#Google

AIJeremy Howard calls Gemini Flash 3.5 disappointing despite praising its intelligence and speed. He argues it was trained to maximize evaluation scores rather than to help humans, so it often performs unrequested actions instead of doing what was asked.

AICursor has added Gemini Flash 3.5 to CursorBench, its main coding agent evaluation. The company says the leaderboard will be updated as new models are released, with results available at

AIGoogle demonstrated Gemini 3.5 Flash in the Gemini app creating an interactive circuit helper that outputs a step-by-step physical build guide and a working simulation. The demo targets students learning complex subjects visually through hands-on interaction. Gemini 3.5 Flash is available globally starting today.

AIGoogle's Gemini 3.5 Flash outperforms Gemini 3.1 Pro on Terminal-Bench 2.1 (76.2%), GDPval-AA (1656 Elo), and MCP Atlas (83.6%). The post also claims it is 4x faster than other frontier models, or 12x in Antigravity, and reports 83.6% on MMMU-Pro for multimodal performance.

Why it matters: The post gives specific benchmark scores against Gemini 3.1 Pro, letting readers compare coding, agentic, and multimodal results directly.

AIGoogle is rolling out Gemini 3.5 Flash globally for consumers in the Gemini app and Search AI Mode. It is also available to developers through the Gemini API, Google Antigravity, and Google AI Studio, and to businesses on the Gemini Enterprise Agent Platform.

Why it matters: The post shows where each Gemini 3.5 Flash access path goes, from consumer apps to developer and enterprise platforms, which helps readers pick the right entry point.

AIAt Google I/O, Google introduced Gemini 3.5 Flash, which the author says has become part of the daily research cycle. The author says a team of agents in Antigravity 2.0 recreated the original AlphaZero paper and built a playable web version from two prompts, coding the reinforcement learning pipeline in JAX/Flax and training a ResNet model via self-play on multi-TPU pods.

AIGoogle's Gemini 3.5 Flash is available today globally, according to Oriol Vinyals. The post invites developers to build agents and apps with it and links to Google's blog for more details.

AIGemini 3.5 Flash produces fully tactile, interactive HTML/SVG hardware simulations in a single shot when given complex agents and tasks. The output includes bump-mapped metals, spring physics, and procedural audio, according to the post.

AIGoogle's Gemini 3.5 Flash reaches 76.2% on Terminal-Bench 2.1, 1656 Elo on GDPval-AA, and 83.6% on MCP Atlas, showing strong performance on multi-step agentic tasks. The model is positioned as an ultra-fast reasoning engine for powering AI agents.

AINVIDIA and Google DeepMind experts will host a live session on Friday, April 24 at 11:00 AM PDT showcasing Gemma 4 running on DGX Spark. The demos cover vision translation, long-context document Q&A, and real-time code generation, with audience questions welcome.

AIAt Google Cloud Next this week, Google introduced its 8th-generation TPUs, split into two variants: 8t for massive-scale training and 8i for low-latency inference. Google presents the launch as a milestone in its accelerator roadmap, aimed at optimizing the full AI stack. The post links to a blog with further details on the systems architecture.

AIGoogle launched Gemini 3.1 Flash Live, a model designed to handle the nuances of live speech such as tone and interruptions. The post says it can be tried in Gemini Live and Search Live.

AIGoogle's newest Gemini 3.1 Flash-Lite model outperforms the previous generation's Gemini 2.5 Flash tier. Google positions it as smarter, faster, and cheaper, with details linked in the Google blog post.

AINano Banana 2, officially released as Gemini 3.1 Flash Image Preview, ranks first in Image Arena text-to-image with a score of 1279. The quoted post says it also ties for first in single-image editing at 1407 and costs $0.067 per image, about half the price of Nano Banana Pro.

AIQuoc Le announced that Aletheia, a math research agent, autonomously solved 6 of 10 FirstProof problems, the best result in the inaugural challenge. The post says this exceeds last year's IMO-gold achievement and points to a paper and thread for full details. The accompanying figure shows 10 unmodified problems, 6 candidate solutions per agent, and expert evaluation yielding 6 solved problems on a best-of-2 basis.

AIGoogle researchers used the Aletheia agent, powered by Gemini 3 Deep Think, to attempt 10 FirstProof challenge problems without modification. The agent operated fully autonomously and solved 6 of the 10 problems, according to the post, with methodology and expert evaluations described in the linked arXiv paper.

Why it matters: The post gives the autonomous setup and expert-evaluated results for an AI agent on FirstProof math problems, useful for judging how far such systems go on research-level math.

AIGoogle has released Gemini 3.1 Pro, reporting 77.1% on ARC-AGI-2 and more than twice the score of Gemini 3 Pro on that benchmark. The model is rolling out to developers in preview through the Gemini API and Google AI Studio, to enterprises via Vertex AI and Gemini Enterprise, and to consumers in the Gemini app and NotebookLM.

Why it matters: The post pairs the release with a benchmark table comparing Gemini 3.1 Pro against Gemini 3 Pro, Claude Sonnet 4.6, Claude Opus 4.6, and GPT-5.2 on reasoning and coding tasks.

