Google is out with a torrent of new AI tools and updates designed to charm writers and creators looking for the utmost in creativity and productivity.
Many of the tools can be especially powerful, given that they’re part of the Google ‘ecosystem,’ and can be easily connected with a number of other Google tools for added performance.
Here are the top ten you’ll want to check-out:
*Doc’s Live: Create and Edit Docs With Your Voice: While AI voice-to-text apps have been around for a while, Google takes this capability a step further by enabling you to create a Google doc with your voice – and pull relevant data for that doc from your Gmail, Google Drive, Google Chat and the Web.
Promised for sometime this summer, Doc’s Live invites you to simply talk and then does the rest by helping you brainstorm, organize your thoughts and structure your document.
Planned for release to Google AI Pro and Google Ultra subscribers, Doc’s Live is also promising to help you tweak the writing style of your doc to your personal preference.
*Ask YouTube: Dig Deeper for Knowledge-Gems on Video: Writers looking to get a better grasp of what YouTube videos have to offer will want to check out this new tool, designed to enable you to do much more complex searches on YouTube.
Each ‘Ask YouTube’ search query will trigger creation of a compilation of relevant videos across YouTube’s entire catalogue, including long-form videos and shorts.
You’ll need a YouTube Premium subscription for the service – which also removes all those ads that YouTube drops into videos.
*Google Pics: A New AI-Powered Designer: Promised for release this summer, Google Pics is a new AI-powered design tool for creating slides, social media posts, business logos, digital brochures, infographics and similar.
Special features include the ability to edit the specific design and text of your work with precision, as well as the ability to integrate Google Pics with Google Workspace.
When available, you’ll need a Google AI Pro or Google AI Ultra subscription to use Google Pics.
*Daily Brief: The Overnight Organizer for Your Life: While the quality of AI agent work varies significantly, Google is promising that its new Daily Brief AI agent will hit a home run for you.
Designed to organize and prioritize your day, Daily Brief – currently available to all Google AI subscribers (18+) in the Gemini app – is a personalized digest that keeps track of your key goals and suggests the next steps for getting there.
Intriguingly, the AI agent works overnight, analyzing your inbox, calendar and tasks while — in a perfect world — connecting all the dots across your life.
*Gemini Spark: An Army of AI Agents Working for You: Promised for release sometime this summer, Gemini Spark is Google’s answer to OpenClaw – an Open Source program that uses multiple AI agents to complete an ongoing series of tasks for you in background.
Such a system – which can be programmed to make decisions for you such as making purchases, sending automated emails, engaging in ongoing research and setting up a flight booking – can be tricky to get right.
Ergo, Google is promising to be extra careful with this one before releasing it wide.
*Google Search Agents: 24/7, Continually Updated Monitoring and Analysis: While my own experience with AI search agents has been mixed – I find they like to make-up citations or simply don’t work as advertised – Google is promising this new issue of search agents will be different.
Ideally, Google’s search agents can take any question dream up and run with it, relentlessly scouring blogs, news sites, social media posts and more to return with regular, synthesized updates relevant to your query.
You can use search agents, for example, to scan the Web for new stories on Open Source AI models that offer the greatest creativity when it comes to writing.
*AI Inbox: Yet Another Email Inbox Organizer: Granted, there seem to be any number of AI tools promising to make email processing a breeze, but Google believes this one is extra special.
Essentially, this upgraded version of AI Inbox – already available for Google AI Ultra subscribers and rolling out to Google AI Plus and Google AI Pro subscribers — is designed to surface the emails that matter most to you each day and prioritize you To-Dos.
AI Inbox will also generate personalized email replies – for your approval — based on contextual information.
Plus, this summer, Google is promising that Google AI Pro and Google Ultra subscribers will be able to use AI Inbox to “talk” to their Gmail without being forced to dig through conversational threads.
*New Gemini 3.5 Flash: AI for the Down and Dirty: While you’ll want to use Google 3.1 Pro for answers that require deeper reasoning and more thoughtful replies, this Flash upgrade for Gemini is for those looking for fast answers that may be a little rough around the edges.
Google is also promising that the new Gemini 3.5 Flash is perfect for tackling long-horizon agentic tasks.
Meanwhile, Gemini 3.5 Pro – Flash’s heftier cousin – is promised for release sometime in June 2026.
*Gemini Omni Flash: Google’s New Spin on Video Creation: Back in the ‘olden days’ – i.e., last week – we were limited to creating video with AI by using a text input.
No more. With Gemini Omni Flash, you can ideally feed Gemini AI any type of input – text, images, audio or video – and the tool will output video for you.
Moreover, videos can be created by adding text instructions regarding what you’re looking for. Or, you can get things going with a conversational interface that pops up when you simply enter a raw input with no instructions.
Available for AI Plus, AI Pro and AI Ultra subscribers, Gemini Omni Flash includes an improved intuitive understanding of forces like gravity, kinetic energy and fluid dynamics, allowing you to create more realistic scenes, according to Koray Kavukcuoglu, a chief AI architect at Google.
