Local AI is experiencing a major boom! Here’s the number of new GGUF models created each month over the past 8 months, along with insights from our internal HF agent (May data is partial): – 176,000 total public GGUF models on HF – Two distinct phases: Oct–Feb averaged ~5.1K new GGUF models per month
LLMS
-
Engineer’s switch to HTML increases token usage 2-4x
By
–
Thariq Claude Code engineer just wrote a whole post about why he switched from Markdown to HTML for agent outputs. His own admission: it uses 2-4x more tokens. Dont know if this is the best advice looking at my rates rn 😀
-
Impact of Token Reduction and Context Engineering on AI Models
By
–
Exactly. At 98.7% fewer tokens, you're not tuning the same system. You're running a different one. And agreed on context engineering. The model that wins isn't the smartest one. It's the one that sees only what it needs, when it needs it.
-
Using Claude Code to automate email API integration
By
–
Thanks man, it's great. I think a few things, the way it's set up with Workers etc is confusing. Luckily Claude Code figured out quickly it could just do a POST to a URL with a token and send emails. I'd look at other email sending companjes to see how they do it Usually it's
-
Shifting from RAG to LLM Knowledge Compilation
By
–
RAG is already becoming the “old way” The future of AI memory is not retrieval.
It’s compilation. Here’s the shift in one sentence: From searching information To structuring knowledge The new model? LLM Wiki Instead of: Chunking documents Running similarity -
Examples of fine-tuning LLMs in practice
By
–
Asking because about once every six months I put out a call for examples of fine tuning LLMs working in practice and, aside from the obvious examples like Cursor, I don't often get many great answers
-
Claude Code provides detailed cost and performance tracking for AI sessions
By
–
15- Transparence des coûts + 25$ de crédit AWS
Claude Code affiche à chaque session : – Coût total des appels API
– Temps d'exécution
– Modèle utilisé
– Nombre de modifications de code Utilité pratique : ces données deviennent des justificatifs de facturation pour tes clients. -
Using Claude Code to develop an AI agent via MCP
By
–
13- Démo 3 : Déléguer la création d'un agent à Claude Code
Instruction donnée à Claude Code, sur un fichier vide : "En te basant sur les autres fichiers, crée un nouvel agent Strands connecté au serveur MCP." Claude Code a : – Lu les fichiers existants pour comprendre la -
Analysis of Claude’s autonomous reasoning and inference capabilities
By
–
10- Ce que la démo révèle sur l'autonomie du modèle Question posée : "Quelle est la météo à San Francisco ?"
Ce que Claude a fait sans qu'on le lui code : – A compris qu'il avait besoin des coordonnées géographiques
– A inféré latitude/longitude depuis sa connaissance interne
– -
Building a Weather Agent in 44 Lines of Code
By
–
9- Démo 1 : Un agent météo en 44 lignes
Agent démontré en live : – Récupère la météo via l'API National Weather Service (
http://
weather.gov)
– Compte les mots du résultat
– Reformule la réponse en langage naturel pour l'utilisateur Total : 44 lignes de code 1 system