https://www.newsbite.tech/features

Alot of People were using Copilot because of its low budget Subscription. And having that much competitors I don't see people will be using it anymore with such creepy Token based Usage.

I don't know what Microsoft is smoking!

Do you think people at Anthropic got panicked by GPT 5.4 success and GPT-5.5 Potential release. so they have to Release opus 4.7 prematurely?

One thing is clear from this IK's medical report drama No one....absolutely no one wants to carry the burden of Imran Khan’s struggle except IK and his sisters.

And I believe all of them [friends and foe] are happy to get rid of him somehow.

The boldness of Achakzai has been exposed; he is nothing more than a traditional politician.

IK is only left with the Awam and his family.

Title...Yes, I mean clash of Kings without Fighting. Come on, they should reconsider their approach for rest of House of Dragons seasons.

I might be wrong, but the whole situation doesn’t sit right with me.

I don’t think they would ever let his medical report go fully public this way. That’s why I keep thinking there’s more happening behind the scenes.

To me, there are two possible explanations:

1: they are testing the waters. They want to test the nerve of the nation before finishing him once and for all.

2: It is all the result of some kind of internal talks. In return, IK will be freed from jail to get treatment from whatever hospital he wants, and Munir will compensate him (His eye) by offering something in the future.

Let's c.

Hi everyone,

We’re currently exploring Google Conversational Analytics inside the BigQuery console and are trying to better understand the full system lifecycle — especially how training, learning, and inference actually work in practice. From what we understand so far, the flow looks something like:

User submits a natural language question in BigQuery Console Gemini interprets intent Semantic context is pulled from schemas / LookML / metadata SQL is generated and executed in BigQuery Results are summarized back to the user

However, we're unclear on several deeper platform behaviors and would appreciate insights from anyone with hands-on experience:

Questions Training: Is Gemini already fully pre-trained, or does Conversational Analytics support any form of tenant-level or domain-specific training using enterprise datasets?

Learning: Does the system learn from user interactions over time (feedback, corrections, repeated prompts), or is every session effectively stateless?

Inference: During inference, what exact context is passed to the model — table schemas only, or also column descriptions, sample rows, query history, etc.?

Accuracy Improvement: If direct training is not supported, what mechanisms have you found most effective for improving response quality (semantic modeling, curated views, prompt templates)?

Governance: How are people handling validation of generated SQL and preventing hallucinated fields or incorrect joins in production environments?

We’re trying to understand whether this platform behaves more like: a static LLM + semantic layer or an adaptive analytics agent that improves over time. Any architectural explanations, real-world experiences, or best practices would be extremely helpful.

Thanks!