*The Whole Kit-and-Kaboodle: Other interesting, new AI and AI upgrades from Google of interest to writers include Personal Intelligence, Google Flow, Google Antigravity, SynthID and Neural Expressive.
You can grab the skinny on all those – as well as a full overview on virtually every important news-making new AI from Google — at “100 Things We Announced at I/O 2026.”
Share a Link: Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.
–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.
The post Google Releases Slew of New AI Tools appeared first on Robot Writers AI.
You have a single gpt-oss-20b deployment. Six teams want to use it. Marketing is running batch summarization jobs at 3am. The fraud team needs sub-second responses 24/7. An intern’s Jupyter notebook is accidentally hammering the endpoint in a tight loop. And your GPU bill is already eye-watering.
Sound familiar? DataRobot gives you two tools to solve this: Rate Limiting and Quota Reservations. This post explains when to reach for each, backed by a real load test example on a staging deployment.
Rate limits sets per-consumer caps across multiple dimensions: requests per minute, token count per hour, concurrent requests, and input sequence length. A default policy applies to all consumers, with per-entity exceptions available for specific overrides.
What it protects against: Any single consumer overconsuming — whether through high request volume, large inputs, or excessive concurrency.
Quota reservations define the deployment’s total possible throughput (value per minute) and a utilization threshold that triggers enforcement. Within that budget, specific entities can be allocated a reserved percentage — guaranteeing them a minimum slice of capacity that other consumers can’t take away.
What it protects against: Priority starvation. Without reservations, a noisy neighbor can consume the entire capacity budget, leaving your critical workloads with nothing.
Used alone, each tool solves a specific problem:
Together, they give you both control surfaces: a ceiling that protects the model and guaranteed floors for the consumers that matter most.
To evaluate these features under pressure, we load-tested a gpt-oss-20b deployment in our staging environment. The setup simulates a real multi-tenant scenario: four consumers sharing one model, each with different priority levels.
| Setting | Value |
|---|---|
| Model | gpt-oss-20b (NVIDIA NIM) |
| Capacity | 1000 RPM |
| Utilization Threshold | 80% (enforcement kicks in at 800 RPM) |
| Consumer | Type | Reserved Capacity | Effective Guarantee |
|---|---|---|---|
| Production Agent A | Deployment | 30% | 300 RPM |
| Production Agent B | Deployment | 20% | 200 RPM |
| Production Agent C | Deployment | 30% | 300 RPM |
| Dev User (unreserved) | User | – | None — shares the 20% unreserved pool |
This left a 20% unreserved pool (200 RPM) for the dev user and any overflow.
We ran six escalating scenarios over 17 minutes to observe behaviour at different saturation levels:
| Scenario | What Happens | Combined Load |
|---|---|---|
| Normal traffic | All four consumers at moderate, throttled rates | ~600 RPM (below utilization threshold) |
| Slight overload | All four consumers ramp up to just over capacity | ~1,200 RPM (1.2× capacity) |
| Heavy overload | All four consumers fire as fast as possible | ~7,200 RPM (7× capacity) |
| Extreme overload | Maximum concurrent workers per consumer | ~12,000 RPM (12× capacity) |
| Late joiner | Three agents flood first, dev user joins 60s later | ~9,000 RPM |
| Reserved-only | Three agents compete, dev user silent | ~7,200 RPM |
Rate limiting by itself is the right choice when:
During the normal traffic scenario (~600 RPM combined, well below the 800 RPM utilization threshold), the rate limiter was invisible and all four consumers achieved 100% success rates with zero rejected requests.
| Scenario | Combined RPM | Success Rate | 429s |
|---|---|---|---|
| Normal traffic | ~600 | 100% | 0 |
Size your reservations based on the absolute minimum throughput each consumer requires during peak contention. This is by design, so you’re not penalizing normal traffic.
And it protects the model even under extreme abuse. During the extreme overload scenario (20,000+ RPM against 1,000 RPM capacity, which is a a 20× overload), the rate limiter rejected 95% of requests. But the model itself stayed perfectly healthy:
| NIM Metric | Under 20× Overload |
|---|---|
| GPU Utilization | 91–95% (stable) |
| E2E Latency | 1.25s → 2.09s (brief spike, then stable) |
| Time to First Token | 35ms (unchanged) |
| Inter-Token Latency | 18ms (unchanged) |
| KV Cache | <3% (not stressed) |
The rate limiter acted as a firewall between chaotic client demand and stable model inference. Without it, those 20,000 requests per minute would have queued up inside the NIM, latency would have ballooned, and the model would have effectively become unusable for everyone.
Takeaway: If your only goal is “don’t let traffic spikes kill the model,” rate limiting alone is sufficient and zero-config beyond setting the capacity number.
Quota reservations become essential when:
Size your reservations based on the absolute minimum throughput each consumer requires during peak contention.
Rules of thumb:
In our example, three production agents received 30%/20%/30% reservations, leaving a 20% unreserved pool for the dev user. This meant the dev user could still use the deployment — they just wouldn’t get guaranteed access during contention.
