The hottest Programming Substack posts right now

And their main takeaways
Category
Top Technology Topics

LLMs running on your laptops and smartphones.

nolano.ai • 78 implied HN points • 11 Mar 23
🕹 Technology AI Hardware Programming Open Source Mobile devices
  1. Large language models (LLMs) can be used for tasks like email completion and code explanation, but currently need hardware accelerators beyond personal devices.
  2. Using on-device LLMs allows greater control over data and the ability to create personalized generation models.
  3. A community of developers is working towards enabling LLM inference locally to empower creators and researchers in utilizing these models for their projects.
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You can't handle Errors

Burning the Midnight Coffee • 83 HN points • 13 Feb 24
🕹 Technology Programming Error Handling
  1. Faults in code lead to errors, which then cause failures in a program's behavior. Understanding this process is crucial for effective error handling.
  2. Handling an error means returning the program to a known, correct state, which usually involves restarting it in some way. Proper failure handling is key.
  3. Exceptions as both error handling and additional return values can lead to more faults and failures. It's important to define and address failures distinctly from errors.

The Sequence Chat: Microsoft's Evan Chaki on Semantic Kernel and Combining LLMs with Conventional Programming Languages

TheSequence • 294 implied HN points • 26 Apr 23
🕹 Technology AI Machine Learning Programming Open Source Development Tools
  1. Semantic Kernel enables developers to create AI applications using large language models without writing complex code or training custom models.
  2. Memory systems and data connectors play a crucial role in enhancing productivity and efficiency in LLM-based applications.
  3. Hybrid programming with natural language and traditional programming languages can automate tasks like creating educational content and contract Q&A, leading to faster, error-free results.

A World without C

Deus In Machina • 72 implied HN points • 07 Mar 24
🕹 Technology Programming Software Languages Innovation Legacy
  1. The push towards memory-safe languages like C++ over C is gaining attention due to concerns about software security, especially in critical systems like government infrastructure and services.
  2. C's simplicity and widespread usage make it a common choice for interlanguage bindings, but its simplicity can also lead to challenges in areas like memory management and handling large projects.
  3. While C has a rich history and legacy, there is growing discussion about the potential for newer languages like Zig to eventually replace C in its core functionalities, driven by advancements in the programming landscape.

The KQL Mysteries: Prologue

Rod’s Blog • 59 implied HN points • 20 Nov 23
🕹 Technology Cybersecurity Programming Data Analysis Digital World Query Language
  1. Jon Block, a top-tier security analyst, used KQL - Kusto Query Language, to tackle cyber threats. This powerful query language helped him root out elusive cyber threats and protect digital landscapes.
  2. Jon's journey into cybersecurity began with self-taught programming and a determined spirit after being a victim of a cyber attack. His dedication led him to become a renowned cybersecurity professional using KQL.
  3. KQL's elegance and power allowed Jon to shine in the cybersecurity realm, offering protection to clients from all levels of society. His mastery of KQL made him a formidable force against cybercriminals.

Don't abuse 0

CodeFaster • 144 implied HN points • 04 Jan 24
🕹 Technology Programming API Software Development
  1. Setting a spend limit of 0 in an API does not mean restricting spending to zero; it actually means allowing infinite spending.
  2. Consider using the string 'infinity' instead of '0' to denote unlimited spending.
  3. If needing to use an integer value for spend limits, consider using -1 to represent infinity, as it is not a common value and prompts further investigation.

pip install sqlmesh-cube

davidj.substack • 23 implied HN points • 19 Dec 24
🕹 Technology Software Programming Data Development Systems
  1. A new package called 'sqlmesh-cube' is available for anyone to use. You can easily install it with pip.
  2. This package helps create a CLI command that outputs JSON, showing how sqlmesh models relate to each other. It's important for building a semantic layer.
  3. This was the author's first package, and they learned a lot about the publishing process along the way. They are open to feedback and requests for updates.

Games as Training

David Friedman’s Substack • 143 implied HN points • 13 Sep 23
🚌 Education Learning Skills Games Teaching Programming
  1. Games like bridge, chess, and Diplomacy can be viewed as training exercises for skills like coordination, tactics, strategy, and commitment.
  2. Playing games can help develop real-world skills like finding your way around environments and accomplishing goals.
  3. Some games are designed specifically to teach skills, such as computer programming or the principles of mutual advantage over conquest.

