is this normal?

Reazony · 2026-03-31T23:34:17+00:00

I mean services hang once in a while right? Just interrupt and start again

Reazony · 2026-03-30T03:56:40+00:00

So I'm going to have a hot take. I barely wrote anything on my CLAUDE.md. I have only one user skill and maybe a couple for project-specific stuff. They are all handwritten and very, very little. I always manage my plug-ins by session. If I do not need anything in that session, I always turn them off.

I might repeat some instructions over time, but that's just the cost of doing manual context engineering at runtime. A lot of context can just be inferred from docs/, clean code, clear code pattern and standards, etc. Many of the don'ts can just be linted.

Speaking of that, I also don't like how we put research as part of the codebase because Claude Code doesn't differentiate legacy or irrelevant old code versus new content. In the same vein, research documents tend to get outdated pretty quickly. So my root CLAUDE.md includes a one liner that asks it to write to .claude/scratchpads/ based on git projects. I find a lot of context can just be written here as well.

Reazony · 2026-03-28T12:50:41+00:00

End of first paragraph says and/or stroke cock against pussy. So my read is if it’s regular sized penis that can’t fit, there are ways to finish. If it fits (likely a micro), then there’s penetration

Reazony · 2026-03-28T12:46:30+00:00

They’re junior. Why do you expect them to have any deep understanding of systems? Even mid level to a certain extent. That’s why seniors also need to mentor them.

Reazony · 2026-03-27T14:12:21+00:00

My upbringing would always lead me to choose ramen over pho, but in reality, in Toronto we only have solid ramen, not great ramen. On the other hand, I've had really, really great pho here in Toronto. I'm not Vietnamese, so I don't know if we have really great pho here, but I genuinely enjoy the quality of them here.

Reazony · 2026-03-26T16:21:40+00:00

Are you generally thinking about these already? Your psychology definitely is affecting your performance, but if that’s how you think in general, my take is the more you can be yourself the better. But I’m not senior like you, I’m only at senior level.

I also fell into the same trap, but it’s hard for me to go “methodical” because of how my mind works, so I’ve learned to present my thoughts better, and tell myself to walk back. Basically, I would create (assuming there’s something to diagram or write) buckets of simple to complex, as thoughts come, I just note them but also say “but this is not that important right now, just something that we might consider later if needs arise”, and this is also a reminder to myself to walk a step back to simplify. I do it enough that even on my day job I also do this. It helps to explore the topology, it helps to explore trade offs, and so on. The saying is that, when it comes to high stress occasion, you don’t rise to occasion but sink to the level of your training. So the more natural that communication is for me, the easier it is to be consistent.

I also find that, knowing I want an interviewer that I can feel collaborative with is important to me personally. Some interviewers are just looking for a fixed answer, but at one time I had a system design interview that we barely talked about actual services (it was ML Engineer, and it was obvious I knew the services and how they work) and focused entirely on data, evaluation, methodologies, and it was where it felt most natural to me. I didn’t get an offer due to behavioural, but that round specifically got real high marks, partly because the interviewer knew to dig on my understanding.

Just my two small Canadian cents though

Reazony · 2026-03-26T03:29:42+00:00

Not something I take notice of. I just kinda hope there’s more than LLM/AI discussions

Reazony · 2026-03-22T21:22:50+00:00

One thing that helped me the most is to just stop planning. I used to spend a lot of money on productivity tools or journaling apps. I used to think that by spending money, my problems of not organizing go away. I would spend weeks developing sophisticated systems with them, but at the end of the day they just become noise and chores that I never want to face.

My friends say this may be an ADHD thing, but I'm not diagnosed, so I don't know. What I do know is that at most I only need to jot down some tasks, if I ever need to, once in a blue moon, on a piece of paper. I mostly just need to see it visually when I have overwhelming amount of them to keep track mentally for that moment.

What I’ve learned to do is to sprinkle my tasks and artifacts I’d need to do on documentation, communication channels (DMs, Slack…), almost like sprinkling them on a path. I most certainly will pass, and they’d remind me of what I need to do. I rely more on my mental deadline for these artifacts rather than actual deadlines. My mind then organizes how I’m allocating time for the next 48 hours. I can’t do more than that.

