AI & Machine Learning — July 2026

19 captures about AI & Machine Learning from July 2026, brought together from your own collection.

[1]

Andrej Karpathy dropped a 22-minute lecture that makes every “prompt engineering expert” look outdated. He exposed what most people still don’t understand: ChatGPT is not trained in one step The base model only predicts

[2]

STANFORD AND ANTHROPIC SPENT $3.1M TO PROVE YOUR AGENT PERFORMS 42% WORSE THAN IT SHOULD - AND FOUND THE FIX most agents have the memory of a goldfish - 30 seconds and everything is forgotten - a graph is elephant memory

[3]

STANFORD AND ANTHROPIC SPENT $3.1M TO PROVE YOUR AGENT PERFORMS 42% WORSE THAN IT SHOULD - AND FOUND THE FIX most agents have the memory of a goldfish - 30 seconds and everything is forgotten - a graph is elephant memory

[4]

Ontologies for AI, by AI As more vendors and people start moving towards the realm of data modeling, knowledge engineering and semantics, inevitably the water gets muddied.

[5]

Google just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to AI agents • 08:09 - Foundations of multi-agent systems • 13:25 - Introduction to Graph engineering • 23:27

[6]

Have you built a language model? You should. It's so much fun to chat with something you made. Anyone can do it, too. I made an app that teaches the fundamentals and gives you everything you need to build your own: pic.

[7]

new AI research dropped and it should worry everyone currently using AI agents... Google Deepmind researchers built 180 different agent team setups, gave every single one the same budget, and let them compete on the same

[8]

AI is moving into the physical world. We're excited about a new wave of startups rebuilding the systems that power the real world, from education and healthcare to defense, finance, infrastructure, and work itself. pic.

[9]

We’re launching the Eval Engineering Skill, a skill that helps coding agents build evals using context from a repository + agent traces. Everything you need to know from ⤵️ — LangChain

[10]

A senior Anthropic engineer just dropped 12-page PDF on "Graph Engineering" for multi-agentic systems. The shift: your agents memory dies with their context window. A knowledge graph makes it permanent.

[11]

One way you can recognize AI slop is when the ideas and the diction don't match — when the ideas are completely ordinary, but the diction is that of someone announcing a brilliant discovery that they're really excited ab

[12]

AI is moving into the physical world. We're excited about a new wave of startups rebuilding the systems that power the real world, from education and healthcare to defense, finance, infrastructure, and work itself. pic.

[13]

Andrew Ng just dropped 8-page PDF on 4 agentic steps "from Loops to Graphs from scartch" The twist: agent has amnesia without both: Loops let agents think - Graphs let agents remember here's 4 workflows, step by step: st

[14]

The Bitter Lesson

[15]

For my first post, I’m sharing a letter signed on why open models matter. AI will transform every industry, power every company, and be built by every country.

[16]

We removed ~80% of the Claude Code system prompt for our newest models, this is what we've learned about writing system prompts, skills and Claude.MDs for them. — Thariq

[17]

AI agents can write code many times faster than a human. What this means is that you, the programmer, have a large amount of time to use those agents to write unit tests, to write acceptance, tests, to write property tes

[18]

🚨 Apple just solved the biggest problem in AI. pic.twitter.com/8H0jhxw8Ab — Peter Dedene

[19]

The use of agentic systems is becoming the go-to when automating complex tasks. As a data scientist, you need to keep up and learn the new paradigm of automation, one in which natural language becomes the primary interface for computation. Once you have seen this new concept, you will realize that the real challenge is an engineering problem: creating reliable, structured, and reproducible systems around probabilistic models. The goal is to learn how to design agentic systems that can redu

[20]

Andrew Ng: "if I had to pick one technology today - it's graphs of self-improving agents I have 4 patterns that necessary" here are 4 rules: rule 1 → reflection make the agent critique its own output, find problems, rewr source

[21]

8 ways to cut your agent bill: (and when each one pays off) 1) prompt caching → the system prompt and the tool schemas never change between calls, so stop paying full price for them. source

[22]

GitHub - petergyang/no-ai-slop: Removes 20+ patterns of AI slop from any piece of writing. Removes 20+ patterns of AI slop from any piece of writing. - petergyang/no-ai-slop source