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Gemini 3.5 is indeed a great model. Apart from its...
Gemini 3.5 is indeed a great model. Apart from its efficiency and improved agentic evals, it's good for long-horizon complex tasks, e.g., built on this model, /teamwork agents in Antigravity created a full training of AlphaZero paper with self-play and all. Try it out!
Google’s AI agents just crossed a major line. 93...
Google’s AI agents just crossed a major line. 93 autonomous agents built a bootable OS kernel in only 12 hours using Antigravity 2.0 powered by Gemini 3.5 Flash. The system: • Wrote kernel-level code • Managed memory + processes • Coordinated agents autonomously • Used 15,000+ model calls • Consumed 2.6B tokens • Cost less than $1,000 • Successfully ran DOOM The craziest part? Humans only gave the goal. The agents handled: • Coding • Verification • Debugging • Tool usage • Multi-agent collaboration This is no longer “AI helping developers.” This is AI operating like a full software engineering organization.
#gemini3 🗒️600+400+300+200m （R jog）×2 🎯3‘20/km...
#gemini3 🗒️600+400+300+200m （R jog）×2 🎯3‘20/km 先週よりも強度上がったけど無事にコンプ、このスピード帯への恐怖心も無くなった😉月末の1500mは5分切れるイメージしかないぜ🔥 #まるお製作所RC https://t.co/jDshByfc71
Gemini 3.5 Flash is out today. Here are numbers fr...
Gemini 3.5 Flash is out today. Here are numbers from my main voice and task agent benchmarks. Some notes: All the Gemini 3 models so far are too slow to work well for voice agents. Gemini 2.5 Flash was a *great* model for voice agents, when it was SOTA. It was fast and good at instruction following. Its big weakness was tool calling. It was quite difficult to prompt Gemini 2.5 Flash to perform tool calling reliably in long context, multi-turn use cases. With Gemini 3, Google improved the tool calling issues a lot. But time to first token is ~1s. We really need TTFT down below 700ms. Google isn't alone in this. All the SOTA models released this year have been reasoning models that aren't optimized for low latency. Claude Haiku 4.5 (released last October) remains the best-performing model with a TTFT under 700ms. Gemini 3.5 Flash is the first Flash model in the 3 family to be released as "generally available." It's quite different from gemini-3-flash-preview, which was released last December. That model actually scored a bit better on my voice agent benchmark. This new model is the new overall top scorer on my task agent benchmark. This benchmark tests a multi-turn task, requiring that models achieve a P50 turn execution time faster than four seconds. Gemini 3.5 Flash with a "high" thinking budget scores significantly better than any other model I've tested. So even though the TTFT isn't what we'd like to see from this model, the overall generation speed makes up for it, and allows us to use the "high" thinking budget and still achieve a per-turn P50 under two seconds. Very impressive. This performance costs money, though. I had become accustomed to thinking of Gemini models as aggressively priced. But Gemini 3.5 Flash is actually more expensive than GPT-5.4 and Claude Sonnet 4.6 on this benchmark. Also note that lower reasoning settings don't always save money. Gemini 3.5 Flash "minimal" costs more, on this benchmark, than "high," because it makes more mistakes, so it uses more tokens to complete the task. Please note that performance of this model on your benchmarks might be very different. My voice agent and task agent results are often wildly out of line with the reported results on standard benchmarks in the model cards and release notes. The voice agent benchmark is 30 turns, and heavily tests tool calling in a long-context scenario. The task agent benchmark injects large streams of structured data events into the context, all tool calls are asynchronous, and the test task takes at least 32 turns to complete. (My motto for evals is "30 turns or it didn't happen.") Make your own benchmarks! (And post the source code and the results for different models, if you can.)
Google introduces Gemini 3.5, an advanced AI built...
Google introduces Gemini 3.5, an advanced AI built for action. It simplifies coding, planning, and complex tasks, making work faster and more efficient for everyone. #Gemini #AI https://t.co/KKnhfBcgrc
100+ AI Tools to replace your tedious work: 1. Re...
100+ AI Tools to replace your tedious work: 1. Research - ChatGPT - YouChat - Abacus - Perplexity - Copilot - Gemini 2. Image - Higgsfield AI Soul - GPT-4o - Midjourney - Grok 3. Productivity - Gamma - Grok 3 - Perplexity AI - Gemini 2.5 Flash 4. Writing - Jasper - Jenny AI - Textblaze - Quillbot 5. Video - Klap - Kling - InVideo - HeyGen - Runway 6. Meeting - Tldv - Otter - Noty AI - Fireflies 7. SEO - VidIQ - Seona AI - BlogSEO - Keywrds ai - Outrank AI 8. Presentation - Decktopus - Slides AI - Gamma AI - Designs AI - Beautiful AI 9. Design - Canva - Flair AI - Designify - Clipdrop - Autodraw - Magician design 10. Audio - Lovo ai - Eleven labs - Songburst AI - Adobe Podcast 11. Marketing - Pencil - Ai-Ads - AdCopy - Simplified - AdCreative 12. Startup - Tome - Ideas AI - Namelix - Pitchgrade - Validator AI 13. Social media management - Tapilo - Typefully - Hypefury - TweetHunter Follow @AdarshChetan for more such amazing stuff ♥️
Join us in celebrating the first year of Gemini ✨�...
Join us in celebrating the first year of Gemini ✨🎂 → https://t.co/MjcEuUZqh8 Explore all that we’ve built together using the Gemini API, Google AI Studio, Gemma open models and beyond, and get ready for all that’s still to come… https://t.co/I1iTI0ThDZ
We just rolled out a new built-in usage dashboard...
We just rolled out a new built-in usage dashboard in Google AI Studio 📈, you can now easily check request + token volumes and spending (with much more coming soon)!! https://t.co/NCqAZZmArR
Today, our research on Co-Scientist was published...
Today, our research on Co-Scientist was published in Nature. It introduces a new multi-agent AI system built with Gemini that iteratively generates, debates, and evolves novel hypotheses for complex scientific problems. Hypothesis Generation introduced today at #GoogleIO📷 as part of the Gemini for Science experimental tools is built with Co-Scientist.⬇️👇
Our latest research on Co-Scientist is out today i...
Our latest research on Co-Scientist is out today in Nature! Built with Gemini, this multi-agent system powers the new Hypothesis Generation tool within Gemini for Science, helping researchers navigate the rigorous cycle of ideation, critique, and refinement. Read more from @ymatias and explore the full announcement: https://t.co/aFqSBETpsh"
Launched today at #GoogleIO, Gemini 3.5 is our lat...
Launched today at #GoogleIO, Gemini 3.5 is our latest family of models that combines frontier intelligence with action. And we’re kicking off the series by releasing 3.5 Flash! @GoogleDeepMind engineered these models from the ground up using our purpose-built AI infrastructure. Businesses can start using Gemini 3.5 Flash in the Gemini Enterprise app to help discover, create, and use the best of Google AI in their workflows starting today. Learn more here → https://t.co/D7Xv7aH9C8
Google just built a working OS from scratch using...
Google just built a working OS from scratch using AI agents Antigravity 2.0 and Gemini 3.5 Flash in 12 hours. 🤖 93 parallel sub-agents running simultaneously 🔄 15,000+ model requests fired 🧠 2.6 billion tokens processed 💸 Less than $1,000 in API credits The cost and time barrier to build serious software is collapsing fast.