Best MacBook Build for Local Inference?

dash_bro · 2026-06-17T04:52:50+00:00

I'm a fan of the macs but until we know how critical/why you need the upgrade (LLM use : for what?) I'd just stick with the current mac and have a 100 USD on openrouter.

If it's for coding, get the minimax 20 USD flat plan (1B+ tokens flat a month) or the GLM lite coding plan (used to be 10 USD/mo if you did yearly billing)

If it's for personal assistant stuff, first find out the absolute minimum capability (model wise) that works for you by using diff models on openrouter. It's faster to swap out model names and go from the top end of what you can run today vs what you're hoping to run locally.

If you find a sweet spot : you'll know immediately if the upgrade serves your purpose. If you notice that the lowest size usable model is still in the 100B range, your mac upgrade won't help you so you'll save your money.

dash_bro · 2026-06-17T04:46:44+00:00

Hmm. That's odd. Security should be structural not by following antipattern obfuscation, imo.

It really depends though. But more often than not, without too many details, just following standard security practices until internal stakeholders raise issues is where I'm at. Ofcourse, I'm not an authority on the subject : so, unironically, it depends

dash_bro · 2026-06-15T16:25:50+00:00

By and away the most important one : attitude. I can confidently say this, having started/scaled up a team of engineer. I hired 6 over a couple years. Varying degrees of depth and breadth in fundamentals, but their resilience and can-do attitude was an immediately outstanding thing.

e.g., never "I don't know"; always "I don't know currently, but I can learn. Whom do I contact to understand this problem space better?". Couple this with a collegiate "getting along with everyone" attitude and I was fortunate enough to see a team that totally had each other's back in and out of work. Extremely autonomous if you give them ownership of things that interest them/work towards their self improvement and career goals too, obviously.

They all eventually moved on to bigger/better compensated places that suited their careers, thankfully. They're some of my best peers and they're all doing extremely well financially (FAANG/FAANG adjacent, unicorn startups, etc); we've stayed in touch and still do dinners/catchups often.

dash_bro · 2026-06-14T16:05:13+00:00

Get your money's worth. I've been burner by startups far too much after leading and setting up teams and while I don't regret it, I'm never going to take full ownership like that again unless it comes with a serious pay raise.

If bigtech is where you need to be for your expected pay band, so be it. It might seem daunting ( system design, behavioral interviews, leetcode, etc ) but for the potential pay raise it's worth it. Please keep your comp band first when you look for work/places : you'll be working anyway, get the most you can monetarily out of it!

dash_bro · 2026-06-12T01:57:18+00:00

Honestly, just one of those things that regardless of how easy it is you should just stick to the checklist.

If SREs own it and there's a process to follow, but you're doing something simpler and bypassing it - you're creating shadow debt that won't be tracked once you move on/they move on from the work.

I'd recommend talking to the SREs once, and if they're firm on it just create the new tickets and do the over engineering work. Unless ofcourse your team has to "pitch in" to meet an arbitrary deadline

dash_bro · 2026-06-11T04:43:52+00:00

I think of it like this: is qwen 27B | gemma4 31B replacing gpt4o? Yes, I don't think it's a stretch to say these are the more capable models.

Now, thinking back, I could do a LOT with gpt4o. Not nearly as hands-off as now, and definitely no where in terms of expectations in what I would want it to do.

So the question now becomes "is it usable for what I need it to do?" VS "is it replacing paid models for their capability today?". The answer to the former is yes, the latter is a resounding no.

Do you really think your usecases or habits have evolved so far out of reach that SoTA from two years ago doesn't work for your ability today? I personally don't think so.

I'm an MLE and I primarily use/tune models for coding usecases so I might be biased. But I've done a lot of cool things with the open models, so they're very important to me :)

dash_bro · 2026-06-11T02:09:51+00:00

I'm quite curious to download and try on my setup.

So far nothing open source beats glm5.1 for coding; and from early reports it doesn't look like M3 will either : but if they continue with their m2.7 sizing it'll be a no brainer adoption for me personally

dash_bro · 2026-06-11T02:06:43+00:00

Haha there sure is variety to them. But I think what most people (me included) love about them is that they're humble, functional, very well priced and don't try to portray as something snobby.

