Dites-vous qu’actuellement, devant moi j’ai 6 terminaux ouverts sur mon ordi, pour 3 projets différents que je code en même temps avec l'IA. – 2 Codex
– 3 Claude Code
– 2 Gemini
– 3 IDE Cursor > Claude Codex, je l’utilise comme le senior dev ultra spé mais un peu débile.
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CODE
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Using Multiple AI Coding Tools Simultaneously
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Microsoft Cancer AI, Mistral Coding Tools, Agent Standards
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Top stories in AI today: – Microsoft's cancer-mapping AI
– Mistral’s Devstral and new coding assistant
– Create a brand kit with Nano Banana Pro
– AI giants to build open agent standards
– 4 new AI tools, community workflows, and more Read more: https://
therundown.ai/p/microsofts-c
ancer-mapping-ai
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Technique 4: Differential Prompting
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Technique 4: Differential Prompting
— God of Prompt (@godofprompt) 10 décembre 2025
Engineers don't ask for one output. They ask for two versions optimized for different criteria, then pick or merge. This exploits the model's ability to hold multiple solution strategies.
Template:
Generate two versions of [output]:
Version… pic.twitter.com/yHkRJkECqf— Technique 4: Differential Prompting Engineers don’t ask for a single output. Instead, they request two versions optimized for different criteria, then select or merge them. This approach leverages the model’s ability to explore multiple solution strategies. Template: Generate two versions of [output]: Version 1:
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[Content optimized for criterion A] Version 2:
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Coding Skills as a Path to AI-Generated Income
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Coding skills unlock Ai income pic.twitter.com/R3neDP8ktl
— Satya Mallick (@LearnOpenCV) 10 décembre 2025Coding skills unlock Ai income
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VLM on Edge Part 3: Frameworks Setup and Inference Guide
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VLM on Edge Part 3: VLM Frameworks Setup and Inference
— Satya Mallick (@LearnOpenCV) 9 décembre 2025
The ultimate guide to deploying Vision Language Models on resource constrained hardware!
In this video we break it down further with the following.
✅Complete CUDA Math Library tailored for NVIDIA Jetson Orin Nano
✅Fail… pic.twitter.com/JVMviNbdICVLM on Edge Part 3: VLM Frameworks Setup and Inference
The ultimate guide to deploying Vision Language Models on resource constrained hardware! In this video we break it down further with the following.
Complete CUDA Math Library tailored for NVIDIA Jetson Orin Nano
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RL Impact on Base Model Performance: Pre-training and Mid-training Interplay
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There are competing views on whether RL can genuinely improve base model's performance (e.g., pass@128). The answer is both yes and no, largely depending on the interplay between pre-training, mid-training, and RL. We trained a few hundreds of GPT-2 scale LMs on synthetic GSM-like reasoning data from scratch. Here are what we found: 🧵
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AI model coding performance on SWE benchmark
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ca veut dire que c'est un model qui code très bien . SWE c'est le benchmark, 68 le score 🙂
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Top AI Models for Different Tasks: A Practical Guide
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ChatLLM model picks we keep coming back to: Everyday → GPT- 5.1 Coding → Sonnet 4.5 / Opus 4.5
Images → Nano Banana Pro, Midjourney → Sora 2, Seedance Pro
TTS → ElevenLabs, Hume
Fast tasks → Gemini Flash
Reasoning → GPT-5.1 Thinking Model switching handled -
Offline AI Coding Model Achieving 68 SWE Performance
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MAIS WWWWWWWWWWHAAAAAAT !!! Mais purée, si c’est vrai, vous n’imaginez même pas le BANGER ASTRONOMIQUE !! Genre pouvoir utiliser une IA qui code à 68 SWE en OFFLINE !!!! Dans l’avion, ou quand t’as pas de réseau dans le train… Je vais tester ça tout de suite, mais ma hype est