Hey everyone, I’m a 16M from the Netherlands. I was hoping to connect with some like-minded people that are interested in ML. I am not talking about GPT wrappers, but more about the fundamentals like LLM architectures, data science, inference, etc.
If you want to connect, you can comment below. Maybe we can even start a project together. Thank you for your time.
I'm a 16M from the Netherlands. I started a project this summer that I really enjoy working on, but it has gotten pretty big and hard to manage. I was hoping to meet some other teens who may have extra ideas and might want to continue development together with me.
The project is a combination of an AI coding harness and also fine-tuned LLMs and inference engines. Basically, I am trying to make a harness optimized for super small models, with very specific guardrails and context assembled in a smart way. I am trying to compensate for smaller models and their quirks, and also trying to automate everything possible so the LLM does less work. I'm currently done with the MVP of the project; I want to refine some features, add a few, and create a proper frontend. I also want to maybe start fine-tuning some SLMs to fit the harness better in the future.
If you want a project to work on, collaborate with someone, and share the interests I mentioned, DM me pls.
Hi all,
As title says, I am currently looking at Macbook Pro with M5 Pro chip and 64GB unified memory. Hoping to put on a MoE like Qwen 35B A3B or something like an 8B model, wondering if it would work well inside a decent AI agent harness like Opencode or a more lightweight one like Pi, since context length seems to matter alot. Also wondering about speed, any room for other apps like an IDE or chromium, and issues with overheating if any? Does anyone have a similar setup? At the edge of my budget at the moment.
The microsoft research on the technology seems extremely promising, yet all models created with this are no larger than 10B. wouldnt it be nice to also experiment with a 30B model for example? Or am I missing something here?
If you don’t know what I mean: Microsoft has about a year ago released the Bitnet models series, where all parameters are basically a -1, 0, or 1 (1,58 bits). They were trained from the ground up at this quant, so their quality is not reduced the same way as taking an unquantized model and quantizing it, similar to Google QAT. Bassically, this reduces compute needed by alot, since a normal LLM weight has more than 4.2 billion possible values, Bitnet has 3. Also it seems to match Llama models that are the same size, while ultimately being less heavy on compute.
They're just making models cost more and making them bigger. At some point it won't improve the model anymore and just stack up compute costs. Also why I think the labs, that open source their models and don't add 5T parameters every new model, will win the ai race. Anyone thinks the same?
Lemme know if u want more.
Very first attempt at making a minimalist poster, any tips?
Hey everyone, complete beginner here. have played lots and lots of shooters, but apex legends for the first time today. played the intro and a forst match, tho i dont understand the game fully, can someone explain it just a bit more, and also things like tactics (only ones that apply to apex, i know tactics in general etc) and things to look out for?
Hey folks, Nyvo here. Basically, I have seen many people trying to get into AI-assisted coding. Sometimes they succeed, but more often, they don’t know where to start or how to use AI efficiently to achieve actual quality results. I used to be one of those people, but after spending over 600 hours experimenting with various AI frameworks and methods, I have created projects for both real clients and myself and I hope to share all the knowledge I have gathered since then below. (None of this is sponsored btw, nor am I trying to sell you anything. Just sharing my real experiences and the resources I use.)
AI-assisted development isn't a luxury anymore. It's the baseline. Roughly 84% of developers now use or plan to use AI tools in their workflow, and more than half reach for them every single day 1 2. The adaptation has already happened.
The interesting part is what happens after adoption. AI has gotten very good, and there is absolutely no reason to say anymore that your AI "isn't working", it is you that is (literally) not working. AI only amplifies your skills, it does not replace them or anything. AI will not work if you don't first.
The single biggest money-pit in this workflow is opening your editor and prompting. The "build-and-refactor" reflex only works when a human being can maintain the entire mental model of the program, but an AI can't, holding only what you tell it within the context of a given chat.
Without planning, files will duplicate, naming will drift, two halves of the same feature may use different patterns simply because the AI decided it preferred a different convention last Tuesday.
I know planning is boring... and slow... but it's also the cheapest form of insurance you can buy. A well-planned project will need many fewer prompts, as the AI will be implementing a plan rather than creating one from scratch and guessing.
