deep1283
deep1283 on Hacker News
Posted by
deep1283 on Tuesday March 10, 2026 @06:26AM
from the self-post dept.
Business owners get hundreds of cold messages daily. Yours gets ignored.
The approach that actually works: find people already talking about the pain your product solves.
Not cold outreach. Not ads. Just being in the right place when someone is already frustrated.
Search Reddit, HN, Twitter for people complaining about the exact problem you solve. Slide into that conversation naturally. That's...
So humans are becoming the hardware layer for AI. The API is just: “Hey, can you go look at this thing in the real world?”
Posted by
deep1283 on Saturday March 07, 2026 @02:23AM
from the self-post dept.
A mistake I see many beginner developers make with AI coding tools is this:They ask the AI to build the entire project.Something like:“Build me a full SaaS app that does this”The result?A messy codebase.AI tries to generate everything at once, and the architecture usually falls apart.After experimenting with AI coding tools, I’ve found a workflow that works much better.Think Like an Architect,...
The token efficiency improvement might be underrated. If the model solves tasks with fewer tokens, that directly translates into lower cost and faster responses for anyone building on the API.
If you’re installing this on a fresh machine, the network installer is usually the smoother option. The full ISO is great if you’re setting up multiple systems or need an offline install, but for most people the net install saves some headaches.
Posted by
deep1283 on Thursday March 05, 2026 @04:44AM
from the self-post dept.
Ever wondered why every form, tutorial, or demo account in tech is named John Doe?The story goes back to medieval England.In the 1300s, English courts had a strange legal trick. When lawyers needed to test a case or file a claim involving someone whose identity was unknown, they used fictional names. The most common ones were John Doe and Richard Roe.They were never real people just...
thats concerning.If the sandbox actually existed at the system level, the model shouldn’t be able to escape it regardless of what it says or tries.
I think a lot of engineers intellectually agree with this idea, but emotionally still default to building the “proper” system.
There’s a strange pressure in tech to reach for architecture, frameworks, and infrastructure even when the problem might only need something scrappy. Sometimes the ugly solution survives longer simply because it’s closer to the actual problem.
This feels like a recurring pattern in the stack. abstraction removes visibility faster than tooling replaces it.
Encryption and higher-level platforms are great for security and productivity, but the debugging surface keeps shrinking. Eventually when something breaks, nobody actually has the layer-by-layer visibility needed to reason about it.
But I’m not sure it’s entirely inaccessible to models either. If you feed them enough signals,logs, incidents, metrics, past debugging threads they might approximate that feedback indirectly. Not the same as being paged at 3am, but maybe closer than we assume.
but your distinction is really good. The feedback loop is probably the key difference.
its a pretty interesting game
I think this is slightly romanticizing the idea that humans “hold the territory” in their heads.
In most real systems no single engineer actually understands the full territory either. People rely on partial mental models, docs, logs, and tribal knowledge. In that sense, LLMs operating on maps might not be that different from how teams already work.
ECH is great from a privacy perspective, but I’m curious how well this will actually work in practice.every time the web encrypts more metadata there’s pushback from middleboxes and network operators.
This is a fun idea. What surprised me is the inversion where MUL ends up faster than ADD because the neural LUT removes sequential dependency while the adder still needs prefix stages.