81–90 of 128 posts
In the next year developers need to realize normal people do not care about the tech stack or the tools used, there are far too many written thoughts and opinions and not enough polished deployed projects. From an industry standpoint it’s business as usual, acquihires from products that LLMs apparently couldn’t save.
I really feel this bit: > With agentic coding, part of what makes the models work today is knowing the mistakes. If you steer it back to an earlier state, you want the tool to remember what went wrong. There is, for lack of a better word, value in failures. As humans we might also benefit from knowing the paths that did not lead us anywhere, but for machines this is critical information. You notice this when you are…
When I find myself in a situation where I’ve been hammering an LLM and it keeps veering down unproductive paths - trying poor solutions or applying fixes that make no difference but eventually we do arrive at the correct answer, the result is often a massive 100+ KB running context. To help mitigate this in the future I'll often prompt: “Why did it take so long to arrive at the solution? What did you do wrong?” Then…
I have a /review-sessions command & a "parse-sessions" skill that tells Claude how to parse the session logs from ~/.claude/projects/, then it classifies the issues and proposes new skills, changes to CLAUDE.md, etc. based on what common issues it saw.
I've tried something similar to DISCOVERIES.md (a structured "knowledge base" of assumptions that were proven wrong, things that were tried, etc.) but haven't had luck keeping this from getting filled with obvious things (that the code itself describes) or slightly-incorrect things, or just too large in general.
I understand the parasocial bit. I actively dislike the idea of gooning, ERP and AI therapists/companions, but I still notice I'm lonelier and more distant on the days when I'm mostly writing/editing content rather than chatting with my agents to build something. It feels enough like interacting with a human to keep me grounded in a strange way.
You guys need to touch grass. Go join a kickball league or something.
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You guys need to touch grass. Go join a kickball league or something.
I'd argue that doing something you don't like with people you're not into is a L. Loneliness isn't optimal but for some people it's the lesser evil. I'm married though, so I have a floor, I'm sure some people are lonely enough to benefit from being around people even under the worst of circumstances.
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I'd argue that doing something you don't like with people you're not into is a L. Loneliness isn't optimal but for some people it's the lesser evil. I'm married though, so I have a floor, I'm sure some people are lonely enough to benefit from being around people even under the worst of circumstances.
Sure, I’m not saying it needs to be kickball. I’m just saying if you find yourself being grounded by an LLM, maybe you should seek out a community of people you do actually like who do something you actually like.
A really interesting point that keeps coming up in discussions about LLMs is “what trade-offs need to be re-evaluated” > I also believe that observability is up for grabs again. We now have both the need and opportunity to take advantage of it on a whole new level. Most people were not in a position where they could build their own eBPF programs, but LLMs can One of my big predictions for ‘26 is the industry followin…
Do you have any example repos of these OSS projects? I'm being reminded of this post every time people keep extolling how "productive" LLMs are: https://mikelovesrobots.substack.com/p/wheres-the-shovelware... Where is the resulting software?
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Difference: companies are not pushing their employees to use stack overflow. Stack overflow doesn't waste massive amounts of water and energy. Stack overflow does not easily abuse millions of copyrights in a second by scraping without permission.
Another difference: stack overflow tells you you are wrong or tells you and do your own research or to read the manual (which in a high percentage of cases is the right answer). It doesn't tell you that you are right and proceeds to hallucinate some non-existent flags for some command invocation.
"Because LLMs now not only help me program, I'm starting to rethink my relationship to those machines. I increasingly find it harder not to create parasocial bonds with some of the tools I use. I find this odd and discomforting [...] I have tried to train myself for two years, to think of these models as mere token tumblers, but that reductive view does not work for me any longer. " It's wild to read this bit. Of cou…
It helps a lot if you treat LLMs like a computer program instead of a human. It always confuses me when I see shared chats with prompts and interactions that have proper capitalization, punctuation, grammar, etc. I've never had issues getting results I've wanted with much simpler prompts like (looking at my own history here) "python grpc oneof pick field", "mysql group by mmyy of datetime", "python isinstance literal…
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When I find myself in a situation where I’ve been hammering an LLM and it keeps veering down unproductive paths - trying poor solutions or applying fixes that make no difference but eventually we do arrive at the correct answer, the result is often a massive 100+ KB running context. To help mitigate this in the future I'll often prompt: “Why did it take so long to arrive at the solution? What did you do wrong?” Then…
Yep - this has worked well for me too. I do it a little differently: I have a /review-sessions command & a "parse-sessions" skill that tells Claude how to parse the session logs from ~/.claude/projects/, then it classifies the issues and proposes new skills, changes to CLAUDE.md, etc. based on what common issues it saw. I've tried something similar to DISCOVERIES.md (a structured "knowledge base" of assumptions that…
I do have to perform more manual adjustments/consolidation to the final postmortem before placing it in the discoveries md file, because as you pointed out LLMs tend to be exceptionally verbose.
Earlier quoted context omitted.
It helps a lot if you treat LLMs like a computer program instead of a human. It always confuses me when I see shared chats with prompts and interactions that have proper capitalization, punctuation, grammar, etc. I've never had issues getting results I've wanted with much simpler prompts like (looking at my own history here) "python grpc oneof pick field", "mysql group by mmyy of datetime", "python isinstance literal…
I've always used "proper" sentences for LLMs since day 1. I think I do a good job at not anthropomorphizing them. It's just software. However, that doesn't mean you have to use it in the exact same ways as other software. LLMs are trained on mostly human-made texts, which I imagine are far more rich with proper sentences than Google search queries. I don't doubt that modern models will usually give you at least somet…
My queries look like the beginning of encyclopedia articles, and my system prompt tells the machine to use that style and tone. It works because it's a continuation engine. I start the article describing what I want to be explained like it's the synopsis at the beginning of the encyclopedia article, and the machine completes the entry.
It doesn't use the first person, and the sycophancy is gone. It also doesn't add cute bullshit, and it helps me avoid LLM psychosis, of which the author of this piece definitely has a mild case.
I'm also tired of seeing claims about productivity improvements from engineers who are self reporting; the METR paper showed those reports are not reliable.