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๐๐ฟ๐ฒ๐ฎ๐ธ๐ถ๐ป๐ด: ๐ง๐ต๐ฒ ๐๐ ๐๐ผ๐ฟ๐น๐ฑ ๐ท๐๐๐ ๐ด๐ผ๐ ๐ฎ ๐น๐ถ๐๐๐น๐ฒ ๐บ๐ผ๐ฟ๐ฒ ๐ถ๐ป๐๐ฒ๐ฟ๐ฒ๐๐๐ถ๐ป๐ด. ๐จ
A new model called ๐๐๐๐ฟ๐ฎ ๐ฒ has reportedly been released, and this time the big story isnโt just about chat or reasoning.
Itโs about computer use.
So what does that actually mean?
Previous AI models could tell you how to do something. Astra is designed to actually use your computer and do the task for you.
On the ๐ข๐ฆ๐ช๐ผ๐ฟ๐น๐ฑ ๐ฏ๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ, Astra reportedly scored ๐ณ๐ฎ.๐ฒ%, compared with ๐ฒ๐ฑ.๐ณ% for ๐๐ฃ๐ง-๐ฑ.๐ฒ ๐ฆ๐ผ๐น.
But the more interesting part is the speed.
The same tasks that took Sol around 75 minutes took Astra about ๐ฐ๐ฌ ๐บ๐ถ๐ป๐๐๐ฒ๐ on average.
And cybersecurity is where things get even more serious.
According to OpenAIโs Preparedness Framework, Astra is the first model to reach the โCriticalโ level for cybersecurity capabilities.
During testing, it reportedly discovered two previously unknown security vulnerabilities and scored 100% on ExploitBench.
Some other reported scores:
โค FrontierMath Tier 4: 97.6%
โค ARC-AGI-3: 98.6%
โค GPQA Diamond: 96%
โค Context window: 1.05 million tokens
That context window is huge. You could potentially give it an entire series of books and still have room to work.
Thereโs one important catch, though.
You canโt just start using Astra today.
Access is initially limited to OpenAIโs Trusted Access and Daybreak programs, with wider access expected later across ChatGPT Plus, Pro, Business, Enterprise, API, AWS, and Azure.
๐ง๐ต๐ฒ ๐ฟ๐ฒ๐ฝ๐ผ๐ฟ๐๐ฒ๐ฑ ๐๐ฃ๐ ๐ฝ๐ฟ๐ถ๐ฐ๐ถ๐ป๐ด ๐ถ๐ $๐ญ๐ฌ ๐ฝ๐ฒ๐ฟ ๐ญ๐ ๐ถ๐ป๐ฝ๐๐ ๐๐ผ๐ธ๐ฒ๐ป๐ ๐ฎ๐ป๐ฑ $๐ฑ๐ฌ ๐ฝ๐ฒ๐ฟ ๐ญ๐ ๐ผ๐๐๐ฝ๐๐ ๐๐ผ๐ธ๐ฒ๐ป๐.
And for advanced cybersecurity tasks, the model will reportedly refuse certain requests for general users.
One more interesting point:
ARC Prizeโs team has pointed out that a 100% ARC-AGI-3 score isnโt completely new. NVIDIAโs AVO architecture, running on Claude Opus 5, has reportedly achieved it before.
So maybe the bigger story isnโt just the model itself.
Itโs the combination of the model + agent system + tools.
And honestly, thatโs the part I find most interesting.
AI is slowly moving from โHereโs how you can do itโ to โIโll do it for you.โ
That shift could change a lot about how we work with computers. ๐
#AI #RainCorraya #ArtificialIntelligence #OpenAI #AITools #FutureOfAI #AIAgents
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My AI assistant runs all day. The expensive model only does the thinking.
Most requests need no AI at all. "Call me in 30 minutes" is handled by plain code. Reminders, schedules and limits are code too.
Short tasks like sorting and scoring go to fast models on a free tier.
The big model only gets planning and long conversations, where it actually has to think.
If it hits a limit, a backup model answers instead of the app going silent.
If your AI app's bill keeps growing, check what really needs the big model.
What does your app send to the most expensive model?
โข Oct 16
Same pattern holds in outbound tooling. Filtering, dedupe and enrichment lookups are plain code. The model only writes the first line once the row is already verified. Cuts cost and the weird outputs.
๐ง๐ฟ๐ฎ๐ฑ๐ถ๐๐ถ๐ผ๐ป๐ฎ๐น ๐๐ถ๐ด๐บ๐ฎ ๐ฑ๐ฒ๐๐ถ๐ด๐ป ๐ถ๐ ๐ฑ๐ฒ๐ฎ๐ฑ ! ! !
As a designer, I love how AI is changing the way we work.
Weโre moving from simply sending clients static designs on Figma to building clickable prototypes that bring their vision to life.
Now, a founder can actually experience how their website will look and feel before development is complete.
They can click through pages, explore interactions, and see animations in action.
And for developers? The handover becomes much smoother because they can see how everything is supposed to work, not just how it should look.
Less guesswork. Better collaboration. A clearer vision.
Honestly, itโs a win-win for everyone.
I think every designer and developer should embrace AI in 2026. Not to replace our skills, but to expand what we can create with them. ๐
So are you still team traditional Figma design ? Or you now use Ai to advance your design work ?
Awesome work!
Picture a consultant who takes 8 sales calls a week.
After each one comes the same routine: replay the recording, write notes, update the pipeline sheet, write the follow-up email. Call it 30 minutes a call. That is 4 hours a week, and on busy days the follow-up slips to tomorrow.
I built an n8n workflow that takes that routine away:
โ Fireflies records and transcribes the call
โ As soon as the transcript is ready, AI writes a summary, the open questions, the next step and whether the prospect is ready to go ahead
โ A follow-up email that fits that outcome is saved in Gmail drafts, addressed to the person on the call
โ The call is logged in Google Sheets and the notes land in your inbox
Nothing is sent automatically. You read the draft, change a line if you want, and press send.
For that consultant, 4 hours of admin a week turns into a few minutes of reading, and every prospect hears back the same day.
I tested it on a real recorded client call and have submitted it to the n8n template library as a free template. If you want it set up for your business, send me a message.
