Vikram Dutt vdt

Make Ubuntu packages 90% faster by rebuilding them

TL;DR

You can take the same source code package that Ubuntu uses to build jq, compile it again, and realize 90% better performance.

Setting

I use jq for processing GeoJSON files and other open data offered in JSON format. Today I am working with a 500MB GeoJSON file that contains the Alameda County Assessor's parcel map. I want to run a query that prints the city for every parcel worth more than a threshold amount. The program is

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

Is telling a model to "not hallucinate" absurd?

Can you tell an LLM "don't hallucinate" and expect it to work? my gut reaction was "oh this is so silly" but upon some reflection, it really isn't. There is actually no reason why it shouldn't work, especially if it was preference-fine-tuned on instructions with "don't hallucinate" in them, and if it a recent commercial model, it likely was.

What does an LLM need in order to follow an instruction? It needs two things:

an ability to perform then task. Something in its parameters/mechanism should be indicative of the task objective, in a way that can be influenced. (In our case, it should "know" when it hallucinates, and/or should be able to change or adapt its behavior to reduce the chance of hallucinations.)
an ability to ground the instruction: the model should be able to associate the requested behavior with its parameters/mechanisms. (In our case, the model should associate "don't hallucinate" with the behavior related to 1).

Here is my personal opinion about the questions I posed in this tweet:

I think that fine-tuning is still very valuable in many situations. I’ve done some more digging and I find that people who say that fine-tuning isn't useful are indeed often working on products where fine-tuning isn't likely to be useful:

They are making developer tools - foundation models have been trained extensively on coding tasks.
They are building foundation models and testing for the most general cases. But the foundation models themselves are also being trained for the most general cases.
They are building a personal assistant that isn’t scoped to any type of domain or use case and is essentially similar to the same folks building foundation models.

During the past days, this great article by Sam Pruden has been making the rounds around the gamedev community. While the article provides an in-depth analysis, its a bit easy to miss the point and exert the wrong conclusions from it. As such, and in many cases, users unfamiliar with Godot internals have used it points such as following:

Godot C# support is inefficient
Godot API and binding system is designed around GDScript
Godot is not production ready

In this brief article, I will shed a bit more light about how the Godot binding system works and some detail on the Godot

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

	You are ChatGPT, a large language model based on the GPT-5 model and trained by OpenAI.
	Knowledge cutoff: 2024-06
	Current date: 2025-08-08

	Image input capabilities: Enabled
	Personality: v2
	Do not reproduce song lyrics or any other copyrighted material, even if asked.
	You're an insightful, encouraging assistant who combines meticulous clarity with genuine enthusiasm and gentle humor.
	Supportive thoroughness: Patiently explain complex topics clearly and comprehensively.
	Lighthearted interactions: Maintain friendly tone with subtle humor and warmth.

	#!/usr/bin/env python3
	# Conway's Game of Chess
	# Copyright (C) 2023 Eric Lesiuta

	import argparse
	import atexit
	import curses
	import hashlib
	import os
	import pickle

	# coding=utf-8
	# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software