标签：#Gemini

Grab a refreshing slice of cold watermelon (because it’s summer in Tokyo and I love it!), and let me...

Voice translations preserve speaker's tone, pacing, pitch—with SynthID watermarks for security.

Imagine speaking in English, and having listeners from all over the world hear you translated into...

This article was originally published on aicoderscope.com TL;DR : Gemini 3.5 Flash went GA on May 19, 2026 and costs 50% less than Claude Sonnet 4.6 on input tokens ($1.50 vs $3.00/M). It generates code at ~284 tokens per second — roughly 4.7× faster than Sonnet 4.6. Cursor already lists it natively; Cline needs one extra config step. The trap: Flash's default thinking level is "medium," which is slower and pricier than "low," the setting Google specifically tuned for coding and tool-use loops. Gemini 3.5 Flash Claude Sonnet 4.6 DeepSeek V4-Flash Best for Fast agent loops, context-heavy analysis Complex refactors, instruction fidelity Cost-capped high-volume tasks Input / Output per 1M tokens $1.50 / $9.00 $3.00 / $15.00 $0.14 / $0.28 Context window 1M tokens 200K tokens 1M tokens Terminal-Bench 2.1 76.2% — — Output speed ~284 t/s ~60 t/s — Max output per request 65,536 tokens 64K tokens 64K tokens The catch Output at $9/M erodes savings on code-gen 15× pricier output than Flash No vision, MIT-licensed Honest take : Use Gemini 3.5 Flash with Cline for multi-step agent tasks where round-trip latency compounds and context windows run large. Stay on Claude Sonnet 4.6 when you need a hard refactor to land perfectly on the first try — Sonnet's 79.6% SWE-bench Verified score still leads Flash's on correctness benchmarks. The cost math that does and doesn't work Gemini 3.5 Flash charges $1.50 per million input tokens and $9.00 per million output tokens. Against Claude Sonnet 4.6 at $3.00/$15.00, the input side is a genuine 2× saving. The output side is almost the same story: $9 vs $15 is 40% cheaper per generated token. Run the numbers on a typical Cline coding session: 8 tool calls, reading 12 files (roughly 20,000 context tokens), generating 500 lines of code output (~7,000 output tokens). Sonnet 4.6: (20K × $3 + 7K × $15) / 1,000,000 = $0.165/session Gemini 3.5 Flash: (20K × $1.50 + 7K × $9) / 1,000,000 = $0.093/session That's 44% cheaper per session. At 50 sessions a m

If you’ve tried building AI applications, you often face a familiar engineering wall. It goes like...

Microsoft shut down dozens of GitHub code repositories for Azure and AI coding tools after a reported hack.

NotebookLM is getting a big upgrade, but it's only for AI Ultra and enterprise accounts right now.

The Model Comparison Problem Most AI model comparisons are useless for developers making real decisions. They benchmark on academic datasets that don't reflect production workloads. They test frontier capabilities that matter for 5% of use cases. They ignore latency, cost, rate limits, and API reliability — which are the things that actually determine whether a model works in your application. This comparison is different. It's focused on what matters when you're building something: how Grok and Gemini perform on the types of tasks developers actually encounter, what each model's API experience is like, and where the genuine tradeoffs lie. I'm deliberately not including benchmark scores. If you want MMLU numbers, there are plenty of leaderboards for that. This is about production utility. What Each Model Actually Is Grok (xAI) Grok is xAI's model family. The current production models are Grok-3 and Grok-3 Mini, with Grok-3 being the flagship. Grok has a large context window (128K tokens standard, with extended context available), real-time access to X (Twitter) data as a differentiating feature, and strong performance on reasoning-heavy tasks. The xAI API follows a familiar REST pattern and is broadly compatible with OpenAI SDK conventions, which makes migration straightforward. Grok's notable characteristics: Strong at structured reasoning and multi-step problem decomposition Real-time web access via the API (useful for tasks needing current information) Relatively generous rate limits compared to some competitors Less restrictive on certain content categories than some other models Gemini (Google DeepMind) Gemini is Google's model family, currently anchored by Gemini 1.5 Pro and Gemini 2.0 Flash. The defining feature of Gemini is its context window — Gemini 1.5 Pro supports up to 1 million tokens in production, which is genuinely useful for certain document-heavy use cases. Gemini also has the tightest integration with Google's ecosystem (Workspace, Cloud, Search)

Keeping up with trending technical topics and new tools on developer forums can be time-consuming. To save time, I wanted to automate the process of finding popular articles, reading the comments to understand community sentiment, and drafting a summary. While I could write a standard Python script to scrape the dev.to API, simple scripts tend to be brittle. If an article doesn't have comments yet, a basic script will likely crash unless you write extensive error-handling logic. Instead of a rigid script, I built an Agent —a program that can dynamically reason about errors and adjust its approach. If one task fails, it can figure out the next best step. In this tutorial, I'll show you how to build a Trend-Spotting Agent using Python, the Google Agent Development Kit (ADK) , and Gemini 2.5 Flash. What We're Building We are going to write a Python application that acts as an autonomous agent. We'll give it three abilities: Search the dev.to API for rising technical articles based on specific tags. Dynamically fetch the top comments of those articles to read real community sentiment. Automatically draft a newsletter-style article on your DEV.to account summarizing its findings. Prerequisites Python 3.9+ installed on your machine. Google ADK . (Check out the Google ADK Docs if you need help installing). A DEV API Key . Grab this from your DEV.to account settings under "Extensions" and throw it in a .env file. Step 1: Giving the Agent its "Hands" (API Tools) Large Language Models (LLMs) are incredibly smart, but out of the box, they can't actually do anything on your computer. The coolest part about Google ADK is that we can write standard Python functions, hand them to the LLM as "tools", and let the AI decide how and when to use them. Let's write our API functions. Tool 1: Finding Rising Articles Here is our function to fetch rising articles. Pay close attention to the docstring ( """Fetches the top...""" ). We aren't writing this for other developers; the ADK actually

Gemini Spark helps automate everyday tasks, from inbox summaries to local event planning, but it’s unclear why Google made it a separate product.

Google’s new AI agent combed through my emails, documents, and calendar to plan a birthday party and still didn’t clock the person most important to me.

Google I/O made it official: AI-generated answers are now front and center in search, and most brands have almost no visibility into how AI is describing them to their customers. For anyone who has spent years building a strategy around 10 blue links, the rules just changed in a pretty significant way. On this episode of TechCrunch’s Equity podcast, Rebecca […]

Google's SynthID, designed to embed imperceptible signals into AI-generated content, is adding a new Content Detection API on Google Cloud's Gemini Enterprise Agent Platform, after gaining adoption by several industry players including Nvidia and OpenAI. By Sergio De Simone