1/10 I catalogued 10,000+ AI tools so you don't have to. But here's the thing — a directory isn't enough. You need the RIGHT tools for YOUR goals. That's why I built something different 🧵👇

The future is going to be AMAZING with AI and robots enabling sustainable ABUNDANCE for all!

GPT-5.2-Codex launches today. It is trained specifically for agentic coding and terminal use, and people at OpenAI have been having great success with it.

We’re expanding the Gemini 3 family with the launch of Gemini 3 Flash. This model: — Combines Gemini 3’s Pro-grade reasoning with Flash-level latency, efficiency, and cost — Delivers frontier-level performance on PHD-level reasoning and knowledge benchmarks — Is our most

Sergey Brin in a recent interview mentioned Google was too scared to ship AI because chatbots say dumb things.Meanwhile OpenAI shipped and took the lead. The lesson: shipping something imperfect beats waiting for perfect. Google had the tech. OpenAI had the nerve.

In the latest issue of The Batch, Andrew Ng shares a simple recipe: using aisuite and MCP tools to spin up a highly autonomous (but unreliable) agent. (Practical agents need more scaffolding!) Plus: 📰 Claude Opus 4.5 is faster, cheaper, and stronger 📰 U.S. launches “Genesis

We need an AI to let us know we need to logoff and touch grass

Most companies using AI in 2025: ChatGPT for emails, maybe some image generation. Most companies claiming to use AI in 2025: We’re building autonomous agent workflows integrated across our entire stack. The gap between marketing and reality is enormous.

8/8 Curious about AI tools and how they work? We cover the space daily. Follow @AIToolsCompass and visit https://t.co/uF1wBE5O08 where we track 10,000+ AI Tools

7/8 RL is computationally expensive and tricky to get right. Reward hacking, where the model games the metric instead of solving the problem, is a constant challenge. But when it works, it unlocks capabilities that supervised learning can't reach.

6/8 RL is also behind reasoning models like o1 and o3. Instead of just predicting the next token, these models learn to search through chains of thought. RL trains them to find reasoning paths that lead to correct answers.

5/8 The process: 1.Generate multiple responses to a prompt 2.Humans rank which response is better 3.Train a reward model on those rankings 4.Use RL to optimize the LLM against that reward model Human preferences become the reward signal.

4/8 Why RL matters for LLMs: RLHF (Reinforcement Learning from Human Feedback). Base models predict text. RLHF trains them to predict text humans actually prefer. This is why ChatGPT feels helpful instead of just autocompleting words.

3/8 Three components in every RL system: 1.Agent: the thing making decisions 2.Environment: the world it operates in 3.Reward signal: feedback on whether actions were good or bad The agent learns a policy, a strategy for picking actions that maximize reward over time.

2/8 The core idea: learn by trial and error. An agent takes actions in an environment, gets rewards or penalties, and adjusts its behavior to maximize future rewards. No labeled data needed. Just outcomes.

1/8 Reinforcement Learning is behind most major AI breakthroughs. Here's how it actually works, explained in simple terms.🧵

8/8 Want to find AI tools using RAG for your workflow? Check https://t.co/uF1wBE5O08 Follow @AIToolsCompass for more breakdowns like this.

7/8 RAG is why AI tools can now search your docs, answer questions about your codebase, and summarize your emails. Simple concept. Massive impact.

6/8 Where RAG struggles: Chunking strategy matters. Bad chunks give bad results. Retrieval can miss relevant info if embeddings don't capture the right nuance. And more context isn't always better. There's a quality vs quantity tradeoff.

5/8 RAG vs fine-tuning: different tools for different jobs. Fine-tuning changes how the model behaves. RAG changes what the model knows. Most enterprise use cases need RAG because the knowledge base updates constantly.

4/8 Why embeddings matter: they capture meaning, not just keywords. "How do I cancel my subscription" and "I want to stop paying" are different words but similar embeddings. This is why RAG finds relevant info even when exact phrases don't match.