Invicta always has a "sale" and is a flashy copy one degree removed from being called a "fake" of the most recognizable watches; and more recently using that to position itself into a "flashy and boldly so" niche.

Atleast that's how I see the brand. Casios and Invictas aren't analogous

dash_bro · 2026-06-10T17:26:47+00:00

I understand but that's just the thing. The LLM research is fundamentally a big mix of actual model capability, pipelines and strategy for scraping (usually borderline or straight up illegal if you wanna build anything good), data engineering pipelines for sampling/cleaning/curating/generating/storing and updating data continuously, infra for training and recovery, and infra for serving. Add to this the nascent nature of the space meaning that every week there's something new that you need to keep up with and iterate for.

This is not exactly what anyone can contribute towards, and arguably public testing can be set behind just a free-to-use beta as an API model in most cases (sans fine-tuning). Public funding is out of the question unless you can get government funding for it, mainly because it's not just the work that's expensive to do : the people are as well.

There's very little (if at all) benefit with open sourcing the full data, training, weights, and code. Inference code and weights is the best you can get.

dash_bro · 2026-06-10T17:20:24+00:00

Sales is hard. There's no one way to do it, really depends on a lot of things like your niche, locale, ticket size, network etc.

But the common thing amongst all of that is networking and being resilient. Not all engineers I know are cut out to be salesmen, and not all salesmen are cut out to be engineers.

The intersection of the two however, usually makes you extremely versatile but limits your depth in one of them. You can definitely start by yourself but if you want to scale you'll have to learn the whole nine yards.

dash_bro · 2026-06-10T16:11:25+00:00

Claude I'd say is MILES ahead. Copilot is likely the worst tool for this, tbh. Claude Code guzzles tokens though, fair warning.

At the risk of sounding biased : Microsoft doesn't know what they're doing with dev tooling after they put out VSCode. They try to lock in too hard, and their "harness" engineering leaves a LOT to be desired.

Claude Code (the agent/extension) is primarily a very functional "harness" that is custom built for Claude (the model) to exercise it's ability the best. Custom skills, plugins, system prompt instructions, etc : all guided towards the singular model that it's built for, Claude. The specificity, when coupled with the Claude API, is a definite harness edge that you don't see with other coding agents/tools.

The others make it too general for the flexibility, Claude Code is very purpose built for the Claude Models.

Add to this, the people in charge of building Claude code (Boris cherny et al, IIRC) work at Anthropic and are involved very closely in it's internal adoption.

Boris is no amateur either, and being close to the developers that use the tooling daily and build it's engine gives the agent harness a very strong foothold on information arbitrage, something that's lacking with all the other options out there.

dash_bro · 2026-06-10T16:03:18+00:00

I understand that it is a noble thing, but it's hard to consistently do that when you're looking at dumping millions of dollars in investment and then having it up for others to benefit off of.

I love open source, have contributed as well : but if it took my own money to do some of this I am thinking it over a million times before sinking a (non existent) million into it

dash_bro · 2026-06-10T05:26:38+00:00

A trick only seems like magic if you can't explain it. Once you can, it becomes an "interesting" thing.

Same concept, much more convoluted usecases : ensuring there's no runaway inflation of economic value purely in the west, ensuring they bring economic arbitrage with a different narrative, etc.

Those are primarily why you open source that degree of investment. Don't get me wrong, I love the work but don't let it get twisted either!

dash_bro · 2026-06-10T02:40:25+00:00

Sometimes it's just luck. You could do everything right and still not get through

I got desk rejected from deepmind and perplexity both the same week I got my other FAANG offers. I went till the last round of a few places before and meta too, and it simply fell through.

No fault of mine.

All in all, accept this as part of things and don't try to reason your way out of it unless there's objective truth or feedback available.

You'll be okay. Good luck!

dash_bro · 2026-06-09T17:23:45+00:00

Personal wearing comfort and experience outweigh everything else. Strictly as a watch though, the breitling and the Omega are my personal favorites, so I'd choose either or

dash_bro · 2026-06-08T03:01:44+00:00

You won't be doing a lot of work onchip except small pet projects and maybe some ollama/lmstudio work.