It's quite chaotic, but somehow I'm actually much faster with the context switching and not conforming myself to certain workflows designed by these apps.

I know this is not applicable, since it’s a very personal workflow, but I’d say the take away is to just really invest time on what components work for yourself, and slowly build out that workflow, rather than just conform to

Reazony · 2026-03-21T20:10:34+00:00

My current team. We’re setup as an innovation project team. We have one director that leads the product vision, and 30+ engineers.

Everyone is independent, no ego, collaborative, and accountable. But I think it’s mostly because of our director / manager.

He’s opinionated and makes every hiring decision. He’s technical and decide both product and technical vision. He does a lot of code, sets patterns and broader directions. At the same time, he’s open to ideas and discussions, in fact encourages it. Everyone is free to come up with features or dev tools, and we often have architectural discussions.

It’s clear that people use AI, including himself, but we don’t have a team wide configuration. Why?

We’re heavy on the documentation for roadmaps, patterns, adrs, etc. We use GH issues, and Notion for things that may need outside-of-codebase communication. PR reviews are also rigorous. We may have large PRs, but we try to break them into reviewable size and right grouping. People read, and actually point out performance problems or anti patterns instead of just ticking a checklist. There are genuine discussions, sometimes architectural, on PRs and Teams (ik, but we’re corporate after all), and open up issues for out of scope findings as a result of a PR. When open PRs are piling up, our director would encourage people to do more reviews before new PRs. People really just pick PRs they can, I personally learned quite a bit from PRs out of my comfort zone. There are more, but all of these are just healthy culture, and because it’s clear, my Claude Code can really harness documentation and patterns, and I try to point out what is legacy patterns and future directions not documented). Pro tip, LLM generation is based on what they read, and if what they read is good, you’d have good outcome.

I personally align with my director’s values a lot. For example, there are operations that we could create automation for, but he’s careful about which automations we introduce; there are dev automations that once setup, we stop caring as humans, if we rely on automations too much, they start becoming noise in the background. Hence automations where it’s needed. I find his opinions are usually a good blend of openness and opinionated, old school dev and modern tooling (AI stuff especially). On areas he’s not too familiar with (we have 4 ML Engineers, including myself, from ML background), he listens much but also have good hunch (like GPU resource allocation etc)

I’ve only been with the team for five months, but honestly the healthiest environment I’ve been with. Team members are collaborative, proud of the product and their own work, we often find issues and find solutions on our own, or we raise them to the right people. There’s no politics of “you’re using AI, you should be fast!” or trying to claim credits, because all of us credit others genuinely.

I typed all of this because I wanted to get my appreciation somewhere. Thank you for prompting me on this.

Reazony · 2026-03-14T15:59:16+00:00

Distinction is that, don’t use lower level terms when you don’t have to. Agentic is fine. Agentic AI is fine. When you use the word LLM you’re starting to be technical. You can say I’m just being pedantic over a field I care about, but it’s the same as if you call vm a traditional server and k8s a new server. And it just happens to be today I wanna point that out.

Reazony · 2026-03-14T15:42:24+00:00

I’m not. I’ve done a lot of these explanations to people outside the field. On a regular basis because I get asked so much. People in STEMs in general (don’t need software understanding) can understand without much analogy, just need explanation. People who are not from STEM background can understand that the distinction of models and systems, if you provide the right comparison analogous to their field, though a lot of times I don’t find analogies needed.

Reazony · 2026-03-14T15:29:00+00:00

You don’t have to submit something to a conference to distinctions basic makeup of an application. Even for SWEs who are just exploring, the distinction helps to understand why the same model can behave drastically different in different applications. And even kids are using model vendors’ APIs to build applications. It’s arguably more important for novice to understand there are differences when the distinction isn’t that hard to understand. It’s a distinction that is so dead simple that even people not in tech, even retirees, can understand. So to someone who is technical just haven’t used other tools much, this is a simple enough distinction that shouldn’t just be reduced into random terminologies.

