Gabbard resigns as director of national intelligence



Trump Gabbard

Tulsi Gabbard resigned as President Donald Trump's director of national intelligence on Friday, saying she needed to step away as her husband battles cancer. She is the fourth Cabinet official to depart during Trump’s second term.

In her resignation letter, which she posted on social media, Gabbard said she told Trump of her decision to leave office on June 30. She said her husband had recently been diagnosed with a rare form of bone cancer and “faces major challenges in the coming weeks and months.”

“At this time, I must step away from public service to be by his side and fully support him through this battle,” she wrote in the letter, which was earlier reported by Fox News.

Trump, in his own social media post announcing her resignation, said “Tulsi has done an incredible job, and we will miss her.” He said her principal deputy, Aaron Lukas, will serve as acting director of national intelligence.

During Trump’s first term, Lukas was as an intelligence aide to the acting director of national intelligence, Ric Grenel, in 2020. A former policy analyst at the Cato Institute, a libertarian think tank, he also served as deputy senior director for Europe and Russia at the National Security Council in the final year of Trump’s previous administration.

There had been rumblings that Gabbard would split with Trump after the president's decision to strike Iran, which caused some division within his administration. Joe Kent, the director of the National Counterterrorism Center, announced his resignation in March, saying he “cannot in good conscience” back the war.

Gabbard, a veteran and former Democratic congresswoman from Hawaii, built her political name on her opposition to foreign wars. This put her in an awkward position when the U.S. joined Israel in launching attacks on Iran on Feb. 28.

During a congressional hearing in March, her measured comments were notable for their careful non-endorsement of Trump’s decision to strike Iran. She repeatedly dodged questions about whether the White House had been warned of potential fallout from the conflict, including Iran’s effective closure of the Strait of Hormuz.

Gabbard said in written remarks to the Senate Intelligence Committee that there had been no effort by Iran to rebuild its nuclear capability after U.S. attacks last year “obliterated” its nuclear program. That statement contradicted Trump, who has repeatedly asserted that the war was necessary to head off an imminent threat from the Islamic Republic.

This created several awkward exchanges with lawmakers who asked Gabbard for her opinion on the threat posed by Iran as the nation’s top intelligence official. She repeatedly said it was Trump’s decision to strike, not hers.

“It is not the intelligence community’s responsibility to determine what is and is not an imminent threat,” she said.

Gabbard’s departure follows Trump having ousted Homeland Security Secretary Kristi Noem in late March, in the midst of mounting criticism over her leadership of the department — including the handling of the administration’s immigration crackdown and disaster response.

The second Cabinet member to leave was Attorney General Pam Bondi, in response to growing frustration over the Justice Department’s handling of files related to Jeffrey Epstein. And Labor Secretary Lori Chavez-DeRemer resigned in April, after being the target of various misconduct investigations.

A surprising choice for the job

A veteran but without any intelligence experience, Gabbard was a surprising choice to head the Office of the Director of National Intelligence, which oversees the nation’s 18 intelligence agencies. She ran for president in 2020 on a progressive platform and her opposition to U.S. involvement in foreign military conflicts.

Citing her military experience, she argued that U.S. wars in the Middle East had destabilized the region, made the U.S. less safe and cost thousands of American lives. Gabbard later dropped out of the race and endorsed the ultimate winner, President Joe Biden.

Two years later she left the Democratic Party to become an independent, saying her old party was dominated by an “elitist cabal of warmongers” and “woke” ideologues. She subsequently campaigned for several high-profile Republicans and became a contributor to Fox News.

She later endorsed Trump, who also was a strong critic of past U.S. wars in the Middle East and campaigned on a pledge to avoid unnecessary wars and nation-building overseas.

Iran caused early tensions

But friction with the president started soon after he began his second term and tapped Gabbard to lead ODNI, which was set up after the Sept. 11, 2001, attacks to improve coordination between the nation’s intelligence agencies.

Shortly after taking on the job, Gabbard testified before lawmakers that there was no intelligence suggesting Iran was seeking to develop nuclear weapons. After Trump launched attacks on Iranian nuclear sites in June he said Gabbard was wrong and that he didn’t care what she said.

She appeared to be back in Trump’s good graces when she took a lead role in Trump’s effort to relitigate his 2020 election loss to Biden, whom Gabbard had endorsed. She appeared at an FBI search of election offices in Fulton County, Georgia, even though her office was created to focus on foreign espionage, not state elections.

Earlier this week, however, she testified to lawmakers during an annual threats hearing that last year’s strikes on Iran’s nuclear sites had “obliterated” their nuclear program and that there had been no subsequent effort to rebuild.

The statement seemed to complicate Trump’s repeated assertions that Iran posed an imminent threat and created several awkward exchanges with lawmakers who asked Gabbard for her opinion on Iran’s threat as the nation’s top intelligence official. She repeatedly said that it was Trump’s decision to strike, not hers.

