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Introducing Google Gemma 4, our most capable open agentic model. It supports high-quality offline code generation with long context and is trained on over 140 languages. Open-source Apache 2.0 licence.
→ View original post on X — @saboo_shubham_, 2026-04-02 16:24 UTC
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So happy to see Google release Gemma 4 today in apache 2.0 that gives you frontier capabilities locally. You can use it right away in all your favorite open agent platforms like openclaw, opencode, pi, Hermes by asking it to change your model to local gemma 4 with
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Today, we’re launching Gemma 4, our most intelligent open models to date. Built with the same breakthrough technology as Gemini 3, Gemma 4 brings advanced reasoning to your personal hardware and devices.
— Google AI (@GoogleAI) 2 avril 2026
Here’s what Gemma 4 unlocks for developers:
— Intelligence-per-parameter:… pic.twitter.com/JgwRZvQHgF
Today, we’re launching Gemma 4, our most intelligent open models to date. Built with the same breakthrough technology as Gemini 3, Gemma 4 brings advanced reasoning to your personal hardware and devices. Here’s what Gemma 4 unlocks for developers: — Intelligence-per-parameter: Our 31B (Dense) and 26B (MoE) models deliver state-of-the-art performance for their size, outcompeting models 20x their size on @arena — Commercial flexibility: Released under a permissive Apache 2.0 license for complete developer flexibility and digital sovereignty — Agentic workflows: Native support for function-calling and structured JSON output allows you to build reliable, autonomous agents — Multimodal edge AI: The E2B and E4B models bring native vision, audio, and low latency to mobile and IoT devices — Long-context reasoning: Up to 256K context windows allow you to process entire repositories or large documents in a single prompt Whether you're building global applications in 140+ languages or local-first AI code assistants, Gemma 4 is built to be your foundation. Explore in @GoogleAIStudio or download the weights on @HuggingFace, @Kaggle, and @Ollama.
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Meet Gemma 4: our new family of open models you can run on your own hardware. Built for advanced reasoning and agentic workflows, we’re releasing them under an Apache 2.0 license. Here’s what’s new 🧵
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Heading to Google Cloud Next? Our team will be at Booth #5006 running demos and available for technical conversations on what you're building with AI agents. See what's happening with LangChain at Next: blog.langchain.com/join-lang…
→ View original post on X — @langchain, 2026-04-02 16:00 UTC
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4/ Here’s what I tried: I dropped in one prompt with everything I needed for a media company idea and let Genspark Claw work through it
— Chubby♨️ (@kimmonismus) 2 avril 2026
It mapped things out step by step and turned it into something I could actually follow
I also tested it inside Telegram and it works directly… pic.twitter.com/hbRtniCg6j
4/ Here’s what I tried: I dropped in one prompt with everything I needed for a media company idea and let Genspark Claw work through it It mapped things out step by step and turned it into something I could actually follow I also tested it inside Telegram and it works directly
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How is @Salesforce’s Agentforce impacting government?
— Helen Yu (@YuHelenYu) 2 avril 2026
AI in the public sector has moved from hesitation to execution. Agents are now live and reducing costs, improving service delivery, and handling millions of citizen interactions. #sponsored
The bigger shift is from reactive… pic.twitter.com/FMumWsBmJk
How is @Salesforce’s Agentforce impacting government? AI in the public sector has moved from hesitation to execution. Agents are now live and reducing costs, improving service delivery, and handling millions of citizen interactions. #sponsored The bigger shift is from reactive systems to proactive, personalized government. This is a redefinition of how government serves citizens: piped.video/EKH6cmeJC3E #AI #GovAI #Agentforce #Missionforce #SalesforcePartner
→ View original post on X — @yuhelenyu, 2026-04-02 15:45 UTC
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1/ Tried Genspark Claw from @genspark_ai and it’s actually better than I expected
— Chubby♨️ (@kimmonismus) 2 avril 2026
I used it to figure out a project and instead of just giving answers, it followed a workflow and walked me through everything step by step while helping me put everything together into something I… pic.twitter.com/tOD6cE8K0r
1/ Tried Genspark Claw from @genspark_ai and it’s actually better than I expected I used it to figure out a project and instead of just giving answers, it followed a workflow and walked me through everything step by step while helping me put everything together into something I
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Static orchestration is the silent killer of multi-agent RAG systems. The query changes, but the agent topology stays the same. The work introduces HERA, a framework that jointly evolves multi-agent orchestration and role-specific agent prompts. At the global level, it optimizes query-specific agent topologies through reward-guided sampling. At the local level, it refines individual agent behaviors via credit assignment and dual-axes prompt adaptation. On six knowledge-intensive benchmarks, HERA achieves an average improvement of 38.69% over recent baselines. Why does it matter? As multi-agent RAG systems scale, the gap between fixed pipelines and adaptive orchestration will only grow. HERA shows that letting the system learn its own coordination structure produces compact, high-utility agent networks. Paper: arxiv.org/abs/2604.00901 Learn to build effective AI agents in our academy: academy.dair.ai/
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Can an AI agent run a startup for a year without going bankrupt? Turns out most can't. New benchmark from Collinear AI puts 12 models to the test. YC-Bench tasks agents with running a simulated startup over hundreds of turns: hiring employees, selecting contracts, and maintaining profitability in a partially observable environment with adversarial clients and compounding consequences. Only three models consistently surpass the $200K starting capital. Claude Opus 4.6 leads at $1.27M average final funds, followed by GLM-5 at $1.21M with 11x lower inference cost. Scratchpad usage, the sole mechanism for persisting information across context truncation, is the strongest predictor of success. Adversarial client detection accounts for 47% of bankruptcies. Long-horizon coherence, not raw intelligence, separates the winners from the bankrupt. Paper: arxiv.org/abs/2604.01212 Learn to build effective AI agents in our academy: academy.dair.ai/