During the slight overload scenario (~1,200 RPM against 1,000 RPM capacity), all four consumers achieved 100% success — the token bucket’s burst capacity absorbed the slight overage. This is the “graceful degradation” zone where reservations aren’t yet needed, but the system is proving it can handle bursts.
When all four consumers fired as fast as possible (7,000–12,000 RPM against a 1,000 RPM capacity), the system was overwhelmed. Here’s what each consumer experienced across the full test:
| Consumer | Reserved | Success Rate | Successful Requests |
|---|---|---|---|
| Production Agent A | 30% | 29.0% | 4,172 |
| Production Agent B | 20% | 30.2% | 4,332 |
| Production Agent C | 30% | 28.9% | 4,176 |
| Dev User (unreserved) | – | 28.9% | 2,828 |
Why the success rates look similar: At 12× overload, even a 300 RPM reservation is only ~2.5% of what each consumer is attempting to send (~3,000 RPM per consumer vs. a 300 RPM guarantee). The reservation works by ensuring each consumer receives its guaranteed 200–300 RPM. However, because 97% of total traffic is rejected during extreme overloads, the relative percentage differences compress.
The more revealing metric is absolute throughput. Reserved consumers completed 4,172–4,332 successful requests. The unreserved dev user completed 2,828 — about 34% fewer. Even accounting for the dev user’s shorter active time, reserved consumers consistently got more requests through during shared scenarios.
In the late joiner scenario, the three production agents were already flooding the system when the dev user joined 60 seconds later. With all reserved capacity spoken for, the dev user was confined to the 20% unreserved pool (~200 RPM). The production agents continued drawing from their guaranteed buckets, unaffected by the new arrival.
This is the scenario that matters most in production. A batch job kicks off, or a new application goes live, and suddenly there’s more demand than supply. Without reservations, the new load pushes everyone’s throughput down equally. With reservations, your critical consumers are shielded.
In the reserved-only scenario, the dev user went silent and only the three production agents competed. Their success rates were nearly identical (28.9%–30.2%) — the system divided throughput proportionally across their reservations.
Client-side metrics (success rates, 429 counts) tell you what your consumers experienced. Server-side OTEL metrics tell you what the platform experienced. Here’s what our example deployment looked like from the inside.
During peak load (20,596 requests/minute hitting the endpoint), the NIM was serving only the ~1,000 RPM that the rate limiter let through:
| What the endpoint saw | What the NIM saw |
|---|---|
| 20,596 requests/min | ~1,000 requests/min (served) |
| 19,603 rate-limited/min | 18–22 concurrent requests |
| — | 1.25s E2E latency (stable) |
| — | 91–95% GPU utilization (healthy) |
Without rate limiting, those 20,000 RPM would have queued inside the NIM. The GPU wouldn’t have gotten more productive — it’s already at 91–95% — but latency would have spiraled as requests stacked up. Instead, the rate limiter rejected excess requests immediately (at 429-response speeds, not inference speeds), keeping the model responsive for the traffic it did accept.
Peak token throughput was ~199,350 tokens/min (total), with ~115,939 input and ~83,411 output. These numbers track directly with the rate limiter’s allowed throughput — not with the attempted request volume. Another way of seeing that the rate limiter is correctly shaping traffic.
Use this flowchart to decide what to configure:
Step 1: Do you have a shared deployment with multiple consumers?
Step 2: Are all consumers equally important?
Step 3: Do any consumers need guaranteed minimum throughput?
Step 4: Configure the unreserved pool.
Start with rate limiting only. Monitor your deployment’s traffic patterns for a week. Look at peak RPM, who’s sending what, and whether anyone is consistently overconsuming. Then add reservations where the data tells you they’re needed.
Set utilization threshold at 70–80%. This gives the token bucket burst room to absorb short spikes without triggering rate limiting on every minor fluctuation. In our example, we used 80% and the system handled 1.2× capacity gracefully before enforcement kicked in.
Monitor with OTEL metrics. After configuring rate limiting, check these server-side metrics to confirm things are working:
Reservation sizing formula:
Reserved RPM = Capacity × Reserved %
Example: 1000 RPM × 30% = 300 RPM guaranteed
Don’t confuse this with a rate limit. A 30% reservation means “you’ll always get at least 300 RPM, even when the system is saturated.” The entity can still use more when capacity is available.
| Feature | Protects Against | Use When |
|---|---|---|
| Rate Limiting | GPU overload, runaway consumers, latency spikes | Always — it’s your safety net |
| Quota Reservations | Priority starvation, noisy neighbors, SLA violations | Multiple consumers with different importance levels |
| Per-entity exceptions | A specific consumer overconsuming | You want to cap a noisy neighbor without reserving capacity for others |
When considering Rate Limiting vs. Quota Reservations: use each tool where it fits. Layer them where the problem demands it.
The post A practical guide for platform teams managing shared AI deployments appeared first on DataRobot.