Nano Banana Pro is the best AI image generator, with caveats
The problem with Nano Banana Pro is that it’s too good.
llms
Nano Banana can be prompt engineered for extremely nuanced AI image generation
Nano Banana allows 32,768 input tokens and I’m going to try to use them all dammit.
Proximal Policy Optimization
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. PPO has become the default reinforcement learning algorithm…
LLM Research Papers: The 2025 List (July to December)
In June, I shared a bonus article with my curated and bookmarked research paper lists to the paid subscribers who make this Substack possible.
2025: The year in LLMs
This is the third in my annual series reviewing everything that happened in the LLM space over the past 12 months. For previous years see Stuff we figured out about …
It turned out that the real unlock of reasoning was in driving tools.
notable result is that AI assisted search actually works now.
LLM Research Papers: The 2025 List (July to December)
A curated list of LLM research papers from July–December 2025, organized by reasoning models, inference-time scaling, architectures, training efficiency, and...
Reasoning
Reinforcement Learning
The State of Reinforcement Learning for LLM Reasoning
Understanding GRPO and New Insights from Reasoning Model Papers
the muted response to GPT-4.5 and Llama 4 (non-reasoning) models suggests we are approaching the limits of what scaling model size and data alone can achieve.
The State Of LLMs 2025: Progress, Problems, and Predictions
A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.
DeepSeek released their R1 paper in January 2025, which showed that reasoning-like behavior can be developed with reinforcement learning, it was a really big deal
DeepSeek R1 was released as an open-weight model
Documentation
Claude API Documentation
Building an AI agent inside a 7-year old Rails application
We run a multi-tenant Rails application with sensitive data and layered authorization. In this post, I walk through how I added the first AI agent tool using RubyLLM, Pundit policies, and our existing Algolia search, without introducing a parallel system or loosening constraints.
LangGraph Explained from Scratch | Aman Kharwal
In this article, I’ll walk you through a complete guide to LangGraph from the ground up. LangGraph Explained from Scratch.
In LangChain, this usually means moving from user input to a prompt, then to the LLM, and finally to the output
ollama pull llama3
2025 LLM Year in Review
2025 Year in Review of LLM paradigm changes
Reinforce
Reinforcement Learning from Verifiable Rewards (RLVR)
Prompt caching: 10x cheaper LLM tokens, but how? | ngrok blog
A far more detailed explanation of prompt caching than anyone asked for.
The Behavioral Economics Of AI Agents - Why Consistency Doesn't Equal Trust - Dataconomy
The AI industry has a trust problem that mirrors a paradox Daniel Kahneman identified decades ago in human decision-making: people
Mistral 3 Live!
Frontier AI by hand ✍️
The 70% factuality ceiling: why Google’s new ‘FACTS’ benchmark is a wake-up call
rbenv and CLI executables
Meet Gemini, Google’s AI assistant. Get help with writing, planning, brainstorming, and more. Experience the power of generative AI.
The Concise Guide to Perplexity
Practical Guide on how to build an Agent from scratch with Gemini 3
A step-by-step practical guide on building AI agents using Gemini 3 Pro, covering tool integration, context management, and best practices for creating effective and reliable agents.
The DoorDash problem and the great AI browser fight
Amazon sued Perplexity this month over its Comet browser, which uses AI agents to do online shopping on your behalf. This is the first major front in the war over who gets to browse the web.
Comparing Memory Systems for LLM Agents: Vector, Graph, and Event Logs
Learn how different memory systems affect multi-agent planning. Comparing Memory Systems for LLM Agents highlights key performance metrics.
Plain Vector RAG
sublinear scaling
Comparing the Top 7 Large Language Models LLMs/Systems for Coding in 2025
Compare the top 7 large language models and systems for coding in 2025. Discover which ones excel for software engineering tasks.
OpenAI GPT-5 / GPT-5-Codex Anthropic Claude 3.5 Sonnet / Claude 4.x Sonnet with Claude Code Google Gemini 2.5 Pro Meta Llama 3.1 405B Instruct DeepSeek-V2.5-1210 (with DeepSeek-V3 as the successor) Alibaba Qwen2.5-Coder-32B-Instruct Mistral Codestral 25.01
Core coding quality: HumanEval, MBPP / MBPP EvalPlus, code generation and repair quality on standard Python tasks. Repo and bug-fix performance: SWE-bench Verified (real GitHub issues), Aider Polyglot (whole-file edits), RepoBench, LiveCodeBench. Context and long-context behavior: Documented…
In a First, AI Models Analyze Language As Well As a Human Expert | Quanta Magazine
If language is what makes us human, what does it mean now that large language models have gained “metalinguistic” abilities?