You need to begin by defining a genuine Minimum Viable Product. The MVP is not "the first version" or a "light version;" it is the smallest possible thing that provides the product's core value, and nothing else.
Unless it's necessary for solving the problem your product is designed to address, that feature isn't needed in the MVP. Auth probably is. Dark mode, email digests, three dashboard themes, login via six social networks; those don't belong. The point is to get something end-to-end working so you can validate the idea, expose genuine bugs, and learn what your users actually want before you invest hours building things they don't care about.
Here's the piece that most articles skip: MVP scope is not merely product discipline; it's context engineering.
AI agents operate with a finite context window. Each file, rule, and conversation you push to the AI's attention window is a token in a shared pot, and your project's entire surface area competes for its focus. A lean MVP keeps this surface area small enough that the AI can keep the entire project in mind simultaneously.
A bloated MVP has the opposite effect: the AI forgets which auth helper function it's supposed to be using, generates a duplicate of a function it has already written, or "refactors" a file it didn't realize three other files depended on. Feature creep does not simply eat your timeline. It actively degrades the AI's understanding of your project.
A tight MVP also means a shorter, more concise AGENTS.md. Fewer features translate to fewer rules, fewer tricky edge cases, and fewer contradictory instructions for the AI to process. Every line of scope you remove is a line of context you gain back.
Once you've outlined the MVP, break it down into a build order/plan. For a tasks app, don't start with "build me a full project;" instead, "build me a database schema and authentication, then the core task CRUD functions, then add filtering and search, then polish the UI." Each section should be small enough to be validated before you proceed.
Split the MVP into distinct modules: the database schema, API endpoints, UI components, etc. Small modules require a smaller context window per prompt, and limit the impact when things go wrong.
The quality of AI-generated output is directly linked with the quality of the context it's given. Treating each prompt as a blank slate is the single biggest waste of this technology and is precisely what characterizes "lazy prompting"; a one-line request and an expectation of magic. The model does not know exactly what you want and will automatically start guessing (it can't read minds), resulting in inconsistent naming, integration errors, and bloated code that you'll spend hours cleaning up. At this point, you are basically gambling on the output with time and money. Giving better instructions is the single best way to improve your odds.
The simplest solution is to create a root file for your project, such as AGENTS.md, that serves as your single source of truth. Keep it concise and specific: explain the project's goal in plain English, detail the exact tech stack and versions used, outline your coding standards (naming conventions, folder structure, formatting), and include a section with "hard rules" that tell the AI explicitly what it should never do. You can also append your MVP scope.
These "hard rules" are more crucial than you might expect. Some practical examples that are well worth stealing:
For small changes, a single line is often sufficient. For new features, however, draft a proper specification. Compare:
To guide the AI's logic, link to relevant GitHub repositories or paste "golden" code snippets that you want it to mimic. For design direction, provide Pinterest or Dribbble links, or upload screenshots. An AI cannot inherently understand a visual aesthetic; it needs a visual or descriptive anchor. Otherwise, you'll just end up with the same generic dashboard over and over.
Also, a tool I often use is OpenAI's prompt optimizer. In general, it works best for OpenAI models, but it can be a great way to quickly improve your prompts.
When integrating external services in your project, always provide the AI with documentation and examples. I have found that Context 7 is a great tool for this so your AI agents can easily find documentation when they need it.
Execution is an iterative and disciplined process: generate, validate, commit. It is not an "on-and-forget" operation.
Your role is to act as the final quality reviewer. Read through the plan, understand the architecture, and keep tabs on what's happening in your code. Pasting in code you don't understand means you aren't building software; you're gambling with it.
Research supports this: AI-assisted pull requests contain approximately 1.7 times more issues than human-only PRs 2, and AI-generated code has been found to have 2.74 times more security vulnerabilities than code written by humans 3. Speed without review simply leads to more bugs, more quickly.
Prevent context drift with one simple rule: one feature per new chat. Long threads accumulate stale code, abandoned ideas, and conflicting instructions, and the AI will begin to unconsciously match patterns to the wrong information. Because your project architecture is already defined in AGENTS.md, a new chat session is essentially free. Simply paste in your agents file and the relevant code snippets, and you're back on track.