AIGoogle's Gemini 3 Deep Think is announced as a model strong in math and coding, with an IMO gold result and a 3455 Codeforces ELO. The post also says it reaches gold-standard performance in physics and chemistry olympiads and sets new records on ARC-AGI-2 and HLE.

AIOriol Vinyals highlights Deep Think's real-world use in helping researchers find errors in high-level mathematics research papers. He notes that such work would be far beyond what an undergraduate math student could do alone.

AIYi Tay introduces Aletheia, a math research agent powered by an advanced version of Gemini Deep Think. The post says it produced two publishable papers, one fully automatic and one human-AI collaboration, and solved multiple open Erdős problems. The attached image shows a Google DeepMind paper titled "Towards Autonomous Mathematics Research" with a generator, verifier, and reviser loop.

AIA new paper shows researchers using "vibe-proving" with Gemini Deep Think to tackle open problems and refute long-standing conjectures. The work also reports discovering obscure mathematical links across theoretical computer science, physics, and other fields.

AIQuoc Le announced a paper describing Aletheia, an agent built on Gemini Deep Think that goes beyond Olympiad problems to PhD-level mathematics. The post says Aletheia iteratively generates and verifies proofs, collaborates on human-AI research, autonomously generates a paper on eigenweights, and solves open Erdős conjectures.

AIGoogle's latest blog post describes how Gemini Deep Think is being applied to advance mathematical and scientific research. The post itself gives no specific benchmarks, figures, or release details beyond this announcement.

AIQuoc Le announced a case study using Gemini to systematically evaluate 700 conjectures labeled open in the Erdős Problems database. The team addressed 13 problems, finding 5 novel autonomous solutions and identifying 8 existing solutions missed by previous literature.

AIOriol Vinyals, who works on Gemini, says Gemini 3 Flash reduces the traditional trade-off between smart and fast LLMs. He congratulated the team on the release, though the post gives no benchmark scores, prices, or other figures.

AIYi Tay says Gemini 3 Flash is out and is an outstanding model, with Flash alone competitive with the best GPT-5 models. Google DeepMind describes Gemini 3 Flash as offering frontier intelligence at a fraction of the cost, built for speed and scale.

AIARC Prize reports that the top Kaggle entry reached 24% on the ARC-AGI-2 private dataset at $0.20 per task, and that all winning solutions and papers are open source. The top verified commercial model, Opus 4.5 (Thinking, 64k), scored 37.6% at $2.20 per task, while a Poetiq refinement on Gemini 3 Pro reached 54% at $30 per task. The author argues that refinement loops are the main driver of 2025 progress, and says ARC-AGI-3 is planned for early 2026.

Why it matters: The post links 2025 competition results to a broader argument about refinement loops, showing how benchmark outcomes are being read as evidence of AI reasoning progress.

AIGoogle's Deep Think was asked to design a fractal explosion in HTML, using extended thinking to plan recursion and aesthetics before writing code. The post presents the result as sophisticated digital art produced from a simple prompt.

AIGoogle's Gemini 3 Deep Think mode is now available in the Gemini app for Ultra users. The post says it uses parallel thinking for difficult coding and scientific tasks and builds on technology that reached gold-medal level at the ICPC World Finals and IMO.

AIQuoc Le announced that Google's Gemini team is recruiting for a new Singapore team focused on advanced reasoning and LLM reinforcement learning. The post invites exceptional engineers and researchers to join the team, which is led by Yi Tay and reports into Quoc Le's Mountain View group.

AIYi Tay announced that Google DeepMind's Gemini team is starting a new research team in Singapore focused on advanced reasoning, LLM/RL, and improving frontier models such as Gemini and Gemini Deep Think. The team is led by Tay and reports to Quoc Le's broader team in Mountain View, which recently contributed to IMO and ICPC gold medal results with Gemini Deep Think. The team is starting small and is recruiting exceptionally capable engineers and researchers from the region and beyond.

AIQuoc Le says Nano Banana Pro's text understanding is impressive, showing an infographic it created for the sequence-to-sequence paper from a single request. The post includes the generated image as its main evidence, with no figures on speed, pricing, or benchmarks.

AIQuoc Le prompted Gemini 3 Deep Think to create a self-contained HTML analog film photography experience, and it produced a demo. The prompt required a viewfinder for uploaded images, adjustable settings, selectable film stocks that affect photo style, and a gallery of overlapping Polaroid-style prints that enlarge on hover.

AIARC-AGI-2 at 45.1% is 🔥

AIGemini 3 has been released, with MathArena Apex rising from under 2% to 23.4% and ARC-AGI-2 reaching 31%. The post presents these jumps on hard benchmarks as a meaningful step forward in AI reasoning.

AIAman Sanger of Cursor says the team is hard at work on Composer 2. The post gives no features, benchmarks, or release timing, though it follows Cursor's $2.3B Series D raise and over $1B in annualized revenue.

AIARC Prize Foundation announced ARC Prize Verified, a program that certifies frontier model scores on the ARC-AGI benchmark using hidden test sets and adds a third-party academic panel to audit and open-source its testing process. Five AI labs, including Google and xAI, are sponsoring ARC-AGI-3 development, and the foundation says donations do not influence verification scoring. Models that pass verification will appear on the official leaderboard with a verification badge.