How I Use Every Claude Code Feature
A brain dump of all the ways I've been using Claude Code.
commish app design
A conversational AI system that listens, learns, and challenges
The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
this section, I’ll focus on two key architectural techniques introduced in DeepSeek V3 that improved its computational efficiency and distinguish it from many other LLMs:
Multi-Head Latent Attention (MLA)
Marktechpost/AI-Tutorial-Codes-Included: Codes/Notebooks for AI Projects
Codes/Notebooks for AI Projects. Contribute to Marktechpost/AI-Tutorial-Codes-Included development by creating an account on GitHub.
5 Common LLM Parameters Explained with Examples
Learn the 5 common LLM parameters explained with examples to optimize your model's performance and generate desired results.
max_completion_tokens, temperature, top_p, presence_penalty, and frequency_penalty
Max Tokens is the maximum number of tokens the model can generate during a run
ChatGPT's Atlas: The Browser That's Anti-Web
A blog about making culture. Since 1999.
Living dangerously with Claude
I gave a talk last night at Claude Code Anonymous in San Francisco, the unofficial meetup for coding agent enthusiasts. I decided to talk about a dichotomy I’ve been struggling …
Qwen3-VL· Ollama Blog
Ollama now supports Alibaba's Qwen3-VL.
https://venturebeat.com/ai/researchers-find-adding-this-one-simple-sentence-to-prompts-makes-ai-models
Verbalized Sampling (VS)
Generate 5 responses with their corresponding probabilities, sampled from the full distribution
Just Talk To It - the no-bs Way of Agentic Engineering | Peter Steinberger
A practical guide to working with AI coding agents without the hype.
Machine Learning Mastery
Making developers awesome at machine learning.
A Guide to Fine-Tuning LLMs using LoRA
In this article, I'll take you through a step-by-step guide to fine-tuning LLMs with LoRA. A Guide to Fine-Tuning LLMs using LoRA.
7 LLM Generation Parameters—What They Do and How to Tune Them?
Seven LLM generation parameters: max tokens, temperature, top-p, top-k, penalties, stop sequences, tuning guidance, defaults
stop sequences
presence penalties
OpenAI’s Windows Play
OpenAI is making a play to be the Windows of AI: the all-encompassing platform that controls both hardware supplier and software developers.
Embracing the parallel coding agent lifestyle
Plus prompt injection attacks against Sora 2 cameos and notes on DSPy and Litestream 0.5.0
Build a Reasoning Model (From Scratch) - Sebastian Raschka
Understand LLM reasoning by creating your own reasoning model–from scratch! LLM reasoning models have the power to tackle truly challenging problems that require finding the right path through multiple steps. In Build A Reasoning Model (From Scratch) you’ll learn how to build a…
Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples
four common ways of evaluating trained LLMs in practice: multiple choice, verifiers, leaderboards, and LLM judges
training loss, perplexity, and rewards, but they are usually used internally during model development.)
A Coding Guide to Build an Autonomous Agentic AI for Time Series Forecasting with Darts and Hugging Face
A Coding Guide to Build an Autonomous Agentic AI for Time Series Forecasting with Darts and Hugging Face. A Step by step guide
LoRA Without Regret
How LoRA matches full training performance more broadly than expected
OpenAI’s Sora Makes Disinformation Extremely Easy and Extremely Real
The new A.I. app generated videos of store robberies and home intrusions — even bomb explosions on city streets — that never happened.
Building the heap: racking 30 petabytes of hard drives for pretraining
How we spent under half a million dollars to build a 30 petabyte data storage cluster in downtown San Francisco
Top 10 Local LLMs (2025): Context Windows, VRAM Targets, and Licenses Compared
Compare top local LLMs for 2025: context windows, VRAM tiers, licenses, quantization, runtimes, dense vs MoE tradeoffs clearly
5 Cutting-Edge Natural Language Processing Trends Shaping 2026
In this article, we discuss five cutting-edge NLP trends that will shape 2026.
transformers’ biggest weakness remains the high compute time and memory consumption of self-attention
Approaches like linear attention and sparse attention
GPT-5-Codex
OpenAI half-relased this model earlier this month, adding it to their Codex CLI tool but not their API. Today they've fixed that - the new model can now be accessed …
Four new releases from Qwen
It's been an extremely busy day for team Qwen. Within the last 24 hours (all links to Twitter, which seems to be their preferred platform for these announcements): Qwen3-Next-80B-A3B-Instruct-FP8 and …
Qwen3 TTS Demo - a Hugging Face Space by Qwen
Enter text and choose a voice and language to generate speech. The app will output the generated audio.
10 Python One-Liners to Optimize Your Hugging Face Transformers Pipelines - KDnuggets
In this article, we present 10 powerful Python one-liners that will help you optimize your Hugging Face pipeline() workflows.