You don’t debug LLMs, you nudge them.
LLMS
-
Free Spanish guide to monetize Claude
By
–
Claude can be very profitable if you know how to use it. Complete Spanish guide with prompts, skills, and monetization. Here is a complete FREE guide in Spanish. To access it:
1. Like
2. Comment "CLAUDE"
3. Follow me (so that -
GPT-5.5 and Opus 4.8 are best models with thinking maxed
By
–
Currently, the best models are GPT-5.5 and Opus 4.8. Make sure thinking is turned all the way up!
-
ChatGPT superapp overhaul rolling out in coming weeks
By
–
OpenAI’s ChatGPT "superapp" overhaul could start rolling out in the coming weeks. The first wave likely won’t be one big launch, but a phased redesign pushing users toward Codex, agents, image generation, and partner apps. As one OpenAI employee told the FT: "Chat is dead", the
-
London event: Codex demos, future plans, and unhinged creations
By
–
Hello London Come hang with us tomorrow and chat about Codex – what it can already do, what’s coming next, and all the slightly unhinged things people are building with it We’ll bring plenty of high-quality GPT-5.5 tokens, some exclusive merch, and banger vibes. Pull up?
-
Vibe Code an Entire Startup in One Afternoon to Build $1B Business
By
–
🚨 BUILD $1 BILLION BUSINESS – VIBE CODE AN ENTIRE START-UP IN AN AFTERNOON
— Abacus.AI (@abacusai) 7 juin 2026
– one prompt to build both web and mobile apps
– accept payments with one click
– no coding required
Abacus AI agent uses top frontier models like Opus 4.8 and GPT 5..5 xHigh to build complete software… pic.twitter.com/5IBKRDKyHWBUILD $1 BILLION BUSINESS – VIBE CODE AN ENTIRE START-UP IN AN AFTERNOON – one prompt to build both web and mobile apps
– accept payments with one click
– no coding required Abacus AI agent uses top frontier models like Opus 4.8 and GPT 5..5 xHigh to build complete software -
Fine-tuned LLM accurately predicts missing chess moves
By
–
Finally, the video shows prompting the LLM before and after fine-tuning.
— Akshay 🚀 (@akshay_pachaar) 7 juin 2026
After fine-tuning, the model is able to find the exact missing chess move instead of randomly generating some moves.
Check this 👇 pic.twitter.com/WPAmLLBg4uFinally, the video shows prompting the LLM before and after fine-tuning. After fine-tuning, the model is able to find the exact missing chess move instead of randomly generating some moves. Check this
-
Define Trainer object by specifying training config
By
–
Define Trainer Here, we create a Trainer object by specifying the training config, like learning rate, model, tokenizer, and more. Check this out
-
Prepare dataset and standardize format for fine-tuning Gemma 4
By
–
Prepare dataset Next, we use a conversation style dataset to fine-tune Gemma 4 12B. The standardize_data_formats method converts the dataset to the correct format for finetuning purposes!
-
Fine-tuning Gemma 4 12B to predict missing chess moves
By
–
Load dataset We'll fine-tune Gemma 4 12B to master chess. Given a set of previous move (one move missing) & the final result it has to predict the missing move. In order to do this we're using the ChessInstruct dataset from HuggingFace. Check this