Gemini 3.5 Flash

“Philipp Schmid recommends Gemini 3.5 Flash for OCR and VQA tasks, highlighting its faster, cheaper, and more accurate performance.”

GenAI PM Daily July 07, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 20 insights for PM Builders, ranked by relevance from Blogs, YouTube, and LinkedIn. #9 𝕏 Philipp Schmid recommends Gemini 3.5 Flash for OCR and VQA tasks, highlighting its faster, cheaper, and more accurate performance.

“Google DeepMind has added native computer use to Gemini 3.5 Flash, giving developers a built-in tool to build custom agents with vision and action capabilities across browser, mobile, and desktop interfaces.”

#1 𝕏 Google DeepMind has added native computer use to Gemini 3.5 Flash, giving developers a built-in tool to build custom agents with vision and action capabilities across browser, mobile, and desktop interfaces.

“Philipp Schmid showcases Google’s new Gemini 3.5 Flash “computer-use” model you can test live on Browserbase.”

The model is presented as a live demo that can be tested on Browserbase. It is later described as enabling agents to drive screens with built-in safeguards.

“#18 𝕏 Philipp Schmid lauds Gemini 3.5 Flash’s underrated multimodal understanding, outpacing Gemini 3.1 Pro. It’s 3× faster and costs half as much, thanks to work by @roboflow.”

#18 𝕏 Philipp Schmid lauds Gemini 3.5 Flash’s underrated multimodal understanding, outpacing Gemini 3.1 Pro. It’s 3× faster and costs half as much, thanks to work by @roboflow.

“Logan Kilpatrick finds Gemini 3.5 Flash on Vending Bench’s Pareto frontier for cost‐per‐intelligence, marking it as one of the most cost‐efficient models for running simulated store operations.”

GenAI PM Daily May 24, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 12 insights for PM Builders, ranked by relevance from X, YouTube, Blogs, and LinkedIn. How CrewAI’s Iris auto-codes PRs in Slack #1 𝕏 Logan Kilpatrick finds Gemini 3.5 Flash on Vending Bench’s Pareto frontier for cost‐per‐intelligence, marking it as one of the most cost‐efficient models for running simulated store operations. #2 𝕏 Google DeepMind expanded its partnership with Singapore to safely deploy AI at scale, launching new programs with country experts to accelerate scientific discovery, strengthen pandemic preparedness, and improve healthcare. #3 ▶️ AI Dev 26 x SF | Luke Kim: The Agent Data Stack—Why Every AI Agent Needs Its Own Data Stack Deeplearning.ai Luke Kim demonstrates how Spice AI’s open-source agent data stack integrates with OpenClaw to federate SQL across Parquet, Iceberg, Snowflake, MySQL, MongoDB, and Elasticsearch and deliver local acceleration via DuckDB/SQLite (backed by Vortex) so an AI agent can diagnose and resolve a simulated production incident in real time. Spice AI replicates working sets from heterogeneous stores into embedded databases (DuckDB or SQLite) accelerated by a custom Vortex engine, exposing them as a unified SQL endpoint and OpenAI-compatible API. In the demo, the presenter scaled a load generator from 1 to 6 replicas—triggering a Grafana latency alert in Slack—after which the OpenClaw agent recommended scaling the order service to 3 replicas and changing the PostgreSQL connection pooler mode from "session" to "transaction". After applying the agent’s recommendations, Grafana metrics showed order service latency and error rates drop back to baseline and request throughput increase, all without granting the agent direct access to backend systems. #4 ▶️ AI Dev 26 x SF | João Moura: Building Recurring, Governed, and Embedded Enterprise Workflows Deeplearning.ai CrewAI built 'Iris', an autonomous Slack-based coding agent that maintains its own memory, writes new skills and flows, and this week altered nearly 50% of all pull requests at the company. Iris answered a designer request by extracting 130 hard-coded color values from the CrewAI application for integration into the design system. Iris self-generates updates by writing its own skills and flows, leading to it altering almost half of the company’s pull requests in a single week. CrewAI published a library of reusable agent skills at skills.creai.com, including a "decide" skill that encodes and surfaces company decision-making processes within engineers’ terminals.

“Logan Kilpatrick shows that Gemini 3.5 Flash outperforms 3.1 Pro on many vision use cases (e.g., a Roboflow eval) while running ~6× faster, showcasing its superior multimodal understanding.”

#18 𝕏 Logan Kilpatrick shows that Gemini 3.5 Flash outperforms 3.1 Pro on many vision use cases (e.g., a Roboflow eval) while running ~6× faster, showcasing its superior multimodal understanding. #19 𝕏 DeepLearning.AI shows how embeddings capture semantic links (e.g., “budget” and “financials”) as the foundation for semantic search.

“Ali Ghodsi rolled out Gemini 3.5 Flash on Databricks, offering blazing-fast AI inference and smart capabilities directly within the platform.”

#3 𝕏 Ali Ghodsi rolled out Gemini 3.5 Flash on Databricks, offering blazing-fast AI inference and smart capabilities directly within the platform.

“Google DeepMind launched Gemini 3.5 Flash, an optimized edition of its large language model engineered for faster, low-latency inference.”

#2 𝕏 Google DeepMind launched Gemini 3.5 Flash, an optimized edition of its large language model engineered for faster, low-latency inference. Also covered by: @Demis Hassabis

“Jeff Dean rolled out Gemini 3.5 Flash globally today, unveiling Google’s latest AI model and inviting users to explore its new capabilities in the linked blog post.”

#1 𝕏 Jeff Dean rolled out Gemini 3.5 Flash globally today, unveiling Google’s latest AI model and inviting users to explore its new capabilities in the linked blog post. Also covered by: @Simon Willison , @Jeff Dean , @Logan Kilpatrick , @Sundar Pichai , @Josh Woodward