed out those who were merely valued for their very basic capability of churning something but never cared neither about engineering nor coding, which is 90% of our industry.

I do feel a big mood shift after late November. I switched to using Cursor and Gemini primarily and it was big change in my ability to get my ideas into code effectively. The Cursor interface for one got to a place that I really like and enjoy using, but its probably more that the results from the agents themselves are less frustrating. I can deal with the output more now.

I'm still a little iffy on the agent swarm idea. I think I will need to see it in action in an interface that works for me. To me it feels like we are anthropomorphizing agents too much, and that results in this idea that we can put agents into roles and them combine them into useful teams. I can't help seeing all agents as the same automatons and I have trouble understanding why giving an agent with different guideliens to follow, and then having them follow along another agent would give me better results than just fixing the context in the first place. Either that or just working more on the code pipeline to spot issues early on - all the stuff we already test for.

Now that it's real, is there a minimum bar of non-AI-generated code that should be required in any production product? Like if 100% of the code is AI generated (or even doom-tabbed) and something goes wrong in prod, (crash, record corruption, data leak, whatever) then what? 99%? 50%? What's the bar where the risk starts outweighing the reward? When do we look around and say "maybe we should start slowing down before we do something that destroys our company"?

Granted it's not a one-size-fits-all problem, but I'm curious if any teams have started setting up additional concrete safeguards or processes to mitigate that specific threat. It feels like a ticking time bomb.

It almost begs the question, what even is the reward? A degradation of your engineering team's engineering fundamentals, in return for...are we actually shipping faster?

  - observe error rate uptick
  - maybe dig in with apm tooling
  - read actual error messages
  - compare what apm and logs said to last commit/deploy
  - if they look even tangentially related deploy the previous commit (aka revert)
  - if its still not fixed do a "debug push", basically stuff a bunch of print statements (or you can do better) around the problem to get more info

The whole thing is about getting rid of experts and let the entry level idiots do all the work. The coders become expendable. And people do not see the chasm staring back at them :D. LLMs in their current form redistributes "intelligence" and expertise to the average joes for mere pennies. It should be much much more expensive, or it will disrupt the whole ecosystem. If it becomes even more intelligent it must be bludgeoned to death a.k.a. regulated like hell, otherwise the ensuing disruption will kill the job market and in the long term human values.

As an added plus: those, who already have wealth will benefit the most, instead of the masses. Since the distribution and dissemination of new projects is at the same level as before, meaning you would need a lot of money. So no matter how clever you are with an llm, if you don't have the means to distribute it you will be left in the dirt.