“It is not the intelligence community’s responsibility to determine what is and is not an imminent threat,” she said at one of this week’s hearings.

Gabbard wrought big changes in one year

Gabbard vowed to eliminate what she said was the politicization of intelligence by government insiders. But she quickly used her office to support some of Trump’s most partisan of arguments — that he won the 2020 election.

She also worked to undermine the results of earlier investigations into Trump’s ties to Russia.

In her year on the job, Gabbard oversaw a sharp reduction in the intelligence workforce, as well as the creation of a new task force that she charged with considering big changes to the intelligence service.

Earlier this year an intelligence sector whistleblower filed a complaint that Gabbard was withholding intelligence for political reasons, a complaint that prompted calls from Democrats for Gabbard’s resignation.

Gabbard, 44, was born in the U.S. territory of American Samoa, raised in Hawaii and spent a year of her childhood in the Philippines. She was first elected as a 21-year-old to Hawaii’s House of Representatives but had to leave after one term when her National Guard unit deployed to Iraq.

As the first Hindu member of the House, Gabbard was sworn into office with her hand on the Bhagavad Gita, the Hindu devotional work. She was also the first American Samoan elected to Congress.

During her four House terms she became known for speaking out against her party’s leadership. Her early support for Sen. Bernie Sanders ’ 2016 Democratic presidential primary run made her a popular figure in progressive politics nationally.



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Elasticsearch Filters – Table of Content

What is Elasticsearch Filters

The bucket is the collection of documents which matches with associated filters. Every bucket is associated with a filter. In elasticsearch filter aggregation defines multi buckets. Filters can also be provided as an array of filters. When it receives requests which form in the form of buckets. They are filtered and those filtered buckets returned in the same order as in request. Its field is also provided as a filter array. Parameters are added in response with which the documents do not match the given filters. Those documents returns to the other bucket or in the same bucket named 

Even other parameters are also used to set key for those documents to give value other than default. When the process of collecting data starts. Documents are separated and formed into buckets. Each bucket flows through filters. While the process is going on the documents which are away from parameters of the given filter are identified. Those identified files are separated and transferred into other buckets or in the same as default. To avoid them from default, new parameters are formed to create keys for them then they are formed into the new bucket. The filters which we used frequently are caught by elasticsearch automatically.

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Why Elasticsearch Filters

It stores the documents in the form of JSON each of them relate to one another. This index makes the documents searchable in real time and also helps the users during searching. It is good at full text search. It is also the platform for real time search.

It is known for its time sensitive use, it works fast with rapid results. By using it users can store, search and analyse the data in huge volume and in real time. With this we get rapid results because instead of searching text directly it searches index. It processes and gives back the data as a response in the form of JSON. Its power lies in the tasks distributed, searched and indexed across the cluster. The Cluster part which helps to store data is known as node. It allows users to make copies of the index that process is called replica.

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How to use Elasticsearch Filters

Generally we need various assistants and applications for searching, storing, filtering, classifying, etc. But, do you ever think that there is a single application which does all those things for us with high speed? Yes, they are named as elasticsearch filters. To use it first we have to submit our text to elasticsearch then it receives our text. Then the text was stored into buckets. Buckets are the collection of documents. When the process is going on these buckets goes through filters which are given for filtering them.

While that process the documents which do not meet the parameters of that filter were identified. Those identified documents are separated from the bucket. Those documents are transferred to other buckets or in the same bucket as default. New parameters are created for those other documents to avoid them from being defaults. Then when we search for the particular topic then our text will be found within seconds. Those text is saved as index instead of saved as text. Because the index helps us a lot in exact results. And also in a short period of time. It filters and searches the exact result for us. Which saves us a lot of time.

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Benefits of Elasticsearch Filters:
  • Used for application search, which rely heavily on search for access and reporting of time.
  • Used for website search, which stores heavy text. Found useful for accurate searches. Steadily gaining place in the search domain sphere.
  • Used for Enterprise search, which allows search that includes documents search. Blog search, people search, etc. It replaced many search solutions of popular websites. We can gain great success in company intranet.
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  • Used for business analytics, works like a good tool for business analytics. It includes learning the curve for implementing this product. Which is felt as a good feature by many organizations. It also allows non technical users, for creating visualization and performs analytical functions.
  • It has rebutted distributed architecture which helped a lot in solving queries. And data processing which is easy to maintain.

Drawbacks of Elasticsearch Filters:
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Finally, companies found a great application for their maintenance. Which helps the organizations a lot in many necessary works. They are like searching, storing, filtering, and organizing into the index. The index is the best feature maintained by it. Because generally search engines save the text as the data presents. But instead it saves the data in the index. Which helps a lot while searching it gave accurate results. With in low time which also saves a lot of time. The requests made by customers and the result it gave as feedback is in the form of JSON. However, its special features gain its position in the market and even holds it in future as the best and useful application for the development of organizations.

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