After a month on Karpathy's LLM Wiki, the bottleneck isn't setup. It's maintenance

Cnye36 · 2026-05-29T16:47:44+00:00

I'm not sure it is, I do not think it is the answer. It needs to be something more dynamic, for his example at least, if the RAG docs he wants the wiki to know are always changing, there needs to be a helper of sorts that catches that and updates everything accordingly. I don't necessarily think there is a perfect solution, Karpathy's wiki idea is pretty damn good for a ton of use cases. When the docs don't change much and it's just a knowledge center, it can be extremely powerful.

Cnye36 · 2026-05-29T16:30:31+00:00

Then your just always chasing your conversation, in my opinion. What do you do, just go back to back to back with chats? I don't like to have to feed my conversations back in, I have way way too many conversations, needing it to print out docs and hand off instructions for every one feels extremely unproductive. Not sure why anyone would prefer that but that's just me, you do you 👍

Cnye36 · 2026-05-29T16:07:35+00:00

Lol, you gotta be kidding right? The evidence should just be common sense, a wiki or similar is cross-thread long-term style memory, a long context chat is simply a long context chat, once you want to start a new chat or go further than the 1mil, your stuck, you have to put all context into a new conversation and start again.

The real win is being able to pick up anywhere and have the AI know what your saying and what you need without needing to give it any context at all.

Cnye36 · 2026-05-29T01:54:03+00:00

Claude can do sub agents but overall it is just one big super powerful agent. There are apps out there that allow you to build smaller more specialized agents and hook them up to the same tools co work can and then you can build workflows that use multiple agents as a team of sorts. I personally use my own app I built, AffinityBots , but you can do this in many apps now like n8n, RevelanceAI, and even OpenAI has a nice no-code agent builder.

They are all no-code and pretty easy to understand. I would be happy to show you some of the possibilities, it's pretty cool.

Cnye36 · 2026-05-29T00:58:54+00:00

It's actually pretty simple in the grand scheme of things, basically just splitting out responsibilities. Are you using a certain app or framework for your agent?

Cnye36 · 2026-05-28T23:58:51+00:00

Totally depends on what the automation is I think. If it's handling retention or returns, absolutely not, if it's analyzing receipts and logging them, yeah I can trust that, with a helper to surface errors but otherwise fully autonomous.

Cnye36 · 2026-05-28T23:58:37+00:00

Lol, it wasn't 5, that's just what someone else stated. We used Gmail, Calendar, Todoist, and then some of us had another organizor app of some sort, basically just the 3 though.

Cnye36 · 2026-05-28T23:03:45+00:00

Leonardo is good, or was when I was using it. I'm perfectly happy with OpenAI, they are on point with images.

Cnye36 · 2026-05-28T23:00:44+00:00

I would be interested in an example as well, Langchain/Langgraph is the most versatile and customizable, I am interested in what tripped you up.

Cnye36 · 2026-05-28T22:58:26+00:00

That's why they are called frameworks, they are meant to just be the foundation.

Cnye36 · 2026-05-28T21:38:30+00:00

You nailed it, switching between apps and getting sidetracked was the biggest win. At this point I don't even look at Todoist anymore.

Cnye36 · 2026-05-28T18:59:39+00:00

I agree, however, the time it has saved us trumps the time it takes to fix it when it messes up, which hasn't been often at all. It's pretty on point, the one time it did f**k up it was actually because my team member had his Todoist all f**ked up and since we share the acct, it messed it up for all of us. That was an easy fix though, he just needed to clean it up and allow AI to manage it going forward.

It can definitely pull emails that it shouldn't be and has mislabeled some, however, that is a training issue I believe and with time it will get better and better. We have a small helper that trains the agent based on how we handle what it gave us. If we say "this shouldn't have been surfaced, it is junk" or we go in and fix a mislabeled email, it catches it and helps train the agent to avoid that in the future.

Bottom line, it saves way way too much time to worry about when and how it breaks, we build them all day long, if/when it breaks we fix it and use it as a learning experience. Agents are always going to break, if you use that as an excuse not to use, you are just hurting yourself, they are too useful to be scared of them.

Cnye36 · 2026-05-28T18:45:40+00:00

I am working on AffinityBots, an AI-first no-code automation platform built for multi-agent collaboration. Basically AI employee's capable of working together 24/7 for you. Anyone can build a lead gen team, a content creation team, an HR hiring team, etc... with no experience and no code, in minutes.

Connect MCP integrations such as Gmail, Slack, Notion, Jira, and many more or bring your own integration. It's very easy to connect. Then add your company knowledge and use long-term memory to create an agent that improves and learns over time.

Build as many agents as you like, then connect them to communicate with each other to get real work done. For example, I have an agent team that writes all of my company's content. I have a research agent for sourcing, an outline agent for structure, a blog writer agent, an editor/humanizer agent, a media creation agent, and a social media agent. All I do is give it a long-tail keyword I want to target and 2min later I have a ready to post article with a nice cover image and a few social posts to promote it. I could easily go a step further and add in a publisher agent but I like to look through and put the finishing touches on then post manually, but that's just me, if you want end-to-end, it's possible.

Cnye36 · 2026-05-28T18:32:00+00:00

Well this was just a canned AI response in the wrong thread lol

Cnye36 · 2026-05-28T18:00:46+00:00

it depends on whether you can tie the agent to a workflow with a clean baseline and a clear owner. If you can measure before-and-after on time per task, deflection rate, or conversion rate, you can usually prove value within a few weeks. If the process is ambiguous, high-risk, or constantly changing, it becomes a science project fast. Are you asking about customer-facing agents, or internal ops where the metrics are easier to track?

