1621
Two different tricks for fast LLM inference
Anthropic and OpenAI both recently announced “fast mode”: a way to interact with their best coding model at significantly higher speeds. These two versions of…
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AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 3,091 posts.
1622
My 25-year adventure in AI and ML
This is the 25-year story of how I fell into the field of AI.
1623
Implementing Natural Conversational Agents with Elixir
In my last post , I discussed some work I had done building Nero, the assistant of the future that I’ve always wanted. I ended up creating an end-to-end example which used Nx, OpenAI APIs, and ElevenLabs to create an in-browser home automation assistant. For a first product, it’s decent. Nero is a neat little party…
1624
AGI is an engineering problem, not a model training problem
LLM models are plateauing, but true AGI isn't about scaling the next breakthrough model—it's about engineering the right context, memory, and workflow systems. AGI is fundamentally a systems engineering problem, not a model training problem.
1625
What will LLM-powered software look like in the medium-term future?
Recently, I started working on a new LLM-oriented project . It’s currently far from feature-complete and probably filled with bugs, but I’m hoping to use it as a platform for exploring a question I’ve been considering a lot lately: what will LLM-powered software look like in the medium-term future? It seems undeniable…
1626
ChatGPT is a bullshit generator but it can still be amazingly useful
The philosopher Harry Frankfurt defined bullshit as speech that is intended to persuade without regard for the truth. By this measure, OpenAI’s new chatbot ChatGPT is the greatest bullshitter ever. Large Language Models (LLMs) are trained to produce
1627
LLM Inflation
One of the signal achievements of computing is data compression 1 : we take in data, make it smaller while retaining all information (“lossless” compression), transmit it, and then decompress it back to the original at the other end.
1628
A Timeline of the OpenAI Board
Yesterday, Sam Altman and Greg Brockman were fired from the Board of Directors of OpenAI.
1629
Hidden Changes in GPT-4, Uncovered
The tool instructions in this article are not up to date as of 2024-01-12, see this post to learn more about the new tool OpenAI added to block conversations about U.S. elections using function calls.
1630
Salesforce regrets firing 4000 experienced staff and replacing them with AI
Salesforces has entered a phase of public reckoning after senior executives publicly admitted that the company overestimated AI’s readiness
1631
AI Slop vs. OSS Security
griff Table of Contents Author's Note What Exactly is the Problem? Human Capital is Limited Burnout Burnout Burnout What AI Slop Looks Like The CV...
1632
LLMs aren't "trained on the internet" anymore
A path to continued model improvement.
1633
Fine-tuning LLMs is a waste of time
People think they can use Fine-Tune for Knowledge Injection. People are Wrong
1634
AI and Machine Learning – The Basics
Artificial Intelligence is a once-in-a lifetime commercial and defense game changer (download a PDF of this article here) Hundreds of billions in public and private capital is being invested in Art…
1635
Push-ups with Python, mediapipe and OpenCV
In the Singapore Army, there is something called IPPT (Individual Physical Proficiency Test). The problem with this test is not how…
1636
PyTorch – Internal Architecture Tour
Update 28 Feb 2019: I added a new blog post with a slide deck containing the presentation I did for PyData Montreal. Introduction Short intro to Python extension objects in C/C++ Zero-copy PyTorch Tensor to Numpy and vice-versa Tensor Storage Shared Memory DLPack: a hope for the Deep Learning frameworks Babel…
1637
A Cookbook for Machine Learning: Vol 1
This was a busy week, I had no time to read anything new, so I'm sharing a note that I wrote for myself, for no other reason than to understand things better. It's a kind of cookbook of various "transformations" you can apply to...
1638
1639
Learnings from 100K lines of Rust with AI (2025)
In the past few months, I’ve been stress-testing how far AI coding agents can take us when building real, production-grade distributed systems. The result: a Rust-based multi-Paxos consensus engine that not only implements all the features of Azure’s Replicated State Library (RSL) [1] — which underpins most major…
1640
I Asked ChatGPT to Explain Some Jokes to Me
Explaining a joke is like dissecting a frog. You understand it better but the frog dies in the process.
1641
If you are good at code review, you will be good at using AI agents
Using AI agents correctly is a process of reviewing code. If you’re good at reviewing code, you’ll be good at using tools like Claude Code, Codex, or the…
1642
Why AI Infrastructure Startups Are Insanely Hard to Build
Level of difficulty: insane
1643
Licensing is neither feasible nor effective for addressing AI risks
Non-proliferation only benefits incumbents
1644
Some thoughts about Anthropic's new cryptanalysis results
Yesterday Anthropic published two new cryptanalysis results, both outputs of Claude Mythos, their (still) unreleased advanced model. The first of these results attacks a signature scheme called HAW…
1645
Building AI products in the probabilistic era
AI turns products from deterministic functions into probabilistic systems. That requires expanding old playbooks (SLOs, funnels, siloed finance), and reasoning in terms of trajectories, Minimum Viable Intelligence thresholds, and data as company operating system.
1646
1647
Voice Recognition and Text to Speech in Python
It’s tough for an aspiring Iron Man to work on creating their personal AI assistant on the weekends. Like any other time-pressured inventor without a PhD in computer science and linguistics, …
1648
Break-a-Scene: Extracting Multiple Concepts from a Single Image
A method for extracting multiple concepts from a single image.
1649
Interpretable Machine Learning
Machine learning is part of our products, processes, and research. But computers usually don’t explain their predictions , which can cause many problems, ranging from trust issues to undetected bugs. This book is about making machine learning models and their decisions interpretable.
1650
Training NanoGPT on My Journal
Wouldn't it be great to have a pet AI chat bot that knows you really well, and can give you free consulting, coaching and therapy ? What could possibly go wrong if an intelligent corporate-controlled system has unlimited access to your private data to provide you the best possible service? Well... a lot of things.