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App Argument Clinic – Monty Python (Official Sketch) [video]

https://www.youtube.com/watch?v=TpQlyUjp3vM
1•zeristor•25s ago•1 comments

The first AI marketing benchmark

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1•Jibran12345•57s ago•1 comments

Legibility of Effort

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Jerry's Map

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4•turtleyacht•4m ago•0 comments

Confidence estimation is a better metric than agreement for LLM judges

https://arxiv.org/abs/2604.20972
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What Is Claude Tag?

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My coworkers read my personal ChatGPT chats via Meta Analytics [video]

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LLMs Are Digitizing Judgment

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Build a local voice assistant in 2026

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A new evolution of Vector Database, add to your toolkit

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Ask HN: What jobs can I escape to outside of tech?

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10% Off Any Accessory

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GLM-5.2 Is the New Best Open Model

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Software Design by Example

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SpaceX launches $25B notes offering, source says

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Elon's iPhone

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The End of Code Review: Coding Agents Supersede Human Inspection

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Confidential Apple Files Leaked on Dark Web After Supplier Cyberattack

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Building Intelligent Games

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Yet Another Piece of AI-Pilled Speculative Fiction Has Gone Dangerously Viral

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TDD is how I trust the code AI agents write [video]

https://www.youtube.com/watch?v=d5x38FCSCyM
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Opinionated Python template: uv under the hood, Makefile as the control surface

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3•alexkey•24m ago•0 comments
Open in hackernews

Ask HN: LLM is useless without explicit prompt

4•revskill•1y ago
After months playing with LLM models, here's my observation:

- LLM is basically useless without explicit intent in your prompt.

- LLM failed to correct itself. If it generated bullshits, it's an inifinite loop of generating more bullshits.

The question is, without explicit prompt, could LLM leverage all the best practices to provide maintainable code without me instruct it at least ?

Comments

ben_w•1y ago
Your expectations are way too high.

> - LLM is basically useless without explicit intent in your prompt.

You can say the same about every dev I've worked with, including myself. This is literally why humans have meetings rather than all of us diving in to whatever we're self-motivated to do.

What does differ is time-scales of the feedback loop with the management:

Humans meetings are daily to weekly.

According to recent research*, the state-of-the-art models are only 50% accurate at tasks that would take a human expert an hour, or 80% accurate at tasks that would take a human expert 10 minutes.

Even if the currently observed trend of increasing time horizons holds, we're 21 months from having an AI where every other daily standup is "ugh, no, you got it wrong", and just over 5 years from them being able to manage a 2-week sprint with an 80% chance of success (in the absence of continuous feedback).

Even that isn't really enough for them to properly "leverage all the best practices to provide maintainable code", as archiecture and maintainability are longer horizon tasks than 2-week sprints.

* https://youtu.be/evSFeqTZdqs?si=QIzIjB6hotJ0FgHm

revskill•1y ago
It's not as high as you think.

LLM failed at the most basic things related to maintainable code. Its code is basicaly a hackery mess without any structure at all.

It's my expectation is that, at least, some kind of maintainable code is generated from what's it's learnt.

ben_w•1y ago
Given your expectation:

> It's my expectation is that, at least, some kind of maintainable code is generated from what's it's learnt.

And your observation:

> LLM failed at the most basic things related to maintainable code. Its code is basicaly a hackery mess without any structure at all.

QED, *your expectations* are way too high.

They can't do that yet.