The current tools are the infancy of AI assisted coding. It’s like the MS-DOS era. Over time maybe the backpropagating from “your comfort language” to “target language” could become commonplace.

Most agent tools right now don't give you good visibility into what sub-agents are doing or what decisions they're making. You zoom out, let it run, come back to a mess. Tools like OpenCode and Amazon's CLI Agent Orchestrator are trying to fix this - letting you watch what each agent is actually doing and step in to correct or redirect.

OpenCode actually removed the ability to message sub-agents directly. I get why - people would message one after it finished, the conversation would fork off, and the main orchestrator lost track. But I don't love that fix because being able to correct or pivot a sub-agent before it finishes was genuinely useful. They bandaided a real problem by removing a good feature.

Honestly the model that works best for me is treating agents like junior devs working under a senior lead. The expert already knows the architecture and what they want. The agents help crank through the implementation but you're reviewing everything and holding them to passing tests. That's where the productivity gain actually is. When non-developers try to use agents to produce entire systems with no oversight that's where things fall apart.

So I wouldn't want agent tools to be "calm" and fade into the background. I want full transparency into what they're doing at all times because that's how you catch wrong turns early. The tooling is still early and rough but it keeps getting better at supporting experts rather than trying to replace them.

I am recently using this tiny[1] skill to generate an order on how to review a PR and it has been very helpful to me.

https://www.dev-log.me/pr_review_navigator_for_claude/

(Waiting for Cerebras coding plan to stop being sold out ;)

I've used them for smaller tasks (making small edits), and the "realtime" aspect of it does provide a qualitative difference. It stops being async and becomes interactive.

A sufficient shift in quantity produces a phase shift in quality.

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That said, the main issue I find with agentic is my mental model getting desynchronized. No matter how fast the models get, it takes a fixed amount of time for me to catch up and understand what they've done.

The most enjoyable way I've found of staying synced is to stay in the driver's seat, and to command many small rapid edits manually. (i.e. I have my own homebrew "agent" that's just a loop of, I prompt it, it proposes edits, I accept or edit, repeat.)

So then the "synchronization" of the mental state is happening continuously, because there is no opportunity for desynchronization. Because you are the one driving. I call that approach semi-auto, or Power Coding (akin to Power Armor, which is wielded manually but greatly enhances speed and strength).

Up until recently, LLMs just plain sucked. You'd set them on a task and then spend hours hand-holding them to output something almost correct.

Nowadays you can have a conversation with the chatbot, hash out a design, rubber duck and discuss what-ifs until you have a solid idea of the thing you're building, codified in a way an agent could understand, and now you have a PLAN.

From there, it's a matter of setting the agent in motion and checking from time to time to make sure it's not getting stuck on something under-specified.

That said, I've found that this kind of workflow works a lot better with claude than with gemini.

I find the inline stuff so incredibly annoying because they move around the text I am looking at.

> “Focus on…” would allow the user to specify what they're interested in changing and present only files and lines of code related to their specified interest.

> “Edit as…” would allow the user to edit the file or selected code as if it were a different programming language or file format.

^ This is a genius idea - someone add this to claude

When a human junior writes code, they leave breadcrumbs of their thinking — commit messages, PR descriptions, comments explaining why they chose approach A over B. You can reconstruct their reasoning from the artifact trail.

Agents don't do this naturally. You get a diff with no context for why it went that direction. So the reviewer has to reverse-engineer the thinking from the code alone, which is actually harder than reviewing human code because there are no "tells" — no familiar coding style, no consistent patterns that hint at the developer's mental model.

The semi-auto approach mentioned upthread works precisely because it solves this: you were there for every decision, so there's nothing to reconstruct. The productivity loss from staying in the loop is offset by the time you save not having to audit opaque changes after the fact.

Yikes.

By the way, the whole website is strange. Just the name alone "haskell for all".

Many years ago when I tried to learn Haskell (and wrote some haskell code that worked but it was sooooo much harder when compared to ruby or python), one of the few things that appeared early on, aside from the monad barrier, was that many haskell people said that Haskell is deliberately not for everyone. Back then this was when IRC was still en vogue, so I "heard" that via various discussions on #haskell.

I did not fully understand this part, because ... why would you write a language that only a few big brain people could use? I found that elitistic and snobbish, even arrogant.

Only at a later time did I understand one part of the meaning. The "we don't want you here" also means "we don't want YOU to change haskell into some other new meta-variant". I understood this much better when some guys wanted to have ruby embrace types. Then I understood that people not only want to change a language but also want to ruin it; whether on purpose or because they prefer something else (such as their brain embraced types-only code bases) is a separate discussion. I still find the haskell attitude very elitistic but I at the least understand that they don't want everyone to use - and change - Haskell.

> For example, someone who was new to Haskell could edit a Haskell file “as Python” and then after finishing their edits the AI attempts to back-propagate their changes to Haskell.

I like the general idea behind "write in any language, have it work in EVERY language". But the whole AI movement seems more about trying to dumb down people really or make them lazy, in many ways. I have seen people use it to great effect, so I am not at all saying AI has no use cases. What I am however had noticing is that it made many normal folks super-lazy. They type on their smartphone, solution comes out, task finished, move on. That's not necessarily only bad, but it comes with trade-offs. My approach is much slower, but it is systematic and I am in full control of what is documented how and where.

> This is obviously not a comprehensive list of ideas, but I wrote this to encourage people to think of more innovative ways to incorporate AI into people's workflows

Oh he has achieved this in a different way. Now I have another reason to not want AI in my "workflows". The whole website also seems super-strange to me. Has he used AI to write the whole content and layout? It's hard to say because I don't know how it used to be in the past, but the paragraphs and the content seem so strange. I suspect he used AI to generate the layout too; and some of the content as well. We are losing "interaction" with real humans here too (ok ok, there is not a lot of interaction with regards to a static website, but if a blog is written by AI, then that is not really any possibility for interaction with a human - you could not even distinguish WHO wrote the content or made the decisions such as which style to choose and so forth; it looks very fake to me or, at the least, in part. I typically don't see this with other blogs.).