Poetiq system achieved 54% score on ARC-AGI-2 benchmark, surpassing Gemini 3 Deep Think at more then twice lower compute cost. Poetiq (Mix) is a combined self learning system that leverages Gemini 3 and GPT-5.1 models. We want to do SVG tests
OPEN SOURCE
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Laude Institute Launches Open Frontier Conference for Open Research
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Excited to see @LaudeInstitute announce Open Frontier — a new conference and collaboration for open research + open-source AI at the frontier. Proud to be a part this effort and excited to see all the collaborations that come out of it. https://
openfrontier.ai -
DeepSeek-V3.2: Open-Source Reasoning Model Rivals GPT-5
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DeepSeek recently dropped a new open-source reasoning champion to rival GPT-5! Introducing DeepSeek-V3.2: 671B param Mixture-of-Experts (37B active) Gold-medal IMO & IOI performance Sparse attention for long contexts Balanced daily driver + tool-use Fully
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Grok 4.20 tested as mystery model
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It turns out that Grok 4.20 is being tested on Alpha Arena as a "mystery model" and shows quite significant performance. "Undisclosed frontier AI lab (not Nof1)" How much would you pay for a model with such performance to run your portfolio?
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Flowise version 3.0.12 available on GitHub
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github.com/FlowiseAI/Flowise… [Translated from EN to English]
→ View original post on X — @flowiseai, 2025-12-05 15:39 UTC
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Flowise 3.0.12 Release: Gemini 3, Code Interpreter, Structured Output
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3.0.12 is out!
— FlowiseAI (@FlowiseAI) 5 décembre 2025
🍌 Nano Banana Pro
Use Gemini 3 with Nano Banana Pro – with support of multi turn conversation
🖥️ Gemini Code Interpreter
Use built in Gemini Code Interpreter and enables the model to generate and run Python code
📜 Agent Structured Output
Agent node can now… pic.twitter.com/97HLu1CDTa3.0.12 is out! 🍌 Nano Banana Pro Use Gemini 3 with Nano Banana Pro – with support of multi turn conversation 🖥️ Gemini Code Interpreter Use built in Gemini Code Interpreter and enables the model to generate and run Python code 📜 Agent Structured Output Agent node can now support returning structured JSON output More bugfix and details in release note 👇
→ View original post on X — @flowiseai, 2025-12-05 15:38 UTC
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Techniques to Reduce Hallucinations and Boost Accuracy
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You can use these techniques RIGHT NOW I've been using these with Claude Sonnet 4.5, GPT-4, and Gemini 2.0 Flash for 6 months. Results: – 100% reduction in hallucinations on technical docs
– 3x faster iteration on code generation
– 90%+ accuracy on complex analysis tasks The -
Meta-Prompting: The Nuclear Option
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Technique 10: Meta-Prompting (The Nuclear Option) This is what OpenAI's red team uses to break their own models and find edge cases. You ask the AI to generate the perfect prompt for itself. Template: I need to accomplish: [high-level goal] Your task:
1. Analyze what would -
Context Injection with Boundaries
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Technique 6: Context Injection with Boundaries Anthropic engineers inject massive context but set clear boundaries on what matters. Template: [CONTEXT]
[paste your documentation, code, research paper] [FOCUS]
Only use information from CONTEXT to answer. If the answer isn't in -
Technique 5: Confidence-Weighted Prompting
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Technique 5: Confidence-Weighted Prompting Google DeepMind uses this technique for high-stakes decisions. Ask the model to rate its confidence and provide alternative answers. Template: Answer this question: [question] For your answer, provide:
1. Your primary answer
2. Confidence level