Git is not optional. AI tools will occasionally delete logic, rename things improperly, or subtly break functioning files. Commit before every significant prompt. If the agent messes up, git reset is your only reliable recourse.
Write your code for the next AI as well. Clean, well-commented code is not just for human collaborators; it's for the version of you that will revisit the file in six months and feed it to a fresh agent. The clearer your code, the faster the next session will be productive.
Also, if your AI agent fails on a task, don't just try to fix it with another prompt. You can also try to reset (use git), and prompt again, but this time in the section of the prompt where you tell your AI what not to do, add a new rule that makes sure it does not make that earlier mistake a second time.
Something that is often also overlooked are agent skills. Skills are basically reusable prompts that you can use to quickly and consistently instruct your AI on how to perform common tasks. For example, you might have a "refactor" skill that tells the AI to look for duplicate code, or a "test" skill that instructs it to write unit tests. By using skills, you can save time and ensure consistency across your project, and at the same time, get better outputs. I recommend browsing skills here.
Not everything I wanted to say really fits in the above sections, so here are some extra tips that are worth mentioning:
AI-assisted development is all about maximizing speed and efficiency. However, speed without caution just simply causes damage. This acceleration is only valuable if you remain as careful as you would be hand-writing every line, and often, even more so.
AI is a powerful tool, but far from perfect. That is why AI will not be a replacement, but an assistant; so treat it like one.
The right side icon on home screen seems to always be smaller than the left one. This applies to literally every icon for me (even native apple ones (except torch and camera)).
Although, when editing the lock screen (slide 2), it does show the icon in correct proportions.
like the title says. I’m thinking about buying this Macbook. What do you think are the best coding models, and at what speeds I could probably run them and if they work in parallel with other software.
Also thinking about using MTP and quantization. Also hearing about MLX and other configs etc.
Hoping one of you redditors has this MacBook config and can share their llm setup.
I don’t need a laptop or anything fancy. I’m looking for a mini PC or similar device to set up at home for local LLM inference. I’m aiming to keep costs low so I probably won’t be running extremely large dense models. Perhaps I’ll use MoE’s or quantised models and likely use MTP.
Has anyone experience in this field? Any recommendations for a computer?
Im sick of thinking of ideas, creating them, and then giving up on them on my own. I'm looking for other teen developers to build a community, learn and share, and try to build something truly original.
I'm a 16-year-old developer from the Netherlands. I'm mostly interested in design, but also artificial intelligence. Portfolio: nyvo.is-a.dev.
As I mentioned earlier, I was hoping to find a few developers to try and build something real with, teaching each other along the way. I think it would be a great experience.
I dont have a plan yet, or product idea, it depends on the skills of the people that are interested.
If you feel the same, DM me.
Im sick of thinking of ideas, creating them, and then giving up on them on my own. I'm looking for other teen developers to build a community, learn and share, and try to build something truly original.
I'm a 16-year-old developer from the Netherlands. I'm mostly interested in design, but also artificial intelligence. Portfolio: nyvo.is-a.dev.
As I mentioned earlier, I was hoping to find a few developers to try and build something real with, teaching each other along the way. I think it would be a great experience.
If you feel the same, DM me.
This makes alot more sense, right? I mean a weapon skin is for matching with the outfit of your player, not playstyle. Things like scopes etc you can keep per contestant, but weapon skin just makes sense to link to outfits, this way I dont have to create a new contestant for every fit haha.
Is it:
- Privacy
- Reduced token costs/no rate limits
- Works offline
- Experimenting/learning purposes
- Something else?
From the base model Macbook Pro, so standard M5 chip, at 16 GB, would you suggest:
- upgrading the chip to M5 Pro and RAM automatically to 24GB (+500$ extra)
- upgrading RAM to 24 GB (+200$ total)/36 GB (+400$ total).
- something else like an older model etc, looking to stay around 2000$ price range, maybe bit above.
What matters most, chip or RAM? Whats the bottleneck? If you own a Macbook, what has been your experience with running local LLM's?