Build Your Own SMTP Server in Go

Valyent's newsletter • 4 HN points • 27 Jul 24
🕹 Technology Software Development Programming Open Source Web Development
  1. Building your own SMTP server helps you understand how emails are sent and received. It allows you to learn the important protocols like SMTP, IMAP, and POP3.
  2. Authentication methods like DKIM, SPF, and DMARC are crucial for ensuring that your emails are delivered successfully and trusted by recipients. They help prevent spam and email spoofing.
  3. Using Go to create your SMTP server involves setting up commands that handle email transactions. You will learn how to manage sender and recipient details, authenticate users, and send emails efficiently.

Tell, don't ask—the gateway to loosely coupled architecture

🔮 Crafting Tech Teams • 39 implied HN points • 15 Jan 24
🕹 Technology Programming Software Engineering
  1. Tell, don't ask is a key concept in loosely coupled architecture to avoid common traps in system coupling.
  2. Applying the tell, don't ask principle can help in building more resilient and scalable software systems.
  3. Software engineering fundamentals include understanding the importance of communication patterns like tell, don't ask.

How to Fine-Tune Your Own Mistral-7B

The Beep • 39 implied HN points • 14 Jan 24
🕹 Technology Machine Learning Natural Language Processing AI Models Data science Programming
  1. You can fine-tune the Mistral-7B model using the Alpaca dataset, which helps the model understand and follow instructions better.
  2. The tutorial shows you how to set up your environment with Google Colab and install necessary libraries for training and tracking the model's performance.
  3. Once you prepare your data and configure the model, training it involves monitoring progress and adjusting settings to get the best results.

Prompt Engineering with GPT-4: Charting and Mapping European Tourism Trends

Data at Depth • 19 implied HN points • 11 Apr 24
🕹 Technology Data Analysis Data Visualization Artificial Intelligence Programming Web Development
  1. Efficiency is highly sought after state of being for coders and data analysts. GPT-4's Code Interpreter functionality significantly streamlines the process of transforming CSV data into data visualizations.
  2. GPT-4 can generate Python code for various types of data visualizations like line charts, bar charts, and area charts. Simply prompting GPT-4 with specific information can quickly produce comprehensive visualizations.
  3. GPT-4 can be utilized to filter datasets, analyze trends, and create innovative visual representations like choropleth maps. Incorporating GPT-4 into data analysis workflows can lead to faster and efficient results.

The devil is old

Wednesday Wisdom • 113 implied HN points • 21 Feb 24
🕹 Technology Programming Mathematics Physics Concurrency Software Development
  1. Experience and age often bring wisdom, knowledge, and a unique perspective.
  2. In technology, while tools and capabilities have evolved, fundamental principles like people dynamics, math, and physics remain constant.
  3. Despite advancements, people still struggle with basic math, concurrent programming, and effective communication in group settings.

Today’s Top 5 HN posts

Top 5 HN Posts of the day • 19 implied HN points • 07 Apr 24
🕹 Technology Programming Software Job Opportunities Web Development Open Source
  1. Today's top 5 HackerNews posts include discussions on SSH backdoors, cartoon face generation in JavaScript, and how performance scales with more agents
  2. A new open-source btrfs driver for Windows called WinBtrfs is being highlighted in the top posts
  3. Additional job opportunities from Bright and Zep AI are shared at the end of the post

Making Sense of Agentic AI | ThoughtWorks’ Birgitta Boeckeler

Dev Interrupted • 28 implied HN points • 12 Nov 24
🕹 Technology AI Software Programming Development Automation
  1. AI tools can help software teams improve their work, but it's important to pick the right ones that actually make a difference. Sometimes the hype around AI doesn't match up with real-world results.
  2. Governance matters when it comes to programming languages. A strict control model can limit a language's potential for growth, so a more open approach might be better.
  3. Reddit is gaining popularity as users appreciate its less polished, more authentic content. It shows that not all platforms need to rely heavily on AI to attract people.

