I’ve been begging for this from OpenAI forever. @elonmusk make it happen for grok
Family Grok Bots
There should be a family or household plan for Grok Bot. Something like Team Bots, but family focused for spouses at least.
My wife has been dipping her toes into using Grok Bot with her X Premium subscription. She's been seeing me utilize it successfully for a good number of things. Even leveraging for a few things that helped her out.
However, there are a lot of things that I've setup that she should have her own access to in order to fully leverage. I've setup a good number of custom MCPs for things like finance and home automation. With about 18 bots now, a good handful of which are for stuff that involves both of us. This involves rules and skills, accessing a home server, and a few dashboards.
Configuring all this isn't just something that is figured out quickly, and I expect will be an ongoing maintenance. Sharing templates is not a solution for this. If I later make more configurations, a new import by my wife creates a new bot, losing history and memory.
Our children are grown, but those with children in school likely have child related tasks/schedules they are constantly dealing with. This shouldn't all be funneled through just one parents bot assistants.
Ultimately to fully leverage Grok Bots in a family there needs to be some kind of connection that can be setup for some bots to help with those things that are shared by the different members of the family, especially the parents.
@SpaceXSI @xai @cursor_ai
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never quit - I mean it. if you ever leave that last job, you're not finding a new one before the heat death of the universe...
How long it took me to find each SWE job:
2014: 1 job applied, 0 interviews, 1 day (nepotism)
2015: 1 job applied, 2 interviews, 1 week
2016: 1 job applied, 4 interviews, 1 week
2016: 1 job applied, 0 interviews, 1 day (friend is a CEO)
2018: 1 job applied, 2 interviews, 1 week
2019: 1 job applied, 5 interviews, 1 week
2021: 1 job applied, 0 interviews, 1 day (invited)
2022: 7 jobs applied, 16 interviews, 3 months
2026: 167 jobs applied, 54 interviews, 1 year 4 months
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Terrible experience. Tried to change my Google password randomly because it couldn’t hit the right buttons on the screen.
To order yours:
1. Only available through our App in the U.S.
2. Download Hark Pro on iOS or Android, and type “order a free pizza” in chat. Hark may need some additional information, like your address or phone number.
3. For this promotion, we require an email to be connected to reduce fraud.
4. Hark will give you 3 options for local pizza places through Doordash - Pick one or tell Hark your favorite instead.
5. Then, Hark will ask you which pizza you want from that restaurant, in what size and with what toppings (P.S. we never share your data, so nobody will find out when you add pineapples).
6. You'll then be prompted to login to Doordash to pass along your $30 gift card. If you don't have an account, Hark can create one for you.
7. Hark will prepare the order.
8. You confirm everything looks right.
9. You eat delicious, free pizza.
We're doing this until we spend $1 million dollars, so send this along to your friends ASAP.
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Meta is not playing - evidence #2
Today we’re releasing Muse Spark Open, the most capable US open-weight model to date.
Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning.
Key Model Specs:
→ 816B total / 42B active parameters
→ 1M token context window
→ Natively multimodal across text, image, video, audio
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Largest investment for AI in EU history
Nvidia will announce $28 of AI investments in Europe this week $NVDA
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Jeff Lewis retweeted
Today we are rolling out Gemini 4 Argon globally in the Gemini API, to Pro + Ultra in Gemini App, and Ultra subs in Antigravity!
Argon is a huge step forward for Gemini, and a labor of love. This model has been battle tested by over 100,000 people at Google over the last month, with so much new innovation, ambition, and hard work baked into this release. The team has been heads down over the last 6 months working to make this possible across the stack (research, infra, safety, eng, etc).
We rolled the model out to cyber defenders as part of our fairwind program and early access customers over the last 2 weeks, it has been amazing to see and talk to folks who are now using this model daily. Seeing the models writing capability, raw knowledge and persistence for coding, paired with new features like 1M token output when the model needs to work more has gotten me even more excited about our path ahead.
This is just the start of the Gemini 4 era. As you saw with 3.5, 3.6, 3.7, and 3.8 Flash, our iteration speed with Gemini is fast : ) Now with Gemini 4 Argon to work from, I am cannot wait to see the progress continue, and most importantly see what this new model enables for the world.
This is such a special moment in the arc of technology, as millions of people everyday tackle problems they could never have thought possible before. Gemini 4 Argon was built to help you tackle the hardest problems. I have personally felt this spark with Argon and hope you do too. Onwards!!
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8/ Methodology notes (from Google’s eval doc):
Most Argon scores are pass@1 at highest thinking settings via the Gemini API. Some self-computed (DeepSWE, Terminal-bench, OSWorld best-of-3, GraphWalks); others from third-party boards (Vals, Zapier, Surge, Proximal, etc.). Competitor numbers mostly from their self-reported max-reasoning results or public boards. Harnesses, frame counts (e.g. LVBench), timeouts, and subsets can differ.
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9/ Independent results
• Artificial Analysis Intelligence Index: ~53 (high), matching GPT-6 Astra (max) and slightly ahead of GPT-6.1 Sol, but behind Claude Opus 5.5 (~58). Strong on agentic tasks + notably low hallucination rate (~15% on AA-Omniscience).
• Vals AI: #1 on the Vals Index (68.9%) and Finance Agent v2.
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6/ ML / Science & math
• PostTrainBench: 45.3% Argon | 44.3% Astra | 40.2% Fable | 49.3% Opus
• Terminal-Bench Science 0.1: 57.6% | 68.1% | 52.6% | 63.3%
• LABBench 2: 88.8% | 85.4% | 68.6% | 73.1%
• RiemannBench: 76.0% | 72.0% | 65.6% | 69.6%
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7/ Long context / Computer use / Multimodal / Cyber
• GraphWalks ≤128k: 99.7% | 98.7% | 91.4% | 90.6%
• GraphWalks 256k–1M: 84.2% | 71.8% | 65.0% | 66.8%
• Agent’s Last Exam: 39.5% | 34.2% | — | 38.2%
• OSWorld-2.0 (offline): 69.2% | 72.6% | — | —
• Chartography: 71.6% | 71.0% | 46.2% | 66.3%
• LVBench: 91.7% | 87.5% | 79.7% | 83.7%
• CWE-bench v1: 68.0% | 68.0% | 58.0% | 67.0%
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2/Official sources
• Model card (full table + methodology): deepmind.google/models/model…
• Eval PDF: storage.googleapis.com/deepm…
• Announcement: blog.google/innovation-and-a…
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