Let that sink in for a moment. DeepSeek v4 pro 75% discount. Permanent! In: $0.43
Out: $0.87 If you read the DeepSeek v4 tech paper you know that this model is insanely good when it comes to efficiency. Only 27% compute and only 10% cache compares to v3.2. SemiAnalysis wrote
LLMS
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DeepSeek v4 Pro 75% Discount, Only 27% Compute and 10% Cache
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Boost coding productivity with GitHub Copilot, ChatGPT, OpenAI
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Supercharged Coding with #GenAI — From vibe coding to best practices using GitHub Copilot, ChatGPT, and OpenAI: http://
amzn.to/3VBH2Su v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: Discover how GitHub Copilot, ChatGPT, and the OpenAI API can boost your coding productivity Push -
Subsidized token industry ending, users still ignore local models
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How it feels to know that the subsidized token industry is coming to an end and people are still not worrying about the right thing (running the models locally on your own compute) No more free DoorDash deliveries / $5 Uber trips for you (after they got you addicted)
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Open Source Deep Research for Agent Harnesses with AI-Q Skills
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Say hello to open source deep research for your favorite agent harness.
— NVIDIA AI (@NVIDIAAI) 22 mai 2026
Our AI-Q agent skill packages the work of building a research pipeline into a portable skill. Drop it into your harness, and the agent delegates a research task to a local or hosted AI-Q server and gets back… pic.twitter.com/u7tVWvCUWdSay hello to open source deep research for your favorite agent harness. Our AI-Q agent skill packages the work of building a research pipeline into a portable skill. Drop it into your harness, and the agent delegates a research task to a local or hosted AI-Q server and gets back
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TRINITY: 0.6B Model Learns to Manage Multiple Tasks
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A 0.6B model learned to manage giants. That is the idea behind TRINITY, a new ICLR 2026 paper by Jinglue Xu, Qi Sun, Peter Schwendeman, Stefan Nielsen, Edoardo Cetin, and Yujin Tang. The paper is not asking: “How do we build one model that knows everything?” It is asking
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TRINITY: 0.6B Model Managing Giants (ICLR 2026)
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A 0.6B model learned to manage giants. That is the idea behind TRINITY, a new ICLR 2026 paper by Jinglue Xu, Qi Sun, Peter Schwendeman, Stefan Nielsen, Edoardo Cetin, and Yujin Tang. The paper is not asking: “How do we build one model that knows everything?” It is asking
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Codex Publishes Model Training Experiment Blogpost
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codex is writing a blogpost about its experiments in training a model all by itself
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Mention of ‘strawberry’ reasoning feature in o1-preview
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Strawberry was the popular code word for reasoning, as implemented in o1-preview, in case that wasn’t clear.
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Google AI Studio adds native Kotlin and Cloud Run support for Gemini
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5/ Google AI Studio updates
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 22 mai 2026
For devs building with Gemini:
🔤 Native Kotlin support for Android apps
☁️ One-click deploy to Cloud Run
🔥 Firebase services support
📤 Seamless export to Antigravity
Build → deploy → scale, all from one place. pic.twitter.com/NMt0zmjLK55/ Google AI Studio updates For devs building with Gemini: Native Kotlin support for Android apps One-click deploy to Cloud Run Firebase services support Seamless export to Antigravity Build → deploy → scale, all from one place.
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Google Releases Gemini 3.5 Flash Model
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1/ Gemini 3.5 Flash is live NOW
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 22 mai 2026
▫️ 4x faster than competing frontier models
▫️ Beats Gemini 3.1 Pro on coding, agentic + multimodal benchmarks
▫️ Available in the Gemini app, Search, Antigravity 2.0 + Gemini API
The "fast AND smart" model finally exists. pic.twitter.com/TctzckYTzu1/ Gemini 3.5 Flash is live NOW 4x faster than competing frontier models Beats Gemini 3.1 Pro on coding, agentic + multimodal benchmarks Available in the Gemini app, Search, Antigravity 2.0 + Gemini API The "fast AND smart" model finally exists.