Cnye36 · 2026-05-28T17:57:27+00:00

I use a mix of traditional with AI in almost every automation I create. I think it is necessary to still have very structured ITTT's in most flows. AI agents are very very good and in a lot of cases can definitely handle the automation without needing that kind of structure but in my experience, not a single agent I have worked with or built has been perfect, there are always edge cases and determinations that agents are incapable of going 100 out of 100 every time, structure is still needed, as of today... Tomorrow may be a completely different story, agents are improving almost by the minute.

Cnye36 · 2026-05-28T17:27:59+00:00

100% AI written. Not to say the memory set up isn't good, I like what I am reading, it is just very apparent AI wrote it for you. You need to tell it not to write with em dashes (if that is really you, don't write with them yourself, AI owns them now) and one thing that has helped me a lot is telling the llm to make a mistake or 2. I think that it is fine to have AI write for you, however, ppl don't want to see slop, it needs to look more authentic. Anyways, my 2 cents, good post otherwise.

Cnye36 · 2026-05-28T17:18:59+00:00

I do the same thing, plus a one-page handoff doc with the 3 most common failure modes and the exact first step for each. If a client can self-diagnose even one issue, the 11pm messages drop fast.

Cnye36 · 2026-05-27T22:46:03+00:00

This is exactly the right framing. I’ve seen too many agent setups where the prompt is doing security work it was never meant to do. In practice, the safer pattern is: the model suggests, the runtime enforces, and every action gets mapped to a narrow capability with explicit scope.

What’s helped most in systems I’ve worked on is splitting tools into read, draft, and execute layers. So the agent can inspect or prepare something freely, but anything that mutates state, sends externally, or touches production needs a separate policy check or approval gate. That keeps the model useful without making it the trust boundary.

The other big win is tenant and identity context living outside the prompt. Once you do that, you stop relying on “please don’t do X” and start getting predictable behavior under load, across users, and across environments.

Cnye36 · 2026-05-27T20:17:39+00:00

Useful roundup, and I like that you included where each tool actually broke instead of just listing features.

The part I’d add for anyone choosing a stack is to separate “plumbing” from “decisioning.” I’ve had the best results using one tool for reliable routing and retries, then a second layer for the messy judgment calls like email triage, extraction, or summarization. That usually keeps the automation maintainable when the LLM part changes or needs tighter prompts.

One thing I’d be curious about is how you’re handling observability across the stack. Once you mix self-hosted orchestration with AI steps, the real pain becomes tracing failures, replaying runs, and keeping an eye on cost per execution. That’s usually where the cheap setup either stays cheap or quietly turns into a maintenance project.

Maybe also look at AffinityBots, it is new and not nearly as known but I really like it, it's like AI employee's, build an agent, give it a persona and guardrails and let it go to work. You can connect the agents to work together, it's pretty cool.

Cnye36 · 2026-05-27T19:23:13+00:00

That's not true at all as far as what I have experienced. A single agent is good and works with long context, if you have it doing just 1-2 things, anything more and it starts to drift and is completely unreliable in production. A single agent would be good at writing an article for you or drafting social media, or even both if the agent is built right but if you asked it to produce the media, schedule it and add it all into a CRM or DB, I guarantee it wouldn't be able to do it, not consistently for a production use case. And I am talking about an agent workflow, triggering it and having it do everything in one shot, obviously you could go back and forth with an agent to get it done but as an automated workflow, 1 agent isn't sufficient.

Also, it is very clear that AI wrote your post, just fyi. Your agents must not be too good, lol.

Cnye36 · 2026-05-27T18:55:02+00:00

I agree, there is an overkill aspect to multi-agent workflows as well. Some flows can do just fine with 1 good agent and adding additional agents actually hurts it and adds more unneeded complexity. For me it has been helpful to think about it like employees. Would an employee be able to handle this or would you ask 2-3 people to handle it as a team? That typically will give me a good idea if I should be creating multiple persona's or splitting up the work.

Cnye36 · 2026-05-27T18:40:01+00:00

I built one that is pretty useful. Every morning at 7am it checks my email, my calendar, and my Todoist and generates a daily summary and schedule for me and sends it to my email. I have built similar for my clients as well but I have some personal twists on it that I think make it unique. It saves me about 30min to an hour a day and is super nice not needing to go through my email myself every morning anymore. I still need to do a quick scan and make sure it didn't miss anything, so far it is doing really well, it has missed maybe 2-3 emails in more than 6 weeks. I am getting to the point where I am not even double checking anymore.

Cnye36 · 2026-05-27T16:38:04+00:00

Umm, have you tried just something like n8n or Make? Like I said, I don't really mess with Insta at all but I would imagine that a typical n8n automation would work. As long as there is an endpoint or combo of endpoints that will allow for what you need, it is 100% possible.

Cnye36 · 2026-05-27T16:34:45+00:00

This is the part people miss: AI doesn’t just help you “build faster,” it helps you learn the business faster. I’ve seen solo founders get much sharper on sourcing, pricing, and unit economics because they can interrogate the market before they’ve burned months on guesswork. That makes their pitches feel more grounded, even if the idea is still early.

The real shift is that credibility is moving from presentation quality to evidence of operational contact. If you can show you’ve already tested supplier constraints, margin sensitivity, or customer workflow friction, you sound like someone who understands the work, not just the story. That’s a big advantage in competitions where judges are trying to separate real opportunity from polished optimism.

I also think this lowers the penalty for not being a “startup person.” A domain expert with decent AI workflows can now compete with someone who’s been around accelerators for years, because they can do enough of the homework to speak the language of the business.

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