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The less your model thinks, the better it codes. Kimi K2.7 Code proved it. The entire reasoning model space has been moving in one direction. More thinking tokens, longer chains, bigger reasoning budgets. Kimi just challenged that. K2.7 Code scores higher than K2.6 on every
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NVIDIA might just have open-sourced one of the most important AI projects right now. everyone is building skills, and we are also pulling in skills other people wrote and downloading them straight off GitHub. the skill is not just text. it bundles instructions and real
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Researchers found a way to make LLMs 8.5x faster!
— Akshay 🚀 (@akshay_pachaar) 11 juin 2026
(without compromising accuracy)
Speculative decoding is quite an effective way to address the single-token bottleneck in traditional LLM inference.
A small "draft" model first generates the next several tokens, then the large… https://t.co/JCdqjCKcKU pic.twitter.com/HbKmRqdF5P
Researchers found a way to make LLMs 8.5x faster! (without compromising accuracy) Speculative decoding is quite an effective way to address the single-token bottleneck in traditional LLM inference. A small "draft" model first generates the next several tokens, then the large
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Apple finally did it. Its new framework, Core AI, runs models entirely on Apple silicon, so inference happens on the user's device with zero server calls and zero token bills. That means Qwen, Mistral, and SAM3 running natively across iPhone, iPad, Mac, and Vision Pro. It's a
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about loop engineering. everyone's saying the same thing this week. you don't prompt agents anymore, you design loops that prompt them. here's the job that loop hands right back to you. a loop running unattended is also a loop failing unattended. loop engineering takes you
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Right. Spans show what got called, not what the agent thought it was doing in between. Ollie walks the causal chain to find where it broke, but intent still gets pieced together from outputs. Logging the model's reasoning as its own span helps, but most frameworks skip it by
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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/WPAmLLBg4u
Finally, 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
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Define Trainer Here, we create a Trainer object by specifying the training config, like learning rate, model, tokenizer, and more. Check this out
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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!
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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