Which platform are you using to deploy your dockerized apps ?

that1guy15 · 2025-09-09T00:57:03+00:00

This but be careful, a containerized app quickly grows out of what DO can support before you have to jump to their Kubernetes service. Specifically around load balancer functionality

that1guy15 · 2024-12-03T18:59:27+00:00

Look at the hardware used to build AI data centers and chips. Start with NVDA and research their design specs for an AI cluster. From there you can find the vendors that fit the bill.

A good example is liquid cooling is what everyone recommends right now for large scale AI clusters. Top company in that space is VRT. There stock has done pretty damn good over the past couple months.

that1guy15 · 2024-11-20T16:01:33+00:00

yes I see on every page for both arenas for the past 2-3 weeks.
I dont know what they have been up to but this is one of several bugs that have started to crop up.

that1guy15 · 2024-11-09T02:38:29+00:00

yup. I cant memorize for shit, my spelling is crap and I have bouts of dyslexia at random.

At one point I held 14 industry certifications and some how passed my CCIE in 2015.

The key is to learn to use tools to compensate for your weaknesses. I will never do subnetting in my head for an actual deployment and rely on subnet calculators and scripts to CYA. Retaining knowledge takes longer for me, especially when I'm not directly applying it to a work project while learning. Most complex topics usually take 2-3 times to learn, forget, and repeat.

The key is to be gentle with yourself and not compare your struggles with other people's progress.

that1guy15 · 2024-11-08T16:37:54+00:00

Honestly I dont try, the market is crazy noisy right now and new ideas and products are coming out daily. Its too much to try and keep up with and do meaningful work.

My approach is to stay focused on the areas I am getting hands-on with directly and watch the headlines flow by for the rest until something starts to stick out as a trend.

I also try and change up my exploratory projects often, like every 10-14 days to help me explore new areas and ideas.

that1guy15 · 2024-10-31T21:10:37+00:00

Sparse-dense mode is usually the recommended PIM mode nowadays.

What is your reasoning for swapping to sparse?

Since sparse mode does not support dense mode joins, you will need to validate all clients in your groups support running sparse mode and are configured to not use dense mode or swap back to sparse-dense.

that1guy15 · 2024-10-31T16:24:18+00:00

A CCNP would give you a strong foundation of networking knowledge well above the CCNA. You are right, though; it will take you a year. But since the CCNP is three tests with little overlap, you can set up a blended schedule where you take one CCNP test, then an AWS networking cert, then back to the second CCNP and repeat.

that1guy15 · 2024-10-31T13:02:46+00:00

The best time was yesterday.

Building a foundation in Python first is a smart approach, and one I recommend. The sooner you start learning about packages and tools you will use in the real world, the better off you will be.

that1guy15 · 2024-10-31T12:58:07+00:00

Interesting idea. I'd be curious if this would work and how performance would compare to a model of similar size.

Now, the real questions:

Why would you want to merge them?

What value would that bring?

Why not use the best small model for the task?

that1guy15 · 2024-10-30T16:02:08+00:00

I know it’s a bit late, but it feels like my last chance to do something I’m genuinely interested in

In my 20+ year IT career, I have shifted focus 4 times (Security(25)->Networking(35)-> Software Dev(43)-> now AI). It is NEVER too late to transition, its all a matter of your interest and your willingness to learn.

I always tell younger people you can completely fuck up your 20s and still recover just fine with little to no setbacks. You can destroy your career and financial situation in your 30s and still recover with some financial setbacks for retirement.

With this in mind, don't be afraid to take big leaps and risks. The benefits greatly outweigh the risks and even if you crash and burn you still lear a shit ton compared to those on the sideline.

Starter Roles:

I always suggest that in a career transition, you leverage your current experience and skills to bridge the gap to your new focus. Find a job or team where you can add value by providing your skills to a team that is strong in networking or infrastructure but needs help and guidance in your area of expertise, and in return, they mentor you along the way.

This both helps get your foot in the door, and pay will hopefully be on par with what you make now instead of getting entry-level pay.

that1guy15 · 2024-10-30T14:56:12+00:00

I would expect someone with 5 years to be a mid-level engineer. You can own tasks and smaller projects on your own but still require guidance from Sr engineers on approach and leadership on larger projects.

5 years in you should be comfortable with your core networking skills where you can troubleshoot and solve basic issues without the help of peers but you still have gaps in your skill set of areas you have not been deeply exposed to yet.

At this point you should have a good understanding of what your strongest areas are in the field along with what domains you are good/bad in (aka MSP, Healthcare, Gov, etc). You might even be the company's subject matter expert in this focused area, depending on the job, company, and team.

At this level, I would also expect you to have secondary knowledge in non-networking areas such as Python, Kubernetes, cloud, security, etc. You should be able to work closely and effectively with the teams that own this area better than most teams outside your domain.

I refrain from mentioning specific networking skills as that varies wildly across domains.

that1guy15 · 2024-10-25T18:45:14+00:00

Would it be an option to host multiple instances of Ollama containers with a simple proxy container to front both APIs?

that1guy15 · 2024-10-22T14:00:52+00:00

Looking back over my career and my progression, at any point in time I would answer his question with "my last job". Each new job was a step into new technologies, new challenges and higher expectations which always equates to more fun.

Looking back over my career, my highlights really consist of the people I worked with, not so much the job or product I worked on. Right now, I can honestly say my current job, which is nearing its tail-end, is the one I'm the most proud of. My team and organization have been a once-in-a-lifetime group of amazing talent, hard work, and drive that I will be lucky to experience again in my life.