Edge 376: The Creators of Vicuna and Chatbot Arena Built SGLang for Super Fast LLM Inference

TheSequence • 98 implied HN points • 07 Mar 24
🕹 Technology Artificial Intelligence Programming Open Source Machine Learning
  1. SGLang is a new open source project from Berkeley University designed to enhance interactions with Large Language Models (LLMs), making them faster and more manageable.
  2. SGLang integrates backend runtime systems with frontend languages to provide better control over LLMs, aiming to optimize the processes involved in working with these models.
  3. The framework created by LMSys offers significant optimizations that can boost the inference times in LLMs by up to 5 times, showcasing advancements in processing vast amounts of data at incredible speeds.

Building a Working Recommendation Engine from Scratch

Building a Recommendation Engine • 3 HN points • 04 Aug 24
🕹 Technology Algorithms Programming APIs Data Management Machine Learning
  1. A recommendation engine can work without complex machine learning. Instead, it can be built using straightforward connections between content to suggest things users might like.
  2. Using an API from a platform like Are.na allows easy access to user content and helps find connections between different channels, making recommendations more relevant.
  3. It's important to filter out content that users already know or follow to give them fresh and exciting recommendations. Regular updates to the recommendations can also help keep things interesting.

Four Essential Skills to Improve Your Python Data Visualization Workflow

Data at Depth • 19 implied HN points • 06 Apr 24
🕹 Technology Data Visualization Python AI Programming Coding
  1. Understanding Python data visualization libraries like Matplotlib, Seaborn, and Plotly can help you create different types of visualizations.
  2. Learning data cleaning and preprocessing techniques with Pandas is crucial to ensure accurate and meaningful visualizations.
  3. Mastering Modular Prompting with tools like ChatGPT can speed up coding tasks by generating code snippets based on specific instructions.

Polymorphic vectors.

Software Bits Newsletter • 206 implied HN points • 08 Jul 23
🕹 Technology Programming Performance Benchmarks
  1. Inheritance can impact performance negatively in C++ due to issues like indirection and virtual function dispatch.
  2. Data-oriented design (DOD) can lead to improved performance by optimizing data organization over code organization.
  3. Using a struct of arrays approach instead of std::variant can offer better performance and minimize memory overhead in certain scenarios.

FaaF: Facts As A Function For Evaluating RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 04 Apr 24
🕹 Technology AI NLP Data Software Programming
  1. RAG systems often struggle to verify facts in generated text. This is because they don't focus enough on assessing the truthfulness of low-quality outputs.
  2. Verifying facts one by one takes a lot of time and resources. It's challenging to check multiple facts in a single generated response efficiently.
  3. The FaaF framework improves fact verification greatly. It simplifies the process, makes it more accurate, and cuts down the time needed for checking facts.

How to Summarize Youtube Video using ChatGPT Api and Node.js

Implementing • 39 implied HN points • 02 Jan 24
🕹 Technology Programming API Web scraping CLI OpenAI
  1. The system architecture for summarizing YouTube videos involves extracting text from videos and generating text summaries using OpenAI's completions API.
  2. The process includes scraping YouTube automatic captioning for text extraction and dividing large text into smaller parts to handle limitations of the completions API.
  3. A command line interface (CLI) was created to allow users to easily summarize YouTube videos by passing the video link and desired language code.

A new kind of coding

Sunday Letters • 139 implied HN points • 06 Feb 23
🕹 Technology Software Development Artificial Intelligence Programming Web Development Machine Learning
  1. Coding with LLMs combines precise programming with flexible models. It's about using the strengths of both to build effective programs.
  2. When creating complex documents, breaking down tasks into smaller pieces is key. This helps models manage and generate content smoothly.
  3. As AI technology grows, we need to be open and experiment. Learning new patterns will help us understand how to best use these models in the future.

Undecidability Isn't So Bad

Bzogramming • 22 implied HN points • 07 Dec 24
🕹 Technology Programming Computing Algorithms Mathematics Software
  1. Some problems in computing are called undecidable, which means we can't find a definite solution for them. However, that doesn’t mean we can’t approach them creatively and get some useful results.
  2. When working with programs, understanding their behavior can often reveal hidden bugs. If a program doesn't behave the way we expect, it might be a sign that something is wrong in the code.
  3. There are smarter ways to analyze code than just throwing our hands up and saying it’s impossible. Advanced tools are already in place in many programming environments, but they often work behind the scenes without us being aware of them.