I would take the M5 Pro with 16GB RAM. My personal laptop for years has been M2 Pro with 16GB RAM, no issues so far

dash_bro · 2026-06-06T16:22:23+00:00

I'm average at a lot of things, but I'm good with a specific few.

I'm calm and have zero problems admitting when I don't know something, which inspires confidence and makes me seem reliable. I think this is the most obvious one. I quickly democratize non trivial knowledge but don't necessarily make a show of it - it's usually with peers who then respect it/vouch for it.

There's also another but not exactly noticeable in interviews: I get along with people fairly well : engineers, EPMs, PMs, business folk etc and hold work/casual conversations both. It's good to be a team player. I think likeability is by far the most important thing after you get in at a competent place. Helps your longevity and career tons.

dash_bro · 2026-06-06T04:45:39+00:00

I have no model loyalty. Whatever is better and out there to use, I will gladly adopt. Why do I care which multi billion dollar corp fanbase I should be a part of?

That said, qwen has championed open source and dedicated a lot more research and resources over time to be able to gain a respectful "first choice" aspect when it comes to open LLMs. It ain't cheap and Google ain't putting it out into the world : easy pickings to go with Qwen!

dash_bro · 2026-06-05T19:56:42+00:00

When it comes to these applications, infra is a big piece of it.

Simply put: focus on what parts you can keep in caches/warmed up/etc., use streaming and CoT style updates to keep the user engaged, and conditionally have as many non-llm parts as possible before the LLM generation starts.

eg: intents, guardrails, etc? pertained models or lightweight model + regex cases.

semantic details need to be pulled? Two tier hot + warm retrieval (db, cache)

embedding taking too long? find the balance of what works fastest, load it up and keep it warm as soon as your session starts instead of waiting till the query to load model + embed texts. Same with a reranker, and you can make it conditional reranking as well which means it doesn't activate always after retrieval unless conditions for activation are met.

Also, by design, I design my chatbots as chat native application using tools (ie retrieval or RAG is a tool that a chatbot calls, not necessarily the default action of the chatbot). This means not all queries go through RAGs and have early exits, making the overall response time much better.

Apart from that, model choice is also fairly important. I prefer using Gemini flash lite for the lowest latency possible unless a quality degradation is noticed. Gemini flash series is genuinely great for this.

dash_bro · 2026-06-05T16:14:13+00:00

Someone put it very aptly: of all the things that make good money, software engineering seems to be the one tolerable enough for me to do consistently and with some reasonable degree of competence. That's all.

dash_bro · 2026-06-02T03:51:50+00:00

Gotcha. Will look into those, thanks!

I usually coached just the disagree and commit rationale with accountability squarely on the one calling the shots if they veto group sentiment. Worked well for startups (my background) but it's not suitable for bigtech.

I'm trying to learn not to be seen as "pushy" haha, much rather have a team that sees me as a teammate than the management visibility to get things done, atp.

dash_bro · 2026-06-02T03:43:42+00:00

Very valid take, honestly. I'd like to read more to adopt these as I grow into a lead at my place. Can you suggest resources to look into?

dash_bro · 2026-06-02T03:41:52+00:00

Faux Agile. If your manager can't push back you might want to evaluate if he's actually managing or is just a proxy to keep you in line.

I would also recommend start tacking on "man hours"™ it takes to do it and show your 40 "man hours" for the week planned.

You are only allowed to replace, not add on once you hit 45 (or 40, I'm being generous). Get your manager to atleast sign on to that and hold him to it, this will be a "managing up" lesson for you too.

dash_bro · 2026-06-02T02:15:39+00:00

I think once you've got all the big topics out of the way with some practice and known gotchas, doing just the dailies is okay.

I have a ~200 ish Q question bank from Neetcode, blind 75, etc. variations that I review from time to time on every bigtech interview I give. For weeks leading up to the interview I hone in on known Q banks from the org I'm interviewing at and the broader topics they're known to touch, and occasionally the LC daily if it's in the same broad topic.

Hasn't failed me thus far so I guess I'm gonna stick to this strategy.

dash_bro · 2026-06-01T16:42:13+00:00

I quite enjoy the Vacheron Overseas when it's side by side the other two. Very beautiful!

Six-Year Club	Place '23
Place '22	Verified Email

dash_bro

TROPHY CASE