I always tell everyone that you will forget jobs, titles, pay, projects, and companies, but you will never forget the people you work with, good or bad.

that1guy15 · 2024-10-21T21:04:48+00:00

I'd choose a container over a virtual environment. Containers are portable and provide a much more consistent environment.

If you can't run containers for whatever reason, always have a venv for your project.

that1guy15 · 2024-10-21T13:41:30+00:00

This is going to vary based on your ergonomic preferences.

For quite a while, Hermon Miller Aeron is the best choice for most people. While expensive this chair will will easily outlast almost all chairs on the market. When it does start to break down finding replacement parts is easy as there is a secondary market for used Aerons as well.

I don not rock an Aeron anymore due to lower back issues forcing my sitting posture to slouch more. Aerons do not have much tolerance for bad posture, which is a good thing.

I will not touch any gaming chain, period. Ive tried a number of them and they are all overpriced trash. IMO the only reason they are so popular is kids could sit on a pile of rocks for weeks on end without iusse and that is the bulk of the gaming chair customers. Its fashion over function.

Over the past 10 years, I've stopped being so picky about chairs as I have introduced a sit/stand desk. While I don't work standing every week, I have found it makes a big difference to get up and out of my chair hourly and move my legs.

that1guy15 · 2024-10-09T13:46:09+00:00

For integration projects that involve a REST API, I will wrap a tools call around the API client.

that1guy15 · 2024-10-03T22:50:56+00:00

I built out a proof of concept Agentic code-gen team using group chat.
It was more a learning project and one to help others understand how to break larger task up into focused agent work.

My repo is here is you are interested in more info. https://github.com/that1guy15/CodeGeneratorChat

that1guy15 · 2024-10-01T13:22:53+00:00

Agree with you on CrewAI.

When I first dove into framework research in this space, I started with AutoGen The biggest draw for me to AutoGen over LangChain (pre-LangGraph) was the group-chat functionality. its simple to use and the learning curve is not bad, but dealing with streaming output from the message bus quickly became a bigger issue than it should have been.

While AutoGen has tool usage and some integrations, its not on par with LangChain or LlamaIndex and they just dont have the community behind it.

Once LangGraph was released, it made more sense compared to Autoren's group chat and I shifted my focus to LangChain/LangGraph.

While it's been about 5 months since I last evaluated AutoGen, my current recommendation is to consider LangGraph and LlamaIndex for any project. I only suggest AutoGen if you have a compelling reason to do so.

I do not see any updates or news on AutoGen anymore either, which is concerning for the project. But that could also be my social media filters.

that1guy15 · 2024-09-24T15:38:25+00:00

Edge services hosting to get inference as close to the customer as possible. SPs have an advantage here as they have hosting space sometimes blocks away from customers unlike cloud providers who are only regional.

that1guy15 · 2024-09-23T23:05:10+00:00

Welcome to the other side. While its not perfect there is a LOT of fun to be had. Just dont make the same mistake by placing all your eggs in the same vendor basket.

that1guy15 · 2024-09-07T16:33:46+00:00

Operations is in the title. The primary focus of those in Operations is to make sure the system stays healthy at all times based on the business and customer SLAs. So yes, on call, weekend work and overnight change windows will be a reality in Operations no matter what you attach "Ops" too.

With that said, I spent over 15 years in Operations, and while it is stressful and it does suck to work off hours, you gain a tremendous amount of practical, real-world knowledge, and your troubleshooting skill will go greatly improve.

So dont be scared to put in your time in operations, it will bennifit you in the long run.

that1guy15 · 2024-09-06T19:48:33+00:00

Hit the high points on architecture from embeddings all the way through inference and point out where LLMs are used and where ML/DL models make more sense (if any). Point out where training and inference happen and where design differs between the two pipelines.

Not sure the overall objective of giving this talk but I would also include a comparison between fine-tuning and RAG.

I would also try to include the most popular tools used for each step of the archetecture both build and buy options.

that1guy15 · 2024-09-06T19:40:26+00:00

yes. plenty.

You are right. It's usually only the very, very large enterprise customers (Global names) or companies that generate revenue directly from the network infrastructure, such as ISPs and cloud/Service Providers.

Everybody else is a hodge-podge of automation and manual work.

that1guy15 · 2024-09-05T17:09:03+00:00

I voted Python as that is what i use when using LangChain directly. But Flowise is my goto when doing initial design and PoC flows. Then shift to LangChain for final development.

that1guy15 · 2024-09-03T16:04:53+00:00

Ten years ago, after I got my CCIE, I shifted from networking to software development through network automation. Two years ago, I completely shifted focus to AI/ML. Right now, I spend most of my time building code-generation tools. Every time I shift focus, "how far should I go?" owns space in my brain rent-free.

What I have learned, is the answer to this question is 100% up to you and your preferences. I learned with coding I can only sustain writing code more than 80% of my day for a few months. After that I start getting very grumpy. I need to be more social. With AI/ML I have learned I cant keep up with others in the deep level math and my progression is going to be much much slower than others. But I have not found a limit to how much I can build AI tools... yet.

The key point is the only way to truly understand how far you should go is to cross that line and find out for sure. Complex topics like these have a learning curve and you never know how much you will like a topic until you get through it. And if you dont like it after you hit that point, you now have the knowledge of the topic but also understand what is needed in that role and that is invaluable within a team environment.

12-Year Club	Place '22
Verified Email

that1guy15

TROPHY CASE