why is the model bad? Would love feedback! It’s the most capable Gemini model we have ever shipped
AI
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DreamLite: ByteDance’s compact 0.39B model for fast image generation and editing
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What if your phone could generate or edit images in under a second? ByteDance’s Intelligent Creation Lab presents DreamLite, a compact 0.39B parameter model that unifies text-to-image generation and editing in one network. It uses a simple trick: concatenating images
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Gemini’s focus on real-world use cases
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we are deeply focused on real world use cases for Gemini, its also exciting to see so many benchmarks get better at capturing these use cases
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Free course on delivering AI-powered analytics and dashboards
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Modern analytics needs more than dashboards. It needs a new foundation. This free one-hour course covers:
• Delivering trusted, AI-powered insights using unified data, governance, business logic, and the lakehouse
• AI/BI Dashboards, Genie spaces, semantic layers, and data -
AI struggles to generate novel research questions
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In science, AI still does a poor job at finding interesting questions to solve in fields that don't have lists of known issues This has always been the hardest thing to teach PhDs: otherwise you find small problems or problems that don't advance the field or don't generalize etc
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Introducing LoRAs for Krea 2: Powerful AI Fine-Tuning System
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introducing LoRAs for Krea 2 (beta).
— Krea (@krea_ai) 21 mai 2026
our most powerful fine-tuning system to date; now you can train Krea 2 on a your own specific style, object, or character with incredible precision.
learn how it works 👇 pic.twitter.com/pbsuw43pMvintroducing LoRAs for Krea 2 (beta). our most powerful fine-tuning system to date; now you can train Krea 2 on a your own specific style, object, or character with incredible precision. learn how it works
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Spotify Engineers Adopt Claude Code; 60% PR Growth
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Chez Spotify, 96 % des ingénieurs codent désormais avec Claude Code.
— Jouhatsu | AI Influence Operator (@Jouhatsu_ai) 21 mai 2026
La fréquence des PR a explosé de +60 % et ils réalisent 4 500 déploiements par jour.
Niklas Gustavsson, Chief Architect de Spotify, vient d'expliquer exactement comment ils ont fait ça, lors d'une… https://t.co/YqYNyJadtD pic.twitter.com/aIAIKQ3nsNChez Spotify, 96 % des ingénieurs codent désormais avec Claude Code. La fréquence des PR a explosé de +60 % et ils réalisent 4 500 déploiements par jour. Niklas Gustavsson, Chief Architect de Spotify, vient d'expliquer exactement comment ils ont fait ça, lors d'une
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Frameworks and Prompts to Validate an AI MVP
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7 prompts. 3 frameworks. One Perplexity deep research session. You go from "what should I build?" to validated MVP scope with a launch plan. Before writing a single line of code. I built these from Christensen's Jobs-to-Be-Done and Kim & Mauborgne's Blue Ocean Strategy. The
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Apply JTBD Demand Detector to an AI Opportunity
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Prompt 5: "The JTBD Demand Detector" "For the strongest opportunity you identified, apply
Clayton Christensen's Jobs-to-Be-Done framework. Research what 'job' the target user is actually hiring
a product to do. Not features. The underlying progress
they're trying to make. -
Incumbent Weakness Mapper: Competitor Analysis Prompt
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Prompt 3: "The Incumbent Weakness Mapper" "Take the top 5 pain points from your research. For each one, do deep research on every existing
product that claims to solve it. For each competitor, I need: their pricing model, the
most common user complaints, specific feature gaps