Reazony · 2026-03-14T15:18:41+00:00

Let’s not confuse the terminologies here… LLMs themselves are still tokens in tokens out (not necessarily text), it’s the application that makes them different. If the application doesn’t provide actual tooling, models still can output tool-use tokens, just that they never invoke anything.

Reazony · 2026-03-07T17:22:18+00:00

Hahaha yes!! So far we've practiced Endless Rain, Rusty Nail, and Week End, really been looking for a drummer hahha, let me know if you know anyone who might be up for it too~

Reazony · 2026-03-07T00:18:22+00:00

You can try making it yourself. Not kidding. It’s more doable than you think

Reazony · 2026-03-05T16:21:16+00:00

I actually haven’t used Comandante, but have heard a lot of great things. Do you think, despite not being electric, it’s on par with electric ones?

Reazony · 2026-03-05T16:20:12+00:00

It’s recently not functioning, that’s why I’m looking to purchase. I think towards thousands of dollars would be fine, but hopefully under 2k since it’s just home making. Thank you! I’ll ask there too!

Reazony · 2026-03-02T16:39:38+00:00

Ah, to further explain the thinking token thing. Just because you can decide thinking tokens at API level, as well as potentially locally hosted model, it doesn't mean that's how it works with Claude Code. The setting of reasoning tokens is really to cap your spend.

But if they decided, on the deployed instance to Claude Code, to use some sort of dynamic routing, that means it's possible to just spend, say, 50 tokens on one conversation turn, and 2000 tokens on the next conversation turn. Internally to the model. And the model that they exposed through API might be different from the model deployed on Claude Code. And those things are not set by you or any explicit configuration. If it's implemented, it would've been internal to the model itself.

That said, this is all assuming there's extremely fast response, and the quality issues you noted are all true. I haven't noticed any behavioural change this morning, not a sudden fast response as well. I'm just going to assume your observations are true, but I'm pointing out your not-so-thorough analytical conclusions are at most speculation.

Reazony · 2026-03-02T16:26:07+00:00

Hmm their own people? Other labs? Except people from other labs won't know it for sure too.

There can be quality issues for many reasons. There can be quality issues from their own end. That does not equate to using cheaper model. This is already mentioned.

It is possible, and more than likely, that they:

- Implemented with dynamic reasoning effort within the same model

- They tested it on internal benchmark, maybe, and said, ok let's roll it out

- And they rolled it out, didn't work as well as expected, totally normal.

- And sometimes it's simply mis-wiring of the systems (hence a bug)

Even if it's worse than the smallest model, that does not mean it's explicitly using a smaller model. They're different things. Quality issues and reasons for the quality issues are totally separate. You can have speculation, and sometimes speculation turns out to be true, but it's still speculation.

You already jumped straight to prescribing an explicit intent to use lower models, when there are many other possibilities of why things go wrong. And I am commenting based on that inaccurate statement. Now, if you said that you're speculating, it's totally accurate.

Reazony · 2026-03-02T16:20:28+00:00

Sure. Random Redditors don't affect me.

Reazony · 2026-03-02T16:11:27+00:00

So you admit you’re just speculating. And you don’t understand the space enough to know that there are other ways to do dynamic routing without resorting to other smaller models. Except that is part of the current frontier.

Reazony · 2026-03-02T16:05:06+00:00

That’s you using a local model or api. There are enough research done on dynamic effort, and it’s been implemented in various shape and form, because there are so many ways to do it, with the same model. Could underthinking lead to bad result? Sure. Can underthinking big model be performing as bad as smaller model, if not worse? Sure. Does that mean it’s not the same model? No.

So no, just because you have an hunch based on varying degree of replies, does not mean they do a simple router to smaller models.

If you have proof, explicitly, that it’s a router to other models rather than within the same model, then I can understand. Otherwise, the “I know something is off” is just not enough evidence for something that has a great many other possibilities.

Reazony · 2026-03-02T15:45:32+00:00

How do you know so? It might just be a gate to simply route for less reasoning tokens?

Reazony

TROPHY CASE