elasticventures

for llm's it's a github repo - spec driven development prompt or skill with a "WIP" (work in progress) status and a broad context summary with <AGENT> instructions to chunk the document.

Trails is a great concept. However wrong monetization strategy.

Exactly, datafusion is implied batteries included apache bigdata ecosystem. Polars is chasing the Python Pandas crowd and uses python syntax, handy if you're already comfortable with ipython.

I found a job and got hired.

RPI Pico is irrelevant in my view. Seriously checkout RISC-V equivalence (there are many, I like the ESP32-C3)

Linux on RISC-V is real, china is using & standardizing on it. Ditch arm, skip RPI, ignore STM-32, and scoff at TI, .. RISC-V is the future.

While you're at it - skip micropython, skip the OS Linux - use RUST and target RISC-V directly.

The cost of RISC-V is cheap and will only continue to plummet, nothing in the IOT space is going to compete with RISC-V except absurdly expensive niche proprietary solutions that were engineered before the blackhole-esque super-nova gravity hole that is RISC-V which is happening right now in the IOT space. I'm not even sure those will survive, ..

The RISC-V options are only going to continue to increase. I say this because China is using RISC-V for most of it's future everything, it's basically the national chip and there are literally tens of thousands of developers & engineers coming out of school into this ecosystem every single day.

Do not use micropython, avoid all these runtime debugging headaches. Yeah, it might take you a few months or even years to learn RUST, but the concurrency, the power saving, the deterministic behaviors - oh joy! The headaches you will save, the extra rest at night and reduced stress will increase your lifespan.

Qemu RISC-V for emulation + testing and you'll save yourself a ton of time only deploying and supporting code that works. Full simulated environments, you can test in parallel in the cloud, few platforms can do that! RISC-V + RUST it's a joy.

Fewer crashes in the field-- and let me tell ya! .. when you're doing anything IOT that is the only thing that matters - never having a crash in the field, that's better than chocolate cake.

Things to look for:

1. How much automata & does the founding team 'care' about technical best practices which will ultimately determine the operational cost of the systems & organization/ type of people to hire. (Do I want to work with the type of people the company is going to need to hire)

2. Do the co-founder(s) understand their market, do they have Realistic & Achievable plan, including visual 'mock ups' of any key behaviors of features, can they articulate the vision to me or will I have discretion on how to implement. I stay away from dubious social science, psychology, anything that is described as "the next xyz"

3. What is the equity structure being offered & compensation relative to the market opportunity (i.e. "how likely are we going to be an exit"). What is the market size, do we have customer #1 (and #2 .. etc.) in mind, a sales strategy, avoid a field of dreams "build it and they will come" mentality.

Fwiw, I have a horrible track record finding co-founders. I prefer odd # of person startups. I don't ever do 50/50 anymore, its always 49/51 or 49.99 and 50.01 whatever it's never 50/50 by contract. One person is 'the decider' I always offer them the 51 but that balance might flip in my favor if they don't deliver on mutually agreed achievable KPI's, sort of like side-bets, and this equity percentage can move a lot at the early stages but it keeps everybody focused. If feelings and egos are going to be bruised I'd rather find out early, if they are going to be greedy and try and screw me later I'd rather not engage at all.

k8s is the best solution for _universally_ bringing legacy pre-container "legacy" application patterns into a cloud. K8s won the battle (vs mesos, openshift) for a number of a reasons - but one of those reasons was absolutely not simplicity, rather k8s was better at handling the edge cases. This is a popular opinion held by Victor Farsick @ DevOps Paradox podcast and I happen to agree with him.

You are absolutely correct that k8s is not necessary for cloud-native environments. Existing comapnies usually have legacy applications, so they can't go "cloud native".

Once a company has started down the k8s path the org (by necessity) will start to specialize a k8s control layer and that will become embedded and you risk career-icide & being labelled a heretic if you try to challenge the rationality of using k8s. K8s creates a nice way sysops to keep devs in little boxes and limit the size of the craters they can make. All everybody wants at the end of the day is not to be hassled and not need to keep learning something new each day.

Dev's are usually horrible system operators since they fundamentally want to use code to solve problems rather than look for solutions others have built. K8s in this respect creates a very non-creative line of demarcation for system responsibilities. Devs should not be writing backup & logging applications.

If you're building a startup and you don't want the k8s complexity what you are suggesting is fine - but if you're going to work in big b0rg org, it's better to get in the k8s bandwagon - what you are saying will get you labelled a heretic, because the k8s admins have enough struggle keeping up with k8s complexity & beating devs into submission, the idea of learning anything else is unpopular.

It's straightforward to hire people with k8s expertise, but now this is a domain of knowledge that humanity has cultivated.

I remain of the unpopular opinion that k8s is really only suitable for companies which are the size of google (imho), the idea of having a massively complex & specialized administration layer for most companies is absolutely stupid. However the idea of refactoring legacy applications to be cloud native will never fly, too many risks & unknowns, disruption, big b0rg orgs dont want none of that.

So k8s is also a safe choice, it's not especially creative, it's burdensome but big b0rg orgs don't really care about any of that - these folks are unfortuantely the decision makers, and they don't understand the topics they are making decisions on, so popularity of tech, groupthink etc. are going to win there.

Finding a good assistant is rough, it requires patience, initiative & chemistry. Once you find 'the one' reward them, because a good assistant is like having a 3rd hemisphere to your own brain -- it sucks when you lose it.

Retooling for an assistant is the most important thing you will ever do for your company, it is what will ultimately allow you to perhaps grow or exit someday.

For new assistants - start them writing down your processes as an operations manual. Every day, they should be making updates and journal entries in the operations manual. The primary purpose of this is to see they can write in a way you can understand, if they can't do this - then fast fire.

If you find somebody who can write/edit/update -- Make sure they know "this role temporary, until it is permanent" and their role is to make themselves indispensable by anticipating your needs.

review your inbox, edit your documents, organize your calendar, and do research, follow up with clients, billing/collections, whatever tasks they can identify and offload from you.

A good assistant once fully trained will be 80% right, 20% wrong, .. you need to accept they aren't you - but eventually you'll both find how to make sure they do the 80% .. and defer/check with you for the other 80%.

A good assistant will write things down for your next assistant. We call this the "BUS" (or Tram) factor.

I know this seems cold & harsh, but having/updating an operations manual should be the first week of any new person as they are training.

while i concur this is a nice notion, it's a special type of rare investor who wants to do all the dilligence, take the risk, and not pursue a large reward.

i came to write this. imagining a future where all plastic was consumed by airbore bacteria and watching the world around us that relies so much on plastics so deeply systematically melt.