about AI, which is really about what I think about AI criticism, and more specifically, how to be a good AI critic. By which I mean: “How to be a critic whose criticism inflicts maximum damage on the parts of AI that are doing the most harm.”

Un article assez intéressant sur l’IA et la démocratie dont voici le résumé:

<< Les démocraties exigent un soutien majoritaire, ce qui pousse les élites à façonner l’opinion publique. L’IA rend cette persuasion plus précise, peu coûteuse et donc plus stratégique. Avec une seule élite, l’optimisation du soutien produit un glissement vers la polarisation, accéléré par la technologie. Avec deux élites alternant au pouvoir, la même technologie encourage des zones de semi-lock, où les préférences deviennent stables et difficiles à renverser. Selon le contexte, l’IA peut donc amplifier ou freiner la polarisation. Au total, la persuasion algorithmique transforme la polarisation en outil de pouvoir, menaçant potentiellement la stabilité démocratique.>>

A list of offensive methods and strategic approaches for facilitating (algorithmic) sabotage, framework disruption, and intentional data poisoning.

A tool to snap pixels to a perfect grid. Designed to fix messy and inconsistent pixel art generated by AI. - Hugo-Dz/spritefusion-pixel-snapper

This is the most realistic and uncanny forevision I ever read.

Artificial intelligence is a fast-moving field. I created Ahead of AI to help you keep up with the latest developments and research trends in the fields of machine learning, deep learning, and artificial intelligence.

No matter how well “AI” works, it has some deeply fundamental problems, that won’t go away with technical progress.

If you are a scientist, an engineer, a philosopher, an anthropologist, or an academic of any stripe, thinking and understanding is your job.

Verbalized Sampling, a training-free prompting strategy to mitigate mode collapse in LLMs by requesting responses with probabilities. Achieves 2-3x diversity improvement while maintaining quality. Model-agnostic framework with CLI/API for creative writing, synthetic data generation, and dialogue simulation. - CHATS-lab/verbalized-sampling

Open-Source Memory Engine for LLMs, AI Agents

What is Memori Memori enables any LLM to remember conversations, learn from interactions, and maintain context across sessions with a single line: memori.enable(). Memory is stored in standard SQL databases (SQLite, PostgreSQL, MySQL) that you fully own and control.

Why Memori?

One-line integration - Works with OpenAI, Anthropic, LiteLLM, LangChain, and any LLM framework SQL-native storage - Portable, queryable, and auditable memory in databases you control 80-90% cost savings - No expensive vector databases required Zero vendor lock-in - Export your memory as SQLite and move anywhere Intelligent memory - Automatic entity extraction, relationship mapping, and context prioritization

Fara-7B is Microsoft’s first agentic small language model (SLM) designed specifically for computer use.

With only 7 billion parameters, Fara-7B is an ultra-compact Computer Use Agent (CUA) that achieves state-of-the-art performance within its size class and is competitive with larger, more resource-intensive agentic systems.

AI-Powered Data Processing: Use LOTUS to process all of your datasets with LLMs and embeddings. Enjoy up to 1000x speedups with fast, accurate query processing, that’s as simple as writing Pandas code - lotus-data/lotus

LOTUS is an open-source query engine that makes programming as easy as writing Pandas and optimizes your programs for up to 400x speedups.

Fast, stateless LLM for your shell: qq answers; qa runs commands - iagooar/qqqa

Oh, dear.