Google Cloud Tech ✓
@GoogleCloudTech · joined 11 Aug 2008
Follow along for how-tos, demos, product news, and more. For company updates, check out @GoogleCloud. Watch #GoogleCloudNext on demand ⬇️
1 314 638Followers
1 598Following
26 422Posts total
929.7KViews on collected posts
Postingan terbaru
Mencari postingan baru…
Claude Opus 5.5 is on Google Cloud!
Built for everyday complex tasks, it handles long-running coding and knowledge work while reporting back clearly on what it did, what it found, and what it needs next at a lower cost per task.
Try it in Model Garden → https://t.co/seLCbcFygF
41K views · 580 likes
· 43 reposts · 27 replies
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"I aspire to be an incredibly lazy prompter." —@_lopopolo
Ryan joins The Agent Factory to make the case for not writing any code manually, and break down how harness engineering can help you get there → https://t.co/tC8Z9I73nx https://t.co/hQ8GcRHhRM
17.1K views · 70 likes
· 13 reposts · 4 replies
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1:01
See how artist Leo Villareal used Gemini and Google Antigravity to supercharge his authoring software in the process of bringing his light sculpture to life. "Buckyball" is a celebration of the vibrancy of NYC and debuted this summer. https://t.co/17UnUZN7sX
20.8K views · 61 likes
· 7 reposts · 3 replies
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2:27
Scaling an AI agent fleet?
We provide full-stack agentic solutions, and now we're expanding our portfolio with Filestore agent volumes: fully managed, elastic file storage purpose-built to give agents isolated, economical, high-performance workspaces → https://t.co/HbJlfE1rMk h
21.6K views · 137 likes
· 22 reposts · 10 replies
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Class is in session with @NVIDIAAI.
Join @ivnardini and @katjasrz for a hands-on course that walks through the end-to-end journey of building, profiling, and scaling JAX models on NVIDIA GPUs—from initial setup to production serving → https://t.co/dlqHCXb6DI https://t.co/7q8PDk
20.7K views · 64 likes
· 11 reposts · 5 replies
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1:01
@GoogleCloudTech our guard shipped with a defect of its own. a PR adds eight PNGs and a stylesheet, description says "9 files changed", our checker read the diff and counted 1 — binary files carry no +++ line, so it couldn't see them at all. it was one click from calling an hones
61 views · 1 likes
· 0 reposts · 0 replies
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@GoogleCloudTech a weather agent can pass the tool-call check while ignoring the forecast
177 views · 1 likes
· 0 reposts · 0 replies
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@GoogleCloudTech Thoughtful treatment of behavioral evaluation as an iterative discipline, not just a scorecard.
Reliable AI agents will also need interfaces that earn attention without overwhelming it. Exploring that creative layer with @PAPAGEDON.
252 views · 2 likes
· 0 reposts · 0 replies
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We’re partnering with @inferact (the team behind @vllm_project) to make TPUs a first-class citizen for open-source AI inference.
Together, we’re delivering:
- Upstreamed open-source TPU kernels
- Native PyTorch integration via TorchTPU
+ so much more ↓
31.8K views · 153 likes
· 23 reposts · 8 replies
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@googlecloud and Inferact are announcing today a partnership to make TPU a first-class citizen in @vllm_project.
This partnership puts both teams on one engineering roadmap to bring TPU to the broader open model ecosystem, optimizing vLLM as the agentic production serving engine
103.1K views · 188 likes
· 38 reposts · 10 replies
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@GoogleCloudTech plugins for coding agents now huh
1 views · 0 likes
· 0 reposts · 0 replies
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@GoogleCloudTech Plugins make sense when they remove setup work. The real test is whether the first install feels boring.
56 views · 0 likes
· 0 reposts · 0 replies
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Introducing Google Cloud plugins for AI coding agents. Designed as installable bundles, agent plugins equip the AI agent of your choice with skills and tools to be more effective on Google Cloud.
Learn more and start building → https://t.co/xndsKuzsuq https://t.co/YecRL2875k
38.2K views · 282 likes
· 41 reposts · 12 replies
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On this week’s livestream you’ll learn how Gemini Enterprise provides a single front door to company data, securely connecting SaaS apps to deploy grounded AI agents.
https://t.co/wOaYKMRcgU
14.5K views · 72 likes
· 9 reposts · 4 replies
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@GoogleCloudTech the missing fifth pattern is idempotency across side effects. once agents react in parallel, every write needs a dedupe key and a durable receipt. otherwise you've built a very fast machine for sending the same email twice.
1.1K views · 6 likes
· 0 reposts · 2 replies
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Find the code path that runs after your fallback fires. If it skips a validation step, you're shipping two different products, while only testing one.
3.6K views · 7 likes
· 0 reposts · 0 replies
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Read the full article ⬇️
https://t.co/Jr4pWXwmMO
4.5K views · 31 likes
· 7 reposts · 3 replies
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Pattern 4: Tiered routing before the expensive call
Look at your own traffic distribution before assuming you need a bigger model. A cheaper first pass usually gets you further.
3.2K views · 15 likes
· 0 reposts · 1 replies
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Pattern 3: A fallback model still has to clear your bar
Find the code path that runs after your fallback fires. If it skips a validation step, you're shipping two different products, while only testing one.
3.6K views · 14 likes
· 0 reposts · 1 replies
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Pattern 2: Let agents react to the same event in parallel
Check whether two of your agents will need to react to the same signal. If your architecture makes one wait behind the other to do it, that's a single-threaded system wearing a multi-agent label.
4.2K views · 22 likes
· 0 reposts · 1 replies
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Pattern 1: The tools you built for yourself can serve other agents too
If your agent talks to its own data over MCP internally, check how much extra work it'd take to expose those same tools externally. Do this before you build a second, human-only API that does the same job.
4.4K views · 31 likes
· 4 reposts · 1 replies
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We just wrapped the Google for Startups AI Agents Challenge. Here are 4 production-grade patterns that you should steal for your next build ⬇️
63.2K views · 542 likes
· 48 reposts · 12 replies
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Dibandingkan akun berukuran sama
37 postingan dari 90 hari terakhir, dibandingkan dengan rentang 1M–10M pengikut. menjangkau lebih sedikit orang dibanding akun seukurannya.
Median tayangan4 703akun ini14 648median untuk 1M–10M
Jangkauan, %0.36%akun ini0.74%median untuk 1M–10M
Interaksi, %0.58%akun ini0.70%median untuk 1M–10M
| Metrik | Akun ini | Median untuk 1M–10M | Rasio |
|---|---|---|---|
| Median tayangan per postingan | 4 703 | 14 648 | 0.32× |
| Jangkauan (tayangan ÷ pengikut) | 0.36% | 0.74% | 0.48× |
| Tingkat interaksi | 0.58% | 0.70% | 0.84× |
Akun lain pada rentang ini → Bandingkan dengan akun lain → Bagaimana tolok ukur ini disusun →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
103.1K14 Sep
31.8K
48.3K15 Sep
252
177
6117 Sep
20.7K
26.9K18 Sep
21.6K19 Sep
28.6K21 Sep
47.3K
20.8K22 Sep
17.1K
41K
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.24%14 Sep
0.58%
1.31%15 Sep
0.79%
0.56%
1.64%17 Sep
0.40%
0.85%18 Sep
0.79%19 Sep
0.83%21 Sep
0.88%
0.34%22 Sep
0.52%
1.59%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 1M–10M accounts is 0.70%.
What the audience does
Likes48.1%4 791 in total
Reposts5.7%568 in total
Replies2.5%244 in total
Quotes0.6%62 in total
Bookmarks43.1%4 286 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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