Mira

@miranetwork229k followers

Building the Trust Layer for AI.

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Oct 17

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Posts 16 posts

#	Date	Post	↓ Outlier	Views	Likes	Reposts	Bookmarks
1	Sep 26, '25	Mira Mainnet is Live. The trust layer for AI has arrived.	246×	1.1M	2.6k	532	168
2	Oct 8	The internet is full of claims that look convincing at first glance. Probe lets you check them where you find them — right in the browser. Don’t trust us. Probe it. Install the ext	2.1×	9.7k	31	6	3
3	Oct 9	Drop the most suspiciously specific thing you've read on the internet this week for us to Probe!	1.7×	7.8k	54	3	0
4	Sep 25	Do you sometimes wish your browser came with a 'Verify' button?	1.5×	6.9k	13	0	0
5	Oct 2	What's the last thing you'd let an AI agent do without checking first?	1.5×	6.9k	18	1	0
6	Sep 23	In Saturn’s test of 18 AI models, financial answers were wrong 57% of the time on average. For complex questions, that rose to 88%. Financial tasks expose a difficult property of A	1.5×	6.9k	20	0	1
7	Sep 28	95% accurate sounds reassuring until your agent is about to act on the 5%. Benchmarks tell you how a model performed across a test set. They don't tell you whether the output sitti	1.5×	6.9k	24	0	0
8	Sep 22	A model can encounter the same wrong number on a thousand websites. It may look like a thousand supporting examples. In reality, they might all trace back to one bad source.	1.4×	6.4k	26	2	0
9	Oct 7	stay verified out there 🫶	1.4×	6.3k	35	2	0
10	Oct 10	The next phase of AI won’t be defined by which model we trust most. It’ll be defined by how little trust the system needs to place in any single model. As agents get access to mone	1.4×	6.2k	44	8	0
11	Oct 1	What AI Verification Actually Requires Accuracy numbers get thrown around a lot, but they don't tell you much about what's actually in front of you. They measure how a model perfor	1.3×	6.1k	46	9	3
12	Oct 5	Is this true? 👀	1.3×	6k	18	1	0
13	Sep 24	How often do you get misled by exaggerated claims in a week?	1.3×	5.8k	19	1	0
14	Oct 13	Most AI debugging still starts with: “Can we use a better model?” But once an AI product has multiple steps, the model is only one place things can break. A bad result could mean:	1.2×	5.4k	19	1	1
15	Oct 12	A model can be 99% accurate and still power an unreliable product. Because the model is only one step in the system. The wrong context gets retrieved. A source is stale. A field ge	1.1×	5k	28	2	0
16	Oct 14	If AI could generate high-accuracy documents, which use-cases would you finally trust it with?	1.0×	4.6k	24	2	0
17	Sep 22	Once the answer matters, the claims need to be checked outside the generation that produced them. Break the output down. Check each claim independently. Compare the results. More d	0.1×	384	0	0	0
18	Sep 22	Self-review helps catch some errors. It can also repeat the same assumption in a more convincing paragraph. The writer and reviewer are working from the same learned patterns.	0.1×	384	0	0	0

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